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Original ArticleSpatial patterns and climate relationships of major plant traits in the New World differ between woody and herbaceous speciesIrena ?ímová1,2,a,b; Cyrille Violle3,a; Jens-Christian Svenning4,x ; Jens Kattge5,6; Kristine Engemann4; Brody Sandel7; Robert K. Peet8; Susan K. Wiser9; Benjamin Blonder10; Brian J. McGill11; Brad Boyle12, 13; Naia Morueta-Holme14, Nathan J. B. Kraft15; Peter M. van Bodegom16; Alvaro G. Gutiérrez17; Michael Bahn18; Wim A. Ozinga19,20; Anna Tosz?gyová1,2, Brian J. Enquist12,21 1Center for Theoretical Study, Charles University in Prague and The Czech Academy of Sciences, 110 00 Praha, Czech Republic; 2Department of Ecology, Faculty of Science, Charles University, 128 44 Praha, Czech Republic; 3Centre d’Ecologie Fonctionnelle et Evolutive (UMR 5175), CNRS - Université de Montpellier - Université Paul-Valéry, 34293 Montpellier, France; 4Section for Ecoinformatics and Biodiversity, Department of Bioscience, Aarhus University, DK-8000 Aarhus C, Denmark; xCenter for Biodiversity Dynamics in a Changing World (BIOCHANGE), Department of Bioscience, Aarhus University, DK-8000 Aarhus C, Denmark Max Planck Institute for Biogeochemistry, 07745 Jena, Germany; 6 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103 Leipzig, Germany;7Department of Biology, Santa Clara University, Santa Clara, CA, 95053 USA; 8Department of Biology, University of North Carolina, Chapel Hill, NC 27599-3280, USA; 9Landcare Research, Lincoln 7640, New Zealand; 10Environmental Change Institute, University of Oxford, Oxford OX1 3QY, Great Britain; 11School of Biology and Ecology / Sustainability Solutions Initiative, University of Maine, Orono, ME 04469, USA; 12Department of Ecology and Evolutionary Biology, University of Arizona, Biosciences West 310, Tucson, AZ 85721, USA; 13Hardner & Gullison Associates, LLC, Amherst, NH 03031, USA;.14Department of Integrative Biology, University of California, Berkeley, CA 94720; 15Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095; 16Institute of Environmental Sciences, Leiden University, 2333 CC Leiden, the Netherlands; 17Departamento de Ciencias Ambientales y Recursos Naturales Renovables, Facultad de Ciencias Agronómicas, Universidad de Chile, La Pintana 8820808, Santiago, Chile; 17Institute of Ecology, University of Innsbruck, Innsbruck, Austria;18Alterra, Wageningen University and Research, PO Box 47, NL-6700 AA Wageningen, The Netherlands;19Department of Ecology, Radboud University Nijmegen, Toernooiveld 1, NL-6525 ED Nijmegen, The Netherlands; 20The Santa Fe Institute, Santa Fe, NM 87501, USAa IS and CV contributed equallyb Corresponding author: Irena ?ímová (simova@cts.cuni.cz)Running head: Biogeography of woody and herbaceous plant traitsThe word count: 7752 (inclusive of abstract, main text and references)The number of journal pages required by figures and tables: 2AbstractAim Despite several recent efforts to map plant traits and to identify their climatic drivers, there are still major gaps. Global trait patterns for major functional groups, in particular the differences between woody and herbaceous plants, have yet to be identified. Here, we take advantage of big data efforts to compile plant species occurrence and trait data and analyse the spatial patterns of assemblage means and variances of key plant traits and their climatic drivers for these two plant groups at a hemispheric scale.LocationNew World (North and South America). MethodsUsing the largest currently available database of plant occurrences, we provide maps of 200 × 200 km grid-cell trait means and variances for both woody and herbaceous species and identify environmental drivers related to these patterns. We focus on six plant traits: maximum plant height, specific leaf area, seed mass, wood density, leaf nitrogen concentration and leaf phosphorus concentration.ResultsFor woody assemblages, we found a strong climate signal for both means and variances of most of the studied traits, consistent with strong environmental filtering. In contrast, for herbaceous assemblages, spatial patterns of trait means and variances were more variable, the climate signal on trait means were often different and weaker. Main conclusionTrait variations for woody versus herbaceous assemblages appear to reflect alternative strategies and differing environmental constraints. Given that most large-scale trait studies are based on woody species, the strikingly different biogeographic patterns of herbaceous traits suggests that a more synthetic framework is needed that addresses how suites of traits within and across broad functional groups respond to climate. Key words: BIEN database, environmental filtering, functional biogeography, growth form, habit, macroecology, plant functional traits, plant functional types, TRY databaseIntroductionThe geography of plant functions is unequivocally a foundation of plant ecology ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2VV8CJDH","properties":{"formattedCitation":"(e.g. Schimper, 1898)","plainCitation":"(e.g. Schimper, 1898)"},"citationItems":[{"id":530,"uris":[""],"uri":[""],"itemData":{"id":530,"type":"book","title":"Pflanzen-geographie auf physiologischer Grundlage","publisher":"G. Fischer","source":"Google Scholar","note":"00608","author":[{"family":"Schimper","given":"Andreas Franz Wilhelm"}],"issued":{"date-parts":[["1898"]]}},"prefix":"e.g."}],"schema":""} (e.g. Schimper, 1898). Just as the functional characterization of species has reinvigorated the field of community ecology ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1fvgjmpp11","properties":{"formattedCitation":"(McGill et al., 2006)","plainCitation":"(McGill et al., 2006)"},"citationItems":[{"id":750,"uris":[""],"uri":[""],"itemData":{"id":750,"type":"article-journal","title":"Rebuilding community ecology from functional traits","container-title":"Trends in Ecology & Evolution","page":"178–185","volume":"21","issue":"4","source":"Google Scholar","call-number":"0709","author":[{"family":"McGill","given":"Brian J."},{"family":"Enquist","given":"Brian J."},{"family":"Weiher","given":"Evan"},{"family":"Westoby","given":"Mark"}],"issued":{"date-parts":[["2006"]]}}}],"schema":""} (McGill et al., 2006), the functional characterization of assemblages at large spatial scales is likely to provide novel insights about the drivers of biogeographic patterns in species diversity and ecosystem functioning ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"TbB9Mozn","properties":{"formattedCitation":"(Lamanna et al., 2014; Stahl et al., 2014)","plainCitation":"(Lamanna et al., 2014; Stahl et al., 2014)"},"citationItems":[{"id":108,"uris":[""],"uri":[""],"itemData":{"id":108,"type":"article-journal","title":"Functional trait space and the latitudinal diversity gradient","container-title":"Proceedings of the National Academy of Sciences","page":"13745-13750","volume":"111","issue":"38","source":"","abstract":"The processes causing the latitudinal gradient in species richness remain elusive. Ecological theories for the origin of biodiversity gradients, such as competitive exclusion, neutral dynamics, and environmental filtering, make predictions for how functional diversity should vary at the alpha (within local assemblages), beta (among assemblages), and gamma (regional pool) scales. We test these predictions by quantifying hypervolumes constructed from functional traits representing major axes of plant strategy variation (specific leaf area, plant height, and seed mass) in tree assemblages spanning the temperate and tropical New World. Alpha-scale trait volume decreases with absolute latitude and is often lower than sampling expectation, consistent with environmental filtering theory. Beta-scale overlap decays with geographic distance fastest in the temperate zone, again consistent with environmental filtering theory. In contrast, gamma-scale trait space shows a hump-shaped relationship with absolute latitude, consistent with no theory. Furthermore, the overall temperate trait hypervolume was larger than the overall tropical hypervolume, indicating that the temperate zone permits a wider range of trait combinations or that niche packing is stronger in the tropical zone. Although there are limitations in the data, our analyses suggest that multiple processes have shaped trait diversity in trees, reflecting no consistent support for any one theory.","DOI":"10.1073/pnas.1317722111","ISSN":"0027-8424, 1091-6490","note":"00003 \nPMID: 25225365","journalAbbreviation":"PNAS","language":"en","author":[{"family":"Lamanna","given":"Christine"},{"family":"Blonder","given":"Benjamin"},{"family":"Violle","given":"Cyrille"},{"family":"Kraft","given":"Nathan J. B."},{"family":"Sandel","given":"Brody"},{"family":"?ímová","given":"Irena"},{"family":"Donoghue","given":"John C."},{"family":"Svenning","given":"Jens-Christian"},{"family":"McGill","given":"Brian J."},{"family":"Boyle","given":"Brad"},{"family":"Buzzard","given":"Vanessa"},{"family":"Dolins","given":"Steven"},{"family":"J?rgensen","given":"Peter M."},{"family":"Marcuse-Kubitza","given":"Aaron"},{"family":"Morueta-Holme","given":"Naia"},{"family":"Peet","given":"Robert K."},{"family":"Piel","given":"William H."},{"family":"Regetz","given":"James"},{"family":"Schildhauer","given":"Mark"},{"family":"Spencer","given":"Nick"},{"family":"Thiers","given":"Barbara"},{"family":"Wiser","given":"Susan K."},{"family":"Enquist","given":"Brian J."}],"issued":{"date-parts":[["2014",9,23]]}}},{"id":311,"uris":[""],"uri":[""],"itemData":{"id":311,"type":"article-journal","title":"Predicting species’ range limits from functional traits for the tree flora of North America","container-title":"Proceedings of the National Academy of Sciences","page":"13739-13744","volume":"111","issue":"38","source":"","abstract":"Using functional traits to explain species’ range limits is a promising approach in functional biogeography. It replaces the idiosyncrasy of species-specific climate ranges with a generic trait-based predictive framework. In addition, it has the potential to shed light on specific filter mechanisms creating large-scale vegetation patterns. However, its application to a continental flora, spanning large climate gradients, has been hampered by a lack of trait data. Here, we explore whether five key plant functional traits (seed mass, wood density, specific leaf area (SLA), maximum height, and longevity of a tree)—indicative of life history, mechanical, and physiological adaptations—explain the climate ranges of 250 North American tree species distributed from the boreal to the subtropics. Although the relationship between traits and the median climate across a species range is weak, quantile regressions revealed strong effects on range limits. Wood density and seed mass were strongly related to the lower but not upper temperature range limits of species. Maximum height affects the species range limits in both dry and humid climates, whereas SLA and longevity do not show clear relationships. These results allow the definition and delineation of climatic “no-go areas” for North American tree species based on key traits. As some of these key traits serve as important parameters in recent vegetation models, the implementation of trait-based climatic constraints has the potential to predict both range shifts and ecosystem consequences on a more functional basis. Moreover, for future trait-based vegetation models our results provide a benchmark for model evaluation.","DOI":"10.1073/pnas.1300673111","ISSN":"0027-8424, 1091-6490","note":"PMID: 25225398","journalAbbreviation":"PNAS","language":"en","author":[{"family":"Stahl","given":"Ulrike"},{"family":"Reu","given":"Bj?rn"},{"family":"Wirth","given":"Christian"}],"issued":{"date-parts":[["2014",9,23]]}}}],"schema":""} (Lamanna et al., 2014; Stahl et al., 2014). Such developments reflect the shift from a ‘biogeography by taxa’ to a ‘biogeography by functions’ ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"v6PdmIcL","properties":{"formattedCitation":"(Chown et al., 2004; Chown & Gaston, 2008; Gaston et al., 2009; Swenson et al., 2012; Reichstein et al., 2014; Violle et al., 2014; Chown & Gaston, 2016)","plainCitation":"(Chown et al., 2004; Chown & Gaston, 2008; Gaston et al., 2009; Swenson et al., 2012; Reichstein et al., 2014; Violle et al., 2014; Chown & Gaston, 2016)"},"citationItems":[{"id":490,"uris":[""],"uri":[""],"itemData":{"id":490,"type":"article-journal","title":"Macrophysiology: large-scale patterns in physiological traits and their ecological implications","container-title":"Functional Ecology","page":"159–167","volume":"18","issue":"2","source":"Google Scholar","note":"00151","shortTitle":"Macrophysiology","author":[{"family":"Chown","given":"S. L."},{"family":"Gaston","given":"K. J."},{"family":"Robinson","given":"D."}],"issued":{"date-parts":[["2004"]]}}},{"id":454,"uris":[""],"uri":[""],"itemData":{"id":454,"type":"article-journal","title":"Macrophysiology for a changing world","container-title":"Proceedings of the Royal Society B: Biological Sciences","page":"1469–1478","volume":"275","issue":"1642","source":"Google Scholar","note":"00133","author":[{"family":"Chown","given":"Steven L."},{"family":"Gaston","given":"Kevin J."}],"issued":{"date-parts":[["2008"]]}}},{"id":2099,"uris":[""],"uri":[""],"itemData":{"id":2099,"type":"article-journal","title":"Macrophysiology – progress and prospects","container-title":"Functional Ecology","page":"330-344","volume":"30","issue":"3","source":"Wiley Online Library","abstract":"* Macrophysiology is the investigation of variation in physiological traits over large geographic and temporal scales and the ecological implications of this variation. It has now been undertaken, as a defined field, for a decade.\n\n\n* Here, we overview its conceptual foundations, methodological approaches and insights, together with challenges the field is facing currently.\n\n\n* Macrophysiology builds on approaches that investigate the ecological and evolutionary significance of physiological trait variation and feedbacks in these processes. One of its key strengths is its ability to provide a basis for examining interactions among the intraspecific, interspecific and assemblage levels.\n\n\n* Macrophysiology is distinct from and typically concerns larger spatial and temporal scales than conservation physiology, whereas it is in several respects similar to, but antecedes, functional biogeography. Contrary to some claims, macrophysiology is not concerned only with the implications for geographic ranges of physiological trait variation.\n\n\n* Several insights, which would not otherwise have been achieved, have arisen from the field, notably the understanding of variation in global patterns of upper and lower lethal temperature limits and organism performance, which have important implications for forecasting the impacts of climate change.\n\n\n* Ten major challenges are identified for the field of macroecology, including better integration of approaches and information for plants and animals. Nonetheless, the prospects for macrophysiology as a significant way to understand our world remain bright.","DOI":"10.1111/1365-2435.12510","ISSN":"1365-2435","journalAbbreviation":"Funct Ecol","language":"en","author":[{"family":"Chown","given":"Steven L."},{"family":"Gaston","given":"Kevin J."}],"issued":{"date-parts":[["2016",3,1]]}}},{"id":2518,"uris":[""],"uri":[""],"itemData":{"id":2518,"type":"article-journal","title":"Macrophysiology: a conceptual reunification","container-title":"The American Naturalist","page":"595-612","volume":"174","issue":"5","source":"PubMed","abstract":"Widespread recognition of the importance of biological studies at large spatial and temporal scales, particularly in the face of many of the most pressing issues facing humanity, has fueled the argument that there is a need to reinvigorate such studies in physiological ecology through the establishment of a macrophysiology. Following a period when the fields of ecology and physiological ecology had been regarded as largely synonymous, studies of this kind were relatively commonplace in the first half of the twentieth century. However, such large-scale work subsequently became rather scarce as physiological studies concentrated on the biochemical and molecular mechanisms underlying the capacities and tolerances of species. In some sense, macrophysiology is thus an attempt at a conceptual reunification. In this article, we provide a conceptual framework for the continued development of macrophysiology. We subdivide this framework into three major components: the establishment of macrophysiological patterns, determining the form of those patterns (the very general ways in which they are shaped), and understanding the mechanisms that give rise to them. We suggest ways in which each of these components could be developed usefully.","DOI":"10.1086/605982","ISSN":"1537-5323","note":"PMID: 19788354","shortTitle":"Macrophysiology","journalAbbreviation":"Am. Nat.","language":"ENG","author":[{"family":"Gaston","given":"Kevin J."},{"family":"Chown","given":"Steven L."},{"family":"Calosi","given":"Piero"},{"family":"Bernardo","given":"Joseph"},{"family":"Bilton","given":"David T."},{"family":"Clarke","given":"Andrew"},{"family":"Clusella-Trullas","given":"Susana"},{"family":"Ghalambor","given":"Cameron K."},{"family":"Konarzewski","given":"Marek"},{"family":"Peck","given":"Lloyd S."},{"family":"Porter","given":"Warren P."},{"family":"P?rtner","given":"Hans O."},{"family":"Rezende","given":"Enrico L."},{"family":"Schulte","given":"Patricia M."},{"family":"Spicer","given":"John I."},{"family":"Stillman","given":"Jonathon H."},{"family":"Terblanche","given":"John S."},{"family":"Kleunen","given":"Mark","non-dropping-particle":"van"}],"issued":{"date-parts":[["2009",11]]}}},{"id":125,"uris":[""],"uri":[""],"itemData":{"id":125,"type":"article-journal","title":"The biogeography and filtering of woody plant functional diversity in North and South America","container-title":"Global Ecology and Biogeography","page":"798–808","volume":"21","issue":"8","source":"Google Scholar","call-number":"0005","note":"00029","author":[{"family":"Swenson","given":"Nathan G."},{"family":"Enquist","given":"Brian J."},{"family":"Pither","given":"Jason"},{"family":"Kerkhoff","given":"Andrew J."},{"family":"Boyle","given":"Brad"},{"family":"Weiser","given":"Michael D."},{"family":"Elser","given":"James J."},{"family":"Fagan","given":"William F."},{"family":"Forero-Monta?a","given":"Jimena"},{"family":"Fyllas","given":"Nikolaos"}],"issued":{"date-parts":[["2012"]]}}},{"id":313,"uris":[""],"uri":[""],"itemData":{"id":313,"type":"article-journal","title":"Linking plant and ecosystem functional biogeography","container-title":"Proceedings of the National Academy of Sciences","page":"13697-13702","volume":"111","issue":"38","source":"","abstract":"Classical biogeographical observations suggest that ecosystems are strongly shaped by climatic constraints in terms of their structure and function. On the other hand, vegetation function feeds back on the climate system via biosphere–atmosphere exchange of matter and energy. Ecosystem-level observations of this exchange reveal very large functional biogeographical variation of climate-relevant ecosystem functional properties related to carbon and water cycles. This variation is explained insufficiently by climate control and a classical plant functional type classification approach. For example, correlations between seasonal carbon-use efficiency and climate or environmental variables remain below 0.6, leaving almost 70% of variance unexplained. We suggest that a substantial part of this unexplained variation of ecosystem functional properties is related to variations in plant and microbial traits. Therefore, to progress with global functional biogeography, we should seek to understand the link between organismic traits and flux-derived ecosystem properties at ecosystem observation sites and the spatial variation of vegetation traits given geoecological covariates. This understanding can be fostered by synergistic use of both data-driven and theory-driven ecological as well as biophysical approaches.","DOI":"10.1073/pnas.1216065111","ISSN":"0027-8424, 1091-6490","note":"00008 \nPMID: 25225392","journalAbbreviation":"PNAS","language":"en","author":[{"family":"Reichstein","given":"Markus"},{"family":"Bahn","given":"Michael"},{"family":"Mahecha","given":"Miguel D."},{"family":"Kattge","given":"Jens"},{"family":"Baldocchi","given":"Dennis D."}],"issued":{"date-parts":[["2014",9,23]]}}},{"id":691,"uris":[""],"uri":[""],"itemData":{"id":691,"type":"article-journal","title":"The emergence and promise of functional biogeography","container-title":"Proceedings of the National Academy of Sciences","page":"13690-13696","volume":"111","issue":"38","source":"","DOI":"10.1073/pnas.1415442111","ISSN":"0027-8424, 1091-6490","note":"00006 \nPMID: 25225414","journalAbbreviation":"PNAS","language":"en","author":[{"family":"Violle","given":"Cyrille"},{"family":"Reich","given":"Peter B."},{"family":"Pacala","given":"Stephen W."},{"family":"Enquist","given":"Brian J."},{"family":"Kattge","given":"Jens"}],"issued":{"date-parts":[["2014",9,23]]}}}],"schema":""} (Chown et al., 2004; Chown & Gaston, 2008; Gaston et al., 2009; Swenson et al., 2012; Reichstein et al., 2014; Violle et al., 2014; Chown & Gaston, 2016). Numerous studies have assessed spatial gradients in plant traits in relation to the environment ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1aWXcN21","properties":{"formattedCitation":"(e.g. Wright et al., 2004, 2005; Chave et al., 2009; Moles et al., 2009; Swenson et al., 2012)","plainCitation":"(e.g. Wright et al., 2004, 2005; Chave et al., 2009; Moles et al., 2009; Swenson et al., 2012)"},"citationItems":[{"id":18,"uris":[""],"uri":[""],"itemData":{"id":18,"type":"article-journal","title":"The worldwide leaf economics spectrum","container-title":"Nature","page":"821-827","volume":"428","issue":"6985","source":"","abstract":"Bringing together leaf trait data spanning 2,548 species and 175 sites we describe, for the first time at global scale, a universal spectrum of leaf economics consisting of key chemical, structural and physiological properties. The spectrum runs from quick to slow return on investments of nutrients and dry mass in leaves, and operates largely independently of growth form, plant functional type or biome. Categories along the spectrum would, in general, describe leaf economic variation at the global scale better than plant functional types, because functional types overlap substantially in their leaf traits. Overall, modulation of leaf traits and trait relationships by climate is surprisingly modest, although some striking and significant patterns can be seen. Reliable quantification of the leaf economics spectrum and its interaction with climate will prove valuable for modelling nutrient fluxes and vegetation boundaries under changing land-use and climate.","DOI":"10.1038/nature02403","ISSN":"0028-0836","call-number":"1503","note":"02262","journalAbbreviation":"Nature","language":"en","author":[{"family":"Wright","given":"Ian J."},{"family":"Reich","given":"Peter B."},{"family":"Westoby","given":"Mark"},{"family":"Ackerly","given":"David D."},{"family":"Baruch","given":"Zdravko"},{"family":"Bongers","given":"Frans"},{"family":"Cavender-Bares","given":"Jeannine"},{"family":"Chapin","given":"Terry"},{"family":"Cornelissen","given":"Johannes H. C."},{"family":"Diemer","given":"Matthias"},{"family":"Flexas","given":"Jaume"},{"family":"Garnier","given":"Eric"},{"family":"Groom","given":"Philip K."},{"family":"Gulias","given":"Javier"},{"family":"Hikosaka","given":"Kouki"},{"family":"Lamont","given":"Byron B."},{"family":"Lee","given":"Tali"},{"family":"Lee","given":"William"},{"family":"Lusk","given":"Christopher"},{"family":"Midgley","given":"Jeremy J."},{"family":"Navas","given":"Marie-Laure"},{"family":"Niinemets","given":"?lo"},{"family":"Oleksyn","given":"Jacek"},{"family":"Osada","given":"Noriyuki"},{"family":"Poorter","given":"Hendrik"},{"family":"Poot","given":"Pieter"},{"family":"Prior","given":"Lynda"},{"family":"Pyankov","given":"Vladimir I."},{"family":"Roumet","given":"Catherine"},{"family":"Thomas","given":"Sean C."},{"family":"Tjoelker","given":"Mark G."},{"family":"Veneklaas","given":"Erik J."},{"family":"Villar","given":"Rafael"}],"issued":{"date-parts":[["2004",4,22]]}},"prefix":"e.g. "},{"id":488,"uris":[""],"uri":[""],"itemData":{"id":488,"type":"article-journal","title":"Modulation of leaf economic traits and trait relationships by climate","container-title":"Global Ecology and Biogeography","page":"411-421","volume":"14","issue":"5","source":"Wiley Online Library","abstract":"Aim? Our aim was to quantify climatic influences on key leaf traits and relationships at the global scale. This knowledge provides insight into how plants have adapted to different environmental pressures, and will lead to better calibration of future vegetation–climate models. Location? The data set represents vegetation from 175 sites around the world. Methods? For more than 2500 vascular plant species, we compiled data on leaf mass per area (LMA), leaf life span (LL), nitrogen concentration (Nmass) and photosynthetic capacity (Amass). Site climate was described with several standard indices. Correlation and regression analyses were used for quantifying relationships between single leaf traits and climate. Standardized major axis (SMA) analyses were used for assessing the effect of climate on bivariate relationships between leaf traits. Principal components analysis (PCA) was used to summarize multidimensional trait variation. Results? At hotter, drier and higher irradiance sites, (1) mean LMA and leaf N per area were higher; (2) average LL was shorter at a given LMA, or the increase in LL was less for a given increase in LMA (LL–LMA relationships became less positive); and (3) Amass was lower at a given Nmass, or the increase in Amass was less for a given increase in Nmass. Considering all traits simultaneously, 18% of variation along the principal multivariate trait axis was explained by climate. Main conclusions? Trait-shifts with climate were of sufficient magnitude to have major implications for plant dry mass and nutrient economics, and represent substantial selective pressures associated with adaptation to different climatic regimes.","DOI":"10.1111/j.1466-822x.2005.00172.x","ISSN":"1466-8238","note":"00318","language":"en","author":[{"family":"Wright","given":"Ian J."},{"family":"Reich","given":"Peter B."},{"family":"Cornelissen","given":"Johannes H. C."},{"family":"Falster","given":"Daniel S."},{"family":"Groom","given":"Philip K."},{"family":"Hikosaka","given":"Kouki"},{"family":"Lee","given":"William"},{"family":"Lusk","given":"Christopher H."},{"family":"Niinemets","given":"?lo"},{"family":"Oleksyn","given":"Jacek"},{"family":"Osada","given":"Noriyuki"},{"family":"Poorter","given":"Hendrik"},{"family":"Warton","given":"David I."},{"family":"Westoby","given":"Mark"}],"issued":{"date-parts":[["2005",9,1]]}}},{"id":187,"uris":[""],"uri":[""],"itemData":{"id":187,"type":"article-journal","title":"Towards a worldwide wood economics spectrum","container-title":"Ecology Letters","page":"351–366","volume":"12","issue":"4","source":"Google Scholar","call-number":"0244","note":"00519","author":[{"family":"Chave","given":"Jerome"},{"family":"Coomes","given":"David"},{"family":"Jansen","given":"Steven"},{"family":"Lewis","given":"Simon L."},{"family":"Swenson","given":"Nathan G."},{"family":"Zanne","given":"Amy E."}],"issued":{"date-parts":[["2009"]]}}},{"id":128,"uris":[""],"uri":[""],"itemData":{"id":128,"type":"article-journal","title":"Global patterns in plant height","container-title":"Journal of Ecology","page":"923–932","volume":"97","issue":"5","source":"Google Scholar","call-number":"0062","note":"00148","author":[{"family":"Moles","given":"Angela T."},{"family":"Warton","given":"David I."},{"family":"Warman","given":"Laura"},{"family":"Swenson","given":"Nathan G."},{"family":"Laffan","given":"Shawn W."},{"family":"Zanne","given":"Amy E."},{"family":"Pitman","given":"Andy"},{"family":"Hemmings","given":"Frank A."},{"family":"Leishman","given":"Michelle R."}],"issued":{"date-parts":[["2009"]]}}},{"id":125,"uris":[""],"uri":[""],"itemData":{"id":125,"type":"article-journal","title":"The biogeography and filtering of woody plant functional diversity in North and South America","container-title":"Global Ecology and Biogeography","page":"798–808","volume":"21","issue":"8","source":"Google Scholar","call-number":"0005","note":"00029","author":[{"family":"Swenson","given":"Nathan G."},{"family":"Enquist","given":"Brian J."},{"family":"Pither","given":"Jason"},{"family":"Kerkhoff","given":"Andrew J."},{"family":"Boyle","given":"Brad"},{"family":"Weiser","given":"Michael D."},{"family":"Elser","given":"James J."},{"family":"Fagan","given":"William F."},{"family":"Forero-Monta?a","given":"Jimena"},{"family":"Fyllas","given":"Nikolaos"}],"issued":{"date-parts":[["2012"]]}}}],"schema":""} (e.g. Wright et al., 2004, 2005; Chave et al., 2009; Moles et al., 2009; Swenson et al., 2012). However, a general set of patterns has yet to emerge, which challenges the assumption of universal and predictable trait-environment relationships ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"139gbb4ct1","properties":{"formattedCitation":"(Shipley et al., 2016)","plainCitation":"(Shipley et al., 2016)"},"citationItems":[{"id":2024,"uris":[""],"uri":[""],"itemData":{"id":2024,"type":"article-journal","title":"Reinforcing loose foundation stones in trait-based plant ecology","container-title":"Oecologia","page":"923-931","volume":"180","issue":"4","source":"link.","abstract":"The promise of “trait-based” plant ecology is one of generalized prediction across organizational and spatial scales, independent of taxonomy. This promise is a major reason for the increased popularity of this approach. Here, we argue that some important foundational assumptions of trait-based ecology have not received sufficient empirical evaluation. We identify three such assumptions and, where possible, suggest methods of improvement: (i) traits are functional to the degree that they determine individual fitness, (ii) intraspecific variation in functional traits can be largely ignored, and (iii) functional traits show general predictive relationships to measurable environmental gradients.","DOI":"10.1007/s00442-016-3549-x","ISSN":"0029-8549, 1432-1939","journalAbbreviation":"Oecologia","language":"en","author":[{"family":"Shipley","given":"Bill"},{"family":"Bello","given":"Francesco De"},{"family":"Cornelissen","given":"J. Hans C."},{"family":"Laliberté","given":"Etienne"},{"family":"Laughlin","given":"Daniel C."},{"family":"Reich","given":"Peter B."}],"issued":{"date-parts":[["2016",4,1]]}}}],"schema":""} (Shipley et al., 2016). First, trait-environment correlations are often weak ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"TU5H4gP2","properties":{"formattedCitation":"(e.g. r2<0.3; Moles et al., 2014)","plainCitation":"(e.g. r2<0.3; Moles et al., 2014)"},"citationItems":[{"id":722,"uris":[""],"uri":[""],"itemData":{"id":722,"type":"article-journal","title":"Which is a better predictor of plant traits: temperature or precipitation?","container-title":"Journal of Vegetation Science","page":"1167-1180","volume":"25","issue":"5","source":"Wiley Online Library","abstract":"Question\n\nAre plant traits more closely correlated with mean annual temperature, or with mean annual precipitation?\n\n\nLocation\n\nGlobal.\n\n\nMethods\n\nWe quantified the strength of the relationships between temperature and precipitation and 21 plant traits from 447,961 species-site combinations worldwide. We used meta-analysis to provide an overall answer to our question.\n\n\nResults\n\nMean annual temperature was significantly more strongly correlated with plant traits than was mean annual precipitation.\n\n\nConclusions\n\nOur study provides support for some of the assumptions of classical vegetation theory, and points to many interesting directions for future research. The relatively low R2 values for precipitation might reflect the weak link between mean annual precipitation and the availability of water to plants.","DOI":"10.1111/jvs.12190","ISSN":"1654-1103","note":"00001","shortTitle":"Which is a better predictor of plant traits","journalAbbreviation":"J Veg Sci","language":"en","author":[{"family":"Moles","given":"Angela T."},{"family":"Perkins","given":"Sarah E."},{"family":"Laffan","given":"Shawn W."},{"family":"Flores-Moreno","given":"Habacuc"},{"family":"Awasthy","given":"Monica"},{"family":"Tindall","given":"Marianne L."},{"family":"Sack","given":"Lawren"},{"family":"Pitman","given":"Andy"},{"family":"Kattge","given":"Jens"},{"family":"Aarssen","given":"Lonnie W."},{"family":"Anand","given":"Madhur"},{"family":"Bahn","given":"Michael"},{"family":"Blonder","given":"Benjamin"},{"family":"Cavender-Bares","given":"Jeannine"},{"family":"Cornelissen","given":"J. Hans C."},{"family":"Cornwell","given":"Will K."},{"family":"Díaz","given":"Sandra"},{"family":"Dickie","given":"John B."},{"family":"Freschet","given":"Grégoire T."},{"family":"Griffiths","given":"Joshua G."},{"family":"Gutierrez","given":"Alvaro G."},{"family":"Hemmings","given":"Frank A."},{"family":"Hickler","given":"Thomas"},{"family":"Hitchcock","given":"Timothy D."},{"family":"Keighery","given":"Matthew"},{"family":"Kleyer","given":"Michael"},{"family":"Kurokawa","given":"Hiroko"},{"family":"Leishman","given":"Michelle R."},{"family":"Liu","given":"Kenwin"},{"family":"Niinemets","given":"?lo"},{"family":"Onipchenko","given":"Vladimir"},{"family":"Onoda","given":"Yusuke"},{"family":"Penuelas","given":"Josep"},{"family":"Pillar","given":"Valério D."},{"family":"Reich","given":"Peter B."},{"family":"Shiodera","given":"Satomi"},{"family":"Siefert","given":"Andrew"},{"family":"Sosinski","given":"Enio E."},{"family":"Soudzilovskaia","given":"Nadejda A."},{"family":"Swaine","given":"Emily K."},{"family":"Swenson","given":"Nathan G."},{"family":"Bodegom","given":"Peter M.","non-dropping-particle":"van"},{"family":"Warman","given":"Laura"},{"family":"Weiher","given":"Evan"},{"family":"Wright","given":"Ian J."},{"family":"Zhang","given":"Hongxiang"},{"family":"Zobel","given":"Martin"},{"family":"Bonser","given":"Stephen P."}],"issued":{"date-parts":[["2014"]]}},"prefix":"e.g. r2<0.3; "}],"schema":""} (e.g. r2<0.3 in Moles et al., 2014). Next, the strength and sign of these correlations can vary across studies. For example, in some studies plant height has been reported to increase most strongly with precipitation ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"717qH5Zc","properties":{"formattedCitation":"{\\rtf (Moles et al., 2009; Swenson et al., 2012; \\uc0\\u352{}\\uc0\\u237{}mov\\uc0\\u225{} et al., 2015)}","plainCitation":"(Moles et al., 2009; Swenson et al., 2012; ?ímová et al., 2015)"},"citationItems":[{"id":128,"uris":[""],"uri":[""],"itemData":{"id":128,"type":"article-journal","title":"Global patterns in plant height","container-title":"Journal of Ecology","page":"923–932","volume":"97","issue":"5","source":"Google Scholar","call-number":"0062","note":"00148","author":[{"family":"Moles","given":"Angela T."},{"family":"Warton","given":"David I."},{"family":"Warman","given":"Laura"},{"family":"Swenson","given":"Nathan G."},{"family":"Laffan","given":"Shawn W."},{"family":"Zanne","given":"Amy E."},{"family":"Pitman","given":"Andy"},{"family":"Hemmings","given":"Frank A."},{"family":"Leishman","given":"Michelle R."}],"issued":{"date-parts":[["2009"]]}}},{"id":125,"uris":[""],"uri":[""],"itemData":{"id":125,"type":"article-journal","title":"The biogeography and filtering of woody plant functional diversity in North and South America","container-title":"Global Ecology and Biogeography","page":"798–808","volume":"21","issue":"8","source":"Google Scholar","call-number":"0005","note":"00029","author":[{"family":"Swenson","given":"Nathan G."},{"family":"Enquist","given":"Brian J."},{"family":"Pither","given":"Jason"},{"family":"Kerkhoff","given":"Andrew J."},{"family":"Boyle","given":"Brad"},{"family":"Weiser","given":"Michael D."},{"family":"Elser","given":"James J."},{"family":"Fagan","given":"William F."},{"family":"Forero-Monta?a","given":"Jimena"},{"family":"Fyllas","given":"Nikolaos"}],"issued":{"date-parts":[["2012"]]}}},{"id":361,"uris":[""],"uri":[""],"itemData":{"id":361,"type":"article-journal","title":"Shifts in trait means and variances in North American tree assemblages: species richness patterns are loosely related to the functional space","container-title":"Ecography","page":"649-658","volume":"38","issue":"7","source":"Wiley Online Library","abstract":"One of the key hypothesized drivers of gradients in species richness is environmental filtering, where environmental stress limits which species from a larger species pool gain membership in a local community owing to their traits. Whereas most studies focus on small-scale variation in functional traits along environmental gradient, the effect of large-scale environmental filtering is less well understood. Furthermore, it has been rarely tested whether the factors that constrain the niche space limit the total number of coexisting species. We assessed the role of environmental filtering in shaping tree assemblages across North America north of Mexico by testing the hypothesis that colder, drier, or seasonal environments (stressful conditions for most plants) constrain tree trait diversity and thereby limit species richness. We assessed geographic patterns in trait filtering and their relationships to species richness pattern using a comprehensive set of tree range maps. We focused on four key plant functional traits reflecting major life history axes (maximum height, specific leaf area, seed mass, and wood density) and four climatic variables (annual mean and seasonality of temperature and precipitation). We tested for significant spatial shifts in trait means and variances using a null model approach. While we found significant shifts in mean species’ trait values at most grid cells, trait variances at most grid cells did not deviate from the null expectation. Measures of environmental harshness (cold, dry, seasonal climates) and lower species richness were weakly associated with a reduction in variance of seed mass and specific leaf area. The pattern in variance of height and wood density was, however, opposite. These findings do not support the hypothesis that more stressful conditions universally limit species and trait diversity in North America. Environmental filtering does, however, structure assemblage composition, by selecting for certain optimum trait values under a given set of conditions.","DOI":"10.1111/ecog.00867","ISSN":"1600-0587","note":"00003","shortTitle":"Shifts in trait means and variances in North American tree assemblages","journalAbbreviation":"Ecography","language":"en","author":[{"family":"?ímová","given":"Irena"},{"family":"Violle","given":"Cyrille"},{"family":"Kraft","given":"Nathan J. B."},{"family":"Storch","given":"David"},{"family":"Svenning","given":"Jens-Christian"},{"family":"Boyle","given":"Brad"},{"family":"Donoghue","given":"John C."},{"family":"J?rgensen","given":"Peter"},{"family":"McGill","given":"Brian J."},{"family":"Morueta-Holme","given":"Naia"},{"family":"Piel","given":"William H."},{"family":"Peet","given":"Robert K."},{"family":"Regetz","given":"Jim"},{"family":"Schildhauer","given":"Mark"},{"family":"Spencer","given":"Nick"},{"family":"Thiers","given":"Barbara"},{"family":"Wiser","given":"Susan"},{"family":"Enquist","given":"Brian J."}],"issued":{"date-parts":[["2015",7,1]]}}}],"schema":""} (Moles et al., 2009; Swenson et al., 2012; ?ímová et al., 2015) whereas others have reported the strongest relationship with mean annual temperature ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1q268g10bp","properties":{"formattedCitation":"(Moles et al., 2014)","plainCitation":"(Moles et al., 2014)"},"citationItems":[{"id":722,"uris":[""],"uri":[""],"itemData":{"id":722,"type":"article-journal","title":"Which is a better predictor of plant traits: temperature or precipitation?","container-title":"Journal of Vegetation Science","page":"1167-1180","volume":"25","issue":"5","source":"Wiley Online Library","abstract":"Question\n\nAre plant traits more closely correlated with mean annual temperature, or with mean annual precipitation?\n\n\nLocation\n\nGlobal.\n\n\nMethods\n\nWe quantified the strength of the relationships between temperature and precipitation and 21 plant traits from 447,961 species-site combinations worldwide. We used meta-analysis to provide an overall answer to our question.\n\n\nResults\n\nMean annual temperature was significantly more strongly correlated with plant traits than was mean annual precipitation.\n\n\nConclusions\n\nOur study provides support for some of the assumptions of classical vegetation theory, and points to many interesting directions for future research. The relatively low R2 values for precipitation might reflect the weak link between mean annual precipitation and the availability of water to plants.","DOI":"10.1111/jvs.12190","ISSN":"1654-1103","note":"00001","shortTitle":"Which is a better predictor of plant traits","journalAbbreviation":"J Veg Sci","language":"en","author":[{"family":"Moles","given":"Angela T."},{"family":"Perkins","given":"Sarah E."},{"family":"Laffan","given":"Shawn W."},{"family":"Flores-Moreno","given":"Habacuc"},{"family":"Awasthy","given":"Monica"},{"family":"Tindall","given":"Marianne L."},{"family":"Sack","given":"Lawren"},{"family":"Pitman","given":"Andy"},{"family":"Kattge","given":"Jens"},{"family":"Aarssen","given":"Lonnie W."},{"family":"Anand","given":"Madhur"},{"family":"Bahn","given":"Michael"},{"family":"Blonder","given":"Benjamin"},{"family":"Cavender-Bares","given":"Jeannine"},{"family":"Cornelissen","given":"J. Hans C."},{"family":"Cornwell","given":"Will K."},{"family":"Díaz","given":"Sandra"},{"family":"Dickie","given":"John B."},{"family":"Freschet","given":"Grégoire T."},{"family":"Griffiths","given":"Joshua G."},{"family":"Gutierrez","given":"Alvaro G."},{"family":"Hemmings","given":"Frank A."},{"family":"Hickler","given":"Thomas"},{"family":"Hitchcock","given":"Timothy D."},{"family":"Keighery","given":"Matthew"},{"family":"Kleyer","given":"Michael"},{"family":"Kurokawa","given":"Hiroko"},{"family":"Leishman","given":"Michelle R."},{"family":"Liu","given":"Kenwin"},{"family":"Niinemets","given":"?lo"},{"family":"Onipchenko","given":"Vladimir"},{"family":"Onoda","given":"Yusuke"},{"family":"Penuelas","given":"Josep"},{"family":"Pillar","given":"Valério D."},{"family":"Reich","given":"Peter B."},{"family":"Shiodera","given":"Satomi"},{"family":"Siefert","given":"Andrew"},{"family":"Sosinski","given":"Enio E."},{"family":"Soudzilovskaia","given":"Nadejda A."},{"family":"Swaine","given":"Emily K."},{"family":"Swenson","given":"Nathan G."},{"family":"Bodegom","given":"Peter M.","non-dropping-particle":"van"},{"family":"Warman","given":"Laura"},{"family":"Weiher","given":"Evan"},{"family":"Wright","given":"Ian J."},{"family":"Zhang","given":"Hongxiang"},{"family":"Zobel","given":"Martin"},{"family":"Bonser","given":"Stephen P."}],"issued":{"date-parts":[["2014"]]}}}],"schema":""} (Moles et al., 2014). In some studies leaf nitrogen concentration increased with decreasing temperature ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"pEqmkVaH","properties":{"formattedCitation":"{\\rtf (Wright et al., 2005; Moles et al., 2014; \\uc0\\u352{}\\uc0\\u237{}mov\\uc0\\u225{} et al., 2017)}","plainCitation":"(Wright et al., 2005; Moles et al., 2014; ?ímová et al., 2017)"},"citationItems":[{"id":488,"uris":[""],"uri":[""],"itemData":{"id":488,"type":"article-journal","title":"Modulation of leaf economic traits and trait relationships by climate","container-title":"Global Ecology and Biogeography","page":"411-421","volume":"14","issue":"5","source":"Wiley Online Library","abstract":"Aim? Our aim was to quantify climatic influences on key leaf traits and relationships at the global scale. This knowledge provides insight into how plants have adapted to different environmental pressures, and will lead to better calibration of future vegetation–climate models. Location? The data set represents vegetation from 175 sites around the world. Methods? For more than 2500 vascular plant species, we compiled data on leaf mass per area (LMA), leaf life span (LL), nitrogen concentration (Nmass) and photosynthetic capacity (Amass). Site climate was described with several standard indices. Correlation and regression analyses were used for quantifying relationships between single leaf traits and climate. Standardized major axis (SMA) analyses were used for assessing the effect of climate on bivariate relationships between leaf traits. Principal components analysis (PCA) was used to summarize multidimensional trait variation. Results? At hotter, drier and higher irradiance sites, (1) mean LMA and leaf N per area were higher; (2) average LL was shorter at a given LMA, or the increase in LL was less for a given increase in LMA (LL–LMA relationships became less positive); and (3) Amass was lower at a given Nmass, or the increase in Amass was less for a given increase in Nmass. Considering all traits simultaneously, 18% of variation along the principal multivariate trait axis was explained by climate. Main conclusions? Trait-shifts with climate were of sufficient magnitude to have major implications for plant dry mass and nutrient economics, and represent substantial selective pressures associated with adaptation to different climatic regimes.","DOI":"10.1111/j.1466-822x.2005.00172.x","ISSN":"1466-8238","note":"00318","language":"en","author":[{"family":"Wright","given":"Ian J."},{"family":"Reich","given":"Peter B."},{"family":"Cornelissen","given":"Johannes H. C."},{"family":"Falster","given":"Daniel S."},{"family":"Groom","given":"Philip K."},{"family":"Hikosaka","given":"Kouki"},{"family":"Lee","given":"William"},{"family":"Lusk","given":"Christopher H."},{"family":"Niinemets","given":"?lo"},{"family":"Oleksyn","given":"Jacek"},{"family":"Osada","given":"Noriyuki"},{"family":"Poorter","given":"Hendrik"},{"family":"Warton","given":"David I."},{"family":"Westoby","given":"Mark"}],"issued":{"date-parts":[["2005",9,1]]}}},{"id":722,"uris":[""],"uri":[""],"itemData":{"id":722,"type":"article-journal","title":"Which is a better predictor of plant traits: temperature or precipitation?","container-title":"Journal of Vegetation Science","page":"1167-1180","volume":"25","issue":"5","source":"Wiley Online Library","abstract":"Question\n\nAre plant traits more closely correlated with mean annual temperature, or with mean annual precipitation?\n\n\nLocation\n\nGlobal.\n\n\nMethods\n\nWe quantified the strength of the relationships between temperature and precipitation and 21 plant traits from 447,961 species-site combinations worldwide. We used meta-analysis to provide an overall answer to our question.\n\n\nResults\n\nMean annual temperature was significantly more strongly correlated with plant traits than was mean annual precipitation.\n\n\nConclusions\n\nOur study provides support for some of the assumptions of classical vegetation theory, and points to many interesting directions for future research. The relatively low R2 values for precipitation might reflect the weak link between mean annual precipitation and the availability of water to plants.","DOI":"10.1111/jvs.12190","ISSN":"1654-1103","note":"00001","shortTitle":"Which is a better predictor of plant traits","journalAbbreviation":"J Veg Sci","language":"en","author":[{"family":"Moles","given":"Angela T."},{"family":"Perkins","given":"Sarah E."},{"family":"Laffan","given":"Shawn W."},{"family":"Flores-Moreno","given":"Habacuc"},{"family":"Awasthy","given":"Monica"},{"family":"Tindall","given":"Marianne L."},{"family":"Sack","given":"Lawren"},{"family":"Pitman","given":"Andy"},{"family":"Kattge","given":"Jens"},{"family":"Aarssen","given":"Lonnie W."},{"family":"Anand","given":"Madhur"},{"family":"Bahn","given":"Michael"},{"family":"Blonder","given":"Benjamin"},{"family":"Cavender-Bares","given":"Jeannine"},{"family":"Cornelissen","given":"J. Hans C."},{"family":"Cornwell","given":"Will K."},{"family":"Díaz","given":"Sandra"},{"family":"Dickie","given":"John B."},{"family":"Freschet","given":"Grégoire T."},{"family":"Griffiths","given":"Joshua G."},{"family":"Gutierrez","given":"Alvaro G."},{"family":"Hemmings","given":"Frank A."},{"family":"Hickler","given":"Thomas"},{"family":"Hitchcock","given":"Timothy D."},{"family":"Keighery","given":"Matthew"},{"family":"Kleyer","given":"Michael"},{"family":"Kurokawa","given":"Hiroko"},{"family":"Leishman","given":"Michelle R."},{"family":"Liu","given":"Kenwin"},{"family":"Niinemets","given":"?lo"},{"family":"Onipchenko","given":"Vladimir"},{"family":"Onoda","given":"Yusuke"},{"family":"Penuelas","given":"Josep"},{"family":"Pillar","given":"Valério D."},{"family":"Reich","given":"Peter B."},{"family":"Shiodera","given":"Satomi"},{"family":"Siefert","given":"Andrew"},{"family":"Sosinski","given":"Enio E."},{"family":"Soudzilovskaia","given":"Nadejda A."},{"family":"Swaine","given":"Emily K."},{"family":"Swenson","given":"Nathan G."},{"family":"Bodegom","given":"Peter M.","non-dropping-particle":"van"},{"family":"Warman","given":"Laura"},{"family":"Weiher","given":"Evan"},{"family":"Wright","given":"Ian J."},{"family":"Zhang","given":"Hongxiang"},{"family":"Zobel","given":"Martin"},{"family":"Bonser","given":"Stephen P."}],"issued":{"date-parts":[["2014"]]}}},{"id":2292,"uris":[""],"uri":[""],"itemData":{"id":2292,"type":"article-journal","title":"Stress from cold and drought as drivers of functional trait spectra in North American angiosperm tree assemblages","container-title":"Ecology and Evolution","page":"7548-7559","volume":"7","issue":"18","source":"PubMed Central","abstract":"Understanding how environmental change alters the composition of plant assemblages, and how this in turn affects ecosystem functioning is a major challenge in the face of global climate change. Assuming that values of plant traits express species adaptations to the environment, the trait‐based approach is a promising way to achieve this goal. Nevertheless, how functional traits are related to species’ environmental tolerances and how trait spectra respond to broad‐scale environmental gradients remains largely unexplored. Here, we identify the main trait spectra for US angiosperm trees by testing hypotheses for the relationships between functional traits and species’ environmental tolerances to environmental stresses, as well as quantifying the environmental drivers of assemblage means and variances of these traits. We analyzed >74,000 community assemblages from the US Forest Inventory and Analysis using 12 functional traits, five traits expressing species’ environmental tolerances and 10 environmental variables. Results indicated that leaf traits, dispersal traits, and traits related to stem hydraulics were related to cold or drought tolerance, and their assemblage means were best explained by minimum temperatures. Assemblage means of traits related to shade tolerance (tree growth rate, leaf phosphorus content, and bark thickness) were best explained by aridity index. Surprisingly, aridity index, rather than minimum temperature, was the best predictors of assemblage variances of most traits, although these relationships were variable and weak overall. We conclude that temperature is likely to be the most important driver of functional community structure of North American angiosperm trees by selecting for optimum strategies along the cold and drought stress trade‐off. In turn, water availability primarily affects traits related to shade tolerance through its effect on forest canopy structure and vegetation openness.","DOI":"10.1002/ece3.3297","ISSN":"2045-7758","note":"PMID: 28944038\nPMCID: PMC5606901","journalAbbreviation":"Ecol Evol","author":[{"family":"?ímová","given":"Irena"},{"family":"Rueda","given":"Marta"},{"family":"Hawkins","given":"Bradford A."}],"issued":{"date-parts":[["2017",8,14]]}}}],"schema":""} (Wright et al., 2005; Moles et al., 2014; ?ímová et al., 2017), whereas in others leaf nitrogen concentration showed the opposite pattern ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"ibAqzA70","properties":{"formattedCitation":"{\\rtf (Ordo\\uc0\\u241{}ez et al., 2009; Swenson et al., 2012)}","plainCitation":"(Ordo?ez et al., 2009; Swenson et al., 2012)"},"citationItems":[{"id":125,"uris":[""],"uri":[""],"itemData":{"id":125,"type":"article-journal","title":"The biogeography and filtering of woody plant functional diversity in North and South America","container-title":"Global Ecology and Biogeography","page":"798–808","volume":"21","issue":"8","source":"Google Scholar","call-number":"0005","note":"00029","author":[{"family":"Swenson","given":"Nathan G."},{"family":"Enquist","given":"Brian J."},{"family":"Pither","given":"Jason"},{"family":"Kerkhoff","given":"Andrew J."},{"family":"Boyle","given":"Brad"},{"family":"Weiser","given":"Michael D."},{"family":"Elser","given":"James J."},{"family":"Fagan","given":"William F."},{"family":"Forero-Monta?a","given":"Jimena"},{"family":"Fyllas","given":"Nikolaos"}],"issued":{"date-parts":[["2012"]]}}},{"id":444,"uris":[""],"uri":[""],"itemData":{"id":444,"type":"article-journal","title":"A global study of relationships between leaf traits, climate and soil measures of nutrient fertility","container-title":"Global Ecology and Biogeography","page":"137–149","volume":"18","issue":"2","source":"Wiley Online Library","abstract":"Aim This first global quantification of the relationship between leaf traits and soil nutrient fertility reflects the trade-off between growth and nutrient conservation. The power of soils versus climate in predicting leaf trait values is assessed in bivariate and multivariate analyses and is compared with the distribution of growth forms (as a discrete classification of vegetation) across gradients of soil fertility and climate.Location All continents except for Antarctica.Methods Data on specific leaf area (SLA), leaf N concentration (LNC), leaf P concentration (LPC) and leaf N:P were collected for 474 species distributed across 99 sites (809 records), together with abiotic information from each study site. Individual and combined effects of soils and climate on leaf traits were quantified using maximum likelihood methods. Differences in occurrence of growth form across soil fertility and climate were determined by one-way ANOVA.Results There was a consistent increase in SLA, LNC and LPC with increasing soil fertility. SLA was related to proxies of N supply, LNC to both soil total N and P and LPC was only related to proxies of P supply. Soil nutrient measures explained more variance in leaf traits among sites than climate in bivariate analysis. Multivariate analysis showed that climate interacted with soil nutrients for SLA and area-based LNC. Mass-based LNC and LPC were determined mostly by soil fertility, but soil P was highly correlated to precipitation. Relationships of leaf traits to soil nutrients were stronger than those of growth form versus soil nutrients. In contrast, climate determined distribution of growth form more strongly than it did leaf traits.Main conclusions We provide the first global quantification of the trade-off between traits associated with growth and resource conservation ‘strategies’ in relation to soil fertility. Precipitation but not temperature affected this trade-off. Continuous leaf traits might be better predictors of plant responses to nutrient supply than growth form, but growth forms reflect important aspects of plant species distribution with climate.","DOI":"10.1111/j.1466-8238.2008.00441.x","ISSN":"1466-8238","language":"en","author":[{"family":"Ordo?ez","given":"Jenny C."},{"family":"Van Bodegom","given":"Peter M."},{"family":"Witte","given":"Jan-Philip M."},{"family":"Wright","given":"Ian J."},{"family":"Reich","given":"Peter B."},{"family":"Aerts","given":"Rien"}],"issued":{"date-parts":[["2009"]]},"accessed":{"date-parts":[["2013",12,18]]}}}],"schema":""} (Ordo?ez et al., 2009; Swenson et al., 2012), and it has also been found to be most strongly related to precipitation ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"cRKhcjbT","properties":{"formattedCitation":"(Swenson & Weiser, 2010)","plainCitation":"(Swenson & Weiser, 2010)"},"citationItems":[{"id":700,"uris":[""],"uri":[""],"itemData":{"id":700,"type":"article-journal","title":"Plant geography upon the basis of functional traits: an example from eastern North American trees","container-title":"Ecology","page":"2234–2241","volume":"91","issue":"8","source":"Google Scholar","call-number":"0022","shortTitle":"Plant geography upon the basis of functional traits","author":[{"family":"Swenson","given":"Nathan G."},{"family":"Weiser","given":"Michael D."}],"issued":{"date-parts":[["2010"]]}}}],"schema":""} (Swenson & Weiser, 2010). Results concerning trait variances diverge even more across studies ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"DV6Kt7fF","properties":{"formattedCitation":"{\\rtf (Swenson & Weiser, 2010; Swenson et al., 2012; \\uc0\\u352{}\\uc0\\u237{}mov\\uc0\\u225{} et al., 2015, 2017)}","plainCitation":"(Swenson & Weiser, 2010; Swenson et al., 2012; ?ímová et al., 2015, 2017)"},"citationItems":[{"id":700,"uris":[""],"uri":[""],"itemData":{"id":700,"type":"article-journal","title":"Plant geography upon the basis of functional traits: an example from eastern North American trees","container-title":"Ecology","page":"2234–2241","volume":"91","issue":"8","source":"Google Scholar","call-number":"0022","shortTitle":"Plant geography upon the basis of functional traits","author":[{"family":"Swenson","given":"Nathan G."},{"family":"Weiser","given":"Michael D."}],"issued":{"date-parts":[["2010"]]}}},{"id":125,"uris":[""],"uri":[""],"itemData":{"id":125,"type":"article-journal","title":"The biogeography and filtering of woody plant functional diversity in North and South America","container-title":"Global Ecology and Biogeography","page":"798–808","volume":"21","issue":"8","source":"Google Scholar","call-number":"0005","note":"00029","author":[{"family":"Swenson","given":"Nathan G."},{"family":"Enquist","given":"Brian J."},{"family":"Pither","given":"Jason"},{"family":"Kerkhoff","given":"Andrew J."},{"family":"Boyle","given":"Brad"},{"family":"Weiser","given":"Michael D."},{"family":"Elser","given":"James J."},{"family":"Fagan","given":"William F."},{"family":"Forero-Monta?a","given":"Jimena"},{"family":"Fyllas","given":"Nikolaos"}],"issued":{"date-parts":[["2012"]]}}},{"id":361,"uris":[""],"uri":[""],"itemData":{"id":361,"type":"article-journal","title":"Shifts in trait means and variances in North American tree assemblages: species richness patterns are loosely related to the functional space","container-title":"Ecography","page":"649-658","volume":"38","issue":"7","source":"Wiley Online Library","abstract":"One of the key hypothesized drivers of gradients in species richness is environmental filtering, where environmental stress limits which species from a larger species pool gain membership in a local community owing to their traits. Whereas most studies focus on small-scale variation in functional traits along environmental gradient, the effect of large-scale environmental filtering is less well understood. Furthermore, it has been rarely tested whether the factors that constrain the niche space limit the total number of coexisting species. We assessed the role of environmental filtering in shaping tree assemblages across North America north of Mexico by testing the hypothesis that colder, drier, or seasonal environments (stressful conditions for most plants) constrain tree trait diversity and thereby limit species richness. We assessed geographic patterns in trait filtering and their relationships to species richness pattern using a comprehensive set of tree range maps. We focused on four key plant functional traits reflecting major life history axes (maximum height, specific leaf area, seed mass, and wood density) and four climatic variables (annual mean and seasonality of temperature and precipitation). We tested for significant spatial shifts in trait means and variances using a null model approach. While we found significant shifts in mean species’ trait values at most grid cells, trait variances at most grid cells did not deviate from the null expectation. Measures of environmental harshness (cold, dry, seasonal climates) and lower species richness were weakly associated with a reduction in variance of seed mass and specific leaf area. The pattern in variance of height and wood density was, however, opposite. These findings do not support the hypothesis that more stressful conditions universally limit species and trait diversity in North America. Environmental filtering does, however, structure assemblage composition, by selecting for certain optimum trait values under a given set of conditions.","DOI":"10.1111/ecog.00867","ISSN":"1600-0587","note":"00003","shortTitle":"Shifts in trait means and variances in North American tree assemblages","journalAbbreviation":"Ecography","language":"en","author":[{"family":"?ímová","given":"Irena"},{"family":"Violle","given":"Cyrille"},{"family":"Kraft","given":"Nathan J. B."},{"family":"Storch","given":"David"},{"family":"Svenning","given":"Jens-Christian"},{"family":"Boyle","given":"Brad"},{"family":"Donoghue","given":"John C."},{"family":"J?rgensen","given":"Peter"},{"family":"McGill","given":"Brian J."},{"family":"Morueta-Holme","given":"Naia"},{"family":"Piel","given":"William H."},{"family":"Peet","given":"Robert K."},{"family":"Regetz","given":"Jim"},{"family":"Schildhauer","given":"Mark"},{"family":"Spencer","given":"Nick"},{"family":"Thiers","given":"Barbara"},{"family":"Wiser","given":"Susan"},{"family":"Enquist","given":"Brian J."}],"issued":{"date-parts":[["2015",7,1]]}}},{"id":2292,"uris":[""],"uri":[""],"itemData":{"id":2292,"type":"article-journal","title":"Stress from cold and drought as drivers of functional trait spectra in North American angiosperm tree assemblages","container-title":"Ecology and Evolution","page":"7548-7559","volume":"7","issue":"18","source":"PubMed Central","abstract":"Understanding how environmental change alters the composition of plant assemblages, and how this in turn affects ecosystem functioning is a major challenge in the face of global climate change. Assuming that values of plant traits express species adaptations to the environment, the trait‐based approach is a promising way to achieve this goal. Nevertheless, how functional traits are related to species’ environmental tolerances and how trait spectra respond to broad‐scale environmental gradients remains largely unexplored. Here, we identify the main trait spectra for US angiosperm trees by testing hypotheses for the relationships between functional traits and species’ environmental tolerances to environmental stresses, as well as quantifying the environmental drivers of assemblage means and variances of these traits. We analyzed >74,000 community assemblages from the US Forest Inventory and Analysis using 12 functional traits, five traits expressing species’ environmental tolerances and 10 environmental variables. Results indicated that leaf traits, dispersal traits, and traits related to stem hydraulics were related to cold or drought tolerance, and their assemblage means were best explained by minimum temperatures. Assemblage means of traits related to shade tolerance (tree growth rate, leaf phosphorus content, and bark thickness) were best explained by aridity index. Surprisingly, aridity index, rather than minimum temperature, was the best predictors of assemblage variances of most traits, although these relationships were variable and weak overall. We conclude that temperature is likely to be the most important driver of functional community structure of North American angiosperm trees by selecting for optimum strategies along the cold and drought stress trade‐off. In turn, water availability primarily affects traits related to shade tolerance through its effect on forest canopy structure and vegetation openness.","DOI":"10.1002/ece3.3297","ISSN":"2045-7758","note":"PMID: 28944038\nPMCID: PMC5606901","journalAbbreviation":"Ecol Evol","author":[{"family":"?ímová","given":"Irena"},{"family":"Rueda","given":"Marta"},{"family":"Hawkins","given":"Bradford A."}],"issued":{"date-parts":[["2017",8,14]]}}}],"schema":""} (Swenson & Weiser, 2010; Swenson et al., 2012; ?ímová et al., 2015, 2017). These inconsistencies could be due to various factors such as differences in sampling scale, sparsity of data, methods of inference, historical legacies, sensitivity to land use, and the specific growth forms studied ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"tl70PhDH","properties":{"formattedCitation":"(Borgy et al., 2017b, 2017a)","plainCitation":"(Borgy et al., 2017b, 2017a)"},"citationItems":[{"id":2164,"uris":[""],"uri":[""],"itemData":{"id":2164,"type":"article-journal","title":"Sensitivity of community-level trait–environment relationships to data representativeness: A test for functional biogeography","container-title":"Global Ecology and Biogeography","page":"729-739","volume":"26","issue":"6","source":"Wiley Online Library","abstract":"Aim\n\nThe characterization of trait–environment relationships over broad-scale gradients is a critical goal for ecology and biogeography. This implies the merging of plot and trait databases to assess community-level trait-based statistics. Potential shortcomings and limitations of this approach are that: (i) species traits are not measured where the community is sampled and (ii) the availability of trait data varies considerably across species and plots. Here we address the effect of trait data representativeness [the sampling effort per species and per plot] on the accuracy of (i) species-level and (ii) community-level trait estimates and (iii) the consequences for the shape and strength of trait–environment relationships across communities.\n\n\nInnovation\n\nWe combined information existing in databases of vegetation plots and plant traits to estimate community-weighted means [CWMs] of four key traits [specific leaf area, plant height, seed mass and leaf nitrogen content per dry mass] in permanent grasslands at a country-wide scale. We propose a generic approach for systematic sensitivity analyses based on random subsampling and data reduction to address the representativeness of incomplete and heterogeneous trait information when exploring trait–environment relationships across communities.\n\n\nMain conclusions\n\nThe accuracy of the CWMs was little affected by the number of individual trait values per species [NIV] but strongly affected by the cover proportion of species with available trait values [PCover]. A PCover above 80% was required for all four traits studied to obtain an estimation bias below 5%. Our approach therefore provides more conservative criteria than previously proposed. Restrictive criteria on both NIV and PCover primarily excluded communities in harsh environments, and such reduction of the sampled gradient weakened trait–environment relationships. These findings advocate systematic measurement campaigns in natural environments to increase species coverage in global trait databases, with special emphasis on species occurring in under-sampled and harsh environmental conditions.","DOI":"10.1111/geb.12573","ISSN":"1466-8238","shortTitle":"Sensitivity of community-level trait–environment relationships to data representativeness","journalAbbreviation":"Global Ecol. Biogeogr.","language":"en","author":[{"family":"Borgy","given":"Benjamin"},{"family":"Violle","given":"Cyrille"},{"family":"Choler","given":"Philippe"},{"family":"Garnier","given":"Eric"},{"family":"Kattge","given":"Jens"},{"family":"Loranger","given":"Jessy"},{"family":"Amiaud","given":"Bernard"},{"family":"Cellier","given":"Pierre"},{"family":"Debarros","given":"Guilhem"},{"family":"Denelle","given":"Pierre"},{"family":"Diquelou","given":"Sylvain"},{"family":"Gachet","given":"Sophie"},{"family":"Jolivet","given":"Claudy"},{"family":"Lavorel","given":"Sandra"},{"family":"Lemauviel-Lavenant","given":"Servane"},{"family":"Mikolajczak","given":"Alexis"},{"family":"Munoz","given":"Fran?ois"},{"family":"Olivier","given":"Jean"},{"family":"Viovy","given":"Nicolas"}],"issued":{"date-parts":[["2017",6,1]]}}},{"id":2741,"uris":[""],"uri":[""],"itemData":{"id":2741,"type":"article-journal","title":"Plant community structure and nitrogen inputs modulate the climate signal on leaf traits","container-title":"Global Ecology and Biogeography","page":"1138-1152","volume":"26","issue":"10","source":"Wiley Online Library","abstract":"Aim\n\nLeaf traits strongly impact biogeochemical cycles in terrestrial ecosystems. Understanding leaf trait variation along environmental gradients is thus essential to improve the representation of vegetation in Earth system models. Our aims were to quantify relationships between leaf traits and climate in permanent grasslands at a biogeographical scale and to test whether these relationships were sensitive to (a) the level of nitrogen inputs and (b) the inclusion of information pertaining to plant community organization.\n\n\nLocation\n\nPermanent grasslands throughout France.\n\n\nMethods\n\nWe combined existing datasets on climate, soil, nitrogen inputs (fertilization and deposition), species composition and four traits, namely specific leaf area, leaf dry matter content and leaf nitrogen and phosphorus concentrations, for 15,865 French permanent grasslands. Trait–climate relationships were tested using the following four climatic variables available across 1,833 pixels (5 km?×?5 km): mean annual temperature (MAT) and precipitation (MAP), and two indices accounting for the length of the growing season. We compared these relationships at the pixel level using either using community-level or species’ trait means.\n\n\nResults\n\nOur findings were as follows: (a) leaf traits related to plant nutrient economy shift consistently along a gradient of growing season length accounting for temperature and soil water limitations of plant growth (GSLtw); (b) weighting leaf traits by species abundance in local communities is pivotal to capture leaf trait–environment relationships correctly at a biogeographical scale; and (c) the relationships between traits and GSLtw weaken for grasslands with a high nitrogen input.\n\n\nMain conclusions\n\nThe effects of climate on plant communities are better described using composite descriptors than coarse variables such as MAT or MAP, but appear weaker for high-nitrogen grasslands. Using information at the community level tends to strengthen trait–climate relationships. The interplay of land management, community assembly and bioclimate appears crucial to the prediction of leaf trait variations and their effects on biogeochemical cycles.","DOI":"10.1111/geb.12623","ISSN":"1466-8238","journalAbbreviation":"Global Ecol Biogeogr","language":"en","author":[{"family":"Borgy","given":"Benjamin"},{"family":"Violle","given":"Cyrille"},{"family":"Choler","given":"Philippe"},{"family":"Denelle","given":"Pierre"},{"family":"Munoz","given":"Fran?ois"},{"family":"Kattge","given":"Jens"},{"family":"Lavorel","given":"Sandra"},{"family":"Loranger","given":"Jessy"},{"family":"Amiaud","given":"Bernard"},{"family":"Bahn","given":"Michael"},{"family":"Bodegom","given":"Peter M.","non-dropping-particle":"van"},{"family":"Brisse","given":"Henry"},{"family":"Debarros","given":"Guilhem"},{"family":"Diquelou","given":"Sylvain"},{"family":"Gachet","given":"Sophie"},{"family":"Jolivet","given":"Claudy"},{"family":"Lemauviel-Lavenant","given":"Servane"},{"family":"Mikolajczak","given":"Alexis"},{"family":"Olivier","given":"Jean"},{"family":"Ordo?ez","given":"Jenny"},{"family":"Ruffray","given":"Patrice","non-dropping-particle":"de"},{"family":"Viovy","given":"Nicolas"},{"family":"Garnier","given":"Eric"}],"issued":{"date-parts":[["2017",10,1]]}}}],"schema":""} (Borgy et al., 2017b, 2017a). Also, many studies have combined woody and herbaceous species in single analyses (e.g., Moles et al. 2014), which may have blurred the climate signal on plant traits. Using traits related to the stature of plants, Diaz et al. (2016) have shown that herbaceous and woody species form two almost independent hotspots in the global spectrum of plant form and function, indicating the fundamental difference between these two groups. Apart from this, some differences between these two groups in their functional adaptations to environmental conditions have also been identified ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"hPmSu9BQ","properties":{"formattedCitation":"{\\rtf (for example coping with disturbances or frost\\uc0\\u8239{}; Reich et al., 1998; Petit & Hampe, 2006; Ordo\\uc0\\u241{}ez et al., 2010)}","plainCitation":"(for example coping with disturbances or frost?; Reich et al., 1998; Petit & Hampe, 2006; Ordo?ez et al., 2010)"},"citationItems":[{"id":461,"uris":[""],"uri":[""],"itemData":{"id":461,"type":"article-journal","title":"Leaf structure (specific leaf area) modulates photosynthesis–nitrogen relations: evidence from within and across species and functional groups","container-title":"Functional Ecology","page":"948–958","volume":"12","issue":"6","source":"Google Scholar","call-number":"0240","shortTitle":"Leaf structure (specific leaf area) modulates photosynthesis–nitrogen relations","author":[{"family":"Reich","given":"P. B."},{"family":"Ellsworth","given":"D. S."},{"family":"Walters","given":"M. B."}],"issued":{"date-parts":[["1998"]]}},"prefix":"for example coping with disturbances or frost ; "},{"id":28,"uris":[""],"uri":[""],"itemData":{"id":28,"type":"article-journal","title":"Some Evolutionary Consequences of Being a Tree","container-title":"Annual Review of Ecology, Evolution, and Systematics","page":"187-214","volume":"37","issue":"1","source":"Annual Reviews","abstract":"AbstractTrees do not form a natural group but share attributes such as great size, longevity, and high reproductive output that affect their mode and tempo of evolution. In particular, trees are unique in that they maintain high levels of diversity while accumulating new mutations only slowly. They are also capable of rapid local adaptation and can evolve quickly from nontree ancestors, but most existing tree lineages typically experience low speciation and extinction rates. We discuss why the tree growth habit should lead to these seemingly paradoxical features.","DOI":"10.1146/annurev.ecolsys.37.091305.110215","note":"00395","author":[{"family":"Petit","given":"Rémy J."},{"family":"Hampe","given":"Arndt"}],"issued":{"date-parts":[["2006"]]}}},{"id":2427,"uris":[""],"uri":[""],"itemData":{"id":2427,"type":"article-journal","title":"Leaf habit and woodiness regulate different leaf economy traits at a given nutrient supply","container-title":"Ecology","page":"3218-3228","volume":"91","issue":"11","source":"Wiley Online Library","abstract":"The large variation in the relationships between environmental factors and plant traits observed in natural communities exemplifies the alternative solutions that plants have developed in response to the same environmental limitations. Qualitative attributes, such as growth form, woodiness, and leaf habit can be used to approximate these alternative solutions. Here, we quantified the extent to which these attributes affect leaf trait values at a given resource supply level, using measured plant traits from 105 different species (254 observations) distributed across 50 sites in mesic to wet plant communities in The Netherlands. For each site, soil total N, soil total P, and water supply estimates were obtained by field measurements and modeling. Effects of growth forms, woodiness, and leaf habit on relations between leaf traits (SLA, specific leaf area; LNC, leaf nitrogen concentration; and LPC, leaf phosphorus concentration) vs. nutrient and water supply were quantified using maximum-likelihood methods and Bonferroni post hoc tests. The qualitative attributes explained 8–23% of the variance within sites in leaf traits vs. soil fertility relationships, and therefore they can potentially be used to make better predictions of global patterns of leaf traits in relation to nutrient supply. However, at a given soil fertility, the strength of the effect of each qualitative attribute was not the same for all leaf traits. These differences may imply a differential regulation of the leaf economy traits at a given nutrient supply, in which SLA and LPC seem to be regulated in accordance to changes in plant size and architecture while LNC seems to be primarily regulated at the leaf level by factors related to leaf longevity.","DOI":"10.1890/09-1509.1","ISSN":"1939-9170","language":"en","author":[{"family":"Ordo?ez","given":"Jenny C."},{"family":"Bodegom","given":"Peter M.","non-dropping-particle":"van"},{"family":"Witte","given":"Jan-Philip M."},{"family":"Bartholomeus","given":"Ruud P."},{"family":"Dobben","given":"Han F.","non-dropping-particle":"van"},{"family":"Aerts","given":"Rien"}],"issued":{"date-parts":[["2010",11,1]]}}}],"schema":""} (Ricklefs & Latham, 1992; Reich et al., 1998; Petit & Hampe, 2006; Ordo?ez et al., 2010). We therefore hypothesize that these two basic groups of plant strategies - herbaceous versus woody plants - should be analysed separately to better identify and understand spatial patterns of traits and trait-climate relationships.Here, we focus on the geographic patterns of plant functional traits across North and South America and ask: What are the spatial patterns of means and variances in trait values of woody and herbaceous assemblages and how do these patterns differ between growth forms? Which environmental drivers are related to these patterns, and do they have similar effects on both woody and herbaceous plants? We take advantage of two plant databases: 1) the database of species’ traits, occurrences and range maps covering the entire New World ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"08e9cBmG","properties":{"formattedCitation":"(Botanical Information and Ecology Network, or Bien; Enquist et al., 2016; Maitner et al., 2017)","plainCitation":"(Botanical Information and Ecology Network, or Bien; Enquist et al., 2016; Maitner et al., 2017)"},"citationItems":[{"id":2167,"uris":[""],"uri":[""],"itemData":{"id":2167,"type":"report","title":"Cyberinfrastructure for an integrated botanical information network to investigate the ecological impacts of global climate change on plant biodiversity","publisher":"PeerJ Preprints","source":"","abstract":"To answer many of the major questions in comparative botany, ecology, and global change biology it is necessary to extrapolate across enormous geographic, temporal and taxonomic scales. Yet much ecological knowledge is still based on observations conducted within a local area or even a few hundred square meters. Understanding ecological patterns and how plants respond to global warming and human alteration of landscapes and ecosystems necessitates a holistic approach. Such an approach must be conducted at a scale that is commensurate with the breadth of the questions being asked. Further, it requires identification, retrieval, and integration of diverse data from a global confederation of collaborating scientists across a broad range of disciplines. We propose to network core databases and data networks to create a novel resource for quantitative plant biodiversity science. The grand challenge is to assemble and share the world’s rapidly accumulating botanical information from plots and collections to create a distributed, web-accessible, readily analyzable data resource. With such a resource, we will answer major questions of direct relevance to plant ecology, plant evolution, plant geography, conservation, global change biology, and protection of biodiversity and ecosystem services. In particular, how does climate influence the distribution and abundance of plant species, how does the phylogenetic diversity of plants vary across broad environmental and climatic gradients, and how are plants assembled into ecological communities? While these and associated questions are at the core of many research endeavors in comparative botany and ecology, our past collective inability to integrate data on a large scale has significantly limited our ability to address these questions head on. This proposed Grand Challenge team will create a data resource of unprecedented size and scope together with the tools for its use, thereby empowering botanists and the general public to better address fundamental issues in plant ecology and global change biology. Although we will focus on plants of the New World, the infrastructure and protocols developed will be scalable to all geographic regions and all types of organisms. Future steps will enable cross-cutting linkages to emerging networks on plant genomics, physiology, and phylogeny, allowing us to address fundamental genetic and evolutionary questions at unprecedented spatial and temporal scales.","URL":"","note":"DOI: 10.7287/peerj.preprints.2615v2","number":"e2615v2","language":"en","author":[{"family":"Enquist","given":"Brian J."},{"family":"Condit","given":"Rick"},{"family":"Peet","given":"Robert K."},{"family":"Schildhauer","given":"Mark"},{"family":"Thiers","given":"Barbara M."}],"issued":{"date-parts":[["2016",12,6]]},"accessed":{"date-parts":[["2017",7,25]]}},"prefix":"Botanical Information and Ecology Network, or Bien; "},{"id":2746,"uris":[""],"uri":[""],"itemData":{"id":2746,"type":"article-journal","title":"The bien r package: A tool to access the Botanical Information and Ecology Network (BIEN) database","container-title":"Methods in Ecology and Evolution","source":"Google Scholar","shortTitle":"The bien r package","author":[{"family":"Maitner","given":"Brian S."},{"family":"Boyle","given":"Brad"},{"family":"Casler","given":"Nathan"},{"family":"Condit","given":"Rick"},{"family":"Donoghue","given":"John"},{"family":"Durán","given":"Sandra M."},{"family":"Guaderrama","given":"Daniel"},{"family":"Hinchliff","given":"Cody E."},{"family":"J?rgensen","given":"Peter M."},{"family":"Kraft","given":"Nathan JB"}],"issued":{"date-parts":[["2017"]]}}}],"schema":""} (Botanical Information and Ecology Network, or Bien; Enquist et al., 2016; Maitner et al., in press), and 2) the TRY Plant Trait Database (try-; ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"79lkbd642","properties":{"formattedCitation":"(Kattge et al., 2011)","plainCitation":"(Kattge et al., 2011)"},"citationItems":[{"id":684,"uris":[""],"uri":[""],"itemData":{"id":684,"type":"article-journal","title":"TRY – a global database of plant traits","container-title":"Global Change Biology","page":"2905-2935","volume":"17","issue":"9","source":"Wiley Online Library","abstract":"Plant traits – the morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs – determine how primary producers respond to environmental factors, affect other trophic levels, influence ecosystem processes and services and provide a link from species richness to ecosystem functional diversity. Trait data thus represent the raw material for a wide range of research from evolutionary biology, community and functional ecology to biogeography. Here we present the global database initiative named TRY, which has united a wide range of the plant trait research community worldwide and gained an unprecedented buy-in of trait data: so far 93 trait databases have been contributed. The data repository currently contains almost three million trait entries for 69?000 out of the world's 300?000 plant species, with a focus on 52 groups of traits characterizing the vegetative and regeneration stages of the plant life cycle, including growth, dispersal, establishment and persistence. A first data analysis shows that most plant traits are approximately log-normally distributed, with widely differing ranges of variation across traits. Most trait variation is between species (interspecific), but significant intraspecific variation is also documented, up to 40% of the overall variation. Plant functional types (PFTs), as commonly used in vegetation models, capture a substantial fraction of the observed variation – but for several traits most variation occurs within PFTs, up to 75% of the overall variation. In the context of vegetation models these traits would better be represented by state variables rather than fixed parameter values. The improved availability of plant trait data in the unified global database is expected to support a paradigm shift from species to trait-based ecology, offer new opportunities for synthetic plant trait research and enable a more realistic and empirically grounded representation of terrestrial vegetation in Earth system models.","DOI":"10.1111/j.1365-2486.2011.02451.x","ISSN":"1365-2486","note":"00349","language":"en","author":[{"family":"Kattge","given":"J."},{"family":"Díaz","given":"S."},{"family":"Lavorel","given":"S."},{"family":"Prentice","given":"I. C."},{"family":"Leadley","given":"P."},{"family":"B?nisch","given":"G."},{"family":"Garnier","given":"E."},{"family":"Westoby","given":"M."},{"family":"Reich","given":"P. B."},{"family":"Wright","given":"I. J."},{"family":"Cornelissen","given":"J. H. C."},{"family":"Violle","given":"C."},{"family":"Harrison","given":"S. P."},{"family":"Van BODEGOM","given":"P. M."},{"family":"Reichstein","given":"M."},{"family":"Enquist","given":"B. J."},{"family":"Soudzilovskaia","given":"N. A."},{"family":"Ackerly","given":"D. D."},{"family":"Anand","given":"M."},{"family":"Atkin","given":"O."},{"family":"Bahn","given":"M."},{"family":"Baker","given":"T. R."},{"family":"Baldocchi","given":"D."},{"family":"Bekker","given":"R."},{"family":"Blanco","given":"C. C."},{"family":"Blonder","given":"B."},{"family":"Bond","given":"W. J."},{"family":"Bradstock","given":"R."},{"family":"Bunker","given":"D. E."},{"family":"Casanoves","given":"F."},{"family":"Cavender-Bares","given":"J."},{"family":"Chambers","given":"J. Q."},{"family":"Chapin Iii","given":"F. S."},{"family":"Chave","given":"J."},{"family":"Coomes","given":"D."},{"family":"Cornwell","given":"W. K."},{"family":"Craine","given":"J. M."},{"family":"Dobrin","given":"B. H."},{"family":"Duarte","given":"L."},{"family":"Durka","given":"W."},{"family":"Elser","given":"J."},{"family":"Esser","given":"G."},{"family":"Estiarte","given":"M."},{"family":"Fagan","given":"W. F."},{"family":"Fang","given":"J."},{"family":"Fernández-Méndez","given":"F."},{"family":"Fidelis","given":"A."},{"family":"Finegan","given":"B."},{"family":"Flores","given":"O."},{"family":"Ford","given":"H."},{"family":"Frank","given":"D."},{"family":"Freschet","given":"G. T."},{"family":"Fyllas","given":"N. M."},{"family":"Gallagher","given":"R. V."},{"family":"Green","given":"W. A."},{"family":"Gutierrez","given":"A. G."},{"family":"Hickler","given":"T."},{"family":"Higgins","given":"S. I."},{"family":"Hodgson","given":"J. G."},{"family":"Jalili","given":"A."},{"family":"Jansen","given":"S."},{"family":"Joly","given":"C. A."},{"family":"Kerkhoff","given":"A. J."},{"family":"Kirkup","given":"D."},{"family":"Kitajima","given":"K."},{"family":"Kleyer","given":"M."},{"family":"Klotz","given":"S."},{"family":"Knops","given":"J. M. H."},{"family":"Kramer","given":"K."},{"family":"Kühn","given":"I."},{"family":"Kurokawa","given":"H."},{"family":"Laughlin","given":"D."},{"family":"Lee","given":"T. D."},{"family":"Leishman","given":"M."},{"family":"Lens","given":"F."},{"family":"Lenz","given":"T."},{"family":"Lewis","given":"S. L."},{"family":"Lloyd","given":"J."},{"family":"Llusià","given":"J."},{"family":"Louault","given":"F."},{"family":"Ma","given":"S."},{"family":"Mahecha","given":"M. D."},{"family":"Manning","given":"P."},{"family":"Massad","given":"T."},{"family":"Medlyn","given":"B. E."},{"family":"Messier","given":"J."},{"family":"Moles","given":"A. T."},{"family":"Müller","given":"S. C."},{"family":"Nadrowski","given":"K."},{"family":"Naeem","given":"S."},{"family":"Niinemets","given":"?."},{"family":"N?llert","given":"S."},{"family":"Nüske","given":"A."},{"family":"Ogaya","given":"R."},{"family":"Oleksyn","given":"J."},{"family":"Onipchenko","given":"V. G."},{"family":"Onoda","given":"Y."},{"family":"Ordo?ez","given":"J."},{"family":"Overbeck","given":"G."},{"family":"Ozinga","given":"W. A."},{"family":"Pati?o","given":"S."},{"family":"Paula","given":"S."},{"family":"Pausas","given":"J. G."},{"family":"Pe?uelas","given":"J."},{"family":"Phillips","given":"O. L."},{"family":"Pillar","given":"V."},{"family":"Poorter","given":"H."},{"family":"Poorter","given":"L."},{"family":"Poschlod","given":"P."},{"family":"Prinzing","given":"A."},{"family":"Proulx","given":"R."},{"family":"Rammig","given":"A."},{"family":"Reinsch","given":"S."},{"family":"Reu","given":"B."},{"family":"Sack","given":"L."},{"family":"Salgado-Negret","given":"B."},{"family":"Sardans","given":"J."},{"family":"Shiodera","given":"S."},{"family":"Shipley","given":"B."},{"family":"Siefert","given":"A."},{"family":"Sosinski","given":"E."},{"family":"Soussana","given":"J.-F."},{"family":"Swaine","given":"E."},{"family":"Swenson","given":"N."},{"family":"Thompson","given":"K."},{"family":"Thornton","given":"P."},{"family":"Waldram","given":"M."},{"family":"Weiher","given":"E."},{"family":"White","given":"M."},{"family":"White","given":"S."},{"family":"Wright","given":"S. J."},{"family":"Yguel","given":"B."},{"family":"Zaehle","given":"S."},{"family":"Zanne","given":"A. E."},{"family":"Wirth","given":"C."}],"issued":{"date-parts":[["2011",9,1]]}}}],"schema":""} Kattge et al., 2011). We use two types of species distribution data: species occurrences and species range maps. Whereas species occurrences data are likely biased by uneven sampling intensity, most species range maps are modelled using climate variables (besides the spatial filters), which can result in some circularity in the trait-climate correlations. Therefore, we restricted the analyses to occurrence data only and use species range maps to verify the occurrence-based spatial trait patterns. We examine plant traits related to key plant ecological strategies ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"27aifi86m0","properties":{"formattedCitation":"{\\rtf (D\\uc0\\u237{}az et al., 2016)}","plainCitation":"(Díaz et al., 2016)"},"citationItems":[{"id":2515,"uris":[""],"uri":[""],"itemData":{"id":2515,"type":"article-journal","title":"The global spectrum of plant form and function","container-title":"Nature","page":"167-171","volume":"529","issue":"7585","source":"","abstract":"Earth is home to a remarkable diversity of plant forms and life histories, yet comparatively few essential trait combinations have proved evolutionarily viable in today’s terrestrial biosphere. By analysing worldwide variation in six major traits critical to growth, survival and reproduction within the largest sample of vascular plant species ever compiled, we found that occupancy of six-dimensional trait space is strongly concentrated, indicating coordination and trade-offs. Three-quarters of trait variation is captured in a two-dimensional global spectrum of plant form and function. One major dimension within this plane reflects the size of whole plants and their parts; the other represents the leaf economics spectrum, which balances leaf construction costs against growth potential. The global plant trait spectrum provides a backdrop for elucidating constraints on evolution, for functionally qualifying species and ecosystems, and for improving models that predict future vegetation based on continuous variation in plant form and function.\nView full text","DOI":"10.1038/nature16489","ISSN":"0028-0836","journalAbbreviation":"Nature","language":"en","author":[{"family":"Díaz","given":"Sandra"},{"family":"Kattge","given":"Jens"},{"family":"Cornelissen","given":"Johannes H. C."},{"family":"Wright","given":"Ian J."},{"family":"Lavorel","given":"Sandra"},{"family":"Dray","given":"Stéphane"},{"family":"Reu","given":"Bj?rn"},{"family":"Kleyer","given":"Michael"},{"family":"Wirth","given":"Christian"},{"family":"Colin Prentice","given":"I."},{"family":"Garnier","given":"Eric"},{"family":"B?nisch","given":"Gerhard"},{"family":"Westoby","given":"Mark"},{"family":"Poorter","given":"Hendrik"},{"family":"Reich","given":"Peter B."},{"family":"Moles","given":"Angela T."},{"family":"Dickie","given":"John"},{"family":"Gillison","given":"Andrew N."},{"family":"Zanne","given":"Amy E."},{"family":"Chave","given":"Jér?me"},{"family":"Joseph Wright","given":"S."},{"family":"Sheremet’ev","given":"Serge N."},{"family":"Jactel","given":"Hervé"},{"family":"Baraloto","given":"Christopher"},{"family":"Cerabolini","given":"Bruno"},{"family":"Pierce","given":"Simon"},{"family":"Shipley","given":"Bill"},{"family":"Kirkup","given":"Donald"},{"family":"Casanoves","given":"Fernando"},{"family":"Joswig","given":"Julia S."},{"family":"Günther","given":"Angela"},{"family":"Falczuk","given":"Valeria"},{"family":"Rüger","given":"Nadja"},{"family":"Mahecha","given":"Miguel D."},{"family":"Gorné","given":"Lucas D."}],"issued":{"date-parts":[["2016",1,14]]}}}],"schema":""} (Díaz et al., 2016): plant height, specific leaf area and seed mass to represent major axes of plant strategies ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"om6xRes2","properties":{"formattedCitation":"(Westoby, 1998)","plainCitation":"(Westoby, 1998)"},"citationItems":[{"id":632,"uris":[""],"uri":[""],"itemData":{"id":632,"type":"article-journal","title":"A leaf-height-seed (LHS) plant ecology strategy scheme","container-title":"Plant and soil","page":"213–227","volume":"199","issue":"2","source":"Google Scholar","call-number":"0563","author":[{"family":"Westoby","given":"Mark"}],"issued":{"date-parts":[["1998"]]}}}],"schema":""} (Westoby, 1998), and leaf nitrogen and phosphorus concentrations per mass as resource-use related traits ADDIN EN.CITE <EndNote><Cite><Author>Reich</Author><Year>2005</Year><RecNum>1191</RecNum><record><database name='Cyrille_biblio_1.enl' path='C:\Users\violle\Desktop\NEW_CV_20012013\Cyrille_biblio_1.enl'>Cyrille_biblio_1.enl</database><source-app name='EndNote' version='8.0'>EndNote</source-app><rec-number>1191</rec-number><ref-type name='Journal Article'>17</ref-type><contributors><authors><author><style face='normal' font='default' size='100%'>Reich, PB</style></author></authors></contributors><titles><title><style face='normal' font='default' size='100%'>Global biogeography of plant chemistry: filling in the blanks</style></title><secondary-title><style face='normal' font='default' size='100%'>New Phytol</style></secondary-title></titles><periodical><full-title><style face='normal' font='default' size='100%'>New Phytol</style></full-title></periodical><pages><style face='normal' font='default' size='100%'>263-268</style></pages><volume><style face='normal' font='default' size='100%'>168</style></volume><dates><year><style face='normal' font='default' size='100%'>2005</style></year></dates><urls></urls></record></Cite><Cite><Author>Chown</Author><Year>2008</Year><RecNum>1715</RecNum><record><database name='Cyrille_biblio_1.enl' path='C:\Users\violle\Desktop\NEW_CV_20012013\Cyrille_biblio_1.enl'>Cyrille_biblio_1.enl</database><source-app name='EndNote' version='8.0'>EndNote</source-app><rec-number>1715</rec-number><ref-type name='Journal Article'>17</ref-type><contributors><authors><author><style face='normal' font='default' size='100%'>Chown, S.L.</style></author><author><style face='normal' font='default' size='100%'>Gaston, K.J.</style></author></authors></contributors><titles><title><style face='normal' font='default' size='100%'>Macrophysiology for a changing world</style></title><secondary-title><style face='normal' font='default' size='100%'>Proc Roy Soc B</style></secondary-title></titles><pages><style face='normal' font='default' size='100%'>1469-1478</style></pages><volume><style face='normal' font='default' size='100%'>275</style></volume><dates><year><style face='normal' font='default' size='100%'>2008</style></year></dates><urls></urls></record></Cite></EndNote>(Chown & Gaston, 2008; Reich, 2005). We also include wood density as a key functional trait for trees ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"C0DFoyWC","properties":{"formattedCitation":"(Chave et al., 2009)","plainCitation":"(Chave et al., 2009)"},"citationItems":[{"id":187,"uris":[""],"uri":[""],"itemData":{"id":187,"type":"article-journal","title":"Towards a worldwide wood economics spectrum","container-title":"Ecology Letters","page":"351–366","volume":"12","issue":"4","source":"Google Scholar","call-number":"0244","note":"00519","author":[{"family":"Chave","given":"Jerome"},{"family":"Coomes","given":"David"},{"family":"Jansen","given":"Steven"},{"family":"Lewis","given":"Simon L."},{"family":"Swenson","given":"Nathan G."},{"family":"Zanne","given":"Amy E."}],"issued":{"date-parts":[["2009"]]}}}],"schema":""} (Chave et al., 2009). MethodsSpecies distribution dataThe BIEN (Botanical Information and Ecology Network) 3.0 database () integrates 20,465,306 plant observations that have been standardized for taxonomy and georeferences and that have their coordinates within North or South America (retrieved on 13.11. 2014; see Appendix 1 for the reference list). Observations stem from herbarium specimens and vegetation plot inventories. The BIEN 3.0 dataset consists of 114,412 plant species in the continental New World. Most of these data are now publicly available via the BIEN R package ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"a1vejjk0661","properties":{"formattedCitation":"(Maitner et al., 2017)","plainCitation":"(Maitner et al., 2017)"},"citationItems":[{"id":2746,"uris":[""],"uri":[""],"itemData":{"id":2746,"type":"article-journal","title":"The bien r package: A tool to access the Botanical Information and Ecology Network (BIEN) database","container-title":"Methods in Ecology and Evolution","source":"Google Scholar","shortTitle":"The bien r package","author":[{"family":"Maitner","given":"Brian S."},{"family":"Boyle","given":"Brad"},{"family":"Casler","given":"Nathan"},{"family":"Condit","given":"Rick"},{"family":"Donoghue","given":"John"},{"family":"Durán","given":"Sandra M."},{"family":"Guaderrama","given":"Daniel"},{"family":"Hinchliff","given":"Cody E."},{"family":"J?rgensen","given":"Peter M."},{"family":"Kraft","given":"Nathan JB"}],"issued":{"date-parts":[["2017"]]}}}],"schema":""} (Maitner et al., in press) with some exceptions concerning the coordinates of endangered species and records from private databases ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"a1i4bfbjii3","properties":{"formattedCitation":"(see Maitner et al., 2017 for details)","plainCitation":"(see Maitner et al., 2017 for details)"},"citationItems":[{"id":2746,"uris":[""],"uri":[""],"itemData":{"id":2746,"type":"article-journal","title":"The bien r package: A tool to access the Botanical Information and Ecology Network (BIEN) database","container-title":"Methods in Ecology and Evolution","source":"Google Scholar","shortTitle":"The bien r package","author":[{"family":"Maitner","given":"Brian S."},{"family":"Boyle","given":"Brad"},{"family":"Casler","given":"Nathan"},{"family":"Condit","given":"Rick"},{"family":"Donoghue","given":"John"},{"family":"Durán","given":"Sandra M."},{"family":"Guaderrama","given":"Daniel"},{"family":"Hinchliff","given":"Cody E."},{"family":"J?rgensen","given":"Peter M."},{"family":"Kraft","given":"Nathan JB"}],"issued":{"date-parts":[["2017"]]}},"prefix":"see","suffix":"for details"}],"schema":""} (see Maitner et al., in press for details). As an additional species distribution dataset, we used the BIEN 2.0 range maps available for 88,417 New World species ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"5e6bCone","properties":{"formattedCitation":"(Enquist et al. in prep; Goldsmith et al., 2016)","plainCitation":"(Enquist et al. in prep; Goldsmith et al., 2016)"},"citationItems":[{"id":2430,"uris":[""],"uri":[""],"itemData":{"id":2430,"type":"article-journal","title":"Plant-O-Matic: a dynamic and mobile guide to all plants of the Americas","container-title":"Methods in Ecology and Evolution","page":"960-965","volume":"7","issue":"8","source":"Wiley Online Library","abstract":"* Advances in both informatics and mobile technology are providing exciting new opportunities for generating, disseminating, and engaging with information in the biological sciences at unprecedented spatial scales, particularly in disentangling information on the distributions and natural history of hyperdiverse groups of organisms.\n\n\n* We describe an application serving as a mobile catalog of all of the plants of the Americas developed using species distribution models estimated from field observations of plant occurrences. The underlying data comprise over 3·5 million standardized observations of over 88?000 plant species.\n\n\n* Plant-O-Matic, a free iOS application, combines the species distribution models with the location services built into a mobile device to provide users with a list of all plant species expected to occur in the 100?×?100?km geographic grid cell corresponding to the user's location. The application also provides ancillary information on species’ attributes (when available) including growth form, reproductive mode, flower color, and common name. Results can be searched and conditionally filtered based on these attributes. Links to externally sourced specimen images further aid in identification of species by the user.\n\n\n* The application's ability to assemble locally relevant lists of plant species and their attributes on demand for anywhere in the Americas provides a powerful new tool for identifying, exploring, and understanding plant diversity. Mobile applications such as Plant-O-Matic can facilitate dynamic new approaches to science, conservation, and science education.","DOI":"10.1111/2041-210X.12548","ISSN":"2041-210X","shortTitle":"Plant-O-Matic","journalAbbreviation":"Methods Ecol Evol","language":"en","author":[{"family":"Goldsmith","given":"Gregory R."},{"family":"Morueta-Holme","given":"Naia"},{"family":"Sandel","given":"Brody"},{"family":"Fitz","given":"Eric D."},{"family":"Fitz","given":"Samuel D."},{"family":"Boyle","given":"Brad"},{"family":"Casler","given":"Nathan"},{"family":"Engemann","given":"Kristine"},{"family":"J?rgensen","given":"Peter M."},{"family":"Kraft","given":"Nathan J. B."},{"family":"McGill","given":"Brian"},{"family":"Peet","given":"Robert K."},{"family":"Piel","given":"William H."},{"family":"Spencer","given":"Nick"},{"family":"Svenning","given":"Jens-Christian"},{"family":"Thiers","given":"Barbara M."},{"family":"Violle","given":"Cyrille"},{"family":"Wiser","given":"Susan K."},{"family":"Enquist","given":"Brian J."}],"issued":{"date-parts":[["2016",3,1]]}},"prefix":"Enquist et al. in prep;"}],"schema":""} (Goldsmith et al., 2016). The method of building the range maps differed depending on the number of occurrences per species available in the database: A species with only one or two occurrence records was assigned a fixed range of 75,000 km2 surrounding each occurrence point. Species with 3-4 records had their ranges defined as convex hulls. Ranges for species with >5 records were modelled using the Maxent species distribution modelling algorithm with a balanced set of climate predictors and spatial eigenvectors ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"8737q29jq","properties":{"formattedCitation":"(Phillips et al., 2006)","plainCitation":"(Phillips et al., 2006)"},"citationItems":[{"id":2011,"uris":[""],"uri":[""],"itemData":{"id":2011,"type":"article-journal","title":"Maximum entropy modeling of species geographic distributions","container-title":"Ecological Modelling","page":"231-259","volume":"190","issue":"3–4","source":"ScienceDirect","abstract":"The availability of detailed environmental data, together with inexpensive and powerful computers, has fueled a rapid increase in predictive modeling of species environmental requirements and geographic distributions. For some species, detailed presence/absence occurrence data are available, allowing the use of a variety of standard statistical techniques. However, absence data are not available for most species. In this paper, we introduce the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data. Maxent is a general-purpose machine learning method with a simple and precise mathematical formulation, and it has a number of aspects that make it well-suited for species distribution modeling. In order to investigate the efficacy of the method, here we perform a continental-scale case study using two Neotropical mammals: a lowland species of sloth, Bradypus variegatus, and a small montane murid rodent, Microryzomys minutus. We compared Maxent predictions with those of a commonly used presence-only modeling method, the Genetic Algorithm for Rule-Set Prediction (GARP). We made predictions on 10 random subsets of the occurrence records for both species, and then used the remaining localities for testing. Both algorithms provided reasonable estimates of the species’ range, far superior to the shaded outline maps available in field guides. All models were significantly better than random in both binomial tests of omission and receiver operating characteristic (ROC) analyses. The area under the ROC curve (AUC) was almost always higher for Maxent, indicating better discrimination of suitable versus unsuitable areas for the species. The Maxent modeling approach can be used in its present form for many applications with presence-only datasets, and merits further research and development.","DOI":"10.1016/j.ecolmodel.2005.03.026","ISSN":"0304-3800","note":"06936","journalAbbreviation":"Ecological Modelling","author":[{"family":"Phillips","given":"Steven J."},{"family":"Anderson","given":"Robert P."},{"family":"Schapire","given":"Robert E."}],"issued":{"date-parts":[["2006",1,25]]}}}],"schema":""} (Phillips et al., 2006; see Goldsmith et al., 2016 for details on the range maps methodology). We overlaid the BIEN 3.0 occurrences on a 200 × 200 km grid (Lambert Azimuthal Equal Area projection) to obtain a species list for each grid cell. We repeated the same procedure with species’ range maps of BIEN 2.0. We chose this resolution as it is robust to potential overestimation of area of occupancy by individual species derived from range maps ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"Q0HFJT6D","properties":{"formattedCitation":"(Hurlbert & Jetz, 2007)","plainCitation":"(Hurlbert & Jetz, 2007)"},"citationItems":[{"id":325,"uris":[""],"uri":[""],"itemData":{"id":325,"type":"article-journal","title":"Species richness, hotspots, and the scale dependence of range maps in ecology and conservation","container-title":"Proceedings of the National Academy of Sciences","page":"13384–13389","volume":"104","issue":"33","source":"Google Scholar","author":[{"family":"Hurlbert","given":"Allen H."},{"family":"Jetz","given":"Walter"}],"issued":{"date-parts":[["2007"]]}}}],"schema":""} (Hurlbert & Jetz, 2007). We only included cells with more than 80% of their area on land. Trait dataWe analysed variation in six functional traits: maximum plant height [m], specific leaf area (SLA) [cm2/g], seed mass [mg], leaf phosphorus and leaf nitrogen concentration per mass (Leaf N and Leaf P) [mg/g], and wood density [g/m2]. We combined the BIEN and TRY trait data (retrieved on 19.10. 2014; a list of the data sources is found in Appendix 1). Merging TRY and BIEN resulted in the largest plant trait compilation for North and South America to date, including more than 70,000 species-level observations for the six plant traits used in the study.Growth form data were taken from ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"DePu9bl1","properties":{"formattedCitation":"(Engemann et al., 2016)","plainCitation":"(Engemann et al., 2016)"},"citationItems":[{"id":2569,"uris":[""],"uri":[""],"itemData":{"id":2569,"type":"article-journal","title":"A plant growth form dataset for the New World","container-title":"Ecology","page":"3243-3243","volume":"97","issue":"11","source":"Wiley Online Library","abstract":"This dataset provides growth form classifications for 67,413 vascular plant species from North, Central, and South America. The data used to determine growth form were compiled from five major integrated sources and two original publications: the Botanical Information and Ecology Network (BIEN), the Plant Trait Database (TRY), the SALVIAS database, the USDA PLANTS database, Missouri Botanical Garden's Tropicos database, Wright (2010), and Boyle (1996). We defined nine plant growth forms based on woodiness (woody or non-woody), shoot structure (self-supporting or not self-supporting), and root traits (rooted in soil, not rooted in soil, parasitic or aquatic): Epiphyte, Liana, Vine, Herb, Shrub, Tree, Parasite, or Aquatic. Species with multiple growth form classifications were assigned the growth form classification agreed upon by the majority (>2/3) of sources. Species with ambiguous or otherwise not interpretable growth form assignments were excluded from the final dataset but are made available with the original data. Comparisons with independent estimates of species richness for the Western hemisphere suggest that our final dataset includes the majority of New World vascular plant species. Coverage is likely more complete for temperate than for tropical species. In addition, aquatic species are likely under-represented. Nonetheless, this dataset represents the largest compilation of plant growth forms published to date, and should contribute to new insights across a broad range of research in systematics, ecology, biogeography, conservation, and global change science.","DOI":"10.1002/ecy.1569","ISSN":"1939-9170","journalAbbreviation":"Ecology","language":"en","author":[{"family":"Engemann","given":"K."},{"family":"Sandel","given":"B."},{"family":"Boyle","given":"B."},{"family":"Enquist","given":"B. J."},{"family":"J?rgensen","given":"P. M."},{"family":"Kattge","given":"J."},{"family":"McGill","given":"B. J."},{"family":"Morueta-Holme","given":"N."},{"family":"Peet","given":"R. K."},{"family":"Spencer","given":"N. J."},{"family":"Violle","given":"C."},{"family":"Wiser","given":"S. K."},{"family":"Svenning","given":"J.-C."}],"issued":{"date-parts":[["2016",11,1]]}}}],"schema":""} Engemann et al. (2016). Species with more than one growth form assignment were included only if >2/3 of the observations of a given species agreed on one growth form (see Engemann et al. 2016 for details). We split the species data into two functional groups: “woody” and “herbaceous”. We considered plants scored as tree, shrub, or liana as “woody”, whereas “herbaceous” were represented by those scored as herbs, grasses, ferns, vines and epiphytes. We excluded mosses and aquatic species. We were able to assign a growth form to 47,784 species having georeferenced occurrence records (21,390 woody and 26,394 herbaceous species). A subset of those species was extracted and we obtained 6107 woody and 6056 herbaceous species with at least one known trait value. The best coverage was for seed mass (3060 woody and 5259 herbaceous species) whereas the lowest coverage was for leaf P (1754 woody and 808 herbaceous species) (see Figs. S2.1-S2.2 and Table S2.1 for details of trait coverage). Prior to analyses, we loge-transformed the values of seed mass, height and wood density to correct for skewness in trait distributions and to improve the normality of the residuals in the fitted statistical models. Additionally, we checked for outlying trait values and manually removed unrealistic outliers assumed to be probable errors in trait observations (ten values total).Environmental dataWe included six climatic predictors that have been commonly used in trait-based studies and/or represent different aspects of climate affecting plant ecophysiology ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"D5wwHFjx","properties":{"formattedCitation":"(e.g. Larcher, 2003; Lambers et al., 2008)","plainCitation":"(e.g. Larcher, 2003; Lambers et al., 2008)"},"citationItems":[{"id":297,"uris":[""],"uri":[""],"itemData":{"id":297,"type":"book","title":"Physiological plant ecology: ecophysiology and stress physiology of functional groups","publisher":"Springer Science & Business Media","source":"Google Scholar","URL":"","note":"05085","shortTitle":"Physiological plant ecology","author":[{"family":"Larcher","given":"Walter"}],"issued":{"date-parts":[["2003"]]},"accessed":{"date-parts":[["2015",5,6]]}},"prefix":"e.g. "},{"id":192,"uris":[""],"uri":[""],"itemData":{"id":192,"type":"book","title":"Plant Physiological Ecology","publisher":"Springer New York","publisher-place":"New York, NY","source":"CrossRef","event-place":"New York, NY","URL":"","ISBN":"978-0-387-78340-6","note":"00056","language":"en","author":[{"family":"Lambers","given":"Hans"},{"family":"Chapin","given":"F. Stuart"},{"family":"Pons","given":"Thijs L."}],"issued":{"date-parts":[["2008"]]},"accessed":{"date-parts":[["2015",10,14]]}}}],"schema":""} (e.g. Larcher, 2003; Lambers et al., 2008). Mean annual temperature [°C], annual precipitation sum [mm], temperature seasonality (standard deviation of monthly temperature multiplied by 100) and precipitation seasonality (coefficient of variation of monthly precipitation) were taken from the WorldClim database ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"pYu3YFZq","properties":{"formattedCitation":"(, Hijmans et al., 2005)","plainCitation":"(, Hijmans et al., 2005)"},"citationItems":[{"id":733,"uris":[""],"uri":[""],"itemData":{"id":733,"type":"article-journal","title":"Very high resolution interpolated climate surfaces for global land areas","container-title":"International journal of climatology","page":"1965–1978","volume":"25","issue":"15","source":"Google Scholar","call-number":"3153","author":[{"family":"Hijmans","given":"Robert J."},{"family":"Cameron","given":"Susan E."},{"family":"Parra","given":"Juan L."},{"family":"Jones","given":"Peter G."},{"family":"Jarvis","given":"Andy"}],"issued":{"date-parts":[["2005"]]}},"prefix":", "}],"schema":""} (, Hijmans et al., 2005). We also included mean annual solar radiation obtained from CliMond ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"mWWZGxBq","properties":{"formattedCitation":"(; Kriticos et al., 2012)","plainCitation":"(; Kriticos et al., 2012)"},"citationItems":[{"id":2267,"uris":[""],"uri":[""],"itemData":{"id":2267,"type":"article-journal","title":"CliMond: global high-resolution historical and future scenario climate surfaces for bioclimatic modelling","container-title":"Methods in Ecology and Evolution","page":"53-64","volume":"3","issue":"1","source":"Wiley Online Library","abstract":"1.?Gridded climatologies have become an indispensable component of bioclimatic modelling, with a range of applications spanning conservation and pest management. Such globally conformal data sets of historical and future scenario climate surfaces are required to model species potential ranges under current and future climate scenarios. 2.?We developed a set of interpolated climate surfaces at 10′ and 30′ resolution for global land areas excluding Antarctica. Input data for the baseline climatology were gathered from the WorldClim and CRU CL1·0 and CL2·0 data sets. A set of future climate scenarios were generated at 10′ resolution. For each of the historical and future scenario data sets, the full set of 35 Bioclim variables was generated. Climate variables (including relative humidity at 0900 and 1500?hours) were also generated in CLIMEX format. The K?ppen–Geiger climate classification scheme was applied to the 10′ hybrid climatology as a tool for visualizing climatic patterns and as an aid for specifying absence or background data for correlative modelling applications. 3.?We tested the data set using a correlative model (MaxEnt) addressing conservation biology concerns for a rare Australian shrub, and a mechanistic niche model (CLIMEX) to map climate suitability for two invasive species. In all cases, the underlying climatology appeared to behave in a robust manner. 4.? This global climate data set has the advantage over the WorldClim data set of including humidity data and an additional 16 Bioclim variables. Compared with the CRU CL2·0 data set, the hybrid 10′ data set includes improved precipitation estimates as well as projected climate for two global climate models running relevant greenhouse gas emission scenarios. 5.?For many bioclimatic modelling purposes, there is an operational attraction to having a globally conformal historical climatology and future climate scenarios for the assessments of potential climate change impacts. Our data set is known as ‘CliMond’ and is available for free download from .","DOI":"10.1111/j.2041-210X.2011.00134.x","ISSN":"2041-210X","shortTitle":"CliMond","language":"en","author":[{"family":"Kriticos","given":"Darren J."},{"family":"Webber","given":"Bruce L."},{"family":"Leriche","given":"Agathe"},{"family":"Ota","given":"Noboru"},{"family":"Macadam","given":"Ian"},{"family":"Bathols","given":"Janice"},{"family":"Scott","given":"John K."}],"issued":{"date-parts":[["2012",2,1]]}},"prefix":"; "}],"schema":""} (; Kriticos et al., 2012). We added the global aridity index obtained from CGIAR-CSI GeoPortal ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"a68ltd8a21","properties":{"formattedCitation":"(; Trabucco & Zomer, 2010)","plainCitation":"(; Trabucco & Zomer, 2010)"},"citationItems":[{"id":2270,"uris":[""],"uri":[""],"itemData":{"id":2270,"type":"article-journal","title":"Global soil water balance geospatial database","container-title":"CGIAR Consortium for Spatial Information, Published online, available from the CGIAR-CSI GeoPortal at: . cgiar-csi. org (last access: January 2013)","source":"Google Scholar","author":[{"family":"Trabucco","given":"A."},{"family":"Zomer","given":"R. J."}],"issued":{"date-parts":[["2010"]]}},"prefix":"; "}],"schema":""} (; Trabucco & Zomer, 2010). This index is calculated as the ratio of annual precipitation to potential evapotranspiration, with higher values of this index representing lower aridity. We projected the climate variables to the Lambert Azimuthal Equal Area projection using nearest neighbour interpolation and resampled each variable to 200 × 200 km grid size by computing mean values for each grid cell using the R raster package ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"3Fe5aqjc","properties":{"formattedCitation":"(Hijmans et al., 2016; R Development Core Team, 2017)","plainCitation":"(Hijmans et al., 2016; R Development Core Team, 2017)"},"citationItems":[{"id":648,"uris":[""],"uri":[""],"itemData":{"id":648,"type":"book","title":"R: A language and environment for statistical computing","publisher":"R Foundation for Statistical Computing","publisher-place":"Vienna, Austria","version":"3.4.0","event-place":"Vienna, Austria","URL":"","ISBN":"9999","call-number":"0000","shortTitle":"R: A language and environment for statistical computing","author":[{"family":"R Development Core Team","given":""}],"issued":{"date-parts":[["2017"]]}}},{"id":2170,"uris":[""],"uri":[""],"itemData":{"id":2170,"type":"book","title":"raster: Geographic Data Analysis and Modeling version 2.5-8. ","version":"2.5-8","source":"R-Packages","abstract":"Reading, writing, manipulating, analyzing and modeling of gridded spatial data. The package implements basic and high-level functions. Processing of very large files is supported.","URL":"","shortTitle":"raster","author":[{"family":"Hijmans","given":"Robert J."},{"family":"Etten","given":"Jacob","dropping-particle":"van"},{"family":"Cheng","given":"Joe"},{"family":"Mattiuzzi","given":"Matteo"},{"family":"Sumner","given":"Michael"},{"family":"Greenberg","given":"Jonathan A."},{"family":"Lamigueiro","given":"Oscar Perpinan"},{"family":"Bevan","given":"Andrew"},{"family":"Racine","given":"Etienne B."},{"family":"Shortridge","given":"Ashton"}],"issued":{"date-parts":[["2016",6,2]]},"accessed":{"date-parts":[["2017",7,25]]}}}],"schema":""} (Hijmans et al., 2016; R Development Core Team, 2017). Data analysesWe first coupled the species occurrences from each grid cell to the species-level trait data. Next, using species’ trait values per grid cell, we calculated per-cell mean and variance for each trait and repeated this calculation using the species occurrences inferred from the range maps. We separately mapped trait patterns based on species occurrences and on occurrences inferred from the range maps. We also separated woody and herbaceous species (except for wood density, which only applies to woody species). Trait maps based on species occurrences per grid cell can be spatially biased, because of differential sampling intensity and the presence of species with extreme trait values ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"epJa1oB1","properties":{"formattedCitation":"(Borgy et al., 2017b)","plainCitation":"(Borgy et al., 2017b)"},"citationItems":[{"id":2164,"uris":[""],"uri":[""],"itemData":{"id":2164,"type":"article-journal","title":"Sensitivity of community-level trait–environment relationships to data representativeness: A test for functional biogeography","container-title":"Global Ecology and Biogeography","page":"729-739","volume":"26","issue":"6","source":"Wiley Online Library","abstract":"Aim\n\nThe characterization of trait–environment relationships over broad-scale gradients is a critical goal for ecology and biogeography. This implies the merging of plot and trait databases to assess community-level trait-based statistics. Potential shortcomings and limitations of this approach are that: (i) species traits are not measured where the community is sampled and (ii) the availability of trait data varies considerably across species and plots. Here we address the effect of trait data representativeness [the sampling effort per species and per plot] on the accuracy of (i) species-level and (ii) community-level trait estimates and (iii) the consequences for the shape and strength of trait–environment relationships across communities.\n\n\nInnovation\n\nWe combined information existing in databases of vegetation plots and plant traits to estimate community-weighted means [CWMs] of four key traits [specific leaf area, plant height, seed mass and leaf nitrogen content per dry mass] in permanent grasslands at a country-wide scale. We propose a generic approach for systematic sensitivity analyses based on random subsampling and data reduction to address the representativeness of incomplete and heterogeneous trait information when exploring trait–environment relationships across communities.\n\n\nMain conclusions\n\nThe accuracy of the CWMs was little affected by the number of individual trait values per species [NIV] but strongly affected by the cover proportion of species with available trait values [PCover]. A PCover above 80% was required for all four traits studied to obtain an estimation bias below 5%. Our approach therefore provides more conservative criteria than previously proposed. Restrictive criteria on both NIV and PCover primarily excluded communities in harsh environments, and such reduction of the sampled gradient weakened trait–environment relationships. These findings advocate systematic measurement campaigns in natural environments to increase species coverage in global trait databases, with special emphasis on species occurring in under-sampled and harsh environmental conditions.","DOI":"10.1111/geb.12573","ISSN":"1466-8238","shortTitle":"Sensitivity of community-level trait–environment relationships to data representativeness","journalAbbreviation":"Global Ecol. Biogeogr.","language":"en","author":[{"family":"Borgy","given":"Benjamin"},{"family":"Violle","given":"Cyrille"},{"family":"Choler","given":"Philippe"},{"family":"Garnier","given":"Eric"},{"family":"Kattge","given":"Jens"},{"family":"Loranger","given":"Jessy"},{"family":"Amiaud","given":"Bernard"},{"family":"Cellier","given":"Pierre"},{"family":"Debarros","given":"Guilhem"},{"family":"Denelle","given":"Pierre"},{"family":"Diquelou","given":"Sylvain"},{"family":"Gachet","given":"Sophie"},{"family":"Jolivet","given":"Claudy"},{"family":"Lavorel","given":"Sandra"},{"family":"Lemauviel-Lavenant","given":"Servane"},{"family":"Mikolajczak","given":"Alexis"},{"family":"Munoz","given":"Fran?ois"},{"family":"Olivier","given":"Jean"},{"family":"Viovy","given":"Nicolas"}],"issued":{"date-parts":[["2017",6,1]]}}}],"schema":""} (Borgy et al., 2017b). To address this we excluded grid cells with a higher variance than the 99% quantile for the respective traits (Figs. S2.3) and two grid cells of extremely high values of mean leaf N and SLA.We used spatial correlations to compare the similarity in geographical patterns of both woody and herbaceous trait means and variances based on species occurrences versus species occurrences inferred from the range maps. We used the Pearson correlation coefficient and Dutilleul’s method of correction for degrees of freedom to account for spatial autocorrelation ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"D4Pa9rcS","properties":{"formattedCitation":"(package SpatialPack; Osorio & Vallejos, 2014)","plainCitation":"(package SpatialPack; Osorio & Vallejos, 2014)"},"citationItems":[{"id":2520,"uris":[""],"uri":[""],"itemData":{"id":2520,"type":"book","title":"SpatialPack: Package for analysis of spatial data. R package version 0.2-3","URL":"CRAN.package=SpatialPack","author":[{"family":"Osorio","given":"Felipe"},{"family":"Vallejos","given":"Ronny"}],"issued":{"date-parts":[["2014"]]}},"prefix":"package SpatialPack;"}],"schema":""} (package SpatialPack; Osorio & Vallejos, 2014). Next, we searched for climatic predictors of trait means and variances using model selection according to the AIC weight ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"VJEGOSWL","properties":{"formattedCitation":"(Burnham & Anderson, 2002; Wagenmakers & Farrell, 2004)","plainCitation":"(Burnham & Anderson, 2002; Wagenmakers & Farrell, 2004)"},"citationItems":[{"id":603,"uris":[""],"uri":[""],"itemData":{"id":603,"type":"book","title":"Model selection and multimodel inference: a practical information-theoretic approach","publisher":"Springer","source":"Google Scholar","URL":"","shortTitle":"Model selection and multimodel inference","author":[{"family":"Burnham","given":"Kenneth P."},{"family":"Anderson","given":"David R."}],"issued":{"date-parts":[["2002"]]},"accessed":{"date-parts":[["2014",4,10]]}}},{"id":2272,"uris":[""],"uri":[""],"itemData":{"id":2272,"type":"article-journal","title":"AIC model selection using Akaike weights","container-title":"Psychonomic Bulletin & Review","page":"192-196","volume":"11","issue":"1","source":"link.","abstract":"The Akaike information criterion (AIC; Akaike, 1973) is a popular method for comparing the adequacy of multiple, possibly nonnested models. Current practice in cognitive psychology is to accept a single model on the basis of only the “raw” AIC values, making it difficult to unambiguously interpret the observed AIC differences in terms of a continuous measure such as probability. Here we demonstrate that AIC values can be easily transformed to so-called Akaike weights (e.g., Akaike, 1978, 1979; Bozdogan, 1987; Burnham & Anderson, 2002), which can be directly interpreted as conditional probabilities for each model. We show by example how these Akaike weights can greatly facilitate the interpretation of the results of AIC model comparison procedures.","DOI":"10.3758/BF03206482","ISSN":"1069-9384, 1531-5320","journalAbbreviation":"Psychonomic Bulletin & Review","language":"en","author":[{"family":"Wagenmakers","given":"Eric-Jan"},{"family":"Farrell","given":"Simon"}],"issued":{"date-parts":[["2004",2,1]]}}}],"schema":""} (Burnham & Anderson, 2002; Wagenmakers & Farrell, 2004). We used the ‘dredge’ function in the R MuMln package ( ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2hi0s07gr2","properties":{"formattedCitation":"(Barton, 2016)","plainCitation":"(Barton, 2016)"},"citationItems":[{"id":2009,"uris":[""],"uri":[""],"itemData":{"id":2009,"type":"book","title":"MuMIn: multi-model inference. R package version 1.15.6. ","source":"Google Scholar","URL":"","note":"00000","shortTitle":"MuMIn","author":[{"family":"Barton","given":"K."}],"issued":{"date-parts":[["2016"]]}}}],"schema":""} Barton, 2016). As the trait-climate relationship can be nonlinear, we used all six climate variables in their linear and quadratic form (12 explanatory variables in total). To reduce the model complexity and identify the most important predictors, we limited the number of terms in the model output to a maximum of six (results presented in the main text). Additionally, we also performed a model with unlimited number of the output terms (results presented in Appendix S3). It has been argued that AIC-approach tends to select overly complex models ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"a1gkg3fjate","properties":{"formattedCitation":"(e.g. Kass & Raftery, 1995)","plainCitation":"(e.g. Kass & Raftery, 1995)"},"citationItems":[{"id":2277,"uris":[""],"uri":[""],"itemData":{"id":2277,"type":"article-journal","title":"Bayes Factors","container-title":"Journal of the American Statistical Association","page":"773-795","volume":"90","issue":"430","source":"amstat. (Atypon)","abstract":"In a 1935 paper and in his book Theory of Probability, Jeffreys developed a methodology for quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now called the Bayes factor, which is the posterior odds of the null hypothesis when the prior probability on the null is one-half. Although there has been much discussion of Bayesian hypothesis testing in the context of criticism of P-values, less attention has been given to the Bayes factor as a practical tool of applied statistics. In this article we review and discuss the uses of Bayes factors in the context of five scientific applications in genetics, sports, ecology, sociology, and psychology. We emphasize the following points: ?From Jeffreys' Bayesian viewpoint, the purpose of hypothesis testing is to evaluate the evidence in favor of a scientific theory.?Bayes factors offer a way of evaluating evidence in favor of a null hypothesis.?Bayes factors provide a way of incorporating external information into the evaluation of evidence about a hypothesis.?Bayes factors are very general and do not require alternative models to be nested.?Several techniques are available for computing Bayes factors, including asymptotic approximations that are easy to compute using the output from standard packages that maximize likelihoods.?In “nonstandard” statistical models that do not satisfy common regularity conditions, it can be technically simpler to calculate Bayes factors than to derive non-Bayesian significance tests.?The Schwarz criterion (or BIC) gives a rough approximation to the logarithm of the Bayes factor, which is easy to use and does not require evaluation of prior distributions.?When one is interested in estimation or prediction, Bayes factors may be converted to weights to be attached to various models so that a composite estimate or prediction may be obtained that takes account of structural or model uncertainty.?Algorithms have been proposed that allow model uncertainty to be taken into account when the class of models initially considered is very large.?Bayes factors are useful for guiding an evolutionary model-building process.?It is important, and feasible, to assess the sensitivity of conclusions to the prior distributions used.","DOI":"10.1080/01621459.1995.10476572","ISSN":"0162-1459","journalAbbreviation":"Journal of the American Statistical Association","author":[{"family":"Kass","given":"Robert E."},{"family":"Raftery","given":"Adrian E."}],"issued":{"date-parts":[["1995",6,1]]}},"prefix":"e.g. "}],"schema":""} (e.g. Kass & Raftery, 1995). Therefore, to verify our results, we additionally performed a Lasso model selection ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"aqvn060qu1","properties":{"formattedCitation":"(Zhao & Yu, 2006)","plainCitation":"(Zhao & Yu, 2006)"},"citationItems":[{"id":2279,"uris":[""],"uri":[""],"itemData":{"id":2279,"type":"article-journal","title":"On Model Selection Consistency of Lasso","container-title":"Journal of Machine Learning Research","page":"2541-2563","volume":"7","issue":"Nov","source":"","ISSN":"ISSN 1533-7928","author":[{"family":"Zhao","given":"Peng"},{"family":"Yu","given":"Bin"}],"issued":{"date-parts":[["2006"]]}}}],"schema":""} (Zhao & Yu, 2006) (results presented in Appendix S3). To compare woody and herbaceous trait-climate relationships, we re-ran the model selection for the combined dataset of standardized trait means (or variances) for both growth forms together. As explanatory variables, we included 1) standardized climate variables of the model outputs (explained above), 2) the interaction terms between all these climate variables (in their linear forms) and the growth form (woody or herbaceous), and 3) the growth form factor. Standardization was done by dividing the centred variables by their standard deviations ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"LvAZJkcP","properties":{"formattedCitation":"{\\rtf (function \\uc0\\u8216{}scale\\uc0\\u8217{} in R; Becker et al., 1988)}","plainCitation":"(function ‘scale’ in R; Becker et al., 1988)"},"citationItems":[{"id":2290,"uris":[""],"uri":[""],"itemData":{"id":2290,"type":"article-journal","title":"The new S language","container-title":"Pacific Grove, Ca.: Wadsworth & Brooks, 1988","source":"Google Scholar","author":[{"family":"Becker","given":"Richard A."},{"family":"Chambers","given":"John M."},{"family":"Wilks","given":"Allan R."}],"issued":{"date-parts":[["1988"]]}},"prefix":"function ‘scale’ in R; "}],"schema":""} (function ‘scale’ in R; Becker et al., 1988). Similarly, as above, we limited the number of terms in the model output to a maximum of six and we additionally performed the selection with unlimited number of terms in the model output and the Lasso model selection (results presented in Appendix S3). Additionally, we examined separate linear regression models for each climate variable with combined dataset of standardized trait means (or variances) for both growth form groups together as response variables and standardized climate (in its linear and quadratic form), the growth form-climate interaction term, and the main effect of growth form as explanatory variables. Specifically, we tested for the significance of the interaction term between climate and growth form. When the trait-climate relationship is the same for both woody and herbaceous species, we expect a significant climate signal, but a non-significant effect of the interaction term.The availability of species trait values is likely to vary geographically, which could bias the results. Therefore, we weighted the regression models by square root of the per-cell number of species with known values of a particular trait (the results presented in the main text), and compared the results with unweighted regression models (results presented in Appendix S3). ResultsComparison of trait patterns based on occurrences to patterns based on range mapsVariation in most trait patterns based on species occurrences per grid cell corresponded well to variation in trait patterns based on species occurrences inferred from species range maps (Table 1). The closest match between the two methods was for all trait means of woody species, whereas the weakest match was for variance in wood density and means and variances of herbaceous leaf N and leaf P. The spatial patterns were generally stronger for woody species compared to herbaceous species.Climate signals on trait means and variances in woody and herbaceous speciesWe found strong trait-climate relationships for trait means of woody species (Table 2, average r2 = 0.67), but much weaker relationships for herbaceous trait means (Table 2, average r2 = 0.22) and for most woody and herbaceous trait variances (Table 2, average r2 = 0.38 for woody and 0.33 for herbaceous species). Mean height of woody species primarily increased with mean annual temperature with tallest trees occurring above 10°C (Fig. S3.6). Although mean height of herbaceous species also increased with increasing temperature, its best predictor was solar radiation, with the tallest species at sites of medium radiation (Fig. S3.6). Mean SLA of woody species increased with increasing temperature and precipitation, although these relationships became flatter after reaching 10°C and 1500mm, respectively (Fig. S3.6). It further curvilinearly increased with increasing temperature seasonality. Herbaceous mean SLA primarily increased with increasing temperature and precipitation, similarly to woody SLA. Woody seed mass strongly increased with increasing precipitation. Mean seed mass of both growth form groups also increased with mean annual temperature, although this relationship was much weaker for herbaceous species. Mean leaf N of woody species increased with increasing temperature and decreasing solar radiation. Although temperature was also the best predictor of herbaceous leaf N, the relationship was much weaker. Mean leaf P of woody species was higher but variable outside the tropics and uniformly lower within the tropics (Fig. 1, columns 1-2), and its variation strongly correlated with temperature seasonality. Little spatial pattern was evident for herbaceous mean leaf P (Fig. 1, columns 3-4), consistent with the weak sensitivity to environmental variables (model r2 = 0.05). Mean wood density increased with increasing temperature and decreasing precipitation. These results remained qualitatively similar when performing a model selection with unlimited number of terms in the model output (Table S3.2) and when performing a Lasso model selection (Table S3.3), except that in the latter case, the importance of the solar radiation was rather weak.In contrast to the high correlations between climate and trait means (for woody species assemblages), correlations between climate and most trait variances were weaker (average r2 = 0.36; Table 2). Trait variances were often predicted by solar radiation (SLA, herbaceous seed mass, woody leaf N, and leaf P) and temperature seasonality (height, herbaceous leaf P), but the form of these relationships was variable (Table 2, Fig. S3.9-S3.11). For instance, whereas variance in height of herbaceous species curvilinearly decreased with increasing temperature seasonality, this relationship was nearly unimodal for variance in woody SLA and height. Similarly, whereas variance in height and herbaceous SLA, and woody leaf P increased with decreasing solar radiation, the relationship was opposite for herbaceous seed mass and woody SLA. These results were qualitatively similar when performing a model selection with unlimited number of terms in the model output (Table S3.2). Nevertheless, when using a Lasso model selection, the results were frequently differentnweightednterationendixthan the the discussion, but I have some hard time putting any predictionsotal number of species as the and solar radiation remained a strong and important predictor of variance in herbaceous specific leaf area only (Table S3.3). This indicates that results concerning trait variances should be interpreted with caution.When testing for the similarity in trait-climate relationships between the growth forms using model selection with standardized variables, the growth form-climate interaction term was relatively strong and important predictor of almost all trait means and variances (Table 3). This indicates that each growth form displays a different relationship with particular climate variables (Fig. 2). The variable with the strongest impact on the dissimilarity in trait-climate relationship between the growth forms was often temperature seasonality. The results largely remained when performing a model selection with unlimited number of terms in the model output and when performing a lasso model selection (Tables S3.5-S3.6). Here, both mean annual temperature and temperature seasonality often had the strongest impact on the difference between woody and herbaceous species. When testing for the effect of the growth form-climate interaction terms using separate linear regression models for each climate variable, the effect of the growth form-climate interaction term was significant in most cases, further supporting the different responses of woody and herbaceous trait means and variances to climate (Figures S3.12-S3.17). Most of the observed relationships between trait means and climate remained when performing a model selection based on the unweighted regression (Table 2 vs. Table S3.4). The only differences occurred for poorly sampled traits such as herbaceous leaf N and leaf P. However, for trait variances the results based on the unweighted regression were frequently different from the weighted resultsnweightednterationendixthan the the discussion, but I have some hard time putting any predictionsotal number of species as the , indicating that these results should be interpreted with caution. As for the weighted models, the unweighted trait-climate relationships for standardized variables differed between woody and herbaceous species (Tables 3 vs. S3.7). Nevertheless, the variable having the strongest impact on the difference between woody and herbaceous species was often mean annual temperature rather than temperature seasonality. The effect of the interaction term of climate and the growth form on trait means was weaker, however, when compared to the results based on the weighted regression. Higher noise in the data of poorly sampled regions (e.g. Amazon basin) can thus partly mask the differences in trait-climate relationships between growth forms.Discussion By using the largest and most complete large-scale plant distribution and trait datasets for the New World, we found strong spatial patterns and climatic associations for several key plant functional traits. Consistent with existing evidence and theoretical expectations ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"9sGspXsb","properties":{"formattedCitation":"(Moles & Westoby, 2003; Kerkhoff et al., 2005; Reich, 2014)","plainCitation":"(Moles & Westoby, 2003; Kerkhoff et al., 2005; Reich, 2014)"},"citationItems":[{"id":364,"uris":[""],"uri":[""],"itemData":{"id":364,"type":"article-journal","title":"Latitude, seed predation and seed mass","container-title":"Journal of Biogeography","page":"105–128","volume":"30","issue":"1","source":"Wiley Online Library","abstract":"Aim We set out to test the hypothesis that rates of pre- and post-dispersal seed predation would be higher towards the tropics, across a broad range of species from around the world. We also aimed to quantify the slope and predictive power of the relationship between seed mass and latitude both within and across species.Methods Seed mass, pre-dispersal seed predation and post-dispersal seed removal data were compiled from the literature. Wherever possible, these data were combined with information regarding the latitude at which the data were collected. Analyses were performed using both cross-species and phylogenetic regressions.Results Contrary to expectations, we found no significant relationship between seed predation and latitude (log10 proportion of seeds surviving predispersal seed predation vs. latitude, P = 0.63; R2 = 0.02; n = 122 species: log10 proportion of seeds remaining after postdispersal seed removal vs. latitude, P = 0.54; R2 = 0.02; n = 205 species). These relationships remained non-significant after variation because of seed mass was accounted for. We also found a very substantial (R2 = 0.21) relationship between seed mass and latitude across 2706 species, with seed mass being significantly higher towards the tropics. Within-species seed mass decline with latitude was significant, but only about two-sevenths, as rapid as the cross-species decline with latitude. Results of phylogenetic analyses were very similar to cross-species analyses. We also demonstrated a positive relationship between seed mass and development time across ten species from dry sclerophyll woodland in Sydney (P < 0.001; R2 = 0.77; Standardized Major Axis slope = 0.14). These data lend support to the hypothesis that growing period might affect the maximum attainable seed mass in a given environment.Main conclusions There was no evidence that seed predation is higher towards the tropics. The strong relationship between seed mass and latitude shown here had been observed in previous studies, but had not previously been quantified at a global scale. There was a tenfold reduction in mean seed mass for every c. 23° moved towards the poles, despite a wide range of seed mass within each latitude.","DOI":"10.1046/j.1365-2699.2003.00781.x","ISSN":"1365-2699","language":"en","author":[{"family":"Moles","given":"A. T."},{"family":"Westoby","given":"M."}],"issued":{"date-parts":[["2003"]]}}},{"id":"PM60htrp/PS8X1Wrz","uris":[""],"uri":[""],"itemData":{"id":"PM60htrp/PS8X1Wrz","type":"article-journal","title":"Plant allometry, stoichiometry and the temperature-dependence of primary productivity","container-title":"Global Ecology and Biogeography","page":"585–598","volume":"14","issue":"6","source":"Google Scholar","author":[{"family":"Kerkhoff","given":"Andrew J."},{"family":"Enquist","given":"Brian J."},{"family":"Elser","given":"James J."},{"family":"Fagan","given":"William F."}],"issued":{"date-parts":[["2005"]]}}},{"id":97,"uris":[""],"uri":[""],"itemData":{"id":97,"type":"article-journal","title":"The world-wide ‘fast–slow’ plant economics spectrum: a traits manifesto","container-title":"Journal of Ecology","page":"275-301","volume":"102","issue":"2","source":"Wiley Online Library","abstract":"*\nThe leaf economics spectrum (LES) provides a useful framework for examining species strategies as shaped by their evolutionary history. However, that spectrum, as originally described, involved only two key resources (carbon and nutrients) and one of three economically important plant organs. Herein, I evaluate whether the economics spectrum idea can be broadly extended to water – the third key resource –stems, roots and entire plants and to individual, community and ecosystem scales. My overarching hypothesis is that strong selection along trait trade-off axes, in tandem with biophysical constraints, results in convergence for any taxon on a uniformly fast, medium or slow strategy (i.e. rates of resource acquisition and processing) for all organs and all resources.\n\n\n\n*\nEvidence for economic trait spectra exists for stems and roots as well as leaves, and for traits related to water as well as carbon and nutrients. These apply generally within and across scales (within and across communities, climate zones, biomes and lineages).\n\n\n\n*\nThere are linkages across organs and coupling among resources, resulting in an integrated whole-plant economics spectrum. Species capable of moving water rapidly have low tissue density, short tissue life span and high rates of resource acquisition and flux at organ and individual scales. The reverse is true for species with the slow strategy. Different traits may be important in different conditions, but as being fast in one respect generally requires being fast in others, being fast or slow is a general feature of species.\n\n\n\n*\nEconomic traits influence performance and fitness consistent with trait-based theory about underlying adaptive mechanisms. Traits help explain differences in growth and survival across resource gradients and thus help explain the distribution of species and the assembly of communities across light, water and nutrient gradients. Traits scale up – fast traits are associated with faster rates of ecosystem processes such as decomposition or primary productivity, and slow traits with slow process rates.\n\n\n\n*\nSynthesis. Traits matter. A single ‘fast–slow’ plant economics spectrum that integrates across leaves, stems and roots is a key feature of the plant universe and helps to explain individual ecological strategies, community assembly processes and the functioning of ecosystems.","DOI":"10.1111/1365-2745.12211","ISSN":"1365-2745","note":"00058","shortTitle":"The world-wide ‘fast–slow’ plant economics spectrum","journalAbbreviation":"J Ecol","language":"en","author":[{"family":"Reich","given":"Peter B."}],"issued":{"date-parts":[["2014",3,1]]}}}],"schema":""} (Moles & Westoby, 2003; Kerkhoff et al., 2005; Reich, 2014), we found that compared to colder environments, warmer and wetter environments are characterized by taller plants with larger seeds and leaves characterised by greater area per unit biomass. Overall, trait-climate relationships differed between woody and herbaceous species, notably marked by different climate predictors or different shapes of the trait-climate relationships. Means and variances of herbaceous traits appeared less strongly linked to climate than woody traits. These differences were strongest for mean leaf phosphorus concentration, seed mass and variance in height and specific leaf area. Such discrepancies may result from the higher diversity in strategies among herbaceous species when compared to woody species. This corresponds to existing evidence that herbaceous species tend to occupy smaller, more specialized niches when compared to woody species ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"a1o6acnud8b","properties":{"formattedCitation":"(Ricklefs & Latham, 1992)","plainCitation":"(Ricklefs & Latham, 1992)"},"citationItems":[{"id":2159,"uris":[""],"uri":[""],"itemData":{"id":2159,"type":"article-journal","title":"Intercontinental Correlation of Geographical Ranges Suggests Stasis in Ecological Traits of Relict Genera of Temperate Perennial Herbs","container-title":"The American Naturalist","page":"1305-1321","volume":"139","issue":"6","source":"JSTOR","abstract":"Disjunct taxa within genera of herbaceous perennial plants relict to temperate eastern Asia and eastern North America exhibit a significant correlation in area of geographical range. This relationship suggests evolutionary stasis of traits related to ecological distribution over periods of at least 10 million and possibly more than 30 million yr. Because woody taxa lack this pattern but appear to exhibit broader ecological distributions on a local scale than do herbaceous taxa, we suggest that stasis goes hand in hand with ecological specialization; some restricted subsets of ecological conditions to which herbs are specialized may persist within a changing ecological mosaic.","ISSN":"0003-0147","author":[{"family":"Ricklefs","given":"Robert E."},{"family":"Latham","given":"Roger Earl"}],"issued":{"date-parts":[["1992"]]}}}],"schema":""} (Ricklefs & Latham, 1992). There are several possible explanations for the weaker climate signal for herbaceous species. In particular, the microclimate perceived by understorey herbaceous communities is not captured by macroclimate variables ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"8n6gqpq14","properties":{"formattedCitation":"(Schneider et al., 2004)","plainCitation":"(Schneider et al., 2004)"},"citationItems":[{"id":2496,"uris":[""],"uri":[""],"itemData":{"id":2496,"type":"article-journal","title":"Ferns diversified in the shadow of angiosperms","container-title":"Nature","page":"553-557","volume":"428","issue":"6982","source":"","abstract":"The rise of angiosperms during the Cretaceous period is often portrayed as coincident with a dramatic drop in the diversity and abundance of many seed-free vascular plant lineages, including ferns. This has led to the widespread belief that ferns, once a principal component of terrestrial ecosystems, succumbed to the ecological predominance of angiosperms and are mostly evolutionary holdovers from the late Palaeozoic/early Mesozoic era. The first appearance of many modern fern genera in the early Tertiary fossil record implies another evolutionary scenario; that is, that the majority of living ferns resulted from a more recent diversification. But a full understanding of trends in fern diversification and evolution using only palaeobotanical evidence is hindered by the poor taxonomic resolution of the fern fossil record in the Cretaceous. Here we report divergence time estimates for ferns and angiosperms based on molecular data, with constraints from a reassessment of the fossil record. We show that polypod ferns (> 80% of living fern species) diversified in the Cretaceous, after angiosperms, suggesting perhaps an ecological opportunistic response to the diversification of angiosperms, as angiosperms came to dominate terrestrial ecosystems.","DOI":"10.1038/nature02361","ISSN":"0028-0836","journalAbbreviation":"Nature","language":"en","author":[{"family":"Schneider","given":"Harald"},{"family":"Schuettpelz","given":"Eric"},{"family":"Pryer","given":"Kathleen M."},{"family":"Cranfill","given":"Raymond"},{"family":"Magallón","given":"Susana"},{"family":"Lupia","given":"Richard"}],"issued":{"date-parts":[["2004",4,1]]}}}],"schema":""} (Schneider et al., 2004). Interestingly, the variable with the strongest impact on the dissimilarity in trait-climate relationship between the two growth forms was temperature seasonality. Differences in strategies to cope with unfavourable seasons thus seem to be the key factor responsible for the difference in woody vs. herbaceous trait values. Our findings that some trait-climate relationships depend on growth form have important implications for studies predicting the functional response of ecosystems to changing climate. Although numerous large-scale studies focus on woody species only and make strong generalizations from this growth form, our results imply that plant woodiness must be considered to be able to adequately assess the importance of climate for plant traits. Importantly, the differences between growth forms may explain the weak trait-climate relationships observed in previous studies that pooled all growth forms for analysis ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1tVJgRqA","properties":{"formattedCitation":"{\\rtf (Ordo\\uc0\\u241{}ez et al., 2009; Moles et al., 2014)}","plainCitation":"(Ordo?ez et al., 2009; Moles et al., 2014)"},"citationItems":[{"id":"C8vNGhAH/TSNQICl8","uris":[""],"uri":[""],"itemData":{"id":"C8vNGhAH/TSNQICl8","type":"article-journal","title":"A global study of relationships between leaf traits, climate and soil measures of nutrient fertility","container-title":"Global Ecology and Biogeography","page":"137–149","volume":"18","issue":"2","source":"Wiley Online Library","abstract":"Aim This first global quantification of the relationship between leaf traits and soil nutrient fertility reflects the trade-off between growth and nutrient conservation. The power of soils versus climate in predicting leaf trait values is assessed in bivariate and multivariate analyses and is compared with the distribution of growth forms (as a discrete classification of vegetation) across gradients of soil fertility and climate.Location All continents except for Antarctica.Methods Data on specific leaf area (SLA), leaf N concentration (LNC), leaf P concentration (LPC) and leaf N:P were collected for 474 species distributed across 99 sites (809 records), together with abiotic information from each study site. Individual and combined effects of soils and climate on leaf traits were quantified using maximum likelihood methods. Differences in occurrence of growth form across soil fertility and climate were determined by one-way ANOVA.Results There was a consistent increase in SLA, LNC and LPC with increasing soil fertility. SLA was related to proxies of N supply, LNC to both soil total N and P and LPC was only related to proxies of P supply. Soil nutrient measures explained more variance in leaf traits among sites than climate in bivariate analysis. Multivariate analysis showed that climate interacted with soil nutrients for SLA and area-based LNC. Mass-based LNC and LPC were determined mostly by soil fertility, but soil P was highly correlated to precipitation. Relationships of leaf traits to soil nutrients were stronger than those of growth form versus soil nutrients. In contrast, climate determined distribution of growth form more strongly than it did leaf traits.Main conclusions We provide the first global quantification of the trade-off between traits associated with growth and resource conservation ‘strategies’ in relation to soil fertility. Precipitation but not temperature affected this trade-off. Continuous leaf traits might be better predictors of plant responses to nutrient supply than growth form, but growth forms reflect important aspects of plant species distribution with climate.","DOI":"10.1111/j.1466-8238.2008.00441.x","ISSN":"1466-8238","language":"en","author":[{"family":"Ordo?ez","given":"Jenny C."},{"family":"Van Bodegom","given":"Peter M."},{"family":"Witte","given":"Jan-Philip M."},{"family":"Wright","given":"Ian J."},{"family":"Reich","given":"Peter B."},{"family":"Aerts","given":"Rien"}],"issued":{"year":2009},"accessed":{"year":2013,"month":12,"day":18},"page-first":"137","container-title-short":"Glob. Ecol. Biogeogr."}},{"id":722,"uris":[""],"uri":[""],"itemData":{"id":722,"type":"article-journal","title":"Which is a better predictor of plant traits: temperature or precipitation?","container-title":"Journal of Vegetation Science","page":"1167-1180","volume":"25","issue":"5","source":"Wiley Online Library","abstract":"Question\n\nAre plant traits more closely correlated with mean annual temperature, or with mean annual precipitation?\n\n\nLocation\n\nGlobal.\n\n\nMethods\n\nWe quantified the strength of the relationships between temperature and precipitation and 21 plant traits from 447,961 species-site combinations worldwide. We used meta-analysis to provide an overall answer to our question.\n\n\nResults\n\nMean annual temperature was significantly more strongly correlated with plant traits than was mean annual precipitation.\n\n\nConclusions\n\nOur study provides support for some of the assumptions of classical vegetation theory, and points to many interesting directions for future research. The relatively low R2 values for precipitation might reflect the weak link between mean annual precipitation and the availability of water to plants.","DOI":"10.1111/jvs.12190","ISSN":"1654-1103","note":"00001","shortTitle":"Which is a better predictor of plant traits","journalAbbreviation":"J Veg Sci","language":"en","author":[{"family":"Moles","given":"Angela T."},{"family":"Perkins","given":"Sarah E."},{"family":"Laffan","given":"Shawn W."},{"family":"Flores-Moreno","given":"Habacuc"},{"family":"Awasthy","given":"Monica"},{"family":"Tindall","given":"Marianne L."},{"family":"Sack","given":"Lawren"},{"family":"Pitman","given":"Andy"},{"family":"Kattge","given":"Jens"},{"family":"Aarssen","given":"Lonnie W."},{"family":"Anand","given":"Madhur"},{"family":"Bahn","given":"Michael"},{"family":"Blonder","given":"Benjamin"},{"family":"Cavender-Bares","given":"Jeannine"},{"family":"Cornelissen","given":"J. Hans C."},{"family":"Cornwell","given":"Will K."},{"family":"Díaz","given":"Sandra"},{"family":"Dickie","given":"John B."},{"family":"Freschet","given":"Grégoire T."},{"family":"Griffiths","given":"Joshua G."},{"family":"Gutierrez","given":"Alvaro G."},{"family":"Hemmings","given":"Frank A."},{"family":"Hickler","given":"Thomas"},{"family":"Hitchcock","given":"Timothy D."},{"family":"Keighery","given":"Matthew"},{"family":"Kleyer","given":"Michael"},{"family":"Kurokawa","given":"Hiroko"},{"family":"Leishman","given":"Michelle R."},{"family":"Liu","given":"Kenwin"},{"family":"Niinemets","given":"?lo"},{"family":"Onipchenko","given":"Vladimir"},{"family":"Onoda","given":"Yusuke"},{"family":"Penuelas","given":"Josep"},{"family":"Pillar","given":"Valério D."},{"family":"Reich","given":"Peter B."},{"family":"Shiodera","given":"Satomi"},{"family":"Siefert","given":"Andrew"},{"family":"Sosinski","given":"Enio E."},{"family":"Soudzilovskaia","given":"Nadejda A."},{"family":"Swaine","given":"Emily K."},{"family":"Swenson","given":"Nathan G."},{"family":"Bodegom","given":"Peter M.","non-dropping-particle":"van"},{"family":"Warman","given":"Laura"},{"family":"Weiher","given":"Evan"},{"family":"Wright","given":"Ian J."},{"family":"Zhang","given":"Hongxiang"},{"family":"Zobel","given":"Martin"},{"family":"Bonser","given":"Stephen P."}],"issued":{"date-parts":[["2014"]]}}}],"schema":""} (e.g., Ordo?ez et al., 2009; Moles et al., 2014). Consistent with expectations of strong and predictable trait-environment relationships ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"fh2t8fh39","properties":{"formattedCitation":"(Lavorel & Garnier, 2002; Shipley et al., 2016)","plainCitation":"(Lavorel & Garnier, 2002; Shipley et al., 2016)"},"citationItems":[{"id":433,"uris":[""],"uri":[""],"itemData":{"id":433,"type":"article-journal","title":"Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail","container-title":"Functional Ecology","page":"545-556","volume":"16","issue":"5","source":"Wiley Online Library","abstract":"* 1The concept of plant functional type proposes that species can be grouped according to common responses to the environment and/or common effects on ecosystem processes. However, the knowledge of relationships between traits associated with the response of plants to environmental factors such as resources and disturbances (response traits), and traits that determine effects of plants on ecosystem functions (effect traits), such as biogeochemical cycling or propensity to disturbance, remains rudimentary.\n* 2We present a framework using concepts and results from community ecology, ecosystem ecology and evolutionary biology to provide this linkage. Ecosystem functioning is the end result of the operation of multiple environmental filters in a hierarchy of scales which, by selecting individuals with appropriate responses, result in assemblages with varying trait composition. Functional linkages and trade-offs among traits, each of which relates to one or several processes, determine whether or not filtering by different factors gives a match, and whether ecosystem effects can be easily deduced from the knowledge of the filters.\n* 3To illustrate this framework we analyse a set of key environmental factors and ecosystem processes. While traits associated with response to nutrient gradients strongly overlapped with those determining net primary production, little direct overlap was found between response to fire and flammability.\n* 4We hypothesize that these patterns reflect general trends. Responses to resource availability would be determined by traits that are also involved in biogeochemical cycling, because both these responses and effects are driven by the trade-off between acquisition and conservation. On the other hand, regeneration and demographic traits associated with response to disturbance, which are known to have little connection with adult traits involved in plant ecophysiology, would be of little relevance to ecosystem processes.\n* 5This framework is likely to be broadly applicable, although caution must be exercised to use trait linkages and trade-offs appropriate to the scale, environmental conditions and evolutionary context. It may direct the selection of plant functional types for vegetation models at a range of scales, and help with the design of experimental studies of relationships between plant diversity and ecosystem properties.","DOI":"10.1046/j.1365-2435.2002.00664.x","ISSN":"1365-2435","note":"00959","shortTitle":"Predicting changes in community composition and ecosystem functioning from plant traits","language":"en","author":[{"family":"Lavorel","given":"S."},{"family":"Garnier","given":"E."}],"issued":{"date-parts":[["2002",10,1]]}}},{"id":2024,"uris":[""],"uri":[""],"itemData":{"id":2024,"type":"article-journal","title":"Reinforcing loose foundation stones in trait-based plant ecology","container-title":"Oecologia","page":"923-931","volume":"180","issue":"4","source":"link.","abstract":"The promise of “trait-based” plant ecology is one of generalized prediction across organizational and spatial scales, independent of taxonomy. This promise is a major reason for the increased popularity of this approach. Here, we argue that some important foundational assumptions of trait-based ecology have not received sufficient empirical evaluation. We identify three such assumptions and, where possible, suggest methods of improvement: (i) traits are functional to the degree that they determine individual fitness, (ii) intraspecific variation in functional traits can be largely ignored, and (iii) functional traits show general predictive relationships to measurable environmental gradients.","DOI":"10.1007/s00442-016-3549-x","ISSN":"0029-8549, 1432-1939","journalAbbreviation":"Oecologia","language":"en","author":[{"family":"Shipley","given":"Bill"},{"family":"Bello","given":"Francesco De"},{"family":"Cornelissen","given":"J. Hans C."},{"family":"Laliberté","given":"Etienne"},{"family":"Laughlin","given":"Daniel C."},{"family":"Reich","given":"Peter B."}],"issued":{"date-parts":[["2016",4,1]]}}}],"schema":""} (Lavorel & Garnier, 2002; Shipley et al., 2016), variation in plant traits showed significant correlations with climate variables. Mean annual temperature, temperature seasonality and solar radiation were among the best predictors of these traits, which is in line with the species-level approach of ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"a2e07vulft9","properties":{"formattedCitation":"(Moles et al., 2014)","plainCitation":"(Moles et al., 2014)"},"citationItems":[{"id":722,"uris":[""],"uri":[""],"itemData":{"id":722,"type":"article-journal","title":"Which is a better predictor of plant traits: temperature or precipitation?","container-title":"Journal of Vegetation Science","page":"1167-1180","volume":"25","issue":"5","source":"Wiley Online Library","abstract":"Question\n\nAre plant traits more closely correlated with mean annual temperature, or with mean annual precipitation?\n\n\nLocation\n\nGlobal.\n\n\nMethods\n\nWe quantified the strength of the relationships between temperature and precipitation and 21 plant traits from 447,961 species-site combinations worldwide. We used meta-analysis to provide an overall answer to our question.\n\n\nResults\n\nMean annual temperature was significantly more strongly correlated with plant traits than was mean annual precipitation.\n\n\nConclusions\n\nOur study provides support for some of the assumptions of classical vegetation theory, and points to many interesting directions for future research. The relatively low R2 values for precipitation might reflect the weak link between mean annual precipitation and the availability of water to plants.","DOI":"10.1111/jvs.12190","ISSN":"1654-1103","note":"00001","shortTitle":"Which is a better predictor of plant traits","journalAbbreviation":"J Veg Sci","language":"en","author":[{"family":"Moles","given":"Angela T."},{"family":"Perkins","given":"Sarah E."},{"family":"Laffan","given":"Shawn W."},{"family":"Flores-Moreno","given":"Habacuc"},{"family":"Awasthy","given":"Monica"},{"family":"Tindall","given":"Marianne L."},{"family":"Sack","given":"Lawren"},{"family":"Pitman","given":"Andy"},{"family":"Kattge","given":"Jens"},{"family":"Aarssen","given":"Lonnie W."},{"family":"Anand","given":"Madhur"},{"family":"Bahn","given":"Michael"},{"family":"Blonder","given":"Benjamin"},{"family":"Cavender-Bares","given":"Jeannine"},{"family":"Cornelissen","given":"J. Hans C."},{"family":"Cornwell","given":"Will K."},{"family":"Díaz","given":"Sandra"},{"family":"Dickie","given":"John B."},{"family":"Freschet","given":"Grégoire T."},{"family":"Griffiths","given":"Joshua G."},{"family":"Gutierrez","given":"Alvaro G."},{"family":"Hemmings","given":"Frank A."},{"family":"Hickler","given":"Thomas"},{"family":"Hitchcock","given":"Timothy D."},{"family":"Keighery","given":"Matthew"},{"family":"Kleyer","given":"Michael"},{"family":"Kurokawa","given":"Hiroko"},{"family":"Leishman","given":"Michelle R."},{"family":"Liu","given":"Kenwin"},{"family":"Niinemets","given":"?lo"},{"family":"Onipchenko","given":"Vladimir"},{"family":"Onoda","given":"Yusuke"},{"family":"Penuelas","given":"Josep"},{"family":"Pillar","given":"Valério D."},{"family":"Reich","given":"Peter B."},{"family":"Shiodera","given":"Satomi"},{"family":"Siefert","given":"Andrew"},{"family":"Sosinski","given":"Enio E."},{"family":"Soudzilovskaia","given":"Nadejda A."},{"family":"Swaine","given":"Emily K."},{"family":"Swenson","given":"Nathan G."},{"family":"Bodegom","given":"Peter M.","non-dropping-particle":"van"},{"family":"Warman","given":"Laura"},{"family":"Weiher","given":"Evan"},{"family":"Wright","given":"Ian J."},{"family":"Zhang","given":"Hongxiang"},{"family":"Zobel","given":"Martin"},{"family":"Bonser","given":"Stephen P."}],"issued":{"date-parts":[["2014"]]}}}],"schema":""} Moles et al. (2014). Seasonality of precipitation had, in turn, the lowest effects on trait means and variances, suggesting that it plays a less important role in the biogeography of these traits at continental scales. Many of the observed trait-climate correlations are broadly consistent with existing hypotheses and past studies focused on single trait-climate correlations. Murray et al. (2004) hypothesized that warmer environments increase metabolic rates, leading to the higher metabolic costs for seedlings and, thus, a need for larger seeds, whereas ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"m0YU6POj","properties":{"formattedCitation":"(Moles & Westoby, 2003)","plainCitation":"(Moles & Westoby, 2003)"},"citationItems":[{"id":364,"uris":[""],"uri":[""],"itemData":{"id":364,"type":"article-journal","title":"Latitude, seed predation and seed mass","container-title":"Journal of Biogeography","page":"105–128","volume":"30","issue":"1","source":"Wiley Online Library","abstract":"Aim We set out to test the hypothesis that rates of pre- and post-dispersal seed predation would be higher towards the tropics, across a broad range of species from around the world. We also aimed to quantify the slope and predictive power of the relationship between seed mass and latitude both within and across species.Methods Seed mass, pre-dispersal seed predation and post-dispersal seed removal data were compiled from the literature. Wherever possible, these data were combined with information regarding the latitude at which the data were collected. Analyses were performed using both cross-species and phylogenetic regressions.Results Contrary to expectations, we found no significant relationship between seed predation and latitude (log10 proportion of seeds surviving predispersal seed predation vs. latitude, P = 0.63; R2 = 0.02; n = 122 species: log10 proportion of seeds remaining after postdispersal seed removal vs. latitude, P = 0.54; R2 = 0.02; n = 205 species). These relationships remained non-significant after variation because of seed mass was accounted for. We also found a very substantial (R2 = 0.21) relationship between seed mass and latitude across 2706 species, with seed mass being significantly higher towards the tropics. Within-species seed mass decline with latitude was significant, but only about two-sevenths, as rapid as the cross-species decline with latitude. Results of phylogenetic analyses were very similar to cross-species analyses. We also demonstrated a positive relationship between seed mass and development time across ten species from dry sclerophyll woodland in Sydney (P < 0.001; R2 = 0.77; Standardized Major Axis slope = 0.14). These data lend support to the hypothesis that growing period might affect the maximum attainable seed mass in a given environment.Main conclusions There was no evidence that seed predation is higher towards the tropics. The strong relationship between seed mass and latitude shown here had been observed in previous studies, but had not previously been quantified at a global scale. There was a tenfold reduction in mean seed mass for every c. 23° moved towards the poles, despite a wide range of seed mass within each latitude.","DOI":"10.1046/j.1365-2699.2003.00781.x","ISSN":"1365-2699","language":"en","author":[{"family":"Moles","given":"A. T."},{"family":"Westoby","given":"M."}],"issued":{"date-parts":[["2003"]]}}}],"schema":""} Moles & Westoby (2003) hypothesized that larger seeds would be favored under warm and wet conditions due to higher competitive pressures ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2hopqv0kvh","properties":{"formattedCitation":"(Murray et al., 2004)","plainCitation":"(Murray et al., 2004)"},"citationItems":[{"id":615,"uris":[""],"uri":[""],"itemData":{"id":615,"type":"article-journal","title":"Geographical gradients in seed mass in relation to climate","container-title":"Journal of Biogeography","page":"379–388","volume":"31","issue":"3","source":"Wiley Online Library","abstract":"Aim To determine whether latitudinal and longitudinal gradients in seed mass are related to variation in climatic features including temperature, solar radiation and rainfall.Location Australia.Methods Seed mass was estimated from over 1600 provenances covering the latitudinal and longitudinal extents of 34 perennial Glycine taxa in Australia. Climatic data were obtained from ANUCLIM 5.1 for collection locations based on long-term meteorological records across Australia. These climatic data were subject to principal components analysis to extract three components as climatic indices. Generalized linear models were used in three separate sets of analyses to evaluate whether seed mass–latitude and seed mass–longitude relationships persisted after taking climatic variation into account. First, relationships were examined across species in analyses that did not explicitly consider phylogenetic relationships. Secondly, phylogenetic regressions were performed to examine patterns of correlated evolutionary change throughout the Glycine phylogeny. Within-species analysis was also performed to examine consistency across different taxonomic levels.Results Geographical variation in seed mass among species was related primarily to temperature and solar radiation, while rainfall was much less influential upon seed mass. Partialing out the influence of temperature and solar radiation in models resulted in the disappearance of significant seed mass–latitude and seed mass–longitude relationships. Patterns within species were generally consistent with patterns among species. However, in several species, factors additional to these climatic variables may contribute to the origin and maintenance of geographical gradients in seed mass, as significant seed mass–latitude and seed mass–longitude relationships remained after controlling for the influence of climatic variables.Main conclusions Our empirical results support the hypotheses that (1) seed mass is larger at low latitudes and in the interior of the Australian continent due to increased metabolic costs at high temperatures, and that (2) higher levels of solar radiation result in an increase in the availability of photosynthate, which in turn leads to an increase in biomass for the production of large seeds. In effect, our findings show that greater energy is available precisely where needed, that is, where high temperatures require large seed mass on the basis of metabolic requirements.","DOI":"10.1046/j.0305-0270.2003.00993.x","ISSN":"1365-2699","language":"en","author":[{"family":"Murray","given":"Brad R."},{"family":"Brown","given":"A. H. D."},{"family":"Dickman","given":"C. R."},{"family":"Crowther","given":"M. S."}],"issued":{"date-parts":[["2004"]]}}}],"schema":""} . Consistent with these predictions, mean seed mass increases with increasing temperature ( ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1t3vAAtC","properties":{"formattedCitation":"{\\rtf (in agreement with Moles et al., 2009; Swenson & Weiser, 2010; \\uc0\\u352{}\\uc0\\u237{}mov\\uc0\\u225{} et al., 2015)}","plainCitation":"(in agreement with Moles et al., 2009; Swenson & Weiser, 2010; ?ímová et al., 2015)"},"citationItems":[{"id":128,"uris":[""],"uri":[""],"itemData":{"id":128,"type":"article-journal","title":"Global patterns in plant height","container-title":"Journal of Ecology","page":"923–932","volume":"97","issue":"5","source":"Google Scholar","call-number":"0062","note":"00148","author":[{"family":"Moles","given":"Angela T."},{"family":"Warton","given":"David I."},{"family":"Warman","given":"Laura"},{"family":"Swenson","given":"Nathan G."},{"family":"Laffan","given":"Shawn W."},{"family":"Zanne","given":"Amy E."},{"family":"Pitman","given":"Andy"},{"family":"Hemmings","given":"Frank A."},{"family":"Leishman","given":"Michelle R."}],"issued":{"date-parts":[["2009"]]}},"prefix":"in agreement with"},{"id":700,"uris":[""],"uri":[""],"itemData":{"id":700,"type":"article-journal","title":"Plant geography upon the basis of functional traits: an example from eastern North American trees","container-title":"Ecology","page":"2234–2241","volume":"91","issue":"8","source":"Google Scholar","call-number":"0022","shortTitle":"Plant geography upon the basis of functional traits","author":[{"family":"Swenson","given":"Nathan G."},{"family":"Weiser","given":"Michael D."}],"issued":{"date-parts":[["2010"]]}}},{"id":361,"uris":[""],"uri":[""],"itemData":{"id":361,"type":"article-journal","title":"Shifts in trait means and variances in North American tree assemblages: species richness patterns are loosely related to the functional space","container-title":"Ecography","page":"649-658","volume":"38","issue":"7","source":"Wiley Online Library","abstract":"One of the key hypothesized drivers of gradients in species richness is environmental filtering, where environmental stress limits which species from a larger species pool gain membership in a local community owing to their traits. Whereas most studies focus on small-scale variation in functional traits along environmental gradient, the effect of large-scale environmental filtering is less well understood. Furthermore, it has been rarely tested whether the factors that constrain the niche space limit the total number of coexisting species. We assessed the role of environmental filtering in shaping tree assemblages across North America north of Mexico by testing the hypothesis that colder, drier, or seasonal environments (stressful conditions for most plants) constrain tree trait diversity and thereby limit species richness. We assessed geographic patterns in trait filtering and their relationships to species richness pattern using a comprehensive set of tree range maps. We focused on four key plant functional traits reflecting major life history axes (maximum height, specific leaf area, seed mass, and wood density) and four climatic variables (annual mean and seasonality of temperature and precipitation). We tested for significant spatial shifts in trait means and variances using a null model approach. While we found significant shifts in mean species’ trait values at most grid cells, trait variances at most grid cells did not deviate from the null expectation. Measures of environmental harshness (cold, dry, seasonal climates) and lower species richness were weakly associated with a reduction in variance of seed mass and specific leaf area. The pattern in variance of height and wood density was, however, opposite. These findings do not support the hypothesis that more stressful conditions universally limit species and trait diversity in North America. Environmental filtering does, however, structure assemblage composition, by selecting for certain optimum trait values under a given set of conditions.","DOI":"10.1111/ecog.00867","ISSN":"1600-0587","note":"00003","shortTitle":"Shifts in trait means and variances in North American tree assemblages","journalAbbreviation":"Ecography","language":"en","author":[{"family":"?ímová","given":"Irena"},{"family":"Violle","given":"Cyrille"},{"family":"Kraft","given":"Nathan J. B."},{"family":"Storch","given":"David"},{"family":"Svenning","given":"Jens-Christian"},{"family":"Boyle","given":"Brad"},{"family":"Donoghue","given":"John C."},{"family":"J?rgensen","given":"Peter"},{"family":"McGill","given":"Brian J."},{"family":"Morueta-Holme","given":"Naia"},{"family":"Piel","given":"William H."},{"family":"Peet","given":"Robert K."},{"family":"Regetz","given":"Jim"},{"family":"Schildhauer","given":"Mark"},{"family":"Spencer","given":"Nick"},{"family":"Thiers","given":"Barbara"},{"family":"Wiser","given":"Susan"},{"family":"Enquist","given":"Brian J."}],"issued":{"date-parts":[["2015",7,1]]}}}],"schema":""} results also found in Moles et al., 2009; Swenson & Weiser, 2010; ?ímová et al., 2015) and precipitation. Similarly, consistent with Ryan and Yoder’s (1997) hydraulic limitation hypothesis for trees, mean height increases towards warm and wet climates as hydraulic pathways are increasingly more vulnerable to frost and drought embolisms ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"4D2TrpoR","properties":{"formattedCitation":"(Ryan & Yoder, 1997; Stegen et al., 2011)","plainCitation":"(Ryan & Yoder, 1997; Stegen et al., 2011)"},"citationItems":[{"id":140,"uris":[""],"uri":[""],"itemData":{"id":140,"type":"article-journal","title":"Hydraulic Limits to Tree Height and Tree Growth","container-title":"BioScience","page":"235-242","volume":"47","issue":"4","source":"CrossRef","DOI":"10.2307/1313077","ISSN":"00063568, 15253244","note":"00690","author":[{"family":"Ryan","given":"Michael G."},{"family":"Yoder","given":"Barbara J."}],"issued":{"date-parts":[["1997",4]]}}},{"id":90,"uris":[""],"uri":[""],"itemData":{"id":90,"type":"article-journal","title":"Variation in above-ground forest biomass across broad climatic gradients","container-title":"Global Ecology and Biogeography","page":"744–754","volume":"20","issue":"5","source":"Google Scholar","note":"00057","author":[{"family":"Stegen","given":"James C."},{"family":"Swenson","given":"Nathan G."},{"family":"Enquist","given":"Brian J."},{"family":"White","given":"Ethan P."},{"family":"Phillips","given":"Oliver L."},{"family":"J?rgensen","given":"Peter M."},{"family":"Weiser","given":"Michael D."},{"family":"Monteagudo Mendoza","given":"Abel"},{"family":"Nú?ez Vargas","given":"Percy"}],"issued":{"date-parts":[["2011"]]}}}],"schema":""} (Ryan & Yoder, 1997; Stegen et al., 2011). The observed increase in wood density with increased temperature is also consistent with the hydraulic limitation hypothesis as denser wood in warmer, drought-prone environments provides increased mechanical support in the form of resistance to xylem conduit implosion or rupture ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"aeir4gnv6","properties":{"formattedCitation":"(Hacke et al., 2001)","plainCitation":"(Hacke et al., 2001)"},"citationItems":[{"id":"Re5U4SCg/9r9ho87W","uris":[""],"uri":[""],"itemData":{"id":"Re5U4SCg/9r9ho87W","type":"article-journal","title":"Trends in wood density and structure are linked to prevention of xylem implosion by negative pressure","container-title":"Oecologia","page":"457–461","volume":"126","issue":"4","source":"Google Scholar","call-number":"0487","author":[{"family":"Hacke","given":"Uwe G."},{"family":"Sperry","given":"John S."},{"family":"Pockman","given":"William T."},{"family":"Davis","given":"Stephen D."},{"family":"McCulloh","given":"Katherine A."}],"issued":{"year":2001},"accessed":{"year":2013,"month":5,"day":13},"page-first":"457","container-title-short":"Oecologia"}}],"schema":""} (Hacke et al., 2001). Consistent with ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1jjmo5if0t","properties":{"formattedCitation":"(Kerkhoff et al., 2005)","plainCitation":"(Kerkhoff et al., 2005)"},"citationItems":[{"id":2268,"uris":[""],"uri":[""],"itemData":{"id":2268,"type":"article-journal","title":"Plant allometry, stoichiometry and the temperature-dependence of primary productivity","container-title":"Global Ecology and Biogeography","page":"585–598","volume":"14","issue":"6","source":"Google Scholar","author":[{"family":"Kerkhoff","given":"Andrew J."},{"family":"Enquist","given":"Brian J."},{"family":"Elser","given":"James J."},{"family":"Fagan","given":"William F."}],"issued":{"date-parts":[["2005"]]}}}],"schema":""} Kerkhoff et al. (2005), leaf phosphorus concentration of woody species tends to increase whereas leaf nitrogen concentration tends to decrease in colder, more seasonal environments. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"pp4GbaFm","properties":{"formattedCitation":"(Kerkhoff et al., 2005)","plainCitation":"(Kerkhoff et al., 2005)"},"citationItems":[{"id":2268,"uris":[""],"uri":[""],"itemData":{"id":2268,"type":"article-journal","title":"Plant allometry, stoichiometry and the temperature-dependence of primary productivity","container-title":"Global Ecology and Biogeography","page":"585–598","volume":"14","issue":"6","source":"Google Scholar","author":[{"family":"Kerkhoff","given":"Andrew J."},{"family":"Enquist","given":"Brian J."},{"family":"Elser","given":"James J."},{"family":"Fagan","given":"William F."}],"issued":{"date-parts":[["2005"]]}}}],"schema":""} Kerkhoff et al. (2005) argued that such environments would select for increased phosphorus concentration relative to nitrogen concentration to increase growth rates and growth efficiencies. It is also possible that lower leaf phosphorus in tropical plant tissues results from lower soil phosphorus concentration in tropical ecosystems ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"FdlCenvI","properties":{"formattedCitation":"(Quesada et al., 2009)","plainCitation":"(Quesada et al., 2009)"},"citationItems":[{"id":182,"uris":[""],"uri":[""],"itemData":{"id":182,"type":"article-journal","title":"Regional and large-scale patterns in Amazon forest structure and function are mediated by variations in soil physical and chemical properties","container-title":"Biogeosciences Discussion","page":"3993–4057","volume":"6","source":"Google Scholar","note":"00090","author":[{"family":"Quesada","given":"C. A."},{"family":"Lloyd","given":"J."},{"family":"Schwarz","given":"M."},{"family":"Baker","given":"TR","dropping-particle":"de"},{"family":"Phillips","given":"Oliver L."},{"family":"Pati?o","given":"Sandra"},{"family":"Czimczik","given":"C."},{"family":"Hodnett","given":"M. G."},{"family":"Herrera","given":"R."},{"family":"Arneth","given":"A."}],"issued":{"date-parts":[["2009"]]}}}],"schema":""} (Quesada et al., 2009). The mean specific leaf area of both woody and herbaceous species decreases with decreasing temperature and with decreasing precipitation ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"sEFNqL7o","properties":{"formattedCitation":"{\\rtf (consistent with empirical findings of Swenson et al., 2012; Hulshof et al., 2013; \\uc0\\u352{}\\uc0\\u237{}mov\\uc0\\u225{} et al., 2015)}","plainCitation":"(consistent with empirical findings of Swenson et al., 2012; Hulshof et al., 2013; ?ímová et al., 2015)"},"citationItems":[{"id":125,"uris":[""],"uri":[""],"itemData":{"id":125,"type":"article-journal","title":"The biogeography and filtering of woody plant functional diversity in North and South America","container-title":"Global Ecology and Biogeography","page":"798–808","volume":"21","issue":"8","source":"Google Scholar","call-number":"0005","note":"00029","author":[{"family":"Swenson","given":"Nathan G."},{"family":"Enquist","given":"Brian J."},{"family":"Pither","given":"Jason"},{"family":"Kerkhoff","given":"Andrew J."},{"family":"Boyle","given":"Brad"},{"family":"Weiser","given":"Michael D."},{"family":"Elser","given":"James J."},{"family":"Fagan","given":"William F."},{"family":"Forero-Monta?a","given":"Jimena"},{"family":"Fyllas","given":"Nikolaos"}],"issued":{"date-parts":[["2012"]]}},"prefix":"consistent with empirical findings of "},{"id":2499,"uris":[""],"uri":[""],"itemData":{"id":2499,"type":"article-journal","title":"Intra-specific and inter-specific variation in specific leaf area reveal the importance of abiotic and biotic drivers of species diversity across elevation and latitude","container-title":"Journal of Vegetation Science","page":"921-931","volume":"24","issue":"5","source":"Wiley Online Library","abstract":"Questions\n\nAre patterns of intra- and inter-specific functional trait variation consistent with greater abiotic filtering on community assembly at high latitudes and elevations, and greater biotic filtering at low latitudes and elevations?\n\n\nLocations\n\nArea de Conservación Guanacaste, Costa Rica; Santa Catalina Mountains, Arizona; Siskiyou Mountains, Oregon.\n\n\nMethods\n\nWe measured woody plant species abundance and a key functional trait associated with competition for resources and environmental tolerance (specific leaf area, SLA) along elevational gradients in low-latitude tropical (Costa Rica), mid-latitude desert (Arizona) and high latitude mediterranean (southern Oregon) biomes. We explored patterns of abiotic and biotic filtering by comparing observed patterns of community-weighted means and variances along elevational and latitudinal gradients to those expected under random assembly. In addition, we related trait variability to niches and explored how total trait space and breadth vary across broad spatial gradients by quantifying the ratio of intra- to inter-specific variation.\n\n\nResults\n\nBoth the community-wide mean and variance of SLA decreased with increasing latitude, consistent with greater abiotic filtering at higher latitudes. Further, low-elevation communities had higher trait variation than expected by chance, consistent with greater biotic filtering at low elevations. Finally, in the tropics and across latitude the ratio of intra- to inter-specific variation was negatively correlated to species richness, which further suggests that biotic interactions influence plant assembly at low latitudes.\n\n\nConclusions\n\nIntra- and inter-specific patterns of SLA variation appeared broadly consistent with the idea that the relative strength of biotic and abiotic drivers on community assembly changes along elevational and latitudinal gradients; evidence for biotic drivers appeared more prominent at low latitudes and elevations and evidence for abiotic drivers appeared more prominent at high latitudes and elevations.","DOI":"10.1111/jvs.12041","ISSN":"1654-1103","journalAbbreviation":"J Veg Sci","language":"en","author":[{"family":"Hulshof","given":"Catherine M."},{"family":"Violle","given":"Cyrille"},{"family":"Spasojevic","given":"Marko J."},{"family":"McGill","given":"Brian"},{"family":"Damschen","given":"Ellen"},{"family":"Harrison","given":"Susan"},{"family":"Enquist","given":"Brian J."}],"issued":{"date-parts":[["2013",9,1]]}}},{"id":361,"uris":[""],"uri":[""],"itemData":{"id":361,"type":"article-journal","title":"Shifts in trait means and variances in North American tree assemblages: species richness patterns are loosely related to the functional space","container-title":"Ecography","page":"649-658","volume":"38","issue":"7","source":"Wiley Online Library","abstract":"One of the key hypothesized drivers of gradients in species richness is environmental filtering, where environmental stress limits which species from a larger species pool gain membership in a local community owing to their traits. Whereas most studies focus on small-scale variation in functional traits along environmental gradient, the effect of large-scale environmental filtering is less well understood. Furthermore, it has been rarely tested whether the factors that constrain the niche space limit the total number of coexisting species. We assessed the role of environmental filtering in shaping tree assemblages across North America north of Mexico by testing the hypothesis that colder, drier, or seasonal environments (stressful conditions for most plants) constrain tree trait diversity and thereby limit species richness. We assessed geographic patterns in trait filtering and their relationships to species richness pattern using a comprehensive set of tree range maps. We focused on four key plant functional traits reflecting major life history axes (maximum height, specific leaf area, seed mass, and wood density) and four climatic variables (annual mean and seasonality of temperature and precipitation). We tested for significant spatial shifts in trait means and variances using a null model approach. While we found significant shifts in mean species’ trait values at most grid cells, trait variances at most grid cells did not deviate from the null expectation. Measures of environmental harshness (cold, dry, seasonal climates) and lower species richness were weakly associated with a reduction in variance of seed mass and specific leaf area. The pattern in variance of height and wood density was, however, opposite. These findings do not support the hypothesis that more stressful conditions universally limit species and trait diversity in North America. Environmental filtering does, however, structure assemblage composition, by selecting for certain optimum trait values under a given set of conditions.","DOI":"10.1111/ecog.00867","ISSN":"1600-0587","note":"00003","shortTitle":"Shifts in trait means and variances in North American tree assemblages","journalAbbreviation":"Ecography","language":"en","author":[{"family":"?ímová","given":"Irena"},{"family":"Violle","given":"Cyrille"},{"family":"Kraft","given":"Nathan J. B."},{"family":"Storch","given":"David"},{"family":"Svenning","given":"Jens-Christian"},{"family":"Boyle","given":"Brad"},{"family":"Donoghue","given":"John C."},{"family":"J?rgensen","given":"Peter"},{"family":"McGill","given":"Brian J."},{"family":"Morueta-Holme","given":"Naia"},{"family":"Piel","given":"William H."},{"family":"Peet","given":"Robert K."},{"family":"Regetz","given":"Jim"},{"family":"Schildhauer","given":"Mark"},{"family":"Spencer","given":"Nick"},{"family":"Thiers","given":"Barbara"},{"family":"Wiser","given":"Susan"},{"family":"Enquist","given":"Brian J."}],"issued":{"date-parts":[["2015",7,1]]}}}],"schema":""} (consistent with empirical findings of Swenson et al., 2012; Hulshof et al., 2013; ?ímová et al., 2015) ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"40fu9h7su","properties":{"formattedCitation":"{\\rtf (similarly as Swenson et al., 2012; \\uc0\\u352{}\\uc0\\u237{}mov\\uc0\\u225{} et al., 2015)}","plainCitation":"(similarly as Swenson et al., 2012; ?ímová et al., 2015)"},"citationItems":[{"id":125,"uris":[""],"uri":[""],"itemData":{"id":125,"type":"article-journal","title":"The biogeography and filtering of woody plant functional diversity in North and South America","container-title":"Global Ecology and Biogeography","page":"798–808","volume":"21","issue":"8","source":"Google Scholar","call-number":"0005","note":"00029","author":[{"family":"Swenson","given":"Nathan G."},{"family":"Enquist","given":"Brian J."},{"family":"Pither","given":"Jason"},{"family":"Kerkhoff","given":"Andrew J."},{"family":"Boyle","given":"Brad"},{"family":"Weiser","given":"Michael D."},{"family":"Elser","given":"James J."},{"family":"Fagan","given":"William F."},{"family":"Forero-Monta?a","given":"Jimena"},{"family":"Fyllas","given":"Nikolaos"}],"issued":{"date-parts":[["2012"]]}},"prefix":"similarly as"},{"id":361,"uris":[""],"uri":[""],"itemData":{"id":361,"type":"article-journal","title":"Shifts in trait means and variances in North American tree assemblages: species richness patterns are loosely related to the functional space","container-title":"Ecography","page":"649-658","volume":"38","issue":"7","source":"Wiley Online Library","abstract":"One of the key hypothesized drivers of gradients in species richness is environmental filtering, where environmental stress limits which species from a larger species pool gain membership in a local community owing to their traits. Whereas most studies focus on small-scale variation in functional traits along environmental gradient, the effect of large-scale environmental filtering is less well understood. Furthermore, it has been rarely tested whether the factors that constrain the niche space limit the total number of coexisting species. We assessed the role of environmental filtering in shaping tree assemblages across North America north of Mexico by testing the hypothesis that colder, drier, or seasonal environments (stressful conditions for most plants) constrain tree trait diversity and thereby limit species richness. We assessed geographic patterns in trait filtering and their relationships to species richness pattern using a comprehensive set of tree range maps. We focused on four key plant functional traits reflecting major life history axes (maximum height, specific leaf area, seed mass, and wood density) and four climatic variables (annual mean and seasonality of temperature and precipitation). We tested for significant spatial shifts in trait means and variances using a null model approach. While we found significant shifts in mean species’ trait values at most grid cells, trait variances at most grid cells did not deviate from the null expectation. Measures of environmental harshness (cold, dry, seasonal climates) and lower species richness were weakly associated with a reduction in variance of seed mass and specific leaf area. The pattern in variance of height and wood density was, however, opposite. These findings do not support the hypothesis that more stressful conditions universally limit species and trait diversity in North America. Environmental filtering does, however, structure assemblage composition, by selecting for certain optimum trait values under a given set of conditions.","DOI":"10.1111/ecog.00867","ISSN":"1600-0587","note":"00003","shortTitle":"Shifts in trait means and variances in North American tree assemblages","journalAbbreviation":"Ecography","language":"en","author":[{"family":"?ímová","given":"Irena"},{"family":"Violle","given":"Cyrille"},{"family":"Kraft","given":"Nathan J. B."},{"family":"Storch","given":"David"},{"family":"Svenning","given":"Jens-Christian"},{"family":"Boyle","given":"Brad"},{"family":"Donoghue","given":"John C."},{"family":"J?rgensen","given":"Peter"},{"family":"McGill","given":"Brian J."},{"family":"Morueta-Holme","given":"Naia"},{"family":"Piel","given":"William H."},{"family":"Peet","given":"Robert K."},{"family":"Regetz","given":"Jim"},{"family":"Schildhauer","given":"Mark"},{"family":"Spencer","given":"Nick"},{"family":"Thiers","given":"Barbara"},{"family":"Wiser","given":"Susan"},{"family":"Enquist","given":"Brian J."}],"issued":{"date-parts":[["2015",7,1]]}}}],"schema":""} . This corresponds to the tradeoff between slow photosynthetic rate and long leaf lifespan under stressful conditions versus fast tissue turnover and high potential for resource capture under more favorable conditions ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"vBMsXY1e","properties":{"formattedCitation":"(Reich, 2014)","plainCitation":"(Reich, 2014)"},"citationItems":[{"id":97,"uris":[""],"uri":[""],"itemData":{"id":97,"type":"article-journal","title":"The world-wide ‘fast–slow’ plant economics spectrum: a traits manifesto","container-title":"Journal of Ecology","page":"275-301","volume":"102","issue":"2","source":"Wiley Online Library","abstract":"*\nThe leaf economics spectrum (LES) provides a useful framework for examining species strategies as shaped by their evolutionary history. However, that spectrum, as originally described, involved only two key resources (carbon and nutrients) and one of three economically important plant organs. Herein, I evaluate whether the economics spectrum idea can be broadly extended to water – the third key resource –stems, roots and entire plants and to individual, community and ecosystem scales. My overarching hypothesis is that strong selection along trait trade-off axes, in tandem with biophysical constraints, results in convergence for any taxon on a uniformly fast, medium or slow strategy (i.e. rates of resource acquisition and processing) for all organs and all resources.\n\n\n\n*\nEvidence for economic trait spectra exists for stems and roots as well as leaves, and for traits related to water as well as carbon and nutrients. These apply generally within and across scales (within and across communities, climate zones, biomes and lineages).\n\n\n\n*\nThere are linkages across organs and coupling among resources, resulting in an integrated whole-plant economics spectrum. Species capable of moving water rapidly have low tissue density, short tissue life span and high rates of resource acquisition and flux at organ and individual scales. The reverse is true for species with the slow strategy. Different traits may be important in different conditions, but as being fast in one respect generally requires being fast in others, being fast or slow is a general feature of species.\n\n\n\n*\nEconomic traits influence performance and fitness consistent with trait-based theory about underlying adaptive mechanisms. Traits help explain differences in growth and survival across resource gradients and thus help explain the distribution of species and the assembly of communities across light, water and nutrient gradients. Traits scale up – fast traits are associated with faster rates of ecosystem processes such as decomposition or primary productivity, and slow traits with slow process rates.\n\n\n\n*\nSynthesis. Traits matter. A single ‘fast–slow’ plant economics spectrum that integrates across leaves, stems and roots is a key feature of the plant universe and helps to explain individual ecological strategies, community assembly processes and the functioning of ecosystems.","DOI":"10.1111/1365-2745.12211","ISSN":"1365-2745","note":"00058","shortTitle":"The world-wide ‘fast–slow’ plant economics spectrum","journalAbbreviation":"J Ecol","language":"en","author":[{"family":"Reich","given":"Peter B."}],"issued":{"date-parts":[["2014",3,1]]}}}],"schema":""} (Reich, 2014). It is also consistent with a recent hypothesis that lower specific leaf area in colder environments helps modulate leaf temperatures ( ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"14fi8fvq67","properties":{"formattedCitation":"(Michaletz et al., 2016)","plainCitation":"(Michaletz et al., 2016)"},"citationItems":[{"id":2530,"uris":[""],"uri":[""],"itemData":{"id":2530,"type":"article-journal","title":"The energetic and carbon economic origins of leaf thermoregulation","container-title":"Nature Plants","page":"16129","volume":"2","source":"Google Scholar","author":[{"family":"Michaletz","given":"Sean T."},{"family":"Weiser","given":"Michael D."},{"family":"McDowell","given":"Nate G."},{"family":"Zhou","given":"Jizhong"},{"family":"Kaspari","given":"Michael"},{"family":"Helliker","given":"Brent R."},{"family":"Enquist","given":"Brian J."}],"issued":{"date-parts":[["2016"]]}}}],"schema":""} Michaletz et al. 2016). Interestingly, specific leaf area of both growth form groups increased with increasing temperature seasonality after accounting for the effect of temperature and precipitation. A possible explanation is that some species (e.g. winter deciduous trees) require higher photosynthetic rates to adapt to a short growing season. Other observed trait correlations with climate are not consistent with any existing hypotheses and do not have any precedent in the literature. For example, in contrast to previous reports of inconsistent relationships between leaf nitrogen concentration and climate ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"otjqgadd1","properties":{"formattedCitation":"{\\rtf (Ordo\\uc0\\u241{}ez et al., 2009; Swenson et al., 2012; Moles et al., 2014)}","plainCitation":"(Ordo?ez et al., 2009; Swenson et al., 2012; Moles et al., 2014)"},"citationItems":[{"id":"Re5U4SCg/p1IobSea","uris":[""],"uri":[""],"itemData":{"id":"Re5U4SCg/p1IobSea","type":"article-journal","title":"A global study of relationships between leaf traits, climate and soil measures of nutrient fertility","container-title":"Global Ecology and Biogeography","page":"137–149","volume":"18","issue":"2","source":"Wiley Online Library","abstract":"Aim This first global quantification of the relationship between leaf traits and soil nutrient fertility reflects the trade-off between growth and nutrient conservation. The power of soils versus climate in predicting leaf trait values is assessed in bivariate and multivariate analyses and is compared with the distribution of growth forms (as a discrete classification of vegetation) across gradients of soil fertility and climate.Location All continents except for Antarctica.Methods Data on specific leaf area (SLA), leaf N concentration (LNC), leaf P concentration (LPC) and leaf N:P were collected for 474 species distributed across 99 sites (809 records), together with abiotic information from each study site. Individual and combined effects of soils and climate on leaf traits were quantified using maximum likelihood methods. Differences in occurrence of growth form across soil fertility and climate were determined by one-way ANOVA.Results There was a consistent increase in SLA, LNC and LPC with increasing soil fertility. SLA was related to proxies of N supply, LNC to both soil total N and P and LPC was only related to proxies of P supply. Soil nutrient measures explained more variance in leaf traits among sites than climate in bivariate analysis. Multivariate analysis showed that climate interacted with soil nutrients for SLA and area-based LNC. Mass-based LNC and LPC were determined mostly by soil fertility, but soil P was highly correlated to precipitation. Relationships of leaf traits to soil nutrients were stronger than those of growth form versus soil nutrients. In contrast, climate determined distribution of growth form more strongly than it did leaf traits.Main conclusions We provide the first global quantification of the trade-off between traits associated with growth and resource conservation ‘strategies’ in relation to soil fertility. Precipitation but not temperature affected this trade-off. Continuous leaf traits might be better predictors of plant responses to nutrient supply than growth form, but growth forms reflect important aspects of plant species distribution with climate.","DOI":"10.1111/j.1466-8238.2008.00441.x","ISSN":"1466-8238","language":"en","author":[{"family":"Ordo?ez","given":"Jenny C."},{"family":"Van Bodegom","given":"Peter M."},{"family":"Witte","given":"Jan-Philip M."},{"family":"Wright","given":"Ian J."},{"family":"Reich","given":"Peter B."},{"family":"Aerts","given":"Rien"}],"issued":{"year":2009},"accessed":{"year":2013,"month":12,"day":18},"page-first":"137","container-title-short":"Glob. Ecol. Biogeogr."}},{"id":125,"uris":[""],"uri":[""],"itemData":{"id":125,"type":"article-journal","title":"The biogeography and filtering of woody plant functional diversity in North and South America","container-title":"Global Ecology and Biogeography","page":"798–808","volume":"21","issue":"8","source":"Google Scholar","call-number":"0005","note":"00029","author":[{"family":"Swenson","given":"Nathan G."},{"family":"Enquist","given":"Brian J."},{"family":"Pither","given":"Jason"},{"family":"Kerkhoff","given":"Andrew J."},{"family":"Boyle","given":"Brad"},{"family":"Weiser","given":"Michael D."},{"family":"Elser","given":"James J."},{"family":"Fagan","given":"William F."},{"family":"Forero-Monta?a","given":"Jimena"},{"family":"Fyllas","given":"Nikolaos"}],"issued":{"date-parts":[["2012"]]}}},{"id":722,"uris":[""],"uri":[""],"itemData":{"id":722,"type":"article-journal","title":"Which is a better predictor of plant traits: temperature or precipitation?","container-title":"Journal of Vegetation Science","page":"1167-1180","volume":"25","issue":"5","source":"Wiley Online Library","abstract":"Question\n\nAre plant traits more closely correlated with mean annual temperature, or with mean annual precipitation?\n\n\nLocation\n\nGlobal.\n\n\nMethods\n\nWe quantified the strength of the relationships between temperature and precipitation and 21 plant traits from 447,961 species-site combinations worldwide. We used meta-analysis to provide an overall answer to our question.\n\n\nResults\n\nMean annual temperature was significantly more strongly correlated with plant traits than was mean annual precipitation.\n\n\nConclusions\n\nOur study provides support for some of the assumptions of classical vegetation theory, and points to many interesting directions for future research. The relatively low R2 values for precipitation might reflect the weak link between mean annual precipitation and the availability of water to plants.","DOI":"10.1111/jvs.12190","ISSN":"1654-1103","note":"00001","shortTitle":"Which is a better predictor of plant traits","journalAbbreviation":"J Veg Sci","language":"en","author":[{"family":"Moles","given":"Angela T."},{"family":"Perkins","given":"Sarah E."},{"family":"Laffan","given":"Shawn W."},{"family":"Flores-Moreno","given":"Habacuc"},{"family":"Awasthy","given":"Monica"},{"family":"Tindall","given":"Marianne L."},{"family":"Sack","given":"Lawren"},{"family":"Pitman","given":"Andy"},{"family":"Kattge","given":"Jens"},{"family":"Aarssen","given":"Lonnie W."},{"family":"Anand","given":"Madhur"},{"family":"Bahn","given":"Michael"},{"family":"Blonder","given":"Benjamin"},{"family":"Cavender-Bares","given":"Jeannine"},{"family":"Cornelissen","given":"J. Hans C."},{"family":"Cornwell","given":"Will K."},{"family":"Díaz","given":"Sandra"},{"family":"Dickie","given":"John B."},{"family":"Freschet","given":"Grégoire T."},{"family":"Griffiths","given":"Joshua G."},{"family":"Gutierrez","given":"Alvaro G."},{"family":"Hemmings","given":"Frank A."},{"family":"Hickler","given":"Thomas"},{"family":"Hitchcock","given":"Timothy D."},{"family":"Keighery","given":"Matthew"},{"family":"Kleyer","given":"Michael"},{"family":"Kurokawa","given":"Hiroko"},{"family":"Leishman","given":"Michelle R."},{"family":"Liu","given":"Kenwin"},{"family":"Niinemets","given":"?lo"},{"family":"Onipchenko","given":"Vladimir"},{"family":"Onoda","given":"Yusuke"},{"family":"Penuelas","given":"Josep"},{"family":"Pillar","given":"Valério D."},{"family":"Reich","given":"Peter B."},{"family":"Shiodera","given":"Satomi"},{"family":"Siefert","given":"Andrew"},{"family":"Sosinski","given":"Enio E."},{"family":"Soudzilovskaia","given":"Nadejda A."},{"family":"Swaine","given":"Emily K."},{"family":"Swenson","given":"Nathan G."},{"family":"Bodegom","given":"Peter M.","non-dropping-particle":"van"},{"family":"Warman","given":"Laura"},{"family":"Weiher","given":"Evan"},{"family":"Wright","given":"Ian J."},{"family":"Zhang","given":"Hongxiang"},{"family":"Zobel","given":"Martin"},{"family":"Bonser","given":"Stephen P."}],"issued":{"date-parts":[["2014"]]}}}],"schema":""} (Ordo?ez et al., 2009; Swenson et al., 2012; Moles et al., 2014), temperature and solar radiation were both strong predictors of woody leaf nitrogen in our study. This may reflect an increased frequency of nitrogen-fixing trees towards lower latitudes, reflecting a shift in nitrogen-fixation strategy ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1vigdi7vci","properties":{"formattedCitation":"(Menge et al., 2014)","plainCitation":"(Menge et al., 2014)"},"citationItems":[{"id":614,"uris":[""],"uri":[""],"itemData":{"id":614,"type":"article-journal","title":"Nitrogen fixation strategies can explain the latitudinal shift in nitrogen-fixing tree abundance","container-title":"Ecology","page":"2236-2245","volume":"95","issue":"8","source":" (Atypon)","abstract":"The rarity of symbiotic nitrogen-fixing trees in higher-latitude compared to lower-latitude forests is paradoxical because higher-latitude soils are relatively N poor. Using national-scale forest inventories from the United States and Mexico, we show that the latitudinal abundance distribution of N-fixing trees (more than 10 times less abundant poleward of 35° N) coincides with a latitudinal transition in symbiotic N-fixation type: rhizobial N-fixing trees (which are typically facultative, regulating fixation to meet nutritional demand) dominate equatorward of 35° N, whereas actinorhizal N-fixing trees (typically obligate, maintaining fixation regardless of soil nutrition) dominate to the north. We then use theoretical and statistical models to show that a latitudinal shift in N-fixation strategy (facultative vs. obligate) near 35° N can explain the observed change in N-fixing tree abundance, even if N availability is lower at higher latitudes, because facultative fixation leads to much higher landscape-scale N-fixing tree abundance than obligate fixation.","DOI":"10.1890/13-2124.1","ISSN":"0012-9658","note":"00006","journalAbbreviation":"Ecology","author":[{"family":"Menge","given":"Duncan N. L."},{"family":"Lichstein","given":"Jeremy W."},{"family":"?ngeles-Pérez","given":"Gregorio"}],"issued":{"date-parts":[["2014",2,10]]}}}],"schema":""} (Menge et al., 2014). Further, in contrast to some previous studies and expectations, we found little evidence that harsh environments reduce the number of viable strategies ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"mpog2cgq5","properties":{"formattedCitation":"(e.g. Swenson et al., 2012)","plainCitation":"(e.g. Swenson et al., 2012)"},"citationItems":[{"id":125,"uris":[""],"uri":[""],"itemData":{"id":125,"type":"article-journal","title":"The biogeography and filtering of woody plant functional diversity in North and South America","container-title":"Global Ecology and Biogeography","page":"798–808","volume":"21","issue":"8","source":"Google Scholar","call-number":"0005","note":"00029","author":[{"family":"Swenson","given":"Nathan G."},{"family":"Enquist","given":"Brian J."},{"family":"Pither","given":"Jason"},{"family":"Kerkhoff","given":"Andrew J."},{"family":"Boyle","given":"Brad"},{"family":"Weiser","given":"Michael D."},{"family":"Elser","given":"James J."},{"family":"Fagan","given":"William F."},{"family":"Forero-Monta?a","given":"Jimena"},{"family":"Fyllas","given":"Nikolaos"}],"issued":{"date-parts":[["2012"]]}},"prefix":"e.g. "}],"schema":""} (e.g. Swenson et al., 2012). Overall, the variation in trait variances along environmental gradients was often rather weak. This is consistent with recent findings indicating that environment affects large-scale assemblage composition by selecting for a certain optimal trait values rather than constraining trait variances ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"IsGE1SpV","properties":{"formattedCitation":"{\\rtf (\\uc0\\u352{}\\uc0\\u237{}mov\\uc0\\u225{} et al., 2015, 2017)}","plainCitation":"(?ímová et al., 2015, 2017)"},"citationItems":[{"id":361,"uris":[""],"uri":[""],"itemData":{"id":361,"type":"article-journal","title":"Shifts in trait means and variances in North American tree assemblages: species richness patterns are loosely related to the functional space","container-title":"Ecography","page":"649-658","volume":"38","issue":"7","source":"Wiley Online Library","abstract":"One of the key hypothesized drivers of gradients in species richness is environmental filtering, where environmental stress limits which species from a larger species pool gain membership in a local community owing to their traits. Whereas most studies focus on small-scale variation in functional traits along environmental gradient, the effect of large-scale environmental filtering is less well understood. Furthermore, it has been rarely tested whether the factors that constrain the niche space limit the total number of coexisting species. We assessed the role of environmental filtering in shaping tree assemblages across North America north of Mexico by testing the hypothesis that colder, drier, or seasonal environments (stressful conditions for most plants) constrain tree trait diversity and thereby limit species richness. We assessed geographic patterns in trait filtering and their relationships to species richness pattern using a comprehensive set of tree range maps. We focused on four key plant functional traits reflecting major life history axes (maximum height, specific leaf area, seed mass, and wood density) and four climatic variables (annual mean and seasonality of temperature and precipitation). We tested for significant spatial shifts in trait means and variances using a null model approach. While we found significant shifts in mean species’ trait values at most grid cells, trait variances at most grid cells did not deviate from the null expectation. Measures of environmental harshness (cold, dry, seasonal climates) and lower species richness were weakly associated with a reduction in variance of seed mass and specific leaf area. The pattern in variance of height and wood density was, however, opposite. These findings do not support the hypothesis that more stressful conditions universally limit species and trait diversity in North America. Environmental filtering does, however, structure assemblage composition, by selecting for certain optimum trait values under a given set of conditions.","DOI":"10.1111/ecog.00867","ISSN":"1600-0587","note":"00003","shortTitle":"Shifts in trait means and variances in North American tree assemblages","journalAbbreviation":"Ecography","language":"en","author":[{"family":"?ímová","given":"Irena"},{"family":"Violle","given":"Cyrille"},{"family":"Kraft","given":"Nathan J. B."},{"family":"Storch","given":"David"},{"family":"Svenning","given":"Jens-Christian"},{"family":"Boyle","given":"Brad"},{"family":"Donoghue","given":"John C."},{"family":"J?rgensen","given":"Peter"},{"family":"McGill","given":"Brian J."},{"family":"Morueta-Holme","given":"Naia"},{"family":"Piel","given":"William H."},{"family":"Peet","given":"Robert K."},{"family":"Regetz","given":"Jim"},{"family":"Schildhauer","given":"Mark"},{"family":"Spencer","given":"Nick"},{"family":"Thiers","given":"Barbara"},{"family":"Wiser","given":"Susan"},{"family":"Enquist","given":"Brian J."}],"issued":{"date-parts":[["2015",7,1]]}}},{"id":2292,"uris":[""],"uri":[""],"itemData":{"id":2292,"type":"article-journal","title":"Stress from cold and drought as drivers of functional trait spectra in North American angiosperm tree assemblages","container-title":"Ecology and Evolution","page":"7548-7559","volume":"7","issue":"18","source":"PubMed Central","abstract":"Understanding how environmental change alters the composition of plant assemblages, and how this in turn affects ecosystem functioning is a major challenge in the face of global climate change. Assuming that values of plant traits express species adaptations to the environment, the trait‐based approach is a promising way to achieve this goal. Nevertheless, how functional traits are related to species’ environmental tolerances and how trait spectra respond to broad‐scale environmental gradients remains largely unexplored. Here, we identify the main trait spectra for US angiosperm trees by testing hypotheses for the relationships between functional traits and species’ environmental tolerances to environmental stresses, as well as quantifying the environmental drivers of assemblage means and variances of these traits. We analyzed >74,000 community assemblages from the US Forest Inventory and Analysis using 12 functional traits, five traits expressing species’ environmental tolerances and 10 environmental variables. Results indicated that leaf traits, dispersal traits, and traits related to stem hydraulics were related to cold or drought tolerance, and their assemblage means were best explained by minimum temperatures. Assemblage means of traits related to shade tolerance (tree growth rate, leaf phosphorus content, and bark thickness) were best explained by aridity index. Surprisingly, aridity index, rather than minimum temperature, was the best predictors of assemblage variances of most traits, although these relationships were variable and weak overall. We conclude that temperature is likely to be the most important driver of functional community structure of North American angiosperm trees by selecting for optimum strategies along the cold and drought stress trade‐off. In turn, water availability primarily affects traits related to shade tolerance through its effect on forest canopy structure and vegetation openness.","DOI":"10.1002/ece3.3297","ISSN":"2045-7758","note":"PMID: 28944038\nPMCID: PMC5606901","journalAbbreviation":"Ecol Evol","author":[{"family":"?ímová","given":"Irena"},{"family":"Rueda","given":"Marta"},{"family":"Hawkins","given":"Bradford A."}],"issued":{"date-parts":[["2017",8,14]]}}}],"schema":""} (?ímová et al., 2015, 2017). It is also possible that trait divergence is more driven by biotic interactions ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2q3b163ggr","properties":{"formattedCitation":"(even if discutable at the grid size under scrutiny, see Damgaard & Weiner, 2017)","plainCitation":"(even if discutable at the grid size under scrutiny, see Damgaard & Weiner, 2017)"},"citationItems":[{"id":2030,"uris":[""],"uri":[""],"itemData":{"id":2030,"type":"article-journal","title":"It's About Time: A Critique of Macroecological Inferences Concerning Plant Competition","container-title":"Trends in ecology & evolution","page":"86–87","volume":"32","issue":"2","source":"Google Scholar","shortTitle":"It's About Time","author":[{"family":"Damgaard","given":"Christian"},{"family":"Weiner","given":"Jacob"}],"issued":{"date-parts":[["2017"]]}},"prefix":"even if discutable at the grid size under scrutiny, see"}],"schema":""} (even if debatable at the grid size under scrutiny, see Damgaard & Weiner, 2017) or environmental heterogeneity, not captured in our analyses. Interestingly, the spatial patterns of traits were largely similar when the underlying data were species occurrences or species occurrences as inferred from species range maps. Species distribution models have improved significantly in recent years ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"yGAOCXPs","properties":{"formattedCitation":"(Thuiller et al., 2006, 2010; Merow et al., 2014)","plainCitation":"(Thuiller et al., 2006, 2010; Merow et al., 2014)"},"citationItems":[{"id":604,"uris":[""],"uri":[""],"itemData":{"id":604,"type":"article-journal","title":"Using niche-based modelling to assess the impact of climate change on tree functional diversity in Europe","container-title":"Diversity and Distributions","page":"49–60","volume":"12","issue":"1","source":"Google Scholar","note":"00149","author":[{"family":"Thuiller","given":"Wilfried"},{"family":"Lavorel","given":"Sandra"},{"family":"Sykes","given":"Martin T."},{"family":"Araújo","given":"Miguel B."}],"issued":{"date-parts":[["2006"]]}}},{"id":215,"uris":[""],"uri":[""],"itemData":{"id":215,"type":"article-journal","title":"Habitat suitability modelling","container-title":"Effects of climate change on birds. Oxford University Press, New York","page":"77–85","source":"Google Scholar","note":"00511","author":[{"family":"Thuiller","given":"Wilfried"},{"family":"Munkemuller","given":"T."},{"family":"Moller","given":"A. P."},{"family":"Fiedler","given":"W."},{"family":"Berthold","given":"P."}],"issued":{"date-parts":[["2010"]]}}},{"id":739,"uris":[""],"uri":[""],"itemData":{"id":739,"type":"article-journal","title":"What do we gain from simplicity versus complexity in species distribution models?","container-title":"Ecography","page":"1267-1281","volume":"37","issue":"12","source":"Wiley Online Library","abstract":"Species distribution models (SDMs) are widely used to explain and predict species ranges and environmental niches. They are most commonly constructed by inferring species' occurrence–environment relationships using statistical and machine-learning methods. The variety of methods that can be used to construct SDMs (e.g. generalized linear/additive models, tree-based models, maximum entropy, etc.), and the variety of ways that such models can be implemented, permits substantial flexibility in SDM complexity. Building models with an appropriate amount of complexity for the study objectives is critical for robust inference. We characterize complexity as the shape of the inferred occurrence–environment relationships and the number of parameters used to describe them, and search for insights into whether additional complexity is informative or superfluous. By building ‘under fit’ models, having insufficient flexibility to describe observed occurrence–environment relationships, we risk misunderstanding the factors shaping species distributions. By building ‘over fit’ models, with excessive flexibility, we risk inadvertently ascribing pattern to noise or building opaque models. However, model selection can be challenging, especially when comparing models constructed under different modeling approaches. Here we argue for a more pragmatic approach: researchers should constrain the complexity of their models based on study objective, attributes of the data, and an understanding of how these interact with the underlying biological processes. We discuss guidelines for balancing under fitting with over fitting and consequently how complexity affects decisions made during model building. Although some generalities are possible, our discussion reflects differences in opinions that favor simpler versus more complex models. We conclude that combining insights from both simple and complex SDM building approaches best advances our knowledge of current and future species ranges.","DOI":"10.1111/ecog.00845","ISSN":"1600-0587","note":"00005","journalAbbreviation":"Ecography","language":"en","author":[{"family":"Merow","given":"Cory"},{"family":"Smith","given":"Mathew J."},{"family":"Edwards","given":"Thomas C."},{"family":"Guisan","given":"Antoine"},{"family":"McMahon","given":"Sean M."},{"family":"Normand","given":"Signe"},{"family":"Thuiller","given":"Wilfried"},{"family":"Wüest","given":"Rafael O."},{"family":"Zimmermann","given":"Niklaus E."},{"family":"Elith","given":"Jane"}],"issued":{"date-parts":[["2014",12,1]]}}}],"schema":""} (Thuiller et al., 2006, 2010; Merow et al., 2014) and range maps are increasingly available for many plant species worldwide. This will facilitate large-scale studies focused on functional traits. An important next step for quantifying spatial variation in traits is to predict changes in ecosystem services ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"DKVkMUgh","properties":{"formattedCitation":"(Violle et al., 2015b)","plainCitation":"(Violle et al., 2015b)"},"citationItems":[{"id":2176,"uris":[""],"uri":[""],"itemData":{"id":2176,"type":"article-journal","title":"Vegetation ecology meets ecosystem science: permanent grasslands as a functional biogeography case study","container-title":"Science of the Total Environment","page":"43–51","volume":"534","source":"Google Scholar","shortTitle":"Vegetation ecology meets ecosystem science","author":[{"family":"Violle","given":"Cyrille"},{"family":"Choler","given":"Philippe"},{"family":"Borgy","given":"Benjamin"},{"family":"Garnier","given":"Eric"},{"family":"Amiaud","given":"Bernard"},{"family":"Debarros","given":"Guilhem"},{"family":"Diquelou","given":"Sylvain"},{"family":"Gachet","given":"Sophie"},{"family":"Jolivet","given":"Claudy"},{"family":"Kattge","given":"Jens"},{"literal":"others"}],"issued":{"date-parts":[["2015"]]}}}],"schema":""} (Violle et al., 2015b) or vegetation dynamics at large spatial scales under global climate change scenarios ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"isk2gc67d","properties":{"formattedCitation":"(Scheiter et al., 2013)","plainCitation":"(Scheiter et al., 2013)"},"citationItems":[{"id":413,"uris":[""],"uri":[""],"itemData":{"id":413,"type":"article-journal","title":"Next-generation dynamic global vegetation models: learning from community ecology","container-title":"New Phytologist","page":"957-969","volume":"198","issue":"3","source":"Wiley Online Library","abstract":"Summary\n\n\n\n\n\n* Dynamic global vegetation models (DGVMs) are powerful tools to project past, current and future vegetation patterns and associated biogeochemical cycles. However, most models are limited by how they define vegetation and by their simplistic representation of competition.\n\n\n* We discuss how concepts from community assembly theory and coexistence theory can help to improve vegetation models. We further present a trait- and individual-based vegetation model (aDGVM2) that allows individual plants to adopt a unique combination of trait values. These traits define how individual plants grow and compete. A genetic optimization algorithm is used to simulate trait inheritance and reproductive isolation between individuals. These model properties allow the assembly of plant communities that are adapted to a site's biotic and abiotic conditions.\n\n\n* The aDGVM2 simulates how environmental conditions influence the trait spectra of plant communities; that fire selects for traits that enhance fire protection and reduces trait diversity; and the emergence of life-history strategies that are suggestive of colonization–competition trade-offs.\n\n\n* The aDGVM2 deals with functional diversity and competition fundamentally differently from current DGVMs. This approach may yield novel insights as to how vegetation may respond to climate change and we believe it could foster collaborations between functional plant biologists and vegetation modellers.","DOI":"10.1111/nph.12210","ISSN":"1469-8137","note":"00069","shortTitle":"Next-generation dynamic global vegetation models","journalAbbreviation":"New Phytol","language":"en","author":[{"family":"Scheiter","given":"Simon"},{"family":"Langan","given":"Liam"},{"family":"Higgins","given":"Steven I."}],"issued":{"date-parts":[["2013",5,1]]}}}],"schema":""} (Scheiter et al., 2013). However, caution must be used in interpreting some results. For instance, merging leaf nitrogen or phosphorus values with range maps of herbaceous species in under-sampled regions of South America generated strong spatial patterns (Fig. 2.4, column 4), but the ecological meaning is unclear. On the other hand, at high latitudes, such as Canada in our case, where species ranges are large and the vegetation is relatively homogenous, species range maps can improve maps of plant functional traits. The estimation of errors and uncertainties when using incomplete and heterogeneous datasets thus remains a priority for assessing the credibility of findings in the emerging field of functional biogeography ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"jkacEP3I","properties":{"formattedCitation":"(Violle et al., 2015a; Borgy et al., 2017b)","plainCitation":"(Violle et al., 2015a; Borgy et al., 2017b)"},"citationItems":[{"id":2161,"uris":[""],"uri":[""],"itemData":{"id":2161,"type":"article-journal","title":"Trait databases: misuses and precautions","container-title":"Journal of Vegetation Science","page":"826-827","volume":"26","issue":"5","source":"Wiley Online Library","abstract":"Trait-based ecology has enjoyed increasing success, aided by the development of trait databases. However their reliability has hardly been tested. Sandel et?al., in this issue, identified uncertainties in trait databases linked to missing species and variable sampling effort among species. This highlights the danger of using trait databases in a blind manner and the need for their thorough evaluation.","DOI":"10.1111/jvs.12325","ISSN":"1654-1103","shortTitle":"Trait databases","journalAbbreviation":"J Veg Sci","language":"en","author":[{"family":"Violle","given":"Cyrille"},{"family":"Borgy","given":"Benjamin"},{"family":"Choler","given":"Philippe"}],"issued":{"date-parts":[["2015",9,1]]}}},{"id":2164,"uris":[""],"uri":[""],"itemData":{"id":2164,"type":"article-journal","title":"Sensitivity of community-level trait–environment relationships to data representativeness: A test for functional biogeography","container-title":"Global Ecology and Biogeography","page":"729-739","volume":"26","issue":"6","source":"Wiley Online Library","abstract":"Aim\n\nThe characterization of trait–environment relationships over broad-scale gradients is a critical goal for ecology and biogeography. This implies the merging of plot and trait databases to assess community-level trait-based statistics. Potential shortcomings and limitations of this approach are that: (i) species traits are not measured where the community is sampled and (ii) the availability of trait data varies considerably across species and plots. Here we address the effect of trait data representativeness [the sampling effort per species and per plot] on the accuracy of (i) species-level and (ii) community-level trait estimates and (iii) the consequences for the shape and strength of trait–environment relationships across communities.\n\n\nInnovation\n\nWe combined information existing in databases of vegetation plots and plant traits to estimate community-weighted means [CWMs] of four key traits [specific leaf area, plant height, seed mass and leaf nitrogen content per dry mass] in permanent grasslands at a country-wide scale. We propose a generic approach for systematic sensitivity analyses based on random subsampling and data reduction to address the representativeness of incomplete and heterogeneous trait information when exploring trait–environment relationships across communities.\n\n\nMain conclusions\n\nThe accuracy of the CWMs was little affected by the number of individual trait values per species [NIV] but strongly affected by the cover proportion of species with available trait values [PCover]. A PCover above 80% was required for all four traits studied to obtain an estimation bias below 5%. Our approach therefore provides more conservative criteria than previously proposed. Restrictive criteria on both NIV and PCover primarily excluded communities in harsh environments, and such reduction of the sampled gradient weakened trait–environment relationships. These findings advocate systematic measurement campaigns in natural environments to increase species coverage in global trait databases, with special emphasis on species occurring in under-sampled and harsh environmental conditions.","DOI":"10.1111/geb.12573","ISSN":"1466-8238","shortTitle":"Sensitivity of community-level trait–environment relationships to data representativeness","journalAbbreviation":"Global Ecol. Biogeogr.","language":"en","author":[{"family":"Borgy","given":"Benjamin"},{"family":"Violle","given":"Cyrille"},{"family":"Choler","given":"Philippe"},{"family":"Garnier","given":"Eric"},{"family":"Kattge","given":"Jens"},{"family":"Loranger","given":"Jessy"},{"family":"Amiaud","given":"Bernard"},{"family":"Cellier","given":"Pierre"},{"family":"Debarros","given":"Guilhem"},{"family":"Denelle","given":"Pierre"},{"family":"Diquelou","given":"Sylvain"},{"family":"Gachet","given":"Sophie"},{"family":"Jolivet","given":"Claudy"},{"family":"Lavorel","given":"Sandra"},{"family":"Lemauviel-Lavenant","given":"Servane"},{"family":"Mikolajczak","given":"Alexis"},{"family":"Munoz","given":"Fran?ois"},{"family":"Olivier","given":"Jean"},{"family":"Viovy","given":"Nicolas"}],"issued":{"date-parts":[["2017",6,1]]}}}],"schema":""} (Violle et al., 2015a; Borgy et al., 2017b). Even though our results are based on the best plant trait and species distribution data currently available at this extensive spatial scale, they must be viewed in light of several important caveats. First, we used only mean species trait values and ignored intraspecific trait variability. Although some traits show greater plasticity than others ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"dIxfR9ym","properties":{"formattedCitation":"(Kattge et al., 2011; Kazakou et al., 2014)","plainCitation":"(Kattge et al., 2011; Kazakou et al., 2014)"},"citationItems":[{"id":684,"uris":[""],"uri":[""],"itemData":{"id":684,"type":"article-journal","title":"TRY – a global database of plant traits","container-title":"Global Change Biology","page":"2905-2935","volume":"17","issue":"9","source":"Wiley Online Library","abstract":"Plant traits – the morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs – determine how primary producers respond to environmental factors, affect other trophic levels, influence ecosystem processes and services and provide a link from species richness to ecosystem functional diversity. Trait data thus represent the raw material for a wide range of research from evolutionary biology, community and functional ecology to biogeography. Here we present the global database initiative named TRY, which has united a wide range of the plant trait research community worldwide and gained an unprecedented buy-in of trait data: so far 93 trait databases have been contributed. The data repository currently contains almost three million trait entries for 69?000 out of the world's 300?000 plant species, with a focus on 52 groups of traits characterizing the vegetative and regeneration stages of the plant life cycle, including growth, dispersal, establishment and persistence. A first data analysis shows that most plant traits are approximately log-normally distributed, with widely differing ranges of variation across traits. Most trait variation is between species (interspecific), but significant intraspecific variation is also documented, up to 40% of the overall variation. Plant functional types (PFTs), as commonly used in vegetation models, capture a substantial fraction of the observed variation – but for several traits most variation occurs within PFTs, up to 75% of the overall variation. In the context of vegetation models these traits would better be represented by state variables rather than fixed parameter values. The improved availability of plant trait data in the unified global database is expected to support a paradigm shift from species to trait-based ecology, offer new opportunities for synthetic plant trait research and enable a more realistic and empirically grounded representation of terrestrial vegetation in Earth system models.","DOI":"10.1111/j.1365-2486.2011.02451.x","ISSN":"1365-2486","note":"00349","language":"en","author":[{"family":"Kattge","given":"J."},{"family":"Díaz","given":"S."},{"family":"Lavorel","given":"S."},{"family":"Prentice","given":"I. C."},{"family":"Leadley","given":"P."},{"family":"B?nisch","given":"G."},{"family":"Garnier","given":"E."},{"family":"Westoby","given":"M."},{"family":"Reich","given":"P. B."},{"family":"Wright","given":"I. J."},{"family":"Cornelissen","given":"J. H. C."},{"family":"Violle","given":"C."},{"family":"Harrison","given":"S. P."},{"family":"Van BODEGOM","given":"P. M."},{"family":"Reichstein","given":"M."},{"family":"Enquist","given":"B. J."},{"family":"Soudzilovskaia","given":"N. A."},{"family":"Ackerly","given":"D. D."},{"family":"Anand","given":"M."},{"family":"Atkin","given":"O."},{"family":"Bahn","given":"M."},{"family":"Baker","given":"T. R."},{"family":"Baldocchi","given":"D."},{"family":"Bekker","given":"R."},{"family":"Blanco","given":"C. C."},{"family":"Blonder","given":"B."},{"family":"Bond","given":"W. J."},{"family":"Bradstock","given":"R."},{"family":"Bunker","given":"D. E."},{"family":"Casanoves","given":"F."},{"family":"Cavender-Bares","given":"J."},{"family":"Chambers","given":"J. Q."},{"family":"Chapin Iii","given":"F. S."},{"family":"Chave","given":"J."},{"family":"Coomes","given":"D."},{"family":"Cornwell","given":"W. K."},{"family":"Craine","given":"J. M."},{"family":"Dobrin","given":"B. H."},{"family":"Duarte","given":"L."},{"family":"Durka","given":"W."},{"family":"Elser","given":"J."},{"family":"Esser","given":"G."},{"family":"Estiarte","given":"M."},{"family":"Fagan","given":"W. F."},{"family":"Fang","given":"J."},{"family":"Fernández-Méndez","given":"F."},{"family":"Fidelis","given":"A."},{"family":"Finegan","given":"B."},{"family":"Flores","given":"O."},{"family":"Ford","given":"H."},{"family":"Frank","given":"D."},{"family":"Freschet","given":"G. T."},{"family":"Fyllas","given":"N. M."},{"family":"Gallagher","given":"R. V."},{"family":"Green","given":"W. A."},{"family":"Gutierrez","given":"A. G."},{"family":"Hickler","given":"T."},{"family":"Higgins","given":"S. I."},{"family":"Hodgson","given":"J. G."},{"family":"Jalili","given":"A."},{"family":"Jansen","given":"S."},{"family":"Joly","given":"C. A."},{"family":"Kerkhoff","given":"A. J."},{"family":"Kirkup","given":"D."},{"family":"Kitajima","given":"K."},{"family":"Kleyer","given":"M."},{"family":"Klotz","given":"S."},{"family":"Knops","given":"J. M. H."},{"family":"Kramer","given":"K."},{"family":"Kühn","given":"I."},{"family":"Kurokawa","given":"H."},{"family":"Laughlin","given":"D."},{"family":"Lee","given":"T. D."},{"family":"Leishman","given":"M."},{"family":"Lens","given":"F."},{"family":"Lenz","given":"T."},{"family":"Lewis","given":"S. L."},{"family":"Lloyd","given":"J."},{"family":"Llusià","given":"J."},{"family":"Louault","given":"F."},{"family":"Ma","given":"S."},{"family":"Mahecha","given":"M. D."},{"family":"Manning","given":"P."},{"family":"Massad","given":"T."},{"family":"Medlyn","given":"B. E."},{"family":"Messier","given":"J."},{"family":"Moles","given":"A. T."},{"family":"Müller","given":"S. C."},{"family":"Nadrowski","given":"K."},{"family":"Naeem","given":"S."},{"family":"Niinemets","given":"?."},{"family":"N?llert","given":"S."},{"family":"Nüske","given":"A."},{"family":"Ogaya","given":"R."},{"family":"Oleksyn","given":"J."},{"family":"Onipchenko","given":"V. G."},{"family":"Onoda","given":"Y."},{"family":"Ordo?ez","given":"J."},{"family":"Overbeck","given":"G."},{"family":"Ozinga","given":"W. A."},{"family":"Pati?o","given":"S."},{"family":"Paula","given":"S."},{"family":"Pausas","given":"J. G."},{"family":"Pe?uelas","given":"J."},{"family":"Phillips","given":"O. L."},{"family":"Pillar","given":"V."},{"family":"Poorter","given":"H."},{"family":"Poorter","given":"L."},{"family":"Poschlod","given":"P."},{"family":"Prinzing","given":"A."},{"family":"Proulx","given":"R."},{"family":"Rammig","given":"A."},{"family":"Reinsch","given":"S."},{"family":"Reu","given":"B."},{"family":"Sack","given":"L."},{"family":"Salgado-Negret","given":"B."},{"family":"Sardans","given":"J."},{"family":"Shiodera","given":"S."},{"family":"Shipley","given":"B."},{"family":"Siefert","given":"A."},{"family":"Sosinski","given":"E."},{"family":"Soussana","given":"J.-F."},{"family":"Swaine","given":"E."},{"family":"Swenson","given":"N."},{"family":"Thompson","given":"K."},{"family":"Thornton","given":"P."},{"family":"Waldram","given":"M."},{"family":"Weiher","given":"E."},{"family":"White","given":"M."},{"family":"White","given":"S."},{"family":"Wright","given":"S. J."},{"family":"Yguel","given":"B."},{"family":"Zaehle","given":"S."},{"family":"Zanne","given":"A. E."},{"family":"Wirth","given":"C."}],"issued":{"date-parts":[["2011",9,1]]}}},{"id":446,"uris":[""],"uri":[""],"itemData":{"id":446,"type":"article-journal","title":"Are trait-based species rankings consistent across data sets and spatial scales?","container-title":"Journal of Vegetation Science","page":"235–247","volume":"25","issue":"1","source":"Google Scholar","note":"00020","author":[{"family":"Kazakou","given":"Elena"},{"family":"Violle","given":"Cyrille"},{"family":"Roumet","given":"Catherine"},{"family":"Navas","given":"Marie-Laure"},{"family":"Vile","given":"Denis"},{"family":"Kattge","given":"Jens"},{"family":"Garnier","given":"Eric"}],"issued":{"date-parts":[["2014"]]}}}],"schema":""} (Kattge et al., 2011; Kazakou et al., 2014), intraspecific trait variation may be more important to incorporate when exploring species assembly processes at smaller scales than at larger scales that cover multiple and strongly heterogeneous biomes ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"vxkdmdsa","properties":{"formattedCitation":"(Albert et al., 2011; Violle et al., 2012; Siefert et al., 2015)","plainCitation":"(Albert et al., 2011; Violle et al., 2012; Siefert et al., 2015)"},"citationItems":[{"id":719,"uris":[""],"uri":[""],"itemData":{"id":719,"type":"article-journal","title":"When and how should intraspecific variability be considered in trait-based plant ecology?","container-title":"Perspectives in Plant Ecology, Evolution and Systematics","page":"217–225","volume":"13","issue":"3","source":"Google Scholar","call-number":"0024","author":[{"family":"Albert","given":"Cécile H."},{"family":"Grassein","given":"Fabrice"},{"family":"Schurr","given":"Frank M."},{"family":"Vieilledent","given":"Ghislain"},{"family":"Violle","given":"Cyrille"}],"issued":{"date-parts":[["2011"]]}}},{"id":275,"uris":[""],"uri":[""],"itemData":{"id":275,"type":"article-journal","title":"The return of the variance: intraspecific variability in community ecology","container-title":"Trends in Ecology & Evolution","page":"244–252","volume":"27","issue":"4","source":"Google Scholar","call-number":"0031","note":"00176","shortTitle":"The return of the variance","author":[{"family":"Violle","given":"Cyrille"},{"family":"Enquist","given":"Brian J."},{"family":"Mcgill","given":"Brian J."},{"family":"Jiang","given":"Lin"},{"family":"Albert","given":"Cécile H."},{"family":"Hulshof","given":"Catherine"},{"family":"Jung","given":"Vincent"},{"family":"Messier","given":"Julie"}],"issued":{"date-parts":[["2012"]]}}},{"id":2032,"uris":[""],"uri":[""],"itemData":{"id":2032,"type":"article-journal","title":"A global meta-analysis of the relative extent of intraspecific trait variation in plant communities","container-title":"Ecology Letters","page":"1406-1419","volume":"18","issue":"12","source":"Wiley Online Library","abstract":"Recent studies have shown that accounting for intraspecific trait variation (ITV) may better address major questions in community ecology. However, a general picture of the relative extent of ITV compared to interspecific trait variation in plant communities is still missing. Here, we conducted a meta-analysis of the relative extent of ITV within and among plant communities worldwide, using a data set encompassing 629 communities (plots) and 36 functional traits. Overall, ITV accounted for 25% of the total trait variation within communities and 32% of the total trait variation among communities on average. The relative extent of ITV tended to be greater for whole-plant (e.g. plant height) vs. organ-level traits and for leaf chemical (e.g. leaf N and P concentration) vs. leaf morphological (e.g. leaf area and thickness) traits. The relative amount of ITV decreased with increasing species richness and spatial extent, but did not vary with plant growth form or climate. These results highlight global patterns in the relative importance of ITV in plant communities, providing practical guidelines for when researchers should include ITV in trait-based community and ecosystem studies.","DOI":"10.1111/ele.12508","ISSN":"1461-0248","journalAbbreviation":"Ecol Lett","language":"en","author":[{"family":"Siefert","given":"Andrew"},{"family":"Violle","given":"Cyrille"},{"family":"Chalmandrier","given":"Lo?c"},{"family":"Albert","given":"Cécile H."},{"family":"Taudiere","given":"Adrien"},{"family":"Fajardo","given":"Alex"},{"family":"Aarssen","given":"Lonnie W."},{"family":"Baraloto","given":"Christopher"},{"family":"Carlucci","given":"Marcos B."},{"family":"Cianciaruso","given":"Marcus V."},{"family":"L. Dantas","given":"Vinícius","non-dropping-particle":"de"},{"family":"Bello","given":"Francesco","non-dropping-particle":"de"},{"family":"Duarte","given":"Leandro D. S."},{"family":"Fonseca","given":"Carlos R."},{"family":"Freschet","given":"Grégoire T."},{"family":"Gaucherand","given":"Stéphanie"},{"family":"Gross","given":"Nicolas"},{"family":"Hikosaka","given":"Kouki"},{"family":"Jackson","given":"Benjamin"},{"family":"Jung","given":"Vincent"},{"family":"Kamiyama","given":"Chiho"},{"family":"Katabuchi","given":"Masatoshi"},{"family":"Kembel","given":"Steven W."},{"family":"Kichenin","given":"Emilie"},{"family":"Kraft","given":"Nathan J. B."},{"family":"Lagerstr?m","given":"Anna"},{"family":"Bagousse-Pinguet","given":"Yoann Le"},{"family":"Li","given":"Yuanzhi"},{"family":"Mason","given":"Norman"},{"family":"Messier","given":"Julie"},{"family":"Nakashizuka","given":"Tohru"},{"family":"Overton","given":"Jacob McC."},{"family":"Peltzer","given":"Duane A."},{"family":"Pérez-Ramos","given":"I. M."},{"family":"Pillar","given":"Valério D."},{"family":"Prentice","given":"Honor C."},{"family":"Richardson","given":"Sarah"},{"family":"Sasaki","given":"Takehiro"},{"family":"Schamp","given":"Brandon S."},{"family":"Sch?b","given":"Christian"},{"family":"Shipley","given":"Bill"},{"family":"Sundqvist","given":"Maja"},{"family":"Sykes","given":"Martin T."},{"family":"Vandewalle","given":"Marie"},{"family":"Wardle","given":"David A."}],"issued":{"date-parts":[["2015",12,1]]}}}],"schema":""} (Albert et al., 2011; Violle et al., 2012; Siefert et al., 2015) and exceed the range limits for most of the component species. Secondly, our measures of trait means and variances are not weighted by the relative abundance of those species within each grid cell. As a result, rare species are given as much statistical weight as common species. We suspect that accounting for trait abundance by using weighted measures of trait means and variances would strengthen the relationships ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"tl70PhDH","properties":{"formattedCitation":"(Borgy et al., 2017b, 2017a)","plainCitation":"(Borgy et al., 2017b, 2017a)"},"citationItems":[{"id":2164,"uris":[""],"uri":[""],"itemData":{"id":2164,"type":"article-journal","title":"Sensitivity of community-level trait–environment relationships to data representativeness: A test for functional biogeography","container-title":"Global Ecology and Biogeography","page":"729-739","volume":"26","issue":"6","source":"Wiley Online Library","abstract":"Aim\n\nThe characterization of trait–environment relationships over broad-scale gradients is a critical goal for ecology and biogeography. This implies the merging of plot and trait databases to assess community-level trait-based statistics. Potential shortcomings and limitations of this approach are that: (i) species traits are not measured where the community is sampled and (ii) the availability of trait data varies considerably across species and plots. Here we address the effect of trait data representativeness [the sampling effort per species and per plot] on the accuracy of (i) species-level and (ii) community-level trait estimates and (iii) the consequences for the shape and strength of trait–environment relationships across communities.\n\n\nInnovation\n\nWe combined information existing in databases of vegetation plots and plant traits to estimate community-weighted means [CWMs] of four key traits [specific leaf area, plant height, seed mass and leaf nitrogen content per dry mass] in permanent grasslands at a country-wide scale. We propose a generic approach for systematic sensitivity analyses based on random subsampling and data reduction to address the representativeness of incomplete and heterogeneous trait information when exploring trait–environment relationships across communities.\n\n\nMain conclusions\n\nThe accuracy of the CWMs was little affected by the number of individual trait values per species [NIV] but strongly affected by the cover proportion of species with available trait values [PCover]. A PCover above 80% was required for all four traits studied to obtain an estimation bias below 5%. Our approach therefore provides more conservative criteria than previously proposed. Restrictive criteria on both NIV and PCover primarily excluded communities in harsh environments, and such reduction of the sampled gradient weakened trait–environment relationships. These findings advocate systematic measurement campaigns in natural environments to increase species coverage in global trait databases, with special emphasis on species occurring in under-sampled and harsh environmental conditions.","DOI":"10.1111/geb.12573","ISSN":"1466-8238","shortTitle":"Sensitivity of community-level trait–environment relationships to data representativeness","journalAbbreviation":"Global Ecol. Biogeogr.","language":"en","author":[{"family":"Borgy","given":"Benjamin"},{"family":"Violle","given":"Cyrille"},{"family":"Choler","given":"Philippe"},{"family":"Garnier","given":"Eric"},{"family":"Kattge","given":"Jens"},{"family":"Loranger","given":"Jessy"},{"family":"Amiaud","given":"Bernard"},{"family":"Cellier","given":"Pierre"},{"family":"Debarros","given":"Guilhem"},{"family":"Denelle","given":"Pierre"},{"family":"Diquelou","given":"Sylvain"},{"family":"Gachet","given":"Sophie"},{"family":"Jolivet","given":"Claudy"},{"family":"Lavorel","given":"Sandra"},{"family":"Lemauviel-Lavenant","given":"Servane"},{"family":"Mikolajczak","given":"Alexis"},{"family":"Munoz","given":"Fran?ois"},{"family":"Olivier","given":"Jean"},{"family":"Viovy","given":"Nicolas"}],"issued":{"date-parts":[["2017",6,1]]}}},{"id":2741,"uris":[""],"uri":[""],"itemData":{"id":2741,"type":"article-journal","title":"Plant community structure and nitrogen inputs modulate the climate signal on leaf traits","container-title":"Global Ecology and Biogeography","page":"1138-1152","volume":"26","issue":"10","source":"Wiley Online Library","abstract":"Aim\n\nLeaf traits strongly impact biogeochemical cycles in terrestrial ecosystems. Understanding leaf trait variation along environmental gradients is thus essential to improve the representation of vegetation in Earth system models. Our aims were to quantify relationships between leaf traits and climate in permanent grasslands at a biogeographical scale and to test whether these relationships were sensitive to (a) the level of nitrogen inputs and (b) the inclusion of information pertaining to plant community organization.\n\n\nLocation\n\nPermanent grasslands throughout France.\n\n\nMethods\n\nWe combined existing datasets on climate, soil, nitrogen inputs (fertilization and deposition), species composition and four traits, namely specific leaf area, leaf dry matter content and leaf nitrogen and phosphorus concentrations, for 15,865 French permanent grasslands. Trait–climate relationships were tested using the following four climatic variables available across 1,833 pixels (5 km?×?5 km): mean annual temperature (MAT) and precipitation (MAP), and two indices accounting for the length of the growing season. We compared these relationships at the pixel level using either using community-level or species’ trait means.\n\n\nResults\n\nOur findings were as follows: (a) leaf traits related to plant nutrient economy shift consistently along a gradient of growing season length accounting for temperature and soil water limitations of plant growth (GSLtw); (b) weighting leaf traits by species abundance in local communities is pivotal to capture leaf trait–environment relationships correctly at a biogeographical scale; and (c) the relationships between traits and GSLtw weaken for grasslands with a high nitrogen input.\n\n\nMain conclusions\n\nThe effects of climate on plant communities are better described using composite descriptors than coarse variables such as MAT or MAP, but appear weaker for high-nitrogen grasslands. Using information at the community level tends to strengthen trait–climate relationships. The interplay of land management, community assembly and bioclimate appears crucial to the prediction of leaf trait variations and their effects on biogeochemical cycles.","DOI":"10.1111/geb.12623","ISSN":"1466-8238","journalAbbreviation":"Global Ecol Biogeogr","language":"en","author":[{"family":"Borgy","given":"Benjamin"},{"family":"Violle","given":"Cyrille"},{"family":"Choler","given":"Philippe"},{"family":"Denelle","given":"Pierre"},{"family":"Munoz","given":"Fran?ois"},{"family":"Kattge","given":"Jens"},{"family":"Lavorel","given":"Sandra"},{"family":"Loranger","given":"Jessy"},{"family":"Amiaud","given":"Bernard"},{"family":"Bahn","given":"Michael"},{"family":"Bodegom","given":"Peter M.","non-dropping-particle":"van"},{"family":"Brisse","given":"Henry"},{"family":"Debarros","given":"Guilhem"},{"family":"Diquelou","given":"Sylvain"},{"family":"Gachet","given":"Sophie"},{"family":"Jolivet","given":"Claudy"},{"family":"Lemauviel-Lavenant","given":"Servane"},{"family":"Mikolajczak","given":"Alexis"},{"family":"Olivier","given":"Jean"},{"family":"Ordo?ez","given":"Jenny"},{"family":"Ruffray","given":"Patrice","non-dropping-particle":"de"},{"family":"Viovy","given":"Nicolas"},{"family":"Garnier","given":"Eric"}],"issued":{"date-parts":[["2017",10,1]]}}}],"schema":""} (Borgy et al., 2017a). Third, our results are largely based on a model selection approach. Although this procedure is justified ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"a1ub15stka7","properties":{"formattedCitation":"(e.g. Burnham & Anderson, 2002)","plainCitation":"(e.g. Burnham & Anderson, 2002)"},"citationItems":[{"id":603,"uris":[""],"uri":[""],"itemData":{"id":603,"type":"book","title":"Model selection and multimodel inference: a practical information-theoretic approach","publisher":"Springer","source":"Google Scholar","URL":"","shortTitle":"Model selection and multimodel inference","author":[{"family":"Burnham","given":"Kenneth P."},{"family":"Anderson","given":"David R."}],"issued":{"date-parts":[["2002"]]},"accessed":{"date-parts":[["2014",4,10]]}},"prefix":"e.g. "}],"schema":""} (e.g. Burnham & Anderson, 2002), the selected ‘best’ model could be spurious due to the collinearity in explanatory variables. Although our results concerning trait means were robust to different model selection approaches, those concerning trait variances were more sensitive and should thus be interpreted with caution. Fourth, our analyses are based on a relatively coarse spatial resolution. Although this resolution should be robust to potential overestimation of species distribution derived from range maps ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2oqhmefq58","properties":{"formattedCitation":"(Hurlbert & Jetz, 2007)","plainCitation":"(Hurlbert & Jetz, 2007)"},"citationItems":[{"id":325,"uris":[""],"uri":[""],"itemData":{"id":325,"type":"article-journal","title":"Species richness, hotspots, and the scale dependence of range maps in ecology and conservation","container-title":"Proceedings of the National Academy of Sciences","page":"13384–13389","volume":"104","issue":"33","source":"Google Scholar","author":[{"family":"Hurlbert","given":"Allen H."},{"family":"Jetz","given":"Walter"}],"issued":{"date-parts":[["2007"]]}}}],"schema":""} (Hurlbert & Jetz, 2007), finer resolution should better capture local environmental conditions and could lead to stronger trait-climate relationships. A more important issue, however, is spatial sampling bias. A substantial fraction of the tropical species, especially the South American species, is lacking trait values. We showed that, whereas trait means were relatively robust to the spatial unevenness of species occurrence records, trait variances were much more sensitive to sampling bias and their relationships to climate should thus be interpreted with caution. Fortunately, the number of trait measurements in large databases continues to increase. Furthermore, our maps of sampling intensity (Figs. S2.1-S2.2) can guide ecologists and plant physiologists to where future field measurements of trait values are needed. Our results have important implications for the emerging field of functional biogeography. First, observed relationships between trait means and variances are helping to assess several prominent hypotheses regarding the climate signal on plant traits (e.g. the hydraulic limitation hypothesis, the seed mass-environmental favourability hypothesis). Second, the differences in trait-climate correlations observed for woody versus herbaceous species imply that there should be a systematic split between woody and herbaceous plants in large-scale, trait-based studies. An important next step for future studies will be to combine the maps of trait means and variances with maps of ecosystem processes (e.g. remotely sensed productivity data). This will enable us to evaluate the relative importance of both in driving ecosystem processes, a long-standing goal of functional ecology ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"Pd3rfaUo","properties":{"formattedCitation":"{\\rtf (D\\uc0\\u237{}az et al., 2007; Lavorel, 2013; Enquist et al., 2015)}","plainCitation":"(Díaz et al., 2007; Lavorel, 2013; Enquist et al., 2015)"},"citationItems":[{"id":523,"uris":[""],"uri":[""],"itemData":{"id":523,"type":"article-journal","title":"Incorporating plant functional diversity effects in ecosystem service assessments","container-title":"Proceedings of the National Academy of Sciences","page":"20684–20689","volume":"104","issue":"52","source":"Google Scholar","note":"00491","author":[{"family":"Díaz","given":"Sandra"},{"family":"Lavorel","given":"Sandra"},{"family":"Bello","given":"Francesco","non-dropping-particle":"de"},{"family":"Quétier","given":"Fabien"},{"family":"Grigulis","given":"Karl"},{"family":"Robson","given":"T. Matthew"}],"issued":{"date-parts":[["2007"]]}}},{"id":415,"uris":[""],"uri":[""],"itemData":{"id":415,"type":"article-journal","title":"Plant functional effects on ecosystem services","container-title":"Journal of Ecology","page":"4-8","volume":"101","issue":"1","source":"Wiley Online Library","abstract":"* The prominent new place of ecosystem services in environmental policy, land management and land planning requires that the best ecological knowledge be applied to ecosystem service quantification. Given strong evidence that functional diversity underpins the delivery of key ecosystem services, assessments of these services may progress rapidly using a trait-based approach.\n\n\n* The trait-based approach shows promising results, especially for plant trait effects on primary production and some processes associated with carbon and nitrogen cycling in grasslands. However, there is a need to extend the proof of concept for a wider range of ecosystems and ecosystem services and to incorporate not only the functional characteristics of plants but those of other organisms with which plants interact for the provision of ecosystem services.\n\n\n* The five papers in this Special Feature illustrate how some of the key conceptual and methodological challenges can be resolved, and provide a range of case studies across three continents. Relevant plant functional traits depict different axes of variation including stature, the leaf economics spectrum, and associated or independent variations in root or stem traits. The application of the trait approach to ecosystem processes underpinned by interactions between plants and other biota is illustrated for soil micro-organisms and granivorous invertebrates. There is strong evidence for the biomass ratio hypothesis (i.e. prevalent effects of the traits of dominant species through the community-weighted mean), along with less prevalent and more complex effects of heterogeneous trait values between species (i.e. functional divergence).\n\n\n* Synthesis. Together, the five papers in this Special Feature illustrate how trait-based approaches may help elucidate the complexity of ecological mechanisms operating in the field to determine ecosystem service delivery. To address scientific and management questions about the provision of multiple services, progress is needed in understanding how functional trade-offs and synergies within organisms scale up to interactions between ecosystem services. Service-oriented ecosystem management within the context of global change, or ecological restoration, remains a major challenge, but trait-based understanding opens new avenues towards more generic, integrated approaches.","DOI":"10.1111/1365-2745.12031","ISSN":"1365-2745","note":"00023","journalAbbreviation":"J Ecol","language":"en","author":[{"family":"Lavorel","given":"Sandra"}],"issued":{"date-parts":[["2013"]]}}},{"id":2533,"uris":[""],"uri":[""],"itemData":{"id":2533,"type":"article-journal","title":"Scaling from Traits to Ecosystems: Developing a General Trait Driver Theory via Integrating Trait-Based and Metabolic Scaling Theories","container-title":"Advances in Ecological Research","page":"249–318","volume":"52","source":"Google Scholar","shortTitle":"Chapter Nine-Scaling from Traits to Ecosystems","author":[{"family":"Enquist","given":"Brian J."},{"family":"Norberg","given":"Jon"},{"family":"Bonser","given":"Stephen P."},{"family":"Violle","given":"Cyrille"},{"family":"Webb","given":"Colleen T."},{"family":"Henderson","given":"Amanda"},{"family":"Sloat","given":"Lindsey L."},{"family":"Savage","given":"Van M."}],"issued":{"date-parts":[["2015"]]}}}],"schema":""} (Díaz et al., 2007; Lavorel, 2013; Enquist et al., 2015). In turn, this will help refine structure and simulation of dynamic vegetation models over large spatial scales ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2g2pemmr27","properties":{"formattedCitation":"(Reichstein et al., 2014)","plainCitation":"(Reichstein et al., 2014)"},"citationItems":[{"id":313,"uris":[""],"uri":[""],"itemData":{"id":313,"type":"article-journal","title":"Linking plant and ecosystem functional biogeography","container-title":"Proceedings of the National Academy of Sciences","page":"13697-13702","volume":"111","issue":"38","source":"","abstract":"Classical biogeographical observations suggest that ecosystems are strongly shaped by climatic constraints in terms of their structure and function. On the other hand, vegetation function feeds back on the climate system via biosphere–atmosphere exchange of matter and energy. Ecosystem-level observations of this exchange reveal very large functional biogeographical variation of climate-relevant ecosystem functional properties related to carbon and water cycles. This variation is explained insufficiently by climate control and a classical plant functional type classification approach. For example, correlations between seasonal carbon-use efficiency and climate or environmental variables remain below 0.6, leaving almost 70% of variance unexplained. We suggest that a substantial part of this unexplained variation of ecosystem functional properties is related to variations in plant and microbial traits. Therefore, to progress with global functional biogeography, we should seek to understand the link between organismic traits and flux-derived ecosystem properties at ecosystem observation sites and the spatial variation of vegetation traits given geoecological covariates. This understanding can be fostered by synergistic use of both data-driven and theory-driven ecological as well as biophysical approaches.","DOI":"10.1073/pnas.1216065111","ISSN":"0027-8424, 1091-6490","note":"00008 \nPMID: 25225392","journalAbbreviation":"PNAS","language":"en","author":[{"family":"Reichstein","given":"Markus"},{"family":"Bahn","given":"Michael"},{"family":"Mahecha","given":"Miguel D."},{"family":"Kattge","given":"Jens"},{"family":"Baldocchi","given":"Dennis D."}],"issued":{"date-parts":[["2014",9,23]]}}}],"schema":""} (Reichstein et al., 2014) and improve predictions of ecosystem services ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"AKVQJpQJ","properties":{"formattedCitation":"(Violle et al., 2015b)","plainCitation":"(Violle et al., 2015b)"},"citationItems":[{"id":2176,"uris":[""],"uri":[""],"itemData":{"id":2176,"type":"article-journal","title":"Vegetation ecology meets ecosystem science: permanent grasslands as a functional biogeography case study","container-title":"Science of the Total Environment","page":"43–51","volume":"534","source":"Google Scholar","shortTitle":"Vegetation ecology meets ecosystem science","author":[{"family":"Violle","given":"Cyrille"},{"family":"Choler","given":"Philippe"},{"family":"Borgy","given":"Benjamin"},{"family":"Garnier","given":"Eric"},{"family":"Amiaud","given":"Bernard"},{"family":"Debarros","given":"Guilhem"},{"family":"Diquelou","given":"Sylvain"},{"family":"Gachet","given":"Sophie"},{"family":"Jolivet","given":"Claudy"},{"family":"Kattge","given":"Jens"},{"literal":"others"}],"issued":{"date-parts":[["2015"]]}}}],"schema":""} (Violle et al., 2015b).AcknowledgementsThis work was conducted as a part of the Botanical Information and Ecology Network (BIEN) Working Group (PIs BJE, Richard Condit, BBoyle, SD, RKP) supported by the National Center for Ecological Analysis and Synthesis (funded by NSF Grant #EF-0553768), the University of California, Santa Barbara, and the State of California. The BIEN Working Group was also supported by iPlant (NSF #DBI-0735191; URL: ). IS and AT were funded by the grant no. 14-36098G from the Grant Agency of The Czech Republic. CV was supported by the French Foundation for Research on Biodiversity (FRB; fondationbiodiversite.fr) in the context of the CESAB project “Assembling, analysing and sharing data on plant functional diversity to understand the effects of biodiversity on ecosystem functioning: a case study with French Permanent Grasslands” (DIVGRASS), by a Marie Curie International Outgoing Fellowship within the 7th European Community Framework Program (DiversiTraits project, no. 221060) and by the European Research Council (ERC) Starting Grant Project “Ecophysiological and biophysical constraints on domestication in crop plants” (Grant ERC-StG-2014-639706-CONSTRAINTS). JCS was supported by the European Research Council (ERC-2012-StG-310886-HISTFUNC) and also considers this work a contribution to his VILLUM Investigator project (VILLUM FONDEN, grant 16549). BBlonder was supported by a UK Natural Environment Research Council independent research fellowship. SKW was supported by Core funding for Crown Research Institutes from the New Zealand Ministry of Business, Innovation and Employment’s Science and Innovation Group. NM-H was supported by a VILLUM Foundation Postdoctoral Fellowship. AGG was supported by FONDECYT 11150835 and CONICYT-PAI 82130046. WO was supported by The Netherlands Organization for Scientific Research (NWO Biodiversity Works). The study was also supported by the TRY initiative on plant traits (). TRY is currently supported by DIVERSITAS/Future Earth and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. We thank all the BIEN and TRY contributors. 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Zhao, P. & Yu, B. (2006) On Model Selection Consistency of Lasso. Journal of Machine Learning Research, 7, 2541–2563. Supporting InformationAppendix S1 – MetadataAppendix S2 – Trait sampling intensity, frequency distribution of trait means and variances.Appendix S3 – Supplementary statistical analyses, plots of the respective contributions of the model termsBiosketch: IS is a postdoctoral researcher interested in macroecology and mechanisms generating species richness patterns in plants at various spatial scales. CV is a senior researcher interested in functional biogeography and community ecology.Author contributions: CV, IS, BJE and JC-S conceived the study; IS analyzed the data with help from AT and PvB, IS and CV led the writing with major contributions from JC-S, KE, RKP, JK, BS, BBlonder, SKW and BJE; BJE, BBoyle, RKP, J-CS, SKW, CV, NJBK, NM-H and BJM developed the BIEN database (), JK provided the TRY database, and PvB, AGG, MB and OW were core TRY contributors. All authors discussed and commented on the manuscript.Editor: Holger KreftData accessibilityAll raster maps in asci format, species occurrence – grid data table and the list of the taxa used in our analyses, main data frame are available in online Supporting Information (Appendix S1). Species coordinates are available via BIEN package ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"a2apc43trpd","properties":{"formattedCitation":"(Maitner, 2017)","plainCitation":"(Maitner, 2017)"},"citationItems":[{"id":2035,"uris":[""],"uri":[""],"itemData":{"id":2035,"type":"book","title":"BIEN: Tools for Accessing the Botanical Information and Ecology Network Database version 1.1.0. ","version":"1.1.0","source":"R-Packages","abstract":"Provides Tools for Accessing the Botanical Information and Ecology Network Database. The BIEN database contains cleaned and standardized botanical data including occurrence, trait, plot and taxonomic data (See <; for more Information). This package provides functions that query the BIEN database by constructing and executing optimized SQL queries.","URL":"","shortTitle":"BIEN","author":[{"family":"Maitner","given":"Brian"}],"issued":{"date-parts":[["2017",3,8]]},"accessed":{"date-parts":[["2017",4,23]]}}}],"schema":""} (Maitner, 2017) with some exceptions of endangered species (see Maitner et al., in press for details). Trait data are available via BIEN () and TRY (try-). Table 1: Pearson correlation coefficients (r) between trait means (means) or variances (vars) based on species occurrences and those based on species ranges maps. ’W’ is woody habit, ’H’ is herbaceous habit. * indicates significant correlation (p<0.05) and (*) indicates marginally significant correlation (p<0.1) when accounting for the effect of space using Duttieul’s method. TraitHabitr (means)r (vars)HeightTrees0.844*0.604*HeightHerbs0.552*0.605SLA Trees0.695*0.473*SLA Herbs0.413*0.315(*)Seed mass Trees0.891*0.470*Seed mass Herbs0.186*0.281*Leaf N Trees0.708*0.571*Leaf NHerbs0.237*0.282(*)Leaf PTrees0.768*0.285*Leaf PHerbs-0.0030.244*Wood densityTrees0.762*0.055*Table 2: The best models explaining trait means and variances of each trait selected according to AIC weight. Coefficients below each explanatory variable are standardized and indicate the relative contribution of this variable to each model. The number of terms in the model output is limited to a maximum of six. See Table S3.2 for the reults concerning unlimited number of terms and Table S3.3 for Lasso model selection. ’W’ is woody habit, ’H’ is herbaceous habit, ‘T’ is mean annual temperature, ‘P’ is annual precipitation, ‘TS’ is temperature seasonality, ’PS’ is precipitation seasonality, ’Arid’ is aridity index (P/PET) and ’Solar’ is annual solar radiation. Each variable is represented by the linear form (e.g. T) and quadratic form (e.g. T2). See Figs. S3.6-S3.11 for plots of the respective contribution of each predictor. All these models were weighted by square root of the per-cell number of species with known trait. See Table S3.4 for the unweighted models. TraitHabitr2TT2PP2TSTS2PSPS2AridArid2SolarSolar2MeanHeightW0.821.27-0.770.33-0.991.07-0.11HeightH0.390.91-0.490.130.381.42-1.43SLA W0.421.53-1.151.20-0.80-0.501.05SLA H0.360.88-0.710.400.19-0.06-0.30Seed mass W0.860.77-0.281.03-0.370.15-0.30Seed mass H0.180.63-0.430.160.40-0.43Leaf N W0.520.90-0.41-0.21-0.15-1.171.08Leaf NH0.130.23-0.38-0.21-0.11-0.12Leaf PW0.830.70-0.690.101.43-0.75-0.17Leaf PH0.050.22-0.26-0.11-0.14Wood densityW0.600.97-0.15-0.16-0.190.19VarianceHeightW0.65-0.33-0.200.90-0.89?-1.171.00HeightH0.56-0.09-1.530.92?-0.06-1.231.25SLAW0.19-0.170.52-0.520.151.54-1.32SLA H0.320.82-0.670.130.30-2.612.14Seed mass W0.46-0.301.20-0.62-0.38-0.13-0.20Seed mass H0.260.190.120.37-0.160.82-0.81Leaf NW0.44-0.18-0.65-0.18-1.131.16Leaf NH0.160.54-0.26-0.24-0.510.17-0.21Leaf PW0.281.12-1.23-0.360.41-2.382.41Leaf PH0.35-0.15-1.140.650.06Wood densityW0.22-0.29???0.09?-0.450.36Table 3: The best models explaining trait means and variances merged for both growth form groups selected according to the AIC weight. Coefficients below each explanatory variable are standardized and indicate the relative contribution of this variable to each model. The number of terms in the model output is limited to a maximum of six. See Table S3.5 for the reults concerning unlimited number of terms and Table S3.6 for Lasso model selection output. See Tavle 2 for explanation of abbreviation of environmental variables. ‘GF’ is growth form (woody/herbaceous). Each variable is represented by the linear term (e.g. T), quadratic term (e.g. T2) and interaction term with growth form (e.g. GF:T). Note that precipitation seasonality is omitted as it was not selected in any case. All variables were standardized prior analyses. All these models were weighted by square-root of the per-cell number of species with known trait. See Table S3.7 for the unweighted models.Traitr2TT2PP2TSTS2AridArid2SolarSolar2GFGF:TGF:PGF:TSGF:AridGF:SolarMean???????????Height0.610.420.140.43-0.23-0.01-0.41SLA 0.54-0.480.98-0.410.860.02-0.48Seed mass 0.560.35-0.390.22-0.20-0.11-0.40Leaf N 0.59-0.55-0.16-0.700.27-0.080.36Leaf P0.76-0.36-0.04-0.120.151.110.21VarianceHeight0.54-0.17-0.760.140.09-0.170.74SLA0.400.56-0.31-0.090.17-0.270.51Seed mass 0.51-0.33-0.410.240.48-0.24-0.10Leaf N0.51-0.21-0.61-0.150.260.05-0.17Leaf P0.43-0.48-0.070.190.090.910.36Figure legendsFig. 1: Trait maps of grid-cell trait means and variances for woody species (the first and third columns) and herbaceous species (the second and fourth columns). Note that trait values of height, seed mass and wood density were loge-transformed prior calculating grid cell trait means and variances. See Figs. S2.4-S2.5 for comparison to trait maps based on species ranges maps.Fig. 2: The relationships of partial effect of standardized (a) trait means and (b) variances for woody species (red circles) and herbaceous species (black circles) plotted against the standardized climatic predictor having the strongest impact on the difference between the growth forms (see Table 3). Note that for variance in seed mass, none of the growth form-climate interaction terms was selected. The variable on the y-axis is calculated as residuals of the linear regression model with standardized trait means (a) and variances (b) for both growth forms together as a response variable and its climate predictors presented in Table 3 (without the variable on the x-axis) as explanatory variables. The model fit is a quadratic regression. ................
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