The biogeography of the Reptiles and Amphibians of ...
A necessarily complex model to explain the biogeography of Madagascar's amphibians and reptiles Jason L. Brown1,2, Alison Cameron3, Anne D. Yoder1, Miguel Vences41 Department of Biology, Duke University, Durham, NC 27708 Durham, NC, USA2 Current address: Department of Biology, The City College of New York, NY, USA3 School of Biological Sciences, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK4 Zoological Institute, Technical University of Braunschweig, Mendelssohnstr. 4, 38106 Braunschweig, GermanyAbstractA fundamental limitation of biogeographic analyses are that pattern and process are inextricably linked, and whereas we can observe pattern, we must infer process. Yet, such inferences are often based on ad-hoc comparisons using a single spatial predictor such as climate, topography, vegetation, or assumed barriers to dispersal without taking into account competing explanatory factors. Here we present an alternative approach, using mixed-spatial models to measure the predictive potential of combinations of spatially explicit hypotheses to explain observed biodiversity patterns. In this study we compiled a comprehensive dataset of 8362 occurrence records from 745 amphibian and reptile species from Madagascar. These data were used to estimate species richness, corrected weighted endemism, and species turnover (based on generalized dissimilarity modeling). We also created or incorporated, when previously available, 18 spatially explicit predictions of 12 major diversification and biogeography hypotheses, such as: mid-domain, topographic heterogeneity, sanctuary, and climate-related factors. Our results clearly demonstrate that mixed-models greatly improved our ability to explain the observed amphibian and reptile biodiversity patterns. Hence, the observed biogeographic patterns were likely influenced by a combination of diversification processes rather than by a single predominant mechanism. Further, selected genera of Malagasy amphibians and reptiles differed in the major factors explaining their spatial patterns of richness and endemism. These differences suggest that key factors in diversification are lineage specific and vary among major endemic clades. Our study therefore emphasizes the importance of comprehensive analyses across taxonomic, temporal, and spatial scales for understanding the complex diversification history of Madagascar's biota. A "one-size-fits-all" model does not exist.Keywords: Conditional Autoregressive Models, Orthogonally Transformed Beta Coefficients, Generalized Dissimilarity Modelling, Species Distribution Modelling The spatial distribution of biodiversity is at the core of biogeography, macroecology, evolutionary biology, and conservation biologyPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5LZW50PC9BdXRob3I+PFllYXI+MjAwNTwvWWVhcj48UmVj
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ADDIN EN.CITE.DATA 5-7, prioritize geographic areas for conservation ADDIN EN.CITE <EndNote><Cite><Author>Kremen</Author><Year>2008</Year><RecNum>5</RecNum><DisplayText><style face="superscript">8</style></DisplayText><record><rec-number>5</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1379361300">5</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kremen, C.</author><author>Cameron, A.</author><author>Moilanen, A.</author><author>Phillips, S. J.</author><author>Thomas, C. D.</author><author>Beentje, H.</author><author>Dransfield, J.</author><author>Fisher, B. L.</author><author>Glaw, F.</author><author>Good, T. C.</author><author>Harper, G. J.</author><author>Hijmans, R. J.</author><author>Lees, D. C.</author><author>Louis, E., Jr.</author><author>Nussbaum, R. A.</author><author>Raxworthy, C. J.</author><author>Razafimpahanana, A.</author><author>Schatz, G. E.</author><author>Vences, M.</author><author>Vieites, D. R.</author><author>Wright, P. C.</author><author>Zjhra, M. L.</author></authors></contributors><titles><title>Aligning conservation priorities across taxa in Madagascar with high-resolution planning tools</title><secondary-title>Science</secondary-title></titles><periodical><full-title>Science</full-title></periodical><pages>222-226</pages><volume>320</volume><number>5873</number><dates><year>2008</year><pub-dates><date>Apr 11</date></pub-dates></dates><isbn>0036-8075</isbn><accession-num>WOS:000254836700041</accession-num><urls><related-urls><url><Go to ISI>://WOS:000254836700041</url></related-urls></urls><electronic-resource-num>10.1126/science.1155193</electronic-resource-num></record></Cite></EndNote>8, assess the effects of conservation measuresPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Ib2ZmbWFubjwvQXV0aG9yPjxZZWFyPjIwMTA8L1llYXI+
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ADDIN EN.CITE.DATA 9, and/or delimit centers of speciation or extinction. These indices, however, are not independent of one another. For instance, species turnover across an area is closely related to the numbers of endemic species within each geographical unit or community, which in turn is often used to estimate areas of endemism (AOE). These represent the coincident restrictedness of taxaPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5QbGF0bmljazwvQXV0aG9yPjxZZWFyPjE5OTE8L1llYXI+
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ADDIN EN.CITE.DATA 10-12 and are often used to identify unique geographic areas for biodiversity conservation or biogeography studies ADDIN EN.CITE <EndNote><Cite><Author>Terborgh</Author><Year>1983</Year><RecNum>541</RecNum><DisplayText><style face="superscript">13,14</style></DisplayText><record><rec-number>541</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384719895">541</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Terborgh, J.</author><author>Winter, B.</author></authors></contributors><titles><title>A method for siting parks and reserves with special reference to Columbia and Ecuador</title><secondary-title>Biological Conservation </secondary-title></titles><periodical><full-title>Biological Conservation</full-title></periodical><pages>45-58</pages><volume>27</volume><dates><year>1983</year></dates><urls></urls></record></Cite><Cite><Author>Ackery</Author><Year>1984</Year><RecNum>528</RecNum><record><rec-number>528</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384717980">528</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Ackery, P. R. </author><author>Vane-Wright, R.I. </author></authors></contributors><titles><title> Milkweed butterflies: Their cladistics and biology</title></titles><dates><year>1984</year></dates><pub-location> London, UK and Ithaca, NY</pub-location><publisher>British Museum of Natural History and Cornell University Press</publisher><urls></urls></record></Cite></EndNote>13,14. Clearly, there is an inescapable circularity to these measures, and thus also to the consequent inferences made regarding biogeographic processes.Inferences of speciation mechanism fall prey to similar limitations. For example, it is generally assumed that species formation and diversification of a range of co-distributed taxa will be triggered or inhibited by similar barriers to gene flow, topographical and geological settings, climatic conditions and shifts, and competition. Accordingly, it is the default expectation that similar barriers (e.g., rivers, ecotones, climatic transitions) will lead to similar patterns of species endemism, turnover, and richness; again, with the underlying assumption that the observation of similar patterns among diverse species reveals a general causal mechanism of diversification across all taxa. But there are additional processes by which species richness may be generated. For example, climatic factors, environmental stability, land area, habitat heterogeneity, paleogeography, and energy available all could be spatially correlated with geographical barriers. Thus, any of these mechanisms might be indirectly, but not causally related to diversification ADDIN EN.CITE <EndNote><Cite><Author>Hawkins</Author><Year>2003</Year><RecNum>484</RecNum><DisplayText><style face="superscript">15</style></DisplayText><record><rec-number>484</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384553015">484</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Hawkins, B. A.</author><author>Field, R.</author><author>Cornell, H. V.</author><author>Currie, D. J.</author><author>Guegan, J. F.</author><author>Kaufman, D. M.</author><author>Kerr, J. T.</author><author>Mittelbach, G. G.</author><author>Oberdorff, T.</author><author>O'Brien, E. M.</author><author>Porter, E. E.</author><author>Turner, J. R. G.</author></authors></contributors><titles><title>Energy, water, and broad-scale geographic patterns of species richness</title><secondary-title>Ecology</secondary-title></titles><periodical><full-title>Ecology</full-title></periodical><pages>3105-3117</pages><volume>84</volume><number>12</number><dates><year>2003</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>0012-9658</isbn><accession-num>WOS:000187973500001</accession-num><urls><related-urls><url><Go to ISI>://WOS:000187973500001</url></related-urls></urls><electronic-resource-num>10.1890/03-8006</electronic-resource-num></record></Cite></EndNote>15. Patterns of endemism, on the other hand, are generally considered to reflect a particular evolutionary history, with areas of endemism corresponding to centers of diversification ADDIN EN.CITE <EndNote><Cite><Author>Jetz</Author><Year>2004</Year><RecNum>488</RecNum><DisplayText><style face="superscript">16</style></DisplayText><record><rec-number>488</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384553275">488</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Jetz, W.</author><author>Rahbek, C.</author><author>Colwell, R. K.</author></authors></contributors><titles><title>The coincidence of rarity and richness and the potential signature of history in centres of endemism</title><secondary-title>Ecology Letters</secondary-title></titles><periodical><full-title>Ecology Letters</full-title></periodical><pages>1180-1191</pages><volume>7</volume><number>12</number><dates><year>2004</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>1461-023X</isbn><accession-num>WOS:000225078000008</accession-num><urls><related-urls><url><Go to ISI>://WOS:000225078000008</url></related-urls></urls><electronic-resource-num>10.1111/j.1461-0248.2004.00678.x</electronic-resource-num></record></Cite></EndNote>16 and often including some element of stochasticity. Consequently, it can be the case that areas of high endemism often are also characterized by high species richness, though the inverse is not necessarily true. Species distribution models (SDMs) allow sophisticated calculations of centers of historical habitat stability ADDIN EN.CITE <EndNote><Cite><Author>Carnaval</Author><Year>2009</Year><RecNum>470</RecNum><DisplayText><style face="superscript">17</style></DisplayText><record><rec-number>470</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384551676">470</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Carnaval, Ana Carolina</author><author>Hickerson, Michael J.</author><author>Haddad, Celio F. B.</author><author>Rodrigues, Miguel T.</author><author>Moritz, Craig</author></authors></contributors><titles><title>Stability Predicts Genetic Diversity in the Brazilian Atlantic Forest Hotspot</title><secondary-title>Science</secondary-title></titles><periodical><full-title>Science</full-title></periodical><pages>785-789</pages><volume>323</volume><number>5915</number><dates><year>2009</year><pub-dates><date>Feb 6</date></pub-dates></dates><isbn>0036-8075</isbn><accession-num>WOS:000263066800048</accession-num><urls><related-urls><url><Go to ISI>://WOS:000263066800048</url></related-urls></urls><electronic-resource-num>10.1126/science.1166955</electronic-resource-num></record></Cite></EndNote>17. Yet, their spatial comparison with current patterns usually follows narrative approaches and is similar to classical hypotheses of diversification mechanisms, with no accounting for autocorrelation among the different explanatory variables. 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ADDIN EN.CITE.DATA 2,7,8,19-21. Despite this progress in conceptual and statistical tools, biological explanation of these patterns is still in its methodological infancy. Here we aim to employ the latest techniques for sophisticated and improved statistical methods for identifying the causal mechanisms that have determined the spatial distribution of Madagascar's herpetofauna. Though the search for the drivers of biological diversification was initially focused on the Neotropics, considerable attention has more recently been focused on other areas such as the Australian wet tropics ADDIN EN.CITE <EndNote><Cite><Author>Graham</Author><Year>2006</Year><RecNum>480</RecNum><DisplayText><style face="superscript">22</style></DisplayText><record><rec-number>480</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384552724">480</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Graham, C. H.</author><author>Moritz, C.</author><author>Williams, S. E.</author></authors></contributors><titles><title>Habitat history improves prediction of biodiversity in rainforest fauna</title><secondary-title>Proceedings of the National Academy of Sciences of the United States of America</secondary-title></titles><periodical><full-title>Proceedings of the National Academy of Sciences of the United States of America</full-title></periodical><pages>632-636</pages><volume>103</volume><number>3</number><dates><year>2006</year><pub-dates><date>Jan 17</date></pub-dates></dates><isbn>0027-8424</isbn><accession-num>WOS:000234727800025</accession-num><urls><related-urls><url><Go to ISI>://WOS:000234727800025</url></related-urls></urls><electronic-resource-num>10.1073/pnas.0505754103</electronic-resource-num></record></Cite></EndNote>22 and Madagascar ADDIN EN.CITE <EndNote><Cite><Author>Vences</Author><Year>2009</Year><RecNum>514</RecNum><DisplayText><style face="superscript">23</style></DisplayText><record><rec-number>514</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384554592">514</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Vences, Miguel</author><author>Wollenberg, Katharina C.</author><author>Vieites, David R.</author><author>Lees, David C.</author></authors></contributors><titles><title>Madagascar as a model region of species diversification</title><secondary-title>Trends in Ecology & Evolution</secondary-title></titles><periodical><full-title>Trends in Ecology & Evolution</full-title></periodical><pages>456-465</pages><volume>24</volume><number>8</number><dates><year>2009</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>0169-5347</isbn><accession-num>WOS:000269051400010</accession-num><urls><related-urls><url><Go to ISI>://WOS:000269051400010</url></related-urls></urls><electronic-resource-num>10.1016/j.tree.2009.03.011</electronic-resource-num></record></Cite></EndNote>23. Madagascar is the world's fourth largest island and hosts an extraordinary number of endemic flora and fauna. For example, 100% of the native species of amphibians and terrestrial mammals, 92% of reptiles, 44% of birds, and >90% of flowering plants occur nowhere else ADDIN EN.CITE <EndNote><Cite><Author>Goodman</Author><Year>2003</Year><RecNum>457</RecNum><DisplayText><style face="superscript">24</style></DisplayText><record><rec-number>457</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1381172526">457</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Goodman, Steven M</author><author>Benstead, Jonathan P</author></authors></contributors><titles><title>The natural history of Madagascar</title></titles><dates><year>2003</year></dates><publisher>University of Chicago Press Chicago</publisher><isbn>0226303063</isbn><urls></urls></record></Cite></EndNote>24. This mega diverse micro-continent, initially part of Gondwana, has been isolated from other continents since the Mesozoic. Its current vertebrate fauna is a mix of only a few ancient Gondwanan clades and numerous endemic radiations, originating from Cenozoic overseas colonizers arriving mainly from AfricaPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Zb2RlcjwvQXV0aG9yPjxZZWFyPjIwMDY8L1llYXI+PFJl
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ADDIN EN.CITE.DATA 25-27.The extraordinary levels of endemism at the level of entire clades in Madagascar, and their long isolation from their sister lineages, provide a unique opportunity to study the mechanisms driving divergence and diversification in situ ADDIN EN.CITE <EndNote><Cite><Author>Yoder</Author><Year>2005</Year><RecNum>527</RecNum><DisplayText><style face="superscript">28</style></DisplayText><record><rec-number>527</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384555350">527</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Yoder, A. D.</author><author>Olson, L. E.</author><author>Hanley, C.</author><author>Heckman, K. L.</author><author>Rasoloarison, R.</author><author>Russell, A. L.</author><author>Ranivo, J.</author><author>Soarimalala, V.</author><author>Karanth, K. P.</author><author>Raselimanana, A. P.</author><author>Goodman, S. M.</author></authors></contributors><titles><title>A multidimensional approach for detecting species patterns in Malagasy vertebrates</title><secondary-title>Proceedings of the National Academy of Sciences of the United States of America</secondary-title></titles><periodical><full-title>Proceedings of the National Academy of Sciences of the United States of America</full-title></periodical><pages>6587-6594</pages><volume>102</volume><dates><year>2005</year><pub-dates><date>May 3</date></pub-dates></dates><isbn>0027-8424</isbn><accession-num>WOS:000229023700012</accession-num><urls><related-urls><url><Go to ISI>://WOS:000229023700012</url></related-urls></urls><electronic-resource-num>10.1073/pnas.0502092102</electronic-resource-num></record></Cite></EndNote>28. Over the past decade, numerous general mechanisms and models have been formulated to explain biodiversity distribution patterns and species diversification in Madagascar, pertaining to environmental stability (or instability), solar energy input, geographic vicariance triggered by topographic or habitat complexity, intrinsic traits of organisms, or stochastic effectsPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5QYXN0b3Jpbmk8L0F1dGhvcj48WWVhcj4yMDAzPC9ZZWFy
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ADDIN EN.CITE.DATA 23,29-36. Evidence has supported numerous hypotheses, though the evidence has typically been marshalled from limited or phylogenetically-constrained taxa. Comprehensive statistical approaches comparing their relative importance are rare ADDIN EN.CITE <EndNote><Cite><Author>Pearson</Author><Year>2009</Year><RecNum>505</RecNum><DisplayText><style face="superscript">37</style></DisplayText><record><rec-number>505</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384554169">505</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Pearson, Richard G.</author><author>Raxworthy, Christopher J.</author></authors></contributors><titles><title>The evolution of local endemism in madagascar: watershed versus climatic gradient hypotheses evaluated by null biogeographic models.</title><secondary-title>Evolution</secondary-title></titles><periodical><full-title>Evolution</full-title><abbr-1>Evolution; international journal of organic evolution</abbr-1></periodical><pages>959-967</pages><volume>63</volume><number>4</number><dates><year>2009</year><pub-dates><date>Apr</date></pub-dates></dates><isbn>0014-3820</isbn><accession-num>WOS:000264379200013</accession-num><urls><related-urls><url><Go to ISI>://WOS:000264379200013</url></related-urls></urls><electronic-resource-num>10.1111/j.1558-5646.2008.00596.x</electronic-resource-num></record></Cite></EndNote>37. In this paper we apply an integrative approach to simultaneously test which of several competing and complementary hypotheses are most strongly correlated with empirical biodiversity patterns (Fig. 1). We first translate a total of 12 diversification mechanisms or diversity models into explicit spatial representations. We then use diverse statistical approaches to assess spatial concordance of these predictor variables with species richness, endemism and turnover as calculated from original occurrence data of Madagascar's amphibians and reptiles, with full species-level coverage. Our results best agree with the hypothesis that various assemblages of species are under the influence of differing causal mechanisms. The clear message is that the distribution of diverse organismal lineages will depend upon idiosyncratic factors determined by their specific organismal life-history traits combined with stochastic historical factors. Thus, any model that endeavors to explain island-wide patterns must necessarily be complex.ResultsTo understand spatial distribution patterns in Madagascar's herpetofauna, we first compared range sizes, and computed species richness and endemism from the modeled distribution areas of amphibians and non-avian reptiles (hereafter reptiles). Mean range size (± standard deviation) in our data set is smaller in amphibians than reptiles taking into account all species (41,673 ± 55,413 km2 vs. 50,205 ± 84,078 km2; t= 3.981, p< 0.001; df=649.7) and after excluding species known from only 1 or 2 localities (64,106 ± 57,532 km2 vs. 95,294 ± 87,495 km2; t= 4.511, p<0.001; df=427.4). Microendemics (species with distributions less than 1000km2) constitute 36.5% of all amphibian and 33.6 % of all reptile species in Madagascar (difference not significant; Z =0.411, p=0.682).Spatial patterns of species richness are quite similar between the two groups (Fig 2A & E) and reach highest values in the eastern rainforest; in amphibians, richness peaks in the central east, whereas in reptiles, it is more evenly distributed across the rainforest biome, with some areas of high diversity also in the north, west, and southwest. Spatial patterns of endemism in both groups (Fig 2B & F) reveal two centers in the north around the Tsaratanana Massif and in the central east. Endemism values for reptiles are also high in southwestern Madagascar, the most arid region of the island. We applied Generalized Dissimilarity Modelling (GDM)PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5GZXJyaWVyPC9BdXRob3I+PFllYXI+MjAwNzwvWWVhcj48
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ADDIN EN.CITE.DATA 38,39 to identify areas of endemism on the basis of turnover patterns for non-avian reptiles and amphibians together. The major AOE obtained in a 4-class categorization of the originally continuous GDM results (Figure 2C & H) largely mirrors the bioclimatic regions of Cornet (1974). Our test includes a total of 12 predictor hypotheses, some of which focus on the geographical pattern in which species diversity is distributed, but without making any clear assumption about how the species originated (e.g., the mid-domain or topography heterogeneity hypotheses). Others explicitly refer to mechanisms of diversification and make predictions about how these processes affected the distribution of species diversity over geographical space (see Supplementary Documents for detailed accounts). We divided all the hypotheses into two categories: one in which predictions for continuous two-dimensional spatial richness and endemism can be derived, and another in which nominal AOE predictions can be derived. The first category includes (1) the Mid-domain Effect, (2) Topographic Heterogeneity, (3) Climatic Refugia, (4) Museum (montane refugia), (5) Disturbance-Vicariance, (6) Climate Stability, (7) Sanctuary and (8) Montane Species Pump. The second category includes (9) the River-Refuge (large river model), (10) Riverine Barrier (minor and major rivers), (11) Climatic Gradient and (12) Watershed. All these hypotheses were transformed into explicit spatial representations (Supplementary Materials) and used as predictor variables for further analyses. We calculated unbiased correlation of the continuous predictor and test variables following the method of Dutilleul ADDIN EN.CITE <EndNote><Cite><Author>Dutilleul</Author><Year>1993</Year><RecNum>475</RecNum><DisplayText><style face="superscript">40</style></DisplayText><record><rec-number>475</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384552155">475</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Dutilleul, Pierre</author><author>Clifford, Peter</author><author>Richardson, Sylvia</author><author>Hemon, Denis</author></authors></contributors><titles><title>Modifying the t test for assessing the correlation between two spatial processes</title><secondary-title>Biometrics</secondary-title></titles><periodical><full-title>Biometrics</full-title></periodical><pages>305-314</pages><dates><year>1993</year></dates><isbn>0006-341X</isbn><urls></urls></record></Cite></EndNote>40, which reduces the degrees of freedom according to the level of spatial autocorrelation between two variables (detailed results in Supplementary Materials Table S3). We found that measures of both reptile and amphibian endemism significantly correlated to the predictor hypotheses of Topographic Heterogeneity, Disturbance-Vicariance and Museum (montane refugia). Reptile endemism (but not amphibian) is also correlated to Sanctuary. Correlations to species richness were not tied to measures of endemisms. Whereas reptile species richness is correlated to the Mid-domain Effect (distance) and Sanctuary hypotheses, amphibian richness is correlated to the Sanctuary hypothesis as well as to the Topographic Heterogeneity, Montane Species Pump, Disturbance-Vicariance, Museum, and River-Refuge hypotheses. In the univariate correlation analyses of nominal geospatial data (those related to AOE predictions) we compared the biogeographic zonation of Madagascar as suggested by the GDM analysis of amphibian and reptile distributions with zonations derived from five predictor hypotheses. We found all predictor variables (corresponding to the hypotheses Riverine-major and Riverine-minor, Gradient, River-refuge, and Watershed) to be significantly correlated to the 15-class GDM, and all but watershed with the 4-class GDM zonation (Table 1). Both GDM classifications share the most overlap with the Riverine and Gradient hypotheses (between 40.9–54.3% and 56.2–71.1%, respectively; Table 1). Given the significant correlation of each of the spatial amphibian and reptile biodiversity patterns with various predictor variables we used mixed conditional autoregressive spatial models (CAR models) to test the influences of various predictors simultaneously. To avoid over-parameterization we used AICc (corrected Akaike Information Criterion), an information-theoretical approach, to compare models with different sets of predictors. We found that complex models including most of the biogeography hypotheses (continuous predictor variables) performed best, based on the lowest AICc values, and consequently used these for further analysis. Detailed contributions of each predictor to the models of richness, endemism and GDM zonation are summarized in Supplementary Materials Table S4. The top-five variables contributed 49.4–75.9% to the models. For a more simplified graphical representation (Fig. 3), we summarized the three Mid-domain Effect hypotheses (latitude, longitude, and distance), the three principal components representing the Climate Gradient hypothesis, and three hypotheses focused on topography (Topographic Heterogeneity, Disturbance-vicariance, Montane Species Pump) were categorized together, respectively (Figs 3 & 4) . We found relevant influences of the Mid-domain Effect especially on the GDM and the species richness and endemism of reptiles (30.9%, 32.9% and 45.5%, respectively). However, it is important to point out that almost all the Mid-domain correlation coefficients were negative. Thus, indicating that Mid-domain Effects do not play a key role in determining spatial patterning. Climate Gradient effects influenced all the models of biodiversity equally, contributing roughly a quarter to each (25.1–27.7%), though in many cases the sign of the contribution varied. However in this case, a positive correlation was not expected. The topography variables contributed positively to the richness and endemism models of amphibians and reptiles, with joint influences of 9.1% and 22.4% on richness, and 6.5% and 17.3% on endemism. The two unique hypotheses, Sanctuary and Museum, each contributed positively to all models, with Museum contributing between 7.1–17.1% (one of the few hypothesis to contribute >5% and to be positively correlated to all biodiversity measurements). The Sanctuary hypothesis also contributed positively to all hypotheses, though to a lesser degree than the Museum hypothesis (which demonstrated little contribution to reptile endemism). To assess variation in biogeography patterns among major groups of the Malagasy herpetofauna, we calculated mixed CAR models using the same methods for richness and endemism of four exemplar sub-clades: the leaf chameleons (Brookesia), tree frogs (Boophis), day geckos (Phelsuma) and Oplurus iguanas (including the monotypic iguana genus Chalarodon). The top contributors to the models were drastically different for several of these clades (Fig. 4). For instance, the topography variables had strong influences on Boophis richness, with a joint contribution of 24.5%, but contributed much less to explaining the patterns of most other groups. Further, the Sanctuary hypothesis had a strong influence on the Brookesia and iguana models, though it contributed very little to the predictions of endemism in Boophis and Phelsuma. Mid-domain Effects were apparent in most models but the sign on the correlation and the contribution of each Mid-domain hypothesis varied considerably. DiscussionThe results of this study clearly demonstrate that single-mechanism explanatory hypotheses of spatial patterning in Madagascar's herpetofauna (and presumably, other Malagasy vertebrates) are inadequate. Thus, we propose a novel method for examining and synthesizing spatial parameters such as species richness, endemism, and community similarity. In this framework, biogeographic hypotheses are explanatory variables. The resulting mixed-model geospatial approach to biogeography analyses is both more robust, and more realistic. 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ADDIN EN.CITE.DATA 45-47, as was applied in this study. The results obtained here for some sub-clades are in agreement with previous analyses, while others are not. For example, the high influence of the mid-domain effect on Boophis treefrogs, one of the most species-rich frog genera in Madagascar, agrees with a previous analysis performed by Colwell & Lees ADDIN EN.CITE <EndNote><Cite><Author>Colwell</Author><Year>2000</Year><RecNum>471</RecNum><DisplayText><style face="superscript">48</style></DisplayText><record><rec-number>471</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384551713">471</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Colwell, R. K.</author><author>Lees, D. C.</author></authors></contributors><titles><title>The mid-domain effect: geometric constraints on the geography of species richness</title><secondary-title>Trends in Ecology & Evolution</secondary-title></titles><periodical><full-title>Trends in Ecology & Evolution</full-title></periodical><pages>70-76</pages><volume>15</volume><number>2</number><dates><year>2000</year><pub-dates><date>Feb</date></pub-dates></dates><isbn>0169-5347</isbn><accession-num>WOS:000085525200011</accession-num><urls><related-urls><url><Go to ISI>://WOS:000085525200011</url></related-urls></urls><electronic-resource-num>10.1016/s0169-5347(99)01767-x</electronic-resource-num></record></Cite></EndNote>48 for all Malagasy amphibians (with a high representation of Boophis). On the contrary, the negative contributions of the mid-domain effects on the biodiversity patterns of the other genera in the analysis are obvious given that their centers of richness and endemism are in either southern or northern Madagascar, but not in central parts of the island. Previous studies postulated a high influence of topography on the diversification of leaf chameleons (Brookesia),PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5SYXh3b3J0aHk8L0F1dGhvcj48WWVhcj4xOTk1PC9ZZWFy
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ADDIN EN.CITE.DATA 41,49 though this is not supported by our analysis. This latter example exemplifies a dilemma of scale, inherent in all comparisons of spatial data sets. In fact, the distribution of Brookesia is highly specific to certain mountain massifs in northern Madagascar while the genus is largely absent from the equally topographically heterogeneous south-east. This absence is probably due to its evolutionary history, with a diversification mainly in the north and limited capacity for range expansion ADDIN EN.CITE <EndNote><Cite><Author>Townsend</Author><Year>2009</Year><RecNum>513</RecNum><DisplayText><style face="superscript">41</style></DisplayText><record><rec-number>513</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384554560">513</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Townsend, Ted M.</author><author>Vieites, David R.</author><author>Glaw, Frank</author><author>Vences, Miguel</author></authors></contributors><titles><title>Testing Species-Level Diversification Hypotheses in Madagascar: The Case of Microendemic Brookesia Leaf Chameleons</title><secondary-title>Systematic Biology</secondary-title></titles><periodical><full-title>Systematic Biology</full-title></periodical><pages>641-656</pages><volume>58</volume><number>6</number><dates><year>2009</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>1063-5157</isbn><accession-num>WOS:000272072700008</accession-num><urls><related-urls><url><Go to ISI>://WOS:000272072700008</url></related-urls></urls><electronic-resource-num>10.1093/sysbio/syp073</electronic-resource-num></record></Cite></EndNote>41. This historical distribution pattern probably accounts for low influence of the topographic hypotheses on Madagascar-wide Brookesia richness and endemism, while at a smaller spatial scale (northern Madagascar) these hypotheses might well have a strong predictive value. While patterns of richness and endemism of the Malagasy herpetofauna have been analyzed several times for various purposes based on partial data setsPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Db2x3ZWxsPC9BdXRob3I+PFllYXI+MjAwMDwvWWVhcj48
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ADDIN EN.CITE.DATA 8,35,37,41,48 the analysis of turnover of species composition and the definition of biogeographic regions following from such explicit analyses are still in their infancy. For reptiles, Angel's ADDIN EN.CITE <EndNote><Cite><Author>Angel</Author><Year>1942</Year><RecNum>529</RecNum><DisplayText><style face="superscript">50</style></DisplayText><record><rec-number>529</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384718119">529</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Angel, F.</author></authors></contributors><titles><title>Les Lézards de Madagascar</title></titles><dates><year>1942</year></dates><publisher>Academie Malgache</publisher><urls></urls></record></Cite></EndNote>50 proposal of biogeographic regions based on classical phytogeography, i.e., regions based on plant community composition ADDIN EN.CITE <EndNote><Cite><Author>Humbert</Author><Year>1955</Year><RecNum>535</RecNum><DisplayText><style face="superscript">51</style></DisplayText><record><rec-number>535</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384718902">535</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Humbert, H. </author></authors></contributors><titles><title>Les territoires phytogéographiques de Madagascar.</title><secondary-title>Année Biologique</secondary-title></titles><periodical><full-title>Année Biologique</full-title></periodical><pages>439–448</pages><volume> 31</volume><dates><year>1955</year></dates><urls></urls></record></Cite></EndNote>51, has usually been adopted ADDIN EN.CITE <EndNote><Cite><Author>Glaw</Author><Year>1994</Year><RecNum>532</RecNum><DisplayText><style face="superscript">52</style></DisplayText><record><rec-number>532</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384718539">532</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Glaw, F.</author><author>Vences, M.</author></authors></contributors><titles><title>Amphibians and Reptiles of Madagascar</title></titles><dates><year>1994</year></dates><pub-location>K?ln</pub-location><publisher>Vences, M. and Glaw Verlags, F. GbR.</publisher><urls></urls></record></Cite></EndNote>52. Later, Schatz ADDIN EN.CITE <EndNote><Cite><Author>Schatz</Author><Year>2000</Year><RecNum>540</RecNum><DisplayText><style face="superscript">53</style></DisplayText><record><rec-number>540</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384719799">540</key></foreign-keys><ref-type name="Book Section">5</ref-type><contributors><authors><author>Schatz, G.E. </author></authors><secondary-authors><author>Louren?o, W.R.</author><author>Goodman, S.M.</author></secondary-authors></contributors><titles><title>Endemism in the Malagasy tree flora</title><secondary-title>Diversity and Endemism in Madagascar</secondary-title></titles><pages> 1–9</pages><dates><year>2000</year></dates><publisher>Société de Biogéographie, MNHN, ORSTOM</publisher><urls></urls></record></Cite></EndNote>53 refined this zonation of Madagascar based on explicit bioclimatic analyses, and Glaw & Vences ADDIN EN.CITE <EndNote><Cite><Author>Glaw</Author><Year>2007</Year><RecNum>533</RecNum><DisplayText><style face="superscript">54</style></DisplayText><record><rec-number>533</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384718655">533</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Glaw, F.</author><author>Vences, M. </author></authors></contributors><titles><title>Field Guide to the Amphibians and Reptiles of Madagascar</title></titles><edition>Third Edition</edition><dates><year>2007</year></dates><pub-location>K?ln</pub-location><publisher>Vences and Glaw Verlag</publisher><urls></urls></record></Cite></EndNote>54 proposed a detailed geographical zonation based on the areas of endemism of Wilmé ADDIN EN.CITE <EndNote><Cite><Author>Wilme</Author><Year>2006</Year><RecNum>523</RecNum><DisplayText><style face="superscript">33</style></DisplayText><record><rec-number>523</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384555012">523</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Wilme, L.</author><author>Goodman, S. M.</author><author>Ganzhorn, J. U.</author></authors></contributors><titles><title>Biogeographic evolution of Madagascar's microendemic biota</title><secondary-title>Science</secondary-title></titles><periodical><full-title>Science</full-title></periodical><pages>1063-1065</pages><volume>312</volume><number>5776</number><dates><year>2006</year><pub-dates><date>May 19</date></pub-dates></dates><isbn>0036-8075</isbn><accession-num>WOS:000237628800048</accession-num><urls><related-urls><url><Go to ISI>://WOS:000237628800048</url></related-urls></urls><electronic-resource-num>10.1126/science.1122806</electronic-resource-num></record></Cite></EndNote>33. The GDM approach herein is the first explicit analysis of a large herpetofaunal dataset to geographically delimit regions distinguished by abrupt changes in their amphibian and reptile communities. This model turned out to agree remarkably well with classical bioclimatic and phytogeographic zonations of Madagascar ADDIN EN.CITE <EndNote><Cite><Author>Humbert</Author><Year>1955</Year><RecNum>535</RecNum><DisplayText><style face="superscript">51,53</style></DisplayText><record><rec-number>535</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384718902">535</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Humbert, H. </author></authors></contributors><titles><title>Les territoires phytogéographiques de Madagascar.</title><secondary-title>Année Biologique</secondary-title></titles><periodical><full-title>Année Biologique</full-title></periodical><pages>439–448</pages><volume> 31</volume><dates><year>1955</year></dates><urls></urls></record></Cite><Cite><Author>Schatz</Author><Year>2000</Year><RecNum>540</RecNum><record><rec-number>540</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384719799">540</key></foreign-keys><ref-type name="Book Section">5</ref-type><contributors><authors><author>Schatz, G.E. </author></authors><secondary-authors><author>Louren?o, W.R.</author><author>Goodman, S.M.</author></secondary-authors></contributors><titles><title>Endemism in the Malagasy tree flora</title><secondary-title>Diversity and Endemism in Madagascar</secondary-title></titles><pages> 1–9</pages><dates><year>2000</year></dates><publisher>Société de Biogéographie, MNHN, ORSTOM</publisher><urls></urls></record></Cite></EndNote>51,53, strongly correlated to climatic explanatory variables (Fig. 3). Especially in the 4-classes GDM, the regions almost perfectly correspond with those proposed by Schatz ADDIN EN.CITE <EndNote><Cite><Author>Schatz</Author><Year>2000</Year><RecNum>540</RecNum><DisplayText><style face="superscript">53</style></DisplayText><record><rec-number>540</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384719799">540</key></foreign-keys><ref-type name="Book Section">5</ref-type><contributors><authors><author>Schatz, G.E. </author></authors><secondary-authors><author>Louren?o, W.R.</author><author>Goodman, S.M.</author></secondary-authors></contributors><titles><title>Endemism in the Malagasy tree flora</title><secondary-title>Diversity and Endemism in Madagascar</secondary-title></titles><pages> 1–9</pages><dates><year>2000</year></dates><publisher>Société de Biogéographie, MNHN, ORSTOM</publisher><urls></urls></record></Cite></EndNote>53 based on bioclimate, i.e., eastern humid, central highland/montane, western arid, southwestern subarid zone. 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ADDIN EN.CITE.DATA 55,56. This implies that no single hypothesis adequately explains the diversification of broad taxonomic groups — our results support this assumption. Richness, endemism and turnover of large and heterogeneous groups exemplified by the all-species amphibian and reptile data sets were in all cases best explained by complex CAR models. These models have the advantage of incorporating most or all of the originally included explanatory variables. Several alternative explanations may account for this outcome. Patterns of biodiversity may not be strongly correlated to any of the predictor mechanisms simply because none of them provide the causal mechanism underlying the diversification processes. As another consideration, spatial predictions of some of the biodiversity hypotheses may have been inaccurate, though we took great care to avoid such mistakes. In any event, improvements in these methods may result in different outcomes in future analyses. Caveats aside, the results of this study almost certainly support a third explanation, that different clades of organisms are each predominantly influenced by a different set of diversification mechanisms. In turn, these are driven by intrinsic factors, such as morphological or physiological constraints, or to extrinsic factors, such as an initial diversification in an area characterized by a certain topography, climate, or biotic composition. This alternative is supported by the observation that the patterns of several of the smaller subgroups in our analysis were indeed best explained by opposing predominant variables, e.g., topographic heterogeneity and museum (Boophis endemism) vs. climate stability and sanctuary (Brookesia endemism). An overarching message is that the taxonomic scale of analysis is of extreme importance when attempting to derive global explanations of biodiversity distribution patterns. Including too many taxa will blur the existing differences among clades and lead to complex explanatory models, whereas patterns within specific clades may be best explained by simple models.The method proposed herein allows for a more objective quantification of the influences of particular diversification mechanisms on biodiversity patterns, compared to traditional, univariate approaches. Further developments of the method should especially focus on including a phylogenetic dimension, and when appropriate (for predictor hypotheses), a temporal component. Geospatial analyses of biodiversity pattern typically use species as equivalent and independent data points, though in reality, they are entities with substantial variation in parameters such as evolutionary age, dispersal capacity and population density, and with different degrees of relatedness depending on their position in the tree of life. This multilayered information can be included in various ways in the CAR/OTBC approach, e.g. by plotting richness and endemism of evolutionary history rather than taxonomic identity, calculating turnover only for sister species with adjacent ranges, or repeating the calculations for sets of species defined by particular nodes on a phylogenetic tree. This latter approach— iterating the analysis for successively more inclusive clades — appears particularly promising for identifying those moments in evolutionary history wherein shifts in prevalent diversification mechanisms have occurred. Given this perspective, we can begin to tease apart the diversification histories of individual clades versus prevailing biogeoclimatic events that shape entire biotas. Materials and MethodsBiodiversity EstimatesSpecies Distribution ModelingSpecies data consisted of 8362 occurrence records of 745 Malagasy amphibian and reptile species (325 and 420 species, respectively). Species distribution models (SDMs) were limited to species that had, at minimum, 3 unique occurrence points at the 30 arc-second spatial resolution (ca. 1 km). The reduced dataset represented 453 species (consisting of 5440 training points of 248 reptile and 205 amphibian species), with a mean of 12 training points per species (max= 131). For 107 amphibian and 119 reptile species with only 1-2 occurrence records a 10km buffer was applied to point localities in place of SDM. The species distribution models were generated in MaxEnt v3.3.3e ADDIN EN.CITE <EndNote><Cite><Author>Phillips</Author><Year>2006</Year><RecNum>506</RecNum><DisplayText><style face="superscript">57</style></DisplayText><record><rec-number>506</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384554205">506</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Phillips, S. J.</author><author>Anderson, R. P.</author><author>Schapire, R. E.</author></authors></contributors><titles><title>Maximum entropy modeling of species geographic distributions</title><secondary-title>Ecological Modelling</secondary-title></titles><periodical><full-title>Ecological Modelling</full-title></periodical><pages>231-259</pages><volume>190</volume><number>3-4</number><dates><year>2006</year><pub-dates><date>Jan 25</date></pub-dates></dates><isbn>0304-3800</isbn><accession-num>WOS:000233859600001</accession-num><urls><related-urls><url><Go to ISI>://WOS:000233859600001</url></related-urls></urls><electronic-resource-num>10.1016/j.ecolmodel.2005.03.026</electronic-resource-num></record></Cite></EndNote>57 using the following parameters: random test percentage = 25, regularization multiplier = 1, maximum number of background points = 10000, replicates = 10, replicated run type = cross validate. One limitation of presence-only data SDM methods is the effect of sample selection bias, where some areas in the landscape are sampled more intensively than others ADDIN EN.CITE <EndNote><Cite><Author>Phillips</Author><Year>2009</Year><RecNum>507</RecNum><DisplayText><style face="superscript">58</style></DisplayText><record><rec-number>507</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384554247">507</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Phillips, Steven J.</author><author>Dudik, Miroslav</author><author>Elith, Jane</author><author>Graham, Catherine H.</author><author>Lehmann, Anthony</author><author>Leathwick, John</author><author>Ferrier, Simon</author></authors></contributors><titles><title>Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data</title><secondary-title>Ecological Applications</secondary-title></titles><periodical><full-title>Ecological Applications</full-title></periodical><pages>181-197</pages><volume>19</volume><number>1</number><dates><year>2009</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>1051-0761</isbn><accession-num>WOS:000263516200014</accession-num><urls><related-urls><url><Go to ISI>://WOS:000263516200014</url></related-urls></urls><electronic-resource-num>10.1890/07-2153.1</electronic-resource-num></record></Cite></EndNote>58. MaxEnt requires an unbiased sample. To account for sampling biases, we used a bias file representing a Gaussian kernel-density of all species occurrence localities. The bias file upweighted presence-only data points with fewer neighbors in the geographic landscape ADDIN EN.CITE <EndNote><Cite><Author>Elith</Author><Year>2011</Year><RecNum>476</RecNum><DisplayText><style face="superscript">59</style></DisplayText><record><rec-number>476</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384552351">476</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Elith, Jane</author><author>Phillips, Steven J.</author><author>Hastie, Trevor</author><author>Dudik, Miroslav</author><author>Chee, Yung En</author><author>Yates, Colin J.</author></authors></contributors><titles><title>A statistical explanation of MaxEnt for ecologists</title><secondary-title>Diversity and Distributions</secondary-title></titles><periodical><full-title>Diversity and Distributions</full-title></periodical><pages>43-57</pages><volume>17</volume><number>1</number><dates><year>2011</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>1366-9516</isbn><accession-num>WOS:000285246700005</accession-num><urls><related-urls><url><Go to ISI>://WOS:000285246700005</url></related-urls></urls><electronic-resource-num>10.1111/j.1472-4642.2010.00725.x</electronic-resource-num></record></Cite></EndNote>59. Species distributions were modeled for the current climate using the 19 standard bioclimatic variables derived from interpolation of climatic records between 1950 and 2000 from weather stations around the globe (Worldclim 1.4 ADDIN EN.CITE <EndNote><Cite><Author>Hijmans</Author><Year>2005</Year><RecNum>35</RecNum><DisplayText><style face="superscript">60</style></DisplayText><record><rec-number>35</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1379365538">35</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Hijmans, R. J.</author><author>Cameron, S. E.</author><author>Parra, J. L.</author><author>Jones, P. G.</author><author>Jarvis, A.</author></authors></contributors><titles><title>Very high resolution interpolated climate surfaces for global land areas</title><secondary-title>International Journal of Climatology</secondary-title></titles><periodical><full-title>International Journal of Climatology</full-title></periodical><pages>1965-1978</pages><volume>25</volume><number>15</number><dates><year>2005</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>0899-8418</isbn><accession-num>WOS:000234519700002</accession-num><urls><related-urls><url><Go to ISI>://WOS:000234519700002</url></related-urls></urls><electronic-resource-num>10.1002/joc.1276</electronic-resource-num></record></Cite></EndNote>60). Non-climatic variables geology, aspect, elevation, solar radiation, and slope were also included ADDIN EN.CITE <EndNote><Cite><Author>Moat</Author><Year>1997</Year><RecNum>539</RecNum><DisplayText><style face="superscript">61,62</style></DisplayText><record><rec-number>539</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384719457">539</key></foreign-keys><ref-type name="Dataset">59</ref-type><contributors><authors><author>Moat, J.</author><author>Du Puy, D.</author></authors><secondary-authors><author>Royal Botanic Gardens, Kew </author></secondary-authors></contributors><titles><title>Simplified Geology of Madagascar</title></titles><dates><year>1997</year></dates><urls><related-urls><url> app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384719256">537</key></foreign-keys><ref-type name="Dataset">59</ref-type><contributors><authors><author>Jarvis, A.</author><author>Reuter, H.I.</author><author>Nelson, A. </author><author>Guevara, E. </author></authors></contributors><titles><title>Hole-filled SRTM for the globe Version 4</title></titles><dates><year>2008</year></dates><pub-location>CGIAR-CSI SRTM 90m Database</pub-location><urls><related-urls><url> . 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ADDIN EN.CITE.DATA 8,37, we clipped each SDM following the approach of Kremen et al. ADDIN EN.CITE <EndNote><Cite><Author>Kremen</Author><Year>2008</Year><RecNum>5</RecNum><DisplayText><style face="superscript">8</style></DisplayText><record><rec-number>5</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1379361300">5</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kremen, C.</author><author>Cameron, A.</author><author>Moilanen, A.</author><author>Phillips, S. J.</author><author>Thomas, C. D.</author><author>Beentje, H.</author><author>Dransfield, J.</author><author>Fisher, B. L.</author><author>Glaw, F.</author><author>Good, T. C.</author><author>Harper, G. J.</author><author>Hijmans, R. J.</author><author>Lees, D. C.</author><author>Louis, E., Jr.</author><author>Nussbaum, R. A.</author><author>Raxworthy, C. J.</author><author>Razafimpahanana, A.</author><author>Schatz, G. E.</author><author>Vences, M.</author><author>Vieites, D. R.</author><author>Wright, P. C.</author><author>Zjhra, M. L.</author></authors></contributors><titles><title>Aligning conservation priorities across taxa in Madagascar with high-resolution planning tools</title><secondary-title>Science</secondary-title></titles><periodical><full-title>Science</full-title></periodical><pages>222-226</pages><volume>320</volume><number>5873</number><dates><year>2008</year><pub-dates><date>Apr 11</date></pub-dates></dates><isbn>0036-8075</isbn><accession-num>WOS:000254836700041</accession-num><urls><related-urls><url><Go to ISI>://WOS:000254836700041</url></related-urls></urls><electronic-resource-num>10.1126/science.1155193</electronic-resource-num></record></Cite></EndNote>8. This method produces models that represent suitable habitat within an area of known occurrence (based on a buffered MCP), excluding suitable habitat greatly outside of observed range. The size of the buffer was based on the area of the MCP. We used buffer distances of 20km, 40km, and 80km, respectively, for three MCP area classes, 0-200km2, 200-1000 km2, and >1000 km2. All corrected SDMs were proofed by taxonomic experts to ensure reliability; if a model did not tightly match knowledge of areas where distributions were well documented, or if little prior information existed regarding a species distribution, or taxonomy was convoluted, and because of, its expected distribution could not be evaluated, the species was excluded from analyses (n= 71). Range Sizes, Species Richness and Corrected Weighted EndemismFor descriptive range-size statistics, distribution range-sizes were sampled for all species at 0.01 degrees2 from corrected SDMs (or buffered point data where applicable) and a student’s t-test with unequal variance was performed between amphibian and reptile species. To assess differences in the frequency of microendemics among the two groups, we converted all distributions that were > or < than 1000 km2 to a value of 0 and 1, respectively. We then calculated the mean frequency for both groups and ran a binomial test among both groups. Species richness was calculated separately for amphibians and reptiles by summing the respective corrected binary SDMs (based on a maximum training sensitivity plus specificity threshold) and, for species with 1-2 occurrence records, buffered points in ArcGIS. This provided a high resolution estimate of richness that is less affected by spatial scale and incomplete sampling than traditional measurements based solely on occurrence records. Measures of endemism are inherently dependent on spatial scale. We chose a grid scale of 82 x 63 km, separating Madagascar into 24 latitudinal and 8 longitudinal rows, to reduce problems associated with estimating endemism over too small or large areasPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DcmlzcDwvQXV0aG9yPjxZZWFyPjIwMDE8L1llYXI+PFJl
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ADDIN EN.CITE.DATA 12,35. Endemism was measured as corrected weighted endemism (CWE), where the proportion of endemics are inversely weighted by their range size (species with smaller ranges are weighted more than those with large ADDIN EN.CITE <EndNote><Cite><Author>Williams</Author><Year>2000</Year><RecNum>543</RecNum><DisplayText><style face="superscript">63</style></DisplayText><record><rec-number>543</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384720180">543</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Williams, P. H. </author></authors></contributors><titles><title>Some properties of rarity scores for site-quality assessment</title><secondary-title>British Journal of Entomology and Natural History</secondary-title></titles><periodical><full-title>British Journal of Entomology and Natural History</full-title></periodical><pages>73-86</pages><volume>13</volume><dates><year>2000</year></dates><urls></urls></record></Cite></EndNote>63) and this value divided by the local species richness ADDIN EN.CITE <EndNote><Cite><Author>Crisp</Author><Year>2001</Year><RecNum>459</RecNum><DisplayText><style face="superscript">12</style></DisplayText><record><rec-number>459</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1381172957">459</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Crisp, M. D.</author><author>Laffan, S.</author><author>Linder, H. P.</author><author>Monro, A.</author></authors></contributors><titles><title>Endemism in the Australian flora</title><secondary-title>Journal of Biogeography</secondary-title></titles><periodical><full-title>Journal of Biogeography</full-title></periodical><pages>183-198</pages><volume>28</volume><number>2</number><keywords><keyword>Biodiversity</keyword><keyword>endemism</keyword><keyword>species richness</keyword><keyword>Australia</keyword><keyword>Pleistocene</keyword><keyword>refugia</keyword><keyword>extinction</keyword><keyword>climate</keyword></keywords><dates><year>2001</year></dates><publisher>Blackwell Science Ltd</publisher><isbn>1365-2699</isbn><urls><related-urls><url>. CWE emphasizes areas that have a high proportion of animals with restricted ranges, but not necessarily high species richness. We calculated CWE separately for reptiles and amphibians using SMDtoolbox v1 (Brown in review). Generalized Dissimilarity ModelingGeneralized Dissimilarity Modeling (GDM) is a statistical technique extended from matrix regressions designed to accommodate nonlinear data commonly encountered in ecological studies ADDIN EN.CITE <EndNote><Cite><Author>Ferrier</Author><Year>2007</Year><RecNum>478</RecNum><DisplayText><style face="superscript">38</style></DisplayText><record><rec-number>478</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384552403">478</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Ferrier, Simon</author><author>Manion, Glenn</author><author>Elith, Jane</author><author>Richardson, Karen</author></authors></contributors><titles><title>Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment</title><secondary-title>Diversity and Distributions</secondary-title></titles><periodical><full-title>Diversity and Distributions</full-title></periodical><pages>252-264</pages><volume>13</volume><number>3</number><dates><year>2007</year><pub-dates><date>May</date></pub-dates></dates><isbn>1366-9516</isbn><accession-num>WOS:000245812100002</accession-num><urls><related-urls><url><Go to ISI>://WOS:000245812100002</url></related-urls></urls><electronic-resource-num>10.1111/j.1472-4642.2007.00341.x</electronic-resource-num></record></Cite></EndNote>38. One use of GDM is to analyze and predict spatial patterns of turnover in community composition across large areas. In short, a GDM is fitted to available biological data (the absence or presence of species at each site and environmental and geographic data) then compositional dissimilarity is predicted at unsampled localities throughout the landscape based on environmental and geographic data in the model. The result is a matrix of predicted compositional dissimilarities (PCD) between pairs of locations throughout the focal landscape. To visualize the PCD, multidimensional scaling was applied, reducing the data to three ordination axes, and in a GIS each axis was assigned a separate RGB color (red, green or blue). Due to computation limitations associated with pairwise comparisons of large datasets, we could not predict composition dissimilarities among all sites in our high resolution Madagascar dataset. To address this, we randomly sampled 2500 points throughout Madagascar from a ca. 10 km2 grid. We then measured the absence or presence of each of the 679 species at each locality. We used the same high resolution environmental and geography data used in the SDM. These 23 layers were reduced to nine vectors in a principal component analyses which represented 99.4% of the variation of the original data. These data were sampled at the same 2500 localities. Both data (species presence and environmental data) were input into a generalized dissimilarity model using the R package: GDM R distribution pack v1.1 (.au/gdm/GDM_R_Distribution_Pack_V1.1.zip). We then extrapolated the GDM into the high resolution climate dataset by assigning ordination scores using k-nearest neighbor classification (k=3, numeric Manhattan distance), calculating each ordination axes independently ADDIN EN.CITE <EndNote><Cite><Author>Ferrier</Author><Year>2007</Year><RecNum>478</RecNum><DisplayText><style face="superscript">38</style></DisplayText><record><rec-number>478</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384552403">478</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Ferrier, Simon</author><author>Manion, Glenn</author><author>Elith, Jane</author><author>Richardson, Karen</author></authors></contributors><titles><title>Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment</title><secondary-title>Diversity and Distributions</secondary-title></titles><periodical><full-title>Diversity and Distributions</full-title></periodical><pages>252-264</pages><volume>13</volume><number>3</number><dates><year>2007</year><pub-dates><date>May</date></pub-dates></dates><isbn>1366-9516</isbn><accession-num>WOS:000245812100002</accession-num><urls><related-urls><url><Go to ISI>://WOS:000245812100002</url></related-urls></urls><electronic-resource-num>10.1111/j.1472-4642.2007.00341.x</electronic-resource-num></record></Cite></EndNote>38. The continuous GDM was transformed into a model with four major classes, and each of these was then classified separately into 3-5 minor classes. The numbers of major and minor classes were based on hierarchical cluster analyses in in SPSS v19 ADDIN EN.CITE <EndNote><Cite><Author>Corporation</Author><Year>2010 </Year><RecNum>536</RecNum><DisplayText><style face="superscript">64</style></DisplayText><record><rec-number>536</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384719075">536</key></foreign-keys><ref-type name="Computer Program">9</ref-type><contributors><authors><author>IBM Corporation </author></authors></contributors><titles><title>IBM SPSS Statistics for Windows</title></titles><edition>19.0</edition><dates><year>2010</year></dates><pub-location> Armonk, NY</pub-location><publisher>IBM Corporation</publisher><urls></urls></record></Cite></EndNote>64 using a “bottom up” approach. The number of classes equaled the number of dendrogram nodes with relative distances (scaled from 0-1) at 0.71 and 0.63 for major and minor groups, respectively. The distance cut off can be somewhat arbitrary, however in our data there were obvious discontinuities (long dendrogram branches between nodes) at these two values. The resulting classified models were interpolated into high resolution climate space using a k-nearest neighbor classification as described above. Biogeography hypotheses We examined which specific spatial predictions for the three biodiversity patterns: species richness, endemism and/or in areas of endemism (AOE- the coincident restrictedness of taxa) in Madagascar could be derived from each of 12 biogeography hypotheses, and then translated these predictions into spatial models in a GIS. In a GIS, spatially explicit predictions of the three biodiversity patterns (species richness, endemism or areas of endemism) were estimated for each biogeography hypothesis. For some of the hypotheses not all three metrics of biodiversity were calculated due to lacking, or incomplete, expectations (e.g. not all hypothesis make predictions about AOE). Because of these incomplete biodiversity pattern predictions, comparisons among hypotheses are statistically non-trivial. This is in part because few diversification hypotheses capture all facets of biodiversity (species richness, endemism, AOE). Further, many estimates of biodiversity patterns rely on components of climate or geography, thus some are based on the same data and are not entirely independent of each other. Each hypothesis was generated at the spatial resolution of 30 arc-seconds (matching the resolution of GDM and species richness estimates, later transformed to 0.91 km2). For the endemism analyses, each biogeography hypothesis was upscaled to match resolution of the endemism analyses by averaging all values encompassed in cell. Spatial Statistics The spatial predictions derived from the various biodiversity hypotheses resulted in either continuous or nominal categorical data. Conducting statistical tests between data types is nontrivial and, in some cases, not logical or impossible. Spatial data are represented in GIS by two different formats: raster and vector. Geospatial raster data are composed of equal sized squares, tessellated in a grid, with each cell representing a value (often continuous data), such as elevation. Spatial vector data (commonly called ‘shapefiles’) can be represented by points, lines, or polygons, such as: localities, roads and countries, respectively. Vector data are non-topological and represent discrete features. They are often used to depict nominal data, where the relationship of data categories to others is unknown or non-linear. Raster data can be converted to vector data (and vice versa) and the data type (i.e. nominal or continuous) may or may not change when converted. For example, in some cases continuous data can be converted to ordered categories (ordinal data) when converted from raster to polygon. However if the same data were converted back to a raster file, it would remain categorical data due to data loss in the first conversion. Regardless of GIS data format, statistical tests need be chosen according to the data types, however GIS data format remain equally important, as often a single data format is required to perform a spatial statistic of interest (i.e. software input limitations). Analyses- Continuous Data To assess a global measurement of correlation between continuous data, we calculated Pearson correlations following the unbiased correlation method of Dutilleul ADDIN EN.CITE <EndNote><Cite><Author>Dutilleul</Author><Year>1993</Year><RecNum>475</RecNum><DisplayText><style face="superscript">40</style></DisplayText><record><rec-number>475</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384552155">475</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Dutilleul, Pierre</author><author>Clifford, Peter</author><author>Richardson, Sylvia</author><author>Hemon, Denis</author></authors></contributors><titles><title>Modifying the t test for assessing the correlation between two spatial processes</title><secondary-title>Biometrics</secondary-title></titles><periodical><full-title>Biometrics</full-title></periodical><pages>305-314</pages><dates><year>1993</year></dates><isbn>0006-341X</isbn><urls></urls></record></Cite></EndNote>40 and using the software Spatial Analysis in Macroecology ADDIN EN.CITE <EndNote><Cite><Author>Rangel</Author><Year>2010</Year><RecNum>510</RecNum><DisplayText><style face="superscript">65</style></DisplayText><record><rec-number>510</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384554390">510</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Rangel, Thiago F.</author><author>Diniz-Filho, Jose Alexandre F.</author><author>Bini, Luis Mauricio</author></authors></contributors><titles><title>SAM: a comprehensive application for Spatial Analysis in Macroecology</title><secondary-title>Ecography</secondary-title></titles><periodical><full-title>Ecography</full-title></periodical><pages>46-50</pages><volume>33</volume><number>1</number><dates><year>2010</year><pub-dates><date>Feb</date></pub-dates></dates><isbn>0906-7590</isbn><accession-num>WOS:000275205200004</accession-num><urls><related-urls><url><Go to ISI>://WOS:000275205200004</url></related-urls></urls><electronic-resource-num>10.1111/j.1600-0587.2009.06299.x</electronic-resource-num></record></Cite></EndNote>65. Analyses- Nominal Categorical Data Comparisons of nominal categorical spatial data (i.e. AOE predictions compared to classified GDM) focused on the spatial distributions of the borders between the subunits. We used the following methods to measure similarities and significance: (1) border overlap, and (2) Pearson correlation coefficients (r) with Dutilleul’s spatial correlation (see above). (1) Border overlap was calculated by sampling the landscape at 1 km resolution for the presence of a border. If present, a point was placed. We then measured the spatial overlap of the sampled borders of two landscapes. In all analyses, a 10 km buffer was applied to the overlap calculation, and points datasets that overlap by 10km or less are were considered overlapping boundaries. To account for differences in the level of subdivision of layers, overlap was converted to a percentage and averaged for both layers being compared. Country outline was excluded from all comparisons and thus, only intra-country boundaries were compared. (2) To assess global correlation between two polygon shapefiles, each shapefile was converted to a distance raster, measuring the closest distance from any point in the landscape to a boundary. Using these layers we measured a Pearson correlation (unbiased correlation after Dutilleul ADDIN EN.CITE <EndNote><Cite><Author>Dutilleul</Author><Year>1993</Year><RecNum>475</RecNum><DisplayText><style face="superscript">40</style></DisplayText><record><rec-number>475</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384552155">475</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Dutilleul, Pierre</author><author>Clifford, Peter</author><author>Richardson, Sylvia</author><author>Hemon, Denis</author></authors></contributors><titles><title>Modifying the t test for assessing the correlation between two spatial processes</title><secondary-title>Biometrics</secondary-title></titles><periodical><full-title>Biometrics</full-title></periodical><pages>305-314</pages><dates><year>1993</year></dates><isbn>0006-341X</isbn><urls></urls></record></Cite></EndNote>40), where high correlation coefficients represent two landscapes that have congruent areas that are isolated from boundaries and others congruent areas that are adjacent to boundaries. Each distance landscape was evenly sampled by 2000 points and correlations were assessed on the values of these points. Analyses- Mixed Models of Continuous Data To determine the influence of each biogeography hypothesis in predicting the observed biodiversity patterns, we integrated all continuous biogeography hypotheses into a single mixed conditional autoregression model (CAR) using the software Spatial Analysis in Macroecology ADDIN EN.CITE <EndNote><Cite><Author>Rangel</Author><Year>2010</Year><RecNum>510</RecNum><DisplayText><style face="superscript">65</style></DisplayText><record><rec-number>510</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384554390">510</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Rangel, Thiago F.</author><author>Diniz-Filho, Jose Alexandre F.</author><author>Bini, Luis Mauricio</author></authors></contributors><titles><title>SAM: a comprehensive application for Spatial Analysis in Macroecology</title><secondary-title>Ecography</secondary-title></titles><periodical><full-title>Ecography</full-title></periodical><pages>46-50</pages><volume>33</volume><number>1</number><dates><year>2010</year><pub-dates><date>Feb</date></pub-dates></dates><isbn>0906-7590</isbn><accession-num>WOS:000275205200004</accession-num><urls><related-urls><url><Go to ISI>://WOS:000275205200004</url></related-urls></urls><electronic-resource-num>10.1111/j.1600-0587.2009.06299.x</electronic-resource-num></record></Cite></EndNote>65. To normalize the predictor variables, Box-Cox transformations (Box and Cox 1964) were performed. The lambda parameter was estimated by maximizing the log-likelihood profile in R package GeoR ADDIN EN.CITE <EndNote><Cite><Author>Diggle</Author><Year> 2007</Year><RecNum>530</RecNum><DisplayText><style face="superscript">47</style></DisplayText><record><rec-number>530</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384718228">530</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Diggle, P.J.</author><author>Ribeiro, P.J. Jr.</author></authors></contributors><titles><title>Model-based Geostatistics</title></titles><dates><year> 2007</year></dates><pub-location>New York, USA</pub-location><publisher>Springer</publisher><urls></urls></record></Cite></EndNote>47. A Gabriel connection matrix was used to describe the spatial relationship among sample points ADDIN EN.CITE <EndNote><Cite><Author>Legendre</Author><Year> 1998</Year><RecNum>538</RecNum><DisplayText><style face="superscript">66</style></DisplayText><record><rec-number>538</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384719371">538</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Legendre, P.</author><author>Legendre, L.</author></authors></contributors><titles><title>Numerical ecology</title></titles><volume>2nd </volume><dates><year> 1998</year></dates><publisher>Elsevier</publisher><urls></urls><language>English</language></record></Cite></EndNote>66. Using Gabriel networks, short connections between neighboring points, is preferable (i.e. more conservative ADDIN EN.CITE <EndNote><Cite><Author>Griffith</Author><Year>1996</Year><RecNum>534</RecNum><DisplayText><style face="superscript">67</style></DisplayText><record><rec-number>534</rec-number><foreign-keys><key app="EN" db-id="29e52v5fndvsw7es5vbp9v5wva5xrpxxa95e" timestamp="1384718755">534</key></foreign-keys><ref-type name="Book Section">5</ref-type><contributors><authors><author>Griffith, D.A.</author></authors><secondary-authors><author>Arlinghaus, S. L.</author></secondary-authors></contributors><titles><title> Some guidelines for specifying the geographic weights matrix contained in spatial statistical models.</title><secondary-title>Practical handbook of spatial statistics</secondary-title></titles><pages>65-82</pages><dates><year>1996</year></dates><publisher>CRC Press</publisher><urls></urls></record></Cite></EndNote>67) than using inverse-decaying distances because in most empirical datasets the residual spatial autocorrelation tends to be stronger at smaller distance classesPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CaW5pPC9BdXRob3I+PFllYXI+MjAwOTwvWWVhcj48UmVj
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ADDIN EN.CITE.DATA 68. The main goal of our mixed spatial analyses were to determine the combination of biogeography hypotheses that best predict the observed biodiversity patterns. If each explanatory variable was incorporated natively, due to considerable multi-colinearity, often only a few variables would end up contributing to a majority of the model. To estimate the true contribution of each hypothesis in context of a mixed model (even if highly correlated to others), we developed a novel approach that removes colinearity from the response variables (but in the process explicit variable identity is temporarily lost). The transformed response variables are then run in a CAR analysis and the resulting standardized model contributions are then transformed back into original response variable identities; reflecting the relative contribution of each in the model. This process is casually referenced here as Orthogonally Transformed Beta Coefficients (OTBCs). Orthogonally Transformed Beta Coefficients Each biogeography hypothesis is standardized from zero to one. This ensured that the component loadings reflected the relative contribution of each biogeography hypothesis. A principal component analysis was performed on the standardized biogeography hypotheses using a covariance matrix. All the resulting principal components (PCs) were extracted and then loaded as explanatory variables in the CAR model. The CAR analyses were run iteratively, starting with all PCs as response variables and then excluding each PC that did not contribute significantly to the model (α = 0.05) until the final model included only PCs that contributed significantly to the model. Because each PC represented a linearly uncorrelated variable, only the relevant, independent data were incorporated into the final CAR model. The resulting standardized beta coefficients (βj from the CAR analyses, Fig. 1 and Equation 1) were then multiplied by the value of the corresponding component loadings (αij from the PCA, see Equation 1). The absolute value of the product reflects the relative contributions of each biogeography hypothesis to each PC, which are weighted by the PC’s contribution in the CAR model (herein termed the weighted component loadings or WCLif , Equation 1). The weighted component loadings (WCLif, Equation 1) were then summed for each biogeography hypothesis across all PCs (Hi) and depict the contributions of each hypothesis in the CAR model. The Hi value was then converted to percentages (HPi) to allow comparison among all CAR analyses. A positive or negative correlation was determined for each biogeography hypothesis by running a separate CAR analysis using the raw biogeography variables as a single response variable (all other parameters were matched). Equation 1 WCL ij= βj ×αij Hi=iWCLij HAll=ijWCLij HPi= HiHall*100 AcknowledgmentsWe are grateful to numerous friends and colleagues who provided invaluable assistance during fieldwork and previous discussions of Madagascar's biogeography, we would like to particularly thank Franco Andreone, Parfait Bora, Christopher Blair, Lauren Chan, Sebastian Gehring, Frank Glaw, Steve M. Goodman, J?rn K?hler, Peter Larsen, David C. Lees, Brice P. Noonan, Maciej Pabijan, Ted Townsend, Krystal Tolley, Roger Daniel Randrianiaina Fanomezana Ratsoavina, David R. Vieites, and Katharina C. Wollenberg. Fieldwork of MV was funded by the Volkswagen Foundation. JLB was supported by the National Science Foundation under Grant No. 0905905 and by Duke University start-up funds to ADY.References ADDIN EN.REFLIST 1Kent, M. Biogeography and macroecology. Progress in Physical Geography 29, 256-264, doi:10.1191/0309133305pp447pr (2005).2Beck, J. et al. What's on the horizon for macroecology? 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Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression. Ecography 32, 193-204, doi:10.1111/j.1600-0587.2009.05717.x (2009).Figure 1. Overview of work protocol and dataflow. Three types of original data were input into the analyses: (1) biogeography hypotheses, (2) geography and climate data and (3) species locality data. These data were used to predict distribution of species, and the distribution models used to calculate biodiversity patterns (species richness, corrected weighted endemism, turnover). We then tested for correlation of these biodiversity patterns with spatial predictions derived from biogeography hypotheses, and used a multivariate model to simultaneously test the influences of these hypotheses on the biodiversity patterns. *The response variables constituted standardized principal components of the raw biogeography hypotheses ** The CAR models were iterated until only response variables that contributed significantly the model were included. Figure 2. Observed Biodiversity Data. Reptile and amphibian species richness (A & E) measures the number of species present. Endemism (B & F), based on a corrected weighted calculation, reflects the proportion of unique species present within certain areas. The generalized dissimiliarity model (GDM) analyzes compositional turnover of communities (here jointly for amphibians and reptiles) and predicts dissimilarity throughout the landscape based on an interpolation based on variation in climate and geographic data. The continuous GDM (G) can then be classified into 4 major and 15 minor areas of endemism (H & C).Figure 3. Explanatory contribution of continuous biogeography hypotheses to a conditional autoregressive spatial model of each observed biodiversity measurements. Only hypotheses contributing >5% are shown; see Supplementary Table S4 for complete and detailed data). Mid-domain: I. latitude, II. longitude, III. distance. Climate-Gradient: I. PC1, II. PC2, III. PC3. Climate-Stability: I. Precipitation Stability, II. Climate Stability (temperature and precipitation). Topography: I. Topographic Heterogeneity, II. Disturbance-vicariance, III. Montane Species Pump. An asterisk marks hypotheses that contributed negatively to the mixed CAR model.Figure 4. Contribution of continuous biogeography hypotheses to a conditional autoregressive spatial model of species richness and endemism for four focal groups. Only hypotheses contributing >5% are shown; see Supplementary Table S4 for complete and detailed data). Mid-domain: I. latitude, II. longitude, III. distance. Climate-Gradient: I. PC1, II. PC2, III. PC3. Climate-Stability: I. Precipitation Stability, II. Climate Stability (temperature and precipitation). Topography: I. Topographic Heterogeneity, II. Disturbance-vicariance, III. Montane Species Pump. An asterisk marks hypotheses that contributed negatively to the mixed CAR model. (1) Brookesia chameleons (number of species = 27, number of original distribution points = 178). (2) Boophis treefrogs (n of spp.= 77, n of pts.=460) (3) Phelsuma day geckos (n of spp.= 28, n of pts.= 304) (4) oplurid iguanas (Oplurus plus the monotypic Chalarodon; n of spp.= 7, n of pts.= 147). Subgroups and colors of pie charts as in Fig. 3. An asterisk marks hypotheses that contributed negatively CAR. For all hypotheses (with exception of the climate-gradient variables) a positive correlation was expected between biodiversity metrics.Table 1. Correlations of nominal biodiversity hypotheses to Generalized Dissimilarity Models (upper: 15-class; lower: 4-class transformation of the original model). The left three columns show calculations based on the percentage of overlapping cells between boundaries. High mean values depict high levels of shared boundaries among GDM and AOE (derived from the respective hypothesis). R-values reflect non-spatial Pearson product-moment correlation coefficients. To assess significance of raster data, we used an unbiased correlation following the method of Dutilleul (1993) that reduces the degrees of freedom according to the level of spatial autocorrelation between the two variables. HypothesisPercent overlapping cells (10km buffer)Correlation to Generalized Dissimilarity Model GDM 15 classesHypothesisMeanrF-statdf p Riverine- Major77.11%35.31%56.21%0.42713.98462.537<.001Riverine- Minor72.43%49.41%60.92%0.50328.59984.415<.001Gradient74.63%67.51%71.07%0.40324.974128.641<.001Watershed 60.14%25.92%43.03%0.2295.887106.8240.017GDM -4100.00%38.71%69.35%0.55126.25760.171<.001Riverine – Refuge71.00%20.69%45.85%0.44011.45047.6930.001GDM- 4 classesHypothesisMeanPearson's rF-statdf p Riverine- Major41.6%40.3%40.9%0.55612.51928.0420.001Riverine- Minor39.4%58.1%48.7%0.5116.34546.550<.001Gradient39.6%69.0%54.3%0.37813.01277.926<.001Watershed 33.2%33.3%33.2%0.2133.77579.7010.056GDM -1552.1%100.0%76.1%0.55126.25760.171<.001Riverine – Refuge30.3%22.1%26.2%0.83752.35522.433<.001SUPPLEMENTARY MATERIALSHypotheses Climate Stability Hypothesis (Fig S2.J)Climate stability is thought to create greater climatic stratification across environmental gradients (Dynesius and Jansson 2000, Jansson and Dynesius 2002). In stable climates, orbitally forced species’ range dynamics (ORD) are low, allowing localized populations to persist, and thus become highly specialized and differentiated. Like the Gradient Hypothesis (following), this model also focuses on bioclimatic disparities, but additionally incorporates climate stability. This hypothesis states that areas of climate stability, particularly those with climatic stratification, should possess higher species richness and endemism. To estimate this model for Madagascar, coliniarity was measured for all BIOCLIM layers (Bio1–19) using a Pearson Coefficient. If R2 values exceeded 0.5, one of the layers was excluded. We preferentially selected layers based on raw data (e.g. selecting mean annual precipitation over seasonality). The following layers were excluded: Bio3, Bio7, Bio9, Bio13–18. For each remaining BIOCLIM layer we calculated the standard deviation of each cell throughout the four time periods for which climate data were available (0 kya, 6kya, 21 kya, 120 kya). The resulting standard deviation of each BIOCLIM layer was standardized to 1 to account for different units in raw data. All standardized stability layers were summed to create the final climate stability layer; with lower values representing higher climate stability through time.Disturbance-Vicariance Hypothesis (Fig S2.I)Under this model, the major factor contributing to diversification was temperature fluctuations (Colinvaux 1993, Bush 1994, Haffer 1997), rather than fluctuations in precipitation and forest fragmentation (as in the preceding Refuge and River hypotheses). This hypothesis states cyclic fluctuations of temperature during the Quaternary caused reoccurring displacement of taxa towards lower and higher altitudes (during cool and warm periods, respectively). Range retractions of taxa into the highlands occasionally resulted in allopatric divergence into new species. The gradual displacement of temperature specialized taxa would cause reoccurring invasions and counter-invasions of heterogeneous landscapes by both montane and lowland taxa. This hypothesis predicts species richness and endemism would be highest in areas of topographic heterogeneity and temperature instability. This hypothesis was generated by measuring colinearity between all BIOCLIM layers corresponding to temperature (Bio1– Bio11, see Climate Stability above for details). The following layers were excluded: Bio3, Bio7 and Bio11. For each remaining BIOCLIM layer we calculated the standard deviation of each cell throughout the four time periods for which climate data were available (0 kya, 6kya, 21 kya, 120 kya). This layer was standardized from 0 to 1 to account for different units between layers. All standardized stability layers were summed to create the final temperature stability layer with lower values representing higher temperature stability. This layer was inverted and multiplied by a standardized version of the final topographic heterogeneity layer (see above). Higher values represented areas with high temperature instability through time and high topographic heterogeneity.Gradient Hypothesis (Fig S1.E)According to the Gradient Hypothesis, diversification of Malagasy taxa was driven by bioclimatic disparities throughout the island (i.e. between the east and west), causing parapatry of populations along environmental gradients. This hypothesis was first formulated by Endler (1982), however more recently it was adapted and formulated in detail for Madagascar by Vences et al. (2010). Vences et al. focused on the climatic stratification longitudinally between the dry west and humid east of Madagascar, terming this species case the Ecogeographic hypothesis. Under the Gradient hypothesis populations adapt to local ecotones, diverging from a generalist ancestor (or one of broader ecological tolerance). Due to local specialization, gene flow within ecological similar sites is higher than those distributed at ecologically different sites across a gradient. The subdivision of populations create a scenario where drift or selection can override gene flow among ecogeographic subpopulations (Fisher 1930) and daughter species occupy separate, adjacent niches. The exact mechanism of speciation is controversial (e.g. prezygotic isolation, behavioral isolation or reproductive barriers) and beyond the focus of this study. This hypothesis invokes no barriers or mechanism of allopatry and predicts species richness and endemism to be highest in areas of high bioclimatic stratification. This GIS prediction was obtained from Pearson & Raxworthy (2009). In our continuous CAR-OBTC analyses, this hypothesis was represented by the first 3 PCs from a PCA on the 19 current BIOCLIM data for Madagascar (Hijmans et al. 2005). Mid-domain Effect Hypothesis (Fig S2. A-C)Initially described to explain latitudinal trends in species richness, this mathematical hypothesis demonstrates that if species’ ranges are distributed randomly between northern and southern geographic limits, the highest overlap of species ranges would be in the middle (Lees 1996, Lees and Colwell 2007). For Madagascar, this hypothesis (in two dimensions) results in increased overlap of species toward the center of country, over the Ankaratra highlands, resulting from the sum of random overlapping species ranges. We explore four variants of this hypothesis: 1 dimension (the mid-domain of latitudinal, altitude or longitudinal gradients) and 2 dimensions, as described above (the mid-domain of both latitudinal and longitudinal gradients). Note the mid-domain of altitude is the same GIS calculation of the Museum hypothesis. Thus, throughout the manuscript this hypothesis is referred to as the Museum hypothesis rather than the mid-domain altitude hypothesis. In one dimension, the latitude hypothesis predicts that species richness would be highest around 18° S, or in two dimensions, centered around 18° S and 46.5° E. The mid-domain hypotheses are included as a null hypothesis for spatial variation in species richness; these hypotheses invoke no barriers or ecological/ habitat heterogeneity and rely solely on the random distribution of species in a defined geographic space. Montane Species Pump Hypothesis (Fig S2.M)This hypothesis predicts that montane regions have higher species richness because of their topographic complexity and climatic zonation - both increase opportunities for allopatric and parapatric speciation (e.g., Moritz et al. 2000; Rahbek and Graves 2001; Hall 2005; Fjeldsa°and Rahbek 2006; Kozak and Wiens 2007). This hypothesis predicts that speciation should be highest in areas of high topographic and climatic heterogeneity. Within those habitats, rates of speciation should be highest in mid-elevations. This hypothesis predicts species richness and endemism would be highest in areas of topographic and climatic heterogeneity.To create a spatially explicit model of this hypothesis we calculated the mean standard deviation of the first three climate PCs (based on Bioclim current data) at ca. 10 km2 square neighborhood of each cell. Each was summed together and then the product was standardized from 0-1. The resulting layer was then multiplied by a standardized (0-1) topographic variation hypothesis. High values depict areas of high topographic and climatic heterogeneity.Museum Hypothesis (Fig S2.D)According to this hypothesis speciation occurred in montane habitats. In the Montane Museum hypothesis, more species exist at intermediate elevation because these elevations were simply occupied the longest, because of this, there has been more time for speciation and the accumulation of species in these habitats when compared to habitats at lower and higher elevations (Stebbins 1974; Stephens and Wiens 2003). This hypothesis predicts levels of endemism and richness will be highest in the middle elevations. Note that in execution, the prediction for this hypothesis is identical to a Mid-domain hypothesis of altitude. The GIS representation of this hypothesis for Madagascar represents the median elevation (378m), where the overlap of species should peak. This elevation was given a value of one and from this elevation, values linearly transitioned to zero at the maximum and minimum elevations.Paleogeographic HypothesisThis hypothesis states that vicariate differentiation of Malagasy lineages is associated with formation of geologic barriers to dispersal. Each hypothesis is specific to the focal paleogeographic event. There are two major classes of geological events that have occurred since the separation from India (ca. 65 MYA). The first, directly a result from separation from India, is the orogeny of eastern escarpment. This hypothesis states that deep lineages, pre-65Mya, should exist between the east and western species. A second, more local barrier is the volcanisms of several regions: Tsaratanana, Manongarivo, Ampasindava (ca. 50 Mya), Ankaratra (phase 1 ca. 28 Mya, phase 2 ca.15 Mya, phase 3 ca. 2 Mya) and Ambre (ca. 2 Mya, Krause 2007). Clades should exhibit breaks around volcanic activity, though given localization of these barriers; biota in most cases should have been able to dispersal around, though perhaps experiencing reduced gene flow. All paleogeographic hypotheses are difficult to test because more strongly than other hypotheses, they are dependent on the location of ancestral populations and the timing of geological events relative to a clade's diversification. Given the variation of ages of origins in Madagascar's vertebrates, it is likely that some clades were affected by most of the major paleogeographical events while others were only affected by some. Because of these factors, it is unlikely that species exhibit congruent biogeographic patterns. Thus in this paper, we were unable to test this hypothesis.Refuge Hypothesis -(Fig S2.D) This hypothesis holds that episodic fragmentation of forests resulted in isolated patches of wet forest and this caused vicariant differentiation between adjacent patches. These transitions were driven by periodic changes (every 20-100 Ky) in insolation associated with Milankovitch cycles (aberrations of the orbit of the earth around the sun due to the slight asymmetrical shape of the earth). Recurrent changes in insolation caused various dry periods followed by humid periods, particularly pronounced at tropical latitudes. The evolutionary consequences of paleoecological changes in climate likely depend on the regional topography, predominant weather patterns and the impact on the ecosystem (for example, a slight reduction in rain may have different biological consequences in a spiny forest versus rainforest). In Amazonia during the late Tertiary and Quaternary during dry climatic periods, it has been argued that extensive humid forests survived due to subtle topographic variation that facilitated rainfall gradients adjacent to the Andes, Guianan highlands, Rondonia and hilly areas east of Pará (Haffer 1969; Vanzolinii1970, 1973; Brown et al., 1974; Prance, 1982, 1996). In addition to changes in insolation associated with Milankovitch cycles, the gradual drifting of Madagascar northward towards the equator likely led to habitat changes, presenting another source of refugial isolation. Thus, it is plausible that refugia persisted and species with narrow ecological niches became isolated and diverged into separate species. To create a continuous spatial representation of this for Madagascar, first colinearity was measured for each BIOCLIM precipitation layer (Bio12 – Bio19) using a Pearson Correlation. If R2 values exceeded 0.5, one of the layers was excluded. This resulted in the exclusion of Bio13, Bio15, Bio16 and Bio17. We preferentially selected layers based on raw data (e.g. selecting mean precipitation over seasonality). For each remaining BIOCLIM layer, we calculated the standard deviation of each cell throughout the four time periods for which climate data were available (0 kya, 6kya, 21 kya, 120 kya). This layer was standardized from 0 to 1 to account for different units/decimal places in raw data between layers. All standardized stability layers were summed to create the final precipitation stability layer with lower values representing higher precipitation stability. Riverine Barrier Hypothesis (Fig S1. A,B). Malagasy rivers are thought to have acted as barriers separating populations, resulting in intra-riverine species and subspecies. As the geology of Madagascar has remained relatively stable since absolute isolation major rivers should have persisted (at least seasonally). Under this hypothesis, we expect vicariate differentiation of Malagasy lineages associated with large tributaries. There have been several criticisms of this hypothesis, such that rivers frequently change course, causing land and its inhabitants to passively transfer across the barrier, rivers cease to act as barriers due to the lack geographic separation at headwaters (Wallace 1852) or temporal fluctuations of climate causing rivers to change in size (e.g. the sizes of any rivers were dramatically reduced during the Pleistocene). To create a spatial prediction for this hypothesis, we selected all major permanent rivers that have headwaters above 1000m and created polygons from the lowland areas between major rivers and 1000m contour line. A second calculation focused on major riverine units composed of areas between rivers with headwaters above 2000m and created biogeographic units from lowland areas between rivers and 1000m contour line.River-Refuge Hypothesis (FigS1.C)The River-Refuge hypothesis, initially described by Haffer (1992, 1993) for the Neotropics, was more recently proposed as a model of Malagasy diversification (Craul et al. 2007). This hypothesis combines aspects of the Riverine Barrier hypothesis and the Refuge hypothesis under which it states that lowland vicariant speciation occurs in refugia separated by broad lowland rivers and by considerable unsuitable terrain in the headwaters. To estimate this model, we combined the Riverine and a binary refuge hypothesis. The continuous refuge layer was converted to a binary model by converting the top quartile to 1 and all other values to 0. To account for differences between precipitation stability in arid areas (versus wet), we excluded areas with less than 50cm in any of the time periods. Areas above 1000m were also excluded. Regions of the Riverine layer and binary refuge layer were then combined into smaller river-refuge subunits.Sanctuary Hypothesis (Fig S2.L)Past climate changes greatly altered the distributions of organisms through time; causing local extinctions, bottlenecks, isolation, range expansion and contraction of populations. Sanctuaries represent specific areas of habitat stability that have remained present through time, differing from refugia (which do not invoke geospatial consistency) and species track suitable habitat across geography (Recuero & García-París 2011). We estimated sanctuaries in Madagascar by calculating SDMs for 453 species (a subset of the GDM dataset, using species with three or more unique occurrence localities). The SDM was projected into three historic time periods: LGM, 120KYA, 6 KYA. All SDMs were converted to binary models using the maximum training sensitivity plus specificity threshold. The maximum training sensitivity plus specificity threshold: 0.080 (SD +-0.047), area: 0.316 (SD +-0.196), training omission: 0.005 (SD +- 0.017), number of training samples (mean: 12.321, max 137, min 3, SD 14.414). For each species, all four binary SDMs were summed. The resulting layer was reclassified so that values of 3 and below were converted to zero and values of 4 were converted to 1. Under this classification, areas where the species was present for all four time periods were considered ‘sanctuaries’. This was repeated for all species and all sanctuaries were summed to estimate areas of higher species richness and ographic Heterogeneity (Fig S2.E)In several studies, the level of topographic variation has been observed to be positively correlated to species richness patterns and centers of endemism (Kerr & Packer 1997; Rahbek and Graves 20001; Jetz & Rahbek 2002; Jetz et al. 2004). To characterize this hypothesis for Madagascar we measured the standard deviation of elevation at ca. 10 km2 of each pixel. Watershed Hypothesis (Fig S1.D)One of the more recent diversification hypotheses is the Watershed hypothesis (Wilmé et al. 2006). According to this model, climatic changes caused retraction of forests to the surrounding major rivers. If the headwaters of adjacent rivers were at lower elevations, the intervening areas between rivers (the watersheds) become arid and forests populations became isolated, serving as areas of speciation. By contrast, if the headwaters of rivers were higher elevations, the watershed served as areas of retreat and forest refugia remained connected among rivers. These watersheds are expected to contain proportionally much lower diversity and endemism. This GIS prediction was obtained from Wilmé et al. (2006). Additional references.Brown Jr., K.S., Sheppard, P.M., Turner, J.R.G. 1974. Quaternary refugia in tropical America: evidence from race formation in Heliconius butterflies. Proc. R. Soc. Lond. B. 187: 369-378.Bush, M.B. 1994. Amazonian speciation: a necessarily complex model. Journal of Biogeography 21::5–17.Colinvaux, P.A. 1993. Pleistocene biogeography and diversity in tropical forests of South America P. Goldblatt (Ed.), Biological Relationships between Africa and South America, Yale University Press, New Haven, CT.Craul, M., Zimmermann, E., Rasoloharijaona, S., Randrianambinina, B., Radespiel, U. 2007. Unexpected species diversity of Malagasy primates (Lepilemur spp.) in the same biogeographical zone: a morphological and molecular approach with the description of two new species. BMC Evolutionary Biology 7:83.Dynesius, M., Jansson, R. 2000. Evolutionary consequences of changes in species_ geographical distributions driven by Milankovitch climate oscillations. Proc. Natl Acad. Sci. U.S.A. 97:9115–9120.Endler, J. 1982. Pleistocene forest refuges: fact or fancy? In Prance, G.T. (Ed.). Biological Diversification in the Tropics. New York: Columbia University Press, p. 179-200.Fisher, R.A. 1930. The Genetical Theory of Natural Selection. Clarendon Press. Goodman, S. M. & Benstead, J. P. The natural history of Madagascar. (University of Chicago Press Chicago, 2003).Fjeldsa°, J., Rahbek, C. 2006. Diversification of tanagers, a species-rich bird group, from the lowlands to montane regions of South America. Integr. Comp. Biol. 46:72–81. (doi:10.1093/icb/icj009)Haffer, J. 1969. Speciation in Amazonian forest birds. Science 165: 131-137.Haffer, J. 1997.Alternative models of vertebrate speciation in Amazonia: an overview Biodiversity and Conservation, 6:451–476.Haffer,, J. 1992. On the “river effect” in some forest birds of southern Amazonia. Bol. Mus. Para. Emilio Goeldi, sér. Zool. 8:217-245.Haffer, J. 1993. Time’s cycle and time’s arrow in the history of Amazonia. Biogeographica 69:15-45.Hall, J. P. 2005. Montane speciation patterns in Ithomiola butterflies (Lepidoptera: Rhiodinidae): are they consistently moving up in the world? Proc. R. Soc. B 272:2457–2466. (doi:10.1098/rspb.2005.3254)Jansson, R., Dynesius, M. 2002. The fate of clades in a world of recurrent climatic change: Milankovitch oscillations and evolution. Annu. Rev. Ecol. Syst. 33:741–777.Jetz, W., Rahbek, C. 2002. Geographic range size and determinants of avian species richness. Science 297:1548–1551.Jetz, W., Rahbek, C., Colwell, R.C. 2004. The coincidence of rarity and richness and the potential signature of history in centers of endemism. Ecol. Lett. 7:1180–1191.Kerr, J.T. Packer, L. 1997. Habitat heterogeneity determines mammalian species richness in high energy environments. Nature. 385:252-254..Kozak, K.H., Wiens, J.J. 2007. Climatic zonation drives latitudinal variation in speciation mechanisms. Proc. R. Soc. B 274:2995-3003. doi: 10.1098/rspb.2007.1106Krause, D. W. 2003. Late Cretaceous vertebrates of Madagascar: A window into Gondwanan biogeography at the end of the Age of Dinosaurs. Pp. 40-47 in S. M. Goodman and J. P. Benstead (eds.), The Natural History of Madagascar. University of Chicago Press, Chicago.Lees, D. C. 1996. The Périnet effect? Diversity gradients in an adaptive radiation of butterflies in Madagascar (Satyrinae: Mycalesina) compared with other rainforest taxa, Pages 479-490 in W. R. Louren?o, ed. Biogéographie de Madagascar. Paris, Editions de l'ORSTOM. Lees, D. C., Colwell R.K. 2007. A strong Madagascan rainforest MDE and no equatorward increase in species richness: Re-analysis of 'The missing Madagascan mid-domain effect', by Kerr J.T., Perring M. & Currie D.J (Ecology Letters 9:149-159, 2006). Ecology Letters 10:E4-E8.Moritz, C., Patton, J. L., Schneider, C. J., Smith, T. B. 2000. Diversification of rainforest faunas: an integrated molecular approach. Annu. Rev. Ecol. Syst. 31:533–563. (doi:10. 1146/annurev.ecolsys.31.1.533)Pearson, R.G., Raxworthy C.J. 2009. The evolution of local endemism in Madagascar: watershed versus climatic gradient hypotheses evaluated by null biogeographic models. Evolution 63:959–967.Prance, G.T. 1982. Biological diversification in the tropics. New York: Columbia University.Prance, G.T. 1996. Islands in Amazonia. Phil. Trans. R. Soc. Lond. B 351: 823-833.Rahbek, C. 1997. The relationship among area, elevation, and regional species richness in Neotropical birds. Am. Nat. 149:875–902.Rahbek, C., Graves, G. R. 2001. Multiscale assessment of patterns of avian species richness. Proc. Natl Acad. Sci. USA 98:4534–4539. (doi:10.1073/pnas.071034898)Recuero, E., García-París, M. 2011. Evolutionary history of Lissotriton helveticus: Multilocus assessment of ancestral vs. recent colonization of the Iberian Peninsula. Molecular Phylogenetics and Evolution 60: 170-182.Stebbins, G. L. 1974. Flowering plants: evolution above the species level. Harvard University Press, Cambridge, Mass.Stephens, P. R., Wiens, J.J. 2003. Explaining species richness from continents to communities: the time-for-speciation effect in emydid turtles. American Naturalist 161:112–128.Vanzolini, PE. 1970. Zoología sistemática, geografía e a origem das espécies. S?o Paulo: Instituto Geográfico de S?o Paulo. 56 pVanzolini, P.E. 1973. Paleoclimates, relief, and species multiplication in equatorial forests. In Meggers, B.J., Ayensu E.S.. Duckworth, W.D. (Eds.). Tropical forest ecosystems in Africa and South America: A comparative review. Washington: Smithsonian Institution. p. 255-258.Wallace, A.R. 1852. On the monkeys of the Amazon. London. Proc. Zool. Soc., 1852:107-110.Wilmé, L., Goodman, S.M., Ganzhorn, J.U. 2006. Biogeographic evolution of Madagascars microendemic biota. Science 312:1063– 1065.Supplementary Figure S1. Nominal Biogeography hypotheses. These hypotheses are comprised of non-order categories from which relationships among contained data are not known or non-linear. Four hypotheses fell into this category: The Riverine (major and minor), River-Refuge, Watershed and Gradient. See table S1 for summary of each hypotheses. Supplementary Figure S2. Continuous Biogeography hypotheses. Nine hypotheses fell into this category: Mid-domain (longitude, latitude and distance), Museum, Topographic Heterogeneity, Gradient* (PC1-PC3), Disturbance-vicariance, Climate Stability, Precipitation Stability, Sanctuary and Montane Species Pump (see table S1 for summary of each hypotheses). Inlayed tables represent the percent contribution of each corresponding hypothesis in the CAR model with the lowest AICc of each observed biodiversity measurements. *The values of the three climate principal components are not necessary assumed to reflect a positive correlation to endemism and species richness, however, we are assuming each reflects a prediction of a linear correlation (either positive or negative). The inclusion of the three climate principal components is the result of the power CAR models and the ability to include multiple explanatory variables. Due to the statistical limitations associated with nominal hypotheses, if a hypothesis could be depicted by continuous data (even if it required several variables) they were converted to this format and incorporated into the CAR. For example, if nominal data represented classified continuous data, we include the continuous data (such as the 3 PCs of climate data). Supplementary Table S1. Major Biogeographic Predictions Relevant to Madagascar. HypothesisDescriptionKey Factors EffectsPredictions for Reptiles and AmphibiansGIS PredictionTemporal ScopeKey CitationsClimate Stability (Fig S2.J)Climate stability creates greater climatic stratification across environmental gradientsStable climate; both seasonally and through geologic time; no barrier is necessaryIn stable climates, orbitally forced species’ range dynamics (ORD) are low, allowing localized populations to persist, and thus become highly specialized and differentiated. Higher levels of endemism in areas of climatic stabilityUse GIS and spatiotemporal explicit climate data to estimate climate stability; stable areas should harbor higher species richness and endemism. NoneDynesius & Jansson, 2000; 2002Disturbance-vicariance (Fig S2.L)Allopatry results from altitudinal range retractions caused by temperature fluctuations. Temperature fluctuations, changes in CO2 and habitat heterogeneity (usually associated with changes in altitude)Decreased temperatures allow cool adapted species to disperse south. Cyclic fluctuations in temperature cause populations to habitat track attitudinally, when temperatures are at their highest, populations become isolated on sky islands Diversity with monophyletic lineages are associated with a single region. Most common ancestor of sister clades date to Pleistocene.Estimate areas of high temperate fluctuations adjacent areas of slope; higher values reflect higher species richnessQuaternaryColinvaux 1993, Bush 1994, Haffer 1997; Raxworthy Nussbaum 1995. Gradient (Fig S1.E)Parapatry of populations due to environmental gradientsDivergent selection and an environmental gradient; no barrier is necessaryParapatric speciation across climate spaceSister taxa are found in different habitats along an environmental gradientCluster analyses of all current climate data to estimate areas of endemism.NoneEndler 1982Mid-domain Effect (Fig S2. A-C)Species’ ranges are distributed randomly between northern and southern geographic limits, the highest overlap of species ranges would be in the middle.Geographic spaceNo mechanism invokedSpecies richness should be highest in mid-domainsRichness is highest in the mid-domain of latitude, longitude and elevation.NoneLees 1996, Lees and Colwell 2007Montane Species Pump (Fig S2. M)Topographic complexity and climatic zonation of mountains increase opportunities for allopatric and parapatric speciationTopographic and climatic heterogeneityAllopatric and parapatric speciation across elevations Sister taxa share common ancestry with montane ancestor. Extant montane species display higher levels of intraspecific genetic variation. Estimate areas of topographic and climatic heterogeneity- high values reflect centers of high endemism and species richnessNoneMoritz et al. 2000; Rahbek and Graves 2001; Hall 2005; Fjeldsa° and Rahbek 2006; Kozak and Wiens 2007, Roy et al 1997, Fjeldsa et al 1999 Museum (Fig S2.D)More species occur at intermediate elevations simply because these elevations were occupied the longest and there has been more time for speciation and the accumulation of species in these habitats relative to those at lower and higher elevationsExtended time occupying in mid-elevationsIncreased differentiation at mid-elevationsMore species occur at intermediate elevations because these elevations were occupied the longest Use GIS to calculate the median elevation of Madagascar which should possess the highest species richness and endemismNoneStephens and Wiens 2003PaleogeographicalVicariant differentiation of Malagasy lineages is associated with formation of geologic barriers to dispersal. Each hypothesis is specific to the focal paleogeographic event.Geological changes resulting in vicariant eventsVicariant differentiation across barriersDistinct east and west lineages associated with the central mountains, and between the southern/central/northern massifs, tsingys. Not CalculatedSpecific to each geological eventGoodman & Benstead 2003Refuge (Fig S2. K)Allopatry due to retraction of wet habitatsRepeated cycles of drastic fluctuation in precipitationEpisodic fragmentation of forests resulting in isolated patches of wet forest causing vicariant differentiation between adjacent patchesEvolutionary lineages associated with refugia (areas of continued precipitation relative to regional mosaic of habitats)Use GIS and spatiotemporal explicit climate data to estimate stable wet habitats, these areas reflect centers of endemismCenozoic (Tertiary and Quaternary)Haffer 1969m 1990, 1999, Endler (1982), Brown (1987) Nores (1999)Riverine (Fig S1. A, B)Allopatry due to rivers acting as barriers.Permanent large riversVicariant differentiation of Malagasy lineages associated with large tributariesReciprocal monophyly of clades on opposite sides of riverMeasured inter-riverine areas which are areas of endemism NoneWallace 1853, Capparella 1991, Patton et al. 1994, Goodman & Ganzhorn, 2004River-Refuge(Fig S1.C)Allopatry due the restriction of wet habitats to lower elevations; higher elevation habitats and intervening rivers act as barriersReduced precipitation, maintenance of permanent riversSimilar to Riverine Hypothesis; fragmental faunal distributions into intra-riverine corridors, isolation is associated with increased aridity adversely affecting habitat suitability at headwater regionsSister taxa are found in adjacent intra-riverine corridorsCombine the Riverine Hypothesis subunits and a binary precipitation stability /low elevation layer. Resulting areas depict areas of high predicted endemism.Late tertiary (Post-Miocene)Haffer 1992; 1993, Craul et al 2007Sanctuary (Fig S2.L)Extinction occurs more often in instable habitats; thus stable areas accumulate species.Climate fluctuations through time across heterogeneous landscapes Areas of niche stability accumulate species through time. Areas of niche stability (specific aspects of climate vs. overall climate in Climate stability) provide sanctuary for species though time. Use GIS and SDM to estimate stable areas in each species distribution through time; areas of highest stability should harbor higher species richness and endemismNoneRecuero & García-Paris 2011 Topographic Heterogeneity(Fig S1. D)The level of topographic variation has been observed to be positively correlated to species richness patterns and centers of endemismTopographyNo mechanism invokedSpecies richness should be highest in areas of high topographic heterogenetyAreas of high topographic heterogeneity harbor higher species richnessNoneKerr & Packer 1997; Rahbek and Graves 20001; Jetz & Rahbek 2002; Jetz et al. 2004Watershed (Fig S1.D)Allopatry results from altitudinal range retractions caused by simultaneous decreases in temperature and precipitation. Lower elevation rivers act as barriers.Repeated cycles of drastic simultaneous increases of both temperature and precipitation. Large permanent rivers and mountains adjacent to lowlands. Dispersal during warm-humid periods allows lowland species to disperse attitudinally, across headwater habitat (previously too arid to occupy). Allopatry occurs as climate cools and the species depends into lowlands and lowland rivers prevent gene flow between populations, now occurring on both sides of river.Sister taxa are found in adjacent intra-riverine corridors. Most common ancestor of sister clades date to Pleistocene.See Wilmé et al. 2006.QuaternaryWilmé et al 2006Supplementary Table S3. Correlations of biodiversity hypotheses to observed biodiversity patterns. R-values reflect non-spatial Pearson product-moment correlation coefficients. To assess significance of raster data, we used an unbiased correlation following the method of Dutilleul (1993). This method reduced the degrees of freedom according to the level of spatial autocorrelation between the two variables. HypothesisReptile Amphibian Correlation to Observed EndemismRF-statdfprF-statdfpMid-domain: Distance-0.1480.53924.0370.470-0.0260.01826.7530.894Topographic Heterogeneity0.3435.42540.7380.025*0.66221.66427.724<.001*Refuge 0.0360.0432.3280.8480.1540.55222.7220.465Montane Species Pump0.3032.63948.4880.1070.61313.85222.9900.001*Disturbance-vicariance 0.3794.10122.4130.0550.61615.75822.829<.001*Climate stability 0.2410.6214.5800.4440.3562.03315.1240.174Sanctuary 0.2962.43825.3130.1310.6067.61113.0800.016*Museum0.2854.97156.4290.030*0.33517.70949.8360.015*River-Refuge (binary)0.2073.8786.8690.0520.0660.954216.9590.330Correlation to Observed Richness RF-statdfp rF-statdfp Mid-domain: Distance-0.48010.26534.3530.003*-0.2041.07224.6980.310Topographic Heterogeneity 0.1402.398119.8520.1240.3076.76964.9520.011*Refuge -0.1010.33231.9480.598-0.0930.21624.6050.646Montane Species Pump0.0560.507160.5760.4770.2965.50657.1960.022*Disturbance-vicariance 0.0192.29762.2510.1350.3035.28641.5540.027*Climate stability 0.2331.50626.1910.2310.2100.72315.6720.408Sanctuary 0.41910.07947.2670.003*0.81834.06916.885<.001*Museum0.2344.03769.4720.048*0.2504.48166.9890.038*River-Refuge (binary)0.1082.378201.1870.1250.2344.9485.0220.029*Supplementary Table S4. Mixed CAR spatial models of observed biodiversity data. A principal component analyses was performed on the standardized biogeography hypotheses. All the resulting principal components (PCs) were extracted and then loaded as explanatory variables. The CAR analyses were run iteratively, starting with all PCs as response variables and then excluding each PC that did not contribute significantly to the model (α = 0.05) until the final model included only PCs that contributed significantly to the model. The standardized beta coefficients (β) were then used to calculate contributions of each biogeography hypothesis (see methods on OTBCs) in the final CAR analysis. To compare the contributions of each biogeography hypothesis among models of observed biodiversity patterns (richness, endemism, GDM), β coefficents from each OTBC/CAR analyses were converted to the percentage of contribution. *The mean of the 3 MDS vectors loadings were calculated and contributed as a single value to the total mean. Percent Contribution to CAREndemismRichnessGDMMean contribution to all observed biodiversity models*AmphibianReptileAmphibianReptileMDS-D1MDS-D2MDS-D3Mean*Mid-domain- Latitude0.0%27.7%3.5%3.6%5.3%20.7%1.6%9.2%8.8%Mid-domain- Longitude8.1%8.0%6.7%9.2%10.9%10.2%8.1%9.7%8.4%Mid-domain- Distance***15.1%9.7%7.7%20.0%14.4%5.7%15.8%11.9%12.9%Climate- PC115.4%0.1%11.4%14.5%15.1%4.5%13.8%11.1%10.5%Climate- PC210.1%12.7%8.5%11.0%10.1%11.7%7.4%9.7%10.4%Climate- PC32.2%12.8%4.6%1.8%4.4%11.9%3.9%6.7%5.6%Refuge 1.4%4.7%6.9%0.0%3.8%10.5%0.0%4.8%3.5%Climate Stability***4.7%4.6%7.4%3.0%0.0%1.0%3.8%1.6%4.2%Topographic Heterogeneity6.5%1.7%7.1%3.7%3.9%0.5%8.0%4.2%4.6%Disturbance-vicariance5.9%4.8%8.1%3.1%3.4%2.2%6.8%4.1%5.2%Montane Species Pump5.0%0.0%7.1%2.3%2.9%0.0%7.2%3.4%3.6%Sanctuary10.9%0.3%13.7%12.6%11.7%3.1%4.7%6.5%8.8%Museum14.8%13.0%7.1%15.1%14.3%18.1%19.0%17.1%13.4%CAR Model SummaryExplained by Predictor Variables: r2 (AICc)0.482(-372.3)0.303(-468.5)0.624 (7861.9)0.309(21849.9)0.869(-4037.8)0.435(-1084.2)0.659(-3327.7)Total Explained (Predictor and Space): r2 (AICc)0.646(-426.0)0.626(-556.4)0.849 (6349.3)0.745(19364.2)0.954(-6659.7)0.942(-6760.4)0.868(-5700.9)Model significance: n, F, p-val141, 42.5, <0.001141, 9.7, <0.0012501,303.5<0.0012501,278.1,<0.0012501, 1265.9,<0.0012501,147.2,<0.0012501, 396.8,<0.001NASupplementary Table S5. Mixed CAR spatial models of focal subgroups. A principal component analyses was performed on the standardized biogeography hypotheses. All the resulting principal components (PCs) were extracted and then loaded as explanatory variables. The CAR analyses were run iteratively, starting with all PCs as response variables and then excluding each PC that did not contribute significantly to the model (α = 0.05) until the final model included only PCs that contributed significantly to the model. The standardized beta coefficients (β) were then used to calculate contributions of each biogeography hypothesis (see methods on OTBCs) in the final CAR analysis. To compare the contributions of each biogeography hypothesis among models of observed biodiversity patterns (richness, endemism, GDM), β coefficents from each OTBC/CAR analyses were converted to the percentage of contribution. *The mean of the 3 MDS vectors loadings were calculated and contributed as a single value to the total mean. Percent Contribution to CAREndemismRichnessBoophisBrookesiaOplurus*PhelsumaBoophisBrookesiaOplurus*PhelsumaMid-domain- Latitude3.0%4.0%2.5%17.0%0.0%5.1%2.3%4.2%Mid-domain- Longitude8.4%9.9%10.6%11.8%6.1%8.3%6.3%10.2%Mid-domain- Distance***16.0%9.9%6.3%6.4%10.6%12.0%11.0%20.4%Climate- PC112.2%12.2%13.5%5.8%14.4%11.7%15.9%13.1%Climate- PC26.0%14.1%13.2%7.0%13.0%11.2%8.9%9.7%Climate- PC35.2%4.3%2.8%18.3%1.6%7.9%0.0%3.9%Refuge 0.0%10.5%11.3%2.3%9.9%4.5%3.3%0.0%Climate Stability***3.6%8.8%11.2%5.9%8.9%3.6%5.8%1.8%Topographic Heterogeneity9.0%2.2%1.0%5.1%2.8%2.1%6.5%4.1%Disturbance-vicariance7.0%4.7%4.1%7.2%5.8%2.5%6.8%2.7%Montane Species Pump8.4%0.6%0.0%4.3%2.7%0.0%4.5%2.7%Sanctuary0.1%18.9%22.6%0.0%20.3%17.2%23.6%7.9%Museum21.1%0.0%0.8%8.8%3.8%13.9%5.2%19.3%CAR Model SummaryExplained by Predictor Variables: r2 (AICc)0.251(-252.2)0.303(-176.1)0.432(-317.9)0.209(-253.4)0.569(9710.5)0.444(5392.1)0.178(9789.1)0.559(6731.4)Total Explained (Predictor and Space): r2 (AICc)0.524(-316.0)0.685(-235.3)0.756 (-436.8)0.759(-354.6)0.863(6851.0)0.812(2680.8)0.504(8530.6)0.794(4837.3)Model significance: n, F, p-val141, 46.6, <0.001141, 49.7, <0.001141, 20.5, <0.001141, 20.1, <0.0012501, 364.7,<0.0012501, 220.6, <0.0012501, 550.1,<0.0012501, 632.4,<0.001 ................
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