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A Study on Biodiversity Offset Assessment Methodology in Japan

－Combination of GIS Screening for Site Selection and On-site Field Assessment－

Abstract: In Japan, there is no legal obligation and also no established assessment methodologies for biodiversity offsets. In this study, a preliminary research framework of forest assessment methodology for a biodiversity offset and banking system was studied as a hypothetical case study in Nagoya City, Japan. Three step approaches were employed. First the wide scale assessment of biodiversity/ ecosystem services (BD/ESs) by GIS was conducted as a screening stage of potential offset site selection. Second, simple field surveys were conducted to do equivalency and offset-possibility assessment in 131 forests in the city. Third, on-site field surveys by utilizing biodiversity assessment methods implemented in other countries were done for testing the applicability of these to Japan.

Keywords: Biodiversity offset and banking, ecosystem service, forest, Japan, Nagoya

Introduction

In Japan, the expansion of urban area caused the degradation of forest and biodiversity. This was remarkable especially for large cities, such as, Tokyo, Osaka, Nagoya, etc. For example, the forest coverage in Nagoya was decreased from 29.8% in 1990 to 23.3% in 2010 (Nagoya City 2012).

One of the policy instruments to compensate the loss of forest and biodiversity by development activities was called biodiversity offset, which was defined in, for example, BBOP (2013). Biodiversity offset and banking systems were widely introduced in many countries (Madsen, Carroll and Kelly 2010), such as, conservation banking (CB) and Mitigation banking(MB) in the USA(State of California 2014), BioBanking(BB) in Australia (NSW Government 2014a), etc. However, there is no legally binding national biodiversity offset system implemented in Japan even though several local governments have already introduced biodiversity offset like systems, such as, Aichi Prefecture (Aichi Prefecture 2013). Ministry of the Environment, Government of Japan (MOE-J) made a draft report on biodiversity offset (MOE-J 2014). The discussion on the possibility of biodiversity offset and banking systems were gradually increased in Japan in recent years.

In Japan, there is no established biodiversity assessment method for biodiversity offset. In the world many kinds of biodiversity offset and banking assessment methods were implemented (Quétier, Lavorel 2011), such as, HSI (Habitat Suitability Index) model (Ito and Hayashi 2014, Dhakal et al. 2014), HH (Habitat Hectares scoring method) (The State of Victoria 2014), BBAM (BioBanking assessment methodology)(NSW Government 2014b). In the discussion of the possibility of biodiversity offset and banking system in Japan, the equivalency and alternativeness between the loss and gain of biodiversity were one of the critical issues (Hayashi and Ooba 2014). Several studies touched on the assessment of the applicability of biodiversity offset assessment methods implemented in other countries into Japan (Hasegawa et al. 2013, Hasegawa and Hayashi 2014, Ito and Hayashi 2014). Most of these were focused on the on-site scale applicability of the existing methods.

Forest provides a variety of benefits to human society, namely, ecosystem services (ESs) (MA 2005). Regarding biodiversity bankings, for example, the CB and MB in the USA, the scope of the assessment of biodiversity components were limited, mainly for evaluating the habitat of endangered species and the ecological function of wetland respectively. Also Ito et al. (2014b) revealed that only one part of biodiversity and ecosystem service (BD/ES) values was included in the credit value of a MB by a contingent valuation method based on environmental economic valuation. However, most of these values were not appropriately included in biodiversity offset and banking systems. The potential scope of BD/ESs should be considered beforehand for a biodiversity offset and banking implementation.

Study objective

In this study, a preliminary research framework of forest assessment methodology of a biodiversity offset and banking system was studied as a hypothetical case study in Nagoya City, Japan. Three step approaches were employed. First the wide scale assessment of BD/ESs by GIS (Geographical information system) was conducted as a screening stage of offset site selection. Second, simple field surveys for forests were conducted to do equivalency and offset-possibility assessment in forests in Nagoya City. Third, on-site field surveys by utilizing biodiversity assessment methods implemented in other countries were done for testing the applicability of these to Japan. Through this case study, the method of a site selection, the spatial assessment of ESs and the preliminary research framework were tested. The results presented here were tentative version and will be updated in the future after further analysis.

Methodology

Study area

Nagoya City is located in Aichi Prefecture (Figure 1(a) and (b)). The City hall is located at 35.181N, 136.906E. The average annual temperature for the city in 2014 was 16.1°C and the average precipitation was 1505.5 mm (Japan Meteorological Agency 2015). The area of the city is approximately 326.43 km2 and the population of the city was 2.27 million, as of April 1st 2014, which was the third largest city in Japan (Nagoya City 2015).

Methods

A hypothetical development activity will damaged a forest area in Nagoya City (Figure 1(c)). The hypothetical development site (4.05ha) was a typical secondary broadleaf deciduous forest dominated by Q. serrata and Q. variabilis in the east part of Nagoya City. To compensate the hypothetical loss of the BD/ESs, the potential offset sites should be considered. In this case, the boundary of the site selection was set to be within the city so that the study was conducted focusing on the city.

The first step was to grasp the BD/ES provisioning potential in Nagoya City as a screening stage (Inagaki et al. 2013, Li et al.2014). It could show the general tendency of BD/ES provisioning potential from each area. Based on the Nagoya land use metric survey GIS data provided by Nagoya City, 6 land use categories were developed by the authors, including urban area, forest area, urban park, water area, paddy field and agricultural land. Then 12 ESs were selected for the ES estimations by utilizing unit value assessment from existing literatures. These included the following: supporting services (soil formation, CO2 absorption, nutrient cycling), provisioning services (agricultural products), regulating services (air purification, climate regulation, rain infiltration capacity, flood mitigation, etc.), cultural services (recreation, spiritual value, aesthetic value, etc.). By Maxent model (Phillips et al. 2006), Li et al. (2014) estimated the potential distributions of mammals, namely, raccoon dog (Nyctereutes procyonoides) and weasel (M. itatsi and/or M. sibirica). By utilizing land use data, the 2 species occurrence data and environment variables (year mean temperature, year mean precipitation, snow cover and elevation), the Maxent model was conducted to explore the potential distributions of these mammals in Nagoya City.

Second, simple filed surveys were conducted focusing on forests in Nagoya City because in the first step, it was difficult to assess the quality of forest, tree species, the situations of understory and litter layer, cultural service use, etc. In the offset site selection, the quality of biodiversity was one of the important points so it needed to collect some field data from each forest. According to the Nagoya green coverage GIS data by Nagoya City, the number of forests (equal or more than 1 ha size) were 239. In this study, the definition of forest was continual tree crown area by tree species, which had equal or more than 1 ha size. Among them, the authors conducted simple field surveys for 131 forests in the Nagoya City from 2013 to 2014. The survey items were listed in Table 1. After conducting simple field surveys, the authors could categorize forest into 13 types tentatively and compared with each other from the perspective of equivalency and offset-possibility of the ES provisioning potential.

Third, on-site more detailed field surveys were conducted for testing a variety of biodiversity assessment methods including HSI, HH and BBAM with several diversity indexes (Simpson’s diversity index, Shannon-Wiener’s index, etc). The authors selected 4 forests near the hypothetical development site in the east part of Nagoya City. The detailed methods of HH and BBAM conducted in this study were summarized in Hasegawa et al. (2013) and Hasegawa and Hayashi (2014). And Ito and Hayashi (2014) developed a forest HSI model by combining integrated SI models for the indicator species, firefly (Luciola parvula), large Japanese field mouse (Apodemus speciosus), and northern goshawk (Accipiter gentilis). In the course of the study, the authors could get the characteristics, limitation and problems of each assessment methods for the potential application to Japan.

The statistical analysis was conducted using Excel ver. 2010(Microsoft corp.), SPSS statistics ver.22 (IBM corp.). The ArcGIS 10.1 (ESRI Japan Inc.) was used for the spatial analysis.

Results and discussion

In the first step, the 12 ES provisioning potential maps and the 2 potential distributions of mammals were calculated. Figure 2 showed tentative examples of BD/ES provisioning potential maps (Inagaki et al. 2013, Li et al. 2014). Based on these, it was easy to understand which area had what kinds of ES provisioning potential compared with the hypothetical development site. Also the trade-off and synergy of ES provisioning potential among ESs could visually identified by GIS.

In the second step, by utilizing simple field survey data, forests were categorised into several groups. According to Iwai and Hayashi (2015), tentative results showed that 131 forests were classified into 13 categories by utilizing 16 ESs by a cluster analysis (Figure 3). These broad categories included bamboo forests, deciduous forests (5 sub categories), evergreen forests (5 sub categories), and parks (2 sub categories). The detailed results will be summarized in the future. Based on this analysis, the forest quality of potential offset sites could be compared with the hypothetical development site. So equivalency and offset-possibility could be roughly grasped by this offset site selection stage.

In the third steps, based on the on-site field surveys, more detailed biodiversity assessment was conducted (Table 2). As for the SI model, only the average SI score of the site D was lower than that of the hypothetical development site. Because, the results of SI models for firefly and large Japanese field mouse were lower than those of the development site. As of the BBAM assessment, the score of the site C was lower than that of the development site. In this case, the score of the site D was highest. Regarding the HH assessment, the score of the site C was lower than that of the development site, too. And the site B was the same score with the development site. The score of site A and B for HSI, HH and BBAM were higher or equal to the development site so that the site A and B could be potential offset sites. However, taking a look at other indexes, such as, individual number and above ground biomass, the scores of the site A and B were lower than those of the development site. So individual assessment methods or indexes had different viewpoints for their own assessment. So the priority of the site assessment for BD/ESs should be considered carefully beforehand. More detailed results were summarized in Ito et al. (2014a), Ito and Hayashi (2014), Hasegawa et al. (2013) and Hasegawa and Hayashi (2014).

Conclusion

In the study, a preliminary research framework of a biodiversity offset and banking assessment methodology was tested from a site selection by spatial GIS assessment and on-site assessment with the inclusion of BD/ESs comprehensively. Individual assessment methods or indexes had different viewpoints for their own assessment. So the priority setting for BD/ESs should be considered carefully beforehand. Future studies included a study on more linkage between spatial ES assessment and on-site assessment. The results presented here were tentative version and will be updated in the future.

References

1) Aichi Prefecture (2013) Biodiversity strategy 2020 of Aichi, pp.160, Aichi Prefecture.(in Japanese)

2) Business and Biodiversity Offsets Programme (BBOP) (2013) To no net loss and beyond: an overview of the Business and Biodiversity Offsets Programme (BBOP), Washington, DC., USA.

3) Dhakal A., Ooba M., Hayashi K. (2014) Assessing impacts of forest conversion on terrestrial vertebrates combining forestry cost with HSI and InVEST: case of Toyota City, Japan, International Journal of Biodiversity Science, Ecosystem Services & Management (on line).

4) Hasegawa Y., Hayashi K., Yoshino N., Malhotra K.(2013) The advantage and issue of a biodiversity measuring method (bio-banking assessment methodology) in Australia - a case study for secondary forest in the Nagoya University campus and its surrounding area, Journal of Human and Environmental Symbiosis, Vol.22, 51-63.(in Japanese)

5) Hasegawa Y., Hayashi K. (2014) A study on the biodiversity assessment method for urban forest in Nagoya, Japan ―a comparative analysis of Habitat Hectares scoring method in Australia to several diversity indexes, the Japan Society for Impact Assessment Papers, 12(2), 49-62. (in Japanese)

6) Hayashi K., Ooba M. (2014) Issues on assessment methods of biodiversity offset/banking ― case studies in Aichi Prefecture -, Proceedings of the 17th Annual Meeting of Japan Association for Human and environmental Symbiosis, 262-265, September 27-28, 2014, Tokushima University, Tokushima, Japan. (in Japanese)

7) Inagaki Y., Ooba M., Hayashi K.(2013) (draft)Quantitative Evaluation of Ecosystem Services of Green Resources in Nagoya by GIS. (unpublished, in Japanese)

8) Ito H., Hayashi K. (2014) Forest evaluation method using integrated SI models, IAIA (International association for impact assessment)14 web proceedings.

9) Ito H., Hayashi K., Hasegawa Y., Ooba M. (2014a) Study on evaluation method for biodiversity on on-site scale ―comparative evaluation by HSI, HH, BB and forest environmental indicators for urban forest in Nagoya ―, Proceedings of the Annual Conference 2014 the Japan Society for Impact Assessment, 98-102.

10) Ito H., Ota T., Hayashi K., Yoshida K., Hu S.(2014b) The importance estimation and economic value on ecosystem services of mitigation bank in California, USA, the Japan Society for Impact Assessment Papers, 12(2), 63-71. (in Japanese)

11) Iwai and Hayashi (2015) (draft) Equivalency and diversity assessment of forests based on ecosystem services : a case of urban forests in Nagoya City, (unpublished, in Japanese)

12) Japan Meteorological Agency (2015) Climate data, Japan Meteorological Agency, URL, , accessed in Feb.2, 2015. (in Japanese)

13) Li R., Ooba M., Hayashi K. (2014) (draft)The potential distributions of mammalian by Maxent model ― Case of Nagoya City. (unpublished)

14) Madsen B., Carroll N, Kelly M. B.(2010) State of biodiversity markets report offset and compensation programs worldwide, pp73, URL, , accessed in Feb.2, 2015.

15) Millennium Ecosystem Assessment (MA) (2005) Ecosystems and human well-being: synthesis, Island Press, Washington, DC., USA.

16) Ministry of the Environment, Government of Japan (MOE-J) (2014) Toward implementation of biodiversity offset in environmental assessment in Japan(draft), pp.62, Ministry of the Environment, Government of Japan. (in Japanese)

17) Nagoya City (2015) Statistics Nagoya web version, Japan, URL, , accessed in Feb.2, 2015. (in Japanese)

18) Nagoya City (2012) Green coverage research in Nagoya, URL, , accessed in Feb.2, 2015, (in Japanese)

19) Nagoya City (2010) Green coverage GIS data.

20) Nagoya City (2007) Nagoya land use metric survey GIS data.

21) New South Wales (NSW) Government (2014a) Biobanking, Australia, URL, , accessed in Feb.2, 2015.

22) New South Wales (NSW) Government (2014b) BioBanking assessment methodology, Australia, URL, , accessed in Feb.2, 2015.

23) Phillips S.J., Anderson R.P., Schapire R.E. (2006) Maximum entropy modelling of species geographic distributions, Ecological Modelling, Vol. 190, 231–259.

24) Quétier F., Lavorel S. (2011) Assessing ecological equivalence in biodiversity offset schemes: key Issues and solutions, Biological Conservation, Vol.144, 12, 2991–2999.

25) State of California, California Department of Fish and Wildlife (2014) Conservation and mitigation banking, USA, URL, , accessed in Feb. 2, 2015.

26) The State of Victoria, Department of Environment and Primary Industries (2014) Vegetation quality assessment manual, Australia, URL, , accessed in Feb. 2, 2015.

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Figure 1. Maps of the study area, (a) Japan with Nagoya City in the star symbol, (b) Nagoya City outlined in red, (c) hypothetical development site in the star symbol

Source: Satellite image: (b)ALOS by JAXA/ Distribution RESTEC, (c) ArcGIS ࠀࠖࠞ࠰࠲࠾with Nagoya green coverage GIS data

Table 1. Simple field survey items by survey size

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Table 2. On-site biodiversity assessment results of the hypothetical development site

and 4 potential offset sites

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Note: BBAM means BioBanking assessment methodology; HH means Habitat Hectares scoring method; Index species for HSI model included firefly (Luciola parvula), northern goshawk (Accipiter gentilis) and large Japanese field mouse (Apodemus speciosus).

Figure 3. Categorization of urban forest in Nagoya City (tentative results)

Note: This results of a cluster analysis using the following parameters under the group-average method and the squared Euclidean distance method: (1) Supporting services (Soil formation, carbon stock, etc.), (2) Regulating services (NO2 absorption, climate regulation, etc.), (3) Provisioning services, (4) Cultural services (Spiritual value, aesthetic value, education value, recreation), (5) Habitat (forest size, naturalness, etc.)

Source: Iwai and Hayashi (2015)

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Figure 2. BD/ES provisioning potential in Nagoya City (tentative examples)

(a) infiltration capacity, (b) CO2 absorption, (c) Potential distributions of raccoon dog

Source: Inagaki et al. (2013) and Li et al. (2014) revised

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12.7mm

12.7mm

Lower margin

2.5cm

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