D3.1 Integrated indicators and test simulations for the assessment of biodiversity impacts

D3.1 Integrated indicators and test simulations for the assessment of biodiversity impacts Climate change and land use response relationships for local ecosystem intactness, species population declines, and global species extinctions D3.1 Integrated indicators and test simulations for the assessment of biodiversity impacts 2 LEGAL DISCLAIMER This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101056898. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Climate, Infrastructure and Environment Executive Agency (CINEA). Neither the European Union nor the granting authority can be held responsible for them. This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). D3.1 Integrated indicators and test simulations for the assessment of biodiversity impacts 3 DOCUMENT INFORMATION Deliverable title D3.1 Integrated indicators and test simulations for the assessment of biodiversity impacts Dissemination level Public Submission deadline 31/03/2024 Version number 2.0 (28/03/2024): Second version Authors Jelle Hilbers (Radboud University) Koen Kuipers (Radboud University) Mark Huijbregts (Radboud University) Reviewers CAMBRIDGE ECONOMETRICS BE E3-MODELLING Scope of the document In this task we identify, further develop and integrate indicators to quantify and analyse the impacts of multiple human drivers on biodiversity. We evaluate responses of biodiversity to climate change and land use change by modelling biodiversity impacts on local ecosystem intactness building on our biodiversity model GLOBIO, on species population declines using our biodiversity model GLOBIOSpecies, and on global species extinctions via the LCIMPACT method. D3.1 Integrated indicators and test simulations for the assessment of biodiversity impacts 4 EXECUTIVE SUMMARY Scope: Biodiversity is declining globally, exemplified by losses of local ecosystem intactness, species population declines, and global species extinctions. In order to halt and/or bend the curve of biodiversity loss, urgent strategies are required that contribute to reaching the biodiversity goals as agreed upon in the KunmingMontreal Global Biodiversity Framework. In order to evaluate the effectiveness of biodiversity policies, models should to be able to quantify impacts of changes in human drivers on biodiversity. To do so requires a key set of operational biodiversity indicators, covering multiple aspects of biodiversity and different drivers of biodiversity decline. Goal : Here, we present response relationships of two main drivers of biodiversity loss (climate change and land use), considering three complementary indicators: the mean species abundance (MSA; indicator of local ecosystem intactness), the Living Planet Index (LPI; indicator of species population declines), and the potentially disappeared fraction of species (PDF; indicator of global species extinctions). Response relationships: The response relationships for MSA are established via metaanalysis studies and quantify the influence of various land use types (such as cropland, pasture and forestry) and greenhouse gas emissions on plants and warmblooded vertebrates compared to an undisturbed situation. The response relationships for the LPI are based on species distribution and habitat suitability models and quantify the average global decline of mammal populations for the two drivers of biodiversity loss. Finally, response relationships for PDF for plants and vertebrates were established for land use by combining species area relationships with regionalised information on species endemism and for climate change by using a comprehesive meta-analysis. All response relationships derived for climate change require the global mean temperature increase (GMTI in °C), or alternatively greenhouse gas (GHG) emissions, as input. The response relationships for land use require the area of certain land use categories for all indicators, either further differentiated by land use intensities (for MSA and PDF) and/or by country (for PDF). The response relationships are derived from state-of-the-art biodiversity modelling approaches and can be linked to integrated assessment models to quantify the combined impacts of land use and climate change on three dimensions of biodiversity. Case study: To showcase how the implementation can work in practice, we linked the response relationships to the SSP2 Middle of the Road (representing a baseline scenario that follows historical trends in social, economic, and technological development). Based on global mean temperature increase and global land use areas from the SSP2 scenario, we quantified the changes in the three biodiversity indicators for the years 2015, 2030, 2050 and 2070. We show that trends in MSA, LPI and PDF are consistent over time, with decreases in MSA of ~18M km2 and ~12M km2 between D3.1 Integrated indicators and test simulations for the assessment of biodiversity impacts 5 2015 and 2070 for plants and warm-blooded vertebrates, respectively, a decrease in the LPI for mammals of ~27% between 2015 and 2070, and increases in the PDF of ~4% and ~14% between 2015 and 2070 for plants and vertebrates, respectively. Conclusions: We presented response relationships for land use and climate change impacts for three key biodiversity indicators based on complementary biodiversity modelling approaches, showed how these response relationships can be used to evaluate policy scenarios and discussed how the response relationships can be further developed. The results presented highlight that strategies to halt and/or bend the curve of biodiversity loss are urgently needed to reach globally agreed goals. D3.1 Integrated indicators and test simulations for the assessment of biodiversity impacts 6 Table of contents 1. Introduction 8 2. Local community intactness 9 2.1 GLOBIO-MSA 9 2.2 Climate change response relationships 10 2.3 Land use response relationships 10 2.4 Aggregating response relationships 11 3. Species population declines 12 3.1 GLOBIO-Species 12 3.2 Climate change response relationships 12 3.3 Land use response relationships 13 3.4 Aggregating response relationships 14 4. Global species extinctions 15 4.1 LC-IMPACT 15 4.2 Climate change response relationships 16 4.3 Land use response relationships 17 4.4 Aggregating response relationships 18 5. Application and outlook 19 5.1 Application 19 5.2 Outlook 21 References 24 Annex A 29 D3.1 Integrated indicators and test simulations for the assessment of biodiversity impacts 7 LIST OF ACRONYMS AND ABBREVIATIONS Acronym Long text AOH Area of habitat FRS Fraction of remaining species IUCN International Union for the Conservation of Nature LCIA Life cycle impact assessment GEP Global extinction probability GHG Greenhouse gas GMTI Global mean temperature increase (C°) LPI Living Planet Index LPIL Living Planet Index loss (1 – LPI) MSA Mean species abundance MSAL Mean species abundance loss (1 – MSA) PDF Potentially disappeared fraction of species SAR Species-area relationship D3.1 Integrated indicators and test simulations for the assessment of biodiversity impacts 8 1. Introduction Biodiversity is declining globally, exemplified by local ecosystem changes (Schipper et al., 2020), species population declines (WWF, 2022), and species extinctions (Barnosky et al., 2012; Ceballos et al., 2015; Ceballos & Ehrlich, 2023). In response to the global biodiversity crisis, 196 countries have agreed upon a framework to protect biodiversity (CBD, 2022). Policy scenarios are important tools that support the identification of strategies that contribute to reaching the biodiversity goals (Pereira et al., 2020). In order to evaluate the effectiveness of policies to reach biodiversity goals, policy scenarios need to be able to quantify impacts of changes in human drivers on biodiversity. Biodiversity is a multifaceted concept that cannot be expressed by a single indicator. Hence, to quantify different dimensions of biodiversity change, several indicators are required that each may reveal distinct trends (Crenna et al., 2020; Santini et al., 2017). Biodiversity indicators are complementary if they assess different dimensions of biodiversity, such as changes in local ecosystem intactness, global species populations, and species extinction risks or rates (Steffen et al., 2015). The five major drivers of global biodiversity change are land use, climate change, pollution, overexploitation, and invasive species (Díaz et al., 2019; IPBES, 2019). With ~70% of the terrestrial surface area (excluding ice) being subjected to some form of human influence, land use is the largest driver of terrestrial biodiversity loss (Arneth et al., 2019). Whereas land use impacts are local in nature, affecting only species whose habitat is being converted, climate change impacts are global, affecting all species on Earth. To cover biodiversity impacts comprehensively, it is key to cover more than a single driver of biodiversity change (Pörtner et al., 2023). Here, we present response relationships for two of the main drivers of biodiversity loss (climate change and land use) (IPBES, 2019), considering three complementary biodiversity indicators: local ecosystem intactness (Section 2), species population declines (Section 3), and global species extinctions (Section 4). These response relationships can be linked to integrated assessment models to enable the quantification of various dimensions of biodiversity. In addition, we apply the response relationships to scenarios to illustrate how they can be used to predict biodiversity loss due to climate change and land use in 2015, 2030, 2050, and 2070 (Section 5). Finally, we present a future outlook for the further development of biodiversity reponse relationships and their integration with scenario and impact assessment models to support the identification of strategies to halt and reverse global biodiversity loss (Section 5). D3.1 Integrated indicators and test simulations for the assessment of biodiversity impacts 9 2. Local community intactness 2.1 GLOBIO-MSA The GLOBIO-MSA model quantifies local terrestrial biodiversity intactness based on the impacts of six drivers (climate change, land use, fragmentation, road disturbance, atmospheric nitrogen deposition, and hunting) on mean species abundance (MSA) (Alkemade et al., 2009; Schipper et al., 2020). MSA ranges from 0 (all original species are locally extinct) to 1 (the assemblage is fully intact). MSA is calculated based on the abundance (N) of individual species (k) in response to a given driver (d) compared to their abundance in an undisturbed natural reference situation (r; Equation 1). To consider intactness relative to the natural reference situation, increases in individual species abundance and species that are not present in the natural reference situation are not considered. That is, the number of species (S) does not exceed the number of species in the reference situation. 𝑀𝑀𝑀𝑀𝑀𝑀𝑑𝑑=∑𝑚𝑚𝑚𝑚𝑚𝑚�𝑁𝑁𝑘𝑘,𝑟𝑟,𝑑𝑑 𝑁𝑁𝑘𝑘,𝑟𝑟, 1�𝑀𝑀−1 𝑆𝑆𝑘𝑘 (Equation 1) MSA per driver intensity is calculated based on response relationships, distinguishing between plants and warm-blooded vertebrates (i.e., mammals and birds). Figure 1 shows the response relationships for climate change (MSA by increasing global mean temperature increase) and land use (MSA by land use class). The GLOBIO-MSA response relationships are spatially generic. Figure 1. GLOBIO-MSA response relationships for (a) climate change (via global mean temperature increase) and (b) land use for plants (green) and warm-blooded vertebrates (red). For land use the following land use classes are considered: intensive cropland (Cr.I), minimal intensity cropland (Cr.M), intensive pasture (Pa.I), minimal intensity pasture (Pa.M), forest plantations (Pl), secondary vegetation (Se), and urban area (Ur) (Schipper et al., 2020). We use the GLOBIO-MSA model to quantify response relationships for climate change (Section 2.2) and land use (Section 2.3) in terms of MSA loss (MSAL = 1 - MSA). We use the area-integrated MSAL.km2 indicator by considering the local MSA response to D3.1 Integrated indicators and test simulations for the assessment of biodiversity impacts 16 4.2 Climate change response relationships The LC-IMPACT response relationships for climate change are based on a metaanalysis of species extinctions in response to GMTI (Urban, 2015). Recently, climate change response relationships have been derived based on climate envelope models considering the spatial distribution and temperature niche of 22,913 vertebrate species (mammals, birds, amphibians and reptiles) (Iordan et al., 2023). The global species extinction response relationships are calculated based on the PDF across 1.875° km grid cells and the GMTI (C°) in 2100 relative to 2010 (Figure 4; Equation 9; Table 4). Figure 4. The fraction of affected species (1 – FRS) due to climate change between 2010 and 2100 across various taxonomic groups (including terrestrial vertebrates) based on climate envelope models. White indicates no affected species, yellow few affected species, and purple many affected species (Iordan et al., 2023). 𝐿𝐿𝑃𝑃𝑃𝑃.𝐺𝐺𝑀𝑀𝐺𝐺𝐿𝐿−1=∑�1−𝐹𝐹𝐹𝐹𝑆𝑆2010−2100,𝑗𝑗�𝐺𝐺𝐺𝐺𝐿𝐿𝑗𝑗𝑗𝑗 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺2010−2100 (Equation 9) We derive taxonomic kingdom-aggregated (vertebrate) response relationships (in PDF/GMTI) based on Iordan et al. (2023). Because GMTI has a global impact, the climate change response relationship is spatially generic. Multiplying the GMTI with the climate change response relationship (in PDF / GMTI) results in the PDF impact. The total PDF impact can also be calculated from greenhouse gas (GHG) emissions by multiplying the GMTI (°C) with the global temperature potential (GTP) of the GHGs (in GMTI / kg GHG) over a certain time horizon (Appendix B2). The total PDF impact is then the sum of the impacts of all GHGs (Iordan et al., 2023). Table 4. LC-IMPACT climate change response relationships (PDF.GMTI-1) PDFCC, vertebrates Climate change 0.0514 D3.1 Integrated indicators and test simulations for the assessment of biodiversity impacts 17 4.3 Land use response relationships The LC-IMPACT response relationships for land use are based on a countryside species-area relationship (cSAR) model that considers the local FRS per land use class (p), the total ecoregional (j) area size (A) of each land use class relative to an undisturbed natural reference situation (r) (Olson et al., 2001), a regional-specific nonlinear slope for the decline in species richness by a reduction in habitat size (z), and a region-specific global extinction probability (Chaudhary et al., 2015). Recently, land use impact models have been advanced by differentiating between land use intensities (Chaudhary & Brooks, 2018) and considering habitat fragmentation (Equation 10) (Scherer et al., 2023). The newest land use impact model considers five land use classes (cropland, pasture, forest plantation, managed forest, and urban area) and three intensity levels (minimal, light, and intense use) and distinguishes biodiversity responses between plants, mammals, birds, amphibians, and reptiles (Scherer et al., 2023). Land use intensity impacts on FRS are derived by mixed linear regression models using PREDICTS data (Hudson et al., 2017; Newbold et al., 2014). Habitat fragmentation impacts are calculated based on the equivalent-connected area (ECA) that considers the extent to which habitat patches within a region are connected via dispersal (Saura et al., 2011; Saura & Pascual-Hortal, 2007). 𝐿𝐿𝑃𝑃𝑃𝑃𝑗𝑗=�1−��𝐺𝐺𝐶𝐶𝑀𝑀𝑟𝑟,𝑗𝑗−∑𝐺𝐺𝐶𝐶𝑀𝑀𝐿𝐿𝐿𝐿,𝑗𝑗𝐿𝐿𝐿𝐿 �+�∑𝐹𝐹𝐹𝐹𝑆𝑆𝐿𝐿𝐿𝐿,𝑗𝑗 1 𝑧𝑧𝑗𝑗 �𝐺𝐺𝐶𝐶𝑀𝑀𝐿𝐿𝐿𝐿,𝑗𝑗𝐿𝐿𝐿𝐿 � 𝐺𝐺𝐶𝐶𝑀𝑀𝑟𝑟,𝑗𝑗�𝑧𝑧𝑗𝑗�𝐺𝐺𝐺𝐺𝐿𝐿𝑗𝑗 (Equation 10) Land use response relationships are calculated as the PDF/km2 of land class-intensity LU in region j (Equation 11), based on the PDF due to total land use in region j (Equation 10), the FRS per land class-intensity, and the total area per land class-intensity. 𝐿𝐿𝑃𝑃𝑃𝑃𝑀𝑀𝐿𝐿,𝑗𝑗.𝑘𝑘𝑚𝑚−2=𝐿𝐿𝑃𝑃𝑃𝑃𝑗𝑗�1−𝐹𝐹𝐹𝐹𝑆𝑆𝐿𝐿𝐿𝐿,𝑗𝑗 𝑧𝑧𝑗𝑗�𝑀𝑀𝐿𝐿𝐿𝐿,𝑗𝑗 ∑�1−𝐹𝐹𝐹𝐹𝑆𝑆𝐿𝐿𝐿𝐿,𝑗𝑗 𝑧𝑧𝑗𝑗�𝑀𝑀𝐿𝐿𝐿𝐿,𝑗𝑗𝐿𝐿𝐿𝐿 ∑�𝑀𝑀𝑀𝑀𝐿𝐿,𝑗𝑗�−1 𝑀𝑀𝐿𝐿 (Equation 11) We use the countryand taxonomic kingdom-aggregated response relationships (in PDF/km2) from Scherer et al. (2023), distinguishing land use impacts between 204 countries, plants and animals (including mammals, birds, amphibians, and reptiles), and five land use classes and three land use intensities (i.e., 15 land use class-intensity combinations) (Appendix B1). Multiplying the land use area per year (in km2) per land use class-intensity results in the PDF impact (i.e., the number of species predicted to go extinct globally relative to the total number of species globally). To avoid underestimating land use impacts on biodiversity, we recommend to use the intense land use category if the land use intensity is unknown. D3.1 Integrated indicators and test simulations for the assessment of biodiversity impacts 18 4.4 Aggregating response relationships To obtain an overall effect on global species extinctions, the PDF impacts for climate change and land use can be combined by summing the PDF per driver d and country j: 𝐿𝐿𝑃𝑃𝑃𝑃=𝐿𝐿𝑃𝑃𝑃𝑃𝐶𝐶𝐶𝐶𝐺𝐺𝑀𝑀𝐺𝐺𝐿𝐿+∑�𝐿𝐿𝑃𝑃𝑃𝑃𝑀𝑀𝐿𝐿,𝑗𝑗𝑀𝑀𝑀𝑀𝐿𝐿,𝑗𝑗� 𝑀𝑀𝐿𝐿,𝑗𝑗 (Equation 12) D3.1 Integrated indicators and test simulations for the assessment of biodiversity impacts 19 5. Application and outlook 5.1 Application In this report we presented response relationships for global land use and climate change impacts on ecosystem intactness (via the MSA indicator), species populations (via the LPI indicator), and species extinction risks (via the PDF indicator) based on state-of-the-art biodiversity modelling approaches. These response relationships can be linked to integrated assessment models to enable the quantification of various dimensions of biodiversity. To showcase this, we linked the derived response relationships in this report to the SSP2 Middle of the Road scenario which is a scenario that follows historical trends in social, economic, and technological development. We derived global land use and climate change impacts on ecosystem intactness, species population declines and species extinction risks for the years 2015, 2030, 2050 and 2070. To do so, input was required on the GMTI (in °C) for the corresponding years and on the land use area for each year (in km2) per land use class. These input data were obtained through GMTI estimates and land use maps from Kok et al. (2023) (Figure 5). Figure 5. The input data related to the SSP2 scenario for GMTI (A) and area per land use category (B) (from (Kok et al., 2023)). D3.1 Integrated indicators and test simulations for the assessment of biodiversity impacts 20 The global mean temperature increase estimates for 2015 (1.032 °C), 2030 (1.477 °C), 2050 (2.064 °C), and 2070 (2.667 °C) were included in equations 2 and 3 to obtain an MSAL.km2 due to climate change for plants and vertebrates, multiplied (after subtracting the GMTI in 1970 of 0.056 °C) with the LPILcc (Table 2) to obtain an LPIL due to climate change for mammals, and multiplied with the PDFCC, vertebrates (Table 4) to obtain a PDF due to climate change for vertebrates. The land use area per land use class (in km2) for 2015, 2030, 2050 and 2070 (Appendix B3, Figure 5) were multiplied with the MSALLU (Table 1) to obtain an MSAL.km2 due to land use for plants and vertebrates, multiplied (after subtracting the land use area per land use class in 1970 (Appendix B3)) with the LPILLU (Table 3) to obtain an LPIL due to land use for mammals, and included in equation 11 to obtain the PDFLU, j due to land use in each country. For the PDFLU, j, we used the response relationships for the intense land use categories to avoid underestimating land use impacts on biodiversity. Finally, we combined the separate results according to equations 4, 7 and 12 to obtain overall impacts of climate change and land use on ecosystem intactness, species population declines and species extinctions. Figure 6. Combined impacts of climate change and land use on (A) plant (green) and warm-blooded vertebrate (orange) local ecosystem intactness (MSAL.km2); (B) mammal (orange) species population declines (LPIL); and (C) global species extinctions (PDF) of plants (green; only land use impacts) and vertebrates (orange). D3.1 Integrated indicators and test simulations for the assessment of biodiversity impacts 21 The results show that the MSA loss for plants increases over time from ~42M km2 in 2015 to ~60M km2 in 2070, and for warm-blooded vertebrates from ~30M km2 in 2015 to ~42M km2 in 2070 (Figure 6). In other words, we would lose an extra ~18M km2 of pristine habitat for plants (equivalent to an area the size of South America) and an extra ~12M km2 of pristine habitat for warm-blooded vertebrates (equivalent to an area the size of Europe) due to climate change and land use alone. Dividing these numbers by the total terrestrial surface area, shows that the fraction of intact communities is predicted to decrease in 2070 to 53% and 67% for plants and warmblooded vertebrates, respectively. These predictions are in line with Kok et al. (2023) and Schipper et al. (2020). The average global decline in mammal populations is predicted to increase from ~21% in 2015 to ~47% in 2070. These results are similar to those found by Kok et al. (2023). Where the decline in mammal populations in 2015 is mainly caused by land use (57% of the total impact), climate change and land use contribute approximately the same proportion of the total impact (i.e., 51% due to land use and 49% due to climate change) in 2070. In other words, the results highlight that reducing the impacts of land use alone will not be sufficient to halt the decline of mammal populations if not accompanied by actions to reduce the impacts of climate change as well. The number of species predicted to go extinct globally relative to the total number of species is predicted to increase from ~20% in 2015 to ~24% in 2070 for plants, and from ~25% in 2015 to ~38% in 2070 for vertebrates. The estimates for 2015 are in line with the estimated proportion of threatened plant species that range between 20% and 39% (Brummitt et al., 2008; Nic Lughadha et al., 2020) and the estimated proportion of threatened vertebrate species (mammals: 26%; amphibians 41%; birds 12%; and reptiles 21%) (IUCN, 2022). Based on an estimated number of 425,035 plant and 74,962 vertebrate species (IUCN, 2022), we would lose ~18,000 plant and ~10,000 vertebrate species between 2015 and 2070 under the baseline scenario. These results for the three complementary biodiversity indicators highlight that integrated strategies are urgently needed that contribute to the halting and/or bending the curve of biodiversity loss. 5.2 Outlook Although the presented response relationships on biodiversity due to climate change and land use covered different aspects of biodiversity, different taxonomic groups and were based on different biodiversity modelling approaches, we foresee several opportunities to further develop the representation of biodiversity responses to human drivers after the DECIPHER project, especially in relation to harmonising the methods underlying the three biodiversity indicators. The PDF land use response relationships consider plants and terrestrial vertebrates (amphibians, birds, mammals and reptiles) (Scherer et al., 2023); and the PDF climate D3.1 Integrated indicators and test simulations for the assessment of biodiversity impacts 22 change response relationships consider terrestrial vertebrates only, excluding plants (Iordan et al., 2023). The MSA response relationships consider plants and terrestrial warm-blooded vertebrates (birds and mammals) (Schipper et al., 2020). The LPI response relationships consider terrestrial mammals only. To improve the harmonisation across the response relationships of the three biodiversity indicators, all response relationships (across the biodiversity indicators and land use and climate change drivers) should consider plants and terrestrial vertebrates (amphibians, birds, mammals, and reptiles). For PDF climate change response relationships this requires an expansion of the climate envelope models to plant species. For MSA response relationships this requires an expansion of the climate change and land use response relationships to amphibians and reptiles. For LPI response relationships this requires an expansion of the GLOBIO-Species model to plants, amphibians, birds, and reptiles. The development of additional plant response relationships is limited by the availability of plant species distirbution data (relevant for the PDF climate change and LPI response relationships). The development of additional plant response relationships is limited by the availability of driver-response relationships (relevant for the MSA and LPI indicators) – this also holds for bird LPI response relationships. The PDF land use response relationships are differentiated by country because the conversion of land in countries characterised by little remaining natural habitat and high numbers of rare species has higher impacts on global biodiversity than land use in countries characterised by much remaining natural habitat and low species richness. LPI response relationships could potentially be differentiated by country as well, but this requires many computationally intensive GLOBIO-Species simulations (the number of countries times the number of drivers). Because MSA quantifies local ecosystem intactness of impacted sites relative to natural undisturbed sites, MSA response relationships remain spatially generic. The MSA land use response relationships differentiate between seven land use classes, including differentiation between minimal and intense land use intensities for cropland and pasture (Table 1) (Schipper et al., 2020). The LPI land use response relationships differentiate between five land use classes, without differentiating between land use intensities (Table 3). The PDF response relationships differentiate between fifteen land use classes. Including differentiation between minimal, light and intense land use for cropland, managed forest, pasture, plantation, and urban area (Appendix B1) (Scherer et al., 2023). Ideally, MSA and LPI land use response relationships are further differentiated to match the PDF land use classification. This is currently hampered by the limited data availability on intactness driver-response relationships to different land use intensities and on species population abundance responses to different land use intensities. Here, we have presented response relationships for two key drivers to global biodiversity (climate change and land use impacts). 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