A protocol for an intercomparison of biodiversity and ecosystem services models using harmonized land-use and climate scenarios

To support the assessments of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), the IPBES Expert Group on Scenarios and Models is carrying out an intercomparison of biodiversity and ecosystem services models using harmonized scenarios (BES-SIM). The goals of BES-SIM are (1) to project the global impacts of land use and climate change on biodiversity and ecosystem services (i.e. nature’s contributions to people) over the coming decades, compared to the 20th century, using a set of common metrics at multiple scales, and (2) to identify model uncertainties and research gaps through the comparisons of projected biodiversity and ecosystem services across models. BES-SIM uses three scenarios combining specific Shared Socio-economic Pathways (SSPs) and Representative Concentration Pathways (RCPs) to explore a wide range of land-use change and climate change futures. This paper describes the rationale for scenarios selection, the process of harmonizing input data for land use, based on the second phase of the Land Use Harmonization Project (LUH2), and climate, the biodiversity and ecosystem service models used, the core simulations carried out, the harmonization of the model output metrics, and the treatment of uncertainty. The results of this collaborative modelling project will support the ongoing global assessment of IPBES, strengthen ties between IPBES and the Intergovernmental Panel on Climate Change (IPCC) scenarios and modelling processes, advise the Convention on Biological Diversity (CBD) on its development of a post-2020 strategic plans and conservation goals, and inform the development of a new generation of nature-centred scenarios.

Walter Jetz | Michael Obersteiner | Tomoko Hasegawa | Wilfried Thuiller | Stefanie Hellweg | Shinichiro Fujimori | Andy Purvis | Benjamin Poulter | Jan H. Janse | Piero Visconti | Simon Ferrier | Henrique M. Pereira | Rebecca Chaplin-Kramer | Mike Harfoot | Almut Arneth | Nicolas Titeux | Carlo Rondinini | Samantha L. L. Hill | Cory Merow | Vanessa Haverd | Rob Alkemade | Adriana De Palma | Moreno Di Marco | Alexander Popp | Fulvio Di Fulvio | Petr Havlík | Tetsuya Matsui | Detlef P van Vuuren | Andrew J. Hoskins | Paul Leadley | Ricardo E. Gonzalez | Carlos Guerra | Florian Wolf | Louise Chini | Peter Anthoni | Kiyoshi Takahashi | S. Hellweg | A. Arneth | C. Guerra | B. Poulter | M. Obersteiner | A. Popp | T. Matsui | Haruka Ohashi | S. Ferrier | G. Hurtt | P. Havlík | P. Anthoni | V. Haverd | L. Chini | H. Pereira | R. Chaplin-Kramer | W. Jetz | P. Leadley | A. Purvis | P. Visconti | Kiyoshi Takahashi | S. Fujimori | D. V. van Vuuren | A. Schipper | R. Alkemade | D. Baisero | C. Rondinini | Isabel M. D. Rosa | Florian Wolf | R. Sharp | W. Thuiller | B. Quesada | T. Hasegawa | T. Harwood | M. Harfoot | HyeJin Kim | J. Janse | Fulvio Di Fulvio | J. Johnson | Inês S. Martins | S. Hill | N. Titeux | C. Merow | M. Guéguen | M. Di Marco | D. Leclère | A. Krause | Daniele Baisero | Adriana De Palma | A. Hoskins | George Hurtt | Andreas Krause | Ines S. Martins | HyeJin Kim | Isabel M.D. Rosa | Emma Caton | Felipe Espinoza | Maya Gueguen | Thomas D. Harwood | Akiko Hirata | Justin A. Johnson | David Leclère | Haruka Ohashi | Benjamin Quesada | Aafke Schipper | Richard Sharp | Christopher Ware | A. Hirata | F. Espinoza | Emma Caton | R. Gonzalez | Christopher B. Ware

[1]  G. Daily,et al.  Biodiversity loss and its impact on humanity , 2012, Nature.

[2]  Matthew J. Smith,et al.  Integrated assessment models for ecologists: the present and the future , 2014 .

[3]  Douglas W. Yu,et al.  The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts , 2014, Ecology and Evolution.

[4]  Benjamin Smith,et al.  Modelling the response of yields and tissue C:N to changes in atmospheric CO2 and N management in the main wheat regions of western Europe , 2015 .

[5]  L. V. Beek,et al.  Coupling global models for hydrology and nutrient loading to simulate nitrogen and phosphorus retention in surface water – description of IMAGE–GNM and analysis of performance , 2015 .

[6]  J. Elith,et al.  Species Distribution Models: Ecological Explanation and Prediction Across Space and Time , 2009 .

[7]  S. Polasky,et al.  Assessing nature's contributions to people , 2018, Science.

[8]  Wolfgang Knorr,et al.  Demographic controls of future global fire risk , 2016 .

[9]  Jonathan Seaquist,et al.  Implications of accounting for land use in simulations of ecosystem carbon cycling in Africa , 2013 .

[10]  R. B. Jackson,et al.  Global biodiversity scenarios for the year 2100. , 2000, Science.

[11]  L. May,et al.  National scale evaluation of the InVEST nutrient retention model in the United Kingdom. , 2018, The Science of the total environment.

[12]  Antoine Guisan,et al.  Predictive habitat distribution models in ecology , 2000 .

[13]  N. Droste,et al.  Assessing Nature’s Contributions to People , 2020, Frontiers for Young Minds.

[14]  Caribbean Community.  The State of Biodiversity in the Caribbean Community:A Review of Progress Towards the Aichi Biodiversity Targets , 2018 .

[15]  Walter Jetz,et al.  Multiscale scenarios for nature futures , 2017, Nature Ecology & Evolution.

[16]  Matthew J. Smith,et al.  Protected areas network is not adequate to protect a critically endangered East Africa Chelonian: Modelling distribution of pancake tortoise, Malacochersus tornieri under current and future climates , 2013, bioRxiv.

[17]  H. Pereira,et al.  Improving extinction projections across scales and habitats using the countryside species-area relationship , 2017, Scientific Reports.

[18]  Stephen Polasky,et al.  Global agriculture and carbon trade-offs , 2014, Proceedings of the National Academy of Sciences.

[19]  P. Kyle,et al.  Land-use futures in the shared socio-economic pathways , 2017 .

[20]  Richard G. Jones,et al.  How representative is the spread of climate projections from the 5 CMIP5 GCMs used in ISI-MIP? , 2016 .

[21]  M. Lomas,et al.  A framework for the cross-sectoral integration of multi-model impact projections: land use decisions under climate impacts uncertainties , 2015 .

[22]  Wilfried Thuiller,et al.  A road map for integrating eco-evolutionary processes into biodiversity models. , 2013, Ecology letters.

[23]  Wilfried Thuiller,et al.  Consequences of climate change on the tree of life in Europe , 2011, Nature.

[24]  Philippe Ciais,et al.  Historical carbon dioxide emissions caused by land-use changes are possibly larger than assumed , 2017 .

[25]  Piero Visconti,et al.  Global habitat suitability models of terrestrial mammals , 2011, Philosophical Transactions of the Royal Society B: Biological Sciences.

[26]  Stephen Sitch,et al.  The Fire Modeling Intercomparison Project (FireMIP), phase 1: Experimental and analytical protocols with detailed model descriptions , 2016 .

[27]  Scott J. Goetz,et al.  Terrestrial and Inland Water Systems , 2014 .

[28]  Matt Walpole,et al.  Progress towards the Aichi Biodiversity Targets: an assessment of biodiversity trends, policy scenarios and key actions , 2014 .

[29]  M. Bierkens,et al.  Global monthly water stress: 1. Water balance and water availability , 2011 .

[30]  Niklaus E. Zimmermann,et al.  Impacts of land cover and climate data selection on understanding terrestrial carbon dynamics and the CO 2 airborne fraction , 2011 .

[31]  Yoan Fourcade,et al.  Comparing species distributions modelled from occurrence data and from expert-based range maps. Implication for predicting range shifts with climate change , 2016, Ecol. Informatics.

[32]  G. Powell,et al.  Mapping More of Terrestrial Biodiversity for Global Conservation Assessment , 2004 .

[33]  J. Eom,et al.  The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview , 2017 .

[34]  H. D. Cooper,et al.  Scenarios for Global Biodiversity in the 21st Century , 2010, Science.

[35]  Wolfgang Cramer,et al.  Biodiversity scenarios neglect future land‐use changes , 2016, Global change biology.

[36]  D. Vuuren,et al.  Assessing inter-sectoral climate change risks: the role of ISIMIP , 2017 .

[37]  Eduardo Zorita,et al.  The PMIP4 contribution to CMIP6 - Part 3: the Last Millennium, Scientific Objective and Experimental Design for the PMIP4 past1000 simulations , 2016 .

[38]  Anni Arponen,et al.  Projecting Global Biodiversity Indicators under Future Development Scenarios , 2016 .

[39]  J. Welbergen,et al.  Climate change and the effects of temperature extremes on Australian flying-foxes , 2008, Proceedings of the Royal Society B: Biological Sciences.

[40]  Ilse R. Geijzendorffer,et al.  An assessment of soil erosion prevention by vegetation in Mediterranean Europe: Current trends of ecosystem service provision , 2016 .

[41]  Brian C. O'Neill,et al.  The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6 , 2016 .

[42]  E. Stehfest,et al.  Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands , 2011 .

[43]  A. Klein,et al.  Global malnutrition overlaps with pollinator-dependent micronutrient production , 2014, Proceedings of the Royal Society B: Biological Sciences.

[44]  Benjamin Smith,et al.  A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis , 2018, Geoscientific Model Development.

[45]  Victoria J. Burton,et al.  Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment , 2016, Science.

[46]  Salvador Mandujano,et al.  The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project , 2016, Ecology and evolution.

[47]  W. Thuiller,et al.  Predicting species distribution: offering more than simple habitat models. , 2005, Ecology letters.

[48]  C. Rahbek,et al.  Spatial predictions at the community level: from current approaches to future frameworks , 2017, Biological reviews of the Cambridge Philosophical Society.

[49]  Rob Alkemade,et al.  GLOBIO3: A Framework to Investigate Options for Reducing Global Terrestrial Biodiversity Loss , 2009, Ecosystems.

[50]  Qiongyu Huang,et al.  Future habitat loss and extinctions driven by land‐use change in biodiversity hotspots under four scenarios of climate‐change mitigation , 2015, Conservation biology : the journal of the Society for Conservation Biology.

[51]  A. Dobson,et al.  Projected Impacts of Climate and Land-Use Change on the Global Diversity of Birds , 2007, PLoS biology.

[52]  M. Kainuma,et al.  SSP3: AIM implementation of Shared Socioeconomic Pathways , 2017 .

[53]  Matthew Hansen,et al.  A global view of shifting cultivation: Recent, current, and future extent , 2017, PloS one.

[54]  EQuiTAblE SHAriNg,et al.  Secretariat of the Convention on Biological Diversity , 2007 .

[55]  J. Biesmeijer,et al.  Fit-for-Purpose: Species Distribution Model Performance Depends on Evaluation Criteria – Dutch Hoverflies as a Case Study , 2013, PloS one.

[56]  C. Müller,et al.  Energy, land-use and greenhouse gas emissions trajectories under a green growth paradigm , 2017 .

[57]  A. Thomson,et al.  The representative concentration pathways: an overview , 2011 .

[58]  F. Piontek,et al.  A trend-preserving bias correction – the ISI-MIP approach , 2013 .

[59]  M. Araújo,et al.  BIOMOD – a platform for ensemble forecasting of species distributions , 2009 .

[60]  Jan H. Janse,et al.  GLOBIO-Aquatic, a global model of human impact on the biodiversity of inland aquatic ecosystems , 2015 .

[61]  Annie Levasseur,et al.  Global guidance on environmental life cycle impact assessment indicators: progress and case study , 2016, The International Journal of Life Cycle Assessment.

[62]  Stephen Sitch,et al.  Dynamic global vegetation modelling: quantifying terrestrial ecosystem responses to large-scale environmental change , 2007 .

[63]  John F. B. Mitchell,et al.  The next generation of scenarios for climate change research and assessment , 2010, Nature.

[64]  N. Crossman,et al.  Quantifying ecosystem services and indicators for science, policy and practice , 2014 .

[65]  K. Arkema,et al.  The Power of Three: Coral Reefs, Seagrasses and Mangroves Protect Coastal Regions and Increase Their Resilience , 2016, PloS one.

[66]  K. Calvin,et al.  Fossil-fueled development (SSP5): An energy and resource intensive scenario for the 21st century , 2017 .

[67]  Catharina J.E. Schulp,et al.  Mapping ecosystem functions and services in Eastern Europe using global-scale data sets , 2012 .

[68]  N. Ramankutty,et al.  Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000 , 2008 .

[69]  W. Cramer,et al.  Global scenarios for biodiversity need to better integrate climate and land use change , 2017 .

[70]  H. Resit Akçakaya,et al.  Analysing biodiversity and conservation knowledge products to support regional environmental assessments , 2016, Scientific Data.

[71]  Chini,et al.  The PMIP 4 contribution to CMIP 6 – Part 3 : the Last Millennium , 2016 .

[72]  S. Ferrier,et al.  Supporting global biodiversity assessment through high-resolution macroecological modelling: Methodological underpinnings of the BILBI framework , 2018, bioRxiv.

[73]  W. Thuiller.  Patterns and uncertainties of species' range shifts under climate change , 2004 .

[74]  S. Bony,et al.  Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5 , 2013, Climate Dynamics.

[75]  Tomoko Hasegawa,et al.  Harmonization of Global Land-Use Change and Management for the Period 850–2100 (LUH2) for CMIP6 , 2020 .

[76]  T. Wigley,et al.  Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 - Part 1: Model description and calibration , 2011 .

[77]  Qi Li,et al.  The 2016 release of the PREDICTS database , 2016 .

[78]  Benjamin Smith,et al.  Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model , 2013 .

[79]  Andy Purvis,et al.  Modelling and projecting the response of local terrestrial biodiversity worldwide to land use and related pressures: the PREDICTS project , 2018 .

[80]  J. Elith,et al.  Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment , 2007 .

[81]  Mark W. Wilson,et al.  Implications of afforestation for bird communities: the importance of preceding land-use type , 2017, Biodiversity and Conservation.

[82]  Tom M. L. Wigley,et al.  Emulating atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 – Part 2: Applications , 2011 .

[83]  K. Riahi,et al.  The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century , 2017 .

[84]  Stephen E. Fick,et al.  WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas , 2017 .

[85]  Matthew J. Smith,et al.  Emergent Global Patterns of Ecosystem Structure and Function from a Mechanistic General Ecosystem Model , 2014, PLoS biology.

[86]  Keywan Riahi,et al.  A new scenario framework for climate change research: the concept of shared socioeconomic pathways , 2013, Climatic Change.

[87]  James E. M. Watson,et al.  Biodiversity: The ravages of guns, nets and bulldozers , 2016, Nature.

[88]  Athanasios S. Kallimanis,et al.  Disentangling the effects of land‐use change, climate and CO2 on projected future European habitat types , 2015 .

[89]  P. Leadley,et al.  Impacts of climate change on the future of biodiversity. , 2012, Ecology letters.

[90]  Brett R. Scheffers,et al.  Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being , 2017, Science.

[91]  P. Kareiva,et al.  Coastal habitats shield people and property from sea-level rise and storms , 2013 .

[92]  George C. Hurtt,et al.  The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6:rationale and experimental design , 2016 .

[93]  S S I T C H,et al.  Evaluation of Ecosystem Dynamics, Plant Geography and Terrestrial Carbon Cycling in the Lpj Dynamic Global Vegetation Model , 2022 .

[94]  S. Hellweg,et al.  Quantifying Land Use Impacts on Biodiversity: Combining Species-Area Models and Vulnerability Indicators. , 2015, Environmental science & technology.

[95]  S. Polasky,et al.  Global Food Demand and Carbon-Preserving Cropland Expansion under Varying Levels of Intensification , 2016, Land Economics.

[96]  Tyler D. Eddy,et al.  Assessing the impacts of 1.5 °C global warming - simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b) , 2016 .

[97]  D. Vuuren,et al.  Integrated Assessment of Global Environmental Change with IMAGE 3.0 : Model description and policy applications , 2014 .

[98]  Keywan Riahi,et al.  A new scenario framework for Climate Change Research: scenario matrix architecture , 2014, Climatic Change.

[99]  F. Piontek,et al.  The Inter-Sectoral Impact Model Intercomparison Project (ISI–MIP): Project framework , 2013, Proceedings of the National Academy of Sciences.

[100]  I. C. Prentice,et al.  Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model , 2003 .