The road to integrate climate change projections with regional land‐use–biodiversity models

Author:

Cabral Juliano Sarmento12ORCID,Mendoza‐Ponce Alma34ORCID,da Silva André Pinto56ORCID,Oberpriller Johannes7ORCID,Mimet Anne8,Kieslinger Julia9ORCID,Berger Thomas10ORCID,Blechschmidt Jana1,Brönner Maximilian9,Classen Alice11ORCID,Fallert Stefan1ORCID,Hartig Florian7ORCID,Hof Christian8ORCID,Hoffmann Markus12ORCID,Knoke Thomas13ORCID,Krause Andreas14ORCID,Lewerentz Anne115ORCID,Pohle Perdita9ORCID,Raeder Uta12,Rammig Anja14ORCID,Redlich Sarah11ORCID,Rubanschi Sven8ORCID,Stetter Christian16ORCID,Weisser Wolfgang8ORCID,Vedder Daniel1171819ORCID,Verburg Peter H.20ORCID,Zurell Damaris21ORCID

Affiliation:

1. Ecosystem Modelling Center for Computational and Theoretical Biology (CCTB) University of Wurzburg Würzburg Germany

2. Biodiversity Modelling and Environmental Change School of Biosciences College of Life and Environmental Sciences University of Birmingham Birmingham UK

3. Research Program on Climate Change Universidad Nacional Autónoma de México Mexico City Mexico

4. International Institute for Applied Systems Analysis Laxenburg Austria

5. Department of Ecology and Genetics Animal Ecology Evolutionary Biology Centre Uppsala University Uppsala Sweden

6. cE3c ‐ Centre for Ecology, Evolution and Environmental Changes & CHANGE ‐ Global Change and Sustainability Institute Faculdade de Ciências Universidade de Lisboa Lisbon Portugal

7. Theoretical Ecology Lab University of Regensburg Regensburg Germany

8. Terrestrial Ecology Research Group Department of Life Science Systems School of Life Sciences Technical University of Munich Freising Germany

9. Chair of Human Geography and Development Studies Institute of Geography Friedrich‐Alexander University Erlangen‐Nuernberg Erlangen Germany

10. Land‐Use Economics in the Tropics and Subtropics Hans‐Ruthenberg Institute Hohenheim University Hohenheim Germany

11. Department of Animal Ecology and Tropical Biology Biocentre, University of Wurzburg Würzburg Germany

12. Chair of Aquatic Systems Biology Department of Life Science Systems Limnologische Station Iffeldorf School of Life Science Technical University of Munich Iffeldorf Germany

13. Department of Life Science Systems School of Life Sciences Institute of Forest Management Technical University of Munich Freising Germany

14. Department of Life Science Systems School of Life Sciences Land Surface‐Atmosphere Interactions Technical University of Munich Freising Germany

15. Institute of Geography and Geoecology Karlsruhe Institute of Technology Karlsruhe Germany

16. Agricultural Production and Resource Economics School of Life Sciences Technical University of Munich Freising Germany

17. Department of Ecosystem Services Helmholtz Center for Environmental Research—UFZ Leipzig Germany

18. Institute of Biodiversity Friedrich Schiller University Jena Jena Germany

19. German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig Leipzig Germany

20. Institute for Environmental Studies VU University Amsterdam Amsterdam The Netherlands

21. Ecology & Macroecology Institute for Biochemistry and Biology University of Potsdam Potsdam Germany

Abstract

Abstract Current approaches to project spatial biodiversity responses to climate change mainly focus on the direct effects of climate on species while regarding land use and land cover as constant or prescribed by global land‐use scenarios. However, local land‐use decisions are often affected by climate change and biodiversity on top of socioeconomic and policy drivers. To realistically understand and predict climate impacts on biodiversity, it is, therefore, necessary to integrate both direct and indirect effects (via climate‐driven land‐use change) of climate change on biodiversity. In this perspective paper, we outline how biodiversity models could be better integrated with regional, climate‐driven land‐use models. We initially provide a short, non‐exhaustive review of empirical and modelling approaches to land‐use and land‐cover change (LU) and biodiversity (BD) change at regional scales, which forms the base for our perspective about improved integration of LU and BD models. We consider a diversity of approaches, with a special emphasis on mechanistic models. We also look at current levels of integration and at model properties, such as scales, inputs and outputs, to further identify integration challenges and opportunities. We find that LU integration in BD models is more frequent than the other way around and has been achieved at different levels: from overlapping predictions to simultaneously coupled simulations (i.e. bidirectional effects). Of the integrated LU‐BD socio‐ecological models, some studies included climate change effects on LU, but the relative contribution of direct vs. indirect effects of climate change on BD remains a key research challenge. Important research avenues include concerted efforts in harmonizing spatial and temporal resolution, disentangling direct and indirect effects of climate change on biodiversity, explicitly accounting for bidirectional feedbacks, and ultimately feeding socio‐ecological systems back into climate predictions. These avenues can be navigated by matching models, plugins for format and resolution conversion, and increasing the land‐use forecast horizon with adequate uncertainty. Recent developments of coupled models show that such integration is achievable and can lead to novel insights into climate–land use–biodiversity relations. Read the free Plain Language Summary for this article on the Journal blog.

Funder

Deutsche Bundesstiftung Umwelt

Deutsche Forschungsgemeinschaft

Deutscher Akademischer Austauschdienst

Publisher

Wiley

Subject

Ecology, Evolution, Behavior and Systematics

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