Continent‐scale global change attribution in European birds ‐ combining annual and decadal time scales |
| |
| Authors: | Peter Søgaard Jørgensen Katrin Böhning‐Gaese Kasper Thorup Anders P Tøttrup Przemysław Chylarecki Frédéric Jiguet Aleksi Lehikoinen David G Noble Jiri Reif Hans Schmid Chris van Turnhout Ian J Burfield Ruud Foppen Petr Voříšek Arco van Strien Richard D Gregory Carsten Rahbek |
| |
| Affiliation: | 1. Center for Macroecology, Evolution and Climate, Department of Biology, University of Copenhagen, Copenhagen, Denmark;2. Biodiversity and Climate Research Centre (BiK‐F) and Senckenberg Gesellschaft für Naturforschung, Frankfurt (Main), Germany;3. Department of Biological Sciences, Goethe Universit?t, Frankfurt (Main), Germany;4. Center for Macroecology, Evolution and Climate, Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark;5. Museum & Institute of Zoology, Polish Academy of Sciences, Warszawa, Poland;6. UMR7204 MNHN‐CNRS‐UPMC, CRBPO CP51, Paris, France;7. Zoological Unit, Finnish Museum of Natural History, University of Helsinki, Helsinki, Finland;8. British Trust for Ornithology, The Nunnery, Thetford, Norfolk, UK;9. Department of Zoology and Laboratory of Ornithology, Faculty of Science, Palacky University Olomouc, Olomouc, Czech Republic;10. Swiss Ornithological Institute, Sempach, Switzerland;11. Sovon Dutch Centre for Field Ornithology, Nijmegen, The Netherlands;12. Department of Animal Ecology, Institute for Wetland and Water Research, Radboud University Nijmegen, Nijmegen, The Netherlands;13. BirdLife International, Wellbrook Court, Cambridge, UK;14. Dutch Centre for Field Ornithology, Nijmegen, The Netherlands;15. European Bird Census Council (EBCC), Nijmegen, The Netherlands;16. Pan‐European Common Bird Monitoring Scheme, Czech Society for Ornithology, Prague 5, Czech Republic;17. Statistics Netherlands, The Hague, The Netherlands;18. Royal Society for the Protection of Birds, The Lodge, Sandy, UK |
| |
| Abstract: | Species attributes are commonly used to infer impacts of environmental change on multiyear species trends, e.g. decadal changes in population size. However, by themselves attributes are of limited value in global change attribution since they do not measure the changing environment. A broader foundation for attributing species responses to global change may be achieved by complementing an attributes‐based approach by one estimating the relationship between repeated measures of organismal and environmental changes over short time scales. To assess the benefit of this multiscale perspective, we investigate the recent impact of multiple environmental changes on European farmland birds, here focusing on climate change and land use change. We analyze more than 800 time series from 18 countries spanning the past two decades. Analysis of long‐term population growth rates documents simultaneous responses that can be attributed to both climate change and land‐use change, including long‐term increases in populations of hot‐dwelling species and declines in long‐distance migrants and farmland specialists. In contrast, analysis of annual growth rates yield novel insights into the potential mechanisms driving long‐term climate induced change. In particular, we find that birds are affected by winter, spring, and summer conditions depending on the distinct breeding phenology that corresponds to their migratory strategy. Birds in general benefit from higher temperatures or higher primary productivity early on or in the peak of the breeding season with the largest effect sizes observed in cooler parts of species' climatic ranges. Our results document the potential of combining time scales and integrating both species attributes and environmental variables for global change attribution. We suggest such an approach will be of general use when high‐resolution time series are available in large‐scale biodiversity surveys. |
| |
| Keywords: | citizen science climate change farmland birds global change attribution land‐use change multiple temporal scales multiscale inference population time series |
|
|
