Modeling direct and indirect climate change impacts on ecological networks: a case study on breeding habitat of Dutch meadow birds
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Climate change can directly affect habitats within ecological networks, but may also have indirect effects on network quality by inducing land use change. The relative impact of indirect effects of climate change on the quality of ecological networks currently remains largely unknown.
The objective of this study was to determine the relative impact of direct and indirect effects of climate change on a network of breeding habitat of four meadow bird species (Black-tailed godwit, Common redshank, Eurasian oystercatcher and Northern lapwing) in the Netherlands.
Habitat models were developed that link meadow bird breeding densities to three habitat characteristics that are sensitive to environmental change (landscape openness, land use and groundwater level). These models were used to assess the impact of scenarios of landscape change with and without climate change on meadow bird breeding habitat quality for a case study area in the peat meadow district of the Netherlands.
All scenarios led to significantly reduced habitat quality for all species, mainly as a result of conversion of grassland to bioenergy crops, which reduces landscape openness. Direct effects of climate change on habitat quality were largely absent, indicating that especially human adaptation to climate change rather than direct effects of climate change was decisive for the degradation of ecological network quality for breeding meadow birds.
We conclude that scenario studies exploring impacts of climate change on ecological networks should incorporate both land use change resulting from human responses to climate change and direct effects of climate change on landscapes.
KeywordsBlack-tailed godwit Common redshank Eurasian oystercatcher Northern lapwing Bioenergy crops Land use change
JvD was financially supported by the Climate Adaptation for Rural Areas (CARE) Project, which was funded by the Knowledge for Climate Programme (http://knowledgeforclimate.climateresearchnetherlands.nl/climateadaptationforruralareas). SOVON Vogelonderzoek Nederland provided the Dutch relative bird density datasets. Harry Dijkstra, Jetty van Lith-Kranendonk and Jaco van der Gaast (all Alterra Wageningen) provided the openness and groundwater datasets. Mara Baudena kindly assisted with programming the reclassification of the openness maps. Paul Opdam gave very useful suggestions for the presentation of the manuscript.
- Bakker M, Alam SJ, Van Dijk J, Rounsevell M, Spek T, Van den Brink A (2014) The feasibility of implementing an ecological network in The Netherlands under conditions of global change. Landscape Ecol. doi: 10.1007/s10980-014-0145-5
- BirdLife International (2004) Birds in Europe: population estimates, trends and conservation status. BirdLife International, CambridgeGoogle Scholar
- BirdLife International (2014) Species factsheet: Limosa limosa. http://www.birdlife.org. Accessed July 2014
- Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New YorkGoogle Scholar
- Burrows MT, Schoeman DS, Buckley LB, Moore P, Poloczanska ES, Brander KM, Brown C, Bruno JF, Duarte CM, Halpern BS, Holding J, Kappel CV, Kiessling W, OÇonner MI, Pandolfi JM, Parmesan C, Schwing FB, Sydeman WJ, Richardson AJ (2011) The pace of shifting climate in marine and terrestrial ecosystems. Science 334:652–655CrossRefPubMedGoogle Scholar
- Dijkstra H, van Lith-Kranendonk J (2000) Schaalkenmerken van het landschap in Nederland. Monitoring Kwaliteit Groene Ruimte (MKGR). Alterra-report 040. Alterra, WageningenGoogle Scholar
- Field A (2009) Discovering statistics using SPSS, 3rd edn. SAGE Publications Ltd, LondonGoogle Scholar
- Sovon VN (2002) Atlas van de Nederlandse broedvogels 1998–2000. Nederlandse fauna 5. Nationaal Natuurhistorisch Museum Naturalis, KNNV Uitgeverij and European Invertebrate Survey-Nederland, LeidenGoogle Scholar
- SPSS Inc. (2009) PASW Statistics for Windows, Version 18.0. SPSS inc., ChicagoGoogle Scholar
- Thunnissen HAM, De Wit AJW (2000) The national land cover database of the Netherlands. In: Beek KJ, Molenaar M (eds) Geoinformation for all. XIX congress of the International Society for Photogrammetry and Remote Sensing, Amsterdam, July 2000, International Archives of Photogrammetry and Remote Sensing, vol 13. ISPRS Amsterdam, pp 223–230Google Scholar
- Van Bodegom PM, Verboom J, Witte JPM, Vos CC, Bartholomeus RP, Geertsema W, Cormont A, Van der Veen M, Aerts R (2014) Synthesis of ecosystem vulnerability to climate change in the Netherlands shows the need to consider environmental fluctuations in adaptation measures. Reg Environ Change 14:933–942Google Scholar
- Van den Hurk B, Klein Tank A, Lenderink G, Van Ulden A, Van Oldenborgh GJ, Katsman C, Van den Brink H, Keller F, Bessembinder J, Burgers G, Komen G, Hazeleger W, Drijfhout S (2006) KNMI climate changes scenarios 2006 for the Netherlands. Report number WR 2006-01. Royal Dutch Meteorological Institute, De BiltGoogle Scholar
- Van der Vliet RE, Oquiñena Valluerca I, Van Dijk J, Wassen MJ (2014) EU protection is inadequate for a declining flyway population of Black-tailed Godwit Limosa limosa: mismatch between future core breeding areas and present special protection areas. Bird Conserv Int. doi: 10.1017/S0959270914000100 Google Scholar
- Van Turnhout C, Vogel R (1997) The new atlas of Dutch breeding birds 1998–2000. Bird Census News 10:26–32Google Scholar
- Vogelzang TA, Venema GS, De Bont CJAM, Wisman JH, Van Leeuwen MGA (2009) Boeren in het Groene Hart; Kansen voor het agrocluster. Report number 2009–012. LEI Wageningen UR, The HagueGoogle Scholar
- Witte JPM, Bartholomeus RP, Van Bodegom PM, Cirkel DG, Van Ek R, Fujita Y, Janssen GMCM, Spek TJ, Runhaar H (2014) A probabilistic eco-hydrological model to predict the effects of climate change on natural vegetation at a regional scale. Landscape Ecol. doi: 10.1007/s10980-14-0086-z