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Benefits of earth observation data for conservation planning in the case of European wetland biodiversity

Published online by Cambridge University Press:  14 November 2012

KERSTIN JANTKE ,
CHRISTINE SCHLEUPNER  and
UWE A. SCHNEIDER
KERSTIN JANTKE*
Affiliation:
Research Unit Sustainability and Global Change, KlimaCampus, University of Hamburg, Grindelberg 5, 20144 Hamburg, Germany
CHRISTINE SCHLEUPNER
Affiliation:
Research Unit Sustainability and Global Change, KlimaCampus, University of Hamburg, Grindelberg 5, 20144 Hamburg, Germany
UWE A. SCHNEIDER
Affiliation:
Research Unit Sustainability and Global Change, KlimaCampus, University of Hamburg, Grindelberg 5, 20144 Hamburg, Germany
*
*Correspondence: Kerstin Jantke Tel: +49 40 42838 2147 Fax: +49 40 42838 7009 e-mail: kerstin.jantke@zmaw.de
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Summary

To evaluate the status of biodiversity and to determine how current conservation efforts can be improved, biodiversity monitoring is crucial. An important aspect of data quality lies in its spatial resolution. It is unclear how finer scale land cover and land value information might further benefit biodiversity conservation. This paper aimed to assess the impacts of scale by modelling the conservation of endangered European wetland species and their corresponding habitats. Fine-scale datasets were derived by integrating existing geographical, biophysical and economic data. A habitat allocation model, based on principles from systematic conservation planning and economic theory, was developed to estimate area requirements and opportunity costs of habitat protection in Europe. Coarse-scale and fine-scale simulations were compared by inputting both resolutions into the model. Habitat locations were restricted either only by historical species occurrence data at UTM 50 resolution or additionally by explicit wetland data at 1-km2 resolution. Coarse country-average land rents were contrasted with spatially detailed land rent estimates at a 5ʹ resolution. Costs of habitat protection and area requirements for reserves may be severely underestimated when conservation planning relies only on coarse-scale data, which may result in notable shortcomings in conservation target achievement. Improvements in conservation benefits far outweigh the additional costs of acquiring fine-scale data.

Keywords

biodiversity policyearth observation data resolutionhomogenous response unitsland use optimization modelmixed integer mathematical programmingspatial wetland distributionsystematic conservation planning
Type
Papers
Information
Environmental Conservation , Volume 40 , Issue 1 , March 2013 , pp. 37 - 47
Copyright
Copyright © Foundation for Environmental Conservation 2012

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References

Andelman, S.J. & Willig, M.R. (2002) Alternative configurations of conservation reserves for Paraguayan bats: considerations of spatial scale. Conservation Biology 16 (5): 1352–1363.CrossRefGoogle Scholar
Araujo, M.B., Thuiller, W., Williams, P.H. & Reginster, I. (2005) Downscaling European species atlas distributions to a finer resolution: implications for conservation planning. Global Ecology and Biogeography 14: 17–30.Google Scholar
Arponen, A., Lehtomaki, J., Leppanen, J., Tomppo, E. & Moilanen, A. (2012) Effects of connectivity and spatial resolution of analyses on conservation prioritization across large extents. Conservation Biology 26 (2): 294–304.CrossRefGoogle ScholarPubMed
Balkovic, J., Schmid, E., Bujnovsky, R., Skalsky, R. & Poltarska, K. (2006) Bio-physical modelling for evaluating soil carbon sequestration potentials on arable lands in the pilot area Baden-Württemberg. Agriculture 52 (4): 1–13.Google Scholar
Balmford, A., Bennun, L., ten Brink, B., Cooper, D., Cote, I.M., Crane, P., Dobson, A., Dudley, N., Dutton, I., Green, R.E., Gregory, R.D., Harrison, J., Kennedy, E.T., Kremen, C., Leader-Williams, N., Lovejoy, T.E., Mace, G., May, R., Mayaux, P., Morling, P., Phillips, J., Redford, K., Ricketts, T.H., Rodriguez, J.P., Sanjayan, M., Schei, P.J., van Jaarsveld, A.S. & Walther, B.A. (2005) The convention on biological diversity's 2010 target. Science 307: 212–213.Google Scholar
Barbosa, A.M., Real, R. & Vargas, J.M. (2010) Use of coarse-resolution models of species’ distributions to guide local conservation inferences. Conservation Biology 24: 1378–1387.Google Scholar
Bode, M., Wilson, K.A., Brooks, T.M., Turner, W.R., Mittermeier, R.A., McBride, M.F., Underwood, E.C. & Possingham, H.P. (2008) Cost-effective global conservation spending is robust to taxonomic group. Proceedings of the National Academy of Sciences USA 105 (17): 6498–6501.Google Scholar
Bohn, U. & Neuhäusel, R., with contributions from Gollub, G., Hettwer, C., Neuhäuslová, Z., Raus, T., Schlüter, H. & Weber, H. (2003) Karte der natürlichen Vegetation Europas / Map of the Natural Vegetation of Europe. Scale 1: 2 500 000. Münster, Germany: Landwirtschaftsverlag.Google Scholar
Busby, J.R. (1991) BIOCLIM: a bioclimatic analysis and prediction system. In: Nature Conservation: Cost Effective Biological Surveys and Data Analysis, ed. Margules, C.R. & Austin, M.P., pp. 64–68. Canberra, Australia: CSIRO.Google Scholar
Early, R. & Thomas, C.D. (2007) Multispecies conservation planning: identifying landscapes for the conservation of viable populations using local and continental species priorities. Journal of Applied Ecology 44 (2): 253–262.CrossRefGoogle Scholar
EEA, ed. (2000) CORINE Land Cover 2000 raster data, 100 m [www document]. URL http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2000-raster-2Google Scholar
Franklin, J. (1995) Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Progress in Physical Geography 19: 474–499.Google Scholar
Fritz, S., Scholes, R., Obersteiner, M., Bouma, J. & Reyers, B. (2008) A conceptual framework for assessing the benefits of a Global Earth Observation System of Systems. IEEE Systems Journal 2: 338–348.Google Scholar
Gasc, J.P., Cabela, A., Crnobrnja-Isailovic, J., Dolmen, D., Grossenbacher, K., Haffner, P., Lescure, J., Martens, H., Martínez Rica, J.P., Maurin, H., Oliveira, M.E., Sofiandou, T.S., Veith, M. & Zuiderwijk, A. (1997) Atlas of Amphibians and Reptiles in Europe. Paris, France: Societas Europaea Herpetologica, Muséum National d'Histoire Naturelle & Service du Petrimone Naturel.Google Scholar
Grand, J., Cummings, M.P., Rebelo, T.G., Ricketts, T.H. & Neel, M.C. (2007) Biased data reduce efficiency and effectiveness of conservation reserve networks. Ecology Letters 10 (5): 364–374.CrossRefGoogle ScholarPubMed
Grantham, H.S., Moilanen, A., Wilson, K.A., Pressey, R.L., Rebelo, T.G. & Possingham, H.P. (2008) Diminishing return on investment for biodiversity data in conservation planning. Conservation Letters 1 (4): 190–198.Google Scholar
Group on Earth Observations (2005) Global Earth Observation System of Systems GEOSS. Noordwijk, Netherlands: ESA Publications Division.Google Scholar
Hagemeijer, W.J.M. & Blair, M.J. (1997) The EBCC Atlas of European Breeding Birds: Their Distribution and Abundance. London, UK: T & A D Poyser.Google Scholar
Henry, P.Y., Lengyel, S., Nowicki, P., Julliard, R., Clobert, J., Celik, T., Gruber, B., Schmeller, D., Babij, V. & Henle, K. (2008) Integrating ongoing biodiversity monitoring: potential benefits and methods. Biodiversity and Conservation 17: 3357–3382.Google Scholar
Hermoso, V. & Kennard, M.J. (2012) Uncertainty in coarse conservation assessments hinders the efficient achievement of conservation goals. Biological Conservation 147 (1): 52–59.CrossRefGoogle Scholar
Herold, M., Woodcock, C.E., Loveland, T.R., Townshend, J., Brady, M., Steenmans, C. & Schmullius, C.C. (2008) Land-cover observations as part of a global earth observation system of systems (GEOSS): progress, activities, and prospects. IEEE Systems Journal 2 (3): 414–423.CrossRefGoogle Scholar
Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. & Jarvis, A. (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965–1978.Google Scholar
Izaurralde, R.C., Williams, J.R., McGill, W.B., Rosenberg, N.J. & Jakas, M.C.Q. (2006) Simulating soil C dynamics with EPIC: model description and testing against long-term data. Ecological Modelling 192: 362–384.Google Scholar
Jantke, K. & Schneider, U.A. (2010) Multiple-species conservation planning for European wetlands with different degrees of coordination. Biological Conservation 143: 1812–1821.Google Scholar
Jantke, K. & Schneider, U.A. (2011) Integrating land market feedbacks into conservation planning: a mathematical programming approach. Environmental Modeling and Assessment 16 (3): 227–238.Google Scholar
Jantke, K., Schleupner, C. & Schneider, U.A. (2011) Gap analysis of European wetland species: priority regions for expanding the Natura 2000 network. Biodiversity and Conservation 20 (3): 581–605.Google Scholar
Joint Research Centre, ed. (2004) European Soil Database version 2.0. European Soil Bureau Network and the European Commission, CD Rom, EUR 19945 EN.Google Scholar
Lee, H.-L., Hertel, T.W., Rose, S. & Avetisyan, M. (2009) An integrated global land use data base for CGE analysis of climate policy options. In: Economic Analysis of Land Use in Global Climate Change Policy, ed. Hertel, T.W., Rose, S.K. & Tol, R.S.J., pp.72–88. New York, NY, USA: Routledge.Google Scholar
Lehner, B. & Döll, P. (2004) Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology 296: 1–22.Google Scholar
Margules, C.R. & Pressey, R.L. (2000) Systematic conservation planning. Nature 405: 243–253.Google Scholar
Marianov, V., ReVelle, C. & Snyder, S. (2008) Selecting compact habitat reserves for species with differential habitat size needs. Computers and Operations Research 35: 475–487.CrossRefGoogle Scholar
McDonnell, M.D., Possingham, H.P., Ball, I.R. & Cousins, E.A. (2002) Mathematical methods for spatially cohesive reserve design. Environmental Modeling and Assessment 7: 107–114.Google Scholar
McPherson, J.M., Jetz, W. & Rogers, D.J. (2006) Using coarse-grained occurrence data to predict species distributions at finer spatial resolutions-possibilities and limitations. Ecological Modelling 192 (3–4): 499–522.CrossRefGoogle Scholar
Merot, P., Squividant, H., Aurousseau, P., Hefting, M., Burt, T., Maitr, V., Kruk, M., Butturini, A., Thenail, C. & Viaud, V. (2003) Testing a climato-topographic index for predicting wetlands distribution along an European climate gradient. Ecological Modelling 163 (1–2): 51–71.Google Scholar
Miller, J., Franklin, J. & Aspinall, R. (2007) Incorporating spatial dependence in predictive vegetation models. Ecological Modelling 202: 225–242.CrossRefGoogle Scholar
Mitchell-Jones, A.J., Amori, G., Bogdanowicz, W., Krystufek, B., Reijnders, P.J.H., Spitzenberger, F., Stubbe, M., Thissen, J.B.M., Vohralík, V. & Zima, J. (1999) The Atlas of European Mammals. London, UK: Academic Press.Google Scholar
Muchoney, D.M. (2008) Earth observations for terrestrial biodiversity and ecosystems. Remote Sensing of Environment 112 (5): 1909–1911.Google Scholar
Muchoney, D.M. & Williams, M. (2010) Building a 2010 biodiversity conservation data baseline: contributions of the Group on Earth Observations. Ecological Research 25 (5): 937–946.CrossRefGoogle Scholar
Naidoo, R., Balmford, A., Ferraro, P.J., Polasky, S., Ricketts, T.H. & Rouget, M. (2006) Integrating economic costs into conservation planning. Trends in Ecology and Evolution 21: 681–687.Google Scholar
Nhancale, B.A. & Smith, R.J. (2011) The influence of planning unit characteristics on the efficiency and spatial pattern of systematic conservation planning assessments. Biodiversity and Conservation 20 (8): 1821–1835.CrossRefGoogle Scholar
Pereira, H.M. & Cooper, H.D. (2006) Towards the global monitoring of biodiversity change. Trends in Ecology and Evolution 21: 123–129.Google Scholar
Possingham, H., Ball, I. & Andelman, S. (2000) Mathematical methods for identifying representative reserve networks. In: Quantitative Methods for Conservation Biology, ed. Ferson, S. & Burgman, M.A., pp. 291–306. New York, NY, USA: Springer.Google Scholar
Prendergast, J.R., Quinn, R.M. & Lawton, J.H. (1999) The gaps between theory and practice in selecting nature reserves. Conservation Biology 13: 484–492.CrossRefGoogle Scholar
Pricewaterhouse Coopers (2006) Socio-Economic Benefits Analysis of GMES [www document]. URL http://esamultimedia.esa.int/docs/GMES/261006_GMES_D10_final.pdfGoogle Scholar
Reside, A.E., Watson, I., VanDerWal, J. & Kutt, A.S. (2011) Incorporating low-resolution historic species location data decreases performance of distribution models. Ecological Modelling 222 (18): 3444–3448.Google Scholar
Richardson, E.A., Kaiser, M.J., Edwards-Jones, G. & Possingham, H.P. (2006) Sensitivity of marine-reserve design to the spatial resolution of socioeconomic data. Conservation Biology 20 (4): 1191–1202.Google Scholar
Sarkar, S., Pressey, R.L., Faith, D.P., Margules, C.R., Fuller, T., Stoms, D.M., Moffett, A., Wilson, K.A., Williams, K.J., Williams, P.H. & Andelman, S. (2006) Biodiversity conservation planning tools: present status and challenges for the future. Annual Review of Environment and Resources 31: 123–159.Google Scholar
Schleupner, C. (2009) GIS as integrating tool in sustainability and global change. Reports on Earth System Science 62. Max Planck Institute for Meteorology, Hamburg, Germany.Google Scholar
Schleupner, C. (2010) GIS-based estimation of wetland conservation potentials in Europe. In: Computational Science and Its Applications, ed. Taniar, D., Gervasi, O., Murgante, B., Pardede, E. & Apduhan, B., pp. 198–213. New York, NY, USA: Springer.Google Scholar
Schmid, E., Balkovic, J., Moltchanova, E., Skalsky, R., Poltarska, K., Müller, B. & Bujnovsky, R. (2006) Biophysical Process Modelling for EU25: Concept, Data, Methods, and Results. Final Research Report for the Integrated Sink Enhancement Assessment Project (INSEA). International Institute for Applied System Analysis, Laxenburg, Austria.Google Scholar
Scholes, R.J., Mace, G.M., Turner, W., Geller, G.N., Jürgens, N., Larigauderie, A., Muchoney, D., Walther, B.A. & Mooney, H.A. (2008) Toward a global biodiversity observing system. Science 321: 1044–1045.Google Scholar
Schwanghart, W., Beck, J. & Kuhn, N. (2008) Measuring population densities in a heterogeneous world. Global Ecology and Biogeography 17: 566–568.Google Scholar
Skalsky, R., Tarasovičova, Z., Balkovič, J., Schmid, E., Fuchs, M., Moltchanova, E., Kindermann, G. & Scholtz, P. (2008) GEO-BENE global database for bio-physical modeling v. 1.0: concepts, methodologies and data. The GEO-BENE database report. International Institute for Applied System Analysis, Laxenburg, Austria.Google Scholar
Stolbovoy, V., Montanarella, L. & Panagos, P. (2007) Carbon Sink Enhancement in Soils of Europe: Data, Modeling, Verification. EUR 23037 EN, European Commission, Ispra, Italy.Google Scholar
Tognelli, M.F., de Arellano, P.I.R. & Marquet, P.A. (2008) How well do the existing and proposed reserve networks represent vertebrate species in Chile? Diversity and Distributions 14: 148–158.CrossRefGoogle Scholar
Trapp, N., Schneider, U.A., McCallum, I., Fritz, S., Schill, C., Borzacchiello, M., Heumesser, C. & Craglia, M. (2012) A meta-analysis on the return of investment of spatial data infrastructures and global earth observation system of systems. Working Paper FNU-199, University of Hamburg and Centre for Marine and Atmospheric Science, Hamburg, Germany.Google Scholar
USGS, ed. (1996) GTOPO30. Available via URL http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/gtopo30_infoGoogle Scholar
Verboom, J., Foppen, R., Chardon, P., Opdam, P. & Luttikhuizen, P. (2001) Introducing the key patch approach for habitat networks with persistent populations: an example for marshland birds. Biological Conservation 100: 89–101.Google Scholar
Warman, L.D., Sinclair, A.R.E., Scudder, G.G.E., Klinkenberg, B. & Pressey, R.L. (2004) Sensitivity of systematic reserve selection to decisions about scale, biological data, and targets: case study from Southern British Columbia. Conservation Biology 18 (3): 655–666.Google Scholar
Williams, J.R. (1995) The EPIC model. In: Computer Models of Watershed Hydrology, ed. Singh, V.P., pp. 909–1000. Colorado, USA: Water Resources Publications.Google Scholar
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