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Coupling Satellite Data with Species Distribution and Connectivity Models as a Tool for Environmental Management and Planning in Matrix-Sensitive Species

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Abstract

Climate change and anthropogenic habitat fragmentation are considered major threats for global biodiversity. As a direct consequence, connectivity is increasingly disrupted in many species, which might have serious consequences that could ultimately lead to the extinction of populations. Although a large number of reserves and conservation sites are designated and protected by law, potential habitats acting as inter-population connectivity corridors are, however, mostly ignored in the common practice of environmental planning. In most cases, this is mainly caused by a lack of quantitative measures of functional connectivity available for the planning process. In this study, we highlight the use of fine-scale potential connectivity models (PCMs) derived from multispectral satellite data for the quantification of spatially explicit habitat corridors for matrix-sensitive species of conservation concern. This framework couples a species distribution model with a connectivity model in a two-step framework, where suitability maps from step 1 are transformed into maps of landscape resistance in step 2 filtered by fragmentation thresholds. We illustrate the approach using the sand lizard (Lacerta agilis L.) in the metropolitan area of Cologne, Germany, as a case study. Our model proved to be well suited to identify connected as well as completely isolated populations within the study area. Furthermore, due to its fine resolution, the PCM was also able to detect small linear structures known to be important for sand lizards’ inter-population connectivity such as railroad embankments. We discuss the applicability and possible implementation of PCMs to overcome shortcomings in the common practice of environmental impact assessments.

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References

  • Addison PFE, Rumpff L, Bau SS, Carey JM, Chee YE, Jarrad FC, McBride MF, Burgman MA (2013) Practical solutions for making models indispensable in conservation decision-making. Divers Distrib 19:490–502. doi:10.1111/ddi.12054

    Article  Google Scholar 

  • Agasyan A, Avci A, Tuniyev B, Lymberakis P, Andrén C, Cogalniceanu D, Wilkinson J, Ananjeva N, Üzüm N, Orlov N, Podloucky R, Tuniyev S, Kaya U, Crnobrnja Isailovic J, Vogrin M, Corti C, Pérez Mellado V, Sá-Sousa P, Cheylan M, Pleguezuelos J, Kyek M, Westerström A, Nettmann HK, Borczyk B, Sterijovski B, Schmidt B (2010) Lacerta agilis. In: IUCN 2012. IUCN red list of threatened species. Version 2012.2. www.iucnredlist.org

  • Andersen LW, Fog K, Damgaard C (2004) Habitat fragmentation causes bottlenecks and inbreeding in the European tree frog (Hyla arborea). Proc R Soc B 271:1293–1302. doi:10.1098/rspb.2004.2720

    Article  Google Scholar 

  • Andrén H (1994) Effects on habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: a review. Oikos 71:355–366. http://www.jstor.org/stable/3545823

  • Baguette M, Blanchet S, Legrand D, Stevens VM, Turlure C (2013) Individual dispersal, landscape connectivity and ecological networks. Biol Rev 88:310–326. doi:10.1111/brv.12000

    Article  Google Scholar 

  • Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F (2012) Impacts of climate change on the future of biodiversity. Ecol Lett 15:365–377. doi:10.1111/j.1461-0248.2011.01736.x

    Article  Google Scholar 

  • Berglind S-A (2000) Demography and management of relict sand lizard Lacerta agilis populations on the edge of extinction. Ecol Bull 48:123–142. http://www.jstor.org/stable/20113253

  • Berglind S-A (2004) Sand lizard (Lacerta agilis) in central Sweden—modeling juvenile reintroduction and spatial management strategies for metapopulation establishment. In: Akcakaya HR (ed) Species conservation and management: case studies. Oxford University Press, Oxford

    Google Scholar 

  • Blanke I (1999) Erfassung und Lebensweise der Zauneidechse (Lacerta agilis) an Bahnanlagen (Capture and life history of the sand lizard (Lacerta agilis) along railroads). Zeitschrift für Feldherpetologie 6:147–159

    Google Scholar 

  • Blanke I (2010) Die Zauneidechse - Zwischen Licht und Schatten (The sand lizard—between light and shade). Laurenti, Bielefeld

    Google Scholar 

  • Caldwell LK (1991) Analysis–assessment–decision: the anatomy of rational policymaking. Impact Assess Bull 9:81–92. doi:10.1080/07349165.1991.9726069

    Article  Google Scholar 

  • Canter L, Ross B (2010) State of practice of cumulative effects assessment and management: the good, the bad and the ugly. Impact Assess Project Apprais 28:261–268. doi:10.3152/146155110X12838715793200

    Article  Google Scholar 

  • Council of the European Commission (1992) Council Directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and flora. Off J Eur Communities Ser L 206:7–49

    Google Scholar 

  • Crist EP, Cicone RC (1984) Application of the tasseled cap concept to simulated thematic mapper data. Photogramm Eng Remote Sens 50:343–352

    Google Scholar 

  • Crooks KR, Sanjayan M (2006) Connectivity conservation. Cambridge University Press, New York

    Book  Google Scholar 

  • De Smedt P (2010) The use of impact assessment tools to support sustainable policy objectives in Europe. Ecol Soc 15:30 http://www.ecologyandsociety.org/vol15/iss4/art30/

  • Devictor V, van Swaay C, Brereton T, Brotons L, Chamberlain D, Heliölä J, Herrando S, Julliard R, Kuussaari M, Lindström Å, Reif J, Roy DB, Schweiger O, Settele J, Stefanescu C, Van Strien A, Van Turnhout C, Vermouzek Z, WallisDe Vries M, Wynhoff I, Jiguet F (2012) Differences in the climatic debts of birds and butterflies at a continental scale. Nat Clim Chang 2:121–124. doi:10.1038/nclimate1347

    Article  Google Scholar 

  • Dickerson W, Montgomery J (1993) Substantive scientific and technical guidance for NEPA analysis: pitfalls in the real world. Environ Prof 15:7–11

    Google Scholar 

  • Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, García Marquéz JR, Gruber B, Lafourcade B, Leitão PJ, Münkemüller T, McClean C, Osborne PE, Reineking B, Schröder B, Skidmore AK, Zurell D, Lautenbach S (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36:27–46. doi:10.1111/j.1600-0587.2012.07348.x

    Article  Google Scholar 

  • Driezen K, Adriaesen F, Rondinini C, Doncaster CP, Matthysen E (2007) Evaluating least-cost model predictions with empirical dispersal data: a case-study using radiotracking data of hedgehogs (Erinaceus europaeus). Ecol Model 209:314–322. doi:10.1016/j.ecolmodel.2007.07.002

    Article  Google Scholar 

  • Duinker PN, Burbidge EL, Boardley SR, Greig LA (2013) Scientific dimensions of cumulative effects assessment: toward improvements in guidance for practice. Environ Rev 21:40–52. doi:10.1139/er-2012-0035

    Article  Google Scholar 

  • Elith J, Graham CH, Anderson RP, Dudík M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JMcC, Peterson AT, Phillips SJ, Richardson KS, Scachetti Pereira R, Schapire RE, Soberón J, Williams S, Wisz MS, Zimmermann NE (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151. http://dx.doi.org/10.1111/j.2006.0906-7590.04596.x

  • Elith J, Phillips SJ, Hastie T (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17:43–57. doi:10.1111/j.1472-4642.2010.00725.x

    Article  Google Scholar 

  • Ellwanger G (2004) Lacerta agilis (Linnaeus, 1758). In: Petersen B, Ellwanger G, Bless R, Boye P, Schröder E, Ssymank A (eds) The European conservation network Natura 2000—ecology and distribution of species listed in the habitat directive in Germany: vertebrates, vol 2. BfN, Bonn Bad-Godesberg, pp 90–97

    Google Scholar 

  • Fagan WF, Calabrese JM (2006) Quantifying connectivity: balancing metric performance with data requirements. In: Crooks KR, Sanjayan M (eds) Connectivity conservation. Cambridge University Press, New York

    Google Scholar 

  • Fahring L (2003) Effects of habitat fragmentation on biodiversity. Ann Rev Ecol Evol Syst 34:487–515

    Article  Google Scholar 

  • Fischer J, Lindenmayer DB (2007) Landscape modification and habitat fragmentation: a synthesis. Glob Ecol Biogeogr 16:265–280. doi:10.1111/j.1466-8238.2007.00287.x

    Article  Google Scholar 

  • Fithian W, Hastie T (2013) Finite-sample equivalence in statistical models for presence-only data. Ann Appl Stat 7:1917–1939. doi:10.1214/13-AOAS667

    Article  Google Scholar 

  • Franklin J (2009) Mapping species distributions: spatial inference and prediction. Cambridge University Press, Cambridge

    Google Scholar 

  • Geneletti D (2006) Some common shortcomings in the treatment of impacts of linear infrastructures on natural habitat. Environ Impact Assess Rev 26:257–267. doi:10.1016/j.eiar.2005.10.003

    Article  Google Scholar 

  • Gilbert-Norton L, Wilson R, Stevens JR, Beard KH (2010) A meta-analytic review of corridor effectiveness. Conserv Biol 24:660–668. doi:10.1111/j.1523-1739.2010.01450.x

    Article  Google Scholar 

  • Glandt D, Bischoff W (1988) Biology and conservation of the sand lizard (Lacerta agilis). Mertensiella 1:1–257

    Google Scholar 

  • Gontier M (2006) Integrating landscape ecology in environmental impact assessment using GIS and ecological modeling. In: Tress B, Tress G, Fry G, Opdam P (eds) From landscape research to landscape planning: aspects of integration, education and application. Springer, Bakkaveen, pp 345–354

    Chapter  Google Scholar 

  • Gontier M, Mörtberg U, Balfors B (2010) Comparing GIS-based habitat models for applications in EIA and SEA. Environ Impact Assess Rev 30:8–18. doi:10.1016/j.eiar.2009.05.003

    Article  Google Scholar 

  • Guillera-Arroita G, Lahoz-Monfort JJ, Elith J, Gordon A, Kujala H, Lentini PE, McCarthy MA, Tingley R, Wintle BA (2015) Is my species distribution model fit for purpose? Matching data and models to applications. Glob Ecol Biogeogr. doi:10.1111/geb.12268

    Google Scholar 

  • Guisan A, Tingley R, Baumgartner JB, Naujokaitis-Lewis I, Sutcliffe PR, Tulloch AI, Reagan TJ, Brotons L, McDonald-Madden E, Mantyka-Pringle C, Martin TG, Rhodes JR, Maggini R, Setterfield SA, Elith J, Schwartz MW, Wintle BA, Broennimann O, Austin M, Ferrier S, Kearney MR, Possingham HP, Buckley YM (2013) Predicting species distributions for conservation decisions. Ecol Lett 16:1424–1435. doi:10.1111/ele.12189

    Article  Google Scholar 

  • Habel JC, Schmitt T (2012) The burden of genetic diversity. Biol Conserv 147:270–274. doi:10.1016/j.biocon.2011.11.028

    Article  Google Scholar 

  • Hale ML, Lurz PWW, Shirley MDF, Rushton S, Fuller RM, Wolff K (2001) Impact of landscape management on the genetic structure of red squirrel populations. Science 293:2246–2248. doi:10.1126/science.1062574

    Article  CAS  Google Scholar 

  • Hanski I (1994) A practical model of metapopulation dynamics. J Anim Ecol 63:151–162

    Article  Google Scholar 

  • Hanski I (1998) Metapopulation dynamics. Nature 396:41–49

    Article  CAS  Google Scholar 

  • Hanski I, Moilanen A, Gyllenberg M (1996) Minimum viable metapopulation size. Am Nat 147:527–541

    Article  Google Scholar 

  • Heikkinen RK, Luoto M, Araújo MB, Virkkala R, Thuiller W, Sykes MT (2006) Methods and uncertainties in bioclimatic envelope modeling under climate change. Prog Phys Geogr 30:751–777. doi:10.1177/0309133306071957

    Article  Google Scholar 

  • Hernandez PA, Graham CH, Master LL, Albert DL (2006) The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29:773–785. doi:10.1111/j.0906-7590.2006.04700.x

    Article  Google Scholar 

  • Hof C, Levinsky I, Araújo MB, Rahbek C (2011) Rethinking species’ ability to cope with rapid climate change. Glob Chang Biol 17:2987–2990. doi:10.1111/j.1365-2486.2011.02418.x

    Article  Google Scholar 

  • Ims RA (1995) Movement patterns related to spatial structures. In: Hansson L, Fahring L, Merriam G (eds) Mosaic landscapes and ecological processes. Chapman and Hall, London

    Google Scholar 

  • ITT (2008) ENVI version 4.5 software, 1133 Westchester Avenue, White Plains, NY 10604, USA

  • Johnson ML, Gaines MS (1987) The selective basis for dispersal of the prairie vole, Microtus ochrogaster. Ecology 68:684–694

    Article  Google Scholar 

  • Keyghobadi N (2007) The genetic implications of habitat fragmentation for animals. Can J Zool 85:1049–1064. doi:10.1139/Z07-095

    Article  Google Scholar 

  • Lambeck RJ (1997) Focal species: a multi-species umbrella for nature conservation. Conserv Biol 11:849–856

    Article  Google Scholar 

  • LANUV NRW (2010) FB 24 ABC Bewertungsbogen 07/10—Lacerta agilis. http://www.naturschutz-fachinformationssysteme-nrw.de. Accessed 12 Mar 2010

  • Mandelik Y, Dayan T, Feitelson E (2005) Issues and dilemmas in ecological scoping: scientific, procedural and economic perspectives. Impact Assess Project Apprais 23:55–63. doi:10.3152/147154605781765724

    Article  Google Scholar 

  • McRae BH, Beier P (2007) Circuit theory predicts gene flow in plant and animal populations. Proc Natl Acad Sci USA 104:19885–19890. doi:10.1073/pnas.0706568104

    Article  CAS  Google Scholar 

  • McRae BH, Dickson BG, Keitt TH, Shah VB (2008) Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology 89:2712–2724. doi:10.1890/07-1861.1

    Article  Google Scholar 

  • Merow C, Smith MJ, Silander JA Jr (2013) A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36:1058–1069. doi:10.1111/j.1600-0587.2013.07872.x

    Article  Google Scholar 

  • Metzger J-P, Décamps H (1997) The structural connectivity threshold: a hypothesis in conservation biology at the landscape scale. Acta Oecol 18:1–12. doi:10.1016/S1146-609X(97)80075-6

    Article  Google Scholar 

  • Mimet A, Houet T, Julliard R, Simon L (2013) Assessing functional connectivity: a landscape approach for handling multiple ecological requirements. Methods Ecol Evol 4:453–463

    Article  Google Scholar 

  • Moilanen A, Hanski I (1998) Metapopulation dynamics: effects of habitat quality and landscape structure. Ecology 79:2503–2515

    Article  Google Scholar 

  • Moilanen A, Hanski I (2006) Connectivity and metapopulation dynamics in highly fragmented landscapes. In: Sanjayan M, Crooks KR (eds) Connectivity conservation. Cambridge University Press, Cambridge

    Google Scholar 

  • Moilanen A, Nieminen M (2002) Simple connectivity measures for metapopulation studies. Ecology 83:1131–1145

    Article  Google Scholar 

  • Morris P, Therivel R (2001) Methods of environmental impact assessment. Spon, London

    Google Scholar 

  • NASA (2002) Landsat 7 science data users handbook, http://landsathandbook.gsfc.nasa.gov/pdfs/Landsat7_Handbook.pdf. Accessed 1 Sep 2011

  • Nicholson E, Ovaskainen O (2009) Conservation priorization using metapopulation models. In: Moilanan A, Wilson KA, Possingham H (eds) Spatial conservation prioritization: quantitative methods & computational tools. Oxford University Press, Oxford

    Google Scholar 

  • Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change impacts across natural systems. Nature 421:37–42. doi:10.1038/nature01286

    Article  CAS  Google Scholar 

  • Pearson RG, Raxworthy CJ, Nakamura M, Peterson AT (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr 34:102–117. doi:10.1111/j.1365-2699.2006.01594.x

    Article  Google Scholar 

  • Peterson AT, Soberón J, Pearson RG, Anderson RP, Martínez-Meyer E, Nakamura M, Araújo MB (2011) Ecological niches and geographic distributions. Princeton University Press, Princeton

    Google Scholar 

  • Petterson B (1985) Extinction of an isolated population of the middle spotted woodpecker Dendrocopos medius (L.) in Sweden and its relation to general theories on extinction. Biol Conserv 32:335–353. doi:10.1016/0006-3207(85)90022-9

    Article  Google Scholar 

  • Phillips SJ, Dudík M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161–175. doi:10.1111/j.0906-7590.2008.5203.x

    Article  Google Scholar 

  • Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Mod 190:231–259. doi:10.1016/j.ecolmodel.2005.03.026

    Article  Google Scholar 

  • Possingham HP, Andelman SJ, Noon BR, Trombulak S, Pulliam HR (2001) Making smart conservation decisions. In: Soule MA, Orians GH (eds) Conservation biology: research priorities for the next decade. Island Press, Washington, pp 225–244

    Google Scholar 

  • Rhodes JR, Wiegand T, McAlpine CA, Callaghan J, Lunney D, Bowen M, Possingham HP (2006) Modeling species’ distributions to improve conservation in semiurban landscapes: Koala case study. Conserv Biol 20:449–459. http://dx.doi.org/10.1111/j.1523-1739.2006.00330.x

  • Roberge JM, Angelstam P (2004) Usefulness of the umbrella species concept as a conservation tool. Conserv Biol 18:76–85

    Article  Google Scholar 

  • Roberts DA, Yamaguchi Y, Lyon RJP (1985) Calibration of airborne imaging spectrometer data to percent reflectance using field measurements. In: Proceedings of the 19th international symposium of remote sensing of the environment, Ann Arbor, October 21–25, 1985

  • Rödder D, Lötters S (2010) Explanative power of variables used in species distribution modeling: an issue of general model transferability or niche shift in the invasive greenhouse frog (Eleutherodactylus planirostris). Naturwissenschaften 97:781–796. doi:10.1007/s00114-010-0694-7

    Article  Google Scholar 

  • Rödder D, Schmidtlein S, Veith M, Lötters S (2009) Alien invasive slider turtle in unpredicted habitat: a matter of niche shift or of predictors studied? PLoS one e7843. http://dx.doi.org/10.1371/journal.pone.0007843

  • Rödder D, Engler JO, Bonke R, Weinsheimer F, Pertel W (2010) Fading of the last giants: an assessment of habitat availability of the Sunda gharial Tomistoma schlegelii and coverage with protected areas. Aquat Conserv Mar Freshw Ecosyst 20:678–684. doi:10.1002/aqc.1137

    Article  Google Scholar 

  • Sawyer SC, Epps CW, Brashares JS (2011) Placing linkages among fragmented habitats: do least-cost models reflect how animals use landscapes? J App Ecol 48:668–678. http://dx.doi.org/10.1111/j.1365-2664.2011.01970.x

  • Schaller J (1990) Geographical information system applications in environmental impact assessment. In: Scholten HJ, Stillwell JCH (eds) Geographical information systems for urban and regional planning. Kluwer Academic Publishers, Dordrecht, pp 107–117

    Chapter  Google Scholar 

  • Schnitter P, Eichen C, Ellwanger G, Neukirchen M, Schröder E (2006) Empfehlungen für die Erfassung und Bewertung von Arten als Basis für das Monitoring nach Artikel 11 und 17 der FFH-Richtlinie in Deutschland (Guidance for the survey and evaluation of species as basis for the monitoring after Art. 11 and 17 of the habitat directive in Germany). Berichte des Landesamtes für Umweltschutz Sachsen-Anhalt (Halle), Sonderheft 2

  • Spear SF, Balkenhol N, Fortin MJ, McRae BH, Scribner KIM (2010) Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysis. Mol Ecol 19:3576–3591. doi:10.1111/j.1365-294X.2010.04657.x

    Article  Google Scholar 

  • Templeton AR, Shaw K, Routman E, Davis SK (1990) The genetic consequences of habitat fragmentation. Ann Missouri Bot Garden 77:13–27. http://www.jstor.org/stable/2399621

  • Therivel R, Ross B (2007) Cumulative effects assessment: does scale matter? Environ Impact Assess Rev 27:365–385. doi:10.1016/j.eiar.2007.02.001

    Article  Google Scholar 

  • Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont IJ, Collingham YC, Erasmus BFN, Ferreira de Siqueira M, Grainger A, Hannah L, Hughes L, Huntley B, Van Jaarsveld AS, Midgely GE, Miles L, Ortega-Huerta MA, Peterson AT, Phillips OL, Williams SE (2004) Extinction risk from climate change. Nature 427:145–148. doi:10.1038/nature02121

    Article  CAS  Google Scholar 

  • Tilman D, May RM, Lehman CL, Nowak MA (1994) Habitat destruction and the extinction dept. Nature 371:65–66

    Article  Google Scholar 

  • Vogt P, Ferrari JR, Lookingbill TR, Gardner RH, Riitters KH, Ostapowicz K (2009) Mapping functional connectivity. Ecol Indic 9:64–71

    Article  Google Scholar 

  • Vos CC, Verboom J, Opdam PFM, ter Braak CJF, Bergers PJM (2001) Towards ecologically scaled landscape indices. Am Nat 157:24–41

    Article  CAS  Google Scholar 

  • Wilson K, Cabeza M, Klein CJ (2009) Fundamental concepts of spatial conservation prioritization. In: Moilanen A, Wilson K, Possingham HP (eds) Spatial conservation prioritization: quantitative methods and computational tools. Oxford Universtiy Press, Oxford, pp 16–27

    Google Scholar 

  • Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A (2008) Effects of sample size on the performance of species distribution models. Divers Distrib 14:763–773. doi:10.1111/j.1472-4642.2008.00482.x

    Article  Google Scholar 

  • Zachos FE, Althoff C, Steynitz YV, Eckert I, Hartl GB (2007) Genetic analysis of an isolated red deer (Cervus elaphus) population showing signs of inbreeding depression. Eur J Wildl Res 53:61–67. doi:10.1007/s10344-006-0065-z

    Article  Google Scholar 

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Acknowledgments

This study was kindly funded by the Cologne environmental state agency. JOE received additional funding by the German Federal Environmental Foundation fellowship programme. We are pleased to Christoph Ring for pre-processing the remote sensing data as well as to Ursula Bott, Michelle Bußler, Veronica Frans, and three anonymous reviewers for giving valuable comments to an earlier version of this manuscript.

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  1. Sven Nekum

    Present address: , Hauptstrasse 240, 53639, Königswinter, Germany

Authors and Affiliations

  1. Zoological Research Museum Alexander Koenig, Adenauerallee 160, 53113, Bonn, Germany

    Dennis Rödder, Sven Nekum & Jan O. Engler

  2. Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Permoserstraße 15, 04318, Leipzig, Germany

    Anna F. Cord

  3. Department of Wildlife Sciences, University of Göttingen, 37077, Göttingen, Germany

    Jan O. Engler

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Rödder, D., Nekum, S., Cord, A.F. et al. Coupling Satellite Data with Species Distribution and Connectivity Models as a Tool for Environmental Management and Planning in Matrix-Sensitive Species. Environmental Management 58, 130–143 (2016). https://doi.org/10.1007/s00267-016-0698-y

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