Modeling spatial distribution of European badger in arid landscapes: an ecosystem functioning approach

Landscape Ecology volume 29, pages843–855(2014)Cite this article

Abstract

Understanding the factors determining the spatial distribution of species is a major challenge in ecology and conservation. This study tests the use of ecosystem functioning variables, derived from satellite imagery data, to explore their potential use in modeling the distribution of the European badger in Mediterranean arid environments. We found that the performance of distribution models was enhanced by the inclusion of variables derived from the Enhanced Vegetation Index (EVI), such as mean EVI (a proxy for primary production), the coefficient of variation of mean EVI (an indicator of seasonality), and the standard deviation of mean EVI (representing spatial heterogeneity of primary production). We also found that distributions predicted by remote sensing data were consistent with the ecological preferences of badger in those environments, which may be explained by the link between EVI-derived variables and the spatial and temporal variability of food resource availability. In conclusion, we suggest the incorporation of variables associated with ecosystem function into species modeling exercises as a useful tool for improving decision-making related to wildlife conservation and management.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

US$ 39.95

Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2

References

  1. Alcaraz D, Paruelo JM, Cabello J (2006) Current distribution of ecosystem functional types in the Iberian Peninsula. Glob Ecol Biogeogr 15:200–210

    Article  Google Scholar 

  2. Alcaraz-Segura D, Paruelo JM, Epstein HE, Cabello J (2012) Environmental and human controls of ecosystem functional diversity in temperate South America. Remote Sens 5(1):127–154

    Google Scholar 

  3. Bardsen BJ, Tveraa T (2012) Density-dependence vs. density-independence—linking reproductive allocation to population abundance and vegetation greenness. J Anim Ecol 81:364–376

    PubMed  Article  Google Scholar 

  4. Barea-Azcón JM, Ballesteros-Duperón E, Gil-Sánchez JM, Virgós E (2010) Badger Meles meles feeding ecology in dry Mediterranean environments of the southwest edge of its distribution range. Acta Theriol 55(1):45–52

    Google Scholar 

  5. Boyce MS (1978) Climatic variability and body size variation in the muskrast (Ondatra zibethicus) of North America. Oecologia 36:1–19

    Article  Google Scholar 

  6. Brown JH, Mehlman DW, Steven GC (1995) Spatial variance in abundance. Ecology 76:2028–2043

    Article  Google Scholar 

  7. Buermann W, Saatchi S, Smith TB, Zutta BR, Chaves JA, Milá B, Graham CH  (2008) Prediction species distributions across the Amazonian and Andean regions using remote sensing data. J Biogeogr 35(7):1160–1176

    Google Scholar 

  8. Burnham KP, Anderson DR (2002) Model selection and multimodel inference. A practical information-theoretic approach. Springer, New York

    Google Scholar 

  9. Cabello J, Fernández N, Alcaraz-Segura D, Oyonarte C, Piñeiro G, Altesor A, Delibes M, Paruelo JM (2012a) The ecosystem functioning dimension in conservation: insights from remote sensing. Biodivers Conserv 21:3287–3305

  10. Cabello J, Alcaraz-Segura D, Ferrero R, Castro AJ, Liras E (2012b) The role of vegetation and lithology in the spatial and inter-annual response of EVI to climate in drylands of Southeastern Spain. J Arid Environ 79:76–83

    Google Scholar 

  11. Chen W, Samuelson FW, Gallas BD, Kang L, Sahiner B, Petrik N (2013) On the assessment of the added value of new predictive biomarkers. BMC Med Res Methodol 13:98

    Google Scholar 

  12. Corbacho C, Sánchez JM, Costillo E (2003) Patterns of structural complexity and human disturbance of riparian vegetation in agricultural landscapes of a Mediterranean area. Agric Ecosyst Environ 95:495–507

    Article  Google Scholar 

  13. DeLong ER, DeLong DM, Clarke-Pearson DL (1998) Comparing the areas under two or more correlated receiver operating characteristic curves: a non-parametric approach. Biometrics 44:837–845

    Article  Google Scholar 

  14. Elith J, Leathwick JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40:677–697

    Article  Google Scholar 

  15. 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 JMM, Peterson AT, Phillips SJ, Richardson K, 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

    Google Scholar 

  16. Ferguson SH, McLoughlin PD (2000) Effect of energy availability, seasonality and geographic range on brown bear life history. Ecography 23:193–200

    Article  Google Scholar 

  17. Ferrier S, Watson G (1997) An Evaluation of the effectiveness of environmental surrogates and modeling techniques in predicting the distribution of biological diversity. Environment Australia, Canberra Australia. http://www.environment.gov.au/archive/biodiversity/publications/technical/surrogates/

  18. García-Rangel S, Pettorelli N (2013) Thinking spatially: the importance of geospatial techniques of carnivore conservation. Ecol Inform 14:84–89

    Article  Google Scholar 

  19. Graham CH, Hijmans RJ (2006) A comparison of methods for mapping species ranges and species richness. Glob Ecol Biogeogr 15:578

    Article  Google Scholar 

  20. Hernández 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

    Google Scholar 

  21. Huete AR, Liu HQ, Batchily K, van Leeuwen W (1997) A comparison of vegetation indices global set of TM images for EOS-MODIS. Remote Sens Environ 59:440–451

    Google Scholar 

  22. Huete AR, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83:195–213

    Google Scholar 

  23. Jepsen JU, Madsen AB, Karlsson M, Groth D (2005) Predicting distribution and density of badger (Meles meles) setts in Denmark. Biodivers Conserv 14:3235–3253

    Google Scholar 

  24. Johnson DD, Jetz W, Macdonald DW (2002) Environmental correlates of badger social spacing across Europe. J Biogeogr 29:411–425

    Article  Google Scholar 

  25. Kruuk H (1989) The social badger: ecology and behaviour of a group-living carnivore (Meles meles). Oxford University Press, Oxford

    Google Scholar 

  26. Lafage D, Secondi J, Georges A, Bouzillé J-B, Pétillon J (2013) Satellite-derived vegetation indices as surrogate of species richness and abundance of ground beetles in temperate floodplains. Insect Conserv Divers. doi:10.1111/icad.12056

  27. Lara-Romero C, Virgós E, Escribano-Ávila G, Mangas JG, Barja I, Pardavila X (2012) Habitat selection by European badgers in Mediterranean semi-arid ecosystems. J Arid Environ 76:43–48

    Google Scholar 

  28. Lavorel S, Canadell J, Rambal S, Terradas J (1998) Mediterranean terrestrial ecosystems: research priorities on global change effects. Glob Ecol Biogeogr Lett 7:157–166

    Google Scholar 

  29. Macdonald DW (1983) The ecology of carnivore social behaviour. Nature 301:379–384

    Article  Google Scholar 

  30. Macdonald DW, Carr GM (1999) Food security and the rewards of tolerance. In: Standen V, Foley R (eds) Comparative socioecology: the behavioural ecology of humans and animals, vol. 8. Blackwell Scientific, Oxford, pp 75–79

    Google Scholar 

  31. Macdonald DW, Newman C (2002) Population dynamics of badgers (Meles meles) in Oxford shires, U.K.: numbers, density and cohort life histories, and a possible role of climate change in population growth. J Zool Lond 256:121–138

    Article  Google Scholar 

  32. Martonne E (1926) Areisme et indice d′aridité. Geogr Rev 17:397–414

    Article  Google Scholar 

  33. Meynard CN, Pillay N, Perrigault M, Caminade P, Ganem G (2012) Evidence of environmental niche differentiation in the striped mouse (Rhabdomys sp.): inference from its current distribution in southern Africa. Ecol Evol 2(5):1008–1023

    Google Scholar 

  34. Monteith JL (1981) Evaporation and surface temperature. R Meteorol Soc 107:1–27

    Article  Google Scholar 

  35. Nemani RR, Keeling CD, Hashimoto H, Jolly WM, Piper SC, Tucker CJ, Myneni RB, Running SW (2003) Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300:1560–1563

    Google Scholar 

  36. Newton-Cross G, White PC, Harris S (2007) Modelling the distribution of badgers Meles meles: comparing predictions from field-based and remotely derived habitat data. Mamm Rev 37(1):54–70

    Article  Google Scholar 

  37. Nilsen EB, Herfindal I, Linnell JD (2005) Can intra-specific variation in carnivore home-range size be explained using remote-sensing estimates of environmental productivity? Ecoscience 12(1):68–75

    Article  Google Scholar 

  38. Oindo BO (2002) Predicting mammal species richness and abundance using multi-temporal NDVI. Photogram Eng Remote Sens 68(6):623–629

    Google Scholar 

  39. Pearce JL, Cherry K, Drielsma M, Ferrier S, Whish G (2001) Incorporating expert opinion and fine-scale vegetation mapping into statistical models of faunal distribution. J Appl Ecol 38:412–424

    Google Scholar 

  40. Pettorelli N, Vik JO, Mysterud A, Gaillard J-M, Tucker CJ, Stenseth NC (2005) Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol Evol 20(9):503–510

    Google Scholar 

  41. Pettorelli N, Gaillard JM, Mysterud A, Duncan P, Stenseth NC, Delorme D, Laere GV, Toïgo C, Klein F (2006) Using a proxy of plant productivity (NDVI) to track animal performance: the case of roe deer. Oikos 112:565–572

    Google Scholar 

  42. Pettorelli N, Ryan S, Mueller T, Bunnefeld N, Jedrzejewska B, Lima M, Kausrud K (2011) The Normalized Difference Vegetation Index (NDVI): unforeseen successes in animal ecology. Clim Res 46:15–27

    Google Scholar 

  43. Phillips SJ (2006) A brief tutorial on MaxEnt. AT & T Research. http://www.cs.princeton.edu/~schapire/maxent/tutorial/tutorial.doc

  44. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259

    Article  Google Scholar 

  45. Pita R, Mira A, Moreira F, Morgado R, Beja P (2009) Influence of landscape characteristics on carnivore diversity and abundance in Mediterranean farmland. Agric Ecosyst Environ 132:57–65

    Google Scholar 

  46. R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org

  47. Revilla E, Palomares F (2002) Spatial organization, group living and ecological correlates in low-density populations of Eurasian badgers, Meles meles. J Anim Ecol 71:497–512

    Article  Google Scholar 

  48. Revilla E, Palomares F, Delibes M (2000) Defining key habitats for low density populations of Eurasian badgers in Mediterranean environments. Biol Conserv 95:269–277

    Article  Google Scholar 

  49. Rodríguez A, Delibes M (1992) Food habits of badgers (Meles meles) in an arid habitat. J Zool 227:347–350

    Article  Google Scholar 

  50. Rosalino LM, Macdonald DW, Santos-Reis M (2004) Spatial structure and land cover use in a low density Mediterranean population of Eurasian badgers. Can J Zool 82:1493–1502

    Article  Google Scholar 

  51. Rosalino LM, Santos MJ, Beiber P, Santos-Reis M (2008) Eurasian badger habitat selection in Mediterranean environments: does scale really matter? Mamm Biol 73:189–198

    Google Scholar 

  52. Running SW, Thornton PE, Nemani R, Glassy JM (2000) Global terrestrial gross and net primary productivity from the earth observing system. In: Sala O, Jackson R, Mooney H (eds) Methods in ecosystem science. Springer, New York, pp 44–57

  53. Sims DA, Rahman AF, Cordova VD, El-Masri BZ, Baldocchi DD, Flanagan LB, Goldstein AH, Hollinger DY, Misson L, Monson RK, Oechel WC, Schmid HP, Wofsy SC, Xu L (2006) On the use of MODIS EVI to asses gross primary productivity of North American ecosystem. J Geophys Res 111:G04015

    Google Scholar 

  54. Tapia L, Domínguez J, Regos A, Vidal M (2013) Using remote sensing data to model European wild rabbit (Oryctolagus cuniculus) occurrence in a highly fragmented landscape in northwestern Spain. Acta Theriol. doi:10.1007/s13364-013-0169-2

  55. Veloz SD (2009) Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models. J Biogeogr 36:2290–2299

    Article  Google Scholar 

  56. Virgós E, Casanovas JG (1999a) Environmental constraints at the edge of a species distribution, the Eurasian badger (Meles meles L.): a biogeographic approach. J Biogeogr 6:559–564

    Article  Google Scholar 

  57. Virgós E, Casanovas JG (1999b) Badger Meles meles sett site selection in low density Mediterranean areas of Central Spain. Acta Theriol 44(2):173–182

    Google Scholar 

  58. Virgós E, Revilla E, Domingo-Roura X, Mangas JG (2005) Conservación del tejón en España: síntesis de resultados y principales conclusiones. In: Virgós E, Revilla E, Mangas JG, Domingo-Roura X (eds) Ecología y conservación del tejón en ecosistemas mediterráneos. Sociedad Española para la Conservación y Estudio de los Mamíferos (SECEM), Málaga, pp 283–294

  59. Wang T, Ye X, Skidmore AK, Toxopeus AG (2010) Characterizing the spatial distribution of giant pandas (Ailuropoda melanoleuca) in fragmented forest landscape. J Biogeogr 37:865–878

    Google Scholar 

  60. Warren DL, Seifert SN (2011) Ecological niche modeling with Maxent: the importance of model complexity and the performance of model selection criteria. Ecol Appl 21(2):335–342

    PubMed  Article  Google Scholar 

  61. Warren DL, Glor RE, Turelli M (2010) ENMTools: a toolbox for comparative studies of environmental niche models. Ecography 33:607–611

    Article  Google Scholar 

  62. Wiegand T, Naves J, Garbulsky MF, Fernández N (2008) Animal habitat quality and ecosystem functioning: exploring seasonal patterns using NDVI. Ecol Monogr 78(1):87–103

    Google Scholar 

  63. Wiley EO, McNyset KM, Peterson AT, Robins CR, Stewart AM (2003) Niche modeling and geographic range predictions in the marine environment using a machine-learning algorithm. Oceanography 16(3):120–127

    Google Scholar 

  64. Willems EP, Barton RA, Hill RA (2009) Remotely sensed productivity, regional home range selection, and local range use by an omnivorous primate. Behav Ecol 20:985–992

    Article  Google Scholar 

  65. Woodroffe R (1995) Body condition affects implantation date in the European badger, Meles meles. J Zool 236:183–188

    Article  Google Scholar 

  66. Woodroffe R, Macdonald DW (1995) Female/female competition in European badgers Meles meles: effects on breeding success. J Anim Ecol 64:12–20

    Article  Google Scholar 

  67. Yackulic CB, Chandler R, Zipkin EF, Royle JA, Nichols JD, Campbell Grant EH, Veran S (2012) Presence-only modelling using MAXENT: when can we trust the inferences? Methods Ecol Evol 4(3):236–243

    Google Scholar 

  68. Yates CJ, McNeill A, Elith J, Midgley GF (2010) Assessing the impacts of climate change and land transformation on Banksia in the South West Australian Floristic Region. Divers Distrib 16:187–201

    Google Scholar 

Download references

Acknowledgments

J.R-M received funding from the Centro Andaluz para la Evaluación y Seguimiento del Cambio Global (CAESCG). The Oklahoma Biological Survey provided support for AJC. Funding was also received from the Andalusian Government (Projects GLOCHARID and SEGALERT P09–RNM-5048), the ERDF, andthe Ministry of Science and Innovation (Project CGL2010-22314, subprogram BOS, National Plant I + D + I 2010).

Author information

Affiliations

  1. Department of Biology and Geology, Andalusian Center for the Assessment and Monitoring of Global Change (CAESCG), University of Almería, 04120, Almería, Spain

    Juan M. Requena-Mullor, Enrique López, Antonio J. Castro, Javier Cabello & Hermelindo Castro

  2. Department of Biology and Geology, University of Almería, La Cañada de San Urbano, 04120, Almería, Spain

    Enrique López, Javier Cabello & Hermelindo Castro

  3. Oklahoma Biological Survey, University of Oklahoma, Norman, OK, 73019, USA

    Antonio J. Castro

  4. Biodiversity and Conservation Area, College of Experimental Science and Technology, University Rey Juan Carlos, Department 1, Office 216, C/Tulipán s/n, 28933, Móstoles, Madrid, Spain

    Emilio Virgós

  5. Society for the Study and Recovery of the Almería′s Biodiversity (SERBAL), C/Baqueira Beret 2, 04720, Aguadulce, Almería, Spain

    Emilio González-Miras

Authors
  1. Juan M. Requena-Mullor
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Enrique López
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antonio J. Castro
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Javier Cabello
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Emilio Virgós
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Emilio González-Miras
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hermelindo Castro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juan M. Requena-Mullor.

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Requena-Mullor, J.M., López, E., Castro, A.J. et al. Modeling spatial distribution of European badger in arid landscapes: an ecosystem functioning approach. Landscape Ecol 29, 843–855 (2014). https://doi.org/10.1007/s10980-014-0020-4

Download citation

Keywords

  • Ecological niche modeling
  • MaxEnt
  • Remote sensing
  • EVI
  • Land use-land cover
  • Mediterranean ecosystems
  • Spain
  • Meles meles