Landscape Ecology

, Volume 31, Issue 6, pp 1241–1260 | Cite as

Shaken but not stirred: multiscale habitat suitability modeling of sympatric marten species (Martes martes and Martes foina) in the northern Iberian Peninsula

  • Maria Vergara
  • Samuel A. Cushman
  • Fermín Urra
  • Aritz Ruiz-González
Research Article



Multispecies and multiscale habitat suitability models (HSM) are important to identify the environmental variables and scales influencing habitat selection and facilitate the comparison of closely related species with different ecological requirements.


This study explores the multiscale relationships of habitat suitability for the pine (Martes martes) and stone marten (M. foina) in northern Spain to evaluate differences in habitat selection and scaling, and to determine if there is habitat niche displacement when both species coexist.


We combined bivariate scaling and maximum entropy modeling to compare the multiscale habitat selection of the two martens. To optimize the HSM, the performance of three sampling bias correction methods at four spatial scales was explored. HSMs were compared to explore niche differentiation between species through a niche identity test.


The comparison among HSMs resulted in the detection of a significant niche divergence between species. The pine marten was positively associated with cooler mountainous areas, low levels of human disturbance, high proportion of natural forests and well-connected forestry plantations, and medium-extent agroforestry mosaics. The stone marten was positively related to the density of urban areas, the proportion and extensiveness of croplands, the existence of some scrub cover and semi-continuous grasslands.


This study outlines the influence of the spatial scale and the importance of the sampling bias corrections in HSM, and to our knowledge, it is the first comparing multiscale habitat selection and niche divergence of two related marten species. This study provides a useful methodological framework for multispecies and multiscale comparatives.


HSM Scale dependency Sampling bias Niche divergence Maxent Pine marten Stone marten 



This study has been partially funded by the Basque Government through the Research group on “Systematics, Biogeography and Population Dynamics” (Ref. IT317-10; GIC10/76). MV (Ref: RBFI-2012-446) and ARG (Ref: DKR-2012-64) were supported by a PhD and post-doctoral fellowships awarded by the Department of Education, Universities and Research of the Basque Government. U.S. Forest Service Rocky Mountain Research Station supported Cushman’s work on this project. The authors wish to thank all the people directly involved in the collection of non-invasive genetic samples and those reporting the tissue specimens and species locations used in this study, including rangers, vets and field researchers and their institutions (Table S1 in Online Appendix). The sampling of Navarre region was supported by the Habitat section, Department of rural development, environment and local administrations, Navarre Government. We are also very grateful to Dr. Jason Brown and Dr. Warren for their useful comments regarding SDMtools and ENMtools, respectively.

Supplementary material

10980_2015_307_MOESM1_ESM.tif (744 kb)
Fig. S1 Comparative of the performance of the multiscale and the single-scale models. The AUC values obtained for the pine marten across scales are shown in green and those for the stone marten are shown in purple. The AUC values obtained for the multiscale models are reported last (TIFF 745 kb)
10980_2015_307_MOESM2_ESM.tif (93.7 mb)
Fig. S2 Distribution of the presences predicted as present and the presences predicted as absent for the raw and two corrected HSMs for Martes spp. Presences predicted as absent are colored in orange while the presences predicted as present are shown in green. The raw Mm_rA and the corrected Mm_cGK2 and Mm_cGK8 are reported in the first column while Mf_rA, Mf_cGK2 and Mf_cGK4 are represented in the second (TIFF 95921 kb)
10980_2015_307_MOESM3_ESM.xlsx (104 kb)
Table S1 Martens locations used to build the habitat suitability models (n = 1286) (XLSX 105 kb)
10980_2015_307_MOESM4_ESM.xlsx (17 kb)
Table S2 Results of the bivariate scaling showing the AUC values of the single variable models across scales and species. The scale with the highest AUC value is represented in bold. Variables underlined (shared by both mustelids) and in italics (species-specific) are those which remained after pruning (XLSX 18 kb)


  1. AEMET (2011) Atlas Climático Ibérico/Iberian Climate Atlas. Agencia Estatal de Meteorología, Ministerio de Medio Ambiente, Rural y Marino, Madrid e Instituto de Meteorologia de Portugal, LisboaGoogle Scholar
  2. Aguirre-Gutiérrez J, Carvalheiro LG, Polce C, Emiel van Loon E, Raer N, Reemer M, Biesmeijer JC (2013) Fit-for-purpose: species distribution model performance depends on evaluation criteria—Dutch Hoverflies as a case study. PLoS ONE. doi: 10.1371/journal.pone.0063708 Google Scholar
  3. Anderson RP, Gonzalez I (2011) Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent. Ecol Model 222:2796–2811CrossRefGoogle Scholar
  4. Anderson R, Peterson A, Gomez-Laverde M (2002) Using niche-based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocket mice. Oikos 98:3–16CrossRefGoogle Scholar
  5. Balestrieri A, Remonti L, Ruiz-González A, Vergara M, Capelli E, Gómez-Moliner BJ, Prigioni C (2010) Food habits of genetically identified pine marten (Martes martes) expanding in agricultural lowlands (NW Italy). Acta Theriol 56:199–207CrossRefGoogle Scholar
  6. Balestrieri A, Remonti L, Ruiz-González A, Zenato M, Gazzola A, Vergara M, Detorri EE, Saino N, Capelli E, Gómez-Moliner BJ, Guidali F, Prigioni C (2015) Distribution and habitat use by pine marten Martes martes in a riparian corridor crossing intensively cultivated lowlands. Ecol Res 30:153–162CrossRefGoogle Scholar
  7. Barrientos R, Virgós E (2006) Reduction of potential food interference in two sympatric carnivores by sequential use of shared resources. Acta Oecol 30:107–116CrossRefGoogle Scholar
  8. Bellamy C, Scott C, Altringham J (2013) Multiscale, presence-only habitat suitability models: fine-resolution maps for eight bat species. J Appl Ecol 50:892–901CrossRefGoogle Scholar
  9. Birks JDS, Messenger JE, Braithwaite TC, Davison A, Brookes RC, Strachan C (2004) Are scat surveys a reliable method for assessing distribution and population status of pine martens? In: Harrison DJ, Fuller AK, Proulx G (eds) Martens and fishers (Martes) in human-altered landscapes: an international perspective. Springer, New York, pp 235–252Google Scholar
  10. Bissonette J, Broekhuizen S (1995) Martes populations as indicators of habitat spatial patterns: the need for a multiscale approach. In: Lidicker WZJ (ed) Landscape approaches in mammalian ecology and conservation. University of Minnesota Press, Minneapolis, pp 95–121Google Scholar
  11. Boria RA, Olson LE, Goodman SM, Anderson RP (2014) Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol Model 275:73–77CrossRefGoogle Scholar
  12. Brainerd S, Rolstad J (2002) Habitat selection by Eurasian pine martens Martes martes in managed forests of southern boreal Scandinavia. Wildl Biol 8:289–297Google Scholar
  13. Brown JL (2014) SDMtoolbox: a python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol Evol 5:694–700CrossRefGoogle Scholar
  14. Buskirk S, Powell RA (1994) Habitat ecology of fishers and American martens. In: Buskirk S, Harestad A, Raphael M, Powell R (eds) Martens, sables, fish: Biological conservation. Cornell University Press, Ithaca, pp 283–296Google Scholar
  15. Cushman SA, McGarigal K (2004) Patterns in the species–environment relationship depend on both scale and choice of response variables. Oikos 105:117–124CrossRefGoogle Scholar
  16. Davison A, Birks JDS, Brookes RC, Braithwaite T, Messenger JE (2002) On the origin of faeces: morphological versus molecular methods for surveying rare carnivores from their scats. J Zool 257:141–143CrossRefGoogle Scholar
  17. Delibes M (1983) Interspecific competition and the habitat of the stone marten Martes foina (Erxleben 1777) in Europe. Acta Zool Fenn 174:229–231Google Scholar
  18. 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 29:129–151CrossRefGoogle Scholar
  19. Elith J, Kearney M, Phillips S (2010) The art of modelling range-shifting species. Methods Ecol Evol 1:330–342CrossRefGoogle Scholar
  20. Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17:43–57CrossRefGoogle Scholar
  21. ESRI (2014) ArcGIS. Environmental Systems Research Incorporated, Redlands, CAGoogle Scholar
  22. Evans J, Oakleaf J, Cushman S, Theobald D (2014) An ArcGIS Toolbox for surface gradient and geomorphometric modeling, Version 2.0-0Google Scholar
  23. Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49CrossRefGoogle Scholar
  24. Fisher JT, Anholt B, Bradbury S, Wheatley M, Volpe JP (2013) Spatial segregation of sympatric marten and fishers: the influence of landscapes and species-scapes. Ecography (Cop) 36:240–248CrossRefGoogle Scholar
  25. Fourcade Y, Engler JO, Rödder D, Secondi J (2014) Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS ONE 9:1–13CrossRefGoogle Scholar
  26. Girvetz EH, Greco SE (2009) Multi-scale predictive habitat suitability modeling based on hierarchically delineated patches: an example for yellow-billed cuckoos nesting in riparian forests, California, USA. Landscape Ecol 24:1315–1329CrossRefGoogle Scholar
  27. Goszczyński J, Posłuszny M, Pilot M, Gralak B (2007) Patterns of winter locomotion and foraging in two sympatric marten species: Martes martes and Martes foina. Can J Zool 85:239–249CrossRefGoogle Scholar
  28. Graf RF, Bollmann K, Suter W, Bugmann H (2005) The importance of spatial scale in habitat models: Capercaillie in the Swiss Alps. Landscape Ecol 20:703–717CrossRefGoogle Scholar
  29. Herr J, Schley L, Roper TJ (2009) Socio-spatial organization of urban stone martens. J Zool 277:54–62CrossRefGoogle Scholar
  30. Herrmann M (1994) Habitat use and spatial organization by the stone marten. In: Buskirk SW, Harestad AS, Raphael MG, Powell RA (eds) Martens, sables and fishers. Cornell University Press, Ithaca, pp 283–296Google Scholar
  31. Jiménez-Valverde A, Lobo JM (2007) Threshold criteria for conversion of probability of species presence to either-or presence–absence. Acta Oecol 31:361–369CrossRefGoogle Scholar
  32. Jiménez-Valverde A, Peterson A, Soberón J, Overton JM, Aragón P, Lobo JM (2011) Use of niche models in invasive species risk assessments. Biol Invasions 13:2785–2797CrossRefGoogle Scholar
  33. Johnson D (1980) The comparison of usage and availability measurements for evaluating resource preference. Ecology 61:65–71CrossRefGoogle Scholar
  34. Khanum R, Mumtaz AS, Kumar S (2013) Predicting impacts of climate change on medicinal asclepiads of Pakistan using Maxent modeling. Acta Oecol 49:23–31CrossRefGoogle Scholar
  35. Koreň M, Find’o S, Skuban M, Kajba M (2011) Habitat suitability modelling from non-point data. Ecol Inform 6:296–302CrossRefGoogle Scholar
  36. Kramer-Schadt S, Niedballa J, Pilgrim JD, Schröder B, Lindenborn J, Reinfelder V, Stillfried M, Heckmann I, Scharf AK, Augeri DM, Cheyne SM, Hearn AJ, Ross J, Macdonald DW, Mathai J, Eaton J, Marshall AJ, Semiadi G, Rustam R, Bernard H, Alfred R, Samejima H, Duckworth JW, Breitenmoser-Wuersten C, Belant JL, Hofer H, Wilting A (2013) The importance of correcting for sampling bias in MaxEnt species distribution models. Divers Distrib 19:1366–1379CrossRefGoogle Scholar
  37. Larroque J, Ruette S, Vandel J-M, Devillard S (2015) Where to sleep in a rural landscape? A comparative study of resting sites pattern in two syntopic Martes species. Ecography. doi: 10.1111/ecog.01133 Google Scholar
  38. Lobo JM, Jiménez-Valverde A, Real R (2008) AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr 17:145–151CrossRefGoogle Scholar
  39. López-Martín J (2007) Martes martes (Linnaeus, 1758). In: Palomo LJ, Gisbert J, Blanco J (eds) Atlas y Libr. Rojo los Mamíferos Terr. España. Dirección General de Biodiversidad-SECEM-SECEMU, Madrid, pp 302–304Google Scholar
  40. Mateo-Sánchez M, Cushman S, Saura S (2013) Scale dependence in habitat selection: the case of the endangered brown bear (Ursus arctos) in the Cantabrian Range (NW Spain). Int J Geogr Inf Sci 1:1–16Google Scholar
  41. McGarigal K, Cushman SA, Ene E (2012) FRAGSTATS v4: spatial pattern analysis program for categorical and continuous maps. Computer Software Programs Products by authors Univ. Massachusetts, Amherst.
  42. Mergey M, Helder R, Roeder J-J (2011) Effect of forest fragmentation on space use patterns in the European pine marten (Martes martes). J Mammal 92:328–335CrossRefGoogle Scholar
  43. Mergey M, Larroque J, Ruette S, Vandel JM, Helder R, Queney G, Devillard S (2012) Linking habitat characteristics with genetic diversity of the European pine marten (Martes martes) in France. Eur J Wildl Res 58(6):909–922CrossRefGoogle Scholar
  44. 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 34:102–117CrossRefGoogle Scholar
  45. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259CrossRefGoogle Scholar
  46. Phillips SJ, Dudik M, Elith J, Graham CH, Lehmann A, Leathwick JR, Ferrier S (2009) Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol Appl 19:181–197CrossRefPubMedGoogle Scholar
  47. Posłuszny M, Pilot M, Goszczyński J, Gralak B (2007) Diet of sympatric pine marten (Martes martes) and stone marten (Martes foina) identified by genotyping of DNA from faeces. Ann Zool Fenn 44:269–284Google Scholar
  48. Proulx G, Aubry K, Birks J, Buskirk S, Fortin C, Frost H, Krohn W, Mayo L, Monakhov V, Payer D, Saeki M, Santos-Reis M, Weir R, Zielinski W (2004) World distribution and status of the genus Martes in 2000. In: Harrison DJ, Fuller AK, Proulx G (eds) Martens and fishers (Martes) in human-altered landscapes: an international perspective. Springer, New York, pp 21–76Google Scholar
  49. Radosavljevic A, Anderson RP (2014) Making better Maxent models of species distributions: complexity, overfitting and evaluation. J Biogeogr 41:629–643CrossRefGoogle Scholar
  50. Rosalino LM, Santos-Reis M (2009) Fruit consumption by carnivores in Mediterranean Europe. Mamm Rev 39:67–78CrossRefGoogle Scholar
  51. Rosellini S, Osorio E, Ruiz-González A, Piñeiro A, Barja I (2008) Monitoring the small-scale distribution of sympatric European pine martens (Martes martes) and stone martens (Martes foina): a multievidence approach using faecal DNA analysis and camera-traps. Wildl Res 35:434–440CrossRefGoogle Scholar
  52. Ruiz-González A, Rubines J, Berdion O, Gomez-Moliner BJ (2008) A non-invasive genetic method to identify the sympatric mustelids pine marten (Martes martes) and stone marten (Martes foina): preliminary distribution survey on the northern Iberian Peninsula. Eur J Wildl Res 54:253–261CrossRefGoogle Scholar
  53. Ruiz-González A, Jose Madeira M, Randi E, Urra F, Gómez-Moliner BJ (2013) Non-invasive genetic sampling of sympatric marten species (Martes martes and Martes foina): assessing species and individual identification success rates on faecal DNA genotyping. Eur J Wildl Res 59:371–386CrossRefGoogle Scholar
  54. Ruiz-González A, Gurrutxaga M, Cushman SA, Randi E, Gómez-Moliner BJ (2014) Landscape genetics for the empirical assessment of resistance surfaces: the European pine marten (Martes martes) as a target-species of a regional ecological network. PLoS ONE 9:19Google Scholar
  55. Ruiz-González A, Cushman SA, Madeira MJ, Etore R, Gómez-Moliner BJ (2015) Isolation by distance, resistance and/or clusters? Lessons learned from a forest-dwelling carnivore inhabiting a heterogeneous landscape. Mol Ecol 24:5110–5129CrossRefPubMedGoogle Scholar
  56. Santos MJ, Santos-Reis M (2010) Stone marten (Martes foina) habitat in a Mediterranean ecosystem: effects of scale, sex, and interspecific interactions. Eur J Wildl Res 56:275–286CrossRefGoogle Scholar
  57. Shirk AJ, Wasserman TN, Cushman SA, Raphael MG (2012) Scale dependency of American marten (Martes americana) habitat relationships. In: Aubry KB, Zielinski WJ, Proulx G, Buskirk SW (eds) Biology and conservation of martens, sables, and fishers: a new synthesis. Cornell University Press, New York, pp 269–283Google Scholar
  58. Shirk AJ, Raphael MG, Cushman SA (2014) Spatiotemporal variation in resource selection: insights from the American marten (Martes americana). Ecol Appl 24:1434–1444CrossRefGoogle Scholar
  59. Spanish Geographical National Institute (CNIG) (2008) Spanish digital elevation model. 250m resolutionGoogle Scholar
  60. Spanish Ministry of Agriculture, Food and Environment (2006) Spanish forest map. 1:50,000Google Scholar
  61. Svenning JC, Normand S, Kageyama M (2008) Glacial refugia of temperate trees in Europe: insights from species distribution modelling. J Ecol 96:1117–1127CrossRefGoogle Scholar
  62. Syfert MM, Smith MJ, Coomes DA (2013) The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models. PLoS ONE. doi: 10.1371/journal.pone.0055158 PubMedGoogle Scholar
  63. Thompson CM, McGarigal K (2002) The influence of research scale on bald eagle habitat selection along the lower Hudson River, New York (USA). Landscape Ecol 17:569–586CrossRefGoogle Scholar
  64. Tikhonov A, Cavallini P, Maran T, Krantz A, Herrero J, Giannatos G, Stubbe M, Libois R, Fernandes M, Yonzon P, Choudhury A, Abramov A, Wozencraft C (2008) Martes foina. The IUCN Red List of Threatened Species, Version 2014.3Google Scholar
  65. Virgós E, García FJ (2002) Patch occupancy by stone martens Martes foina in fragmented landscapes of central Spain: the role of fragment size, isolation and habitat structure. Acta Oecol 23:231–237CrossRefGoogle Scholar
  66. Virgós E, Recio M, Cortés Y (2000) Stone marten (Martes foina) use of different landscape types in the mountains of central Spain. Z Säugetierkd 65:375–379Google Scholar
  67. Virgós E, Zalewski A, Rosalino L, Mergey M (2012) Habitat ecology of genus Martes in Europe: a review of the evidences. In: Aubry KB, Zielinski WJ, Proulx G, Buskirk SW (eds) Biology and conservation of martens, sables, and fishers: a new synthesis. Cornell University Press, New York, pp 255–266Google Scholar
  68. Warren DL, Seifert SN (2011) Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol Appl 21:335–342CrossRefPubMedGoogle Scholar
  69. Warren DL, Glor RE, Turelli M (2008) Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution 62:2868–2883CrossRefPubMedGoogle Scholar
  70. Warren DL, Glor RE, Turelli M (2010) ENMTools: a toolbox for comparative studies of environmental niche models. Ecography 1:607–611Google Scholar
  71. Wasserman TN, Cushman SA, Wallin DO, Hayden J (2012) Multi scale habitat relationships of Martes americana in northern Idaho, U.S.A. USDA Forest Service RMRS Research Paper RMRS-RP-94Google Scholar
  72. Wellenreuther M, Larson KW, Svensson EI (2012) Climatic niche divergence or conservatism? Environmental niches and range limits in ecologically similar damselflies. Ecology 93:1353–1366CrossRefPubMedGoogle Scholar
  73. Wereszczuk A, Zalewski A (2015) Spatial niche segregation of sympatric stone marten and pine marten—avoidance of competition or selection of optimal habitat? PLoS ONE 10:e0139852. doi: 10.1371/journal.pone.0139852 CrossRefPubMedPubMedCentralGoogle Scholar
  74. Wiens J, Rotenberry JT, van Horne B (1987) Habitat occupancy patterns of North American shrubsteppe birds: the effects of spatial scale. Oikos 48:132–147CrossRefGoogle Scholar
  75. Yackulic CB, Chandler R, Zipkin EF, Royle JA, Nichols JD, Campbell Grant EH, Veran S (2013) Presence-only modelling using MAXENT: when can we trust the inferences? Methods Ecol Evol 4:236–243CrossRefGoogle Scholar
  76. Zalewski A, Włodzimierz JW (2006) Spatial organisation and dynamics of the pine marten Martes martes population in Białowieza Forest (E Poland) compared with other European woodlands. Ecography 29:31–43CrossRefGoogle Scholar
  77. Zalewski A, Jedrzejewski W, Jedrzejewska B (2004) Mobility and home range use by pine martens (Martes martes) in a Polish primeval forest. Ecoscience 11:113–122Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Maria Vergara
    • 1
    • 2
  • Samuel A. Cushman
    • 3
  • Fermín Urra
    • 4
  • Aritz Ruiz-González
    • 1
    • 2
  1. 1.Department of Zoology and Animal Cell BiologyUniversity of the Basque Country, UPV/EHUVitoria-GasteizSpain
  2. 2.Systematics, Biogeography and Population Dynamics Research Group, Lascaray Research CenterUniversity of the Basque Country, UPV/EHUVitoria-GasteizSpain
  3. 3.U.S. Forest Service, Rocky Mountain Research StationFlagstaffUSA
  4. 4.Biodiversity UnitEnvironmental Management of Navarre S.A.PamplonaSpain

Personalised recommendations