Skip to main content
Log in

Modelling the spatial distribution of wildlife animals using presence and absence data

  • Published:
Contemporary Problems of Ecology Aims and scope

Abstract

This study was conducted to analyze the habitat preference of six major mammals for various environmental factors based on 100 random points within a mountain area in South Korea. In-situ presence and absence data for the mammals were surveyed and collected, and twelve explanatory variables related to topography, water, greenness, and anthropogenic influence were applied to create a habitat distribution model. The best combination of variables was determined using Moran’s I coefficients and Akaike criteria information, and applied to estimate the habitat preference for each species using GRASP v.3.0. The predictive map showed that wildlife animals in this study were mainly categorized into two groups: Group I (Korean squirrel, Sciurus vulgaris, mole, Talpa micrura and water deer, Hydropotes inermis), showed equal preference for all mountainous areas; Group II (weasel, Mustela sibirica, leopard cat, Felis bengalensis and raccoon dog, Nyctereutes procyonoides) showed different preferences in a mountain.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Akaike, H., A new look at the statistical model identification, IEEE Trans. Autom. Contrib., 1974, vol. 19, pp. 716–723.

    Article  Google Scholar 

  • Andrén, H., Effect of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: a review, Oikos, 1994, vol. 71, pp. 355–366.

    Article  Google Scholar 

  • Aspinall, R. and Veitch, N., Habitat mapping from satellite imagery and wildlife survey data using a Bayesian modeling procedure in a GIS, Photogramm. Eng. Remote Sens., 1993, vol. 59, pp. 537–543.

    Google Scholar 

  • Austin, M.P., Spatial prediction of species distribution: an interface between ecological theory and statistical modeling, Ecol. Model., 2002, vol. 157, pp. 101–118.

    Article  Google Scholar 

  • Brotons, L., Thuiller, W., Araújo, M.B., and Hirzel, A.H., Presence-absence versus presence-only modeling methods for predicting bird habitat suitability, Ecography, 2004, vol. 27, pp. 437–448.

    Article  Google Scholar 

  • Burnham, K.P. and Anderson, D.R., Kullback-Leibler information as a basis for strong inference in ecological studies, Wildl. Res., 2001, vol. 28, pp. 111–119.

    Article  Google Scholar 

  • Cornwell, W.K. and Ackerly, D.D., Community assembly and shifts in plant trait distributions across an environmental gradient in coastal California, Ecol. Monogr., 2009, vol. 79, pp. 109–126.

    Article  Google Scholar 

  • Dangjin City, The Statistical Yearbook in 2014, Dangjin, 2014.

  • Darveau, M., Beauchesne, P., Bélanger, L., Huot, J., and Larue, P., Riparian forest strips as habitat for breeding birds in boreal forest, J. Wildl. Manage., 1995, vol. 59, pp. 67–78.

    Article  Google Scholar 

  • De Alba, S., Lindstrom, M., Schmacher, T.E., and Malo, D.D., Soil landscape evolution due to soil redistribution by tillage: a new conceptual model of soil catena evolution in agricultural landscapes, Catena, 2004, vol. 58, pp. 77–100.

    Article  Google Scholar 

  • Dubayah, R. and Rich, P.M., Topographic solar radiation models for GIS, Int. J. Geogr. Inf. Syst., 1995, vol. 9, pp. 405–419.

    Article  Google Scholar 

  • Eldridge, M.D.B. and Pearson, D.J., Black-footed rock wallaby Petrogale lateralis, in The Mammals of Australia, van Dyck, S.M. and Strahan, R., Eds., Sydney: New Holland, 2008, pp. 376–380.

    Google Scholar 

  • Elith, J. and Burgman, M.A., Habitat models for population viability analysis, in Population Viability in Plants, Brigham, C.A. and Schwanz, M.W., Eds., New York: Springer-Verlag, 2003, pp. 203–235.

    Chapter  Google Scholar 

  • Elith, J., Graham, C.H., Anderson, R.P., Dudik, M., Ferrier, S., Guisan, A., Hijmans, R.J., Huettmann, F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle, B.A., Manion, G., Moritz, C., et al., Novel methods improve prediction of species’ distributions from occurrence data, Ecography, 2006, vol. 29, pp. 129–151.

    Article  Google Scholar 

  • Fahrig, L., Effect of habitat fragmentation on the extinction threshold: a synthesis, Ecol. Appl., 2002, vol. 12, pp. 346–353.

    Google Scholar 

  • Ferrier, S., Watson, G., Pearce, J., and Drielsma, M., Extended statistical approaches to modeling spatial pattern in biodiversity in northeast New South Wales. I. Species-level modeling, Biodiversity Conserv., 2002, vol. 11, pp. 2275–2307.

    Article  Google Scholar 

  • Ferrier, S. and Guisan, A., Spatial modeling of biodiversity at the community level, J. Appl. Ecol., 2006, vol. 43, pp. 393–404.

    Article  Google Scholar 

  • Franklin, J., McCullough, P., and Gray, C., Terrain variables used for predictive mapping of vegetation communities in southern California, in Terrain Analysis: Principles and Applications, Wilson, J.P. and Gallant, J.C., Eds., New York: Wiley, 2000.

    Google Scholar 

  • Franklin, J., Mapping Species Distributions: Spatial Inference and Prediction, New York: Cambridge Univ. Press, 2010, pp. 123–124.

    Book  Google Scholar 

  • Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.B., Frith, C.D., and Frackowiak, R.S.J., Statistical Parametric Maps in functional imaging: a general linear approach, Hum. Brain Mapp., 1995, vol. 2, pp. 189–210.

    Article  Google Scholar 

  • Gibbons, J.W., Terrestrial habitat: a vital component for herpetofauna of isolated wetlands, Wetlands, 2003, vol. 23, pp. 630–635.

    Article  Google Scholar 

  • Gormley, A.M., Forsyth, D.M., Griffon, P., Lindeman, M., Ramsey, D.S., Scroggie, M.P., and Woodford, L., Using presence-only and presenceabsence data to estimate the current and potential distributions of established invasive species, J. Appl. Ecol., 2011, vol. 48, pp. 25–34.

    Article  PubMed  PubMed Central  Google Scholar 

  • Grillet, P., Cheylan, M., Thirion, J., Doré, F., Bonnet, X., Dauge, C., Chollet, S., and Marchand, M.A., Rabbit burrows or artificial refuges are a critical habitat component for the threatened lizard, Timon lepidus (Asuria, Lacertidae), Biodiversity Conserv., 2010, vol. 19, pp. 2039–2051.

    Article  Google Scholar 

  • Grimbacher, P.S. and Catterall, C.P., How much do site age, habitat structure, and spatial isolation influence the restoration of rainforest beetle species assemblages? Biol. Conserv., 2007, vol. 135, pp. 107–118.

    Article  Google Scholar 

  • Guisan, A. and Zimmermann, N.E., Predictive habitat distribution models in ecology, Ecol. Model., 2000, vol. 135, pp. 147–186.

    Article  Google Scholar 

  • Guisan, A. and Thuiller, W., Predicting species distribution: offering more than simple habitat models, Ecol. Lett., 2005, vol. 8, pp. 993–1009.

    Article  Google Scholar 

  • Hastie, T. and Tibshirani, R., Generalized Additive Models, London: Chapman and Hall, 1990.

    Google Scholar 

  • Hernandez, P.A., Graham, C.H., Master, L.L., and Albert, D.L., The effect of sample size and species characteristics on performance of different species distribution modeling methods, Ecography, 2006, vol. 29, pp. 773–785.

    Article  Google Scholar 

  • Kim, J.Y., Seo, C.W., Kwon, H.S., Ryu, J.E. and Kim, M.J., A study on the species distribution modeling using national ecosystem survey data, Environ. Impact Asses., 2012, vol. 21, pp. 593–607.

    Google Scholar 

  • Koenig, W.D., Spatial autocorrelation of ecological phenomena, Trends Ecol. Evol., 1999, vol. 14, pp. 22–26.

    Article  PubMed  Google Scholar 

  • Kullback, S. and Leibler, R.A., On information and sufficiency, Ann. Math. Stat., 1951, vol. 22, pp. 79–86.

    Article  Google Scholar 

  • Lawton, J.H. and Woodroffe, G.L., Habitat and the distribution of water voles: why are there gaps in a species’ range? J. Anim. Ecol., 1991, vol. 60, pp. 79–91.

    Article  Google Scholar 

  • Lehmann, A., Overton, J.M., and Leathwick, J.R., GRASP: generalized regression analysis and spatial prediction, Ecol. Model., 2002, vol. 157, pp. 189–207.

    Article  Google Scholar 

  • Legendre, P., Spatial autocorrelation: trouble or new paradigm? Ecology, 1993, vol. 74, pp. 1659–1673.

    Article  Google Scholar 

  • Macdonald, D.W., Tew, T.E., and Tod, I.A., The ecology of weasels Mustela nivalis on mixed farmland in southern England, Biologia, 2004, vol. 59, pp. 235–241.

    Google Scholar 

  • Mace, R.D., Waller, J.S., Manley, T.L., Ake, K., and Wittinger, W.T., Landscape evaluation of grizzly bear habitat in western Montana, Conserv. Biol., 1999, vol. 13, pp. 367–377.

    Article  Google Scholar 

  • MacKenzie, D.I. and Royle, J.A., Designing occupancy studies: general advice and allocating survey effort, J. Appl. Ecol., 2005, vol. 42, pp. 1105–1114.

    Article  Google Scholar 

  • Manel, S., Dias, J.M., and Ormerod, S.J., Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird, Ecol. Model., 1999, vol. 120, pp. 337–347.

    Google Scholar 

  • McCollin, D., Forest edges and habitat selection in birds: a functional approach, Ecography, 1998, vol. 21, pp. 247–260.

    Article  Google Scholar 

  • McCullagh, P. and Nelder, J.A., Generalized Linear Models, London: Chapman and Hall, 1989.

    Book  Google Scholar 

  • Papiernik, S.K., Schumacher, T.E., Lobb, D.A., Lindstrom, M.J., Lieser, M.L., Eynard, A., and Schumacher, J.A., Soil properties and productivity as affected by topsoil movement within an eroded landform, Soil Tillage Res., 2009, vol. 102, pp. 67–77.

    Article  Google Scholar 

  • Patz, J.A., Graczyk, T.K., Geller, N., and Vittor, A.Y., Effects of environmental change on emerging parasitic diseases, Int. J. Parasitol., 2000, vol. 30, pp. 1395–1405.

    Article  CAS  PubMed  Google Scholar 

  • Phillips, S.J., Anderson, R.P., and Schapire, R.E., Maximum entropy modeling of species geographic distributions, Ecol. Model., 2006, vol. 190, pp. 231–259.

    Article  Google Scholar 

  • Rangel, T.F., Diniz-Filho, J.A.F., and Bini, L.M., SAM: a comprehensive application for spatial analysis in macroecology, Ecography, 2010, vol. 33, pp. 46–50.

    Article  Google Scholar 

  • Rhim, S.J. and Lee, W.S., Influence of forest fragmentation on the winter abundance of mammals in Mt. Chirisan National Park, South Korea, J. Wildl. Manage., 2007, vol. 71, pp. 1404–1408.

    Article  Google Scholar 

  • Sakaki, H. and Ono, Y., Habitat use and selection of the Siberian weasel Mustela sibirica coreana during the non-mating season, J. Mamm. Soc. Jpn., 1994, vol. 19, pp. 21–32.

    Google Scholar 

  • Scott, J.M., Heglund, P.J., Samson, F., Haufler, J., Morrison, M., Raphael, M., and Wall, B., Predicting Species Occurrences: Issues of Accuracy and Scale, Covelo: Island Press, 2002, p. 868.

    Google Scholar 

  • Thuiller, W., BIOMOD—optimizing predictions of species distributions and projecting potential future shifts under global change, Global Change Biol., 2003, vol. 9, pp. 1353–1362.

    Article  Google Scholar 

  • Tratalos, J., Fuller, R.A., Warren, P.H., Davies, R.G., Gaston, K.J., Urban form, biodiversity potential and ecosystem services, Landscape Urban Plan., 2007, vol. 83, pp. 308–317.

    Google Scholar 

  • Vos, C.C. and Chardon, J.P., Effects of habitat fragmentation and road density on the distribution pattern of the moor frog Rana arvalis, J. Appl. Ecol., 1998, vol. 35, pp. 44–56

    Article  Google Scholar 

  • William, J.R. and Robert, L.B., Wolves and the ecology of fear: can predation risk structure ecosystem? BioScience, 2004, vol. 54, pp. 755–766.

    Article  Google Scholar 

  • Wisz, M.S., Hijmans, R.J., Li, J., Peterson, A.T., Graham, C.H., Guisan, A., NCEAS predicting species distributions working group, effects of sample size on the performance of species distribution models, Diversity Distrib., 2008, vol. 14, pp. 763–773.

    Article  Google Scholar 

  • Yackulic, C.B., Chandler, R., Zipkin, E.F., Royle, J.A., Nichols, J.D., Grant, E.H.C., and Veran, S., Presenceonly modeling using MAXENT: When can we trust the inferences? Methods Ecol. Evol., 2012, vol. 4, pp. 236–243.

    Article  Google Scholar 

  • Zhang, E.D., Teng, L.W., and Wu, Y.B., Habitat suitability evaluation for the Chinese water deer Hydropotes inermis in Yancheng Nature Reserve, China, Acta Theriol. Sin., 2006, vol. 26, pp. 368–372.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gab-Sue Jang.

Additional information

The article is published in the original.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kwon, HS., Kim, BJ. & Jang, GS. Modelling the spatial distribution of wildlife animals using presence and absence data. Contemp. Probl. Ecol. 9, 515–528 (2016). https://doi.org/10.1134/S1995425516050085

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1995425516050085

Keywords

Navigation