Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-25T19:56:23.849Z Has data issue: false hasContentIssue false
  • English
  • Français

Comparative assessment of different methods for using land-cover variables for distribution modelling of Salamandra salamandra longirotris

Published online by Cambridge University Press:  16 August 2012

DAVID ROMERO ,
JESÚS OLIVERO  and
RAIMUNDO REAL
DAVID ROMERO*
Affiliation:
Departamento de Biología Animal, Facultad de Ciencias, Universidad de Málaga, 29071 Malaga, Spain
JESÚS OLIVERO
Affiliation:
Departamento de Biología Animal, Facultad de Ciencias, Universidad de Málaga, 29071 Malaga, Spain
RAIMUNDO REAL
Affiliation:
Departamento de Biología Animal, Facultad de Ciencias, Universidad de Málaga, 29071 Malaga, Spain
*
*Correspondence: David Romero Tel: +34 952132383 Fax: +34 952131668 e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Predictive models are frequently used to define the most suitable areas for species protection or reintroduction. Land-cover variables can be used in different ways for distribution modelling. The surface area of a set of land-cover classes is often used, each land-cover presence/absence or the distance to them from any point of the study area can be preferred; multiple types of land-cover variables may be combined to produce a single model. This paper assesses whether different approaches to using land-cover variables may lead to different ecological conclusions when interpreted for conservation by focusing on the distribution of the salamader Salamandra salamandra longirostris, an endangered amphibian subspecies in the south of the Iberian Peninsula. Twenty-eight land-cover classes and another 42 environmental variables were used to construct four different models. Three models used a unique type of land-cover variable: either the presence of each class, the surface area of each class or the distance to each class, with all three variable types jointly entered in a fourth model. All models attained acceptable scores according to some criteria (discrimination, descriptive and predictive capacities, classification accuracy and parsimony); however most of the assessment parameters computed indicated a better performance of the models using either the surface area of land classes or the distance to them from every sampled square, compared to the model using class presences. The best scores were obtained with the fourth model, which combined different types of land-cover variables. This model suggested that oak forest fragmentation in favour of herbaceous crops and pastures may have negative effects on the distribution of S. s. longirostris. This was only partially suggested by the first three models, which considered a single type of land-cover variable, demonstrating the importance of considering a multi-variable analysis for conservation planning.

Keywords

class presenceclass surface areadistance to classenvironmental favourability modelshabitat fragmentationland-cover variables
Type
Papers
Information
Environmental Conservation , Volume 40 , Issue 1 , March 2013 , pp. 48 - 59
Copyright
Copyright © Foundation for Environmental Conservation 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Acevedo, P. & Cassinello, J. (2009) Human-induced range expansion of wild ungulates causes niche overlap between previously allopatric species: red deer and Iberian ibex in mountainous regions of southern Spain. Annales Zoologici Fennici 46: 39–50.CrossRefGoogle Scholar
Acevedo, P., Cassinello, J. & Gortázar, C. (2007) The Iberian ibex is under an expansion trend but displaced to suboptimal habitats by the presence of extensive goat livestock in central Spain. Biodiversity and Conservation 16: 3361–3376.CrossRefGoogle Scholar
Acevedo, P., Farfán, M.A., Márquez, A.L., Delibes-Mateos, M., Real, R. & Vargas, J.M. (2011) Past, present and future of wild ungulates in relation to changes in land use. Landscape Ecology 26: 19–31.Google Scholar
Akaike, H. (1973) Information theory and an extension of the maximum likelihood principle. In: Proceedings of the Second International Symposium on Information Theory, ed. Petrov, B.N. & Csaki, F., pp. 267–281. Budapest, Hungary: Akademiai Kiado.Google Scholar
Alzaga, V., Tizzani, P., Acevedo, P., Ruiz-Fons, F., Vicente, J. & Gortázar, C. (2009) Deviance partitioning of host factors affecting parasitation in the European brown hare (Lepus europaeus). Naturwissenschaften 96: 1157–1168.Google Scholar
Araújo, M.B., Thuiller, W. & Pearson, R.G. (2006) Climate warming and the decline of amphibians and reptiles in Europe. Journal of Biogeography 33: 1712–1728.Google Scholar
Baasch, D.M., Fischer, J.W., Hygnstrom, S.E., VerCauteren, K.C., Tyre, A.J., Millspaugh, J.J., Merchant, J.W. & Volesky, J.D. (2010) Resource selection by elk in an agro-forested landscape of northwestern Nebraska. Environmental Management 46: 725–737.Google Scholar
Benjamini, Y. & Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B 57: 289–300.Google Scholar
Bousbouras, D. & Ioannidis, Y. (1997) The distribution and habitat preferences of the amphibians of Prespa National Park. Hydrobiologia 351: 127–133.CrossRefGoogle Scholar
Cruz, M.J., Rebelo, R. & Crespo, E.G. (2006) Effects o fan introduced crayfish, Procambarus clarkii, on the distribution of south-western Iberian amphibians in their breeding habitats. Ecography 29: 329–338.Google Scholar
Cushman, S.A. (2006) Effects of habitat loss and fragmentation on amphibians: a review and prospectus. Biological Conservation 128: 231–240.Google Scholar
Delibes-Mateos, M., Farfán, M.A., Olivero, J., Márquez, A.L. & Vargas, J.M. (2009) Long-term changes in game species over a long period of transformation in the Iberian Mediterranean landscape. Environmental Management 46: 1256–1268.Google Scholar
Delibes-Mateos, M., Farfán, M.A., Olivero, J. & Vargas, J.M. (2010) Land-use changes as critical factor for long-term wild Rabbit conservation in the Iberian Peninsula. Environental Conservation 37: 1–8.Google Scholar
Dubois, A. & Raffaëli, J. (2009) A new ergotaxonomy of the family Salamandridae Goldfuss, 1820 (Amphibia, Urodela). Alytes 26: 1–85.Google Scholar
Egea-Serrano, A., Oliva-Paterna, F.J. & Torralva, M. (2006) Breeding habitat selection of Salamandra salamandra (Linnaeus, 1758) in the most arid zone of its European distribution range: application to conservation management. Hydrobiologia 560: 363–371.CrossRefGoogle Scholar
Farr, T.G. & Kobrick, M. (2000) Shuttle Radar Topography Mission produces a wealth of data. EOS Transaction of the American Geophysical Union 81: 583–585.Google Scholar
Fielding, A.H. & Bell, J.F. (1997) A review of methods for the assessment of prediction errors in conservation presence-absence models. Environmental Conservation 24: 38–49.Google Scholar
Fielding, A.H. & Haworth, P.F. (1995) Testing the generality of bird-habitat models. Conservation Biology 9: 1466–1481.Google Scholar
Font, I. (1983) Atlas Climático de España. Madrid, Spain: Instituto Nacional de Meteorología.Google Scholar
Font, I. (2000) Climatología de España y Portugal. Salamanca, Spain: Ediciones Universidad de Salamanca.Google Scholar
García-París, M., Alcobendas, M. & Alberch, P. (1998) Influence of the Guadalquivir river basin on mitochondrial DNA evolution of Salamandra salamandra (Caudata: Salamandridae) from Southern Spain. Copeia 1: 173–176.Google Scholar
García-París, M., Montori, A. & Herrero, P. (2004) Amphibia, Lissamphibia. In: Fauna Ibérica, Volumen 24, ed. Ramos, M.A., Alba, J., Bellés, X., Gosálbez, J., Guerrera, A., Macpherson, E., Serrano, J., Templado, J., pp. 43–275. Madrid, Spain: Museo Nacional de Ciencias Naturales, CSIC.Google Scholar
Greenwald, K.R., Gibbs, H.L. & Waite, T.A. (2009 a) Efficacy of land-cover models in predicting isolation of marbled salamander populations in a fragmented landscape. Conservation Biology 25: 1232–1241.Google Scholar
Greenwald, K.R., Purrenhage, J.L. & Savage, W.K. (2009 b) Landcover predicts isolation in Ambystoma salamanders across region and species. Biological Conservation 142: 2493–2500.CrossRefGoogle Scholar
Gibbs, J.P. (1998) Distribution of woodland amphibians along a forest fragmentation gradient. Landscape Ecology 13: 263–268.Google Scholar
Guerry, A.D. & Hunter, M.L. (2002) Amphibian distributions in a landscape of forests and agriculture: an examination of landscape composition and configuration. Conservation Biology 16: 745–754.CrossRefGoogle Scholar
Hausdorf, B. & Henning, C. (2003) Biotic element analysis in biogeography. Systematic Biology 52: 717–723.Google Scholar
Herrmann, H.L., Babbitt, K.J. & Baber, M.J. (2005) Congalton RG. Effects of landscape characteristics on amphibian distribution in a forest-dominated landscape. Biological Conservation 123: 139–149.CrossRefGoogle Scholar
Hosmer, D.W. & Lemeshow, S. (2000) Applied Logistic Regression. Second edition. New York, NY, USA: John Wiley and Sons.Google Scholar
Hirzel, A.H., Hausser, J., Chessel, D. & Perrin, N. (2002) Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? Ecology 83: 2027–2036.Google Scholar
Instituto Geológico y Minero de España (1979) Explicación de los mapas de lluvia útil, de reconocimiento hidrogeológico y de síntesis de los sistemas acuíferos. In: Mapa Hidrogeológico Nacional. Second Edition. Madrid, Spain: Instituto Geológico y Minero de España.Google Scholar
Instituto Geográfico Nacional (1999) Mapa de Carreteras. Península Ibérica, Baleares y Canarias. Madrid, Spain: Instituto Geográfico Nacional, Ministerio de Fomento.Google Scholar
Junta de Andalucía (2009) Mapa de usos y coberturas vegetales de Andalucía 1956–1999–2003, escala 1:25,000. Sevilla, Spain: Consejería de Medio Ambiente.Google Scholar
Landis, J.R. & Koch, G.C. (1977) The measurement of observer agreement for categorical data. Biometrics 33: 159–174.Google Scholar
Legendre, P. (1993) Spatial autocorrelation: trouble or new paradigm? Ecology 74: 1659–1673.Google Scholar
Legendre, P. & Legendre, L. (1998) Numerical Ecology. Second edition. Amsterdam, the Netherlands: Elsevier Science.Google Scholar
Lobo, J., Jiménez-Valverde, A. & Real, R. (2008) AUC: a misleading measure of the performance of predictive distribution models. Global Ecolology Biogeography 17: 145–151.Google Scholar
López Fernández, M.L., Piñas, S. & López, F.M.S. (2008) Macrobioclimas, bioclimas y variantes bioclimáticas de la España peninsular y balear, y su cartografía. Publicaciones de Biología. Universidad de Navarra, Serie Botánica 17: 229–236.Google Scholar
Manenti, R., Ficetola, G.F. & De Bernardi, F. (2009) Water, stream morphology and landscape: complex habitat determinants for the fire salamander Salamandra Salamandra. Amphibia-Reptilia 30: 7–15.Google Scholar
Montgomery, D.C. & Peck, E.A. (1992) Introduction to Linear Regression Analysis. New York, NY, USA: Wiley.Google Scholar
Miñano, P.A., Egea, A., Oliva-Paterna, F.J. & Torralba, M. (2003) Hábitat reproductor de Salamandra salamandra (Linnaeus, 1758) en el Noroeste de la Región de Murcia (S. E. Península Ibérica): distribución actualizada. Anales de Biología 25: 203–205.Google Scholar
Montero de Burgos, J.L. & González-Rebollar, J.L. (1974) Diagramas bioclimáticos. Madrid, Spain: Instituto Nacional para la Conservación de la Naturaleza.Google Scholar
Muñoz, A.R. & Real, R. (2006) Assessing the potential range expansion of the exotic monk parakeet in Spain. Diversity and Distributions 12: 656–665.Google Scholar
Muñoz, A.R., Real, R., Barbosa, A.M. & Vargas, J.M. (2005) Modelling the distribution of Bonelli's eagle in Spain: implications for conservation planning. Diversity and Distributions 11: 477–486.Google Scholar
Oja, T., Alamets, K. & Pärnamets, H. (2005) Modelling bird habitat suitability base d on landscape parameters at different scales. Ecological Indicators 5: 314–321.Google Scholar
Oak Ridge National Laboratory (2001) LandScan 2000 Global Population Database. Tennesse, USA: Oak Ridge National Laboratory.Google Scholar
Pearson, R.G. & Dawson, T.P. (2003) Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecology Biogeography 12: 361–371.Google Scholar
Phillips, S.J., Anderson, R.P. & Schapire, R.E. (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: 231–259.CrossRefGoogle Scholar
Pleguezuelos, J.M., Márquez, R. & Lizana, M. (2004) In: Atlas y Libro Rojo de los Anfibios y Reptiles de España. Madrid, Spain: Dirección General de Conservación de la Naturaleza-Asociación Herpetológica Española.Google Scholar
Price, S.J., Dorcas, M.E., Gallant, A.L., Klaver, R.W. & Willson, J.D. (2006) Three decades of urbanization: estimating the impact of land-cover change on stream salamander populations. Biological Convervation 133: 436–441.Google Scholar
Real, R., Barbosa, A.M. & Vargas, J.M. (2006) Obtaining environmental favourability functions from logistic regression. Environmental and Ecological Statistics 13: 237–245.Google Scholar
Rittenhouse, T.A.G. & Semlitsch, R.D. (2006) Grasslands as movement barriers for a forest-associated salamander: Migration behavior of adult and juvenile salamanders at a distinct habitat edge. Biological Conservation 131: 14–22.Google Scholar
Rojas, A.B., Cotilla, I., Real, R. & Palomo, L.J. (2001) Determinación de las áreas probables de distribución de los mamíferos terrestres en la provincial de Málaga. Galemys 13: 217–229.Google Scholar
Salvador, A. & García-París, M. (2001) Anfibios Españoles. Talavera, Spain: Esfagnos-Canseco.Google Scholar
Seoane, J., Bustamante, J. & Díaz-Delgado, R. (2004) Competing roles for landscape, vegetation, topography and climate in predictive models of bird distribution. Ecological Modelling 171: 209–222.Google Scholar
Sokal, R.R. & Rohlf, F.J. (1979) Biometría. Principios y Métodos Estadísticos en la Investigación Biológica. Madrid, Spain: Blume.Google Scholar
Stuart, S.N., Chanson, J.S., Cox, N.A., Young, B.E., Rodrigues, A.S.L., Fischman, D.L. & Waller, R.W. (2004) Status and trends of amphibian declines and extinctions worldwide. Science 306: 1783.Google Scholar
Svenning, J.C., Baktoft, K.H. & Balslev, H. (2009) Land-use history affects understory plant species distributions in a large temperate-forest complex, Denmark. Plant Ecology 201: 221–234.CrossRefGoogle Scholar
Tejedo, M., Reques, R., Gasent, J.M., González, J.P., Morales, J., García, L., González, E., Donaire, D., Sánchez, M.J. & Marangoni, F. (2003) Distribución de los Anfibios Endémicos de Andalucía: Estudio Genético y Ecológico de las Poblaciones. Madrid, Spain: Consejería de Medio Ambiente (Junta de Andalucía), CSIC.Google Scholar
Thuiller, W., Araújo, M.B. & Lavorel, S. (2004) Do we need land-cover data to model species distributions in Europe? Journal of Biogeography 31: 353–361.Google Scholar
US Geological Survey (1996) GTOPO30. Land Processes Distributed Active Archive Center. EROS Data Center [www document]. URL http://edcdaac.usgs.gov/gtopo30.aspGoogle Scholar
Yost, A.C., Petersen, S.L., Gregg, M. & Miller, R. (2008) Predictive modeling and mapping sage grouse (Centrocercus urophasianus) nesting habitat using maximum entropy and a long-term dataset from Southern Oregon. Ecological Informatics 3: 375–386.Google Scholar
Supplementary material: File

D.ROMERO Supplementary Material

Appendix

Download D.ROMERO Supplementary Material(File)
File 88.1 KB