A maximum entropy model for predicting wild boar distribution in Spain

  1. Bosch López, Jaime
  2. Mardones, Fernando
  3. Pérez, Andrés
  4. de la Torre Reoyo, Ana Isabel
  5. Muñoz Reoyo, María Jesús
Revista:
Spanish journal of agricultural research

ISSN: 1695-971X,  2171-9292

Año de publicación: 2014

Volumen: 12

Número: 4

Páginas: 984-999

Tipo: Artículo

DOI: 10.5424/SJAR/2014124-5717 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Spanish journal of agricultural research

Objetivos de desarrollo sostenible

Resumen

Wild boar (Sus scrofa) populations in many areas of the Palearctic including the Iberian Peninsula have grown continuously over the last century. This increase has led to numerous different types of conflicts due to the damage these mammals can cause to agriculture, the problems they create in the conservation of natural areas, and the threat they pose to animal health. In the context of both wildlife management and the design of health programs for disease control, it is essential to know how wild boar are distributed on a large spatial scale. Given that the quantifying of the distribution of wild species using census techniques is virtually impossible in the case of large-scale studies, modeling techniques have thus to be used instead to estimate animals� distributions, densities, and abundances. In this study, the potential distribution of wild boar in Spain was predicted by integrating data of presence and environmental variables into a MaxEnt approach. We built and tested models using 100 bootstrapped replicates. For each replicate or simulation, presence data was divided into two subsets that were used for model fitting (60% of the data) and cross-validation (40% of the data). The final model was found to be accurate with an area under the receiver operating characteristic curve (AUC) value of 0.79. Six explanatory variables for predicting wild boar distribution were identified on the basis of the percentage of their contribution to the model. The model exhibited a high degree of predictive accuracy, which has been confirmed by its agreement with satellite images and field surveys.

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Referencias bibliográficas

  • References
  • Abaigar T, 1993. Régimen alimentario del jabalí (Sus scrofa, L. 1758) en el sureste ibérico. Do-ana Acta Vert 20(1): 35-48.
  • Acevedo P, Quiros-Fernandez F, Casal J, Vicente J, 2014. Spatial distribution of wild boar population abundance: Basic information for spatial epidemiology and wildlife management. Ecol Indic 36: 594-600. http://dx.doi.org/10.1016/j.ecolind.2013.09.019
  • Anderson BJ, Akçakaya HR, Araújo MB, Fordham DA, Martínez-Meyer E, Thuiller W, Brook BW, 2009. Dynamics of range margins for metapopulations under climate change. Proc R Soc B 276: 1415-1420. http://dx.doi.org/10.1098/rspb.2008.1681
  • Araújo MB, Luoto M, 2007. The importance of biotic interactions for modelling species distributions under climate change. Glob Chang Biol 16: 743-753.
  • Araújo MB, New M, 2007. Ensemble forecasting of species distributions. Trends Ecol Evol 22: 42-47. http://dx.doi.org/10.1016/j.tree.2006.09.010
  • Araújo MB, Guilhaumon F, Neto DR, Pozo I, Calmaestra R, 2011a. Biodiversidade e alterações climáticas/biodiversidad y alteraciones climáticas. Ministerio do Ambiente e Ordenamiento do Territorio & Ministerio de Medio Ambiente y Medio Rural y Marino. Lisboa /Madrid. 656 pp. Available in http://www.ibiochange.mncn.csic.es/iberiachange/wordpress/wp-content/uploads/2008/07/Libro-1-junio-2012.pdf?goback=.gde_2716697_member_121228272. [15 September 2012].
  • Araújo MB, Guilhaumon F, Neto DR, Pozo I, Calmaestra R, 2011b. Impactos, vulnerabilidad y adaptación al cambio climático de la biodiversidad española. 2. Fauna de vertebrados. Dirección General de Medio Natural y Política Forestal. Ministerio de Medio Ambiente, y Medio Rural y Marino. Madrid, 640 pp. Available in http://www.magrama.gob.es/es/biodiversidad/temas/inventarios-nacionales/inventario-especies-terrestres/efectos_cambio_climatico.aspx. [02 July 2012].
  • Araújo MB, Whittaker RJ, Ladle RJ, Erhard M, 2005. Reducing uncertainty in projections of extinction risk from climate change. Global Ecol Biogeogr 14: 529-538. http://dx.doi.org/10.1111/j.1466-822X.2005.00182.x
  • Austin MP, 1985. Continuum concept, ordination methods, and niche theory. Annu Rev Ecol Syst 16: 39–61. http://dx.doi.org/10.1146/annurev.es.16.110185.000351
  • Baldwin RA, 2009. Use of maximum entropy modeling in wildlife research. Entropy 11(4): 854-866. http://dx.doi.org/10.3390/e11040854
  • Baselga A, Araújo MB, 2009. Individualistic vs. community modelling of species distributions under climate change. Ecography 32: 55-65. http://dx.doi.org/10.1111/j.1600-0587.2009.05856.x
  • Benito de Pando B, Pe-as de Giles J, 2007. Aplicación de modelos de distribución de especies a la conservación de la biodiversidad en el sureste de la Península Ibérica. GeoFocus (Artículos) 7: 100-119.
  • Bosch J, Peris S, Fonseca C, Martínez M, De la Torre A, Iglesias I, Mu-oz MJ, 2012. Distribution, abundance and density of the wild boar, Sus scrofa L., on the Iberian Peninsula, based on the CORINE program and hunting statistics. Folia Zool 61(2): 138–151.
  • Brook BW, Akçakaya HR, Keith DA, Mace GM, Pearson RG, Araújo MB, 2009. Integrating bioclimate with population models to improve forecasts of species extinctions under climate change. Biol Lett 5: 723-725. http://dx.doi.org/10.1098/rsbl.2009.0480
  • Colino-Rabanal VJ, Bosch J, Mu-oz MJ, Peris SJ, 2012. Influence of new irrigation croplands on wild boar Sus scrofa road kills in NW Spain. Anim Biodiv & Conserv 35(2): 97-102.
  • De Candolle AI, 1855. Géographique botanique raisonneè. Masson, Paris.
  • De la Torre A, Bosch J, Iglesias I, Mu-oz MJ, Mur L, Martínez-López B, Martínez M, Sánchez-Vizcaíno JM, 2013. Assessing the risk of African swine fever introduction into the European Union by wild boar. Transbound Emerg Dis doi:10.1111/tbed.12129. http://dx.doi.org/10.1111/tbed.12129
  • De Miguel JM, 1999. Nature and configuration of the agricultural-forestry-pasture landscape in the conservation of biological diversity in Spain. Rev Chil Hist Nat 72: 547-557.
  • De Miguel JM, 2002. Ecología, diversidad y desarrollo sostenible en sistemas agroforestales tradicionales en Espa-a. Cuad Soc Esp Cien For 14: 23-32.
  • DiMiceli CM, Carroll ML, Sohlberg A, Huang C, Hansen MC, Townshend JRG, 2011. Annual global automated MODIS vegetation continuous fields (MOD44B) at 250 m spatial resolution for data years beginning day 65, 2000-2010, Collection 5 Percent Tree Cover, University of Maryland, College Park, MD, USA.
  • Ellenberg H, 1988. Vegetation ecology of Central Europe, 4th ed. Cambridge University Press, Cambridge, UK.
  • Elith J, Leathwick JR, 2009. Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40: 677-697. http://dx.doi.org/10.1146/annurev.ecolsys.110308.120159
  • Elith J, Graham CH, Anderson RP, Dudík M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick J, Lehmann A, et al., 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, Dudík M, Chee YE, Yates CJ, 2011. A statistical explanation of MaxEnt for ecologists. Diversity Distrib 17: 43-57. http://dx.doi.org/10.1111/j.1472-4642.2010.00725.x
  • Ferrier S, 2002. Mapping spatial pattern in biodiversity for regional conservation planning: Where to from here? Syst Biol 51: 331-363. http://dx.doi.org/10.1080/10635150252899806
  • Fielding AH, Bell JF, 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24(1): 38–49. http://dx.doi.org/10.1017/S0376892997000088
  • Fithian W, Hastie T, 2013. Finite-sample equivalence of several statistical models for presence-only data. Ann Appl Stat 7(4): 1917-1939. http://dx.doi.org/10.1214/13-AOAS667
  • García M, Maldonado J, Morla C, Sainz H, 2002. Fitogeografía histórica de la península Ibérica. In: La diversidad biológica de Espa-a (Pineda FD, de Miguel JM, Casado JM, Montalvo J, eds.), Pearson Educación, Prentice Hall, Madrid. pp: 45-63.
  • Guisan A, Zimmermann NE, 2000. Predictive habitat distribution models in ecology. Ecol Model 135(2-3): 147–186. http://dx.doi.org/10.1016/S0304-3800(00)00354-9
  • Guisan A, Thuiller W, 2005. Predicting species distribution: offering more than simple habitat models. Ecol Let 8(9): 993–1009. http://dx.doi.org/10.1111/j.1461-0248.2005.00792.x
  • Hansen MC, DeFries RS, Townshend JRG, Carroll M, Dimiceli C, Sohlberg RA, 2003. Global percent tree cover at a spatial resolution of 500 meters: First results of the MODIS vegetation continuous fields algorithm. Earth Interact 7: 1–15. Available in: http://ftp.glcf.umd.edu/library/pdf/ei_hansen.pdf.
  • Hawkins BA, Porter EE, 2003. Relative influences of current and historical factors on mammal and bird diversity patterns in deglaciated North America. Glob Ecol Biogeogr 12: 475–481. http://dx.doi.org/10.1046/j.1466-822X.2003.00060.x
  • Heffner RA, Butler MJ, Reilly CK, 1996. Pseudoreplication revisited. Ecology 77: 2558–2562. http://dx.doi.org/10.2307/2265754
  • Heikkinen R, Luoto M, Virkkala R, Pearson RG, Körber JH, 2007. Biotic interactions improve prediction of boreal bird distributions at macro-scales. Glob Ecol Biogeogr 16: 754-763. http://dx.doi.org/10.1111/j.1466-8238.2007.00345.x
  • 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. http://dx.doi.org/10.1111/j.0906-7590.2006.04700.x
  • Herrero J, Irízar I, Laskurain NA, García-Serrano A, García-González R, 2005. Fruits and roots: wild boar foods during the cold season in the southwestern Pyrenees. Ital J Zool 72(1): 49-52. http://dx.doi.org/10.1080/11250000509356652
  • Herrero J, García-Serrano A, Couto S, Ortu-o V, García-González R, 2006. Diet of wild boar Sus scrofa L. and crop damage in an intensive agroecosystem. Eur J Wildl Res 52: 245-250. http://dx.doi.org/10.1007/s10344-006-0045-3
  • Hirzel AH, Le Lay G, 2008. Habitat suitability modelling and niche theory. J Appl Ecol 45: 1272-1381. http://dx.doi.org/10.1111/j.1365-2664.2008.01524.x
  • Hijmans RJ, van Etten J, 2012. Raster: Geographic analysis and modeling with raster data. R package version 2.0-12. Available in http://CRAN.R-project.org/package=raster [10 December 2012].
  • Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A, 2005. Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25(15): 1965–1978. http://dx.doi.org/10.1002/joc.1276
  • Hull V, Zhang J, Zhou S, Huang J, Vi-a A, Liu W, Tuanmu MN, Li R, Liu D, Liu D, Xu W, et al. 2014. Impact of livestock on giant pandas and their habitat. J Nat Conserv 22(3): 256-264. http://dx.doi.org/10.1016/j.jnc.2014.02.003
  • Huntley B, Berry PM, Cramer W, McDonald AP, 1995. Modelling present and potential future ranges of some European higher plants using climate response surfaces. J Biogeog 22(6): 967–1001. http://dx.doi.org/10.2307/2845830
  • Jiménez-Valverde A, 2012. Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling. Global Ecol Biogeogr 21: 498–507. http://dx.doi.org/10.1111/j.1466-8238.2011.00683.x
  • Keith DA, Akçakaya HR, Thuiller W, Midgley GF, Pearson RG, Phillips SJ, Regan H, Araújo M, Rebelo T, 2008. Predicting extinction risks under climate change: Coupling stochastic population models with dynamic bioclimatic habitat models. Biol Lett 4: 560-563. http://dx.doi.org/10.1098/rsbl.2008.0049
  • Keuling O, Baubet E, Duscher A, Ebert C, Fischer C, Monaco A, Podgórski T, Prevot C, Ronnenberg K, Sodeikat G, 2013. Mortality rates of wild boar Sus scrofa L. in central Europe. Eur J Wildl Res 59(6): 805-814. http://dx.doi.org/10.1007/s10344-013-0733-8
  • Laplace PS, 1820. Théorie analytique des probabilités. Courcier. Paris.
  • Lawler JJ, White D Neilson, RP, Blaustein AR, 2006. Predicting climate-induced range shifts: model differences and model reliability. Glob Chang Biol 12: 1–17. http://dx.doi.org/10.1111/j.1365-2486.2006.01191.x
  • Lobo JM, Jiménez-Valverde A, Real R, 2008. AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr 17(2): 145-151. http://dx.doi.org/10.1111/j.1466-8238.2007.00358.x
  • Lobo JM, Jiménez-Valverde A, Hortal J, 2010. The uncertain nature of absences and their importance in species distribution modelling. Ecography 33: 103–114. http://dx.doi.org/10.1111/j.1600-0587.2009.06039.x
  • Long JL, 2003. Introduced mammals of the world. CSIRO Publishers, Collingwood, Australia.
  • Manel S, Williams HC, Ormerod SJ, 2001. Evaluating presence-absence models in ecology: the need to account prevalence. J Appl Ecol 38: 921-931. http://dx.doi.org/10.1046/j.1365-2664.2001.00647.x
  • Markina-Lamonja FA, 1998. Estudio de las poblaciones de corzo (Capreolus capreolus L.) y jabalí (Sus scrofa L.) y análisis de su explotación cinegética en el territorio histórico de Álava. Doctoral thesis. University of León, Spain.
  • Marmion M, Parviainen M, Luoto, M, Heikkinen RK, Thuiller W, 2009. Evaluation of consensus methods in predictive species distribution modelling. Divers Distrib 15: 59–69. http://dx.doi.org/10.1111/j.1472-4642.2008.00491.x
  • Martí R, del Moral JC (Eds.), 2003. Atlas de las aves reproductoras de Espa-a. Dirección General de Conservación de la Naturaleza-Sociedad Espa-ola de Ornitología. Madrid.
  • Martínez-Jauregui M, Arenas C, Herruzo AC, 2011. Understanding long-term hunting statistics: the case of Spain (1972-2007). Forest Syst 20(1): 139-150. http://dx.doi.org/10.5424/fs/2011201-10394
  • Massei G, Genov P, 2000. Il cinghiale. Calderini Edagricole. Bologna.
  • Massei G, Genov P, 2004. The environmental impact of wild boar. Galemys 16: 135–145.
  • Mateo RG, Croat TB, Felicísimo AM, Mu-oz J, 2010. Profile or group discriminative techniques? Generating reliable species distribution models using pseudo-absences and target-group absences from natural history collections. Divers Distrib 16: 84–94. http://dx.doi.org/10.1111/j.1472-4642.2009.00617.x
  • Melis C, Szafrańska PA, Jędrzejewska B, Bartoń K, 2006. Biogeographical variation in the population density of wild boar (Sus scrofa) in western Eurasia. J Biogeogr 33: 803–811. http://dx.doi.org/10.1111/j.1365-2699.2006.01434.x
  • Neilson RP, 1995. A model for predicting continental-scale vegetation distribution and water balance. Ecol Appl 5 (2): 362–385. http://dx.doi.org/10.2307/1942028
  • Oliver W, Leus K, 2008. Sus scrofa. In: IUCN 2012. IUCN Red List of Threatened Species. Vers. 2012.2. Available in www.iucnredlist.org [11 February 2012].
  • Palomo LJ, Gisbert J, Blanco JC, 2007. Atlas y libro rojo de los mamíferos terrestres de Espa-a. Dirección General para la Biodiversidad-SECEM-SECEMU, Madrid, 588 pp.
  • Papanastasis VP, Mantzanas K, Dini-Papanastasi O, Ispikoudis I, 2009. Traditional agroforestry systems and their evolution in Greece. In: Agroforestry in Europe: current status and future prospects (Rigueiro-Rodríguez A, McAdam J, Mosquera-Losada MR, eds). Springer Science + Business Media B.V., Dordrecht, pp: 89–109.
  • Pardini A, 2009. Agroforestry systems in Italy: traditions towards modern management. In: Agroforestry in Europe: current status and future prospects (Rigueiro-Rodríguez A, McAdam J, Mosquera-Losada MR, eds). Springer Science + Business Media BV, Dordrecht, pp: 255–267.
  • Pearce J, Ferrier S, 2000. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Model 133: 225–245. http://dx.doi.org/10.1016/S0304-3800(00)00322-7
  • Peris S, Baquedano R, Sánchez A, Pescador M, 2005. Mortalidad del jabalí (Sus scrofa) en carreteras de la provincia de Salamanca (NO de Espa-a). ¿Influencia de su comportamiento social? Galemys 17 (1-2): 13-23.
  • Peterson AT, Papes M, Soberón J, 2008. Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol Model 213: 63-72. http://dx.doi.org/10.1016/j.ecolmodel.2007.11.008
  • Peterson AT, Soberón J, Pearson RG, Anderson RP, Martínez-Meyer E, Nakamura M, Araújo MB, 2011. Ecological niches and geographic distributions (MPB-49). Princeton University Press.
  • Phillips S, 2008. Response to transferability and model evaluation in ecological niche modelling. Ecography 31: 272–278. http://dx.doi.org/10.1111/j.0906-7590.2008.5378.x
  • Phillips SJ, Dudík M, 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31: 161–175. http://dx.doi.org/10.1111/j.0906-7590.2008.5203.x
  • Phillips SJ, Anderson RP, Schapire RE, 2006. Maximum entropy modeling of species geographic distributions. Ecol Model 190: 231–259. http://dx.doi.org/10.1016/j.ecolmodel.2005.03.026
  • Pinzon J, Brown ME, Tucker CJ, 2005. Satellite time series correction of orbital drift artifacts using empirical mode decomposition. In: Hilbert-Huang Transform: introduction and applications (Huang NE & Shen SSP, eds). World Sci Publ Co. Pte. Ltd, Singapore. pp: 167-186. http://dx.doi.org/10.1142/9789812703347_0008
  • Podgórski T, Baś G, Jędrzejewska B, Sönnichsen L, Śnieżko S, Jędrzejewski W, Okarma H, 2013. Spatiotemporal behavioral plasticity of wild boar (Sus scrofa) under contrasting conditions of human pressure: primeval forest and metropolitan area. J Mammal 94: 109–119. http://dx.doi.org/10.1644/12-MAMM-A-038.1
  • Prasad AM, Iverson LR, Liaw A, 2006. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9: 181–199. http://dx.doi.org/10.1007/s10021-005-0054-1
  • Rosell C, Herrero J, 2007. Sus scrofa Linnaeus, 1758. In: Atlas y libro rojo de los mamíferos terrestres de Espa-a (Palomo LJ, Gisbert J, Blanco JC, eds). Dirección General para la Biodiversidad-SECEM-SECEMU, Madrid (Spain), pp: 348-351.
  • Rosell C, Fernández-Llario P, Herrero J, 2001. El jabalí (Sus scrofa Linnaeus, 1758). Galemys 13: 1–25.
  • Ruiz de la Torre J, 2002. Vegetación forestal espa-ola. In: La diversidad biológica de Espa-a (Pineda FD, de Miguel JM, Casado MA, Montalvo J, coords.). Prentice Hall, Madrid (Spain). pp: 65-79.
  • Sáez–Royuela C, Tellería JL, 1986. The increased population of the wild boar (Sus scrofa L.) in Europe. Mammal Review 16: 97–101. http://dx.doi.org/10.1111/j.1365-2907.1986.tb00027.x
  • Sainz H, Sánchez de Dios R, García-Cervigón A, 2010. La cartografía sintética de los paisajes vegetales espa-oles: una asignatura pendiente en geobotánica. Ecología 23: 249-272.
  • Sanderson E, Jaiteh M, Levy M, Redford K, Wannebo A, Woolmer G, 2002. The human footprint and the last of the wild. Bioscience 52(10): 891-904. http://dx.doi.org/10.1641/0006-3568(2002)052[0891:THFATL]2.0.CO;2
  • Sarasa M, Sarasa, JA, 2013. Intensive monitoring suggests population oscillations and migration in wild boar Sus scrofa in the Pyrenees. Anim Biodivers Conserv 36(1): 79-88.
  • Schley L, Dufrêne M, Krier A, Frantz AC, 2008. Patterns of crop damage by wild boar (Sus scrofa) in Luxembourg over a 10-year period. Eur J Wildl Res 54: 589–599. http://dx.doi.org/10.1007/s10344-008-0183-x
  • Spencer PBS, Hampton JO, 2005. Illegal translocation and genetic structure of feral pigs in Western Australia. J Wildl Manag 69: 377–384. http://dx.doi.org/10.2193/0022-541X(2005)069<0377:ITAGSO>2.0.CO;2
  • Stockwell D, Peters D, 1999. The GARP modelling system: Problems and solutions to automated spatial prediction. Int J Geogr Inf Sci. 13: 143-158. http://dx.doi.org/10.1080/136588199241391
  • Suri M, Hofierka J, 2004. A new GIS-based solar radiation model and its application to photovoltaic assessments. Trans GIS 8:175-190. http://dx.doi.org/10.1111/j.1467-9671.2004.00174.x
  • Taylor R, Hellgren E, Gabor T, Ilse L, 1998. Reproduction of feral pigs in Southern Texas. J Mammal 79 (4): 1325- 1331. http://dx.doi.org/10.2307/1383024
  • Tellería JL, Sáez-Royuela C, 1985. L'evolution demographique du sanglier (Sus scrofa) en Espagne. Mammalia 49(2): 195-202. http://dx.doi.org/10.1515/mamm.1985.49.2.195
  • Thuiller W, 2004. Patterns and uncertainties of species' range shifts under climate change. Global Change Biol 10(12): 2020-2027. http://dx.doi.org/10.1111/j.1365-2486.2004.00859.x
  • Tsoar A, Allouche O, Steinitz O, Rotem D, Kadmon R, 2007. A comparative evaluation of presence-only methods for modelling species distribution. Diversity Distrib 13: 397-405. http://dx.doi.org/10.1111/j.1472-4642.2007.00346.x
  • Tucker CJ, Pinzon JE, Brown ME, 2004. Global inventory modeling and mapping studies, NA94apr15b.n11-VIg, 2.0, Global Land Cover Facility, University of Maryland, College Park, Maryland 04/15/1994.
  • Tucker CJ, Pinzon JE, Brown ME, Slayback D, Pak EW, Mahoney R, Vermote EF, El Saleous N, 2005. An extended AVHRR 8-km NDVI data set compatible with MODIS and SPOT vegetation NDVI data. Int J Remote Sens 26(20): 4485-5598. http://dx.doi.org/10.1080/01431160500168686
  • UNESCO, 1977. Mediterranean forest and maquis: ecology, conservation and management. MaB technical notes 2, France, 79 pp.
  • USGS, 2004. Shuttle radar topography mission, 1 arc second scene SRTM_u03_n008e004, Unfilled Unfinished 2.0, Global Land Cover Facility, University of Maryland, College Park, Maryland, February 2000.
  • Vitorino FJ, Fonseca JM, 2004. Wild boar in Portugal. Galemys 16 (NE): 243-251.
  • Von Humboldt A, Bonpland A, 1807. Essai sur la géographie des plantes. Facsimile reprint, Sherborn Fund no 1. Society for the Bibliography of Natural History, London.
  • Whittaker RJ, Nogués-Bravo D, Araújo MB, 2007. Geographical gradients of species richness: a test of the water-energy conjecture of Hawkins et al. (2003) using European data for five taxa. Glob Chang Biol 16: 76–89.
  • Wood GW, Barrett RH, 1979. Status of wild pigs in the United States. Wildl Soc Bull 7: 237–246.
  • Woodward FI, 1987. Climate and plant distribution. Cambridge University Press, Cambridge, UK. 174 pp.
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