Abstract
espanolEn los ultimos anos, el tamano de poblacion del lince iberico (Lynx pardinus) y su area de distribucion han aumentado de forma significativa. Actualmente, la especie habita zonas en las que habia estado extinto durante decadas y en las que se han identificado nuevos puntos negros de mortalidad en carretera. La conservacion de esta especie requiere que se haga una evaluacion exhaustiva del riesgo de muerte en carretera en su posible distribucion futura. En este estudio se emplea el algoritmo MaxEnt para identificar las carreteras donde hay mayor riesgo de atropellar un lince iberico. Mas de 1.150 tramos de carreteras fueron evaluados en Andalucia (sur de Espana). En el modelo se utilizaron variables relacionadas con las carreteras y el habitat circundante. En total, 1.395 km de los 7.384 km evaluados (el 18,9%) se han calificado como de alto riesgo. Nuestros resultados podrian ayudar a planificar futuras estrategias de conservacion. Hasta donde conocemos, esta es la primera vez que se utiliza el algoritmo MaxEnt para predecir de forma espacialmente explicita la mortalidad de fauna silvestre en carreteras. EnglishIn recent years, the Iberian lynx (Lynx pardinus) has experienced a significant increase in the size of its population and in its distribution. The species currently occupies areas in which it had been extinct for decades and new road mortality black spots have been identified. Its conservation requires an intensive risk assessment of road–deaths in potential future distribution areas. Using the MaxEnt algorithm we aimed to identify the roads where there is a greater risk of road collision for the Iberian lynx. More than 1,150 stretches of road were evaluated in Andalusia (southern Spain). Both road–related and habitat variables were included in the model. A total of 1,395 km of the 7,384 km evaluated (18.9%) were classified as high risk road. Our results could help plan future conservation strategies. To our knowledge, this is the first time that the MaxEnt algorithm has been used to provide spatially–explicit predictions about wildlife road mortality.
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