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Identifying Breeding Habitat for the Iberian Lynx: Inferences from a Fine-Scale Spatial Analysis

Néstor Fernández, Miguel Delibes, Francisco Palomares and David J. Mladenoff
Ecological Applications
Vol. 13, No. 5 (Oct., 2003), pp. 1310-1324
Published by: Wiley
Stable URL: http://www.jstor.org/stable/4134715
Page Count: 15
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Identifying Breeding Habitat for the Iberian Lynx: Inferences from a Fine-Scale Spatial Analysis
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Abstract

Empirical landscape habitat models are valuable tools for addressing species conservation issues in heterogeneous landscapes. These have been particularly useful for animal populations requiring extensive areas, like large mammalian carnivores. Although models are scale-dependent, they are often based exclusively on coarse-grained information on landscape structure and species distribution. However, accurate discrimination and quality assessment of breeding habitats may require more detailed information on both individuals' habitat use and landscape patterns. Here, we modeled breeding habitat for the Iberian lynx (Lynx pardinus), an endangered specialist of Mediterranean native ecosystems, in a human-dominated landscape. For this, we used radiotelemetry data on breeding individuals and fine-grained landscape variables obtained over an areal extent encompassing an entire lynx metapopulation. In addition, we restricted the study domain to areas of potential presence within this extent based on previous habitat analyses. This fine-scale landscape analysis and design aimed to test whether it was possible to obtain detailed inferences on breeding habitat distribution and quality within more coarsely defined habitats that only indicate where the species can be found. Landscape composition factors (i.e., fine-grain variables of landscape pattern within territories) and context factors (variables on geographical location of territories) were combined in different candidate habitat models. These reflected various hypotheses involving territory presence and size, including vegetation structure characteristics, landscape heterogeneity and complexity, favorable prey habitat, and human disturbance. The best approximating model for territory presence included only one landscape composition variable, namely, density of ecotones between scrubland and pastureland, which favored presence. For this model, classification accuracy was >80% in 94% of cross-validation tests performed. The best approximating model for territory size explained 65% of the variation in size and included also density of ecotones, plus mean coverage of tall shrubs within territories; both correlated negatively with size. We show how these predictors are also related to prey density and suggest that this low-hierarchical-level relationship between the prey and patterns of the landscape can be interpreted as a likely mechanism explaining breeding habitat distribution and quality at a higher level. Our approach shows the potential of finescale landscape analysis to understand patterns of breeder abundance, and to better manage population viability of threatened species in human-populated areas.

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