Importance of the interplay between land cover and topography in modeling habitat selection

Anthropogenic land cover change is one of the greatest threats to the persistence of organisms. Therefore, identifying and safeguarding optimal land covers for declining organisms is a major challenge of the Anthropocene. Priority land covers are typically identified through habitat selection studies. These traditionally use land cover and/or topographic features as separate, additive predictors of organismal occurrence, despite the fact that each land cover is superimposed on specific topographic features, such as elevation or slope, which may affect its attractiveness. Here, we tested the importance of considering the largely overlooked interplay between land cover and topography, using as a model a golden eagle Aquila chrysaetos population which is sharply declining due to human-induced land cover alteration. We found that the overlay of a key land cover type (old-growth forest) on underlying terrain known to be favorable for the population (steep slopes at higher elevations than the nest) dramatically increased its attractiveness. Conversely, the matching of the same land cover with unfavorable topography (gentle slopes at lower elevations) deteriorated its attractiveness for the population. Thus, underlying topography acted as the major determinant of land cover suitability for the eagle population. The conservation implications could be profound, because modeling land cover per se could waste conservation resources on low quality sites (old-growth forest on gentle terrain at low elevations) with unlikely benefits for the threatened eagle population. We expect topographic modulators of land cover quality to be more common than previously appreciated in many or most study systems, as numerous organisms inhabiting terrestrial and aquatic environments, regardless of taxonomic group, exhibit selectivity for specific topographic features and specific land cover types. In conclusion, we encourage modelers to take more into account underlying modulators that may drive differences in quality within the same land cover. This would make wildlife-habitat models more realistic, improve their applicability, and enhance the cost-effectiveness of conservation efforts.