Thinking outside the patch: a multi-species comparison of conceptual models from real-world landscapes

Abstract

Context: When modeling a species’ distribution, landscapes can alternatively be conceptualized following patch- or gradient-based approaches. However, choosing the most suitable conceptualization is difficult and methods for empirical validation are still lacking. Objectives: To address the conditions under which a given conceptual model is more suitable, taking into account landscape context and species trait dependency effects. Patch- and gradient-based conceptualizations were built based on two structurally different landscapes: variegated and mosaic. We hypothesize that: (H1) gradient-based models better describe variegated landscapes while patch-based models perform better in mosaic landscapes; and (H2) gradient-based models will fit generalist species better while patch-based models will suit specialists better. Methods: We modeled the distribution of eleven bird species in each landscape using each conceptualization. We determined the suitability of each conceptual model to fit statistical models by looking for cross- species responses and deviations from best models. Results: We found no clear support for our hypotheses. Although patch-based models performed better in mosaic landscapes (H1), they also provided useful conceptualizations in variegated landscapes. However, when patches showed high heterogeneity, gradient- based approaches better fit specialist species (H2). Conclusions: The suitability of a given conceptual model depends on the interaction between species habitat specialization, and the intrinsic spatial heterogeneity of the landscape and the ability of each conceptualization to capture it. Gradient-based models provide better information on resource allocation, while patch-based models offer a simplified perspective on landscape attributes. Future research should consider the nature of both species and landscapes in order to avoid bias from inadequate landscape conceptualizations.