Landscape connectivity modeling from the perspective of animal dispersal
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Dispersal plays a key role in linking populations, habitat (re)-colonization, and species range expansion. As fragmentation and habitat loss are ubiquitous threats and can disrupt dispersal, landscape connectivity modeling has become a valuable tool in conservation planning.
We provide an overview of how current connectivity modeling has incorporated the different aspects of animal dispersal. We describe the most popular connectivity models and highlight their main assumptions related to dispersal, suggesting a series of questions that could clarify the advantages and disadvantages of using a particular approach.
We review the structure of the connectivity models based on least-cost analysis, circuit theory, and the individual-based dispersal models. We use some studies as case examples to discuss how important elements of animal dispersal were considered through models to predict movement routes.
Ongoing developments in connectivity modeling have made it possible to represent animal dispersal in a more realistic way by implementing key elements such as dispersal behaviors, mortality, and inter-individual variability. However, the potential to consider such elements and how this is done in connectivity modeling depends on the selected approach, since each model represents animal dispersal through a different perspective.
We recommend that the choice of a connectivity model should be made after considering the study objectives, the species dispersal mechanism, and the prior knowledge available about it. By understanding and incorporating dispersal behavior into connectivity modeling, we can improve our capacity to generate useful information aimed to construct more effective conservation strategies.
KeywordsCircuit theory Dispersal movement Least-cost path analysis Individual-based models Resistance surface Resistant kernels
We thank the two anonymous referees for their careful reading and their many helpful and insightful comments that have greatly improved this manuscript. We also thank Daniel Brito for discussions and suggestions. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001. M. F. Diniz is supported by a graduate fellowship from CAPES. The Brazilian National Research Council (CNPq) provided a Research Grant (#306838/2016-8) to R. B. Machado.
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