| dc.description.abstract |
Landscape connectivity is an important component of systematic conservation planning. Step‐selection functions (SSFs) is a highly promising method for connectivity modeling. However, differences in movement behavior across individuals and seasons are usually not considered in current SSF‐based analyses, potentially leading to imprecise connectivity models. Here, our objective was to use SSFs to build functional connectivity models for African elephants Loxodonta africana in a seasonal environment to illustrate the temporal variability of functional landscape connectivity. We provide a methodological framework for integrating detected inter‐individual variability into resistance surface modeling, for assessing how landscape connectivity changes across seasons, and for evaluating how seasonal connectivity differences affect predictions of movement corridors. Using radio‐tracking data from elephants in the Borderland area between Kenya and Tanzania, we applied SSFs to create seasonal landscape resistance surfaces. Based on seasonal models, we predicted movement corridors connecting major protected areas (PAs) using circuit theory and least‐cost path analysis. Our findings demonstrate that individual variability and seasonality lead to substantial changes in landscape connectivity and predicted movement corridors. Specifically, we show that the models disregarding seasonal resource fluctuations underestimate connectivity for the wet and transitional seasons, and overestimate connectivity for the dry season. Based on our seasonal models, we predicted a connectivity network between large PAs and highlight seasonal and consistent patterns that are most important for effective management planning. Our findings reveal that elephant movements in the borderland between Kenya and Tanzania are essential for maintaining connectivity in the dry season, and that existing corridors do not protect these movements in full extent. |
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