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Connectivity plays a crucial role in determining the spread, viability, and persistence of populations across space. Dispersal across landscapes, or the movement of individuals or genes among resource patches, is critical for functional connectivity. Yet current connectivity modelling typically uses information on species location or habitat preference rather than movement, which unfortunately may not capture key dispersal limitations. We argue that recent developments in species distribution modelling provide insightful lessons for addressing this gap and advancing our understanding of connectivity. We suggest shifting the focus of connectivity modelling from locating where animals potentially disperse to a process‐based approach directed towards understanding and mapping factors that limit successful dispersal. To do so, we propose defining species dispersal requirements through identifying spatial, environmental and intrinsic constraints to successful dispersal, analogous to identifying environmental dimensions that define niches. We discuss the benefits of this constraint‐based framework for understanding the distribution of species, predicting species responses to climate change, and connectivity conservation practice. We illustrate how the framework can aid in identifying potential detrimental effects of human activities on connectivity and species persistence, and can spur the implementation of innovative conservation strategies. The proposed framework clarifies the validity and contextual utility of objectives and measures in existing connectivity models, and identifies gaps that may impede our understanding of connectivity and its integration into successful conservation strategies. We expect that this framework will facilitate a mechanistic approach to understanding and conserving connectivity, which will aid in effectively predicting and mitigating effects of ongoing environmental change.

Crop and livestock depredation by wildlife is a primary driver of human-wildlife conflict, a problem that threatens the coexistence of people and wildlife globally. Understanding mechanisms that underlie depredation patterns holds the key to mitigating conflicts across time and space. However, most studies do not consider imperfect detection and reporting of conflicts, which may lead to incorrect inference regarding its spatiotemporal drivers. We applied dynamic occupancy models to elephant crop depredation data from India between 2005 and 2011 to estimate crop depredation occurrence and model its underlying dynamics as a function of spatiotemporal covariates while accounting for imperfect detection of conflicts. The probability of detecting conflicts was consistently <1.0 and was negatively influenced by distance to roads and elevation gradient, averaging 0.08-0.56 across primary periods (distinct agricultural seasons within each year). The probability of crop depredation occurrence ranged from 0.29 (SE 0.09) to 0.96 (SE 0.04). The probability that sites raided by elephants in primary period t would not be raided in primary period t + 1 varied with elevation gradient in different seasons and was influenced negatively by mean rainfall and village density and positively by distance to forests. Negative effects of rainfall variation and distance to forests best explained variation in the probability that sites not raided by elephants in primary period t would be raided in primary period t + 1. With our novel application of occupancy models, we teased apart the spatiotemporal drivers of conflicts from factors that influence how they are observed, thereby allowing more reliable inference on mechanisms underlying observed conflict patterns. We found that factors associated with increased crop accessibility and availability (e.g., distance to forests and rainfall patterns) were key drivers of elephant crop depredation dynamics. Such an understanding is essential for rigorous prediction of future conflicts, a critical requirement for effective conflict management in the context of increasing human-wildlife interactions.