An integrated model of territory occupancy and mark‐recapture data for estimating spatial variation in survival

Abstract Spatial variation in survival is central to understanding population dynamics and guiding conservation. However, assessing it is hard, since capture‐mark‐recapture (CMR) data required for such inference must be collected over large spatial extents, which is seldom possible. By contrast, territory occupancy (TO) data are typically spatially rich and widely available for territorial species, but they do not directly inform individual survival. We developed an integrated model combining CMR and TO data. The model links site‐level occupancy dynamics, governed by site persistence and colonization probabilities, to survival of the territory owner, allowing both data sources to jointly inform spatiotemporal variation in survival. An estimable scaling parameter () accommodates potential violation of a deterministic occupancy‐survival link arising from breeding dispersal or alternative colonization dynamics. We evaluated our model using simulation across different survival scenarios (constant, spatial, spatiotemporal), life histories and CMR detection probabilities, and assessed estimator robustness when the deterministic occupancy‐survival link is violated. We applied the model to long‐term peregrine falcon ( Falco peregrinus ) data in Hungary, evaluating the effects on survival of the presence of a predator (eagle owl) and the proportion of agricultural land. Survival estimated with the integrated model showed negligible bias and good coverage across all simulation scenarios, while substantially improving precision relative to CMR‐only analyses. Precision gains were largest for spatial regression coefficients (up to 80%) and temporal standard deviation parameters (up to 88%); gains increased with greater model complexity and lower detection probability. When the deterministic occupancy‐survival link was violated, adequately absorbed the discrepancy and prevented bias in survival. In the case study, integration substantially improved the precision of spatiotemporal survival estimates and revealed a negative association with eagle owl presence and a positive one with the proportion of agricultural land. Our new integrated model improves estimation of spatial and temporal variation in survival by leveraging shared information across data sources, extending spatially explicit demographic inference to systems where CMR data alone are insufficient, and thereby allowing spatial survival inference in a broader range of applications.