AI Insight
Researchers developed an enhanced space-for-time mark-recapture model that incorporates age, time, and individual characteristics to estimate survival of migrating animals detected at multiple locations. The traditional Cormack-Jolly-Seber models were extended using finite-mixture models to account for age uncertainty and generalized linear models to include spatiotemporal and individual covariates. Applied to juvenile steelhead trout in Idaho's Snake River Basin, the model successfully estimated survival trends across different populations, ages, time periods, and individual body lengths.
Why it matters
This statistical tool enables more accurate assessment of survival rates in migratory species with complex life histories, which is critical for conservation management and understanding population dynamics. The generalized framework can be applied to diverse animal populations with different monitoring systems, improving ecological research and wildlife management decisions.
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⚠️ Preprint – Noch nicht peer-reviewed
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Space-for-time Cormack-Jolly-Seber (CJS) models have been developed to estimate survival of migrating animals that are imperfectly detected at spatially discrete sampling stations. However, space-for-time CJS models typically ignore survival over time and age-specific probability of movement, missing the diversity of life history strategies. To address these limitations, we extended space-for-time Cormack-Jolly-Seber models to explicitly incorporate time, age, and individual-level covariates to account for diverse life history strategies. We incorporate a sub-model that includes uncertainty in individuals’ age using a finite-mixture model. The detection and transition probabilities were parameterized using generalized linear models, facilitating flexible model specification and inclusion of spatiotemporal and individual covariates. We apply the model to detections of juvenile steelhead (Oncorhynchus mykiss) from two populations in the Snake River Basin in Idaho, USA, to estimate trends in survival with respect to population, age, time, and the length of individuals. Additionally, we generalized the model so that it can be applied to other systems and populations with different life history strategies and monitoring infrastructure.
Source: An age- and time-specific space-for-time mark-recapture model