Accurately estimating survival rate and identifying the drivers of its variation is essential for our understanding of population dynamics and life history strategies (Sæther and Bakke 2000), as well as for population management and conservation (Francis et al. 1998, Doherty et al. 2014). Many studies estimate survival from capture–recapture data using the Cormack–Jolly–Seber (CJS) model (Lebreton et al. 1992). However, survival estimates are confounded with permanent emigration from the study area, which can be particularly problematic for mobile species. This is problematic, not only because CJS models under estimate true survival in populations where permanent emigration occurs (i.e. they estimate “apparent” survival), but also because some factors of interest may affect both survival and emigration (e.g., habitat quality, Paquet et al. 2020), leaving the interpretation of results challenging, for example in terms of management decisions.

Several methods have been developed to account for permanent emigration when estimating survival, for example by jointly analyzing CMR data with data on individuals’ locations at each capture/resighting site (to estimate their dispersal distances; Schaub and Royle 2013, Badia Boher et al. 2023), with telemetry data (Powel et al. 2000), mark recovery data (Burnham 1993, Fay et al. 2019), or with live-resight data (Barker 1997).

The Barker joint live-recapture/live-resight (JLRLR) model can estimate survival when resight data are continuous over a long interval and from a larger area than the capture recapture data. This model becomes particularly promising with the growing collection of data from citizen science, or remote detection tools (Dzul et al. 2023). However, as pointed out by Dzul et al., this model assumes that resight probability is homogeneous across the area where individuals can move, and this assumption is likely violated for example because of non-random movements or because of non-random location of resighting sites.

In their manuscript, Dzul et al. performed a thorough simulation study to evaluate the accuracy of survival estimates from JLRLR models under various study designs regarding the location of resight sites (global, random, fixed including the capture site, and fixed excluding the capture site). They simulated data with varying survival and movement values, varying recapture and resight probabilities, and varying sample sizes. Finally, they also developed and fitted a multi state version of the JLRLR model. They show that JLRLR models performed better than CJS models. Survival estimates were still often biased (either positively or negatively) but they were less biased when sesight sites were randomly located (rather than at fixed locations), when recapture sites were included in the resighting design, and when using the multi state JLRLR model they developed.

This study highlights (multistate) JLRLR models as an alternative to CJS models one should consider when auxiliary resight data can be collected. Moreover, it shows the importance of evaluating not only model performance, but also the efficiency of alternative sampling designs before choosing one for our studies. Hopefully, this study will help the authors and other researchers making a more informed and efficient choice of model and design to estimate survival in their study populations.

References

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