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Continuous predictors of survival present a challenge in the analysis of data from studies of marked individuals if they vary over time and can only be observed when individuals are captured. Existing methods to study the effects of such variables have followed one of two approaches. The first is to model the joint distribution of the predictor and the observed capture histories, and the second is to draw inference from the likelihood conditional on events that depend only on observed predictor values, called the trinomial model. Previous comparison of these approaches found that joint modelling provided more precise inference about the effect of the covariate while the trinomial model was less prone to issues of model mis‐specification. However, we believe that an important issue was missed. We show through mathematical analysis and numerical simulation that the trinomial model is not identifiable when the predictor has no effect on the survival probability. This also causes inferences from the trinomial model to be imprecise when the effect of the covariate on the survival probability is small. We also analyse data on the effect of body mass on the survival of meadow voles to demonstrate the importance of this issue in real applications. [ABSTRACT FROM AUTHOR]

Abundance estimation is a vital goal in wildlife monitoring. Camera‐traps are a tool to survey wildlife populations noninvasively and can be used for abundance estimation if individuals are identifiable. However, for species without individual identification characteristics, camera‐trap surveys have often been combined with some other survey method such as capture‐recapture (CR, using traditional tags or DNA through hair snags or scat) to inform an integrated model. We discuss and apply two integrated models involving presence‐absence data from camera traps and CR data from hair traps to compare bias and precision to estimate the population density of grizzly bears of the central Rocky Mountains of Alberta, Canada. Unlike many other studies, we found that integrating presence‐absence data with CR data does not improve the precision of the density estimates. The possible reasons for such results are discussed in detail. [ABSTRACT FROM AUTHOR]

Schofield et al. (2018, Biometrics 74, 626–635) presented simple and efficient algorithms for fitting continuous‐time capture‐recapture models based on Poisson processes. They also demonstrated by real examples that the standard method of discretizing continuous‐time capture‐recapture data and then fitting traditional discrete‐time models may lead to information loss in population size estimation. In this article, we aim to clarify that key to the approach of Schofield et al. (2018) is the Poisson model assumed for the number of captures of each individual throughout the study, rather than the fact of data being collected in continuous time. We further show that the method of data discretization works equally well as the method of Schofield et al. (2018), provided that a Poisson model is applied instead of the traditional Bernoulli model to the number of captures for each individual on each sampling occasion. [ABSTRACT FROM AUTHOR]

Time-varying individual covariates present a challenge in modelling data from mark-recapture-recovery ( MRR) experiments of wild animals. Many values of the covariate will be unknown because they can be observed only when an individual is captured, and the missing values cannot be ignored., Catchpole et al. [ Journal of the Royal Statistical Society: Series B ( Statistical Methodology), 70, 445-460, 2008] presents one solution to this problem by constructing a conditional likelihood depending only on the observed covariate information - the so-called trinomial model., This paper describes the link between the trinomial model and the mark-recapture-recovery model of Burnham ( Marked Individuals in the Study of Bird Population, 199-213, 1993) and shows how the trinomial model can be implemented in the software package program mark. This provides the user with access to all of the features of program mark including the facilities for model building and model selection without having to write custom code., I provide details on the analysis of a simulated data set and discuss an r package developed to help users format their data and to implement the model through the existing rmark package. [ABSTRACT FROM AUTHOR]