Use of PIT tags to assess individual heterogeneity of laboratory-reared juveniles of the endangered Cumberlandian combshell (Epioblasma brevidens) in a mark-recapture study

The federally endangered Cumberlandian combshell (Epioblasma brevidens) was propagated and reared to taggable size (5–10 mm), and released to the Powell River, Tennessee, to augment a relict population. Methodology using passive integrated transponder (PIT) tags on these mussels greatly facilitated the detection process. The overall mean detection probability and survival rate of released individuals reached 97.8 to 98.4% and 99.7 to 99.9% (per month), respectively, during nine successive recapture occasions in the 2-year study period, regardless of seasonality. Nonhierarchical models and hierarchical models incorporating individual and seasonal variations through a Bayesian approach were compared and resulted in similar performance of prediction for detection probability and survival rate of mussels. This is the first study to apply the mark–recapture method to laboratory-reared mussels using PIT tags and stochastic models. Quantitative analyses for individual heterogeneity allowed examination of demographic variance and effects of heterogeneity on population dynamics, although the individual and seasonal variations were small in this study. Our results provide useful information in implementing conservation strategies of this faunal group and a framework for other species or similar studies.

Digging through model complexity: using hierarchical models to uncover evolutionary processes in the wild

The growing interest for studying questions in the wild requires acknowledging that eco-evolutionary processes are complex, hierarchically structured and often partially observed or with measurement error. These issues have long been ignored in evolutionary biology, which might have led to flawed inference when addressing evolutionary questions. Hierarchical modelling (HM) has been proposed as a generic statistical framework to deal with complexity in ecological data and account for uncertainty. However, to date, HM has seldom been used to investigate evolutionary mechanisms possibly underlying observed patterns. Here, we contend the HM approach offers a relevant approach for the study of eco-evolutionary processes in the wild by confronting formal theories to empirical data through proper statistical inference. Studying eco-evolutionary processes requires considering the complete and often complex life histories of organisms. We show how this can be achieved by combining sequentially all life-history components and all available sources of information through HM. We demonstrate how eco-evolutionary processes may be poorly inferred or even missed without using the full potential of HM. As a case study, we use the Atlantic salmon and data on wild marked juveniles. We assess a reaction norm for migration and two potential trade-offs for survival. Overall, HM has a great potential to address evolutionary questions and investigate important processes that could not previously be assessed in laboratory or short time-scale studies.

Estimating demographic parameters using hidden process dynamic models

Structured population models are widely used in plant and animal demographic studies to assess population dynamics. In matrix population models, populations are described with discrete classes of individuals (age, life history stage or size). To calibrate these models, longitudinal data are collected at the individual level to estimate demographic parameters. However, several sources of uncertainty can complicate parameter estimation, such as imperfect detection of individuals inherent to monitoring in the wild and uncertainty in assigning a state to an individual. Here, we show how recent statistical models can help overcome these issues. We focus on hidden process models that run two time series in parallel, one capturing the dynamics of the true states and the other consisting of observations arising from these underlying possibly unknown states. In a first case study, we illustrate hidden Markov models with an example of how to accommodate state uncertainty using Frequentist theory and maximum likelihood estimation. In a second case study, we illustrate state-space models with an example of how to estimate lifetime reproductive success despite imperfect detection, using a Bayesian framework and Markov Chain Monte Carlo simulation. Hidden process models are a promising tool as they allow population biologists to cope with process variation while simultaneously accounting for observation error.

Simple estimation and test procedures in capture-mark-recapture mixed models

The need to consider in capture-recapture models random effects besides fixed effects such as those of environmental covariates has been widely recognized over the last years. However, formal approaches require involved likelihood integrations, and conceptual and technical difficulties have slowed down the spread of capture-recapture mixed models among biologists. In this article, we evaluate simple procedures to test for the effect of an environmental covariate on parameters such as time-varying survival probabilities in presence of a random effect corresponding to unexplained environmental variation. We show that the usual likelihood ratio test between fixed models is strongly biased, and tends to detect too often a covariate effect. Permutation and analysis of deviance tests are shown to behave properly and are recommended. Permutation tests are implemented in the latest version of program E-SURGE. Our approach also applies to generalized linear mixed models.

Individual heterogeneity in studies on marked animals using numerical integration: capture-recapture mixed models

In conservation and evolutionary ecology, quantifying and accounting for individual heterogeneity in vital rates of open populations is of particular interest. Individual random effects have been used in capture-recapture models, adopting a Bayesian framework with Markov chain Monte Carlo (MCMC) to carry out estimation and inference. As an alternative, we show how numerical integration via the Gauss-Hermite quadrature (GHQ) can be efficiently used to approximate the capture-recapture model likelihood with individual random effects. We compare the performance of the two approaches (MCMC vs. GHQ) and finite mixture models using two examples, including data on European Dippers and Sociable Weavers. Besides relying on standard statistical tools, GHQ was found to be faster than MCMC simulations. Our approach is implemented in program E-SURGE. Overall, capture recapture mixed models (CR2Ms), implemented either via a GHQ approximation or MCMC simulations, have potential important applications in population biology.