Estimating transitions between states using measurements with imperfect detection: application to serological data

Abstract

Abstract. Classifying the states of an individual and quantifying transitions between states are crucial while modeling animal behavior, movement, and physiologic status. When these states are hidden or imperfectly known, it is particularly convenient to relate them to appropriate quantitative measurements taken on the individual. This task is, however, challenging when quantitative measurements are not available at each sampling occasion. For capture-recapture data, various ways of incorporating such non-discrete information have been used, but they are either ad hoc and/or use a fraction of the available information by relying on a priori thresholds to assign individual states. Here we propose assigning discrete states based on a continuous measurement, and then modeled survival and transition probabilities based on these assignments. The main advantage of this new approach is that a more informative use of the non-discrete information is done. As an illustrative working example, we applied this approach to eco-epidemiological data collected across a series of years in which individuals of a long-lived seabird, the Black-legged Kittiwake (Rissa tridactyla), could either be visually detected or physically recaptured and blood sampled for subsequent immunological analyses. We discuss how this approach opens many perspectives in eco-epidemiology, but also more broadly, in population ecology.

Inference of the infection status of individuals using longitudinal testing data from cryptic populations: Towards a probabilistic approach to diagnosis

“…Detecting infection with confidence in individuals can be difficult when diagnostic tests are imperfect because the true state of infection is not, or is only partially, determinable 1 . Cryptic populations of wild animals add an extra layer of complexity because they are often unobserved for long periods and may be difficult to catch and sample.…”

Next‐generation serology: integrating cross‐sectional and capture–recapture approaches to infer disease dynamics

“…Finally, considering the recent advances made in quantitative ecology, this approach could be applied to more complex scenarios than the one we considered here, by being combined with methods accounting for state misclassification by repeating sampling (McClintock et al 2010, Lahoz-Monfort et al 2016, using the information contained in quantitative measurements (Choquet et al 2013), combining assays such as serology and direct detection (Viana et al 2016, Buzdugan et al 2017 or by integrating individual traits more explicitly (Plard et al 2019). Notably, it is important to note that, because we used a simulated data set, the performances of the three presented approaches could have been overestimated.…”

R2ucare: An r package to perform goodness‐of‐fit tests for capture–recapture models

“…R2ucare allows evaluating the quality of fit of standard capture–recapture models for open populations. Future developments will focus on implementing goodness‐of‐fit tests for models combining different sources of data (McCrea, Morgan, & Pradel, ) and residual‐based diagnostics (Choquet, Carrie, Chambert, & Boulinier, ; Warton, Stoklosa, Guillera‐Arroita, MacKenzie, & Welsh, ).…”