A state-space concomitant capture-recapture integrated model to improve population parameter estimates of sparse datasets
Description
The present work demonstrated that a state-space formulation of an integrated concurrent marking-observation capture-recapture model (C-MOM) allows for concomitant marking and observing data collection processes, which is a violation of assumptions of classical mark-resight models available, such as the zero-truncated Poisson log-normal mixed effects (ZPNE). To assess C-MOM’s performance under different scenarios, its population parameters’ estimates were compared in terms of bias, precision and accuracy to estimates produced by a classical mark recapture (CMR) (based on Jolly-Seber) and the ZPNE in a virtual ecology study of the rock cavy (Kerodon rupestris). This small colonial rodent presents low capture, but high observation rates. In comparison to the CMR and the ZPNE, the C-MOM presented improved accuracy without overestimating precision. This approach enables scientists studying colonial or gregarious species to produce reliable population parameter’s estimates even budget and time restrictions result in sparse capture-recapture datasets. This analysis might be reproduced by running the R codes in this reprository, in the numbered order. You will need to have the software MARK installed in your computer. It is necessary to change the directory on the copied files before running (i.e. setwd() <- file.path(getwd())).
Files
Steps to reproduce
In this repository you will find all codes and data needed to run all analysis presented in the manuscript Micheletti et al. in prep. "Relaxing the assumption of independent capturing, marking, and observing events of a classical integrated capture-resight and counting model when using a comparable state-space integrated model can improve population parameter estimates for sparse datasets" The files are numbered in the order that they should run. You will need to have the software MARK installed in your computer. It is necessary to change the directory on the copied files before running (i.e. setwd() <- file.path(getwd())).