Incorporating space in hierarchical capture mark recapture models: can we better capture variance?

Abstract

Abstract 1. Capture mark recapture (CMR) models allow the estimation of various components of animal populations, such as survival and recapture probabilities and often assume homogenous detection of individuals. However, individual detection probability is not heterogeneous for a range of different reasons, for example due to the location and environmental context of traps within an individual’s home range or individual characteristics such as age. Spatial CMR models incorporate this heterogeneity by including the spatial coordinates of traps, data which is often already collected in standard CMR approaches. 2. We compared how the inclusion of spatial data changed estimations of survival, detection probability, and the probability of seroconversion to an arenavirus, in the multimammate mouse. We used a Bayesian framework to develop non spatial, partially spatial and fully spatial models alongside multievent CMR models and used simulations to test whether parameters were sensitive to starting parameters. 3. We found that bias and precision were similar for all three different model types, with simulations always returning estimates within the 95% credible intervals. When applied to field data, our models predicted a lower survival of individuals exposed to Morogoro virus (MORV) in non spatial models while survival was similar in spatially explicit models. 4. We suggest that spatial coordinates of traps should always be recorded when carrying out CMR and spatially explicit analysis should be used whenever possible, particularly as incorporating spatial variation may capture ecological processes without the need for additional data collection that can be challenging to acquire with wild animals.