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

Abstract

AbstractCapture mark recapture (CMR) models allow the estimation of various components of animal populations, such as survival and recapture probabilities. In recent years, incorporating the spatial distribution of the devices used to detect an animals’ presence has become possible. By incorporating spatial information, we explicitly acknowledge the fact that there will be spatial structuring in the ecological processes which give rise to the capture data. Individual detection probability is not heterogeneous for a range of different reasons, for example the location of traps within an individual’s home range, the environmental context around the trap or the individual characteristics of an animal such as its age. Spatial capture recapture models incorporate this heterogeneity by including the spatial coordinates of traps, data which is often already collected in standard CMR approaches. Here, we compared how the inclusion of spatial data changed estimations of survival, detection probability, and to some extent the probability of seroconversion to a common arenavirus, using the multimammate mouse as our model system. We used a Bayesian framework to develop non spatial, partially spatial and fully spatial models alongside multievent CMR models. First, we used simulations to test whether certain parameters were sensitive to starting parameters, and whether models were able to return the expected values. Then we applied the non-spatial, partially spatial and fully spatial models to a real dataset. We found that bias and precision were similar for the three different model types, with simulations always returning estimates within the 95% credible intervals. When applying our models to the real data set, we found that the non-spatial model predicted a lower survival of individuals exposed to Morogoro virus (MORV) compared to unexposed individuals, yet in the spatial model survival between exposed and non-exposed individuals was the same. This suggests that the non-spatial model underestimated the survival of seropositive individuals, most likely due to an age effect. We suggest that spatial coordinates of traps should always be recorded when carrying out CMR and spatially explicit methods of analysis should be used whenever possible, particularly as incorporating spatial variation may more easily capture ecological processes without the need for additional data collection that can be challenging to acquire with wild animals.