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Data from: Incorporating capture heterogeneity in the estimation of autoregressive coefficients of animal population dynamics using capture-recapture data


Nicolau, Pedro G.; Sørbye, Sigrunn H.; Yoccoz, Nigel G. (2021), Data from: Incorporating capture heterogeneity in the estimation of autoregressive coefficients of animal population dynamics using capture-recapture data, Dryad, Dataset,


Population dynamics models combine density-dependence and environmental effects. Ignoring sampling uncertainty might lead to biased estimation of the strength of density-dependence. This is typically addressed using state-space model approaches, which integrate sampling error and population process estimates. Such models seldom include an explicit link between the sampling procedures and the true abundance, which is common in capture-recapture settings. However, many of the models proposed to estimate abundance in the presence of capture heterogeneity lead to incomplete likelihood functions and cannot be straightforwardly included in state-space models.

We assessed the importance of estimating sampling error explicitly by taking an intermediate approach between ignoring uncertainty in abundance estimates and fully specified state-space models for density-dependence estimation based on autoregressive processes. First, we estimated individual capture probabilities based on a heterogeneity model, using a conditional multinomial likelihood, followed by a Horvitz-Thompson estimate for abundance. Second, we estimated coefficients of autoregressive models for the log abundance. Inference was performed using the methodology of integrated nested Laplace approximation (INLA). We performed an extensive simulation study to compare our approach with estimates disregarding capture history information, and using R-package VGAM, for different parameter specifications. The methods were then applied to a real dataset of gray-sided voles Myodes rufocanusfrom Northern Norway.

We found that density-dependence estimation was improved when explicitly modelling sampling error in scenarios with low process variances, in which differences in coverage reached up to 8% in estimating the coefficients of the autoregressive processes. In this case, the bias also increased assuming a Poisson distribution in the observational model. For high process variances, the differences between methods were small and it appeared less important to model heterogeneity.


See original publication for sampling details and spatial map of the stations. This dataset corresponds to a processed version of the raw dataset, after correcting for typing errors. To make it easier to analyse, the captures on different days for the same individual have been aggregated into a single row containing the capture history associated with that same individual, using the mean weight measured on both capture days for a given vole.

Usage Notes

Capture recapture histories with two sampling days of gray-sided voles for different spatially independent locations, sampled in Spring and Fall, from 2000 to 2017. Some missing values present in weight and sex of individuals. Animals collected at different time points (6 months apart) are assumed to be different (no long-term memory expected).