Estimating demographic parameters using a combination of known-fate and open N-mixture models

TL;DR

This paper introduces an integrated modeling approach combining known-fate and open N-mixture models to improve estimation of demographic parameters like survival and recruitment from ecological count and mark-resight data.

Contribution

The authors develop a novel combined model and R package that jointly estimates demographic parameters, enhancing accuracy and precision over separate methods.

Findings

The integrated model reliably recovers parameters with no bias.

Estimates are more precise under the joint model.

Joint estimation may better represent overall population survival.

Abstract

1. Accurate estimates of demographic parameters are required to infer appropriate ecological relationships and inform management actions. Recently developed N-mixture models use count data from unmarked individuals to estimate demographic parameters, but a joint approach combining the strengths of both analytical tools has not been developed. 2. We present an integrated model combining known-fate and open N-mixture models, allowing the estimation of detection probability, recruitment, and the joint estimation of survival. We first use a simulation study to evaluate the performance of the model relative to known values. We then provide an applied example using 4 years of wolf survival data consisting of relocations of radio-collared wolves within packs and counts of associated pack-mates. The model is implemented in both maximum-likelihood and Bayesian frameworks using a new R package kfdnm and the BUGS language. 3. The simulation results indicated that the integrated model was able to reliably recover parameters with no evidence of bias, and estimates were more precise under the joint model as expected. Results from the applied example indicated that the marked sample of wolves was biased towards individuals with higher apparent survival rates (including losses due to mortality and emigration) than the unmarked pack-mates, suggesting estimates of apparent survival based on joint estimation could be more representative of the overall population. Estimates of recruitment were similar to direct observations of pup production, and overlap of the credible intervals suggested no clear differences in recruitment rates. 4. Our integrated model is a practical approach for increasing the amount of information gained from future and existing radio-telemetry and other similar mark-resight datasets.