Estimating demographic parameters using a combination of known‐fate and open N‐mixture models. Issue 10 (1st October 2015)

• Record Type: Journal Article
• Authors: Schmidt, Joshua H.; Johnson, Devin S.; Lindberg, Mark S.; Adams, Layne G.
• Is Part Of: Ecology. Volume 96:Issue 10(2015)
• Page Start: 2583
• Page End: 2589
• Publication Date: 2015-10-01
• Subjects: Canis lupus -- detection probability -- Gates of the Arctic National Park, Alaska, USA -- integrated model -- known-fate models -- mark–resight data -- N-mixture models -- recruitment -- survival -- wolves; Ecology -- Periodicals; Ecology -- Periodicals; Écologie -- Périodiques; Ecologie; Écologie; Écologie animale; Écologie végétale; Ecology; Periodicals; 577.05
• DOI: 10.1890/15-0385.1
• Languages: English
• ISSNs: 0012-9658

Abstract

Abstract : Accurate estimates of demographic parameters are required to infer appropriate ecological relationships and inform management actions. Known‐fate data from marked individuals are commonly used to estimate survival rates, whereas N ‐mixture models use count data from unmarked individuals to estimate multiple demographic parameters. However, a joint approach combining the strengths of both analytical tools has not been developed. Here we develop 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 demonstrate our approach through both simulations and an applied example using four years of known‐fate and pack count data for wolves ( Canis lupus ). Simulation results indicated that the integrated model reliably recovered parameters with no evidence of bias, and survival estimates were more precise under the joint model. Results from the applied example indicated that the marked sample of wolves was biased toward individuals with higher apparent survival rates than the unmarked pack mates, suggesting that joint estimates may be more representative of the overall population. Our integrated model is a practical approach for reducing bias while increasing precision and the amount of information gained from mark–resight data sets. We provide implementations in both the BUGS language and an R package.