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370 results on '"Lindberg, Mark S."'

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

Author

Schmidt, Joshua H., Johnson, Devin S., Lindberg, Mark S., and Adams, Layne G.

Subjects
Quantitative Biology - Quantitative Methods, Quantitative Biology - Populations and Evolution
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., Comment: 22 pages, 2 figures, 2 appendices

Published
2015
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48. Bayesian mark–recapture–resight–recovery models: increasing user flexibility in the BUGS language

Author

Riecke, Thomas V., Gibson, Dan, Leach, Alan G., Lindberg, Mark S., Schaub, Michael, and Sedinger, James S.

Subjects
demography, mark���resight, Ecology, mark-resight, robust design, mark–resight, unobservable state, Bayesian, Ecology, Evolution, Behavior and Systematics, QH540-549.5, fitness
Abstract

Estimating demographic parameters of interest is a critical component of applied conservation biology and evolutionary ecology, where demographic models and demographic data have become increasingly complex over the last several decades. These advances have been spurred by the development and use of information theoretic approaches, programs such as MARK and SURGE, and Bayesian inference. The use of Bayesian analyses has also become increasingly popular, where WinBUGS, JAGS, Stan, and NIMBLE provide increased user flexibility. Despite recent advances in Bayesian demographic modeling, some capture–recapture models that have been implemented in Program MARK remain unavailable to quantitative ecologists that wish to use Bayesian modeling approaches. We provide novel parameterizations of capture–mark–recapture–resight–recovery models implemented in Program MARK that have not yet been implemented in the BUGS language. Simulations show that the models described herein provide accurate parameter estimates. Our parameterizations of these models can easily be extended to estimate additional parameters such as entry probability, additional live states, or cause-specific mortality rates. Additionally, implementing these models in a Bayesian framework allows users to readily estimate parameters as mixtures, incorporate random individual or temporal variation, and use informative priors to assist with parameter estimation.

Published
2021

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