R code and output supporting: Time series sightability modeling of animal populations

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Collection Period

2005
2016

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Northeastern Minnesota

Source information

This work represents original analysis and interpretation of Minnesota Department of Natural Resources moose survey (sightability trials and operational surveys) data.

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ArchMiller, Althea A
althea.archmiller@gmail.com

Abstract

The goal of our study was to expand a previously developed model-based approach to include random effects and a temporal spline for time series modeling of multiple years of operational survey data. We developed a Bayesian hierarchical model as our framework to build and compare fixed-effects and temporal model-based sightability models applied to 12 years of MN moose operational survey data. Here, we share the Program R code and data necessary to replicate the manuscript results that demonstrate how our time series sightability modeling approach can increase the precision of population estimators and predict population dynamics with smoother (and thus more realistic trends) through time.

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Referenced by

ArchMiller, Dorazio, St. Clair, Fieberg. 2018. Time series sightability modeling of animal populations. PLOS ONE.
https://doi.org/10.1371/journal.pone.0190706

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Attribution-NonCommercial-ShareAlike 3.0 United States
http://creativecommons.org/licenses/by-nc-sa/3.0/us/

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Funding Information

Minnesota Department of Natural Resources
Wildlife Restoration (Pittman-Robertson) Program
Minnesota Agricultural Experimental Station

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Suggested Citation

ArchMiller, Althea A; Fieberg, John R; Dorazio, Robert M; St. Clair, Katherine. (2017). R code and output supporting: Time series sightability modeling of animal populations. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/D6N30B.

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Version History

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2*
2018-01-16 04:34:52
The corresponding paper has been published in PLOS ONE, and the g_plots_results.html, programs_R.zip, and model_diagram.pdf files have been updated to reflect changes in the final publication.
2017-03-29 08:43:52
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