Simulation to evaluate response of population models to annual trends in detectability
August 21, 2019
In 'Simulation to evaluate response of population models to annual trends in detectability', we provide data and R code necessary to create simulation scenarios and estimate trends with different population models (Monroe et al. 2019).
Literature cited: Monroe, A. P., G. T. Wann, C. L. Aldridge, and P. S. Coates. 2019. The importance of simulation assumptions when evaluating detectability in population models. Ecosphere 10(7):e02791. 10.1002/ecs2.2791, http://onlinelibrary.wiley.com/doi/10.1002/ecs2.2791/full.
Citation Information
| Publication Year | 2019 |
|---|---|
| Title | Simulation to evaluate response of population models to annual trends in detectability |
| DOI | 10.5066/P91L28PG |
| Authors | Adrian P Monroe, Greg Wann, Cameron Aldridge, Peter S Coates |
| Product Type | Data Release |
| Record Source | USGS Digital Object Identifier Catalog |
| USGS Organization | Fort Collins Science Center |
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The importance of simulation assumptions when evaluating detectability in population models
Population monitoring is important for investigating a variety of ecological questions, and N-mixture models are increasingly used to model population size (N) and trends (lambda) while estimating detectability (p) from repeated counts within primary periods (when populations are closed to changes). Extending these models to dynamic processes with serial dependence across primary periods may relax
Authors
Adrian P. Monroe, Gregory T. Wann, Cameron L. Aldridge, Peter S. Coates
Cameron L Aldridge, PhD
Branch Chief / Supervisory Research Ecologist
Branch Chief / Supervisory Research Ecologist
Email
Phone
Peter Coates
Research Wildlife Biologist
Research Wildlife Biologist
Email
Phone
Related Content
The importance of simulation assumptions when evaluating detectability in population models
Population monitoring is important for investigating a variety of ecological questions, and N-mixture models are increasingly used to model population size (N) and trends (lambda) while estimating detectability (p) from repeated counts within primary periods (when populations are closed to changes). Extending these models to dynamic processes with serial dependence across primary periods may relax
Authors
Adrian P. Monroe, Gregory T. Wann, Cameron L. Aldridge, Peter S. Coates
Cameron L Aldridge, PhD
Branch Chief / Supervisory Research Ecologist
Branch Chief / Supervisory Research Ecologist
Email
Phone
Peter Coates
Research Wildlife Biologist
Research Wildlife Biologist
Email
Phone