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Simulation to evaluate response of population models to annual trends in detectability

Dates

Publication Date
Start Date
2012-04-23
End Date
2017-06-30

Citation

Monroe, A.P., Wann, G., Aldridge, C., and Coates, P.S., 2019, Simulation to evaluate response of population models to annual trends in detectability: U.S. Geological Survey data release, https://doi.org/10.5066/P91L28PG.

Summary

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.

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Attached Files

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lek_attend_age.csv 51.13 MB
lek_attend_noage.csv 21.82 MB
simulation_metadata_final.xml
Original FGDC Metadata

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27.13 KB
code.zip 9.54 KB

Purpose

These simulations are used to evaluate effects of annual trends in detectability on estimates of population trends from different hierarchical population models. This analysis first simulates hypothetical populations of greater sage-grouse (Centrocercus urophasianus) attending leks and then uses posterior samples of model coefficients characterizing lek attendance by yearling and adult greater sage-grouse from 'lek_attend_age.csv' to inform simulations of the detection process during lek counts. The observed counts are then analyzed with population models that either use the maximum number of grouse observed from repeated counts at lek (peak counts) or uses repeated counts to estimate detectability (N-mixture model). Posterior samples of coefficients from a lek attendance model that averaged attendance rates across age classes ('lek_attend_noage.csv') are used to correct peak counts for variation in attendance or to provide an informative prior for annual attendance in N-mixture models. The files 'lek_attend_age.csv' and 'lek_attend_noage.csv' are required for analyses with R scripts 'run.sim.R' and 'model.run.fun.R'

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Communities

  • Fort Collins Science Center (FORT)
  • USGS Data Release Products

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Additional Information

Identifiers

Type Scheme Key
DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/P91L28PG
USGS_ScienceCenter https://www.sciencebase.gov/vocab/category/item/identifier Fort Collins Science Center
USGS_MissionArea https://www.sciencebase.gov/vocab/category/item/identifier Ecosystems

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