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Quantitative literature review of statistical methods in ecology that account for imperfect detection
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data
figures
models
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LICENSE.md
README.md
analysis_detection_covs.R
analysis_id_alltaxa.R
analysis_id_septaxa.R

README.md

imperfect-detection

A quantitative literature review of ecological statistical methods accounting for imperfect detection. We read 500+ ecology papers from 40+ years (collected using a stratified sampling method) and assessed which papers accounted for imperfect detection, if necessary. Probability that a paper accounted for imperfect detection was modeled as a function of several covariates including year, journal type, and the taxa studied. We used a hierarchical logistic regression model fit in a Bayesian framework.

The results are published in the following article:

Kellner, Kenneth F.; Swihart, Robert K. 2014. Accounting for imperfect detection in ecology: a quantitative review. PLoS ONE 9(10): e111436.

Metadata

The analysis_id_alltaxa.R file contain the framework of the Bayesian analysis in JAGS for the project. Additional information is provided in the comments of the file.

The analysis_id_septaxa.R file contain the framework of the Bayesian analysis in JAGS when each taxa was analyzed separately. Additional information is provided in the comments of the file.

The analysis_detection_covs.R file contains some simple calculations and characterizations of how reported detection probabilities varied in the sampled papers.

The models folder contains the BUGS models used; models/model_id_alltaxa.R for the full analysis and models/model_id_septaxa.R for the analysis separated by taxonomic group.

The figures folder contains code for Figure 1 in the paper as well as figures used in an oral presentation at the Midwest Fish & Wildlife Conference in 2015.

The data folder contains the raw data files (CSV format) used in the analyses as well as a script used to generate a basic version of the paper database.

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