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Partitioning detectability components in populations subject to within-season temporary emigration using binomial
Katherine M O'Donnell1, Frank R Thompson2, Raymond D Semlitsch1
1Division of Biological Sciences, University of Missouri, 105 Tucker Hall, Columbia, Missouri, 65211, United States of America.
Plos One
|March 17, 2015
Summary
Accurate animal population estimates require accounting for imperfect detection. This study developed advanced hierarchical models to better estimate abundance and detection probability, improving ecological understanding and reliability of population trends.
Area of Science:
- Ecology
- Wildlife population modeling
- Statistical ecology
Background:
- Estimating animal population sizes and trends reliably requires accounting for imperfect detection, where not all individuals are detected.
- Hierarchical models can estimate abundance and detection probability simultaneously, but variations in detectability due to factors like temporary emigration can cause biased estimates.
- Existing models may not fully capture the complexities of animal availability and detection probability.
Purpose of the Study:
- To extend hierarchical binomial mixture models to simultaneously account for multiple sources of variation in detectability, including temporary emigration.
- To improve the accuracy and reliability of population size and trend estimates in ecological studies.
- To provide a more congruent statistical framework aligning with ecological understanding of animal populations.
Main Methods:
- Developed an extended hierarchical binomial mixture model incorporating a state process for abundance patterns and an observation model for imperfect detection.
- Accounted for temporary emigration between sampling periods and other factors influencing availability and conditional detection probability.
- Applied the model to a case study of southern red-backed salamanders (Plethodon serratus) using survey data from 2010-2012.
Main Results:
- The extended model demonstrated the influence of ecological factors on salamander abundance and detectability.
- Aspect (northeasterly) was the strongest predictor of salamander abundance.
- Time since rainfall negatively impacted salamander surface activity, while woody cover and rocks increased detection probability.
Conclusions:
- Explicitly modeling temporary emigration and other detectability components enhances the congruence between statistical models and ecological understanding.
- Survey design, including maximizing species availability and conditional detection probability, is crucial for reliable population parameter estimation.
- The developed hierarchical model offers a more robust approach for estimating animal populations with imperfect detection.
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