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Related Experiment Video

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Balancing precision and risk: should multiple detection methods be analyzed separately in N-mixture models?

Tabitha A Graves¹, J Andrew Royle, Katherine C Kendall

¹School of Forestry, Northern Arizona University, Flagstaff, Arizona, United States of America. Tabitha.Graves@colostate.edu

|December 20, 2012

Summary

Combining multiple detection methods for population abundance estimates can improve precision. However, separate analyses may be needed if landscape influences differ between methods, revealing distinct ecological drivers for grizzly bears.

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Basics of Multivariate Analysis in Neuroimaging Data

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Published on: July 24, 2010

Area of Science:

Wildlife Ecology
Population Dynamics
Conservation Biology

Background:

Multiple detection methods can enhance population abundance estimates' accuracy and precision.
Separate analysis of detection methods may be crucial when landscape influences vary, impacting abundance estimations.

Purpose of the Study:

To evaluate the impact of combining two detection methods (hair traps and bear rubs) on identifying variables important to grizzly bear local abundance.
To compare single-method versus joint-method analyses within hierarchical abundance models (N-mixture models).

Main Methods:

Utilized hierarchical abundance models (N-mixture models) with distinct components for hair trap and bear rub detection data.
Assessed if single-method analyses identified the same important variables and provided similar abundance estimates compared to joint analysis.

Main Results:

Joint analysis of hair trap and bear rub data increased precision and certainty in variable and model selection.
Single-method analyses identified different variables and predicted distinct spatial distributions of abundance.
Heterogeneity between detection methods can lead to different ecological insights.

Conclusions:

Comparing single-method and joint-method results is recommended to detect heterogeneity in N-mixture models.
Increased precision from combined methods must be weighed against the risk of obscuring method-specific ecological drivers.
The presented framework is valuable for species with detection heterogeneity across methods.