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Related Experiment Video

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Precision gain versus effort with joint models using detection/non-detection and banding data.

Jamie S Sanderlin¹, William M Block¹, Brenda E Strohmeyer¹

¹Rocky Mountain Research Station U.S.D.A. Forest Service Flagstaff Arizona.

Ecology and Evolution

|February 16, 2019

Summary

Combining multiple data sources improves wildlife population estimates. A new framework helps balance data precision gains against costs, optimizing study designs for better ecological inference.

Keywords:

abundance data integration integrated population models recruitment study design survival western bluebird wildfire effects

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Area of Science:

Ecology
Wildlife Population Dynamics
Statistical Modeling

Background:

Capture-recapture methods are informative but costly at large scales.
Integrating multiple data sources enhances parameter precision but requires cost-benefit analysis.
Existing models often lack detailed evaluation of precision gain versus data acquisition effort.

Purpose of the Study:

To present a general framework for evaluating the trade-offs between precision gained and costs of acquiring multiple data sources.
To demonstrate the application of Bayesian hierarchical joint models for improving population inference using combined data.
To quantify data source costs and evaluate parameter precision gains in a western bluebird population.

Main Methods:

Utilized an 8-year dataset of detection/non-detection and banding data from western bluebirds.
Constructed separate single data source models and a joint model incorporating both data types.
Employed Bayesian hierarchical joint models to estimate abundance, survival, and recruitment.

Main Results:

Joint models yielded more precise parameter estimates than single data models, with varying precision across parameters (survival > abundance > recruitment).
Banding data provided higher apparent survival precision than detection/non-detection data.
Adding detection/non-detection data to banding data offered minimal precision gain but increased spatial coverage and inference capabilities.

Conclusions:

The developed framework aids in designing cost-effective population monitoring studies by evaluating precision gains against effort.
Justification for increased costs of additional data types depends on specific project objectives and desired inference.
Joint data models offer a flexible approach to optimize ecological study designs and improve population parameter estimation.