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Related Experiment Video

Updated: Jun 26, 2026

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

Efficient estimation of abundance for patchily distributed populations via two-phase, adaptive sampling.

Michael J Conroy¹, Jonathan P Runge, Richard J Barker

¹U.S. Geological Survey, Georgia Cooperative Fish and Wildlife Research Unit, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia 30602, USA. mconroy@uga.edu

|January 14, 2009

Summary

Estimating populations of rare, patchy species is challenging. This new Bayesian two-phase sampling method efficiently estimates abundance using detection and capture-mark-recapture data, proving accurate in simulations.

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Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

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Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

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Published on: July 30, 2019

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

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Published on: November 20, 2017

Area of Science:

Ecology
Population Biology
Statistical Ecology

Background:

Organisms often exhibit patchy distributions, making abundance estimation difficult and inefficient with traditional methods like capture-mark-recapture (CMR).
Intensive sampling approaches struggle with low detection rates and spatial heterogeneity in patchily distributed populations.

Purpose of the Study:

To develop and validate a novel two-phase sampling scheme and Bayesian model for efficiently estimating abundance in patchily distributed populations.
To provide a robust method for estimating global abundance when complete sampling of all patches is infeasible.

Main Methods:

A two-phase Bayesian sampling framework combining occupancy estimation (binomial detection) with intensive sampling (CMR) in selected sites.
Joint likelihood modeling to integrate detection and CMR data, estimating abundance-detection relationships.
Bayesian inference to estimate abundance, detection, and capture probabilities, accounting for spatial heterogeneity.

Main Results:

The proposed two-phase adaptive approach demonstrated low bias and mean-square error (MSE) in simulation studies.
Bayesian credibility intervals showed near-nominal coverage of true parameter values across various population and design scenarios.
The method successfully estimated abundance for vole populations (Microtus spp.) in Montana.

Conclusions:

The two-phase Bayesian sampling strategy offers an efficient and accurate method for estimating abundance of rare and patchily distributed species.
This approach is particularly valuable for ecological studies requiring global abundance estimates when comprehensive sampling is not possible.
The model effectively integrates limited detection data with intensive sampling data for robust population estimation.