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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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Published on: February 25, 2013

Inference about density and temporary emigration in unmarked populations.

Richard B Chandler¹, J Andrew Royle, David I King

¹USGS Patuxent Wildlife Research Center, 12100 Beech Forest Rd., Laurel, Maryland 20708-4039, USA. rchandler@usgs.gov

|August 30, 2011

Summary

This study introduces a new hierarchical model for estimating animal population density, accounting for temporary movement and imperfect detection. The model accurately estimates density for unmarked populations using standard survey methods.

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Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Area of Science:

Ecology
Wildlife Population Modeling
Quantitative Biology

Background:

Traditional population density models often assume closed populations and uniform spatial distribution, which rarely hold true for mobile organisms.
Mobile populations experience temporary emigration and imperfect detection, complicating accurate density estimation.
Existing survey methods may not fully account for these dynamic population processes.

Purpose of the Study:

To develop a flexible hierarchical model for estimating the density of unmarked populations.
To incorporate temporary emigration and imperfect detection into population density estimation.
To provide a robust framework applicable to various standard wildlife survey methods.

Main Methods:

Developed a hierarchical statistical model for density inference in open populations.
The model accommodates temporary emigration and imperfect detection.
Applied the model to data from repeated point counts using removal, double-observer, or distance sampling, and cue counts.

Main Results:

Simulation studies confirmed unbiased parameter estimation under various temporary emigration scenarios.
The model accurately estimated Chestnut-sided Warbler density (1.09 birds/ha) using repeated removal sampling data.
Density estimates were comparable to those from intensive spot-mapping efforts.

Conclusions:

The presented hierarchical model offers a robust and adaptable approach for estimating population density in mobile species.
It effectively addresses limitations of traditional models by incorporating temporary emigration and imperfect detection.
The model's applicability across diverse survey methods enhances its utility for ecological research and wildlife management. The model is available in the R package unmarked.