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Related Experiment Video

Updated: Jun 11, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Bias, precision, and parameter redundancy in complex multistate models with unobservable states.

Larissa L Bailey¹, Sarah J Converse, William L Kendall

¹USGS Patuxent Wildlife Research Center, 12100 Beech Forest Road, Laurel, Maryland 20708, USA. Larissa.Bailey@colostate.edu

|June 30, 2010

Summary

Multistate mark-recapture models can estimate survival and transition probabilities, even with temporary emigration. However, parameter redundancy can bias estimates, necessitating careful model evaluation for accurate ecological insights.

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Last Updated: Jun 11, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

Ecology
Wildlife Biology
Conservation Science

Background:

Multistate mark-recapture models are crucial for estimating survival and transition probabilities in animal populations.
Temporary emigration, where individuals are temporarily unavailable for capture, presents challenges in traditional mark-recapture studies.
Unobservable states in these models can complicate parameter estimation and model selection.

Purpose of the Study:

To examine complex multistate mark-recapture models with both observable and unobservable states.
To investigate the impact of parameter redundancy on the accuracy and precision of survival and transition probability estimates.
To provide guidance for practitioners using these models in ecological research.

Main Methods:

Utilized numerical methods to analyze models with two observable and two unobservable states.
Explored model identifiability and potential parameter redundancy issues.
Considered biological systems involving island-nesting albatross and pond-breeding amphibians as case studies.

Main Results:

Found that while many models are theoretically identifiable, practical estimation of parameters can be challenging.
Parameter redundancy was identified as a significant issue, potentially leading to biased estimates.
Model complexity and the presence of unobservable states can hinder accurate parameter estimation.

Conclusions:

Practitioners should focus on improving detection probabilities and employing robust design sampling to enhance estimate quality.
Investigating theoretical identifiability and potential near-singularity is recommended before applying complex models.
Careful consideration of model structure and parameterization is essential for reliable ecological inference from mark-recapture data.