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Probit Models For Capture-Recapture Data Subject To Imperfect Detection, Individual Heterogeneity And Misidentification, Brett T. Mcclinktock, Larissa L. Bailey, Brian P. Dreher, William A. Link
Probit Models For Capture-Recapture Data Subject To Imperfect Detection, Individual Heterogeneity And Misidentification, Brett T. Mcclinktock, Larissa L. Bailey, Brian P. Dreher, William A. Link
United States Geological Survey: Staff Publications
As noninvasive sampling techniques for animal populations have become more popular, there has been increasing interest in the development of capture-recapture models that can accommodate both imperfect detection and misidentification of individuals (e.g., due to genotyping error). However, current methods do not allow for individual variation in parameters, such as detection or survival probability. Here we develop misidentification models for capture-recapture data that can simultaneously account for temporal variation, behavioral effects and individual heterogeneity in parameters. To facilitate Bayesian inference using our approach, we extend standard probit regression techniques to latent multinomial models where the dimension and zeros of the …
Probit Models For Capture-Recapture Data Subject To Imperfect Detection, Individual Heterogeneity And Misidentification1, Brett T. Mcclintock, Larissa L. Bailey, Brian P. Dreher, William A. Link
Probit Models For Capture-Recapture Data Subject To Imperfect Detection, Individual Heterogeneity And Misidentification1, Brett T. Mcclintock, Larissa L. Bailey, Brian P. Dreher, William A. Link
United States Geological Survey: Staff Publications
As noninvasive sampling techniques for animal populations have become more popular, there has been increasing interest in the development of capture-recapture models that can accommodate both imperfect detection and misidentification of individuals (e.g., due to genotyping error). However, current methods do not allow for individual variation in parameters, such as detection or survival probability. Here we develop misidentification models for capture-recapture data that can simultaneously account for temporal variation, behavioral effects and individual heterogeneity in parameters. To facilitate Bayesian inference using our approach, we extended standard probit regression techniques to latent multinomial models where the dimension and zeros of the …
Spatially Explicit Models For Inference About Density In Unmarked Or Partially Marked Populations, Richard B. Chandler, J. Andrew Royle
Spatially Explicit Models For Inference About Density In Unmarked Or Partially Marked Populations, Richard B. Chandler, J. Andrew Royle
United States Geological Survey: Staff Publications
Recently developed spatial capture-recapture (SCR) models represent a major advance over traditional capture-recapture (CR) models because they yield explicit estimates of animal density instead of population size within an unknown area. Furthermore, unlike non-spatial CR methods, SCR models account for heterogeneity in capture probability arising from the juxtaposition of animal activity centers and sample locations. Although the utility of SCR methods is gaining recognition, the requirement that all individuals can be uniquely identified excludes their use in many contexts. In this paper, we develop models for situations in which individual recognition is not possible, thereby allowing SCR concepts to be …
Spatially Explicit Models For Inference About Density In Unmarked Or Partially Marked Populations, Richard B. Chandler, J. Andrew Royle
Spatially Explicit Models For Inference About Density In Unmarked Or Partially Marked Populations, Richard B. Chandler, J. Andrew Royle
United States Geological Survey: Staff Publications
Recently developed spatial capture-recapture (SCR) models represent a major advance over traditional capture-recapture (CR) models because they yield explicit estimates of animal density instead of population size within an unknown area. Futhermore, unlike nonspatial CR methods, SCR models account for heterogeneity in capture probability arising from the juxtaposition of animal activity centers and sample locations. Although the utility of SCR methods is gaining recognition, the requirement that all individuals can be uniquely identified exludes their use in many contexts. In this paper, we develop models for situations in which individual recognition is not possible, thereby allowing SCR concepts to be …