scholarly journals Individual heterogeneity and capture-recapture models: what, why and how?

Oikos ◽  
2017 ◽  
Vol 127 (5) ◽  
pp. 664-686 ◽  
Author(s):  
Olivier Gimenez ◽  
Emmanuelle Cam ◽  
Jean-Michel Gaillard
Keyword(s):  
Individual Heterogeneity ◽  
Capture Recapture

scholarly journals Probit models for capture–recapture data subject to imperfect detection, individual heterogeneity and misidentification

2014 ◽  
Vol 8 (4) ◽  
pp. 2461-2484 ◽  
Author(s):  
Brett T. McClintock ◽  
Larissa L. Bailey ◽  
Brian P. Dreher ◽  
William A. Link
Keyword(s):  
Individual Heterogeneity ◽  
Imperfect Detection ◽  
Probit Models ◽  
Capture Recapture ◽  
Data Subject

scholarly journals EM and component-wise boosting for Hidden Markov Models: a machine-learning approach to capture-recapture

2016 ◽  
Author(s):  
Robert W. Rankin
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Markov Models ◽  
Hidden Markov ◽  
Individual Heterogeneity ◽  
Learning Approach ◽  
Unified Framework ◽  
Machine Learning Approach ◽  
Capture Recapture ◽  
Linear Effects

AbstractThis study introduces statistical boosting for capture-mark-recapture (CMR) models. It is a shrinkage estimator that constrains the complexity of a CMR model in order to promote automatic variable-selection and avoid over-fitting. I discuss the philosophical similarities between boosting and AIC model-selection, and show through simulations that a boosted Cormack-Jolly-Seber model often out-performs AICc methods, in terms of estimating survival and abundance, yet yields qualitatively similar estimates. This new boosted CMR framework is highly extensible and could provide a rich, unified framework for addressing many topics in CMR, such as non-linear effects (splines and CART-like trees), individual-heterogeneity, and spatial components.

scholarly journals Spatial Mark-Resight for Categorically Marked Populations with an Application to Genetic Capture-Recapture

2018 ◽  
Author(s):  
Ben C. Augustine ◽  
Frances E. C. Stewart ◽  
J. Andrew Royle ◽  
Jason T. Fisher ◽  
Marcella J. Kelly
Keyword(s):  
Individual Heterogeneity ◽  
Positive Bias ◽  
Telemetry Data ◽  
Detection Function ◽  
Noninvasive Methods ◽  
Data Set ◽  
Density Estimates ◽  
Mark Recapture ◽  
Capture Recapture ◽  
Scr Model

AbstractThe estimation of animal population density is a fundamental goal in wildlife ecology and management, commonly met using mark recapture or spatial mark recapture (SCR) study designs and statistical methods. Mark-recapture methods require the identification of individuals; however, for many species and sampling methods, particularly noninvasive methods, no individuals or only a subset of individuals are individually identifiable. The unmarked SCR model, theoretically, can estimate the density of unmarked populations; however, it produces biased and imprecise density estimates in many sampling scenarios typically encountered. Spatial mark-resight (SMR) models extend the unmarked SCR model in three ways: 1) by introducing a subset of individuals that are marked and individually identifiable, 2) introducing the possibility of individual-linked telemetry data, and 3) introducing the possibility that the capture-recapture data from the survey used to deploy the marks can be used in a joint model, all improving the reliability of density estimates. The categorical spatial partial identity model (SPIM) improves the reliability of density estimates over unmarked SCR along another dimension, by adding categorical identity covariates that improve the probabilistic association of the latent identity samples. Here, we combine these two models into a “categorical SMR” model to exploit the benefits of both models simultaneously. We demonstrate using simulations that SMR alone can produce biased and imprecise density estimates with sparse data and/or when few individuals are marked. Then, using a fisher (Pekania pennanti) genetic capture-recapture data set, we show how categorical identity covariates, marked individuals, telemetry data, and jointly modeling the capture survey used to deploy marks with the resighting survey all combine to improve inference over the unmarked SCR model. As previously seen in an application of the categorical SPIM to a real-world data set, the fisher data set demonstrates that individual heterogeneity in detection function parameters, especially the spatial scale parameter σ, introduces positive bias into latent identity SCR models (e.g., unmarked SCR, SMR), but the categorical SMR model provides more tools to reduce this positive bias than SMR or the categorical SPIM alone. We introduce the possibility of detection functions that vary by identity category level, which will remove individual heterogeneity in detection function parameters than is explained by categorical covariates, such as individual sex. Finally, we provide efficient SMR algorithms that accommodate all SMR sample types, interspersed marking and sighting periods, and any number of identity covariates using the 2-dimensional individual by trap data in conjunction with precomputed constraint matrices, rather than the 3-dimensional individual by trap by occasion data used in SMR algorithms to date.

scholarly journals A Generalized Estimating Equations Approach to Model Heterogeneity and Time Dependence in Capture-Recapture Studies

2017 ◽  
Vol 3 (1) ◽  
pp. 9-17 ◽  
Author(s):  
Md. Abdus Salam Akanda ◽  
Russell Alpizar-Jara
Keyword(s):  
Time Dependence ◽  
Linear Correlation ◽  
Generalized Estimating Equations ◽  
Estimating Equations ◽  
Real Data ◽  
Individual Heterogeneity ◽  
Animal Population ◽  
Population Parameters ◽  
Capture Recapture ◽  
Generalized Estimating

AbstractIndividual heterogeneity in capture probabilities and time dependence are fundamentally important for estimating the closed animal population parameters in capture-recapture studies. A generalized estimating equations (GEE) approach accounts for linear correlation among capture-recapture occasions, and individual heterogeneity in capture probabilities in a closed population capture-recapture individual heterogeneity and time variation model. The estimated capture probabilities are used to estimate animal population parameters. Two real data sets are used for illustrative purposes. A simulation study is carried out to assess the performance of the GEE estimator. A Quasi-Likelihood Information Criterion (QIC) is applied for the selection of the best fitting model. This approach performs well when the estimated population parameters depend on the individual heterogeneity and the nature of linear correlation among capture-recapture occasions.

scholarly journals Individual heterogeneity and capture-recapture models: what, why and how?

2017 ◽  
Author(s):  
Olivier Gimenez ◽  
Emmanuelle Cam ◽  
Jean-Michel Gaillard
Keyword(s):  
Population Dynamics ◽  
Life History ◽  
Finite Mixture Models ◽  
Random Effect ◽  
Life History Evolution ◽  
Individual Heterogeneity ◽  
Future Research ◽  
Imperfect Detection ◽  
Trade Offs ◽  
Capture Recapture

AbstractVariation between and within individuals in life history traits is ubiquitous in natural populations. When affecting fitness-related traits such as survival or reproduction, individual heterogeneity plays a key role in population dynamics and life history evolution. However, it is only recently that properly accounting for individual heterogeneity when studying population dynamics of free-ranging populations has been made possible through the development of appropriate statistical models. We aim here to review case studies of individual heterogeneity in the context of capture-recapture models for the estimation of population size and demographic parameters with imperfect detection. First, we define what individual heterogeneity means and clarify the terminology used in the literature. Second, we review the literature and illustrate why individual heterogeneity is used in capture-recapture studies by focusing on the detection of life-history trade-offs, including senescence. Third, we explain how to model individual heterogeneity in capture-recapture models and provide the code to fit these models (https://github.com/oliviergimenez/indhet_in_CRmodels). The distinction is made between situations in which heterogeneity is actually measured and situations in which part of the heterogeneity remains unobserved. Regarding the latter, we outline recent developments of random-effect models and finite-mixture models. Finally, we discuss several avenues for future research.

Capture recapture électroniques en phosphorescence

1952 ◽  
Vol 12 (7) ◽  
pp. 749-801 ◽  
Author(s):  
Daniel Curie
Keyword(s):  
Capture Recapture

An Urn Model for the Multi-Sample Capture/Recapture Sequential Tagging Process.

1987 ◽  
Author(s):  
Jose G. Leite ◽  
Carlos A. De Braganca Pereira
Keyword(s):  
Urn Model ◽  
Capture Recapture
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