Estimating Population Growth Rate From Capture–Recapture Data in Presence of Capture Heterogeneity

Pradel, Roger

doi:10.1198/jabes.2009.08064

Cited by 9 publications

(10 citation statements)

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“…This is expected to reduce estimation bias (Cormack, 1972) but it is also known that capture heterogeneity needs to be addressed (Abadi, Botha, & Altwegg, 2013) to avoid biased estimates of local survival and colonization. For that reason, mixtures of species effects on detection probability were fitted, with two or three components ("PradelRecMix" in RMark, Pradel, Choquet, Lima, Merritt, & Crespin, 2009). Models were compared using AICc to select the minimum adequate model with lowest AICc.…”

Section: Capture-recapture Analysis Of Species Encounter Historiesmentioning

confidence: 99%

Assessing species richness trends: Declines of bees and bumblebees in the Netherlands since 1945

Dooren

2019

Ecology and Evolution

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Estimating and predicting temporal trends in species richness is of general importance, but notably difficult because detection probabilities of species are imperfect and many datasets were collected in an opportunistic manner. We need to improve our capabilities to assess richness trends using datasets collected in unstandardized procedures with potential collection bias. Two methods are proposed and applied to estimate richness change, which both incorporate models for sampling effects and detection probability: (a) nonlinear species accumulation curves with an error variance model and (b) Pradel capture–recapture models. The methods are used to assess nationwide temporal trends (1945–2018) in the species richness of wild bees in the Netherlands. Previously, a decelerating decline in wild bee species richness was inferred for part of this dataset. Among the species accumulation curves, those with nonconstant changes in species richness are preferred. However, when analyzing data subsets, constant changes became selected for non‐Bombus bees (for samples in collections) and bumblebees (for spatial grid cells sampled in three periods). Smaller richness declines are predicted for non‐Bombus bees than bumblebees. However, when relative losses are calculated from confidence intervals limits, they overlap and touch zero loss. Capture–recapture analysis applied to species encounter histories infers a constant colonization rate per year and constant local species survival for bumblebees and other bees. This approach predicts a 6% reduction in non‐Bombus species richness from 1945 to 2018 and a significant 19% reduction for bumblebees. Statistical modeling to detect species richness time trends should be systematically complemented with model checking and simulations to interpret the results. Data inspection, assessing model selection bias, and comparisons of trends in data subsets were essential model checking strategies in this analysis. Opportunistic data will not satisfy the assumptions of most models and this should be kept in mind throughout.

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Section: Capture-recapture Analysis Of Species Encounter Historiesmentioning

confidence: 99%

Assessing species richness trends: Declines of bees and bumblebees in the Netherlands since 1945

Dooren

2019

Ecology and Evolution

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“…Unequal detection rates may lead to biases in abundance estimate, estimated survival probability or population growth in the context of open populations (Carothers 1973, Schwarz 2001. Heterogeneity in detection probability is still receiving attention in contemporary studies (Crespin et al 2008, Cubaynes et al 2010, Pradel et al 2010, Marescot et al 2011, Oliver et al 2011, Fletcher et al 2012, Abadi et al 2013, White and Cooch 2017, and new methods are being developed to address heterogeneity, such as multievent models (Pradel 2009).…”

Section: Individual Heterogeneity Seen As a Nuisancementioning

confidence: 99%

Individual heterogeneity and capture–recapture models: what, why and how?

Giménez

Cam

Gaillard

2017

Oikos

101

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Variation 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 tradeoffs, 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.

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“…In these models, a latent variable is used to assign individuals to one of the mixture components characterized by specific parameters. CR mixture models have had several applications in both ecology and evolution (Dorazio & Royle 2003;Ve´ran et al 2007;Bunge & Barger 2008;Pledger & Phillpot 2008;Morgan & Ridout 2009;Tyrrell et al 2009;Wanger et al 2009;Cubaynes et al 2010;Pe´ron et al 2010;Pradel et al 2010;Oliver et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Assessing individual heterogeneity using model selection criteria: how many mixture components in capture–recapture models?

et al. 2012

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Summary1. Capture-recapture mixture models are important tools in evolution and ecology to estimate demographic parameters and abundance while accounting for individual heterogeneity. A key step is to select the correct number of mixture components i) to provide unbiased estimates that can be used as reliable proxies of fitness or ingredients in management strategies and ii) classify individuals into biologically meaningful classes. However, there is no consensus method in the statistical literature for selecting the number of components. 2. In ecology, most studies rely on the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) that has recently gained attention in ecology. The Integrated Completed Likelihood criterion (ICL; IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22, 719) was specifically developed to favour well-separated components, but its use has never been investigated in ecology. 3. We compared the performance of AIC, BIC and ICL for selecting the number of components with regard to a) bias and accuracy of survival and detection estimates and b) success in selecting the true number of components using extensive simulations and data on wolf (Canis lupus) that were used for management through survival and abundance estimation. 4. Bias in survival and detection estimates was <0.02 for both AIC and BIC, and more than 0.09 for ICL, while mean square error was <0.05 for all criteria. As expected, bias increased as heterogeneity increased. Success rates of AIC and BIC in selecting the 'true' number of components were better than ICL (68% for AIC, 58% for BIC, and 16% for ICL). As the degree of heterogeneity increased, AIC (and BIC in a lesser extent) overestimated the number of components, while ICL often underestimated this number. For the wolf study, the 2-class model was selected by BIC and ICL, while AIC could not decide between the 2-and 3-class models. 5. We recommend using AIC or BIC when the aim is to estimate parameters. Regarding classification, we suggest taking the classification quality into account by using ICL in conjunction with BIC, pending further work to adapt its penalty term for capture-recapture data.

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Estimating Population Growth Rate From Capture–Recapture Data in Presence of Capture Heterogeneity

Cited by 9 publications

References 0 publications

Assessing species richness trends: Declines of bees and bumblebees in the Netherlands since 1945

Assessing species richness trends: Declines of bees and bumblebees in the Netherlands since 1945

Individual heterogeneity and capture–recapture models: what, why and how?

Assessing individual heterogeneity using model selection criteria: how many mixture components in capture–recapture models?

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