Approximations of the Bias in the Jolly-Seber Capture-Recapture Model

Gilbert, R.O.

doi:10.2307/2529173

Cited by 69 publications

(40 citation statements)

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“…If CH is strong and some individuals have very low capture probabilities, it is also possible to detect apparent age effects in / (Pre´vot-Julliard, Lebreton & Pradel 1998), in a fashion similar to that induced by transients (Pradel, Cooch & Cooke 1995). Given that CH is likely to occur for many populations, we can expect some bias in/ if our model does not allow for it (Cormack 1972;Carothers 1973Carothers , 1979Gilbert 1973;Nichols and Pollock 1983;Stromborg et al 1988;Pollock et al 1990;Hwang & Chao 1995;Manly, McDonald, & McDonald 1999;Pledger, Pollock & Norris 2003;Devineau, Choquet & Lebreton 2006;Cubaynes et al 2010). Carothers (1973) suggested that the bias in/ caused by CH will often be small, especially when compared with the bias CH causes to estimates of abundance.…”

Section: Introductionmentioning

confidence: 99%

Bias in estimation of adult survival and asymptotic population growth rate caused by undetected capture heterogeneity

et al. 2011

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Summary 1.Mark-recapture studies are often used to estimate adult survival probability / ð Þ, which is an important demographic parameter for long-lived species, as it can have a large impact on the population growth rate. We consider the impact of variation in capture probability among individuals (capture heterogeneity) on the estimation of / from a mark-recapture study and thence on estimation of the asymptotic population growth rate k ð Þ. 2. We review the mechanisms by which capture heterogeneity arises, methods of allowing for it in the analysis, and use simulation to assess the power of detecting three types of capture heterogeneity (two-group heterogeneity, trap-response and temporary emigration) using standard mark-recapture lack-of-fit tests. 3. We use simulation to assess the bias that can arise in the estimation of / from a mark-recapture study when we do not allow for capture heterogeneity. Using a generic population model, we assess the effect this bias has on estimation of k. 4. We use our results on the power of the lack-of-fit tests, together with a measure of the size of the bias relative to the standard error of the estimate of /, to assess which situations might lead to an important level of undetected bias. Our results suggest that undetected bias is not likely to be an issue when there is trap-response, owing to the lack-of-fit tests having sufficient power to detect any trap-response that could lead to non-negligible bias. For two-group heterogeneity, the worst bias generally occurs when the difference between the capture probabilities for the two groups is moderate and both capture probabilities are low. For temporary emigration, the worst bias generally occurs when the rate of emigration and the capture probability are both low. 5. We illustrate the issues for conservation management using data from studies of Hector's dolphin (Cephalorhynchus hectori) in New Zealand and wolves (Canis lupus) in France. 6. Previous studies have suggested that capture heterogeneity will generally lead to a relatively small bias in the estimate of /. However, given the high sensitivity of the asymptotic population growth rate to adult survival, a small bias in / might lead to nontrivial bias in the estimate of k.

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Section: Introductionmentioning

confidence: 99%

Bias in estimation of adult survival and asymptotic population growth rate caused by undetected capture heterogeneity

et al. 2011

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“…Similar mean percent error were determined for the Schnabel (26), the Overton (34), the calendar-of-catches (27), and the 5-day removal (25) (Manly, 1970;Carothers, 1973) and the intrinsic bias of the estimator when all assumptions are met (Gilbert, 1973). This study, using data generated for 2 small mammal species in enclosed systems replicated for season and community-type, allowed the evaluation of various estimates under the constraints of field conditions.…”

Section: Resultsmentioning

confidence: 62%

Comparative accuracy of population estimators for enclosed small mammal populations

Gorynska¹

1981

Acta Theriol.

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“…It does not address bias due to heterogeneity of capture probabilities that can produce negatively biased estimates with the bias being more severe with smaller sample sizes (Gilbert 1973, Hammond 1986. If the abundance estimates are negatively biased, this bias will be greatest in the estimate using only photographs scored as quality 1 and 2 because of the reduced sample size and the higher probability of frequently captured whales being represented by a high-quality photograph in the data.…”

Section: Discussionmentioning

confidence: 99%

“…The selection procedure described above does not address potential bias due to heterogeneity of capture probabilities that can negatively bias estimates with the bias being more severe with smaller sample sizes (Gilbert 1973, Hammond 1986. To determine if the decrease in sample size might increase the possible effects of heterogeneity, we simulated this sample size decrease by randomly removing photographs equal to the number removed during the three photographic quality removal steps.…”

Section: Effects On Abundance Estimatesmentioning

confidence: 99%

“…Because the subset of photographs used to evaluate the variability in the evaluation process was heavily weighted toward whole and nonpartial flukes, estimated probabilities of changing half and partial scores were imprecise. Because of this imprecision, this analysis was rerun where only overall quality scores were reassigned, and the results were comparable.The selection procedure described above does not address potential bias due to heterogeneity of capture probabilities that can negatively bias estimates with the bias being more severe with smaller sample sizes (Gilbert 1973, Hammond 1986. To determine if the decrease in sample size might increase the possible effects of heterogeneity, we simulated this sample size decrease by randomly removing photographs equal to the number removed during the three photographic quality removal steps.…”

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Balancing bias and precision in capture‐recapture estimates of abundance

Friday¹,

Smith

Stevick

et al. 2008

Marine Mammal Science

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Capture-recapture estimates of abundance using photographic identification data are sensitive to the quality of photographs used and distinctiveness of individuals in the population. Here analyses are presented for examining the effects of photographic quality and individual animal distinctiveness scores and for objectively selecting a subset of data to use for capture-recapture analyses using humpback whale (Megaptera novaeangliae) data from a 2-year study in the North Atlantic. Photographs were evaluated for their level of quality and whales for their level of individual distinctiveness. Photographic quality scores had a 0.21 probability of changing by a single-quality level, and there were no changes by two or more levels. Individual distinctiveness scores were not independent of photographic quality scores. Estimates of abundance decreased as poor-quality photographs were removed. An appropriate balance between precision and bias in abundance estimates was achieved by removing the lowest-quality photographs and those of incompletely photographed flukes given our assumptions about the true population abundance. A simulation of the selection process implied that, if the estimates are negatively biased by heterogeneity, the increase in bias produced by decreasing the sample size is not more than 2%. Capture frequencies were independent of individual distinctiveness scores.Key words: abundance estimation, capture-recapture, humpback whale, individual animal distinctiveness, individual identification, mark-recapture, Megaptera novaeangliae, North Atlantic, photographic identification, photographic quality.Photographs of natural markings are used to identify individual animals in a process known as photo-identification. Photo-identification has been used in a number of capture-recapture studies to estimate animal abundance and survival rates of several species (e.g., Buckland 1990, Hammond et al. 1990, Flatt et al. 1997, Langtimm et al. 1998, Baker 1999, Zeh et al. 2002, Calambokidis and Barlow 2004, Mizroch et al. 2004. When photo-identification is used in capture-recapture studies, the ability to accurately identify individuals from photographs needs to be examined. Accurate identification of individuals will depend on the quality of the photographs used and the distinctiveness of the natural markings of the individuals (Hammond 1986). Errors in identification can occur as false negatives and false positives and affect the accuracy of the resulting estimates.Photographic quality (hereafter referred to as quality) includes the clarity and contrast of the photograph, the angle of the markings to the plane of the photograph (angle), and the proportion of the animal or identifying feature that is photographed. Natural markings include color patterns, the shape of features, and scars. The distinctiveness of markings can depend on the complexity of a color pattern or a feature's shape and the number and size of scars.False-positive errors occur when different individuals are incorrectly identified as the same ...

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Approximations of the Bias in the Jolly-Seber Capture-Recapture Model

Cited by 69 publications

References 3 publications

Bias in estimation of adult survival and asymptotic population growth rate caused by undetected capture heterogeneity

Bias in estimation of adult survival and asymptotic population growth rate caused by undetected capture heterogeneity

Comparative accuracy of population estimators for enclosed small mammal populations

Balancing bias and precision in capture‐recapture estimates of abundance

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