Incorporating Genotype Uncertainty into Mark–Recapture‐Type Models For Estimating Abundance Using DNA Samples

Wright, Janine; Barker, Richard J.; Schofield, Matthew; Frantz, Alain C.; Byrom, Andrea E.; Gleeson, Dianne

doi:10.1111/j.1541-0420.2008.01165.x

Cited by 84 publications

(110 citation statements)

References 40 publications

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“…A similar approach was used in the wolf example; genotypes with a mean 'quality index' less than a specified threshold were not used in the analysis. In addition, when using DNA samples, it is possible to allow for misidentification using modelling techniques (Lukacs & Burnham 2005;Wright et al 2009). A related issue is that CH can sometimes be avoided by the use of an appropriate sampling design; Ebert et al (2010) provided an example in which intense sampling effort on a small study area with few sampling occasions led to CH being less likely to be detected.…”

Section: Discussionmentioning

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: Discussionmentioning

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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“…Moreover, the models have a problem with the model structure and identifiably of the parameters (Yoshizaki 2007). Recently, other CR models have been developed which incorporate genotype errors for estimating abundance using DNA samples (Knapp et al 2009;Wright et al 2009). It will be interesting to test model performance in several real case studies and situations where abundance is known.…”

Section: Cr Analysis Of a Non-invasive Genetic Datasetmentioning

confidence: 99%

Bridging the gaps between non-invasive genetic sampling and population parameter estimation

Marucco¹,

Boitani

Pletscher

et al. 2010

Eur J Wildl Res

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Reliable estimates of population parameters are necessary for effective management and conservation actions. The use of genetic data for capture-recapture (CR) analyses has become an important tool to estimate population parameters for elusive species. Strong emphasis has been placed on the genetic analysis of non-invasive samples, or on the CR analysis; however, little attention has been paid to the simultaneous overview of the full non-invasive genetic CR analysis, and the important insights gained by understanding the interactions between the different parts of the technique. Here, we review the three main steps of the approach: designing the appropriate sampling scheme, conducting the genetic lab analysis, and applying the CR analysis to the genetic results; and present a synthesis of this topic with the aim of discussing the primary limitations and sources of error. We discuss the importance of the integration between these steps, the unique situations which occur with non-invasive studies, the role of ecologists and geneticists throughout the process, the problem of error propagation, and the sources of biases which can be present in the final estimates. We highlight the importance of team collaboration and offer a series of recommendations to wildlife ecologists who are not familiar with this topic yet but may want to use this tool to monitor populations through time

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“…However, in itself, this subsampling generates uncertainty, as chance events such as the movement of organisms, the heterogeneous distribution of individuals, and the skill of the observers influence recorded population abundance (Muhlfeld et al 2006). While there are a variety of sophisticated methods that seek to address the problems raised by spatial subsampling (e.g., Wright et al 2009;Ford et al 2012), the uncertainty generated at the sampling stage can never be fully resolved.…”

Section: Introductionmentioning

confidence: 99%

Factors Influencing the Detectability of Early Warning Signals of Population Collapse

Clements

Drake

Griffiths

et al. 2015

The American Naturalist

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The recent description of potentially generic early warning signals is a promising development that may help conservationists to anticipate a population's collapse prior to its occurrence. So far, the majority of such warning signals documented have been in highly controlled laboratory systems or theoretical models. Data from wild populations, however, are typically restricted both temporally and spatially due to limited monitoring resources and intrinsic ecological heterogeneity -limitations that may affect the detectability of generic early warning signals, as they add additional stochasticity to population abundance estimates. Consequently, spatial and temporal subsampling may serve either to muffle or magnify early warning signals. Using a combination of theoretical models and analysis of experimental data, we evaluate the extent to which statistical warning signs are robust to data corruption. Online enhancements: appendixes, zip file.abstract: The recent description of potentially generic early warning signals is a promising development that may help conservationists to anticipate a population's collapse prior to its occurrence. So far, the majority of such warning signals documented have been in highly controlled laboratory systems or in theoretical models. Data from wild populations, however, are typically restricted both temporally and spatially due to limited monitoring resources and intrinsic ecological heterogeneity-limitations that may affect the detectability of generic early warning signals, as they add additional stochasticity to population abundance estimates. Consequently, spatial and temporal subsampling may serve to either muffle or magnify early warning signals. Using a combination of theoretical models and analysis of experimental data, we evaluate the extent to which statistical warning signs are robust to data corruption.

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Incorporating Genotype Uncertainty into Mark–Recapture‐Type Models For Estimating Abundance Using DNA Samples

Cited by 84 publications

References 40 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

Bridging the gaps between non-invasive genetic sampling and population parameter estimation

Factors Influencing the Detectability of Early Warning Signals of Population Collapse

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