Incorporating Genotyping Error Into Non‐Invasive DNA‐Based Mark—Recapture Population Estimates

Knapp, Shannon M.; Craig, Bruce A.; Waits, Lisette P.

doi:10.2193/2007-156

Cited by 36 publications

(37 citation statements)

References 26 publications

(31 reference statements)

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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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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

View full text Add to dashboard Cite

show abstract

“…It is timely to review N C and N e estimators because the field is advancing rapidly with recent publications reporting new estimators and performance evaluations for both N C (Boulanger et al 2008a, b;Kendall et al 2008;Settlage et al 2008;Knapp et al 2009) and N e (Jorde and Ryman 2007;Waples and Yokota 2007;Nomura 2008;Palstra and Ruzzante 2008;Tallmon et al 2008;Wang 2009;Waples and Do in press). We focus on estimators that are currently the most useful for conservation and wildlife management, e.g.…”

Section: Introductionmentioning

confidence: 99%

Estimation of census and effective population sizes: the increasing usefulness of DNA-based approaches

et al. 2010

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Population census size (N C ) and effective population sizes (N e ) are two crucial parameters that influence population viability, wildlife management decisions, and conservation planning. Genetic estimators of both N C and N e are increasingly widely used because molecular markers are increasingly available, statistical methods are improving rapidly, and genetic estimators complement or improve upon traditional demographic estimators. We review the kinds and applications of estimators of both N C and N e , and the often undervalued and misunderstood ratio of effective-to-census size (N e /N C ). We focus on recently improved and well evaluated methods that are most likely to facilitate conservation. Finally, we outline areas of future research to improve N e and N C estimation in wild populations.

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“…, Knapp et al. ), where the magnitude of uncertainty in individual identity scales with population abundance alone.…”

Section: Introductionmentioning

confidence: 99%

Spatial capture–recapture for categorically marked populations with an application to genetic capture–recapture

et al. 2019

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Recently introduced unmarked spatial capture–recapture (SCR), spatial mark–resight (SMR), and 2‐flank spatial partial identity models (SPIMs) extend the domain of SCR to populations or observation systems that do not always allow for individual identity to be determined with certainty. For example, some species do not have natural marks that can reliably produce individual identities from photographs, and some methods of observation produce partial identity samples as is the case with remote cameras that sometimes produce single‐flank photographs. Unmarked SCR, SMR, and SPIM share the feature that they probabilistically resolve the uncertainty in individual identity using the spatial location where samples were collected. Spatial location is informative of individual identity in spatially structured populations because a sample is more likely to have been produced by an individual living near the trap where it was recorded than an individual living further away from the trap. Further, the level of information about individual identity that a spatial location contains is related to two key ecological concepts, population density and home range size, which we quantify using a proposed Identity Diversity Index (IDI). We show that latent and partial identity SCR models produce imprecise and biased density estimates in many high IDI scenarios when data are sparse. We then extend the unmarked SCR model to incorporate categorical, partially identifying covariates, which reduce the level of uncertainty in individual identity, increasing the reliability and precision of density estimates, and allowing reliable density estimation in scenarios with higher IDI values and with more sparse data. We illustrate the performance of this “categorical SPIM” via simulations and by applying it to a black bear data set using microsatellite loci as categorical covariates, where we reproduce the full data set estimates with only slightly less precision using fewer loci than necessary for confident individual identification. We then discuss how the categorical SPIM can be applied to other wildlife sampling scenarios such as remote camera surveys, where natural or researcher‐applied partial marks can be observed in photographs. Finally, we discuss how the categorical SPIM can be added to SMR, 2‐flank SPIM, or other latent identity SCR models.

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Incorporating Genotyping Error Into Non‐Invasive DNA‐Based Mark—Recapture Population Estimates

Cited by 36 publications

References 26 publications

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

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

Estimation of census and effective population sizes: the increasing usefulness of DNA-based approaches

Spatial capture–recapture for categorically marked populations with an application to genetic capture–recapture

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