The Jolly–Seber Model with Tag Loss

Cowen, Laura; Schwarz, Carl

doi:10.1111/j.1541-0420.2006.00523.x

Cited by 50 publications

(41 citation statements)

References 18 publications

(28 reference statements)

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“…Such models may incorporate, for example, the fact that older tags may be more readily lost as the tags degrade with time. We would then have the analogous a tag (t, l) term, where l corresponds to the age of the tag [see for example Cowen and Schwarz (2006) for further discussion of similar tag-loss models]. However, we do not consider this case further, as we do not have sufficient data in our example to estimate age-dependent tag-loss.…”

Section: Estimating Mark-loss Ratesmentioning

confidence: 98%

Estimating demographic parameters for capture–recapture data in the presence of multiple mark types

2010

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In mark-recapture studies, various techniques can be used to uniquely identify individual animals, such as ringing, tagging or photo-identification using natural markings. In some long-term studies more than one type of marking procedure may be implemented during the study period. In these circumstances, ignoring the different mark types can produce biased survival estimates since the assumption that the different mark types are equally catchable (homogeneous capture probability across mark types) may be incorrect. We implement an integrated approach where we simultaneously analyse data obtained using three different marking techniques, assuming that animals can be cross-classified across the different mark types. We discriminate between competing models using the AIC statistic. This technique also allows us to estimate both relative mark-loss probabilities and relative recapture efficiency rates for the different marking methods. We initially perform a simulation study to explore the different biases that can be introduced if we assume a homogeneous recapture probability over mark type, before applying the method to a real dataset. We make use of data obtained from an intensive long-term observational study of UK female grey seals (Halichoerus grypus) at a single breeding colony, where three different methods are used to identify individuals within a single study: branding, tagging and photo-identification based on seal coat pattern or pelage.

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Section: Estimating Mark-loss Ratesmentioning

confidence: 98%

Estimating demographic parameters for capture–recapture data in the presence of multiple mark types

2010

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“…Current methods of marking many different species indicate that problems with mark retention occur with small mammals (Fokidis et al 2006), large terrestrial mammals (Fosgate et al 2006), aquatic mammals (Bradshaw et al 2000), fish (Cowen and Schwarz 2006), and reptiles (Rivalan et al 2005). Animals that lose all marks become part of the unmarked population and estimates of population parameters (e.g., survival and abundance) will be biased (Arnason and Mills 1981;Diefenbach and Alt 1998).…”

Section: Introductionmentioning

confidence: 98%

“…A number of studies have incorporated mark loss in open and closed models but most have some limitations, such as assuming independence for double mark loss events (Cowen and Schwarz 2006), or double mark loss does not occur (Tavecchia et al 2012), or the permanent mark does not uniquely identify individuals (Hyun et al 2012). Conn et al (2004) integrated a mark loss model with a resight-recovery model, which required recovery of the animal to record mark loss and Juillet et al (2010) extended this model to account for heterogeneity in reporting rates related to mark loss.…”

Section: Introductionmentioning

confidence: 99%

Hidden Markov Model for Dependent Mark Loss and Survival Estimation

Laake

Johnson

Diefenbach

et al. 2014

JABES

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Mark-recapture estimators assume no loss of marks to provide unbiased estimates of population parameters. We describe a hidden Markov model (HMM) framework that integrates a mark loss model with a Cormack-Jolly-Seber model for survival estimation. Mark loss can be estimated with single-marked animals as long as a sub-sample of animals has a permanent mark. Double-marking provides an estimate of mark loss assuming independence but dependence can be modeled with a permanently marked sub-sample. We use a log-linear approach to include covariates for mark loss and dependence which is more flexible than existing published methods for integrated models. The HMM approach is demonstrated with a dataset of black bears (Ursus americanus) with two ear tags and a subset of which were permanently marked with tattoos. The data were analyzed with and without the tattoo. Dropping the tattoos resulted in estimates of survival that were reduced by 0.005-0.035 due to tag loss dependence that could not be modeled. We also analyzed the data with and without the tattoo using a single tag. By not using.Supplementary materials accompanying this paper appear on-line.

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“…: +27 46 6038416; fax: +27 46 6222487. Tag loss directly affects the estimation of population size through introducing negative bias in the estimate of apparent survival (McDonald et al, 2003;Cowen and Schwarz, 2006). If, for example, 80% of the tags are retained despite 100% of the fish surviving between sampling occasions, then only 80% of the available marked fish will be noticeable as being marked; apparent survival is therefore under-estimated and needs to be adjusted by 0.80 −1 = 1.25.…”

Section: Introductionmentioning

confidence: 99%