An extension of the Jolly-Seber model combining two sources of capture-recapture data
I propose a modification of the Jolly-Seber model, the two-source Jolly-Seber (TSJS)model, to estimate population size by combining two sources of capture-recapture data of thesame population where there might be an unknown overlap between twodatasets. This is the case with recent surveys of whales and dolphins where researchers useindividual identification records from both photo-identification and DNA profiling of skinbiopsy samples. This sampling configuration results in two datasets that might contain thesame individuals. This new approach enables the estimation of the overlap and the calculation ofthe population size using capture-recapture information arising from both sampling methods.Monte Carlo simulations are used to assess the properties of the present estimator. When allthe assumptions are met, the estimator seems to be unbiased as long as the occasion-specificsimultaneous sampling probability is above 0.2. Simulation analyses also indicate that theproposed method performs better than existing closed-population estimators when there islittle heterogeneity among individuals in capture probabilities and when the average captureprobability is high. Alternatives have been explored and a two-source version of model M0has also been developed and compared to the TSJS estimator. Traditional closed-populationestimators have been compared to the new approaches (TSJS and two-source M0 models)when the population is open and the assumption of homogeneous capture probability isviolated. Both procedures are finally applied to real data on the humpback whale Megapteranovaeangliae, on the wintering grounds of New Caledonia (South Pacific), where individualshave been sampled independently by skin sampling biopsy and photo-identification orsimultaneously by both methods on a same capture occasion. The proposed methods holdgreat promise in monitoring by providing researchers and managers with a method allowing adiversity of sampling protocols. It could be more efficient in estimating population size, interms of both precision and bias, than models based only on one type of data. And as it isimportant to control variation in a sampling design, this methodology could also provide auseful way to reduce variation by increasing the sample size and, hence, to enhance theestimator precision.
Year of publication: |
2010-01-28
|
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Authors: | Madon, Bénédicte |
Other Persons: | Brian McArdle (contributor) ; Scott Baker (contributor) |
Publisher: |
Auckland |
Saved in:
freely available
Saved in favorites
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