Estimation of bird distribution based on ring re‐encounters: precision and bias of the division coefficient and its relation to multi‐state models

Korner‐Nievergelt, Fränzi; Schaub, Michael; Thorup, Kasper; Vock, Michael; Kania, Wojciech

doi:10.1080/00063650903154439

Cited by 18 publications

(21 citation statements)

References 15 publications

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“…Furthermore, it is necessary that not all groups have the same division coefficient (ie there are differences between groups) in order to obtain precise estimates. The precision of the division-coefficient estimate ( x T ) increases with increasing differences in the division coefficient between the groups, with increasing sample size, and with decreasing difference in reencounter probability between the destination areas (Korner-Nievergelt et al 2010). For further underlying assumptions and data requirements see Kania & Busse (1987) and Korner-Nievergelt et al (2010).…”

Section: Division Coefficientmentioning

confidence: 96%

“…The function bootci.dc() gives the bootstrap confidence intervals for the division coefficients and the re-encounter probabilities estimated by dc(). For details see Korner-Nievergelt et al (2010).…”

Section: Division Coefficientmentioning

confidence: 98%

“…The division coefficient is the proportion of birds of group G migrating to destination area T. Confidence intervals of the estimated division coefficients can be obtained through bootstrapping (Korner-Nievergelt et al 2010). Note that the division coefficient method gives unbiased results only if (i) there are no group differences in the area-specific re-encounter probabilities, (ii) at least a small proportion of each group migrates to each destination area, and (iii) the sum of the destination areas covers the entire range of all groups.…”

Section: Division Coefficientmentioning

confidence: 99%

See 2 more Smart Citations

Introducing the R-package ‘birdring’

Korner‐Nievergelt

Robinson

2014

Ringing & Migration

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Section: Division Coefficientmentioning

confidence: 96%

Section: Division Coefficientmentioning

confidence: 98%

Section: Division Coefficientmentioning

confidence: 99%

See 1 more Smart Citation

Introducing the R-package ‘birdring’

Korner‐Nievergelt

Robinson

2014

Ringing & Migration

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“…Furthermore, reencounter probability during the non-breeding period was allowed to differ between the flyways, but is assumed to be similar for all individuals within a flyway independent of the origin of the individual. This is a key assumption of the multinomial mark-reencounter model, making the model parameters identifiable (see Korner-Nievergelt et al 2010b). Finally, the total of the flyway areas is assumed to cover the whole non-breeding range for the four populations analysed.…”

Section: Sensitivity To Violation Of Model Assumptionsmentioning

confidence: 99%

“…All these methods are based on the same core assumption that makes reencounter probabilities estimable: the probability of being reencountered is equal for all birds present in the same area and independent of the birds' origin. Further, more than one group of birds that differ in their spatial distribution have to be analysed to make reencounter probabilities and proportions of birds in different areas estimable (Bauthian et al 2007; Busse and Kania 1977;Korner-Nievergelt et al 2010b;Thorup and Conn 2009).…”

Section: Introductionmentioning

confidence: 99%

Migratory connectivity derived from sparse ring reencounter data with unknown numbers of ringed birds

2011

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The large databases on ring reencounters, e.g. Euring database, contain extant information on the spatial distribution and potentially, on migratory connectivity of birds. However, reencounter data are normally sparse due to low reencounter probability. Further, to extract unbiased information about the spatial distribution of birds, spatial variation in reencounter probability has to be corrected for. To do so, knowledge of the total numbers of ringed birds is crucial but often not available. We present a general, combined statistical model to estimate population specific migration patterns based on the European reencounter data for which the number of ringed birds is unknown. Our approach combines a Cormack-Jolly-Seber model with a multinomial model. We present, for the first time, estimates and credible intervals of the spatial distribution of different populations of a migrant bird during the non-breeding period based on imperfect ringing data. Here, we used the Common Nightingale (Luscinia megarhynchos) as a representative long-distance migrant. The model allowed estimation of which proportions of the different breeding populations use a western, central or eastern flyway. Sensitivity analysis based on simulated data showed that most of these estimates were robust against violation of the most important model assumptions, i.e. homogeneity in recapture probability, homogeneity in breeding area return probability, and in reencounter probability within the flyways. We provide a general technique to account for spatial variation in reencounter probability when analysing migratory connectivity based on ring reencounter data with unknown numbers of ringed individuals. It is applicable for almost all migrating species with reencounter data.

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Estimating migratory connectivity of birds when re‐encounter probabilities are heterogeneous

Cohen

Hostetler

Royle

et al. 2014

Ecology and Evolution

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Understanding the biology and conducting effective conservation of migratory species requires an understanding of migratory connectivity – the geographic linkages of populations between stages of the annual cycle. Unfortunately, for most species, we are lacking such information. The North American Bird Banding Laboratory (BBL) houses an extensive database of marking, recaptures and recoveries, and such data could provide migratory connectivity information for many species. To date, however, few species have been analyzed for migratory connectivity largely because heterogeneous re-encounter probabilities make interpretation problematic. We accounted for regional variation in re-encounter probabilities by borrowing information across species and by using effort covariates on recapture and recovery probabilities in a multistate capture–recapture and recovery model. The effort covariates were derived from recaptures and recoveries of species within the same regions. We estimated the migratory connectivity for three tern species breeding in North America and over-wintering in the tropics, common (Sterna hirundo), roseate (Sterna dougallii), and Caspian terns (Hydroprogne caspia). For western breeding terns, model-derived estimates of migratory connectivity differed considerably from those derived directly from the proportions of re-encounters. Conversely, for eastern breeding terns, estimates were merely refined by the inclusion of re-encounter probabilities. In general, eastern breeding terns were strongly connected to eastern South America, and western breeding terns were strongly linked to the more western parts of the nonbreeding range under both models. Through simulation, we found this approach is likely useful for many species in the BBL database, although precision improved with higher re-encounter probabilities and stronger migratory connectivity. We describe an approach to deal with the inherent biases in BBL banding and re-encounter data to demonstrate that this large dataset is a valuable source of information about the migratory connectivity of the birds of North America.

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Estimation of bird distribution based on ring re‐encounters: precision and bias of the division coefficient and its relation to multi‐state models

Cited by 18 publications

References 15 publications

Introducing the R-package ‘birdring’

Introducing the R-package ‘birdring’

Migratory connectivity derived from sparse ring reencounter data with unknown numbers of ringed birds

Estimating migratory connectivity of birds when re‐encounter probabilities are heterogeneous

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