Estimating uncertainty of North American landbird population sizes
| Autor: | Wayne E. Thogmartin, Kenneth V. Rosenberg, Peter J. Blancher, Arvind O. Panjabi, Jessica C. Stanton |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
population size
education.field_of_study breeding bird survey Ecology partners in flight Population size Population Plant culture Breeding bird survey species conservation assessment SB1-1110 Environmental sciences Geography Animal Science and Zoology GE1-350 QK900-989 education uncertainty Plant ecology Ecology Evolution Behavior and Systematics Nature and Landscape Conservation |
| Zdroj: | Avian Conservation and Ecology, Vol 14, Iss 1, p 4 (2019) |
| ISSN: | 1712-6568 |
| Popis: | An important metric for many aspects of species conservation planning and risk assessment is an estimate of total population size. For landbirds breeding in North America, Partners in Flight (PIF) generates global, continental, and regional population size estimates. These estimates are an important component of the PIF species assessment process, but have also been used by others for a range of applications. The PIF population size estimates are primarily calculated using a formula designed to extrapolate bird counts recorded by the North American Breeding Bird Survey (BBS) to regional population estimates. The extrapolation formula includes multiple assumptions and sources of uncertainty, but there were previously no attempts to quantify this uncertainty in the published population size estimates aside from a categorical data quality score. Using a Monte Carlo approach, we propagated the main sources of uncertainty arising from individual components of the model through to the final estimation of landbird population sizes. This approach results in distributions of population size estimates rather than point estimates. We found the width of uncertainty of population size estimates to be generally narrower than the order-of-magnitude distances between the population size score categories PIF uses in the species assessment process, suggesting confidence in the categorical ranking used by PIF. Our approach provides a means to identify species whose uncertainty bounds span more than one categorical rank, which was not previously possible with the data quality scores. Although there is still room for additional improvements to the estimation of avian population sizes and uncertainty, particularly with respect to replacing categorical model components with empirical estimates, our estimates of population size distributions have broader utility to a range of conservation planning and risk assessment activities relying on avian population size estimates. |
| Databáze: | OpenAIRE |
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