SolymosTomsMatsuokaEtAl2020

Référence

Sólymos, P., Toms, J.D., Matsuoka, S.M., Cumming, S.G., Barker, N.K.S., Thogmartin, W.E., Stralberg, D., Crosby, A.D., Dénes, F.V., Haché, S., Mahon, C.L., Schmiegelow, F.K.A., Bayne, E.M. (2020) Lessons learned from comparing spatially explicit models and the Partners in Flight approach to estimate population sizes of boreal birds in Alberta, Canada. Condor, 122(2). (Scopus )

Résumé

Estimating the population abundance of landbirds is a challenging task complicated by the amount, type, and quality of available data. Avian conservationists have relied on population estimates from Partners in Flight (PIF), which primarily uses roadside data from the North American Breeding Bird Survey (BBS). However, the BBS was not designed to estimate population sizes. Therefore, we set out to compare the PIF approach with spatially explicit models incorporating roadside and off-road point-count surveys. We calculated population estimates for 81 landbird species in Bird Conservation Region 6 in Alberta, Canada, using land cover and climate as predictors. We also developed a framework to evaluate how the differences between the detection distance, time-of-day, roadside count, and habitat representation adjustments explain discrepancies between the 2 estimators. We showed that the key assumptions of the PIF population estimator were commonly violated in this region, and that the 2 approaches provided different population estimates for most species. The average differences between estimators were explained by differences in the detection-distance and time-of-day components, but these adjustments left much unexplained variation among species. Differences in the roadside count and habitat representation components explained most of the among-species variation. The variation caused by these factors was large enough to change the population ranking of the species. The roadside count bias needs serious attention when roadside surveys are used to extrapolate over off-road areas. Habitat representation bias is likely prevalent in regions sparsely and non-representatively sampled by roadside surveys, such as the boreal region of North America, and thus population estimates for these regions need to be treated with caution for certain species. Additional sampling and integrated modeling of available data sources can contribute towards more accurate population estimates for conservation in remote areas of North America. © 2020 Her Majesty the Queen in Right of Canada, as represented by the Minister of Environment, 2020.

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@ARTICLE { SolymosTomsMatsuokaEtAl2020,
    AUTHOR = { Sólymos, P. and Toms, J.D. and Matsuoka, S.M. and Cumming, S.G. and Barker, N.K.S. and Thogmartin, W.E. and Stralberg, D. and Crosby, A.D. and Dénes, F.V. and Haché, S. and Mahon, C.L. and Schmiegelow, F.K.A. and Bayne, E.M. },
    JOURNAL = { Condor },
    TITLE = { Lessons learned from comparing spatially explicit models and the Partners in Flight approach to estimate population sizes of boreal birds in Alberta, Canada },
    YEAR = { 2020 },
    NOTE = { cited By 3 },
    NUMBER = { 2 },
    VOLUME = { 122 },
    ABSTRACT = { Estimating the population abundance of landbirds is a challenging task complicated by the amount, type, and quality of available data. Avian conservationists have relied on population estimates from Partners in Flight (PIF), which primarily uses roadside data from the North American Breeding Bird Survey (BBS). However, the BBS was not designed to estimate population sizes. Therefore, we set out to compare the PIF approach with spatially explicit models incorporating roadside and off-road point-count surveys. We calculated population estimates for 81 landbird species in Bird Conservation Region 6 in Alberta, Canada, using land cover and climate as predictors. We also developed a framework to evaluate how the differences between the detection distance, time-of-day, roadside count, and habitat representation adjustments explain discrepancies between the 2 estimators. We showed that the key assumptions of the PIF population estimator were commonly violated in this region, and that the 2 approaches provided different population estimates for most species. The average differences between estimators were explained by differences in the detection-distance and time-of-day components, but these adjustments left much unexplained variation among species. Differences in the roadside count and habitat representation components explained most of the among-species variation. The variation caused by these factors was large enough to change the population ranking of the species. The roadside count bias needs serious attention when roadside surveys are used to extrapolate over off-road areas. Habitat representation bias is likely prevalent in regions sparsely and non-representatively sampled by roadside surveys, such as the boreal region of North America, and thus population estimates for these regions need to be treated with caution for certain species. Additional sampling and integrated modeling of available data sources can contribute towards more accurate population estimates for conservation in remote areas of North America. © 2020 Her Majesty the Queen in Right of Canada, as represented by the Minister of Environment, 2020. },
    AFFILIATION = { Boreal Avian Modelling Project, University of Alberta, Edmonton, AB, Canada; Alberta Biodiversity Monitoring Institute, University of Alberta, Department of Biological Sciences, Edmonton, AB, Canada; Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada; Canadian Wildlife Service, Environment and Climate Change Canada, Edmonton, AB, Canada; U.S. Geological Survey, Alaska Science Center, Anchorage, Alaska, United States; Département des Sciences du Bois et de la Forêt, Université Laval, Québec, QC, Canada; Department of Renewable Resources, University of Alberta, Edmonton, AB, Canada; U.S. Geological Survey, Upper Midwest Environmental Sciences Center, La Crosse, Wisconsin, United States; Canadian Wildlife Service, Environment and Climate Change Canada, Yellowknife, Northwest Territories, Canada; Canadian Wildlife Service, Environment and Climate Change Canada, Whitehorse, Canada },
    ART_NUMBER = { duaa007 },
    AUTHOR_KEYWORDS = { abondance; abundance; biais d'échantillonnage; biais des relevés le long de routes; detectability; détectabilité; roadside bias; sampling bias },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1093/condor/duaa007 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087074148&doi=10.1093%2fcondor%2fduaa007&partnerID=40&md5=4d3c5c96641a3f7b40a7c89f76e4cf3d },
}

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