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Distance models as a tool for modelling detection probability and density of native bumblebees

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Darin J. McNeil, Clint R. V. Otto, Erin L. Moser, Katherine R. Urban-Mead, David E. King, Amanda D. Rodewald, and Jeffrey L. Larkin, 2018, Distance models as a tool for modelling detection probability and density of native bumblebees: Journal of Applied Entomology.

Summary

Effective monitoring of native bee populations requires accurate estimates of population size and relative abundance among habitats. Current bee survey methods, such as netting or pan trapping, may be adequate for a variety of study objectives but are limited by a failure to account for imperfect detection. Biases due to imperfect detection could result in inaccurate abundance estimates or erroneous insights about the response of bees to different environments. To gauge the potential biases of currently employed survey methods, we compared abundance estimates of bumblebees (Bombus spp.) derived from hierarchical distance sampling models (HDS) to bumblebee counts collected from fixed‐area net surveys (“net counts”) and fixed‐width transect [...]

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Type Scheme Key
local-index unknown 70200978
local-pk unknown 70200978
doi http://www.loc.gov/standards/mods/mods-outline-3-5.html#identifier doi:10.1111/jen.12583
series unknown Journal of Applied Entomology

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citationTypeArticle
editionOnline First
journalJournal of Applied Entomology
languageEnglish

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