Detection probabilities for sessile organisms

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Detection probabilities for sessile organisms

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dc.contributor.author Berberich, Gabriele M.
dc.contributor.author Dormann, Carsten F.
dc.contributor.author Klimetzek, Dietrich
dc.contributor.author Berberich, Martin B.
dc.contributor.author Sanders, Nathan J.
dc.contributor.author Ellison, Aaron M.
dc.date.accessioned 2017-01-06T21:29:53Z
dc.date.issued 2016
dc.identifier Quick submit: 2016-07-10T10:16:57-0400
dc.identifier.citation Berberich, Gabriele M., Carsten F. Dormann, Dietrich Klimetzek, Martin B. Berberich, Nathan J. Sanders, and Aaron M. Ellison. 2016. “Detection Probabilities for Sessile Organisms.” Ecosphere 7 (11) (November): e01546. Portico. doi:10.1002/ecs2.1546. en_US
dc.identifier.issn 2150-8925 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:29847623
dc.description.abstract Estimation of population sizes and species ranges are central to population and conservation biology. It is widely appreciated that imperfect detection of mobile animals must be accounted for when estimating population size from presence-absence data. Sessile organisms also are imperfectly detected, but correction for detection probability in estimating their population sizes is rare. We illustrate challenges of detection probability and population estimation of sessile organisms using censuses of red wood ant (Formica rufa-group) nests as a case study. These ants, widespread in the northern hemisphere, can make large (up to 2-m tall), highly visible nests. Using data from a mapping campaign by eight observers with varying experience of sixteen 3600-m2 plots in the Black Forest region of southwest Germany, we compared three different statistical approaches (a nest-level data-augmentation patch-occupancy model with event-specific covariates; a plot-level Bayesian and maximum likelihood model; non-parametric Chao-type estimators) for quantifying detection probability of sessile organisms. Detection probabilities by individual observers of red wood ant nests ranged from 0.31 – 0.64 for small nests, depending on observer experience and nest size (detection rates were approximately 0.17 higher for large nests), but not on habitat characteristics (forest type, local vegetation). Robust estimation of population density of sessile organisms – even highly apparent ones such as red wood ant nests – thus requires estimation of detection probability, just as it does when estimating population density of rare or cryptic species. Our models additionally provide approaches to calculate the number of observers needed for a required level of accuracy. Estimating detection probability is vital not only when censuses are conducted by experts, but also when citizenscientists are engaged in mapping and monitoring of both common and rare species. en_US
dc.description.sponsorship Organismic and Evolutionary Biology en_US
dc.language.iso en_US en_US
dc.publisher Wiley-Blackwell en_US
dc.relation.isversionof 10.1002/ecs2.1546 en_US
dash.license LAA
dc.subject ants en_US
dc.subject citizen-science en_US
dc.subject detection probability en_US
dc.subject Formica rufa-group en_US
dc.subject Formicidae en_US
dc.subject Bayesian dataaugmentation en_US
dc.subject non-parametric richness estimator en_US
dc.subject plot-level detection model en_US
dc.subject red wood ants en_US
dc.subject sessile organisms en_US
dc.title Detection probabilities for sessile organisms en_US
dc.type Journal Article en_US
dc.date.updated 2016-07-10T14:17:03Z
dc.description.version Accepted Manuscript en_US
dc.relation.journal Ecosphere en_US
dash.depositing.author Ellison, Aaron M.
dc.date.available 2016
dc.date.available 2017-01-06T21:29:53Z

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