Detection probabilities for sessile organisms
Berberich, Gabriele M.
Dormann, Carsten F.
Berberich, Martin B.
Sanders, Nathan J.
MetadataShow full item record
CitationBerberich, 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.
AbstractEstimation 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.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:29847623
- FAS Scholarly Articles