AI Article Synopsis

  • - Aerial surveys are used to estimate wildlife populations in large, hard-to-reach areas, focusing on how distance affects bird detection during helicopter counts in the Arctic.
  • - Two observers counted birds within a limited view and used distance categories to record data, which was then analyzed through two different modeling approaches: one without distance data and one that included distance as a factor.
  • - The study found that the model incorporating distance (MRD) provided significantly better data fit compared to the simpler model, although both methods showed some bias in their estimates, with the MR model underestimating bird numbers by 2%-5%.

Article Abstract

Aerial survey is an important, widely employed approach for estimating free-ranging wildlife over large or inaccessible study areas. We studied how a distance covariate influenced probability of double-observer detections for birds counted during a helicopter survey in Canada's central Arctic. Two observers, one behind the other but visually obscured from each other, counted birds in an incompletely shared field of view to a distance of 200 m. Each observer assigned detections to one of five 40-m distance bins, guided by semi-transparent marks on aircraft windows. Detections were recorded with distance bin, taxonomic group, wing-flapping behavior, and group size. We compared two general model-based estimation approaches pertinent to sampling wildlife under such situations. One was based on double-observer methods without distance information, that provide sampling analogous to that required for mark-recapture (MR) estimation of detection probability, , and group abundance, , along a fixed-width strip transect. The other method incorporated double-observer MR with a categorical distance covariate (MRD). A priori, we were concerned that estimators from MR models were compromised by heterogeneity in due to un-modeled distance information; that is, more distant birds are less likely to be detected by both observers, with the predicted effect that would be biased high, and biased low. We found that, despite increased complexity, MRD models (ΔAICc range: 0-16) fit data far better than MR models (ΔAICc range: 204-258). However, contrary to expectation, the more naïve MR estimators of were biased low in all cases, but only by 2%-5% in most cases. We suspect that this apparently anomalous finding was the result of specific limitations to, and trade-offs in, visibility by observers on the survey platform used. While MR models provided acceptable point estimates of group abundance, their far higher stranded errors (0%-40%) compared to MRD estimates would compromise ability to detect temporal or spatial differences in abundance. Given improved precision of MRD models relative to MR models, and the possibility of bias when using MR methods from other survey platforms, we recommend avian ecologists use MRD protocols and estimation procedures when surveying Arctic bird populations.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362609PMC
http://dx.doi.org/10.1002/ece3.4824DOI Listing

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