Data from: Effects of distance on detectability of Arctic waterfowl using double-observer sampling during helicopter surveys

• Main Authors: Alisauskas, Ray T, Conn, Paul B
• Language: unknown
• Published: 2019
• Subjects: Life sciences; medicine and health care; Arctic
• Online Access: http://nbn-resolving.org/urn:nbn:nl:ui:13-do-28qr; https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:119314

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, urn:x-wiley:20457758:media:ece34824:ece34824-math-0001, and group abundance, urn:x-wiley:20457758:media:ece34824:ece34824-math-0002, 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 urn:x-wiley:20457758:media:ece34824:ece34824-math-0003 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 urn:x-wiley:20457758:media:ece34824:ece34824-math-0004 would be biased high, and urn:x-wiley:20457758:media:ece34824:ece34824-math-0005 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 urn:x-wiley:20457758:media:ece34824:ece34824-math-0006 were biased low in all cases, but only by 2%–5% in most cases. We suspect that this apparently anomalous finding ...