Alisauskas Ray T, Conn Paul B
Environment and Climate Change Canada Prairie and Northern Wildlife Research Centre Saskatoon Saskatchewan Canada.
Marine Mammal Laboratory, Alaska Fisheries Science Center National Marine Fisheries Service, NOAA Seattle Washington.
Ecol Evol. 2019 Feb 5;9(2):859-867. doi: 10.1002/ece3.4824. eCollection 2019 Jan.
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.
航空调查是一种重要且广泛应用的方法,用于在大型或难以进入的研究区域估计自由放养的野生动物数量。我们研究了距离协变量如何影响在加拿大北极中部进行直升机调查时计数鸟类的双观察者检测概率。两名观察者前后排列,但彼此视觉上相互遮挡,在一个不完全共享的视野中对距离达200米的鸟类进行计数。每位观察者根据飞机窗户上的半透明标记,将检测结果分配到五个40米距离区间中的一个。记录检测结果时包括距离区间、分类群、翅膀拍打行为和群体大小。我们比较了两种在此类情况下与野生动物抽样相关的基于模型的一般估计方法。一种基于无距离信息的双观察者方法,该方法提供的抽样类似于标记重捕(MR)估计检测概率、以及沿固定宽度带状样带的群体丰度所需的抽样。另一种方法将双观察者MR与分类距离协变量(MRD)相结合。事先,我们担心由于未建模的距离信息,MR模型的估计量会因的异质性而受到影响;也就是说,距离较远的鸟类被两名观察者同时检测到的可能性较小,预计会导致被高估,被低估。我们发现,尽管复杂性增加,但MRD模型(ΔAICc范围:0 - 16)比MR模型(ΔAICc范围:204 - 258)对数据的拟合要好得多。然而,与预期相反,在所有情况下,较为简单的MR估计量都被低估了,但在大多数情况下仅低2% - 5%。我们怀疑这一明显异常的发现是调查平台上观察者的能见度存在特定限制和权衡的结果。虽然MR模型提供了可接受的群体丰度点估计,但与MRD估计相比,其高得多的标准误差(0% - 40%)会损害检测丰度的时间或空间差异的能力。鉴于MRD模型相对于MR模型的精度提高,以及在使用来自其他调查平台的MR方法时可能存在偏差,我们建议鸟类生态学家在调查北极鸟类种群时使用MRD方案和估计程序。