Estimating multispecies abundance using automated detection systems: ice‐associated seals in the Bering Sea

Abstract

Summary1. Automated detection systems employing advanced technology (e.g. infrared imagery, auditory recording systems, pattern recognition software) are compelling tools for gathering animal abundance and distribution data since investigators can often collect data more efficiently and reduce animal disturbance relative to surveys using human observers. 2. Even with these improvements, analysing animal abundance with advanced technology can be challenging because of potential for incomplete detection, false positives and species misidentification. We argue that double sampling with an independent sampling method can provide the critical information needed to account for such errors.3. We present a hierarchical modelling framework for jointly analysing automated detection and double sampling data obtained during animal population surveys. Under our framework, observed counts in different sampling units are conceptualized as having arisen from a thinned log-Gaussian Cox process subject to spatial autocorrelation (where thinning accounts for incomplete detection). For multispecies surveys, our approach handles incomplete species observations owing to (i) structural uncertainties (e.g. in cases where the automatic detection data do not provide species observations) and (ii) species misclassification; the latter requires auxiliary information on the misclassification process. 4. As an example of combining an automated detection system and a double sampling procedure, we consider the problem of estimating animal abundance from aerial surveys that use infrared imagery to detect animals, and independent, high-resolution digital photography to provide information on species composition and thermal detection accuracy. We illustrate our approach by analysing simulated data and data from a survey of four iceassociated seal species in the eastern Bering Sea. 5. Our analysis indicated reasonable performance of our hierarchical modelling approach, but suggested a need to balance model complexity with the richness of the data set. For example, highly parameterized models can lead to spuriously high predictions of abundance in areas that are not sampled, especially when there are large gaps in spatial coverage. 6. We recommend that ecologists employ double sampling when enumerating animal populations with automated detection systems to estimate and correct for detection errors. Combining multiple data sets within a hierarchical modelling framework provides a powerful approach for analysing animal abundance over large spatial domains.

An approach for using off-the-shelf object-based image analysis software to detect and count birds in large volumes of aerial imagery

“…In some cases, it may be possible to reduce commission error rates to the point where manual review of automated analysis results is not necessary, while in other cases, like ours, varying degrees of manual postanalysis effort may be required. Although animal contrast in thermal-infrared imagery has proven useful for automated detection of mammals (Conn et al 2014, Chrétien et al 2015, 2016, Seymour et al 2017, the very coarse pixel resolution of thermal cameras compared to RGB cameras generally renders them ineffective for aerial detection of comparatively smaller birds (Chabot and Francis 2016). It should be noted that any aerial image-based survey will only allow detection of subjects that are visible from overhead and miss subjects that are, for example, concealed under canopy or diving underwater.…”

Spectral Reflectance of Polar Bear and Other Large Arctic Mammal Pelts; Potential Applications to Remote Sensing Surveys

“…Airborne UVA/VIS/NIR/SWIR camera systems could also be used. One option would be to use moderate resolution multi-spectral imagery (e.g., 0.5-1.0 m pixels) to detect potential bears based on the spectral signature, combined with higher resolution (e.g., 0.1 m) standard photographs to verify species identification visually, using techniques analogous to those of [13].…”

“…Polar bear fur is highly absorbent in the lower ultraviolet (UVA) region, in contrast to snow, and as such, when imaged in the UVA range, polar bears appear black against a white snow background [19,20,22]. Thermal infrared (TIR) (3000-12,000 nm) imaging has been used to survey a variety of arctic mammals, including walrus [23] and seals (e.g., [13]), and could potentially detect polar bears [21]. Early attempts to detect polar bears with thermal imaging met with limited success [24], but this was likely due to the limited sensitivity of the equipment used, given that subsequent studies have shown that exercising bears radiate heat several degrees warmer than the ambient temperatures [21].…”

Aerial surveys for pack-ice seals along the Ingrid Christensen and Princess Astrid Coasts, East Antarctica

“…It is possible that more hauled-out seals may have been encountered had the aerial surveys been conducted mid-day as recommended by Lake et al (1997), and if more frequent surveys were conducted within and between seasons. We, therefore, recommend that future surveys in the area take into account the time of the aerial surveys and adopt standardized, quantitative methods for design or model-based estimates such as those used by Southwell (2005), Conn et al (2013), andver Hoef et al (2014).…”