Fig 1.
A map of the study area used in 2016 aerial surveys for seals and polar bears. Black lines represent aerial survey tracks, while small blue circles represent locations of bear tracks. Breaks in transect lines represent times where survey crew went “off effort” because of dense fog. Red triangles represent thermal detections of polar bear groups, and orange squares represent additional groups seen by human observers (U.S. and Russia) or in post hoc examination of photographs (Russia only). The ≈625km2 grid cells used for abundance estimation appear in the background (beige lines). Land masses (gray) include Alaska, U.S.A. to the east and Russia, to the west. The blue shading represents the area associated with the Chukchi subpopulation of polar bears as determined by the polar bear specialist working group (PBSG), while yellow shading shows the area where a long term capture-recapture study of polar bears [8] was previously conducted. For an animation depicting survey effort and observations overlayed on sea ice as a function of survey day, see S1 Video.
Fig 2.
Paired infrared and color imagery of a polar bear.
Color (right) and IR (left) imagery collected from 300 m during the 2016 aerial surveys from the U.S. platform containing a polar bear (zoomed inset). The IR image collected with the long wavelength infrared cooled camera (FLIR A6750sc SLS) provides a visual representation of apparent temperature in greyscale where darker shades are cool and lighter shades are warm. The paired color image, collected with the Prosilica GT6600c fitted with a 100 mm Zeiss lens, confirms that the heat signature detected in the IR image is from a polar bear.
Fig 3.
Randomized quantile residuals (RQRs) for assessing goodness-of-fit for models fit to polar bear encounter data. RQRs should be uniformly distributed on (0,1) for a well-fitting model. Also presented are χ2 test p-values to assess uniformity.
Fig 4.
Spatio-temporal maps of sea ice and predicted polar bear distribution.
Remotely sensed sea ice concentration values (top row), estimated polar bear track index (middle row), and predictions of polar bear abundance at the beginning, middle, and end of 2016 aerial surveys of the eastern Chukchi Sea (g(0) = 0.8 scenario). The polar bear track index is an estimate of the proportion of photographs that would contain polar bear tracks had photographs been taken in all grid cells and on all days of the survey. Predicted abundance is calculated as . Note that the scale of shading on abundance plots is nonlinear (i.e., low densities are visible as light blue and teal colors).
Fig 5.
Estimated smooth effects of covariates on polar bear abundance (black line), together with 95% confidence intervals (grey shading). Note that distance from land, easting, and northing effects were standardized to have a mean of 1.0 prior to analysis. Polar bear tracks were modeled as a simple linear effect on abundance so do not appear here.
Table 1.
Estimates of polar bear abundance for different regions and assumptions about g(0) in Russian survey flights, along with 95% log-based confidence intervals.
Regions include the full study grid (‘Chukchi’), mean (i.e., time-averaged) abundance for those cells of the survey grid with centroids in U.S. waters (‘U.S.’), mean abundance in Russian waters (‘Russia’), mean abundance in the portions of our study area that overlapped the polar bear specialist group boundary (‘PBSG’), and an estimate for the area used for capture and release of bears in a mark-recapture study of polar bears in the Chukchi Sea (‘Regehr’ [8]). For a map of the study area and related regions, see Fig 1).