Drones reveal spatial patterning of sympatric Alaskan pinniped species and drivers of their local distributions

Abstract

The Arctic and its adjacent ecosystems are undergoing rapid ecological reorganization in response to the effects of global climate change, and sentinel species provide critical updates as these changes unfold. This study leverages emerging remote sensing techniques to reveal fine-scale drivers of distribution and terrestrial habitat use of two sympatric sentinel species of the central Bering Sea, the Pacific harbor seal (<i>Phoca vitulina richardsi</i>) and the northern fur seal (<i>Callorhinus ursinus</i>), at non-breeding haul-outs in the Pribilof Islands. We surveyed these species using unoccupied aircraft systems with thermal and visible-light photography, and we applied distributional modeling techniques to quantify the relative influence of habitat characteristics and social dynamics on the local distributions of these species. Drone imagery yielded locations and population counts of each species, and spatial data products allowed quantitative characterization of occupied sites, revealing that conspecific attraction is a driver of local site selection for both species, and Pacific harbor seals and northern fur seals are differentially limited by terrain characteristics. These findings represent new applications of species distribution modeling at local scales, made possible by ultra-high resolution drone surveillance and photogrammetric techniques, which add new spatial context to past observations and future scenarios in this changing ecosystem.

Review of Satellite Remote Sensing and Unoccupied Aircraft Systems for Counting Wildlife on Land

“…For canopy-dwelling species, Wich and Piel [24] recommend that a correction is applied to account for individuals hidden under vegetation that are missed on thermal and RGB images [55]. Traditionally, thermal cameras were swapped interchangeably with the existing camera, flown on separate aircraft synoptically (e.g., [63]) or mounted as an additional payload on UAS [64]. However, some newer payloads have multiple sensors that collect RGB imagery at the same time as thermal IR data are acquired [65].…”

Growth and opportunities for drone surveillance in pinniped research

“…The spatial qualities of drone data provide clear benefits for demographic and abundance surveys by obviating the potential bias of undercounting in complex terrain, where animals may be partially or completely hidden from non‐nadir perspectives. Additionally, high‐resolution mapping products and orthorectified positional data can reveal precise, fine‐scale relationships between pinnipeds and landcover or physical topography that might not resolve in comparable stereoscopic products from high‐altitude aerial photography or satellite imagery (Larsen et al 2022b). Such species–habitat relationships can reveal preferences and limitations of pinniped habitat selection that might be driven by terrestrial locomotive ability (Beentjes 1990, Garrett & Fish 2015, Fish 2018) or thermoregulatory behaviours (White & Odell 1971, Liwanag et al 2014, Montero‐Serra et al 2014, Chaise et al 2018), linking individual energetic costs to emergent patterns of terrestrial occupancy.…”

“…Some current drone applications with pinnipeds leverage thermal or multispectral imagery to facilitate detection by high contrast in drone imagery (Seymour et al 2017, Sweeney et al 2019, Larsen et al 2022b), but many more studies rely exclusively on visible‐light photography to detect pinnipeds. With visible‐light aerial imagery, deep learning techniques have already been applied to estimate aggregate pinniped counts (Hoekendijk et al 2021), detect individual pinnipeds (Dujon et al 2021), and classify pinnipeds by age class (Salberg 2015, Infantes et al 2022), though success and generalisability vary widely between examples.…”

Unmanned Aerial Vehicles (UAVs) in Marine Mammal Research: A Review of Current Applications and Challenges

“…Still images or video footage are processed and reviewed to count individuals and classify the animals by species and/or age classes (e.g., adults and pups). In most studies, images are previously processed to compile them into an orthomosaic of the study area [14,15,17,36,40,55,57,94,99,101]. In addition, UAV-based orthomosaic imagery allows for the study of terrain characteristics and social factors in haul-out sites, enabling spatial analysis of the distribution and site selection of pinniped haul-outs [17].…”