Salt marshes provide important services to coastal ecosystems in the southeastern United States. In many locations, salt marsh habitats are threatened by coastal development and erosion, necessitating large-scale monitoring. Assessing vegetation height across the extent of a marsh can provide a comprehensive analysis of its health, as vegetation height is associated with Above Ground Biomass (AGB) and can be used to track degradation or growth over time. Traditional methods to do this, however, rely on manual measurements of stem heights that can cause harm to the marsh ecosystem. Moreover, manual measurements are limited in scale and are often time and labor intensive. Unoccupied Aircraft Systems (UAS) can provide an alternative to manual measurements and generate continuous results across a large spatial extent in a short period of time. In this study, a multirotor UAS equipped with optical Red Green Blue (RGB) and multispectral sensors was used to survey five salt marshes in Beaufort, North Carolina. Structure-from-Motion (SfM) photogrammetry of the resultant imagery allowed for continuous modeling of the entire marsh ecosystem in a three-dimensional space. From these models, vegetation height was extracted and compared to ground-based manual measurements. Vegetation heights generated from UAS data consistently under-predicted true vegetation height proportionally and a transformation was developed to predict true vegetation height. Vegetation height may be used as a proxy for Above Ground Biomass (AGB) and contribute to blue carbon estimates, which describe the carbon sequestered in marine ecosystems. Employing this transformation, our results indicate that UAS and SfM are capable of producing accurate assessments of salt marsh health via consistent and accurate vegetation height measurements.

Monitoring body length and body condition of individuals helps determine overall population health and assess adaptation to environmental changes. Aerial photogrammetry from drone‐based videos is a valuable method for obtaining body length and body condition measurements of cetaceans. However, the laborious manual processing of drone‐based videos to select frames to measure animals ultimately delays assessment of population health and hinders conservation actions. Here, we apply deep learning methods to expedite the processing of drone‐based videos to improve efficiency of obtaining important morphological measurements of whales. We develop two user‐friendly models to automatically (1) detect and output frames containing whales from drone‐based videos (“DeteX”) and (2) extract body length and body condition measurements from input frames (“XtraX”). We use drone‐based videos of gray whales to compare manual versus automated measurements (n = 86). Our results show automated methods reduced processing times by one‐ninth, while achieving similar accuracy as manual measurements (mean coefficient of variation <5%). We also demonstrate how these methods are adaptable to other species and identify remaining challenges to help further improve automated measurements in the future. Importantly, these tools greatly speed up obtaining key morphological data while maintaining accuracy, which is critical for effectively monitoring population health.

Describing individual morphology and growth is key for identifying ecological niches and monitoring the health and fitness of populations. Eastern North Pacific ((ENP), approximately 16 650 individuals) gray whales primarily feed in the Arctic/sub-Arctic regions, while a small subgroup called the Pacific Coast Feeding Group (PCFG, approximately 212 individuals) instead feeds between northern California, USA and British Columbia, Canada. Evidence suggests PCFG whales have lower body condition than ENP whales. Here we investigate morphological differences (length, skull, and fluke span) and compare length-at-age growth curves between ENP and PCFG whales. We use ENP gray whale length-at-age data comprised of strandings, whaling, and aerial photogrammetry (1926–1997) for comparison to data from PCFG whales collected through non-invasive techniques (2016–2022) to estimate age (photo identification) and length (drone-based photogrammetry). We use Bayesian methods to incorporate uncertainty associated with morphological measurements (manual and photogrammetric) and age estimates. We find that while PCFG and ENP whales have similar growth rates, PCFG whales reach smaller asymptotic lengths. Additionally, PCFG whales have relatively smaller skulls and flukes than ENP whales. These findings represent a striking example of morphological adaptation that may facilitate PCFG whales accessing a foraging niche distinct from the Arctic foraging grounds of the broader ENP population.

Understanding how individual animals respond to stressors behaviourally and physiologically is a critical step towards quantifying long-term population consequences and informing management efforts. Glucocorticoid (GC) metabolite accumulation in various matrices provides an integrated measure of adrenal activation in baleen whales and could thus be used to investigate physiological changes following exposure to stressors. In this study, we measured GC concentrations in faecal samples of Pacific Coast Feeding Group (PCFG) gray whales (Eschrichtius robustus) collected over seven consecutive years to assess the association between GC content and metrics of exposure to sound levels and vessel traffic at different temporal scales, while controlling for contextual variables such as sex, reproductive status, age, body condition, year, time of year and location. We develop a Bayesian Generalized Additive Modelling approach that accommodates the many complexities of these data, including non-linear variation in hormone concentrations, missing covariate values, repeated samples, sampling variability and some hormone concentrations below the limit of detection. Estimated relationships showed large variability, but emerging patterns indicate a strong context-dependency of physiological variation, depending on sex, body condition and proximity to a port. Our results highlight the need to control for baseline hormone variation related to context, which otherwise can obscure the functional relationship between faecal GCs and stressor exposure. Therefore, extensive data collection to determine sources of baseline variation in well-studied populations, such as PCFG gray whales, could shed light on cetacean stress physiology and be used to extend applicability to less-well-studied taxa. GC analyses may offer greatest utility when employed as part of a suite of markers that, in aggregate, provide a multivariate measure of physiological status, better informing estimates of individuals’ health and ultimately the consequences of anthropogenic stressors on populations.

Monitoring Adélie penguin (Pygoscelis adeliae) populations on the Western Antarctic Peninsula (WAP) provides information about the health of the species and the WAP marine ecosystem itself. In January 2017, surveys of Adélie penguin colonies at Avian Island and Torgersen Island off the WAP were conducted via unoccupied aircraft systems (UAS) collecting optical Red Green Blue (RGB), thermal, and multispectral imagery. A semi-automated workflow to count individual penguins using a fusion of multispectral and thermal imagery was developed and combined into an ArcGIS workflow. This workflow isolates colonies using multispectral imagery and detects and counts individuals by thermal signatures. Two analysts conducted manual counts from synoptic RGB UAS imagery. The automated system deviated from analyst counts by −3.96% on Avian Island and by 17.83% on Torgersen Island. However, colony-by-colony comparisons revealed that the greatest deviations occurred at larger colonies. Matched pairs analysis revealed no significant differences between automated and manual counts at both locations (p > 0.31) and linear regressions of colony sizes from both methods revealed significant positive relationships approaching unity (p < 0.0002. R² = 0.91). These results indicate that combining UAS surveys with sensor fusion techniques and semi-automated workflows provide efficient and accurate methods for monitoring seabird colonies in remote environments.