Mon, Aug 02, 2021:On Demand
Background/Question/Methods
Animals make decisions regarding habitat use and movement that influence their fitness and survival. Animal-borne tags allow researchers to observe how animals move through their environment. When coupled with remotely-sensed environmental metrics, tag data can identify habitat features that are important to foraging predators. However, our ability to accurately identify habitat features requires animal movement and environmental data collected at corresponding spatiotemporal scales. Here, we investigated the fine-scale foraging behavior of blue whales (Balaenoptera musculus) using high-resolution multi-sensor tags from 2016-2020 along the California Coast (n=13, mean duration = 6.5 ± 4.5 days). To match our tag data’s resolution, we quantified blue whale foraging in relation to sub-mesoscale surface current features identified from concurrently sampled high-frequency radar (6 km, hourly resolution). We used a Lagrangian approach to calculate the backward-in-time finite-time Lyapunov exponent (FTLE), a measure of aggregative transport processes (e.g., fronts and eddies) that have been shown to influence shark, seabird, and marine mammal movements at broader scales. To assess habitat selection, we compared the distribution of FTLE values at blue whale feeding and non-feeding locations to the available habitat. We also used a generalized linear mixed model (GLMM) to quantify the relationship between FTLE and foraging state (feeding vs non-feeding).
Results/Conclusions To sustain their large body size, blue whales rely on consuming substantial quantities of small-bodied prey (e.g., krill, Euphausia spp.). We found that for feeding locations, blue whales significantly selected for areas with higher FTLE values than the surrounding environment and that a higher probability of feeding was associated with higher FTLE values. Regions of elevated FTLE represent aggregative surface current features, which can transport nutrient-rich water and influence the aggregation of organisms such as krill through physical forcing. Our data span multiple regions, seasons, and years, indicating that FTLE may be a robust metric for identifying dynamic, ephemeral habitat features in the California Current System. Our results give insight into the functional ecological relationships that influence blue whale movement at sub-daily scales and support the growing evidence that fine and intermediate-scale processes are critical in structuring marine ecosystems.
Results/Conclusions To sustain their large body size, blue whales rely on consuming substantial quantities of small-bodied prey (e.g., krill, Euphausia spp.). We found that for feeding locations, blue whales significantly selected for areas with higher FTLE values than the surrounding environment and that a higher probability of feeding was associated with higher FTLE values. Regions of elevated FTLE represent aggregative surface current features, which can transport nutrient-rich water and influence the aggregation of organisms such as krill through physical forcing. Our data span multiple regions, seasons, and years, indicating that FTLE may be a robust metric for identifying dynamic, ephemeral habitat features in the California Current System. Our results give insight into the functional ecological relationships that influence blue whale movement at sub-daily scales and support the growing evidence that fine and intermediate-scale processes are critical in structuring marine ecosystems.