Inferring Cetacean Population Densities from the Absolute Dynamic Topography of the Ocean in a Hierarchical Bayesian Framework

Abstract

We inferred the population densities of blue whales (Balaenoptera musculus) and short-beaked common dolphins (Delphinus delphis) in the Northeast Pacific Ocean as functions of the water-column’s physical structure by implementing hierarchical models in a Bayesian framework. This approach allowed us to propagate the uncertainty of the field observations into the inference of species-habitat relationships and to generate spatially explicit population density predictions with reduced effects of sampling heterogeneity. Our hypothesis was that the large-scale spatial distributions of these two cetacean species respond primarily to ecological processes resulting from shoaling and outcropping of the pycnocline in regions of wind-forced upwelling and eddy-like circulation. Physically, these processes affect the thermodynamic balance of the water column, decreasing its volume and thus the height of the absolute dynamic topography (ADT). Biologically, they lead to elevated primary productivity and persistent aggregation of low-trophic-level prey. Unlike other remotely sensed variables, ADT provides information about the structure of the entire water column and it is also routinely measured at high spatial-temporal resolution by satellite altimeters with uniform global coverage. Our models provide spatially explicit population density predictions for both species, even in areas where the pycnocline shoals but does not outcrop (e.g. the Costa Rica Dome and the North Equatorial Countercurrent thermocline ridge). Interannual variations in distribution during El Niño anomalies suggest that the population density of both species decreases dramatically in the Equatorial Cold Tongue and the Costa Rica Dome, and that their distributions retract to particular areas that remain productive, such as the more oceanic waters in the central California Current System, the northern Gulf of California, the North Equatorial Countercurrent thermocline ridge, and the more southern portion of the Humboldt Current System. We posit that such reductions in available foraging habitats during climatic disturbances could incur high energetic costs on these populations, ultimately affecting individual fitness and survival.

Predicting cetacean distributions in data‐poor marine ecosystems

“…Pardo et al (2015) also found higher blue whale densities in waters with intermediate values of absolute dynamic topography (SSH is one of the variables used to calculate absolute dynamic topography). Intermediate SSH was an important indicator of the upwelling-modified waters associated with blue whale habitat in our models for both eastern Pacific Ocean ecosystems (i.e.…”

Scale of inference: on the sensitivity of habitat models for wide‐ranging marine predators to the resolution of environmental data

“…fronts, eddies; Scales et al , Cotté et al ). Where mechanistic linkages between seasonal or climatological fields and animal responses can be clearly inferred, synoptic environmental data fields can be useful (Block et al , Louzao et al , Hazen et al , , Scales et al , Pardo et al ). Moreover, the inclusion of a suite of contemporaneous and climatological data fields in the same models may be necessary to capture animal–environment interactions over scales relevant to conservation and management.…”

Estimation of population size and trends for highly mobile species with dynamic spatial distributions

“…Pardo et al. () developed multiyear models for blue whales ( Balaenoptera musculus ) and short‐beaked common dolphins ( Delphinus delphis ), with an ecological submodel for the distribution process. Estimated densities at each sampling unit varied in direct response to temporal variation in the habitat covariate, leading to substantial interannual variation in the aggregate population size across the entire survey region.…”

“…We represented the conventional approach with a mixed‐effects model (Model H0), in which the ecological process submodel comprises a fixed density–habitat relationship with random effects on year to allow for interannual variation in population size and habitat availability. There is no explicit submodel for the population process—instead, the population size within the survey region is inferred by integrating predicted densities over a fitted spatial density surface for the survey region (as per Pardo et al., ). This mixed‐effects model approximates the standard line‐transect estimation process currently used to estimate Dall's porpoise abundance in the CCE survey region except that the survey region is not partitioned into strata (Barlow & Forney, ).…”