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Towards a better characterisation of deep-diving whales' distributions by using prey distribution model outputs?

ABSTRACT: In habitat modelling, environmental variables are assumed to be proxies of lower trophic levels distribution and by extension, of marine top predator distributions. More proximal variables, such as potential prey fields, could refine relationships between top predator distributions and their environment. In situ data on prey distributions are not available over large spatial scales but, a numerical model, the Spatial Ecosystem And POpulation DYnamics Model (SEAPODYM), provides simulations of the biomass and production of zooplankton and six functional groups of micronekton at the global scale. Here, we explored whether generalised additive models fitted to simulated prey distribution data better predicted deep-diver densities (here beaked whales Ziphiidae and sperm whales Physeter macrocephalus) than models fitted to environmental variables. We assessed whether the combination of environmental and prey distribution data would further improve model fit by comparing their explanatory power. For both taxa, results were suggestive of a preference for habitats associated with topographic features and thermal fronts but also for habitats with an extended euphotic zone and with large prey of the lower mesopelagic layer. For beaked whales, no SEAPODYM variable was selected in the best model that combined the two types of variables, possibly because SEAPODYM does not accurately simulate the organisms on which beaked whales feed on. For sperm whales, the increase model performance was only marginal. SEAPODYM outputs were at best weakly correlated with sightings of deep-diving cetaceans, suggesting SEAPODYM may not accurately predict the prey fields of these taxa. This study was a first investigation and mostly highlighted the importance of the physiographic variables to understand mechanisms that influence the distribution of deep-diving cetaceans. A more systematic use of SEAPODYM could allow to better define the limits of its use and a development of the model that would simulate larger prey beyond 1,000 m would probably better characterise the prey of deep-diving cetaceans.

SUBMITTER: Virgili A

PROVIDER: S-EPMC8336804 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Dive capacity among toothed whales (suborder: Odontoceti) has been shown to generally increase with body mass in a relationship closely linked to the allometric scaling of metabolic rates. However, two odontocete species tagged in this study, the Blainville's beaked whale Mesoplodon densirostris and the Cuvier's beaked whale Ziphius cavirostris, confounded expectations of a simple allometric relationship, with exceptionally long (mean: 46.1 min & 65.4 min) and deep dives (mean: 1129 m & 1179 m), and comparatively small body masses (med.: 842.9 kg & 1556.7 kg). These two species also exhibited exceptionally long recovery periods between successive deep dives, or inter-deep-dive intervals (M. densirostris: med. 62 min; Z. cavirostris: med. 68 min). We examined competing hypotheses to explain observed patterns of vertical habitat use based on body mass, oxygen binding protein concentrations, and inter-deep-dive intervals in an assemblage of five sympatric toothed whales species in the Bahamas. Hypotheses were evaluated using dive data from satellite tags attached to the two beaked whales (M. densirostris, n = 12; Z. cavirostris, n = 7), as well as melon-headed whales Peponocephala electra (n = 13), short-finned pilot whales Globicephala macrorhynchus (n = 15), and sperm whales Physeter macrocephalus (n = 27). Body mass and myoglobin concentration together explained only 36% of the variance in maximum dive durations. The inclusion of inter-deep-dive intervals, substantially improved model fits (R2 = 0.92). This finding supported a hypothesis that beaked whales extend foraging dives by exceeding aerobic dive limits, with the extension of inter-deep-dive intervals corresponding to metabolism of accumulated lactic acid. This inference points to intriguing tradeoffs between body size, access to prey in different depth strata, and time allocation within dive cycles. These tradeoffs and resulting differences in habitat use have important implications for spatial distribution patterns, and relative vulnerabilities to anthropogenic impacts.

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Towards a better characterisation of deep-diving whales' distributions by using prey distribution model outputs?

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