Fine‐scale Habitat Modeling of a Top Marine Predator: Do Prey Data Improve Predictive Capacity

Abstract

Predators and prey assort themselves relative to each other, the availability of resources and refuges, and the temporal and spatial scale of their interaction. Predictive models of predator distributions often rely on these relationships by incorporating data on environmental variability and prey availability to determine predator habitat selection patterns. This approach to predictive modeling holds true in marine systems where observations of predators are logistically difficult, emphasizing the need for accurate models. In this paper, we ask whether including prey distribution data in fine-scale predictive models of bottlenose dolphin (Tursiops truncatus) habitat selection in Florida Bay, Florida, U.S.A., improves predictive capacity. Environmental characteristics are often used as predictor variables in habitat models of top marine predators with the assumption that they act as proxies of prey distribution. We examine the validity of this assumption by comparing the response of dolphin distribution and fish catch rates to the same environmental variables. Next, the predictive capacities of four models, with and without prey distribution data, are tested to determine whether dolphin habitat selection can be predicted without recourse to describing the distribution of their prey. The final analysis determines the accuracy of predictive maps of dolphin distribution produced by modeling areas of high fish catch based on significant environmental characteristics. We use spatial analysis and independent data sets to train and test the models. Our results indicate that, due to high habitat heterogeneity and the spatial variability of prey patches, fine-scale models of dolphin habitat selection in coastal habitats will be more successful if environmental variables are used as predictor variables of predator distributions rather than relying on prey data as explanatory variables. However, predictive modeling of prey distribution as the response variable based on environmental variability did produce high predictive performance of dolphin habitat selection, particularly foraging habitat.

Habitat selection of a coastal shark species estimated from an autonomous underwater vehicle

“…The distribution of marine nekton is influenced by abiotic and biotic environmental cues on multiple spatial and temporal scales (Bowler & Benton 2005, Nathan et al 2008, Torres et al 2008, Huijbers et al 2012. Animals selecting for habitats in patchy environments may also have patchy distributions, reflecting variable oceanic conditions.…”

“…Studies identifying mesoscale habitat use commonly use acoustic telemetry or visual sightings records to document species locations in the coastal ocean and estuaries (Heithaus et al 2006, Torres et al 2008, Huijbers et al 2012, Kneebone et al 2012. The coastal ocean presents additional challenges in habitat selection studies due to its physical dynamics.…”

“…Currents, tides, freshwater inputs, nutrient loading and patchy prey distribution create an environment that varies on scales much smaller than those in the open ocean (Epifanio & Garvine 2001). Within these mesoscale study regions, environmental conditions are measured using temperature loggers at fixed points (Kneebone et al 2012), bathymetry maps and sediment type (Heithaus et al 2006, Torres et al 2008, or point source measurements of temperature, salinity, turbidity, and dissolved oxygen recorded near sightings of animals (Torres et al 2008). In addition, auditory, olfactory and visual cues at smaller scales have been shown to affect juvenile reef fish habitat association in a laboratory setting (Huijbers et al 2012), and studies have shown that salmonids use chemical cues in the water for natal stream homing behavior (Scholz et al 1976, Dittman & Quinn 1996.…”

Habitat preferences among three top predators inhabiting a degraded ecosystem, the Black Sea

“…Although they can often be related to physical oceanographic features such as water depth, it is likely that prey abundance and movements are the most important factors in determining the occurrence and movements of cetaceans (Gaskin 1982, Evans 1987, Nøttestad et al 2014. Oceanographic features have therefore been widely used as proxies of ecological variables such as prey distribution, which is difficult to obtain or cannot be measured (Torres et al 2008). Different circulation features, bathymetric complexity, physico-chemical and strong weather patterns are known to exist within the study area and are well documented (Oğuz et al 1995, BSC 2008.…”

“…Management of whale and dolphin populations can benefit from accurate, model-derived predictions of their habitat to mitigate anthropogenic effects such as fisheries by-catch (Kaschner et al 2012), foresee impacts of habitat alterations on ecosystem function (D'Amico et al 2003), protect critical habitats or select suitable areas for protection (Cañadas et al 2005, de Stephanis et al 2008, and aid our understanding of the ecology of these animals (Hamazaki 2002). Environmental factors can then be applied to predict marine predator distribution based on the relationships between predator, prey and environment, allowing more robust results than when one relies on prey distribution alone (Torres et al 2008). Therefore, by assuming that the distribution of a species is non-random relative to environmental variability, predictive models of distribution typically identify the ecological relationships between the environment and species habitat selection.…”