Prey Patch Patterns Predict Habitat Use by Top Marine Predators with Diverse Foraging Strategies

Abstract

Spatial coherence between predators and prey has rarely been observed in pelagic marine ecosystems. We used measures of the environment, prey abundance, prey quality, and prey distribution to explain the observed distributions of three co-occurring predator species breeding on islands in the southeastern Bering Sea: black-legged kittiwakes (Rissa tridactyla), thick-billed murres (Uria lomvia), and northern fur seals (Callorhinus ursinus). Predictions of statistical models were tested using movement patterns obtained from satellite-tracked individual animals. With the most commonly used measures to quantify prey distributions - areal biomass, density, and numerical abundance - we were unable to find a spatial relationship between predators and their prey. We instead found that habitat use by all three predators was predicted most strongly by prey patch characteristics such as depth and local density within spatial aggregations. Additional prey patch characteristics and physical habitat also contributed significantly to characterizing predator patterns. Our results indicate that the small-scale prey patch characteristics are critical to how predators perceive the quality of their food supply and the mechanisms they use to exploit it, regardless of time of day, sampling year, or source colony. The three focal predator species had different constraints and employed different foraging strategies – a shallow diver that makes trips of moderate distance (kittiwakes), a deep diver that makes trip of short distances (murres), and a deep diver that makes extensive trips (fur seals). However, all three were similarly linked by patchiness of prey rather than by the distribution of overall biomass. This supports the hypothesis that patchiness may be critical for understanding predator-prey relationships in pelagic marine systems more generally.

Winter and spring diving behavior of bowhead whales relative to prey

“…Thus, the field metabolic rate and any other extra energetic demands of bowhead whales in Disko Bay can only be met if the whales feed on much higher copepod densities located in patches, likely near the seabed. Kenney et al [12] suggested that foraging in North Atlantic right whales must occur on dense and discrete layers of zooplankton to meet the energetic requirements, and generally marine predators rely on local patches or clusters of patches of prey rather than on areal average prey densities [13]. This study suggests that bowhead whales spend a considerable amount of time feeding close to the seabed where copepod densities are higher than densities closer to the surface.…”

Dolphin Prey Availability and Calorific Value in an Estuarine and Coastal Environment

“…The overall biomass of PDP and concentration of available energy appears to be higher in the winter seasons, which does not coincide with the higher abundance of dolphins sighted in the summer months. The suitability of prey includes abundance or biomass, as well as the distribution and ease of capture, all of which influence predator distribution (Benoit-Bird et al, 2013) and population size (Benoit-Bird, 2004). However, assessing the quality (i.e., nutritional value) of available prey may be a better indicator for assessing the value of prey for managing cetacean populations.…”

Individual variation in seasonal movements and foraging strategies of a land-locked, ice-breeding pinniped

“…Therefore large foraging areas may be related to better foraging habitat than smaller areas. For example, northern fur seals avoided the smallest prey patches and those separated by large distances, when their foraging paths were compared to aggregations of prey (pollock) (Benoit-Bird et al 2013).…”

“…Firstly, the spatio-temporal resolution of the data is such that foraging areas identified here are likely to constitute hierarchical clusters of foraging patches, rather than individual prey patches (Kotliar & Wiens 1990, Benoit-Bird et al 2013, and the delineation of patches can be sensitive to polygon definition as sumptions. Secondly, since we employed a 2 state SSM behavioural model to utilise data from both position only and TDR tags, resting and foraging locations may not always be differentiated.…”