The application of a drift-foraging bioenergetic model to evaluate the relative influence of prey abundance (invertebrate drift) and habitat (e.g. pool frequency) on habitat quality for young-of-the-year (YOY) and yearling juvenile cutthroat trout, Oncorhynchus clarki (Richardson) is described. Experiments and modelling indicated simultaneous limitation of fish growth by prey abundance and habitat, where depth and current velocity limit the volume of water and prey flowing through a fishÕs reactive field as well as swimming costs and prey capture success. Predicted energy intake and growth increase along a depth gradient, with slower deeper pool habitat generating higher predicted growth for both YOY and yearling trout. Bioenergetic modelling indicated that fish are constrained to use progressively deeper habitats to meet increasing energy requirements as they grow. Sensitivity of growth to prey abundance identified the need to better understand how variation in invertebrate drift and terrestrial drop affects habitat quality and capacity for drift-feeding fishes.K E Y W O R D S : bioenergetic modelling, habitat limitation, habitat quality, habitat quantity, invertebrate drift.

As the effects of climate change become more pronounced, variation in the direction and magnitude of shifts in species occurrence in space and time may disrupt interspecific interactions in ecological communities. In this study, we examined how the fall and winter ichthyoplankton community in the Newport River Estuary located inshore of Pamlico Sound in the southeastern United States has responded to environmental variability over the last 27 yr. We relate the timing of estuarine ingress of 10 larval fish species to changes in sea surface temperature (SST), the Atlantic Multidecadal Oscillation, the North Atlantic Oscillation, wind strength and phenology, and tidal height. We also examined whether any species exhibited trends in ingress phenology over the last 3 decades. Species varied in the magnitude of their responses to all of the environmental variables studied, but most shared a common direction of change. SST and northerly wind strength had the largest impact on estuarine ingress phenology, with most species ingressing earlier during warm years and delaying ingress during years with strong northerly winds. As SST warms in the coming decades, the average date of ingress of some species (Atlantic croaker Micropogonias undulatus, summer flounder Paralichthys dentatus, pinfish Lagodon rhomboides) is projected to advance on the order of weeks to months, assuming temperatures do not exceed a threshold at which species can no longer respond through changes in phenology. These shifts in ingress could affect larval survival and growth since environmental conditions in the estuarine and pelagic nursery habitats of fishes also vary seasonally.

Seascape ecology has demonstrated that marine fishes are associated with multiscale habitat characteristics; however, most species distribution models focus on only a few predictors (e.g. depth, temperature), and this limits knowledge of essential fish habitat characteristics. Our objectives were to (1) determine habitat associations of offshore predatory marine fishes using a comprehensive suite of predictors, including area of nearby estuarine environments, (2) assess variable influence, and (3) model the spatial distribution of selected fishes in the families Carcharhinidae and Lutjanidae. We hypothesized that the concept of coastal outwelling would be evidenced by species associations with areas of nearby estuarine environments, and prey abundance would correlate with predator distributions. Species distribution models were developed for 2 snapper and 3 shark species in the northern Gulf of Mexico, USA. We used 34 multiscale predictors to evaluate how fish probability of presence or catch per unit effort (CPUE) were associated with oceanography, geography, substrate, area of nearby wetlands and estuaries, and prey abundance. Boosted regression trees, a machine-learning technique, modeled the most influential variables and predicted distributions. Model validation showed an overall accuracy of 79-86%, and CPUE models explained >40% of model deviance. Oceanographic variables, particularly mixed layer depth, were most influential and most frequently selected. As hypothesized, predatory fish distributions were predicted by prey abundances, and shark distributions were predicted by area of nearby coastal wetlands and estuaries. Our findings suggest that spatial models can provide novel insights into prey associations and linkages of marine species with nearby wetlands and estuaries.