Surface and subsurface oceanographic features drive forage fish distributions and aggregations: Implications for prey availability to top predators in the US Northeast Shelf ecosystem.

Forage fishes are a critical food web link in marine ecosystems, aggregating in a hierarchical patch structure over multiple spatial and temporal scales. Surface-level forage fish aggregations (FFAs) represent a concentrated source of prey available to surface- and shallow-foraging marine predators. Existing survey and analysis methods are often imperfect for studying forage fishes at scales appropriate to foraging predators, making it difficult to quantify predator-prey interactions. In many cases, general distributions of forage fish species are known; however, these may not represent surface-level prey availability to predators. Likewise, we lack an understanding of the oceanographic drivers of spatial patterns of prey aggregation and availability or forage fish community patterns. Specifically, we applied Bayesian joint species distribution models to bottom trawl survey data to assess species- and community-level forage fish distribution patterns across the US Northeast Continental Shelf (NES) ecosystem. Aerial digital surveys gathered data on surface FFAs at two project sites within the NES, which we used in a spatially explicit hierarchical Bayesian model to estimate the abundance and size of surface FFAs. We used these models to examine the oceanographic drivers of forage fish distributions and aggregations. Our results suggest that, in the NES, regions of high community species richness are spatially consistent with regions of high surface FFA abundance. Bathymetric depth drove both patterns, while subsurface features, such as mixed layer depth, primarily influenced aggregation behavior and surface features, such as sea surface temperature, sub-mesoscale eddies, and fronts influenced forage fish diversity. In combination, these models help quantify the availability of forage fishes to marine predators and represent a novel application of spatial models to aerial digital survey data.

Predicting the offshore distribution and abundance of marine birds with a hierarchical community distance sampling model.

Proposed offshore wind energy development on the Atlantic Outer Continental Shelf has brought attention to the need for baseline studies of the distribution and abundance of marine birds. We compiled line transect data from 15 shipboard surveys (June 2012-April 2014), along with associated remotely sensed habitat data, in the lower Mid-Atlantic Bight off the coast of Delaware, Maryland, and Virginia, USA. We implemented a recently developed hierarchical community distance sampling model to estimate the seasonal abundance of 40 observed marine bird species. Treating each season separately, we included six oceanographic parameters to estimate seabird abundance: three static (distance to shore, slope, sediment grain size) and three dynamic covariates (sea surface temperature [SST], salinity, primary productivity). We expected that avian bottom-feeders would respond primarily to static covariates that characterize seafloor variability, and that surface-feeders would respond more to dynamic covariates that quantify surface productivity. We compared the variation in species-specific and community-level responses to these habitat features, including for rare species, and we predicted species abundance across the study area. While several protected species used the study area in summer during their breeding season, estimated abundance and observed diversity were highest for nonbreeding species in winter. Distance to shore was the most common significant predictor of abundance, and thus useful in estimating the potential exposure of marine birds to offshore development. In many cases, our expectations based on feeding ecology were confirmed, such as in the first winter season, when bottom-feeders associated significantly with the three static covariates (distance to shore, slope, and sediment grain size), and surface-feeders associated significantly with two dynamic covariates (SST, primary productivity). However, other cases revealed significant relationships between static covariates and surface-feeders (e.g., distance to shore) and between dynamic covariates and bottom-feeders (e.g., primary productivity during that same winter). More generally, we found wide interannual, seasonal, and interspecies variation in habitat relationships with abundance. These results show the importance of quantifying detection and determining the ecological drivers of a community's distribution and abundance, within and among species, for evaluating the potential exposure of marine birds to offshore development.

Effects of selective adaptation on coding sugar and salt tastes in mixtures.

Little is known about coding of taste mixtures in complex dynamic stimulus environments. A protocol developed for odor stimuli was used to test whether rapid selective adaptation extracted sugar and salt component tastes from mixtures as it did component odors. Seventeen human subjects identified taste components of "salt + sugar" mixtures. In 4 sessions, 16 adapt-test stimulus pairs were presented as atomized, 150-µL "taste puffs" to the tongue tip to simulate odor sniffs. Stimuli were NaCl, sucrose, "NaCl + sucrose," and water. The sugar was 98% identified but the suppressed salt 65% identified in unadapted mixtures of 2 concentrations of NaCl, 0.1 or 0.05 M, and sucrose at 3 times those concentrations, 0.3 or 0.15 M. Rapid selective adaptation decreased identification of sugar and salt preadapted ambient components to 35%, well below the 74% self-adapted level, despite variation in stimulus concentration and adapting time (<5 or >10 s). The 96% identification of sugar and salt extra mixture components was as certain as identification of single compounds. The results revealed that salt-sugar mixture suppression, dependent on relative mixture-component concentration, was mutual. Furthermore, like odors, stronger and recent tastes are emphasized in dynamic experimental conditions replicating natural situations.

Time and intensity factors in identification of components of odor mixtures.

Identification of odors of compounds introduced into changeable olfactory environments is the essence of olfactory coding, which focuses perception on the latest stimulus with the greatest salience. Effects of stimulus intensity and adapting time on mixture component identification after adapting with one component were each studied in 10 human subjects. Odors of 1 and 5 mM vanillin (vanilla) and phenethyl alcohol (rose) were identified, with adapting time varied by sniffing naturally once or twice, or sniffing 5 times, once every 2 s. Odors of water-adapted single compounds were identified nearly perfectly (94%), self-adapted to 51% but did not cross-adapt (94%), showing the 2 compounds had quickly adapting independent odors. Identifications of the vanilla and rose odors in water-adapted mixtures were reduced to 59% and 79%, respectively. Following single-component adaptation, the average 33% identification of odors of adapted (ambient) mixture components contrasted with the greater average 86% identification of new unadapted (extra) mixture components. Identifications were lower for 1 than 5 mM components when concentrations were not matched, and ambient component identifications were lower after 10-s adaptation than after 1 or 2 sniffs. Rapid selective adaptation and mixture component suppression manipulate effective intensity to promote emergence of characteristic odor qualities in dynamic natural settings.

Characteristic component odors emerge from mixtures after selective adaptation.

Humans cannot reliably identify the distinctive characteristic odors of components in mixtures containing more than three compounds. In the present study, we demonstrate that selective adaptation can improve component identification. Characteristic component odors, lost in mixtures, were identifiable after presenting other mixture constituents for a few seconds. In mixtures of vanillin, isopropyl alcohol, l-menthol and phenethyl alcohol, this rapid selective adaptation unmasked each component. We suggest that these findings relate directly to how olfactory qualities are coded: olfactory receptors do not act as detectors of isolated molecular features, but likely recognize entire molecules closely associated with perceived olfactory qualities or "notes". Rapid and focused activation of a few distinct receptor types may dominate most odor percepts, emphasizing the importance of many dynamic and specific neural signals. An interaction between two fundamental coding strategies, mixture suppression and selective adaptation, with hundreds of potential olfactory notes, explains humans experiencing the appearance and disappearance of identifiable odors against ambient mixture backgrounds.