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The Atlantic surfclam (Spisula solidissima solidissima) is an economically valuable clam species that supports a major US fishery. Until recently, fishery efforts along the southern edge of the surfclam range were low due to clam mortalities there in the 1990s. Recent surfclam fishing efforts off Virginia raised questions of whether the surfclam population has returned in the southern region or if a single cohort is supporting the fishery there. Questions have also arisen about whetherS. s. similisis among the population fished off the coast of VA.Spisula solidissima similisis a warm-water cryptic subspecies of the Atlantic surfclam. Although morphologically indistinguishable,S. s. similisgrows to a smaller size and is genetically distinct. Atlantic surfclams (n = 103) were collected from the fishing grounds off the coast of VA. Each surfclam was aged, and shell length and tissue weight recorded for comparison to surfclams of the same age from the center of the population. Analyses of mitochondrial (mtCOI) sequences suggests that the two groups sampled off VA are genetically homogeneous, both groups contain two divergent mitochondrial lineages, and one surfclam sampled shares theS. s. similismtCOI sequence. There are multiple cohorts of surfclams, suggesting that environmental conditions may have improved for surfclams in the south, or that this population has acclimated to altered conditions. Further research should investigate the potential for subspecies hybridization.

Taphonomic indicators are often used to assess time-since-death of skeletal remains. These indicators frequently have limited accuracy, resulting in the reliance of other methodologies to age remains. Arctica islandica, commonly known as the ocean quahog, is a relatively widespread bivalve in the North Atlantic, with an extended lifespan that often exceeds two hundred years; hence, their shells are often studied to evaluate climate change over time. This report evaluates taphonomic age using 117 A. islandica shells collected from the Mid-Atlantic Bight offshore of the Delmarva Peninsula with radiocarbon dates extending from 60–4,400 cal years BP. These shells had varying degrees of taphonomic alteration produced by discoloration and degradation of periostracum. To determine if a relationship exists between taphonomic condition and time-since-death, radiocarbon ages were compared with the amount of remaining periostracum and type of discoloration. Old shells (individuals that died long ago) were discolored orange with no periostracum while younger shells (individuals that died more recently) had their original color, with some periostracum. Both the disappearance of periostracum and appearance of discoloration followed a logistic process, with 50% of shells devoid of periostracum and 50% discolored in about 1,000 years. The logistic form of long-term taphonomic processes degrading shell condition is first reported here, as are the longest time series for taphonomic processes in death assemblages within the Holocene record. This relationship can be utilized for triage when deciding what shells to age from time-averaged assemblages, permitting more efficient application of expensive methods of aging such as radiocarbon dating.

Rising water temperatures along the northeastern U.S. continental shelf have resulted in an offshore range shift of the Atlantic surfclamSpisula solidissimato waters still occupied by ocean quahogsArctica islandica. Fishers presently are prohibited from landing both Atlantic surfclams and ocean quahogs in the same catch, thus limiting fishing to locations where the target species can be sorted on deck. Wind energy development on and around the fishing grounds will further restrict the fishery. A spatially explicit model of the Atlantic surfclam fishery (Spatially Explicit Fishery Economics Simulator) has the ability to simulate the consequences of fishery displacement due to wind energy development in combination with fishery and stock dynamics related to the species' overlap with ocean quahogs. Five sets of simulations were run to determine the effect of varying degrees of species overlap due to Atlantic surfclam range shifts in conjunction with fishing constraints due to wind farm development. Simulations tracked changes in relative stock status, fishery performance, and the economic consequences for the fishery. Compared to a business‐as‐usual scenario, all scenarios with less‐restrictive fishing penalties due to species overlap exhibited higher raw catch numbers but also greater reductions in revenue and increases in cost after the implementation of wind farms. This analysis serves to demonstrate the response of the Atlantic surfclam fishery to combined pressures from competing ocean uses and climate change and emphasizes the potential for economic disruption of fisheries as climate change interacts with the evolution of ocean management on the continental shelf.

The Atlantic surfclamSpisula solidissimafishery, which spans the U.S. Northeast continental shelf, is among the most exposed to offshore wind energy development impacts because of the overlap of fishing grounds with wind energy lease areas, the hydraulic dredges used by the fishing vessels, and the location of vessel home ports relative to the fishing grounds. The Atlantic surfclam federal assessment survey is conducted using a commercial fishing vessel in locations that overlap with the offshore wind energy development. Once wind energy turbines, cables, and scour protection are installed, survey operations within wind energy lease areas may be curtailed or eliminated due to limits on vessel access, safety requirements, and assessment survey protocols. The impact of excluding the federal assessment survey from wind energy lease areas was investigated using a spatially explicit, agent‐based modeling framework that integrates Atlantic surfclam stock biology, fishery captain and fleet behavior, and federal assessment survey and management decisions. Simulations were designed to compare assessment estimates of spawning stock biomass (SSB) and fishing mortality (F) for scenarios that excluded the survey from (1) wind energy lease areas or (2) wind energy lease areas and potential wind energy lease areas (“call areas”). For the most restricted scenario, the simulated stock assessment estimated 17% lower SSB relative to an unrestricted survey, placing it below the SSB target. The simulatedFincreased by 7% but was still less than the acceptedFthreshold. Changes in biological reference points were driven by the inability to access the Atlantic surfclam biomass within the wind energy lease areas. Deviations in reference points reflected the proportion of the population excluded from the survey. Excluding the Atlantic surfclam assessment surveys from the regions designated for offshore wind development can alter long‐term stock assessments by increasing uncertainty in metrics that are used to set fishing quotas.

A survey of the region eastward of Nantucket provided an opportunity to examine the cold temperate–boreal boundary along the high‐energy Great South Channel. Here described are the benthic macroinvertebrate community types encountered, with a focus on the influence of climate change on the range boundaries of the benthic biomass dominants and the potential existence of transient multiple stable states. The survey identified three primary community types. The shallowest sites were occupied by a surfclam‐dominated community, comprising an abundance of large (≥150 mm) surfclams, and a few common attached epibiota primarily attached to exposed surfclam shell. Two communities exist at intermediate depths, one dominated by submarket and small market‐size surfclams (<150 mm) and the other, created by mussel mats and their attendant epibiota, crabs, sea urchins, and other mobile epifauna. Mussels are a foundational species, establishing a hard‐bottom terrain conducive to these other denizens in soft‐bottom habitat. Cobbles were nearly ubiquitous, rocks were routinely recovered, and boulders were encountered occasionally. Slow growing attached epibionts were exceedingly rare and mobile epifauna were not obviously associated with these large sedimentary particles; nor were the surfclam or mussel communities. The frequency of barnacle scars suggests sediment scour under the high‐flow regime characteristic of the surveyed region, which voids the habitat potential of these sedimentary particles. The abundance of surfclam shell indicates that surfclams have inhabited the shoaler depths for an extended time; limited shell at deeper sites supports the inference from the absence of large animals that these sites are relatively newly colonized and represent further evidence of an offshore shift in range brought on by increasing bottom water temperatures. The dichotomous nature of the two primary community types at mid‐depths suggests that these two communities represent multiple stable states brought on by the interaction of an invading cold temperate species with the receding boreal fauna resulting in a transient intermingling of species, which, however, structure the habitat into exclusionary stable states rather than overlapping in a co‐occurrence ecotone.

Discards from commercial fisheries have been linked to detrimental effects on ecosystems and stocks of living marine resources. Understanding spatial and temporal patterns of discards may assist in devising regulatory practices and mitigation strategies and promote sustainable management policies. This study investigates data from bycatch monitoring programs using a machine learning approach. We used a gradient boosting classifier for describing catch and bycatch patterns in the U.S. Mid-Atlantic Black Seabass (Centropristis striata), Summer Flounder (Paralichthys dentatus), Scup (Stenotomus chrysops), and Longfin Squid (Doryteuthis pealeii) fisheries. We used oceanographic, biological, spatial, and fisheries data as explanatory model features. We found positive associations between target species volume and bycatch. Although we found that sea surface temperature and year were important model features, the direction of impact of those predictors was variable. From our findings, we conclude that machine learning approaches are promising in supplementing traditional methodologies, especially with the increase in data availability trends.