Climate change and decadal variability are impacting marine fish and invertebrate species worldwide and these impacts will continue for the foreseeable future. Quantitative approaches have been developed to examine climate impacts on productivity, abundance, and distribution of various marine fish and invertebrate species. However, it is difficult to apply these approaches to large numbers of species owing to the lack of mechanistic understanding sufficient for quantitative analyses, as well as the lack of scientific infrastructure to support these more detailed studies. Vulnerability assessments provide a framework for evaluating climate impacts over a broad range of species with existing information. These methods combine the exposure of a species to a stressor (climate change and decadal variability) and the sensitivity of species to the stressor. These two components are then combined to estimate an overall vulnerability. Quantitative data are used when available, but qualitative information and expert opinion are used when quantitative data is lacking. Here we conduct a climate vulnerability assessment on 82 fish and invertebrate species in the Northeast U.S. Shelf including exploited, forage, and protected species. We define climate vulnerability as the extent to which abundance or productivity of a species in the region could be impacted by climate change and decadal variability. We find that the overall climate vulnerability is high to very high for approximately half the species assessed; diadromous and benthic invertebrate species exhibit the greatest vulnerability. In addition, the majority of species included in the assessment have a high potential for a change in distribution in response to projected changes in climate. Negative effects of climate change are expected for approximately half of the species assessed, but some species are expected to be positively affected (e.g., increase in productivity or move into the region). These results will inform research and management activities related to understanding and adapting marine fisheries management and conservation to climate change and decadal variability.

The term river herring collectively refers to alewife (Alosa pseudoharengus) and blueback herring (A. aestivalis), two anadromous fishes distributed along the east coast of North America. Historically, river herring spawning migrations supported important fisheries, and their spawning runs continue to be of cultural significance to many coastal communities. Recently, substantial declines in spawning run size prompted a petition to consider river herring for listing under the Endangered Species Act (ESA). The ESA status review process requires an evaluation of a species’ response to multiple stressors, including climate change. For anadromous species that utilize a range of habitats throughout their life cycle, the response to a changing global climate is inherently complex and likely varies regionally. River herring occupy marine habitat for most of their lives, and we demonstrate that their relative abundance in the ocean has been increasing in recent years. We project potential effects of ocean warming along the US Atlantic coast on river herring in two seasons (spring and fall), and two future periods (2020–2060 and 2060–2100) by linking species distribution models to projected temperature changes from global climate models. Our analyses indicate that climate change will likely result in reductions in total suitable habitat across the study region, which will alter the marine distribution of river herring. We also project that density will likely decrease for both species in fall, but may increase in spring. Finally, we demonstrate that river herring may have increased sensitivity to climate change under a low abundance scenario. This result could be an important consideration for resource managers when planning for climate change because establishing effective conservation efforts in the near term may improve population resiliency and provide lasting benefits to river herring populations.

Multispecies stock assessment models require predator diet data, e.g. stomach samples. Diet data can be unavailable, sparse, of small sample size, or very noisy. It is unclear if multispecies interactions can be estimated without bias when interactions are weak. Research is needed about how model performance is affected by the availability or quality of diet data and by the method for fitting it. We developed seven age-structured operating models that simulate trophic interactions for two fish species and different scenarios of diet data availability or quality. The simulated data sets were fitted using four statistical catch-at-age models that estimated fishing, predation and residual natural mortality and differed in the way the diet data was fitted. Fitting the models to diet data averaged over time should be avoided since it resulted in estimation bias. Fitting annual diet composition per stomach produced bias estimates due to the occurrence of zeros in the observed proportions and the statistical assumptions for the diet model. Fitting to annual stomach proportions averaged across stomachs led to unbiased results even if the number of stomachs was small, the interactions were weak or some sampled years and ages were missing. These methods should be preferred when fitting multispecies models.