Recent shifts in the geographic distribution of marine species have been linked to shifts in preferred thermal habitats. These shifts in distribution have already posed challenges for living marine resource management, and there is a strong need for projections of how species might be impacted by future changes in ocean temperatures during the 21st century. We modeled thermal habitat for 686 marine species in the Atlantic and Pacific oceans using long-term ecological survey data from the North American continental shelves. These habitat models were coupled to output from sixteen general circulation models that were run under high (RCP 8.5) and low (RCP 2.6) future greenhouse gas emission scenarios over the 21st century to produce 32 possible future outcomes for each species. The models generally agreed on the magnitude and direction of future shifts for some species (448 or 429 under RCP 8.5 and RCP 2.6, respectively), but strongly disagreed for other species (116 or 120 respectively). This allowed us to identify species with more or less robust predictions. Future shifts in species distributions were generally poleward and followed the coastline, but also varied among regions and species. Species from the U.S. and Canadian west coast including the Gulf of Alaska had the highest projected magnitude shifts in distribution, and many species shifted more than 1000 km under the high greenhouse gas emissions scenario. Following a strong mitigation scenario consistent with the Paris Agreement would likely produce substantially smaller shifts and less disruption to marine management efforts. Our projections offer an important tool for identifying species, fisheries, and management efforts that are particularly vulnerable to climate change impacts.

The geographic distributions of marine species are changing rapidly, with leading range edges following climate poleward, deeper, and in other directions and trailing range edges often contracting in similar directions. These shifts have their roots in fine-scale interactions between organisms and their environment—including mosaics and gradients of temperature and oxygen—mediated by physiology, behavior, evolution, dispersal, and species interactions. These shifts reassemble food webs and can have dramatic consequences. Compared with species on land, marine species are more sensitive to changing climate but have a greater capacity for colonization. These differences suggest that species cope with climate change at different spatial scales in the two realms and that range shifts across wide spatial scales are a key mechanism at sea. Additional research is needed to understand how processes interact to promote or constrain range shifts, how the dominant responses vary among species, and how the emergent communities of the future ocean will function.

Species around the world are shifting their ranges in response to climate change. To make robust predictions about climate‐related colonizations and extinctions, it is vital to understand the dynamics of range edges. This study is among the first to examine annual dynamics of cold and warm range edges, as most global change studies average observational data over space or over time. We analyzed annual range edge dynamics of marine fishes—both at the individual species level and pooled into cold‐ and warm‐edge assemblages—in a multi‐decade time‐series of trawl surveys conducted on the Northeast US Shelf during a period of rapid warming. We tested whether cold edges show stronger evidence of climate tracking than warm edges (due to non‐climate processes or time lags at the warm edge; the biogeography hypothesis or extinction debt hypothesis), or whether they tracked temperature change equally (due to the influence of habitat suitability; the ecophysiology hypothesis). In addition to exploring correlations with regional temperature change, we calculated species‐ and assemblage‐specific sea bottom and sea surface temperature isotherms and used them to predict range edge position. Cold edges shifted further and tracked sea surface and bottom temperature isotherms to a greater degree than warm edges. Mixed‐effects models revealed that for a one‐degree latitude shift in isotherm position, cold edges shifted 0.47 degrees of latitude, and warm edges shifted only 0.28 degrees. Our results suggest that cold range edges are tracking climate change better than warm range edges, invalidating the ecophysiology hypothesis. We also found that even among highly mobile marine ectotherms in a global warming hotspot, few species are fully keeping pace with climate.

Concerns over fishing impacts on marine populations and ecosystems have intensified the need to improve ocean management. One increasingly popular market-based instrument for ecological stewardship is the use of certification and eco-labeling programs to highlight sustainable fisheries with low environmental impacts. The Marine Stewardship Council (MSC) is the most prominent of these programs. Despite widespread discussions about the rigor of the MSC standards, no comprehensive analysis of the performance of MSC-certified fish stocks has yet been conducted. We compared status and abundance trends of 45 certified stocks with those of 179 uncertified stocks, finding that 74% of certified fisheries were above biomass levels that would produce maximum sustainable yield, compared with only 44% of uncertified fisheries. On average, the biomass of certified stocks increased by 46% over the past 10 years, whereas uncertified fisheries increased by just 9%. As part of the MSC process, fisheries initially go through a confidential pre-assessment process. When certified fisheries are compared with those that decline to pursue full certification after pre-assessment, certified stocks had much lower mean exploitation rates (67% of the rate producing maximum sustainable yield vs. 92% for those declining to pursue certification), allowing for more sustainable harvesting and in many cases biomass rebuilding. From a consumer’s point of view this means that MSC-certified seafood is 3–5 times less likely to be subject to harmful fishing than uncertified seafood. Thus, MSC-certification accurately identifies healthy fish stocks and conveys reliable information on stock status to seafood consumers.

Species distribution models (SDMs) are a common approach to describing species’ space‐use and spatially‐explicit abundance. With a myriad of model types, methods and parameterization options available, it is challenging to make informed decisions about how to build robust SDMs appropriate for a given purpose. One key component of SDM development is the appropriate parameterization of covariates, such as the inclusion of covariates that reflect underlying processes (e.g. abiotic and biotic covariates) and covariates that act as proxies for unobserved processes (e.g. space and time covariates). It is unclear how different SDMs apportion variance among a suite of covariates, and how parameterization decisions influence model accuracy and performance. To examine trade‐offs in covariation parameterization in SDMs, we explore the attribution of spatiotemporal and environmental variation across a suite of SDMs. We first used simulated species distributions with known environmental preferences to compare three types of SDM: a machine learning model (boosted regression tree), a semi‐parametric model (generalized additive model) and a spatiotemporal mixed‐effects model (vector autoregressive spatiotemporal model, VAST). We then applied the same comparative framework to a case study with three fish species (arrowtooth flounder, pacific cod and walleye pollock) in the eastern Bering Sea, USA. Model type and covariate parameterization both had significant effects on model accuracy and performance. We found that including either spatiotemporal or environmental covariates typically reproduced patterns of species distribution and abundance across the three models tested, but model accuracy and performance was maximized when including both spatiotemporal and environmental covariates in the same model framework. Our results reveal trade‐offs in the current generation of SDM tools between accurately estimating species abundance, accurately estimating spatial patterns, and accurately quantifying underlying species–environment relationships. These comparisons between model types and parameterization options can help SDM users better understand sources of model bias and estimate error.

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Species richness has long been used as an indicator of ecosystem functioning and health. Global richness is declining, but it is unclear whether sub-global trends differ. Regional trends are especially understudied, with most focused on island regions where richness is strongly impacted by novel colonisations. We addressed this knowledge gap by testing for multi-decade trends in species richness in nine open marine regions around North America (197 region-years) while accounting for imperfect observations and grounding our findings in species-level range dynamics. We found positive richness trends in eight of nine regions, four of which were statistically significant. Species' range sizes generally contracted pre-extinction and expanded post-colonisation, but the ranges of transient species expanded over the long-term, slowly increasing their regional retention and driving increasing richness. These results provide more evidence that sub-global richness trends are stable or increasing, and highlight the utility of range size for understanding richness dynamics.

Background Studies that attempt to measure shifts in species distributions often consider a single species in isolation. However, understanding changes in spatial overlap between predators and their prey might provide deeper insight into how species redistribution affects food web dynamics. Predator–prey overlap metrics Here, we review a suite of 10 metrics [range overlap, area overlap, the local index of collocation (Pianka's O), Hurlbert's index, biomass‐weighted overlap, asymmetrical alpha, Schoener's D, Bhattacharyya's coefficient, the global index of collocation and the AB ratio] that describe how two species overlap in space, using concepts such as binary co‐occurrence, encounter rates, spatial niche similarity, spatial independence, geographical similarity and trophic transfer. We describe the specific ecological insights that can be gained using each overlap metric, in order to determine which is most appropriate for describing spatial predator–prey interactions for different applications. Simulation and case study We use simulated predator and prey distributions to demonstrate how the 10 metrics respond to variation in three types of predator–prey interactions: changing spatial overlap between predator and prey, changing predator population size and changing patterns of predator aggregation in response to prey density. We also apply these overlap metrics to a case study of a predatory fish (arrowtooth flounder, Atheresthes stomias) and its prey (juvenile walleye pollock, Gadus chalcogrammus) in the Eastern Bering Sea, AK, USA. We show how the metrics can be applied to understand spatial and temporal variation in the overlap of species distributions in this rapidly changing Arctic ecosystem. Conclusions Using both simulated and empirical data, we provide a roadmap for ecologists and other practitioners to select overlap metrics to describe particular aspects of spatial predator–prey interactions. We outline a range of research and management applications for which each metric may be suited.