Merging trait-based ecology and regime shift theory to anticipate community responses to warming.

Anthropogenic warming is altering species abundance, distribution, physiology, and more. How changes observed at the species level alter emergent community properties is an active and urgent area of research. Trait-based ecology and regime shift theory provide complementary ways to understand climate change impacts on communities, but these two bodies of work are only rarely integrated. Lack of integration handicaps our ability to understand community responses to warming, at a time when such understanding is critical. Therefore, we advocate for merging trait-based ecology with regime shift theory. We propose a general set of principles to guide this merger and apply these principles to research on marine communities in the rapidly warming North Atlantic. In our example, combining trait distribution and regime shift analyses at the community level yields greater insight than either alone. Looking forward, we identify a clear need for expanding quantitative approaches to collecting and merging trait-based and resilience metrics in order to advance our understanding of climate-driven community change.

A Vulnerability Assessment of Fish and Invertebrates to Climate Change on the Northeast U.S. Continental Shelf.

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 Effects of Sub-Regional Climate Velocity on the Distribution and Spatial Extent of Marine Species Assemblages.

Many studies illustrate variable patterns in individual species distribution shifts in response to changing temperature. However, an assemblage, a group of species that shares a common environmental niche, will likely exhibit similar responses to climate changes, and these community-level responses may have significant implications for ecosystem function. Therefore, we examine the relationship between observed shifts of species in assemblages and regional climate velocity (i.e., the rate and direction of change of temperature isotherms). The assemblages are defined in two sub-regions of the U.S. Northeast Shelf that have heterogeneous oceanography and bathymetry using four decades of bottom trawl survey data and we explore temporal changes in distribution, spatial range extent, thermal habitat area, and biomass, within assemblages. These sub-regional analyses allow the dissection of the relative roles of regional climate velocity and local physiography in shaping observed distribution shifts. We find that assemblages of species associated with shallower, warmer waters tend to shift west-southwest and to shallower waters over time, possibly towards cooler temperatures in the semi-enclosed Gulf of Maine, while species assemblages associated with relatively cooler and deeper waters shift deeper, but with little latitudinal change. Conversely, species assemblages associated with warmer and shallower water on the broad, shallow continental shelf from the Mid-Atlantic Bight to Georges Bank shift strongly northeast along latitudinal gradients with little change in depth. Shifts in depth among the southern species associated with deeper and cooler waters are more variable, although predominantly shifts are toward deeper waters. In addition, spatial expansion and contraction of species assemblages in each region corresponds to the area of suitable thermal habitat, but is inversely related to assemblage biomass. This suggests that assemblage distribution shifts in conjunction with expansion or contraction of thermal habitat acts to compress or stretch marine species assemblages, which may respectively amplify or dilute species interactions to an extent that is rarely considered. Overall, regional differences in climate change effects on the movement and extent of species assemblages hold important implications for management, mitigation, and adaptation on the U.S. Northeast Shelf.

Ecosystem Model Skill Assessment. Yes We Can!

NEED TO ASSESS THE SKILL OF ECOSYSTEM MODELS: Accelerated changes to global ecosystems call for holistic and integrated analyses of past, present and future states under various pressures to adequately understand current and projected future system states. Ecosystem models can inform management of human activities in a complex and changing environment, but are these models reliable? Ensuring that models are reliable for addressing management questions requires evaluating their skill in representing real-world processes and dynamics. Skill has been evaluated for just a limited set of some biophysical models. A range of skill assessment methods have been reviewed but skill assessment of full marine ecosystem models has not yet been attempted. NORTHEAST US ATLANTIS MARINE ECOSYSTEM MODEL: We assessed the skill of the Northeast U.S. (NEUS) Atlantis marine ecosystem model by comparing 10-year model forecasts with observed data. Model forecast performance was compared to that obtained from a 40-year hindcast. Multiple metrics (average absolute error, root mean squared error, modeling efficiency, and Spearman rank correlation), and a suite of time-series (species biomass, fisheries landings, and ecosystem indicators) were used to adequately measure model skill. Overall, the NEUS model performed above average and thus better than expected for the key species that had been the focus of the model tuning. Model forecast skill was comparable to the hindcast skill, showing that model performance does not degenerate in a 10-year forecast mode, an important characteristic for an end-to-end ecosystem model to be useful for strategic management purposes. SKILL ASSESSMENT IS BOTH POSSIBLE AND ADVISABLE: We identify best-practice approaches for end-to-end ecosystem model skill assessment that would improve both operational use of other ecosystem models and future model development. We show that it is possible to not only assess the skill of a complicated marine ecosystem model, but that it is necessary do so to instill confidence in model results and encourage their use for strategic management. Our methods are applicable to any type of predictive model, and should be considered for use in fields outside ecology (e.g. economics, climate change, and risk assessment).