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Under climate change, species composition and abundances in high-latitude waters are expected to substantially reconfigure with consequences for trophic relationships and ecosystem services. Outcomes are challenging to project at national scales, despite their importance for management decisions. Using an ensemble of six global marine ecosystem models we analyzed marine ecosystem responses to climate change from 1971 to 2099 in Canada’s Exclusive Economic Zone (EEZ) under four standardized emissions scenarios. By 2099, under business-as-usual emissions (RCP8.5) projected marine animal biomass declined by an average of −7.7% (±29.5%) within the Canadian EEZ, dominated by declines in the Pacific (−24% ± 24.5%) and Atlantic (−25.5% ± 9.5%) areas; these were partially compensated by increases in the Canadian Arctic (+26.2% ± 38.4%). Lower emissions scenarios projected successively smaller biomass changes, highlighting the benefits of stronger mitigation targets. Individual model projections were most consistent in the Atlantic and Pacific, but highly variable in the Arctic due to model uncertainties in polar regions. Different trajectories of future marine biomass changes will require regional-specific responses in conservation and management strategies, such as adaptive planning of marine protected areas and species-specific management plans, to enhance resilience and rebuilding of Canada’s marine ecosystems and commercial fish stocks.


Climate change is already altering the physical and biogeochemical properties of the ocean, with impacts on species abundances, distributions, ecosystem functioning, and the provision of ecosystem services (Cheung et al. 2016Worm and Lotze 2016). Rising temperatures and enhanced stratification alter primary productivity, changes which can then amplify through food webs with consequences for higher trophic levels (Kwiatkowski et al. 2018Lotze et al. 2019). Other stressors, such as ocean acidification and oxygen declines, are impacting ocean productivity, nutrient and carbon cycling, leading to metabolic consequences and behavioural changes in many species (Doney et al. 2009Keeling et al. 2010). Species range shifts are already occurring and expected to continue into the future, particularly in high-latitude and polar waters, resulting in reconfigurations of ecological communities (Cheung et al. 2010Poloczanska et al. 2013). Therefore, projections of climate-change impacts in the fast-changing oceans of high-latitude countries such as Canada, where commercial and subsistence fisheries are economically, nutritionally, and socially valuable, are urgently needed to inform fisheries management and marine conservation.

Canada has one of the longest coastlines in the world and borders three different oceans—the Atlantic, Pacific, and Arctic—making it a pertinent case study for investigating marine biomass responses to climate change within its Exclusive Economic Zone (EEZ). To do so requires the use of coupled physical, biogeochemical, and ecological models. Using outputs such as water temperature, primary production, and other physical and biochemical variables from Earth System Models (ESMs) as forcing variables, marine ecosystem models can be used to project changes in animal biomass, species distributions, and food webs (Bopp et al. 2013Lefort et al. 2015Tittensor et al. 2018a). Individual ecosystem models are based on model-specific building blocks, such as species types, size classes, or functional groups, and ecological processes considered are unique to each model, hence they vary in their response to changing ocean conditions (Tittensor et al. 2018a). Past studies have typically used single marine ecosystem models, forced by one or several ESMs to derive patterns of biological changes on multiple scales (Blanchard et al. 2012Barange et al. 2014Jones et al. 2015), which can underrepresent the variety of underlying ecosystem processes and underestimate the range of projection uncertainty (Bryndum-Buchholz et al. 2019). Combining projections from multiple ecosystem models into ensembles allows the quantification of mean trends and an assessment of variation in projections due to differing model structures, parameters and processes (Tittensor et al. 2018a). Such model intercomparison projects (MIPs) are commonly used in climate impact research and have proven invaluable in the understanding of physical climate-change projections (e.g., Bopp et al. 2013), yet they have only recently been adopted for global ocean ecosystems (Tittensor et al. 2018aLotze et al. 2019).

We used ensemble projections from the Fisheries and Marine Ecosystem Model Intercomparison Project (Fish-MIP; Tittensor et al. 2018a) to quantify spatio-temporal changes in marine animal biomass within the Canadian EEZ under multiple climate-change scenarios over the 21st century. We analyzed outputs from six global marine ecosystem models forced with standardized outputs from two ESMs under four emissions scenarios (Tittensor et al. 2018b). We compared mean trends and variation in total marine animal biomass due to changing climate conditions; quantified responses to differences in projected climate-change mitigation efforts in Canada’s Pacific, Atlantic, and Arctic oceans; and examined the policy and management consequences of our findings.

Materials and methods

Data sources

We extracted historical (1970–2005) and future (2006–2099) spatially explicit projections of marine animal biomass (total marine animal biomass; including all vertebrates and invertebrates of trophic level >1, excluding zooplankton) from six global marine ecosystem models included in Fish-MIP simulation round 2a: APECOSM (Maury 2010), BOATS (Carozza et al. 2016), DBEM (Cheung et al. 2010), DPBM (Blanchard et al. 2012), EcoOcean (Christensen et al. 2015), and Macroecological (Jennings and Collingridge 2015Tittensor et al. 2018bTable S1). Each ecosystem model was forced with standardized outputs from two ESMs (GFDL-ESM2M and IPSL-CM5A-LR; APECOSM and DPBM runs were only available for IPSL-CM5A-LR in Fish-MIP simulation round 2a) provided by the Coupled Model Intercomparison Project Phase 5 (CMIP5, under four contrasting greenhouse gas (GHG) concentration scenarios (representative concentration pathways (RCPs), further referred to as emissions scenarios; DBEM runs were only available for RCP2.6 and 8.5 in the Fish-MIP simulation round 2a). One of the ESMs, GFDL-ESM2M, projects moderate changes in variables such as sea surface temperature (SST) and oceanic net primary productivity (NPP) over the 21st century, whereas IPSL-CM5A-LR projects stronger SST warming and NPP declines (Bopp et al. 2013). The four RCPs were: RCP2.6, a low emissions and strong mitigation scenario, assuming peak emissions by 2010–2020 with a substantial reduction until 2100 (van Vuuren et al. 2011); RCP4.5 and 6.0, two stabilization emissions scenarios that level off at intermediate GHG concentration levels by 2100 (Masui et al. 2011Thomson et al. 2011); and RCP8.5, a high business-as-usual emissions pathway, assuming continuous emissions increase until 2100 (Riahi et al. 2011). In this study, we focus on Fish-MIP model runs under no-fishing scenarios, since spatially explicit future projections of fishing at global scales are as of yet unavailable, and outputs that include a simplified fishing projection (using an assumption of constant and spatially unchanged fishing pressure at 2005 levels to 2100) are only available for three marine ecosystem models (Tittensor et al. 2018a2018b). Therefore, our analysis isolates the climate-change signal on marine animal biomass.

Study area

Our study area spanned the entire Canadian EEZ across three oceans (Fig. 1). The Canadian Pacific along the coast of British Columbia is characterized by warm waters carried onshore by the North Pacific Current (Okey et al. 2014). Canada’s Atlantic Ocean ranges from the Gulf of Maine to northern Labrador and is influenced by the warm Gulf Stream and cold Labrador Current (Saba et al. 2016). The Canadian Arctic spans the entire northern Canadian coast and is characterized by cryospheric elements sensitive to warming (Prowse et al. 2009a2009bDufresne et al. 2013).

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