Future Scenarios Developing a model for the US Sea Scallop fishery that incorporates ocean acidification and warming References Cooley, S. R., J. E. Rheuban, D. Hart, V. Luu, D. Glover, J. A. Hare, and S. C. Doney. 2015. An integrated assessment model for helping the United States sea scallop (P. magellanicus) fishery plan ahead for ocean acidification and warming. PLoS ONE. Hart, D. R., and A. S. Chute. 2004. Essential fish habitat source document: Sea scallop. Placopecten magellanicus, life history and habitat characteristics. Northeast Fisheries Science Center. Jennie E. Rheuban 1 , Sarah R. Cooley 2 , Deborah Hart 3 , Victoria Luu 4 , David M. Glover 1 , Jonathan A. Hare 3 , and Scott C. Doney 1 1. Woods Hole Oceanographic Institution, Woods Hole, MA 2. Ocean Conservancy, 3. NOAA NMFS NEFSC, 4. Boston University [email protected] Atlantic Sea Scallop Placopecten magellanicus • High value wild-caught fishery (500 million USD ex-vessel revenue, Fig. 1) • Spatially separate populations (Mid-Atlantic Bight and Georges Bank) Overfished • Management successful in improving fishery but does not account for potential long term change Climate Impacts 10 year • Ocean Acidification (OA): reduction in ocean pH and CaCO 3 saturation state caused by increased atmospheric carbon dioxide (Fig. 3). • Mollusk growth (Fig. 4) and reproduction more difficult • Warming can alter species distribution, growth rates, and mortality. Influence of OA on Scallops Fig. 5. From 8 species in 6 studies (Cooley et al. 2015). * * * * * *Likely Influenced by OA Pelagic Benthic Fig. 5. Life cycle of sea scallop. (Adapted from Hart and Chute 2004). Model Links 3 submodels (Fig. 5) through relationships from other mollusk species (e.g. Fig. 4), IPCC future scenarios, and management decisions. Size based population dynamics from NOAA NMFS management models Two-box biogeochemical model, driven by seasonal thermal stratification Economic decision- making and NOAA NMFS Economics data Fig. 5. Model schematic (Cooley et al. 2015) Model Fit Size-specific abundances (Fig. 6), landings, revenue (Fig. 7), and biomass (not shown) match historical data well Fig. 6 (left). (A) actual and (B) modeled size- specific abundance Fig. 7. Revenue (A) and landings (B). Scenario/Driver Pressures Climate (T, CO 2 ) RCP2.6 RCP4.5 RCP6 RCP8.5 Fuel Base growth rate (%) 0 0.35 0.7 1.4 Tax Carbon tax $/ton/100 Price Base fuel price + Fuel tax OA Impacts None L G+L G+L+P Management None ABC ABC + F msy ABC + F msy + Closure Exploring the impacts of possible futures in a 4x4x4x4 scenario framework with differing degrees of OA impacts, management levels, fuel costs, and future climate scenarios (Table 1) (256 possible futures!) Table 1. Possible driver choices for future scenario framework. L = larval impacts, G = growth rate impacts, P = predation impacts, ABC = management set allowable biological catch, F msy = variable F msy , Closure = 10% biomass in areas closed to fishing 2010 2040 2070 2100 0 1 2 3 4 5 ΔT (°C from 2006-2010 mean) 300 400 500 600 700 800 900 1000 2000 2010 2030 2050 2070 2090 Atmospheric CO 2 (IPCC RCP pathways) Future trajectories vary considerably based on climate pathways, impacts, and degrees of management. Outcomes from this model will be used to inform management and leaders of the industry of possible futures. Results RCP 8.5 High Management RCP 2.6 High Management RCP 8.5 No Management RCP 2.6 No Management