Gavin Fay

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).

Quantifying Patterns of Change in Marine Ecosystem Response to Multiple Pressures

The ability to understand and ultimately predict ecosystem response to multiple pressures is paramount to successfully implement ecosystem-based management. Thresholds shifts and nonlinear patterns in ecosystem responses can be used to determine reference points that identify levels of a pressure that may drastically alter ecosystem status, which can inform management action. However, quantifying ecosystem reference points has proven elusive due in large part to the multi-dimensional nature of both ecosystem pressures and ecosystem responses. We used ecological indicators, synthetic measures of ecosystem status and functioning, to enumerate important ecosystem attributes and to reduce the complexity of the Northeast Shelf Large Marine Ecosystem (NES LME). Random forests were used to quantify the importance of four environmental and four anthropogenic pressure variables to the value of ecological indicators, and to quantify shifts in aggregate ecological indicator response along pressure gradients. Anthropogenic pressure variables were critical defining features and were able to predict an average of 8-13% (up to 25-66% for individual ecological indicators) of the variation in ecological indicator values, whereas environmental pressures were able to predict an average of 1-5 % (up to 9-26% for individual ecological indicators) of ecological indicator variation. Each pressure variable predicted a different suite of ecological indicator’s variation and the shapes of ecological indicator responses along pressure gradients were generally nonlinear. Threshold shifts in ecosystem response to exploitation, the most important pressure variable, occurred when commercial landings were 20 and 60% of total surveyed biomass. Although present, threshold shifts in ecosystem response to environmental pressures were much less important, which suggests that anthropogenic pressures have significantly altered the ecosystem structure and functioning of the NES LME. Gradient response curves provide ecologically informed transformations of pressure variables to explain patterns of ecosystem structure and functioning. By concurrently identifying thresholds for a suite of ecological indicator responses to multiple pressures, we demonstrate that ecosystem reference points can be evaluated and used to support ecosystem-based management.

Operationalizing integrated ecosystem assessments within a multidisciplinary team: lessons learned from a worked example

Operationalizing integrated ecosystem assessments within a multidisciplinary team: lessons learned from a worked example Geret S. DePiper*, Sarah K. Gaichas, Sean M. Lucey, Patricia Pinto da Silva, M. Robin Anderson, Heather Breeze, Alida Bundy, Patricia M. Clay, Gavin Fay, Robert J. Gamble, Robert S. Gregory, Paula S. Fratantoni, Catherine L. Johnson, Mariano Koen-Alonso, Kristin M. Kleisner, Julia Olson, Charles T. Perretti, Pierre Pepin, Fred Phelan, Vincent S. Saba, Laurel A. Smith, Jamie C. Tam, Nadine D. Templeman, and Robert P. Wildermuth NOAA Northeast Fisheries Science Center, 166 Water Street, Woods Hole, MA 02543, USA Fisheries and Oceans Canada, Northwest Atlantic Fisheries Centre, 80 East White Hills, St. John’s, NL A1C 5X1, Canada Fisheries and Oceans Canada, Bedford Institute of Oceanography, 1 Challenger Drive, Dartmouth, NS B2Y 4A2, Canada School for Marine Science & Technology, University of Massachusetts Dartmouth, 200 Mill Road, Suite 30, Fairhaven, MA 02719, USA Environmental Defense Fund, Floor 28, 123 Mission Street, San Francisco, CA 94105, USA National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Northeast Fisheries Science Center, Geophysical Fluid Dynamics Laboratory, Princeton University Forrestal Campus, 201 Forrestal Road, Princeton, NJ 08540, USA

Methods for estimating spatial trends in Steller sea lion pup production using the Kalman filter.

Many species exhibit spatially varying trends in population size and status, often driven by differences among factors affecting individual subpopulations. Estimation and differentiation of such trends may be important for management, and a driving force for monitoring programs. The ability to estimate spatial differences in population trend may depend on assumptions regarding connectivity among subpopulations (stock structure or spatial overlap in stressors), information that is often poorly known. Linear state-space models using the Kalman filter were developed, tested, and applied for trend estimation of pup production for the western Alaska stock of Steller sea lions (Eumetopias jubatus), given only count data. Models were able to estimate trends and abundance even when data were missing. Models that assumed spatial correlation in trend among rookeries were more robust to stock structure assumptions when the stock structure was potentially mis-specified. High levels of spatial correlation among rookeries estimated from Steller sea lion pup count data are consistent with large-scale covariance of population trend within the Steller sea lion metapopulation.

Combining stock, multispecies, and ecosystem level fishery objectives within an operational management procedure: simulations to start the conversation

Original Article Combining stock, multispecies, and ecosystem level fishery objectives within an operational management procedure: simulations to start the conversation Sarah K. Gaichas*, Michael Fogarty, Gavin Fay, Robert Gamble, Sean Lucey, and Laurel Smith NOAA NMFS Northeast Fisheries Science Center, 166 Water Street, Woods Hole, MA 02543, USA School for Marine Science and Technology, University of Massachusetts Dartmouth, 200 Mill Road, Fairhaven, MA 02719, USA *Corresponding author: tel: þ1 508 495 2016; fax þ1 508 495 2258; e-mail: sarah.gaichas@noaa.gov