Robert J. Gamble

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).

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

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

Dynamic Complexity in Exploited Marine Ecosystems

Abstract Marine ecosystems are increasingly recognized as exhibiting the principal hallmarks of complex systems, including the possibility of sudden shifts in state among alternative basins of attraction and both periodic and aperiodic dynamical behavior. Over the last several decades a well-defined theory of complexity has emerged integrating earlier (and inter-related) concepts based on catastrophe, bifurcation, and chaos theory. In this review, we trace aspects of the historical development of these ideas and their application to marine systems. The manifestations of nonlinear dynamics in marine ecosystems include regime shifts; mirage correlations in which causally-connected system components can appear to be in-phase, asynchronous, or anti-correlated over different stanzas of time; and related state-dependent behavior in which the response of a focal variable to a driver differs depending on the present state of the system. We describe the analytical underpinnings of each of these dynamical behaviors. Although nonlinear dynamical systems are often portrayed in a deterministic setting, we emphasize the phenomenon of stochastic resonance in which an underlying nonlinear system acts as a noise amplifier in the presence of random perturbations. We next review the tools available for analyzing nonlinear dynamical systems based on the concept of state-space reconstruction and the application of techniques in nonlinear time series analysis. Finally, we address the management implications of nonlinear dynamics in exploited marine species and argue that considerations of predictability and forecast skill can serve as effective criteria for model selection and inference.