Decision‐making for ecosystem‐based management: evaluating options for a krill fishery with an ecosystem dynamics model

Abstract

Decision-makers charged with implementing ecosystem-based management (EBM) rely on scientists to predict the consequences of decisions relating to multiple, potentially conflicting, objectives. Such predictions are inherently uncertain, and this can be a barrier to decision-making. The Convention on the Conservation of Antarctic Marine Living Resources requires managers of Southern Ocean fisheries to sustain the productivity of target stocks, the health and resilience of the ecosystem, and the performance of the fisheries themselves. The managers of the Antarctic krill fishery in the Scotia Sea and southern Drake Passage have requested advice on candidate management measures consisting of a regional catch limit and options for subdividing this among smaller areas. We developed a spatially resolved model that simulates krill-predator-fishery interactions and reproduces a plausible representation of past dynamics. We worked with experts and stakeholders to identify (1) key uncertainties affecting our ability to predict ecosystem state; (2) illustrative reference points that represent the management objectives; and (3) a clear and simple way of conveying our results to decision-makers. We developed four scenarios that bracket the key uncertainties and evaluated candidate management measures in each of these scenarios using multiple stochastic simulations. The model emphasizes uncertainty and simulates multiple ecosystem components relating to diverse objectives. We summarize the potentially complex results as estimates of the risk that each illustrative objective will not be achieved (i.e., of the state being outside the range specified by the reference point). This approach allows direct comparisons between objectives. It also demonstrates that a candid appraisal of uncertainty, in the form of risk estimates, can be an aid, rather than a barrier, to understanding and using ecosystem model predictions. Management measures that reduce coastal fishing, relative to oceanic fishing, apparently reduce risks to both the fishery and the ecosystem. However, alternative reference points could alter the perceived risks, so further stakeholder involvement is needed to identify risk metrics that appropriately represent their objectives.

From strategic ambiguity to technical reference points in the Antarctic krill fishery: the worst journey in the world?

“…The modelled area is divided into coastal (dark) and oceanic (light) small-scale management units. (Watters et al 2013| = ) proposed management measures to meet these objectives. Evaluation approaches often use models to simulate the effect of management measures on the future state of the ecosystem (Rademeyer et al 2007).…”

“…Butterworth and Thomson (1995) and Thomson et al (2000) sought to refine the soft reference points for krill to better account for predator requirements. Others evaluated catch allocation options, which are spatial subdivisions of the regional catch limit intended to minimize localized impacts on krill predators (WG-EMM [Working Group on Ecosystem Monitoring and Management] 2008; Plagányi & Butterworth 2012;Watters et al 2013). The evaluation involved many millions of simulations using spatially resolved ecosystem dynamics models to project the future state of krill, its predators and the fishery.…”

“…The requirement to prevent changes that are not potentially reversible (hereafter the reversibility principle) implies objectives that could be stated in terms of recovery reference points. These reference points specify the state of the system two to three decades after the cessation of fishing (Watters et al 2013). However, operational objectives should refer to measurable states such as current abundance (depletion) rather than future abundance (recovery).…”

From Ice to Penguins: The Role of Mathematics in Antarctic Research

“…The scenarios were based on two different model 29! structures, described in Plagányi and Butterworth (2012) and Watters et al (2013) and 30! several alternative parameterizations of each model structure.…”

“…circulation models (Hill et al 2007a). Watters et al (2013) developed four 2! parameterizations, each of which combined one of these extreme values for plausible 3!…”