Which ecological indicators can robustly detect effects of fishing?

ICES Journal of Marine Science, Jan 2005

Fulton, Elizabeth A., Smith, Anthony D.M., Punt, André E.

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Which ecological indicators can robustly detect effects of fishing?

ICES Journal of Marine Science Which ecological indicators can robustly detect effects of fishing? Elizabeth A. Fulton 0 Anthony D. M. Smith 0 Andre´ E. Punt 0 0 E. A. Fulton and A. D. M. Smith: CSIRO Marine Research , GPO Box 1538, Hobart, TAS 7001 , Australia. A. E. Punt: Formerly CSIRO, now School of Aquatic and Fishery Sciences , E. A. Fulton: tel: C61 3 6232 5018; fax: C61 3 6232 5053 Many ecological indicators have been proposed to detect and describe the effects of fishing on marine ecosystems, but few have been evaluated formally. Here, simulation models of two marine systems off southeastern Australia (a large marine embayment, and an EEZscale regional marine ecosystem) are used to evaluate the performance of a suite of ecological indicators. The indicators cover species, assemblages, habitats, and ecosystems, including quantities derived from models such as Ecopath. The simulation models, based on the Atlantis framework, incorporate the effects of fishing from several fishing gears, and also the confounding impacts of other broad-scale pressures on the ecosystems (e.g. increased nutrient loads). These models are used to provide fishery-dependent and fisheryindependent pseudo-data from which the indicators are calculated. Indicator performance is quantified by the ability to detect and/or predict trends in key variables of interest (''attributes''), the true values of which are known from the simulation models. The performance of each indicator is evaluated across a range of ecological and fishing scenarios. Results suggest that indicators at the community level of organization are the most reliable, and that it is necessary to use a variety of indicators simultaneously to detect the full range of impacts from fishing. Several key functional groups provide a good characterization of ecosystem state, or indicate the cause of broader ecosystem changes in most instances. ecosystem; fishery management; fishing effects; indicators; metrics; simulation Introduction The concept of sustainable fishing has evolved over the past two decades to focus increasingly on the wider ecological impacts of fisheries on marine ecosystems (Constable, 2001; Sainsbury and Sumaila, 2003) . In Australia, this has led to legislation that requires assessment of fisheries impacts on the environment (including habitats and foodwebs), not just on target species (Environment Australia, 2001) . Unfortunately, the legislation requiring such evaluation has developed ahead of the science needed to provide appropriate assessments. Together with suitably chosen reference points, ecological indicators can serve two purposes in relation to managing the impacts of fishing. First, they can be used to define performance measures to track how well management objectives are being achieved. Second, they can be used as part of decision rules to determine adaptive management strategies to respond to those impacts. Both uses are common in single-species fishery management, but are yet to be widely adopted in managing the broader ecological impacts of fishing (Sainsbury et al., 2000) . Many ecological indicators have been proposed for use in assessing impacts of fishing, and there have been several recent reviews (Vandermeulen, 1998; Hall, 1999; Murawski, 2000; Rice, 2000; ICES, 2001; Rochet and Trenkel, 2003) . Field tests have been used to evaluate a restricted number of indicators (Link et al., 2002; Trenkel and Rochet, 2003; Nicholson and Jennings, 2004) , but a formal evaluation of the robustness of many others is still lacking. Robustness in this context refers to the consistency of performance across alternative ecosystem types, levels of perturbation intensity, and sampling uncertainty. Empirical evaluation of indicator robustness requires large bodies of data from well-studied systems. Computer simulation can provide a cost-effective alternative where 2005 International Council for the Exploration of the Sea. Published by Elsevier Ltd. All rights reserved. such data are lacking. Although this approach cannot guarantee that the indicators identified are indeed robust, it can provide an efficient screening tool to eliminate those that are unlikely to perform well with real data. An additional benefit of using modelled data is that the analyst is certain about the true properties of the system generating the data, which is a difficulty for real systems, even when they are well studied. We use computer simulations to evaluate a range of potential indicators (including those derived from network theory and existing ecosystem models), using as case studies two marine ecosystems in Australian waters. The simulations take into account aspects of the data collection scheme, including sampling design and the statistical precision of the samples. Methods Operating model In the fisheries context, operating models are caricatures of the real world that seek to incorporate sufficient aspects of the dynamics of real systems to serv (...truncated)


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Fulton, Elizabeth A., Smith, Anthony D.M., Punt, André E.. Which ecological indicators can robustly detect effects of fishing?, ICES Journal of Marine Science, 2005, pp. 540-551, Volume 62, Issue 3, DOI: 10.1016/j.icesjms.2004.12.012