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)