Identifying indicators of the effects of fishing using ...

ICES Journal of Marine Science (2011), 68(7), 1417–1425. doi:10.1093/icesjms/fsr050

Identifying indicators of the effects of fishing using alternative models, uncertainty, and aggregation error Sarah J. Metcalf 1,2*, Matthew B. Pember 2, and Lynda M. Bellchambers 2 1

School of Biological Sciences and Biotechnology, Murdoch University, South Street, Murdoch, Perth, WA 6150, Australia Western Australian Fisheries and Marine Research Laboratories, Department of Fisheries, PO Box 20, North Beach, Perth, WA 6920, Australia

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*Corresponding Author: tel: +61 8 9203 0213; fax: +61 8 9203 0199; e-mail: sarah.metcalf@fish.wa.gov.au. Metcalf, S. J., Pember, M. B., and Bellchambers, L. M. 2011. Identifying indicators of the effects of fishing using alternative models, uncertainty, and aggregation error. – ICES Journal of Marine Science, 68: 1417 – 1425. Received 26 July 2010; accepted 3 March 2011; advance access publication 13 April 2011.

Keywords: complexity, ecosystem, qualitative modelling, structural uncertainty, trophic web.

Introduction The need to consider the broader ecosystem within fisheries management in addition to traditional aspects, e.g. stock abundance, recruitment, and growth rates, has been widely recognized. However, the capacity to measure and monitor all aspects of ecosystems has also been recognized as unrealistic because of their complexity (Kremen, 1992). Funding and time constraints determine that data are often only collected for species with economic value (Hilborn and Liermann, 1998), so data are often lacking for other species. Methods of identifying the most-effective variables for monitoring ecosystem change using limited data could therefore be beneficial in focusing data collection and allowing the most efficient use of resources. Methods of identifying indicators that have been used in the past include quantitative and semi-quantitative ecosystem models such as the Ecopath suite of Ecosim and Ecospace (Fulton et al., 2005; Moloney et al., 2005; Shin et al., 2010) and Atlantis (Fulton et al., 2005, 2011). These models require substantial data, including foodweb information and data on non-trophic interactions, fisheries catch rates, and proposed management strategies. Ordination techniques, such as principal components analysis and multidimensional scaling, can also be used to identify indicators (e.g. Rice, 2003). Using these techniques, species or community abundance data are assessed against an underlying influential gradient, e.g. sea surface temperature, pollution levels. Other indicators that are widely used focus on the management of target species rather than the broader effects of fishing on an

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ecosystem. Such traditional indicators include changes to size spectra and sex ratio that are often identified through the observation of the effects of fishing over time. Some indicators simply track changes in fisheries catches over time for a suite of species, generally including important target species such as the Western Australian dhufish (Glaucosoma herbraicum; Fletcher and Head, 2006). When data are not widely available for an ecosystem, the use of quantitative and semi-quantitative ecosystem models may be impossible. If these models can be produced with limited data, the uncertainty associated with model predictions may be so high as to make the model ineffective. In addition, the use of traditional fisheries indicators, e.g. target species, to identify broader ecosystem changes will generally be ineffective because the species must be a key driver of change within the ecosystem for such effects to be observed. Qualitative ecosystem models use ecosystem structure to provide insight into the dynamics (Puccia and Levins, 1985), particularly in data-limited situations. Press perturbation analysis, i.e. changing the abundance or level of activity of one or more variables of a model at equilibrium, provides expected directions of change (increase, decrease, no change) and indices of uncertainty for all model variables as a consequence of direct and indirect effects. Qualitative models have been used to identify species (or species groups) that may be useful indicators of change (e.g. Hayes et al., 2008; Dambacher et al., 2009) and the specific relationships between model variables that drive responses to change (Hosack et al., 2009).

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The identification of indicators of the indirect effects of fishing is often an issue for fisheries management, particularly if just commercial catch data are available. Complex, intermediate, and simplified qualitative models were produced for a fishery case study off Western Australia to identify potential indicators of ecosystem change attributable to western rock lobster (Panulirus cygnus) extraction and bait input. Models of intermediate complexity were used to identify indicators because they produced the least aggregation error. Structural uncertainty was considered through a series of structurally different intermediate models. These alternate models consistently predicted that extraction of rock lobster may positively impact small fish of low economic value, such as old wife (Enoplosus armatus), footballer sweep (Neatypus obliquus), and king wrasse (Coris auricularis). These small fish were therefore identified as potential indicators of the effects of rock lobster extraction. Small crustaceans (amphipods and isopods) also displayed positive impacts attributable to bait input from the rock lobster fishery and were identified as potential indicators of bait effects. Monitoring of these indicators may aid the detection of ecosystem change caused by the rock lobster fishery.

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impact of fisheries extraction on the suite. However, there are, as yet, no indicators of the indirect effects (e.g. change in the structure of the trophic web) of fishing. Such indicators are crucial to an assessment of the overall ecosystem effects of target species removal and to allow holistic fisheries management. The western rock lobster fishery is of particular economic and social importance in WA, with a number of regional communities relying largely on the fishery for employment. The fishery has a typical annual catch of 11 000 t (Department of Fisheries Western Australia, 2010); however, the catch in recent years has dropped as a consequence of low levels of recruitment. Ecological risk assessments have ranked the paucity of information on the ecosystem effects of removing western rock lobster biomass as a concern in waters deeper than 40 m (Bellchambers, 2010). Further, indicators of indirect fishing effects may have importance in the rock lobster fishery in evaluating an ecosystem response to bait input. Multiple model formulations representing different possible ecosystem structures were produced to assess structural uncertainty. The alternative models were used to study the potential indirect effects of fishing and bait input off the coast of WA and

Figure 1. Map of WA showing the Department of Fisheries Bioregional boundaries (Fletcher and Santoro, 2009), including Jurien Bay, where the rock lobster ecosystem is located.


A Western Australian ecosystem, Jurien Bay, was selected to investigate suitable indicator species for monitoring the effects of the local fishery for rock lobster (Panulirus cygnus). Jurien Bay is a mid-shelf (40–60 m) benthic ecosystem in the centre of the West Coast Bioregion (WCB) of Western Australia (WA; Figure 1). The Jurien Bay ecosystem was selected for investigation because it is located towards the centre of distribution of the rock lobster fishery and is representative of the wider fishery in terms of habitats, fishing effort, and lobster catch (Bellchambers, 2010). This ecosystem is of particular interest, because rock lobster biomass would be expected to accumulate in the absence of fishing as a result of the offshore migration of lobster near maturity (Phillips and Melville-Smith, 2006; Bellchambers et al., 2010). Very little is known about the impact of bait on the broader ecosystem, but some impacts may be expected given the high ratio of bait to lobster caught (1.4 kg bait for 1 kg lobster caught; E. Barker, Department of Fisheries Western Australia, pers. comm.). The Department of Fisheries Western Australia currently uses a number of target species as indicators of fishery impacts on a larger suite of target species (Department of Fisheries Western Australia, 2010) to identify the direct effects of fishing, i.e. the

S. J. Metcalf et al.

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Identifying indicators of the effects of fishing to identify potential indicators of these effects that may be monitored to gauge ecosystem impacts. The models belonged to three classes of complexity (complex, intermediate, and simplified) to evaluate the effect of simplification (aggregation of variables) and select the most appropriate model to use for the identification of indicators.

Methods Qualitative models

A = 1. Prey 2. Pred

1. 2. − − . + −

(1)

Both pulse and press perturbations can influence ecosystem dynamics (Bender et al., 1984). A pulse perturbation is a shortterm change in the abundance or activity of one or several variables followed by a return to equilibrium. Press perturbations affect the ecosystem on a longer time-scale and may arise from sustained, long-term changes to one or more system parameters. Following the established mathematical protocols, the signs of the adjoint of the negative community matrix (adj-A) [Equation (2)] was used to detail qualitative predictions of response (direction of change in population abundances) to press perturbation using both direct and indirect effects among community members (Dambacher et al., 2002). The predicted response is read across the matrix rows and the perturbed variable (the one that increased) is read down the matrix columns (sensu Metcalf et al., 2008) adj − A = 1. Prey 2. Pred

1. + +

2. + . −

(2)

In complex systems, a perturbation may impact a variable through multiple pathways. A single variable may therefore have

Diet information

Figure 2. Signed digraph representing the relationship between a predator and its prey. The predator has a negative direct effect on the prey (dot and line), whereas the prey has a positive direct effect on the predator (arrowed line). Negative self-effects represent a reliance on factors external to the system or density-dependence.

Information on dietary composition for a range of species residing within the ecosystem was collected from the published literature and from researchers. If available, dietary information from the specific ecosystem was used as input in the qualitative models. However, when site-specific information was not available, dietary data from similar areas in southwestern Australia were used. Both published literature and expert scientific opinion were used to determine the species or groups to be included in the complex model. Dietary data were included in the model qualitatively. For example, a predator would receive a direct positive effect from its prey, whereas the prey received a negative impact. Species were included if they were assumed to spend most of their time within the ecosystem.


Models were constructed using signed digraphs to represent the signs of interactions between species or groups (variables) in the system. For instance, the direct effect of a predator on its prey, i.e. removal through predation, was represented by a negative link, and the direct effect of a prey on its predator, i.e. as a resource, gave rise to a positive link (Figure 2). Negative self-effects were used to represent a reliance on factors external to the modelled system, such as nutrients or light, as well as densitydependent effects. These direct interactions between variables (+, 2, 0) in a model, i.e. system structure, can also be represented in the community matrix (A; Dambacher et al., 2002)

multiple potential responses to the perturbation, and the potential responses may have opposing signs (increase, +, or decrease, 2). The opposing signs cancel each other out and create ambiguity in the predicted response to change (Dambacher et al., 2002). The balance of signs determines the sign of the prediction (Dambacher et al., 2003). Weights can be given to the predictions to provide an assessment of the level of ambiguity (Dambacher et al., 2003). For instance, the weighted prediction for a response with one negative and one positive pathway will be zero, because there is an equal chance of the response being positive or negative. If there are six positive pathways and four negative ones, the negative pathways cancel the positive, leaving two positive pathways. The weighted prediction will therefore be the net pathways (+2) divided by the total number of pathways (10), equalling 0.2. Dambacher et al. (2003) used quantitative simulations to investigate the impact of countervailing feedback cycles (pathways) on sign stability, i.e. that the predicted sign will not change. They randomly varied interaction strengths of community matrix elements by two orders of magnitude in 5000 different matrices, whereas the sign structure remained unchanged. They found that weighted predictions .0.5 had a high percentage (.90%) of stable matrices and that the prediction sign could therefore be assumed to be reliable and not likely to be influenced by the countervailing feedback cycles. Hosack et al. (2008) took this work a step further and, instead of using a uniform distribution as in Dambacher et al. (2003), employed four separate distributions with markedly different types of skew, because they realized that a uniform distribution would not adequately represent the relationships being modelled. In addition, trophic dependence was included through the use of trophic transfer efficiencies, i.e. the energetic cost of processing food. Monte Carlo simulations were undertaken to generate 500 stable numeric matrices, and the proportion of simulations with the “correct” sign, i.e. that matched the sign in the original adj-A across all modelled systems, was calculated. A logistic-type function was then used to incorporate the influence of prediction weights and absolute feedback on the expected proportion of correct sign. This allows a flexible curve to be fitted to the simulation results with a priori bounds between 0.5 (where there is an equal chance of the response being positive or negative) and 1.0 (perfectly sign determined). We used the methods of Hosack et al. (2008) to provide an index of sign determinacy and indicate the reliability of the prediction signs.

1420 Model construction, indicator identification, and aggregation error Five steps were followed in producing each trophic model for analysing the ecosystem effects of fishing. 1. A complex trophic model was constructed to include all available dietary information. Species or species groups were selected for input if they were major (15% index of relative importance or % weight, sensu Metcalf et al., 2008) prey, predators, competitors, or habitat for rock lobster (Figure 3a).

3. Following simplification, alternative models were produced to deal with uncertainty in how the simplified models should be

structured. Structural uncertainty was found in the simplified Jurien Bay ecosystem model for two reasons. First, the overall impact of bait on octopus and cuttlefish as well as the rock lobster ecosystem in general is unknown. Waddington and Meeuwig (2009) suggest that the input of bait may have a substantial impact on deep-water (.36 m) trophic systems by providing an additional resource to cephalopods (octopus and cuttlefish) and small crustaceans, particularly isopods. Positive links from bait to these variables (the cephalopods and the small crustaceans) have therefore been retained in some alternative models (models A, B, and C for small crustaceans only; Table 1) and excluded in others (models D and E; Table 1). A second source of structural uncertainty arose because the extent to which the rock lobster fishery injures cephalopods during pot-lifting is unknown. A negative link from the fishery to cephalopods was therefore included in models C and D, but excluded in models A, B, and E. Discrepancies in the results between structurally different models were then used to highlight specific impacts that bait and/or fishery impacts on cephalopods may have on ecosystem dynamics. The results from models with different structures were compared to allow the identification of variables in the simplified model that are sensitive to structural uncertainty, i.e. results differ between models, and those that are predicted despite the uncertainty, i.e. results are consistent between models (Hayes et al., 2008). Results that were consistent between different models suggest that the response will occur regardless of structural uncertainty and that this species or species group may be a good indicator of change in the perturbed variable. The index of sign determinacy (Hosack et al., 2008) was used to indicate the reliability of the prediction sign. Variables that produced both consistent response signs and a high (.0.80; Hayes et al., 2008) index of sign determinacy between different models were identified as the best indicators of change. At this stage, indicators were generally variables that had been aggregated during the simplification process. 4. The identification of more specific indicators of change was investigated in a model of intermediate complexity. The variables that had been identified as indicators from the simplified models were disaggregated to determine which, if any, specific groups and species could provide a better indication of change than the aggregated variable. Following the assessment of results from the alternative models, the general fish indicator variable was disaggregated into four variables in the intermediate model: sweep and wrasse, foxfish, old wife, and small fish (Figure 4). Separation of species into these variables was based on their similarity of prey and predators. 5. The level of aggregation error (Gardner et al., 1982; Cale et al., 1983; Auger et al., 2000) was evaluated to determine the extent to which the loss of detail between a simplified model (model A) and an intermediate model affected the results. Aggregation error was not assessed for models B–E because experts suggested that these scenarios were less likely than model A. The use of models with minimum inconsistency between complex and simple models is thought to provide the best approximation of model dynamics (Iwasa et al., 1987). Aggregation error was calculated as the percentage of predictions in the simplified and intermediate models that differed from the complex model.


2. The complex model was simplified to reduce the ambiguity associated with model predictions (Metcalf et al., 2008). Simplification was undertaken using an iterative process to aggregate variables or to remove variables and/or links from the model. Three methods were used in the simplification process: logic (i.e. do not aggregate predators with their prey), expert opinion, and the REGE algorithm (Luczkovich et al., 2003; www.analytictech.com/downloaduc6.htm), which identifies variables with equivalent neighbours, i.e. predators and prey. Variables with similar life history and ecology were aggregated initially. For instance, dhufish, baldchin groper (Choerodon rubescens), pink snapper (Pagrus auratus), and breaksea cod were aggregated into the single variable referred to as demersal scalefish (Figure 3b). Each of these species has a comparable functional role in the trophic web and the rock lobster fishery, i.e. are common bycatch species (Fletcher and Santoro, 2009), as well as some similarities in life history and habitat requirements (Nardi et al., 2006; Moore et al., 2007; Fletcher and Santoro, 2009). Footballer sweep (Neatypus obliquus), old wife (Enoplosus armatus), king wrasse (Coris auricularis), foxfish (Bodianus frenchii), and small fish (unidentified) were aggregated into one variable (referred to as general fish) for the same reason, as were amphipods and isopods (referred to as small crustaceans). Variables were also aggregated if they were identified as having high regular equivalence using the REGE algorithm. The REGE algorithm was used in a fashion similar to Metcalf et al. (2008), to objectively identify like variables for aggregation. Because of high regular equivalence (using the REGE algorithm), gastropods were aggregated with bivalves, as were Australian sea lions and small sharks (referred to as predators). Filtering of links using expert opinion was also used to simplify the models, because all links are assumed to be equal. Weaker links needed to be removed from the system to ensure that greater importance was not attributed to these ecosystem dynamics and relationships than is warranted. For example, sponges were not included as a major dietary item for rock lobster, because sponges in the diet of rock lobster were considered to be a consequence of foraging for isopods and amphipods (small benthic crustaceans; K. Waddington, University of Western Australia, pers. comm.). The aggregation and filtering of links reduced the model to 11 variables, which was considered to be an appropriate size for the identification of indicator groups (Figure 3b; Metcalf et al., 2008). Any further aggregation may have resulted in the aggregation of dissimilar groups, i.e. predators with prey and long-lived with short-lived species, and may have confounded the results (O’Neill and Rust, 1979; Gardner et al., 1982).


Identifying indicators of the effects of fishing

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Figure 3. (a) The complex Jurien Bay model with 33 variables before simplification, and (b) the simplified model with 11 variables. Variations on this model that can be attributed to structural uncertainty are complex (Table 1).

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Aggregation error is an indication of the level of uncertainty produced through the aggregation of variables (in the simplified and intermediate models) compared with the complex model (sensu Metcalf et al., 2008).

Results Structural uncertainty and the identification of indicators

Table 1. Variations on the simplified Jurien Bay models (Figure 3b) to allow investigation of structural uncertainty on model predictions. Model A B C D E

Links included/excluded Bait links to small crustaceans and cephalopods included Bait link to bivalves and gastropods included, but bait link to cephalopods excluded Same as model B, plus a negative link from the rock lobster fishery to cephalopods All bait links excluded, but negative links from the rock lobster fishery to cephalopods included All bait links and negative link from rock lobster fishery to cephalopods excluded

Specific indicator identification and aggregation error Following the identification of general fish as the most reliable indicator for change in models A–E, this variable was disaggregated to form an intermediate model. Similar to the simplified model (model A), three of the four disaggregated variables (old wife, sweep and wrasse, and small fish) were predicted to increase in response to a perturbation to rock lobster fishing (Table 3); those variables had a very high index of sign determinacy and may therefore be good indicators of change in the rock lobster fishery. Foxfish was the only disaggregated general fish variable that was predicted to decline as a result of perturbation to the fishery. This decline was predicted because foxfish do not consume small crustaceans in the model and therefore do not receive an indirect benefit from the fishery through bait. In contrast, old wife, small fish, and sweep and wrasse all received an indirect positive impact from the fishery through the input of bait and the subsequent increase in small crustaceans. Small fish were assumed to be an inappropriate indicator because they were not adequately defined in the dietary literature and contained unidentified teleosts from the diets of dhufish, breaksea cod, and sea lions. Monitoring of change in this variable would therefore be difficult because it would be unclear which species should be the focus of monitoring efforts. The aggregation error associated with model A was calculated at 21.49%. In comparison, the intermediate model had a moderate level of aggregation error (9.18%) and was more likely to result in the same predictions as the complex model. This reduced aggregation error in the intermediate model arose because the extensive

Figure 4. The Jurien Bay intermediate model where the indicator variable identified in the simplified model, general fish, has been disaggregated into the four variables old wife, foxfish, sweep and wrasse, and small fish.


The category general fish was predicted to increase in abundance following a press perturbation (increase) in rock lobster fishing. It was identified as the best indicator of change in the rock lobster fishery owing to the consistency of response signs between different models and a high index of sign determinacy (Table 2). In addition, small crustaceans may be a useful indicator of the specific influence of bait in the system because they were predicted to increase in abundance when bait was included in the model (models A–C) and may decline in abundance when bait was assumed to have little influence on the system (models D and E; Table 2). The predicted decline in abundance when

bait was removed must be viewed with caution, however, because of the relatively low index of sign determinacy (0.72 and 0.58, respectively). Data collection and further investigation using quantitative analyses would be useful in clarifying this point.

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Identifying indicators of the effects of fishing Table 2. Predicted sign of responses to change in the impact of the fishery on the rock lobster population with the index of sign determinacy (ISD) in parenthesis to indicate the reliability of the predicted response. Group Predators Cephalopods Demersal fish Crabs and polychaetes Algae General fish Bivalves/gastropods Small crustaceans

Model A + (0.68) + (0.58) + (0.63) + (0.80) + (0.80) + (0.94) 2 (0.80) + (0.99)

Model B + (0.58) 2 (0.82) 2 (0.64) + (0.66) + (0.66) + (0.98) 2 (0.66) + (0.97)

Model C 2 (0.52) 2 (0.97) 2 (0.85) 2 (0.55) 2 (0.55) + (0.99) + (0.55) + (0.95)

Model D 2 (0.66) 2 (0.94) 2 (0.94) + (0.68) 2 (0.77) + (0.99) + (0.77) 2 (0.72)

Model E 2 (0.55) 2 (0.72) 2 (0.83) + (0.83) 2 (0.62) + (0.97) + (0.62) 2 (0.58)

The variable “general fish” has both consistent signs between different models and a high ISD, and “small crustaceans” may be used as an indicator of the effect of bait on the ecosystem because of specific differences in the results from models including bait effects.

Variable Old wife Foxfish Sweep and wrasse Small fish

Predicted response + (1.00) 2 (0.85) + (1.00) + (1.00)

feedback involved in the general fish variables in the complex model was retained in the intermediate model. The aggregation of these general fish variables in the simplified model resulted in the loss of this information and therefore produced a greater aggregation error.

Discussion The complexity and data limitations associated with ecosystems are a major source of uncertainty in scientific studies, and researchers need to be able to work with this uncertainty to produce meaningful results and advice for consideration by resource managers. Qualitative models can easily incorporate structural uncertainty into ecosystem investigations (Hayes et al., 2008), and this uncertainty can actually be made useful by aiding the identification of indicators of change and construction of the simplest practicable model. The capacity to investigate structural uncertainty using qualitative models is important because parameter uncertainty is often taken into account by fisheries scientists while structural uncertainty is often neglected (Hill et al., 2007). Ignoring uncertainty or attempting to monitor every aspect of an ecosystem would likely produce meaningless results and would be expensive. Using these techniques, the identification of species/groups that are most likely to be impacted by fishing can be useful in that the likelihood of structural changes to the trophic web can be evaluated, an important management consideration. For instance, if algae were predicted to decline through fishing with high sign determinacy, this could have major implications for the trophic structure under continued fishing pressure and therefore for the sustainability of current fishing practices. The models constructed here underscored the need for monitoring of indicator species abundance as well as the establishment of a quantitative threshold for indicator abundance at which changes to fisheries management (to reduce

fishing impacts) would be triggered. By identifying these points for future monitoring and assessment, qualitative models can aid the implementation of ecosystem-based fisheries management (EBFM) in WA. Comparison of the results from structurally different models revealed general fish as robust to the structural uncertainties assessed and were therefore categorized as good indicators of change caused by the rock lobster fishery. Disaggregating this variable from the simplified models allowed the identification of old wife, footballer sweep, and king wrasse as potential indicators. In order for these species to be useful for resource management, the methods available for data collection need to be cost-efficient and capable of providing sufficient statistical power to detect change. For example, if indicator species tend to hide among algae and are not visible from the water column, this would limit the methods available for monitoring their abundance. Similarly, if visual census using scuba were the only effective method of data collection for footballer sweep, king wrasse, and old wife in the Jurien Bay ecosystem, their use as indicators would be impractical because of the depths involved and the subsequent costs and safety issues associated with deepdiving. Those species tend to be visible within the water column, making methods such as towed video or stereo BRUV (Baited Remote Underwater Video) possible. Such methods are relatively cost-efficient, overcome occupational health and safety issues associated with deep-diving, and can provide data with sufficient power to detect change. In addition, the use of BRUV allows comparisons to be made across different habitats, rendering the results comparable with other local and international studies (Harvey et al., 2007; Langlois et al., 2010). Further, footballer sweep and king wrasse are very common within the Jurien Bay ecosystem. This increases the ease of data collection and makes monitoring practical, because their large numbers and relatively low variability provide good power to detect change compared with larger demersal fish, which are targeted by commercial and recreational fisheries. As a result, old wife, footballer sweep, and king wrasse can be assumed to be appropriate indicators because they are predicted to react to perturbation, based on model outputs, and can be monitored in a cost-effective manner that can detect change. Indirect trophic responses to fishing are well known, having been observed in a range of different ecosystems (Murphy, 1977; van der Elst, 1979; Hay, 1984; Hixon and Beets, 1993). For instance, Watson et al. (2007) identified a range of small non-target fish as potential indicators of fishing pressure at


Table 3. Predicted sign of responses with the index of sign determinacy (ISD) in parenthesis for general fish variables in the Jurien Bay intermediate model (Figure 4); old wife and sweep and wrasse were identified as the most appropriate indicators.

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(Dambacher et al., 2009; Metcalf et al., 2009). The capacity for incorporating uncertainty and evaluating aggregation error underscores the fact that qualitative modelling can be particularly beneficial in data-limited situations and may prove to be useful as a precursor to quantitative analyses focusing on specific indicators.

Acknowledgements We thank Geoff Hosack, Brett Molony, Rod Lenanton, and Brent Wise for their helpful suggestions, and Kris Waddington and Tim Langlois for useful ideas on rock lobster and indicator species. The Western Australian Marine Science Institution (WAMSI) provided financial support to SJM, and WAMSI and Fisheries Research and Development Corporation (FRDC) project No. 2008/013 to MBP and LMB.

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the Abrolhos Islands, WA. Old wife, small fish, and sweep and wrasse benefitted indirectly from rock lobster fishing through reduced competition with rock lobster for shared food resources. The addition of bait to the system may also lead to an increase in these fish groups by boosting the availability of the prey group small crustaceans, an indicator of bait effects. This result lends support to the suggestion that the addition of bait may impact the trophic dynamics of the ecosystem (Waddington and Meeuwig, 2009). Empirical fisheriesindependent data on small crustacean abundance in areas closed, i.e. without bait input, and open, i.e. with bait input, to commercial fishing should increase the capacity of researchers to quantify the ecological effects of bait in the rock lobster fishery. Furthermore, the use of all identified indicators of change attributable to the rock lobster fishery (small crustaceans, old wife, footballer sweep, and king wrasse) could provide evidence of indirect fishery impacts at multiple trophic levels within the ecosystem. The most important difference between the intermediate and simplified models was that, despite using a similar number of variables (11 and 14), the intermediate models had a much lower aggregation error than the more simplified versions. Note, that lower aggregation error does not indicate that the results are more correct. Rather, the results from models with a low aggregation error would be more likely to represent the complex model because they retained more detailed information than the simplified models. Assuming that the complex model is an appropriate representation of the system, it follows that results with a lower aggregation error may be useful in determining indicators of change and assessing ecosystem dynamics. Comparatively, the complex models contain the most information, but they also have lower predictability (Dambacher et al., 2003). Forcing model complexity can therefore be problematic when data are limited because it increases uncertainty (Kimmins et al., 2008), yet less-detailed models have been criticized for being unable to produce realistic behaviour (Fulton et al., 2003). The results of this study have revealed that models of intermediate complexity reduce the disadvantages associated with both very simple, highly aggregated models and very detailed, complex models and support the view of Hulot et al. (2000) that models of intermediate complexity can optimize the understanding of a community. The identification of a suite of potential indicators of ecosystem change, as suggested by Cury and Christensen (2005) when only commercial catch data are available, remains a significant challenge for fisheries management. Long time-series (decades) of fishery-independent data are scarce (Shin et al., 2010), particularly regarding species of low or no commercial or recreational value (Fletcher, 2005). Despite this issue, pressure remains to provide accurate and sensitive predictions for the impacts of fishing. This study has shown that qualitative models can be useful for combating this lack of data by predicting the direction of change in species or group abundance, identifying specific variables (indicators) likely to be robust to uncertainty, and investigating the impact of different types of perturbations to the system, e.g. fisheries extraction and bait input. Such information can be of use in risk assessments for EBFM (Fletcher et al., 2010), and in the prioritization of funding for data collection and fisheries research. Qualitative models can also aid the progression of EBFM through the identification of social and economic change in fishery systems


Identifying indicators of the effects of fishing

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