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These pages are still under construction
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Indicator summary
Summary of indicator structure and function (or another title??)
| Indicator | Attribute | Purpose | If restricted to taxa, list which ones | Ecosystem applicability | Identified capability | Biological classification level | Response variable | Drivers | Robustness | ||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Biomass of species of interest | community Community structure; trophic Trophic structure, population Population structure; predatorPredator-prey balance | fisheriesFisheries | Should be suitable for all ecosystems | D or ADemonstrable | Ecosystem; communityCommunity; population | trophodynamic | trophodynamicPopulation | Size-baaed, Trophodynamic, Environmental | Trophodynamic | Potentially high |
Examples of how the indicators is used for ecosystem management and ecosystem status and trends
| Indicator examples | Current status and trends | Management objective/direction | Stakeholder/Public acceptability |
|---|---|---|---|
| Examples of how the indicator is used. | Pick one of the following:
| Pick one of the following:
| Pick one of the following:
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| George Bank | increasing exploitation of the groundfish fishery | ||
| no-fish area of Tasmania | tenfold increase in biomass of rock lobsters (Janus edwardsii) after a six year period of closure | ||
Definition and/or background
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Various ways of treating biomass data can be used by researchers in investigating impacts of fishing on community structure. Aside from using simple measures (e.g. biomass expressed as mean percent change by weight for aggregated species or individuals of species over time), examples of more specific treatments give insight into how measures of biomass may be used as potential indicators. In field studies, trends in biomass can give some indication of the health of the system. For example, when biomass of one or more important species or assemblages falls below a defined minimum acceptable limit for successful recruitment, recovery time, or for a species threatened with extinction, the ecosystem can be regarded as overfished (Murawski 2000). Measures of biomass are also used in a wide range of ecological models. For example, to measure dominance changes over time of a keystone species (sea urchin), McClanahan (1995) used biomass in an energy-based coral reef simulation model (Figure 5.31) to examine the impact of fishing on community structure. The model indicated that the intensity and selectivity of fishing can affect reef structure and processes, as the abundance and interactions of the coral and algae are controlled by the herbivores, which are in turn controlled by carnivores (often the targets of fishing). The changes in system state predicted by the model are obvious in the biomass trends it predicts, an example of which is given in Figure 51.3.
In traditional single species management measures of biomass (frequently trends over time in biomass / pristine biomass) have been used to judge the health of the stock, and thus the fishery. Thus, measures in biomass can be informative at a population level, but by extending the number of species tracked or choosing species carefully (e.g. using keystone or vulnerable species) more system-level information maybe encapsulated in these simple measures.
Figure 5.31: The model output of biomass (wet weight) when all fish components are fished after 39 years of simulation. It shows the response of the coral ecosystem to overfishing, in particular, the increased biomass of sea urchins, which are keystone species in this system (after McClanahan 1995).
need to add figure 5.3
(Figure provided by Fulton et al 2004a).
Abundance of keystone species
Keystone species are those that play a pivotal role in the ecology of a community. There are some examples in the marine environment that could be used as indicator species (such as some shark species; sea urchins; some seagrass species), particularly if their densities are monitored.
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Substantial changes in the abundance of keystone species can have immense and widespread impacts on marine ecosystems. As a result, measuring changes in abundance of keystone species can be an effective indicator for some effects of fishing. For example, the overfishing of urchin predators (e.g. rock lobster, sea otter and grey nurse sharks) has been shown to lead to urchin dominance and subsequent overgrazing of algal communities in temperate habitats and the erosion of coral reefs in tropical habitats (McClanahan and Shafir 1990, McClanahan 1992, Goni 1998, Estes et al. 1998, Tegner and Dayton 2000).
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Biomass of prey species
The biomass of certain groups can also be used to consider aspects of the system such as community composition. Predator-prey responses to fishing impacts can provide important and potentially robust indicators of biomass changes in an ecosystem. This is true not only of field data, but also of the output of simulation models. There have been numerous simulation modelling studies recently investigating the impacts of removing key predators from an ecosystem (e.g. for chondrichthyans, Stevens et al. 2000), the over-harvesting of prey species on seabird species (Furness 1999), and the over-harvesting of prey species in Port Phillip Bay, Victoria (Fulton and Smith in press). An example of the time-series output of these kinds of models is given in Figure 52.4.
Figure 5.42: ECOSIM simulation of the functional response to an aggressive sand lance fishery showing biomass declines in seabirds (which prey on sand lance) and other species (after Okey and Pauly 1999)need to add figure 5.4. (Figure provided by Fulton et al 2004a).
Attribute
Community structure; trophic structure; population structure; predator-prey balance
Purpose
fisheriesFisheries
Taxa
This indicator is not restricted to any particular species but there are possible taxa that could be used as an indicator species include some shark species; , sea urchins; , and some seagrass species.
Data required
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The following is from Fulton et al 2004a -
Should be suitable for all ecosystems. For certain species of interest the method may be more restricted. For example, the use of the biomass of keystone species will only be applicable in ecosystems where there is a keystone species that determines system state. Unfortunately, the existence of such a species may not be apparent before large-scale changes in an ecosystem have already occurred.
Identified capability
Biological classification level
Response variable
Drivers
Robustness
The following is from Fulton et al 2004a -
Potentially high: Measures of biomass may be simple, but they can be immensely informative even if only used in a qualitative sense to track system state. This is particularly true, if a suite of species is followed as this allows for a summary of the entire state of the system and may allow for the identification of the cause of any changes in system state (Fulton and Smith in press). One draw back to many indicators reviewed in this report is the requirement that data must be collected on all, or the majority of species in a system. One way to avoid this problem is to only measure indicator assemblages, on the proviso that these species really do summarise system state, as fishing effects are largely confined to these groups. The major limitation to using measures of biomass as an indicator is the collection of reliable data either through time or between locations so that reliable baselines can be established and any trends in biomass can be identified. Changes in biomass are only informative so long as the data collected is representative of the true state of the system. Thus, fisheries dependent data is not always reliable (due to changes in effort and market driven targeting). Fisheries independent and ecological data will be required. Data from reference areas would also be useful, as it would highlight any widespread decreases in biomass that may result from large-scale environmental cycles, displacement by invading species, pathogens and anthropogenic activities other than fishing.
Keystone species: potentially medium to high (especially for fished coral reef systems and macrophyte dominated temperate systems), but the limitations are that other environmental or trophic interactions may produce the same effects and the identity and impact of keystone species may not be known until the system is heavily impacted.
Simulation modelling: Medium to high, if used in a qualitative sense of predicting likely (and potentially unforeseen) effects of fishing. Major limitations are concerned with model specification and the quality of the data used to tune the model.
Current status and trends
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Stevens et al. (2000) is a good example of the methodology. They used ECOSIM to model the impacts of removing shark predators by fishing over a 100-year period in 3 ecosystems of differing latitudes and regimes (Alaskan Gyre, Venezuela and Pacific-Hawaii). The outputs of the model provide a very preliminary indication of the sorts of changes that could be expected to occur with the removal of trophically important top predators like sharks. The output was also dependent on the ecosystem type and the results differed widely between tropical Pacific ecosystems and boreal systems. The results indicate that for boreal systems (Alaskan Gyre), there the removal of top predators (sharks) would result in a rapid increase to a peak in the biomass of many of the shark’s main prey species after about 15-20 years from the onset of the removal of sharks. This is then followed by a slow, sustained decline back to baseline values (taking about 80 years) for these groups, though a few declined still further (e.g. the “large fish” ends the 100 year period at 50% of its initial biomass, despite booming soon after the intensive fishing of sharks begins). In contrast, on the Venezuelan shelf there were strong and persistent changes (up to 2.5x original biomass) in relative abundance of many species, some of which were only minor components of the diet of sharks. More surprisingly, two groups that are not preyed upon by sharks also showed substantial changes after the removal of sharks began, squid and benthic producers fell by about 10% and 15% respectively. All of these results for the Venezuelan shelf suggest that shark depletion propagates through the food chain in a complex way. The results for the coastal Hawaiian coral reef system were different again. There was a counterintuitive total and rapid crash in the abundance of tuna and jacks (possibly triggered by a twofold increase in abundance of the seabirds, which are their main predators), whereas the other species preyed up on by sharks all increased in abundance.
Management strategies and/or objectives
define a standard set of management objectives?? ie from Indiseas
- Conservations biodiversity
- Ecosystem stability and resistance to perturbations
- ecosystem structure and functioning
- resource potential
has it been used in a management strategy? if so how?
relationship to management strategies/ objectives
Stakeholder/public acceptability
Acceptability with stakeholders
- by all stakeholder
- by the public
- understandable to the stakeholders
Associated links
References
Fulton, E.A., Smith, A.D.M., Webb, H., and Slater, J. (2004a) Ecological indicators for the impacts of fishing on non-target species, communities and ecosystems: Review of potential indicators. AFMA Final Research Report, report Number R99/1546.
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Fulton, E.A., Fuller,M., Smith, A.D.M., and Punt, A. (2004) Ecological indicators of the ecosystem effects of fishing: Final report. AFMA Final Research Report, report Number R99/1546.
Possible references for updating this indicator
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