Fishery and ecosystem assessment with inadequate data
(Norm Hall is at the Centre for Fish & Fisheries Research, School of Biological Sciences and Biotechnology, Murdoch University.
He can be contacted at: normhall@central.murdoch.edu.au)
Abstract
If management plans for fisheries are to be effective, they must ensure that the principles of ecologically sustainable development are achieved. Recognition of the need to consider the direct and indirect impacts of fishing activities on the fish community and the habitat with which it is associated has highlighted the inadequacy of the data that are available for many fisheries. For instance, data are often collected for target species that are recreationally or commercially important. However, there is often little information available regarding the interactions of those species with other members of the associated fish assemblages, or interactions between the target species and the habitats that they occupy at different stages of their life cycles. For many low-value Australian fisheries, research advice will continue to be provided within a context of inadequate biological and fishery data. Indeed, for most of Australia’s fisheries, the immediate needs of fishery managers for research advice are likely to be met through intelligent use of inadequate data.
The modelling techniques that are used when faced with inadequate data are considered in this paper and an approach to deal with the problem is proposed. Appropriate use of the results from meta-analyses of the findings of other research studies may offer an interim solution to the problem of inadequate data for many of our ecosystems. The uncertainty associated with the use of inadequate data must also be recognized within the models that use these data.
Introduction
Estuarine and nearshore species of fish are exploited in Western Australian (WA) waters by both commercial and recreational fishers. The majority of these species have a relatively low commercial value compared to the various shellfish and crustacean species that are also fished in WA. It is likely that most of the stocks of these fish species are fully exploited, and some may be over-exploited. However, precise assessment of the state of the stocks is hindered by the inadequacy of the available data, precluding the use of many traditional fishery models. Although the numbers of commercial fishing vessels operating within the fisheries for these species are constrained, there are currently no controls on the number of recreational fishers. Most of Western Australia’s recreational fishing is controlled using spatial and temporal closures, gear controls, minimum and maximum legal sizes, and bag limits.
Despite facing increasing levels of fishing effort arising from growth in recreational fishing, limited biological and fisheries data exist in WA for most exploited species of fish. Commercial catch and effort statistics may be available for these species, however only recently has a systematic five-yearly survey been established to obtain regular estimates of the recreational catch and fishing effort. It is unlikely that the quality of the data will be greatly improved in the future, because characteristics of the estuarine and nearshore fisheries make it both difficult and expensive to obtain biological samples and/or reliable catch and effort data for recreational fishers at more frequent intervals than the five-yearly survey periods. Factors contributing to this include the number of species involved, the geographic extent of the fisheries, the number of landing points, and the large number of recreational fishers.
The increasing exploitation of these Western Australian fish stocks will inevitably place a greater demand on fisheries managers and scientists to develop more effective strategies for sustaining the stocks. The challenge for fisheries scientists is to develop models that can use the types of data that are currently available for these fisheries, coupled with appropriate information drawn from general ecological principles and from comparative systems, to evaluate the current state of each ecosystem and the effectiveness of alternative fishery management strategies.
Possible approaches to overcome data limitations
A number of modelling techniques are frequently used when data are inadequate. For instance, the model assumptions can be simplified to avoid the need for missing or inadequate data, or to allow calculation of those data from other data. Data that would otherwise be required in fitting the model can also be used to calculate the missing or inadequate data, parameters that must be estimated can also be treated as missing data, and appropriate results from other studies can be applied to the models.
Simplifying assumptions are frequently used to avoid the need to utilise missing data. Examples of this approach are evident when we assume a model structure that is independent of age, length, sex, other species, habitat, and environment. We may also assume a closed unit stock, a constant natural mortality, or the homogeneous distribution of stock over a single area. Such assumptions may also enable missing data to be calculated from the existing data. An example of this is the assumption of an unfished equilibrium that enables the determination of the initial system state in an age-structured model. Such assumptions are not the only ones that are possible, and alternative results might be obtained by applying different sets of assumptions.
Another approach that is often employed when modelling is to reserve some of the data that would otherwise be used in fitting the model to calculate the missing data. Examples of this technique are evident in the calculation of an initial age structure from the first sample(s) of age composition, or in the calculation of initial biomass from the first observation(s) of catch per unit of effort. Occasionally, we may also use other available data. For example, an estimate of the growth of a species might be obtained from an independent sample of length-at-age data, and the resulting growth curves may then be used in the fishery model.
Missing data may also be represented as parameters within the model and their values estimated, with other parameters, when the model is fitted to the available data. The advantage of this approach is that the uncertainty associated with the parameter estimates of the missing data is recognised. Examples of this approach include the estimation of natural mortality, initial biomass, or initial age composition in an age-structured model.
Increasingly, results from other studies are used to provide information that is missing for the stock being modelled. Such meta-analysis has been used to determine many empirical equations that represent the general relationships that exist between parameters describing biological processes. For example, Pauly (1980) used the results from 175 other studies to develop an empirical relationship between natural mortality, the parameters of the von Bertalanffy growth equation, and water temperature. More recently, Froese and Binohlan (2000) have used numerous other research findings to describe the relationships between parameters including maximum length and length at maturity. Insights into the ability of a stock to compensate for a reduction in the abundance of spawners have been provided by Myers et al. (1999), using the results from the meta-analysis of a stock-recruitment database. This meta-analysis showed that the number of spawners produced per spawner each year at low populations is relatively constant among species. Finally, in ecosystem studies, data from FishBase (Froese and Pauly 2000) have been used to determine many of the initial values required in EcoPath and EcoSim (Christensen and Pauly 1992).
Before using results derived from such meta-analyses, it is important to ensure that the data are relevant to the species currently being studied. Thus, before the results of such studies are applied to Western Australia’s estuarine and coastal fisheries, we need to ensure that the results are drawn from selected studies for similar species. Furthermore, when such data are applied as proxies, the uncertainties associated with estimates of the missing data must be considered when assessing the status of the fisheries to which they are applied.
Model selection
Although the approaches described above may assist in dealing with the problems that arise from the paucity of the data that exist for WA’s estuarine and nearshore fisheries, the selection of models for these fisheries remains an issue. The traditional single species models and analyses that are used for fisheries stock assessment include biomass dynamics models, age- and sex-structured models, virtual population analysis (VPA) and stock synthesis or integrated analysis models. These models and methods of analysis may be extended further for analysis of data from a multispecies fishery, and incorporate interactions between species. Such interactions may be dependent on the age and size of the fish and of each predator and prey species. Multispecies VPA is frequently used for the stock assessment of such fisheries. Interactions between species may also occur as a result of fleet dynamics. Ecosystem models extend the multispecies framework further, by describing the flow of energy and elements such as nitrogen and carbon between functional groups within the ecosystem. These models thereby couple the physical and biological processes to describe primary production, trophic interactions and the effect of environmental variables on the ecosystem.
As model complexity grows, there is an increasing demand for data. While it is desirable to use an appropriate structure when modelling a fishery, the limitations of the available data must be considered. This consideration may require that simpler single species models are applied for the individual species in a multispecies fishery, or that simpler formulations (such as those of biomass dynamics models) are used. Such simplifying assumptions will inevitably introduce additional uncertainty into the results of stock assessments and must be considered when evaluating the sustainability of the fisheries.
Conclusions
Assessment of some of the data-limited fisheries of the nearshore and estuarine environments of WA may be enhanced by using data from meta-analyses of similar species and fisheries. These analyses can provide proxies for missing data, but the complexity of our ecosystem models needs to be appropriate to the quality of the data that are available. The uncertainty that is introduced through the use of data derived from other fisheries or that results from simplifying assumptions should be considered when evaluating the results of our assessments. While this may offer an approach by which the impacts on the ecosystems of these Western Australian estuarine and nearshore fisheries may be assessed, it does not eliminate the need to improve the quality of the data that exist for these fisheries. An essential element of the ecosystem models that are developed through this strategy should be an investigation of the areas in which research effort might be concentrated to achieve the greatest benefit from the resulting information.
Acknowledgments
This study, which is being undertaken by the Centre for Fish and Fisheries Research at Murdoch University, is funded by the Fisheries Research and Development Corporation. Their support is gratefully acknowledged.
References
Christensen, V. and Pauly, D. 1992. ECOPATH II - A software for balancing steady-state ecosystem models and calculating network characteristics. Ecological Modelling 61: 169-185.
Froese, R. and Binohlan, C. 2000. Empirical relationships to estimate asymptotic length, length at first maturity and length at maximum yield per recruit in fishes, with a simple method to evaluate length frequency data. Journal of Fish Biology 56: 758-773.
Froese, R. and Pauly, D. (eds). 2000. FishBase 2000: concepts, design and data sources. ICLARM, Los Baņos, Laguna, Philippines. 344 pp.
Myers, R. A., Bowen, K. G. and Barrowman, N. J. 1999. Maximum reproductive rate of fish at low population sizes. Canadian Journal of Fisheries and Aquatic Sciences 56: 2404-2419.
Pauly, D. 1980. On the interrelationships between natural mortality, growth parameters, and mean environmental temperature in 175 fish stocks. Journal du Conseil. Int. Exploration de Mer 39: 175-192.
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