Strengths and weaknesses
of fish stock assessment techniques.
Accurate stock assessment is of growing importance as the human population and demand for fish increases. Continued technological advances in fishing fleets increase efficiency directly effecting natural fish stocks. Attempting to match natural stock fluctuation with fishing effort may help to avoid any further long term damage of exploited species; this is of great importance as fish provide vital contributions to food supplies and influence employment in coastal areas.
Various methods are applied to calculate estimates of recruitment, stock sizes, and age groups. It is apparent that stock assessment techniques are highly dependent on available data, whether long or short-term predictions are the aim, both strengths and weaknesses are influenced by the abundance of this information. For correct predictions many techniques require large inputs of unbiased data, therefore the strength of any stock biomass prediction will be influenced by the weakness of the available inputs; validating final modal estimates of a fishery.
Rose (1997) offers another view for these problems, indicating that
fisheries scientists have lost track of their science by becoming 'keyboard
ecologists' whom rarely, if ever work directly with real fisheries. This
of course does not reflect on the collection of fisheries data but more
the interpretation and wisdom required to gain results. This diversion
may lead to incorrect long-term analysis; potentially undermining fishery
techniques, therefore it is crucial that all stakeholders in a fishery
increase an understanding and trust in stock assessment procedures (Anon
|When age data is sparse or the species cannot easily be aged, length based
assessments are an alternative. Chen's (1997) comparison between age and
length structured yield-per-recruit models showed length structured techniques
better incorporated information observed from fisheries, but age structured
gave more precise and conservative estimates of yield-per-recruit. This
is the main reason why age structured models are chosen from the conservation
perspective in fisheries management. The obvious difference between age
and length is that age is a linear measure of time whereas length is non-linear.
This makes data interpretations more difficult, more assumptions of growth
reductions due to age must be made. Assumptions removed from a model increase
accuracy, this is why age methods are preferred if feasible.
A potential strength of fishery science will be the adoption of multi-species models to fisheries that currently utilise single species methods. These models, although essential for future management purposes, seem unreliable and more imprecise than the currently used methods. They require more data that could lead to inaccurate assumptions, thus leaving fisheries in a worse state.
The key area that multi-species models address is predation. It is often assumed that fishing mortality alone is responsible for the variation in fish survival, but in some fisheries, losses to predation can exceed losses to fisheries (Bax 1998). This could indicate that assumptions of natural mortality in single species models are drastically misleading. Mertz & Myers (1997) point out that if bad estimations of natural mortality are used in calculations of cohort strength derived from catch data, the accuracy may be greatly corrupted. Pereiro (1995) supports that where species are not linked to a specific substratum natural mortality will always predominate over fishing mortality thus fishing mortality is not the subsidiary factor. Either way the addition of accurate natural mortality estimations into models must be welcomed.
This review has shown some major problems encountered when estimating populations from a fishery. Strengths seem sparse, maybe the biggest being that these techniques are the only available methods for estimating stock dynamics. Assessment techniques have strengths over each other and it is imperative the correct method is paired to its purpose.
Weaknesses seemed over bearing and many writers have tried to remove errors from previously presented models resulting in a claim that theirs is now the most accurate. Until data collection methods have improved there will always be inaccuracies in results. The addition of computer programs should aid time-consuming calculations allowing scientists to return to the field of study to uncover new methods of improving the currently used stock assessment techniques.
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