Stock assessment methodology based on mathematical-statistical models and, here and there, on acoustic surveys is employed as the main tool in fisheries management, as the "best available science" required by law in some countries. Claimed by many as lacking in its quantitative and qualitative inputs and, hence, producing at best very approximate results: can it be considered scientific?
I think the best examples of stock assessments can be fairly considered as a valid scientific output. The best examples clearly identify the assumptions, often use multiple methods, have extensive peer review processes, state the precision of the estimates, and the sources of uncertainty. In my experience, such outputs receive a very high level of scrutiny, both from peers and the general public. The extent of review is often considerably higher than traditional peer-reviewed papers.
It can only be considered scientific, if it disposes of any pretense of precision and produces its results in a form of a range (e.g., plus-minus 25%). MB-Y
Fish stocks assessments range from the scientifically well founded, based on theory, extensively peer-reviewed and quality controlled with estimates of variability, to poor assessments based on unclear methods and little or no quality control or peer review. The question therefore does not have one absolute answer, but will vary on the ecosystem and stocks in question. Likewise are generalizing answers like that of Menakhem above not very useful as they do not pick up on the nuances of the stock assessments.
Erik, my "generalization" is valid also for your "nuances". If an assessment is as well founded, etc., as you describe, than it's range of reliability may be narrower than the
+/- 25%. I mentioned (I wrote: e.g.). But it becomes IMO rather fallatious than scientific when it produces precises figures down to tens or hundreds of tonnes without any qualification. MB-Y
To understand how nature works, both from the point of view of quality and quantity, it is necessary to "build" a perfect picture of the phenomenon to be studied. The better this constructed representation is, the easier it will be to understand the phenomenon both qualitatively and quantitatively. Mathematical models are just an identikit of a phenomenon to be studied. These identikits only vaguely resemble photographs since the identikits are based on very few variables and parameters while the phenomena themselves depend on a large number of variables and parameters, many of which are unknown or unknowable. In order to estimate the parameters it is necessary to measure the variables. If the measurements (samples) are too few or if the sampling is not statistically (scientifically) programmed in space and time, the precision of the parameter estimates will be low regardless of the perfection of the mathematical model. Estimates have a relative value, not absolute, i.e. it is important to know how the estimates change over time (for example: from year to year). Therefore it is imperative to adopt mathematical models and sampling plans in accordance with objective and scientific methods: increase the precision of the estimates by adopting or creating better mathematical models (with a larger number of parameters and variables), optimize sampling according to objective and scientific criteria. In this perspective, the results are as scientific as the methods used to produce them.
I agree, Leonardo, as long as you use mathematical models for studying how changes in one or a few parameters affect the general picture in relative terms. But, pretending that using them for real time quantitative assessment produces precise figures, turns science into guesswork or outright fallacy. Maybe, one day the technology and survey techniques would advance into what today is still utopia.
I tend to agree with John & Erik. The best stock assessments are good enough to get a fair impression on how the stock is changing over time. The absolute figures may be wrong and numbers to the tenth and hundreds of tons may imply a false precision, but still relative interannual changes in stock size and age structure can be tracked. And sometimes even meaningful catch recommendations can be obtained from the assessments...
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