Fish Population Dynamic and Stock Assessment

1.7.4. Biological indicators

Relative biomass : specifically of gelatinous zooplankton, cephalopods, small pelagic, scanvengers, demersal fish, piscivores, top predators, and biogenic habitat (cover forming species). Ease of sampling differs strongly between these groups (for instance external bodies, such as NGOs, often monitor the charismatic top predators, tourism can focus attention on coral reef fishes, snapshots of mangrove forests and shallow benthic habitat may be available from satellite images. It is possible to find proxies for even some of the most difficult groups. For example, changes in relative biomass of gelatinous zooplankton may be captured by frequency of bloom events, as this is a much more easily obtainable statistic due to the ability to generate it from expert information and fisher interviews.

Biomass ratios: in particular the biomass ratios of piscivore: planktivore (PS:ZP), pelagic: demersal (P:D) and infauna: epifauna. The last of these is probably not feasible in data poor situations, but the other two can be done.

Size spectra: which give an indication of perturbation in system structure (using the slope of the curve), but can also highlight changes in system productivity (via the intercept).

Maximum (or mean) length: this indicator is observed to work in practice even if simple rules of thumb regarding are used. Caution is needed regarding market driven changing in preferred sizes, however.

Total fisheries removals: (catch + bycatch + discards): this indicator considers the total biomass removed from the system versus what is left cycling in the system. While similar in concept to the widely used comparison of primary production vs. removals from the system, there is concern that it will not be suitable for data poor and dispersed small scale fisheries due to the lack of data on removals let alone discards.

Diversity: (counts of species): It remains an informative and fundamental piece of information about the system. Alternative measures of diversity or system structure may need to be considered too, depending on which operating model is used or what empirical data is available. One possibility is considering the value of changes in species-area curves (e.g. the slope and asymptote of the curve) through time (or spatially if under a perturbation gradient). It is likely that some simple or ordinal data can be collected on this from the fishers themselves given that they are acutely aware of what they catch. Looking at changes in these ranking could be highly informative.

Size at maturity (weight and length): It is a strong means of detecting change in the system and stock structure. It may be too difficult to use in data poor situations.

Biophysical (Chlorophyll a, temperature, dissolved inorganic nitrogen, and level of contaminants): these may be drawn from water quality monitoring programs or remotely sensed data sets, but are needed even if they aren’t already being collected as they are means of teasing out causation. Indicators respond to any system change, including those caused by sectors other than fisheries, so any information that helps elucidate causation is extremely helpful.

While all of these indicators are not equally easily calculated in data poor situations, they are a good “straw-man” to start testing indicators for such fisheries in this study. It is critical that a suite of indicators, which are not all highly correlated, is used;

· Multiple time and space scales are spanned by the data sets.