You could contact Jeremy Wade of River Monsters fame. He has a lot of experience with this – [email protected] :-)

which fishery James?

Hi James,

sounds like you want to do a multi-species approach. Fishing with different nets by different people, especially gill-nets, may make things complicated. Do you have any possibility for capture-recapture experiments? Are the species you want to investigate suited for that? Do you have any time series of your data? Or you may think about catch-depletion methods, if that is feasible, a brief description for a start is found here: http://derekogle.com/fishR/examples/oldFishRVignettes/Depletion.pdf.

I don't know PASGEAR, but from its description it sound quite adequate. I also once worked with FiSAT II from FAO, but probably you know this already? FiSAT is better suited for analysis than for data storage. For analysis there are also plenty of R packages available, maybe fishR would be good. At least it's free, even though you might need people who know how to work with it.

Anyway good luck with your campaign!

With the proviso that I haven’t actually done this or seen it done in quite this way, but based on a human dimensions and traditional ecological knowledge approach in which I have participated, I would try something like this:

Interview fishermen of different ages, preferably experienced fishermen (and women if they fish … but see also below), both older and younger ones, asking them both in general and in terms of a few species what their catch (rates) are now compared to five or ten years ago (you need to anchor on current catch rates). Ask what their species mix is now, compared to five - ten years ago. Ask about their gear types and changes over the past five-ten years. And wrap up with a question about what they see as the overall difference in their fishery and what is causing that difference over the past five-ten years.

Ask the people in the community (often women) who handle fish (e.g., people who run markets, sell fish, process the catch, etc.) what their experience is in terms of what is brought home (quantity and species mix) … now and in the past five or ten years.

The key thing is to use their perceptions of the past, recall bias though there may be, to give an idea of changes in the fishery. By using fishermen of different ages you get a sense of current versus historical experience. This should provide some context, and even compare/contrast, with your creel surveys.

Ambitious perhaps but talking to fishermen about their experiences, and not just quizzing their catch, is rewarding.

Hope this helps. Aloha.

HI,

There has been a lot of methodological development in latest years regarding data-poor fisheries assessment. ICES has been quite active developing data-poor methods though WKLIFE workshops. This year was the 6th. You can find a lot of info following http://ices.dk/community/groups/Pages/WKLIFEVI.aspx. Many of the methods developed require only landings and life history parameters. You can also find a lot of information regarding species life history at  www.fishbase.org.

hi james, big challenge! your question reminds me of a reference i went to look up now in my 2000 FAO fisheries paper on the samoan student census - another participatory way of assessing coastal fisheries, but taking more than 6 weeks! (i like samuel pooley's take above to work with and through the fishermen - at least for part of what you are trying to achieve). the reference is as follows:

Fox, P. (1986) A manual of rapid appraisal techniques for Philippine coastal fisheries. Bureau of Fisheries and Aquatic Resources Research Division (BFAR)

it is dated, but i remember it to have been really useful, comprehensive and well written. you may find other useful references in the same paper - notably the p. dalzell (1998), m. king (1993) and r.a. maine (1996) ones. noting your wish to possibly adopt an approach which is sustainable and may be maintained and used by the local community after your departure, mind sophistication, and keep it simple and effective. few manage to create functional and sustainable science / community interfaces. in resource poor environments, snapshot surveys that are not going to be repeated - regardless of their sophistication - have very little chance of substantially contributing to fisheries management. simple and sustainable data collection methods - if supported by the local community seeing a benfit in such information - do.

The use of students in surveying subsistence fisheries - a Pacific Island case study. Available from: https://www.researchgate.net/publication/264334255_The_use_of_students_in_surveying_subsistence_fisheries_-_a_Pacific_Island_case_study.

Technical Report The use of students in surveying subsistence fisheries - a P...

 We recently completed a stock assessment of data limited species in the Gulf of Mexico (http://sedarweb.org/sedar-49-final-stock-assessment-report-gulf-mexico-data-limited-species) using the Data-Limited Methods Toolkit  (http://www.datalimitedtoolkit.org/ ) package in R (Carruthers and Hordyk 2016).  The package has consolidated methods and enabled simultaneous analysis of performance using management strategy evaluation (MSE). It requires landing estimates and assumptions about stock status, but is relatively easy to parameterize.

Good Luck! 

The RFAMS method is an very helpful tool for recording catches at fish landing sites in tropical developing countries where the catches are highly diverse with many fishing types involved. on the other hand, a potentially main issue is whether a large portion of the catches is being missed for any particular reason, e.g. catches by-passing the surveyed part of the landing site, sale direct from boats prior to catches entering landing site, etc. This needs to be determined on a case by case basis depending on the landing site being surveyed. Thus, one of the critical first steps of using this method is to develop a good understanding of the dynamics of the landing site or sites being surveyed. Using this method without understanding how a landing site operates can lead to significant underestimation of landings and potentially erroneous data being collected

Fisheries have been defined as data-poor if information is insufficient to produce a defensible

quantitative stock assessment (Dowling et al., 2011), meaning that the best scientific information

available is inadequate for determining meaningful reference points or current stock status relative

to such reference points. This is consistent with the FAO definition that data-limited fisheries are

considered to be fisheries lacking sufficient biological information to infer the exploitation status

of the targeted stocks. It is often difficult therefore to bring data-poor fisheries into alignment with

legislative requirements concerning estimation of reference points and relative stock status. In the

United States for example, the Magnuson-Stevens Act requires fisheries to be managed on the basis

of MSY, and a similar situation exists in New Zealand (Sections 1.2 and 1.3). Shortcomings in data

provide an incentive for the development of assessment methods that have lower data requirements

than those currently in use.

Data-poor stocks are therefore usually those with limited data or limited information content in the

data that have been collected. Within the concept of data poor it is possible to further distinguish

fisheries that are not necessarily data limited, but limited by technical capacity (i.e. the scientific

capacity to carry out a stock assessment is not available). These situations could be classified as

capacity-poor. However, using the above definitions they are often grouped together with strictly

data-poor fisheries, since they are all similarly deficient in a quantitative assessment.

A lack of informative data and technical capacity are often concurrent characteristics for economic

reasons. Data-poor fisheries are often characterised as such because they are of low value and

have prohibitive costs of data collection, which similarly implies a lack of support for technical

analysis (Dowling et al., 2011). In New Zealand specifically, the costs of data collection are offset by

cost-recovery and therefore the quantity of data collected tends to be related to economic value of the

fishery.

good details

I agree with the answer of Dr. Wolfgang Nikolaus Probst.

Regards

SM Najim

FiSAT is good way for analyses 

Hello James

see https://dlmtool.github.io/DLMtool/userguide/userguide.pdf

cheers, BM

Dear Bruno,

Good web site

Thanks

Noori

this link is useful

www.fao.org/docrep/003/x8692e/x8692e09.htm

regards

Dear Hassan

Good link

Regards

Software LFDA (MRAG) is an alternative to FISAT, but only for determining growth and mortality parrameters. From these last parameterrs, you can get the exploitation rate of the stocks.