Title: Fisheries Biology in R (Advanced Course): Stock Assessment
Subtitle: Stock Assessment Course Using Novel Methods for Data-poor Fisheries in the Mediterranean and the Black Sea
Date: 11-15 November 2019
Venue: School of Biology of Aristotle University of Thessaloniki (AUTh), Thessaloniki, Greece
Organizers: GEOMAR and the Laboratory of Ichthyology, School of Biology, AUTh
Following a series of international courses on CMSY and related data-poor methods, the purpose of this course was to introduce stock assessment scientists from around the Mediterranean and Black Sea to a string of new tools for application in data-poor situations. Participants were then encouraged to apply these new tools to their respective stocks and present their results to the group for general discussion.
Course Description: An advanced stock assessment course for scientists interested in the assessment of data-poor fisheries that includes novel methods based on catch and resilience (CMSY), abundance (AMSY) and length frequency data (LBB).
In particular, BCrumb is a tool that uses a Markov Chain Monte Carlo approach (JAGS) to close gaps in time series of CPUE, taking into account the overall trend and variability. It also facilitates combination of different time series of CPUE with different uncertainties into an overall harmonized time series for use in subsequent analysis. Participants made extensive use of this tool and encountered no major problems.
LBB (Froese et al. 2018a, 2019) is a tool that analyses length frequency data and estimates from that relative stock status B/B0. This output can then be used directly for management advice or as prior input for subsequent analysis together with additional data. Participants had length frequency data for fish and invertebrate species and were able to get satisfactory estimates of relative abundance.
CMSY (Froese et al. 2016, 2018b) is a tool that analyses catch data combined with prior information on productivity and stock status, where objective stock status priors are preferably derived from LBB. If in addition CPUE data are available (e.g. after harmonization with BCrumb), then CMSY applies a full Bayesian Schaefer model to the data. Output are regular stock assessment reference points (Fmsy, Bmsy), MSY, indicators (F/Fmsy, B/Bmsy), graphs and statistics. All participants were able to run CMSY/BSM on their respective stocks, with results that were very similar to independent assessments (if available) or general perception of the stocks.
AMSY (Froese et al. in press) is a tool for situations where CPUE data but no reliable catch data are available. Outputs are exploitation F/Fmsy and stock status B/Bmsy. All participants were able to run AMSY on their respective stocks, with results that were similar to independent assessments (if available).
Finally, JABBA (Winker et al. 2018) was introduced as an advanced Bayesian implementation of a state-space surplus production model, with many analytical options and outputs.
RESOURCE PERSONS (INSTRUCTORS)
GEOMAR Helmholtz Centre for Ocean Research
Centre for Statistics in Ecology Environment and Conservation
University of Cape Town, South Africa
Laboratory of Ichthyology, School of Biology
Aristotle University of Thessaloniki, Greece
by Athanassios Tsikliras, Donna Dimarchopoulou, and Rainer Froese
The Fisheries Biology in R Advanced Course (Stock assessment) was organized by the Centre of Life Long Learning and the Laboratory of Ichthyology, led by Athanassios Tsikliras, and was held at the School of Biology of Aristotle University of Thessaloniki, Thessaloniki, Greece on 11-15 November 2019. Twenty scientists were selected out of fifty applicants and fifteen of them finally attended the course (five successful applicants couldn’t make it for financial and visa issues), three on a fee scholarship, whereas the General Fisheries Commission for the Mediterranean (GFCM) supported three other participants. The fifteen participants came from eight Mediterranean and Black Sea countries and Columbia. Rainer Froese and Henning Winker were the main instructors.
During the course, the updated CMSY version (CMSY+) was successfully tested in a number of stocks that had been previously assessed using CMSY (Froese et al. 2018b) and for which there is an official assessment available. LBB and AMSY were externally tested by experts for the first time in stocks that had been previously assessed using age-based and surplus production models and the comparison indicated a very good agreement. Finally, several preliminary assessments were performed for the first time at MSY-level for stocks with only length frequency distributions or abundance data available in the Caribbean, the Mediterranean and the Black Seas.
Versions of models used:
LBB_33a.R available at http://oceanrep.geomar.de/44832/ http://oceanrep.geomar.de/43182/
CMSY_2019_8r.R available at http://oceanrep.geomar.de/33076/
AMSY_68x.R available at http://oceanrep.geomar.de/47135
Froese R, Demirel N, Coro G, Kleisner KM, Winker H (2016) Estimating fisheries reference points from catch and resilience. Fish and Fisheries 18: 506–526
Froese R, Winker H, Coro G, Demirel N, Tsikliras AC, Dimarchopoulou D, Scarcella G, Probst WN, Dureuil M, Pauly D (2018a) A new approach for estimating stock status from length-frequency data. ICES Journal of Marine Science 75: 2004-2015
Froese R, Winker H, Coro G, Demirel N, Tsikliras AC, Dimarchopoulou D, Scarcella G, Quaas M, Matz-Lück N (2018b) Status and rebuilding of European fisheries. Marine Policy 93: 159-170
Froese R, Winker H, Coro G, Demirel N, Tsikliras AC, Dimarchopoulou D, Scarcella G, Probst WN, Dureuil M, Pauly D (2019) On the pile-up effect and priors for Linf and M/K: Response to a Comment by Hordyk et al. on “A new approach for estimating stock status from length-frequency data”. ICES Journal of Marine Science 76: 461-465
Froese R, Winker H, Coro G, Demirel N, Tsikliras AC, Dimarchopoulou D, Scarcella G, Palomares MLD, Dureuil M, Pauly D (in press). Estimating stock status from abundance and resilience. ICES Journal of Marine Science
Winker H, Carvalho F, Kapur M (2018) JABBA: Just Another Bayesian Biomass Assessment. Fisheries Research 204: 275-288