Two Promising Methods for Assessment of Data-Poor Stocks Rainer Froese, GEOMAR, Germany Daniel Pauly, FC-UBC, Canada 9 November 2014, San Francisco, USA.

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Presentation transcript:

Two Promising Methods for Assessment of Data-Poor Stocks Rainer Froese, GEOMAR, Germany Daniel Pauly, FC-UBC, Canada 9 November 2014, San Francisco, USA

The Challenge Globally, about ¾ of exploited fish stocks are not “fully assessed”, i.e., stock status is unknown This is also true for developed areas, such as Europe or USA Two new approaches are presented which provide reasonable proxies for stock status

The Catch-MSY Method This method gives robust estimates of the maximum sustainable yield (MSY) It also gives (biased) estimates of productivity and stock size Despite the known bias, these stock status estimates were better than other methods (outcome of simulations by FAO workshop)

Daniel just added one slide: one my students used this method…

First New Method: CMSY CMSY is a further development of Catch-MSY CMSY overcomes the bias and gives reasonable estimates of F msy and B msy Simulation testing and evaluation of CMSY has been done, submission planned for December Recent ICES workshop (WKLIFE IV) applied CMSY successfully to several data-limited stocks

CMSY Simulation testing I Simulated high to low biomass, for a species with medium resilience

CMSY Simulation testing II Simulated low to high biomass, for a species with low resilience

CMSY Simulation testing III Simulated constant low biomass, for a species with high resilience

Preliminary Simulation Results CMSY was tested against 24 simulated stocks where true r, k, MSY and biomass were known CMSY included the true r-k within its 95% confidence limit in all cases The final true biomass was in included in the 95 percentile range of CMSY in 23(96%) of the cases

CMSY Evaluation I Evaluation testing against full assessment data for Celtic Sea cod

CMSY Evaluation II Evaluation testing against full assessment data for Faroe Haddock

CMSY Evaluation III Evaluation testing against full assessment data for North Sea herring

CMSY Evaluation IV Evaluation testing against full assessment data for Norway lobster in the Bay of Biscay

Preliminary Results of the Evaluation CMSY was tested on 114 fully assessed global stocks 95% Confidence limits of CMSY and the full Schaefer overlapped in 97% of the stocks r and k of the full Schaefer were included in the CMSY estimates in 93% and 92% of the cases, respectively

CMSY Summary Requires: catch or landings, resilience (e.g. from FishBase), broad stock status (good or bad) Predicts: MSY, B msy, F msy, current biomass & exploitation. Note that relative predictions such as B/B msy are more robust than absolute values Limitations: needs at least 10 years of data; assumes average productivity and is too optimistic if that assumption is not true, e.g. at very low stock sizes

Second New Method: hsCPUE hockey-stick analysis of Catch-Per-Unit-of-Effort CPUE data are split into juveniles and adults Abundance of juveniles is plotted over that of their parents A hockey-stick fit shows the point below which recruitment declines Current stock status can be assessed relative to the hockey stick

Stock-Recruitment Plot with Fisheries Reference Points

Proof of Concept hsCPUE was applied to 12 data-limited stocks

hsCPUE fit to data-poor North Sea dab

Adult CPUE of North Sea dab shown against hsCPUE reference points

hsCPUE Summary Requires: size-structured CPUE (best from surveys Predicts: stock size relative to reference points Limitations: needs at least 10 years of data, including a range of stock sizes

Thank You