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DATA LIMITED STOCK ASSESSMENT OF POPULATION DYNAMICS BY CMSY MODEL ON THE EXAMPLE OF BLACK SEA SPRAT IN THE RUSSIAN WATERS
- Source :
- Fisheries. 2021:76-82
- Publication Year :
- 2021
- Publisher :
- FSBI TSUREN, 2021.
-
Abstract
- This study performs approbation of trend CMSY model on the example of Black sea sprat fishing unit, localized in Russian waters. Data sources has been reduced to the level of data limited modeling for indicator and trend models approach. CMSY population model results were compared with previously performed estimations by more powerful cohort model - XSA. CMSY results shows no significant deviations from the XSA results. Forecast scenarios and conclusions based on CMSY model fitting leads to the same statements with previously published results by XSA. CMSY model shows next results: stock biomass in 2019 B2019 = 63,9 ths. t, fishing mortality – F2019 = 0,29. Stock biomass in 2019 was significant below the target reference point BMSY = 105 ths. t and higher then limit reference point Blim = 52,7 ths. t. Some uncertain overexploitation in 2019 was underlined, F2019/FMSY = 1,12. Investigation of forecast scenarios with different total allowed catch levels indicates that there are no features for increasing the catch capacity in short-term projection. CMSY model fitting have passed the necessary stability tests and confirm previously founded results. In summary of this study, we can recommend to use CMSY model for stock assessment procedure in terms of data-limited information background.
- Subjects :
- 0106 biological sciences
education.field_of_study
Stock assessment
biology
010604 marine biology & hydrobiology
Population
Fishing
Cohort model
Sprat
04 agricultural and veterinary sciences
biology.organism_classification
01 natural sciences
Overexploitation
Population model
Statistics
040102 fisheries
0401 agriculture, forestry, and fisheries
Environmental science
education
Stock (geology)
Subjects
Details
- ISSN :
- 01316184
- Volume :
- 2021
- Database :
- OpenAIRE
- Journal :
- Fisheries
- Accession number :
- edsair.doi...........10533081a0a68bda6d5cfb4f12ca13c8