DBPapers
DOI: 10.5593/SGEM2016/B32/S15.099

IDENTIFYING AND QUANTIFYING MARINE ECOSYSTEM EFFECTS ON THE BLACK SEA SPRAT BIOLOGY

G. Sarbu, A. Totoiu, M.I. Nenciu, L. Boicenco, G. Radu
Wednesday 7 September 2016 by Libadmin2016

References: 16th International Multidisciplinary Scientific GeoConference SGEM 2016, www.sgem.org, SGEM2016 Conference Proceedings, ISBN 978-619-7105-62-9 / ISSN 1314-2704, June 28 - July 6, 2016, Book3 Vol. 2, 755-762 pp

ABSTRACT
Environmental fluctuations like temperature, food abundance etc. cause major changes in fish productivity and can lead to rapid fluctuations in fishing opportunities. Such fluctuations are not foreseen or taken into account by most management advisory frameworks for short-lived species such as sprat, which generally assume environmental stability and constant productivity.
The paper summarizes the results obtained so far in the project “IntelliGent Oceanographically-based short-term fishery FORecastIng applicaTions” (GOFORIT), which will identify links between the ecology of short-lived fish species and climate and oceanographic conditions at time scales relevant to annual stock assessment and advisory cycles, and use this new knowledge in forecasts.
We have identified new relationships between ecosystem status (temperature, phytoplankton and zooplankton) and some parameters like: recruitment, spawning stock biomass, stock biomass, L-infinity, mean length, mean weight, growth parameter (k), lipids, protein etc.
The highest positive correlations (this means that as one variable increases/decreases in value, the second variable also increase/decrease in value), based on Pearson’s r (r = [0.8,1]) was: between phytoplankton and biomass, between spawning stock biomass and stock biomass, between temperature and protein contents, between zooplankton and recruitment.
The highest negative correlations (this means that as one variable increases in value, the second variable decreases in value), based on Pearson’s r (r = [-1,-0.6]) was: between lipids and protein content, between phytoplankton and spawning stock biomass, between temperature and stock biomass, between temperature and growth parameter (k), between temperature and recruitment, between temperature and zooplankton, between zooplankton and lipids content.
Based on the significance coefficient (p <= 0.05), we can conclude that there is a statistically significant correlation (which means that increases or decreases in one variable do significantly relate to increases or decreases in the second variable) between: biomass and catches, L-infinity and mean weight, spawning stock biomass and stock biomass, spawning stock biomass and recruitment, spawning stock biomass and catches, temperature and protein, lipids and protein, temperature and stock biomass, temperature and growth parameter (k), temperature and recruitment.

Keywords: GOFORIT, forecast, fishery, ecosystem, correlations