An observing system for the collection of fishery and oceanographic data

[摘要] Fishery Observing System (FOS) was developed as a first and basic steptowards fish stock abundance nowcasting/forecasting within the framework ofthe EU research program Mediterranean Forecasting System: Toward anEnvironmental Prediction (MFSTEP). The study of the relationship betweenabundance and environmental parameters also represents a crucial pointtowards forecasting. Eight fishing vessels were progressively equipped withFOS instrumentation to collect fishery and oceanographic data. The vesselsbelonged to different harbours of the Central and Northern Adriatic Sea. Forthis pilot application, anchovy (Engraulis encrasicolus, L.) was chosen as the target species.Geo-referenced catch data, associated with in-situ temperature and depth, were theFOS products but other parameters were associated with catch data as well.MFSTEP numerical circulation models provide many of these data. Inparticular, salinity was extracted from re-analysis data of numericalcirculation models. Satellite-derived sea surface temperature (SST) andchlorophyll were also used as independent variables. Catch and effort datawere used to estimate an abundance index (CPUE – Catch per Unit of Effort).Considering that catch records were gathered by different fishing vesselswith different technical characteristics and operating on different fishdensities, a standardized value of CPUE was calculated. A spatial andtemporal average CPUE map was obtained together with a monthly mean timeseries in order to characterise the variability of anchovy abundance duringthe period of observation (October 2003–August 2005). In order to studythe relationship between abundance and oceanographic parameters, GeneralizedAdditive Models (GAM) were used. Preliminary results revealed a complexscenario: the southern sector of the domain is characterised by a strongerrelationship than the central and northern sector where the interactionsbetween the environment and the anchovy distribution are hidden by a higherpercentage of variability within the system which is still unexplained.

GAM analysis showed that increasing the number of explanatory variables alsoincreased the portion of variance explained by the model. Data exchange andinterdisciplinary efforts will therefore be crucial for the success of thisresearch activity.

[学科分类] 海洋学与技术