Environmental conditions, satellite imagery, and clupeoid recruitment in the northern Benguela upwelling system

Abstract

The relationship between oceanographic conditions and clupeoid (pilchard, Sardinops sagax, and anchovy, Engraulis capensis) recruitment in the northern Benguela upwelling system was investigated from 1981 to 1987 using a time‐series of mean weekly SST images. Two approaches were taken. The first involved correlating recruitment success with the number of weekly coastal `SST events' above various cut‐off temperatures during the main reproductive season. The second involved constructing a multiple regression model of recruitment success with two independent environmental variables: namely, the number of coastal `SST events' greater than 19°C, and an onshore retention index for the early life‐history stages. The retention index was derived from a spatial time‐series analysis of the SST images using principal components analysis. In general, pilchard recruitment showed a positive relationship with the `number of SST events' whilst anchovy recruitment had a negative relationship; 1987 was an outlier year, during which there were exceptionally high levels of both pilchard and anchovy recruitment. The multiple regression R2 values were high and significant for both species (pilchard R2 = 0.88, anchovy R2 = 0.96). The regression model also accounted for the 1987 outlier according to levels of onshore retention which, despite low inshore SSTs, were particularly high during the 1986/87 reproductive season. Although these results need to be validated with data from a longer time period, they show how satellite data might be used for predicting clupeoid recruitment success in the northern Benguela.

Integrating Biogeochemistry and Ecology Into Ocean Data Assimilation Systems

“…Environmental variables such as SST, mixed-layer depth, and chlorophyll are used as covariates along with fishingrelated variables to explain observed variation in catch rate (e.g., Herron et al, 1989;Cole, 1999;Zagaglia and Stech, 2004;Bigelow and Maunder, 2007).…”

Critical regions: A GIS-based model of marine productivity hotspots

“…Many studies have analyzed monitored fisheries production data to verify relations among marine productivity hotspots and pelagic fisheries resources, e. g., on anchovy stocks (Olivar, 1990;Le Clus, 1991;Cole, 1999) and squid stocks (Pierce, 1995;Yatsu and Watanabe, 1996;Bakun and Csirke, 1998;Waluda et al, 1999;Xavier et al, 1999;Faure et al, 2000;Anderson and Rodhouse, 2001;Arguelles et al, 2001). Du et al (2000) used GIS to analyze a time series of mean weekly SST images and corresponding purse net statistic productivity for the period 1987 -1997 in East China Sea.…”

An observing system for the collection of fishery and oceanographic data

“…Studies regarding how environmental conditions influence distribution and abundance of small pelagics pointed out that temperature could be an important factor (Yáñez et al, 2001;Beare et al, 2004) but salinity (Regner, 1996), currents (Nishimoto and Washburn, 2002), wind as an upwelling driving force (Cole, 1999) and eventually lunar effects (Agenbag et al, 2003) could play some roles in driving aggregation. Understanding how the environment influences fish distribution is complicated.…”

“…These measurements provide a parameter generally considered important in determining fish aggregation, directly or indirectly. Many studies have pointed out the relationship between fish abundance and SST (Simpson, 1994;Cole, 1999;Waluda et al, 2001;Yanez et al, 2001), the latter being obtained by satellite measurements. Within the framework of MFSTEP, SST data and also surface chlorophyll are made available for the entire Adriatic Sea by the ISAC-CNR, Rome (http://www.bo.ingv.it/adricosm, Böhm et al, 2003) and temperature at depth data are also available as model outputs.…”