Remotely Sensing the Biophysical Drivers of Sardinella aurita Variability in Ivorian Waters

Abstract

Abstract:The coastal regions of the Gulf of Guinea constitute one of the major marine ecosystems, producing essential living marine resources for the populations of Western Africa. In this region, the Ivorian continental shelf is under pressure from various anthropogenic sources, which have put the regional fish stocks, especially Sardinella aurita, the dominant pelagic species in Ivorian industrial fishery landings, under threat from overfishing. Here, we combine in situ observations of Sardinella aurita catch, temperature, and nutrient profiles, with remote-sensing ocean-color observations, and reanalysis data of wind and sea surface temperature, to investigate relationships between Sardinella aurita catch and oceanic primary producers (including biomass and phenology of phytoplankton), and between Sardinella aurita catch and environmental conditions (including upwelling index, and turbulent mixing). We show that variations in Sardinella aurita catch in the following year may be predicted, with a confidence of 78%, based on a bilinear model using only physical variables, and with a confidence of 40% when using only biological variables. However, the physics-based model alone is not sufficient to explain the mechanism driving the year-to-year variations in Sardinella aurita catch. Based on the analysis of the relationships between biological variables, we demonstrate that in the Ivorian continental shelf, during the study period 1998-2014, population dynamics of Sardinella aurita, and oceanic primary producers, may be controlled, mainly by top-down trophic interactions. Finally, based on the predictive models constructed here, we discuss how they can provide powerful tools to support evaluation and monitoring of fishing activity, which may help towards the development of a Fisheries Information and Management System.

Interannual monsoon wind variability as a key driver of East African small pelagic fisheries

“…While the herrings and shads reproduce throughout the year in tropical regions 46 , the regional anchovy species, which are found along the Tanzanian coast 47 spawn near the surface between October and March 48 . Since the survival of larval stages is dependent upon the variable ocean conditions 49 , 50 , most small pelagic fish spawn in locations and during seasons that minimize these losses 9 . In our case study, the strong surface currents (and moderate mixing, Supplementary Fig.…”

“…The FAO has also officially acknowledged catch reconstructions such as those of Sea Around Us help fill the gaps in national fisheries data and demonstrate how catches have realistically changed over time 76 . Furthermore, the latter data have been used to derive the interplay between the environmental variability (such as Chl-a concentrations, phytoplankton phenology and SST) and marine fish responses 77 in different coastal regions like those of Tanzania and Kenya 30 and Senegal 50 .…”

A Major Ecosystem Shift in Coastal East African Waters During the 1997/98 Super El Niño as Detected Using Remote Sensing Data

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“…Historically, these have respectively caused 25% and 55% of the disputes between fishers and governments in Tanzania between 1990 and 2017 [10], and tense competition between a variety of users in Kenya [11]. Worldwide it is acknowledged that changes in coastal upwelling systems, which provide more than 40% of global fish catch [12], can lead to changes in fish abundance [13,14]. In the last few decades, such changes have occurred in many upwelling systems around the globe.…”

Satellite Retrieval of Surface Water Nutrients in the Coastal Regions of the East China Sea

“…However, these routine means are limited by small spatial coverage and nonsynchronous observations; they also require significant manpower and material resources, which makes it difficult for sustained observations over the long term. However, satellite remote sensing is a powerful tool for large-scale and long-term observations with high temporal and spatial resolution [5][6][7][8]. In particular, the first Geostationary Ocean Color Imager (GOCI), launched by South Korea in 2010, provides eight hourly images per day with a spatial resolution of 500 m. This higher spatial and temporal resolution improved the observations of highly dynamic and small-scale changes in coastal waters [9][10][11].…”