Unravelling links between squid catch variations and biophysical mechanisms in South African waters

Jebri, Fatma; Raitsos, Dionysios Ε.; Gittings, John A.; Jacobs, Zoe; Srokosz, Meric; Gornall, Jessica; Sauer, W. H. H.; Roberts, Michael J.; Popova, Ekaterina

doi:10.1016/j.dsr2.2022.105028

Cited by 9 publications

(10 citation statements)

References 86 publications

(129 reference statements)

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“…However, this relationship degraded when updated with data from recent years (Sauer et al, 2013). Similarly, Jebri et al (2022) found a statistical relationship between catch and chlorophyll a concentration on the bank, with the latter being controlled primarily by changes in wind.…”

Section: Introductionmentioning

confidence: 80%

“…For instance, the timing of wind-driven upwelling and its impact on primary production can be captured correctly, whereas mesoscale variability, e.g., meanders in the Agulhas current, cannot be accurately captured due to its intrinsically chaotic nature (Jacobs et al, 2020(Jacobs et al, , 2022Robinson et al, 2016;Srokosz et al, 2015). We hypothesise that weather-driven interannual variability in upwelling over the Agulhas Bank (Jebri et al, 2022) leads to predictable changes in the size structure of the plankton community (total biomass and/or the ratio of small to large plankton types). These changes then lead to a response in the size spectrum of higher trophic levels, notably the 1 mg-1 g wet mass range that the size spectrum model reliably reproduces.…”

Section: Possible Mechanismsmentioning

confidence: 92%

“…There is a long history of modelling relationships between environment and squid catch for the purpose of prediction or forecasting (Moustahfid et al, 2021;Rodhouse et al, 2014). In most of this work, however, the emphasis lies on empirical statistical relationships between stock size (or CPUE) and environmental variables, e.g., temperature (Roberts, 2005;Waluda et al, 2001) or chlorophyll (Jebri et al, 2022). The use of process-based models is less common and often takes the form of highly complex end-to-end ecosystem models such as Ecopath with Ecosim (Gasalla et al, 2010) or Atlantis (Jackson et al, 2007); a comprehensive overview is given by de la Chesnais et al (2019).…”

Section: Predicting Catch Of Cephalopodsmentioning

confidence: 99%

See 2 more Smart Citations

The paralarval stage as key to predicting squid catch: Hints from a process-based model

Bruggeman¹,

Jacobs²,

Popova³

et al. 2022

Deep Sea Research Part II: Topical Studies in Oceanography

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Section: Introductionmentioning

confidence: 80%

Section: Possible Mechanismsmentioning

confidence: 92%

Section: Predicting Catch Of Cephalopodsmentioning

confidence: 99%

See 1 more Smart Citation

The paralarval stage as key to predicting squid catch: Hints from a process-based model

Bruggeman¹,

Jacobs²,

Popova³

et al. 2022

Deep Sea Research Part II: Topical Studies in Oceanography

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“…To achieve this, we apply an unsupervised ML approach, the neural clustering Self Organizing Maps (SOM, Kohnen, 1982;Kohonen, 2014), to 23 years of satellite observations of surface currents, Sea Surface Temperature (SST) and chlorophyll-a (Chl-a, proxy for phytoplankton biomass and productivity). Note that although the Cold Ridge upwelling is triggered by south-westerly winds (Probyn et al, 1994;Roberts, 2005), here we only focus on the surface currents influence which enhances the upwelling productivity via westward advection (Jebri et al, 2022;Jacobs et al, submitted a). This multi-parameter SOM analysis is used to identify the most characteristic modes of the circulation that may lead to high and/or low productivity during the Cold Ridge season.…”

Section: Introductionmentioning

confidence: 99%

Earth Observation and Machine Learning Reveal the Dynamics of Productive Upwelling Regimes on the Agulhas Bank

et al. 2022

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The combined application of machine learning and satellite observations offers a new way for analysing complex ocean biological and physical processes. Here, an unsupervised machine learning approach, Self Organizing Maps (SOM), is applied to discover links between surface current variability and phytoplankton productivity during seasonal upwelling over the Agulhas Bank (South Africa), from 23 years (November-March 1997-2020) of daily satellite observations (surface current, sea surface temperature, chlorophyll-a). The SOM patterns extracted over this dynamically complex region, which is dominated by the Agulhas Current (AC), revealed 4 topologies/modes of the AC system. An AC flowing southwestward along the shelf edge is the dominant mode. An AC with a cyclonic meander near shelf is the second most frequent mode. An AC with a cyclonic meander off shelf and AC early retroflection modes are the least frequent. These AC topologies influence the circulation and the phytoplankton productivity on the shelf. Strong (weak) seasonal upwelling is seen in the AC early retroflection, the AC with a cyclonic meander near shelf modes and in part of the AC along the shelf edge mode (the AC with a cyclonic meander off shelf mode and in part the AC along the shelf edge mode). The more productive patterns are generally associated with a strong southwestward flow over the central bank caused by the AC intrusion to the east Bank or via an anticyclonic meander. The less productive situations can be related to a weaker southwest flow over the central bank, strong northeast flow on the eastern bank, and/or to a stronger northwest flow on the central bank. The SOM patterns show marked year-to-year variability. The high/low productivity events seem to be linked to the occurrence of extreme phases in climate variability modes (El Niño Southern Oscillation, Indian Ocean Dipole).

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“…In the Western Indian Ocean (WIO), coastal upwelling systems are seasonal (only present for part of the year), driven by changing wind directions over the year, leading to changes in their productivity (e.g., Kämpf and Chapman, 2016b). They play a key role in regulating regional ecosystem productivity and sustaining food security and livelihoods for millions of people via fishing activity (Bakun et al, 1998;Jacobs et al, 2020a;Jacobs et al, 2020b;Jebri et al, 2020;Jebri et al, 2022a).…”

Section: Introductionmentioning

confidence: 99%

Automated detection of coastal upwelling in the Western Indian Ocean: Towards an operational “Upwelling Watch” system

Hammond

Jebri²,

Srokosz³

et al. 2022

Front. Mar. Sci.

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Coastal upwelling is an oceanographic process that brings cold, nutrient-rich waters to the ocean surface from depth. These nutrient-rich waters help drive primary productivity which forms the foundation of ecological systems and the fisheries dependent on them. Although coastal upwelling systems of the Western Indian Ocean (WIO) are seasonal (i.e., only present for part of the year) with large variability driving strong fluctuations in fish catch, they sustain food security and livelihoods for millions of people via small-scale (subsistence and artisanal) fisheries. Due to the socio-economic importance of these systems, an "Upwelling Watch" analysis is proposed, for producing updates/alerts on upwelling presence and extremes. We propose a methodology for the detection of coastal upwelling using remotely-sensed daily chlorophyll-a and Sea Surface Temperature (SST) data. An unsupervised machine learning approach, K-means clustering, is used to detect upwelling areas off the Somali coast (WIO), where the Somali upwelling – regarded as the largest in the WIO and the fifth most important upwelling system globally – takes place. This automatic detection approach successfully delineates the upwelling core and surrounds, as well as non-upwelling ocean regions. The technique is shown to be robust with accurate classification of out-of-sample data (i.e., data not used for training the detection model). Once upwelling regions have been identified, the classification of extreme upwelling events was performed using confidence intervals derived from the full remote sensing record. This work has shown promise within the Somali upwelling system with aims to expand it to the rest of the WIO upwellings. This upwelling detection and classification method can aid fisheries management and also provide broader scientific insights into the functioning of these important oceanographic features.

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Unravelling links between squid catch variations and biophysical mechanisms in South African waters

Cited by 9 publications

References 86 publications

The paralarval stage as key to predicting squid catch: Hints from a process-based model

The paralarval stage as key to predicting squid catch: Hints from a process-based model

Earth Observation and Machine Learning Reveal the Dynamics of Productive Upwelling Regimes on the Agulhas Bank

Automated detection of coastal upwelling in the Western Indian Ocean: Towards an operational “Upwelling Watch” system

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