The influence of environmental factors and hydrodynamics on sardine (Sardina pilchardus, Walbaum 1792) abundance in the southern Alboran Sea

Jghab, A.; Vargas‐Yáñez, Manuel; Reul, Andreas; García-Martínez, M.C.; Hidalgo, Manuel; Moyá, Francina; Bernal, M.; Omar, M. Ben; Benchoucha, Saïd; Lamtai, A.

doi:10.1016/j.jmarsys.2018.12.002

Cited by 15 publications

(17 citation statements)

References 54 publications

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“…Sardine, for example shows preference for colder waters compared to round sardinella and anchovy, and appears to confine its distribution and shrink its spawning grounds to colder waters when SST increases [46], a condition that affects its fisheries. This negative relationship between sardine landings and SST, indicates that the long-term temperature changes in the Mediterranean could have a negative impact on sardine abundance [72]. In contrast, there is a positive relationship between sardine landings and chlorophyll concentration in the Alboran Sea [72] which has also been related to other areas of the Mediterranean Sea [73].…”

Section: Discussionmentioning

confidence: 98%

“…According to the findings of the present work (Figure 5, Table 7), temperature and salinity are the main drivers of the distribution of most small and medium pelagic fishes in the Mediterranean. The spatial distribution and the abundance of small and medium pelagic species may be directly affected by sea surface temperature (SST) [72], but the effect of SST can be also indirect through changes in the planktonic components of the food webs [13] that constitute the main prey for small pelagic fishes [64]. The effect of SST, however, is not uniform across species and it depends on their thermal preferences [7], which may vary among the pelagic fishes [9].…”

Section: Discussionmentioning

confidence: 99%

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Species Distribution Modelling via Feature Engineering and Machine Learning for Pelagic Fishes in the Mediterranean Sea

et al. 2020

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In this work a fish species distribution model (SDM) was developed, by merging species occurrence data with environmental layers, with the scope to produce high resolution habitability maps for the whole Mediterranean Sea. The final model is capable to predict the probability of occurrence of each fish species at any location in the Mediterranean Sea. Eight pelagic, commercial fish species were selected for this study namely Engraulis encrasicolus, Sardina pilchardus, Sardinella aurita, Scomber colias, Scomber scombrus, Spicara smaris, Thunnus thynnus and Xiphias gladius. The SDM environmental predictors were obtained from the databases of Copernicus Marine Environmental Service (CMEMS) and the European Marine Observation and Data Network (EMODnet). The probabilities of fish occurrence data in low resolution and with several gaps were obtained from Aquamaps (FAO Fishbase). Data pre-processing involved feature engineering to construct 6830 features, representing the distribution of several mean-monthly environmental variables, covering a time-span of 10 years. Feature selection with the ensemble Reciprocal Ranking method was used to rank the features according to their relative importance. This technique increased model’s performance by 34%. Ten machine learning algorithms were then applied and tested based on their overall performance per species. The XGBoost algorithm performed better and was used as the final model. Feature categories were explored, with neighbor-based, extreme values, monthly and surface ones contributing most to the model. Environmental variables like salinity, temperature, distance to coast, dissolved oxygen and nitrate were found the strongest ones in predicting the probability of occurrence for the above eight species.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Species Distribution Modelling via Feature Engineering and Machine Learning for Pelagic Fishes in the Mediterranean Sea

et al. 2020

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“…The most positive WeMOi value (+5.99) of the 1951-2016 study period refers to 2 December 1976 ( Fig. 3a), when an intense precipitation episode was recorded in the Basque Country (northern Spain), according to the European Climate Assessment (ECA) dataset (Klein Tank et al, 2002;Cornes et al, 2018). The most negative WeMOi value (−5.97) during the 1951-2016 period corresponds to 28 November 2014 ( Fig.…”

Section: Daily Wemoi Valuesmentioning

confidence: 99%

“…Daily NAO index (NAOi) values are also used for comparison with WeMOi values and to enhance the role played by the WeMO in torrential precipitation. Following the calculation method based on daily WeMOi values, daily NAOi values are computed by means of surface pressure data from the San Fernando (SW Spain) and Reykjavík (SW Iceland) weather stations; the data for Reykjavík were provided by the ECA dataset (Klein Tank et al, 2002). The NAOi values present the same percentage as that of the negative WeMOi daily values (55.1 %), and almost half of the days are around 0.…”

Section: Daily Wemoi Valuesmentioning

confidence: 99%

Intra-annual variability of the Western Mediterranean Oscillation (WeMO) and occurrence of extreme torrential precipitation in Catalonia (NE Iberia)

López-Bustins

Arbiol-Roca

Martín‐Vide

et al. 2020

Nat. Hazards Earth Syst. Sci.

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Abstract. In previous studies the Western Mediterranean Oscillation index (WeMOi) at daily resolution has proven to constitute an effective tool for analysing the occurrence of episodes of torrential precipitation over eastern Spain. The western Mediterranean region is a very sensitive area, since climate change can enhance these weather extremes. In the present study we created a catalogue of the extreme torrential episodes (≥200 mm in 24 h) that took place in Catalonia (NE Iberia) during the 1951–2016 study period (66 years). We computed daily WeMOi values and constructed WeMOi calendars. Our principal result reveals the occurrence of 50 episodes (0.8 cases per year), mainly concentrated in the autumn. We confirmed a threshold of WeMOi ≤ −2 to define an extreme negative WeMO phase at daily resolution. Most of the 50 episodes (60 %) in the study area occurred on days presenting an extreme negative WeMOi value. Specifically, the most negative WeMOi values are detected in autumn, from 11 to 20 October, coinciding with the highest frequency of extreme torrential events. On comparing the subperiods, we observed a statistically significant decrease in WeMOi values in all months, particularly in late October and in November and December. No changes in the frequency of these extreme torrential episodes were observed between both subperiods. In contrast, a displacement of the extreme torrential episodes is detected from early to late autumn; this can be related to a statistically significant warming of sea temperature.

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“…https://doi.org/10.5194/nhess-2019-374 Preprint. Caloiero et al, 2011;El Kenawy et al, 2012;Coll et al, 2014;Ríos-Cornejo et al, 2015b;Lana et al, 2017;Jghab et al, 2019).…”

Section: Selection Of Torrential Eventsmentioning

confidence: 99%

Intra-annual variability of the Western Mediterranean Oscillation (WeMO) and occurrence of extreme torrential rainfall in Catalonia (NE Iberia)

López-Bustins¹,

Martín‐Vide²,

Arbiol-Roca³

et al. 2019

Preprint

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Abstract. In previous studies the Western Mediterranean Oscillation index (WeMOi) at daily resolution has proven to constitute an effective tool for analysing the occurrence of episodes of torrential rainfall over eastern Spain. The Western Mediterranean region is therefore a very sensitive area, since climate change can enhance these weather extremes. In the present study we selected the extreme torrential episodes (≥ 200 mm in 24 hours) that took place in Catalonia (NE Iberia) during the 1951–2016 study period (66 years). We computed daily WeMOi values and constructed WeMOi calendars. Our principal results reveal the occurrence of 50 episodes (0.8 cases per year), mainly concentrated in the autumn months. We inferred a threshold of WeMOi ≤ −2 to define an extreme negative WeMO phase at daily resolution. Most of the 50 episodes (60 %) in the study area occurred on days presenting an extreme negative WeMOi value. Specifically, the most negative WeMOi values are detected in autumn, during the second 10-day period of October (11th–20th), coinciding with the highest frequency of extreme torrential events. On comparing the subperiods, we observed a statistically significant decrease in WeMOi values in all months, particularly in late October, and in November and December. No changes in the frequency of these extreme torrential episodes were observed between both subperiods; in contrast, a displacement of the episodes is detected from early to late autumn.

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The influence of environmental factors and hydrodynamics on sardine (Sardina pilchardus, Walbaum 1792) abundance in the southern Alboran Sea

Cited by 15 publications

References 54 publications

Species Distribution Modelling via Feature Engineering and Machine Learning for Pelagic Fishes in the Mediterranean Sea

Species Distribution Modelling via Feature Engineering and Machine Learning for Pelagic Fishes in the Mediterranean Sea

Intra-annual variability of the Western Mediterranean Oscillation (WeMO) and occurrence of extreme torrential precipitation in Catalonia (NE Iberia)

Intra-annual variability of the Western Mediterranean Oscillation (WeMO) and occurrence of extreme torrential rainfall in Catalonia (NE Iberia)

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