Using self organizing maps to analyze larval fish assemblage vertical dynamics through environmental-ontogenetic gradients
|Author(s)||Álvarez I.1, Font Munoz Joan Salvador1, 3, Hernández-Carrasco I.1, Díaz-Gil C.1, 2, Salgado-Hernanz P.M.1, Catalán I.A.1|
|Affiliation(s)||1 : IMEDEA (UIB-CSIC). Miquel Marqués 21, 07190, Esporles, Spain
2 : Laboratori d' Investigacions Marines i Aqüicultura, LIMIA (Balearic Government), Port d'Andratx, Illes Balears, Spain
3 : IFREMER, French Institute for Sea Research, DYNECO PELAGOS, 29280, Plouzané, France
|Source||Estuarine Coastal And Shelf Science (0272-7714) (Elsevier BV), 2021-09 , Vol. 258 , P. 107410 (12p.)|
|Keyword(s)||Larval fish assemblages, Self-organizing maps, Neural networks, Seasonal thermocline, Vertical distribution|
We analyzed the influence of the stratification process in the vertical distribution of larval fish in a microtidal coastal Mediterranean zone. By applying a Self Organizing Maps (SOM) technique, we could analyze a complex dataset accounting for non-linear processes. The analysis integrated multivariate data on larval fish and environmental parameters in two depth strata through two-time components (nictimeral and fortnightly through the main spawning seasons), and considered size-based information. Although causal relationships cannot be constructed, the use of SOM analyses enabled the description of the whole system evolution overcoming the constraints of linear approaches in complex multivariate datasets with multiple dependencies in the data. We contend that this approach can help to unveil the intricate patterns of settlement/recruitment of young fish, which is often hampered by the rigidity of some formal statistical approaches.