FN Archimer Export Format
PT J
TI Using self organizing maps to analyze larval fish assemblage vertical dynamics through environmental-ontogenetic gradients
BT 
AF Álvarez, I.
   FONT MUNOZ, Joan Salvador
   Hernández-Carrasco, I.
   Díaz-Gil, C.
   Salgado-Hernanz, P.M.
   Catalán, I.A.
AS 1:1;2:1,3;3:1;4:1,2;5:1;6:1;
FF 1:;2:;3:;4:;5:;6:;
C1 IMEDEA (UIB-CSIC). Miquel Marqués 21, 07190, Esporles, Spain
   Laboratori d' Investigacions Marines i Aqüicultura, LIMIA (Balearic Government), Port d'Andratx, Illes Balears, Spain
   IFREMER, French Institute for Sea Research, DYNECO PELAGOS, 29280, Plouzané, France
C2 IMEDEA, SPAIN
   LIMIA, SPAIN
   IFREMER, FRANCE
SI BREST
SE PDG-ODE-DYNECO
IN WOS Ifremer UPR
   copubli-europe
IF 3.229
TC 0
UR https://archimer.ifremer.fr/doc/00695/80754/84043.pdf
LA English
DT Article
DE ;Larval fish assemblages;Self-organizing maps;Neural networks;Seasonal thermocline;Vertical distribution
AB 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. 
PY 2021
PD SEP
SO Estuarine Coastal And Shelf Science
SN 0272-7714
PU Elsevier BV
VL 258
UT 000677911800001
DI 10.1016/j.ecss.2021.107410
ID 80754
ER

EF