@article{fdi:010089926,
  title = {{D}emonstrating the relevance of spatial-functional statistical analysis in marine ecological studies : the case of environmental variations in micronektonic layers},
  author = {{K}ande, {Y}. and {D}iogoul, {N}. and {B}rehmer, {P}atrice and {D}abo-{N}iang, {S}. and {N}gom, {P}. and {P}errot, {Y}annick},
  editor = {},
  language = {{ENG}},
  abstract = {{I}n this study, we conducted an analysis of a multifrequency acoustics dataset acquired from scientific echosounders in the {W}est {A}frican water. {O}ur objective was to explore the spatial arrangement of marine organism aggregations. {W}e investigated various attributes of these intricate biological entities, such as thickness, relative density, and depth, in relation to their surroundings. {T}hese environmental conditions were represented at a fine scale using a towed multiparameter system. {T}his study is closely intertwined with two key domains: {F}isheries acoustics techniques and functional data analysis. {F}isheries acoustics techniques facilitate the collection of high-resolution spatial and temporal data concerning marine organisms at various depths and spatial scales, all without causing any disturbance. {O}n the other hand, spatial-functional data analysis is a statistical approach for examining data characterised by functional attributes distributed across a spatial domain. {T}his analysis encompasses dimension reduction techniques, as well as supervised and unsupervised methods, which take into consideration spatial dependencies within extensive datasets. {W}e began by applying multivariate statistical techniques and subsequently employed {F}unctional {D}ata {A}nalysis ({FDA}). {I}n the modeling section, we introduced the spatial dimension with the spatial coordinates as covariates in the {G}eneral {A}dditive {M}odel ({GAM}) and {F}unctional {G}eneralized {S}pectral {A}dditive {M}odel ({FGSAM}) models, aiming to underscore its relevance in those contexts. {I}n an exploratory phase, {M}ultivariate {F}unctional {P}rincipal {C}omponent {A}nalysis provided detailed insights into the variations of parameters at different depths, a capability not offered by traditional {P}rincipal {C}omponent {A}nalysis. {W}hen it came to regression tasks, we explored the interactions between descriptors of {S}ound {S}cattering {L}ayers and key environmental variables, both with and without considering spatial dimensions. {O}ur findings revealed significant distinctions between northern and southern {S}ound {S}cattering {L}ayers, as well as between coastal and high-sea regions. {T}he use of the spatial locations enhanced the performance of {GAM} and {FGSAM}, particularly in the case of salinity, reflecting the influence of water mixing and seawater temperature. {T}he multifaceted effects of environmental variations on {S}ound {S}cattering {L}ayers underscore the importance of spatial-functional statistical analysis in ecological studies involving complex, spatially functional objects. {B}eyond the scope of this specific case study, the application of functional data analysis shows promise for a wide array of ecological studies dealing with extensive spatial datasets.},
  keywords = {{SENEGAL} ; {ATLANTIQUE}},
  booktitle = {},
  journal = {{E}cological {I}nformatics},
  volume = {81},
  numero = {},
  pages = {102547 [18 ]},
  ISSN = {1574-9541},
  year = {2024},
  DOI = {10.1016/j.ecoinf.2024.102547},
  URL = {https://www.documentation.ird.fr/hor/fdi:010089926},
}