A functional perspective of nematode assemblages as proxy of quality in tropical estuarine tidal flats

Justino, Juciélia Tenório; Demetrio, Guilherme Ramos; Neres, Patrícia Fernandes; Meneses, Diógenes; Pinto, Taciana Kramer

doi:10.1016/j.marenvres.2023.105922

Cited by 1 publication

(1 citation statement)

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“…Since the simplicity of the method allows us to avoid the time-consuming of activities without renouncing the reliability of results in marine ecosystems (see Semprucci et al ., 2022 ; Justino et al ., 2023 ), we tested the same approach to soil habitats. We considered four morphological characters e.g.…”

Section: Introductionmentioning

confidence: 99%

Simple, inexpensive, and rapid approach to detect changes in the structure of soil free-living nematodes

Semprucci,

Catani,

Grassi

et al. 2024

Helminthologia

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Summary A general limitation of ecological investigations based on nematodes is related to the difficult and time-consuming taxonomic identification of species. Therefore, nematologists are investing many efforts to develop alternative approaches as proxies applicable in biomonitoring assessment. Recently, an alternative method that combines morpho-functional traits was proposed for detecting assemblage changes of marine nematodes. In view of the promising results, it was tested the same approach to document taxonomic structure changes of soil free-living and plant parasitic nematodes. Specifically, this attempt was carried out using three data sets that include studies from various European regions and different types of ecosystems: forests, grasslands and maize crops. Multivariate statistical analysis revealed that the simple combination of the four traits (i.e., buccal cavity cuticularization occurrence, amphideal fovea size and shape, morphology of the cuticle and pharynx) in a single code number perfectly mirrors the taxonomic structure trends of the nematode assemblage at genus level. Therefore, we predict that similar results can be also obtained by directly encoding nematode specimens with the selected traits and we point to new important advances if this procedure can be coupled with advanced machine learning.

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Section: Introductionmentioning

confidence: 99%