Dataset Information

Diversity patterns of free-living nematode assemblages in seagrass beds from the Cuban archipelago (Caribbean Sea).

ABSTRACT: Diversity patterns of free-living marine nematodes in tropical seagrass beds are understudied. Here, we describe the species richness and assemblage composition of nematodes in 13 seagrass sites covering the whole Cuban archipelago. Nematodes were collected from Thalassia testudinum seagrass beds and identified to species level. We provide a checklist of nematode species from seagrass beds. The species richness of nematode assemblages is high, with 215 species, 138 genus, 35 families, seven orders and two classes. That γ-diversity is higher than other studies and points to seagrass beds as diversity hotspots of free-living marine nematodes. Local species richness in seagrass bed sites is about 57 ± 17 species and broadly similar across the sites, despite the environmental heterogeneity. The geographical distance plays a weak, but significant, role on the decay of similarity likely affected by limited dispersal of nematodes. The pairwise similarity values, related to poor-coloniser nematodes, were twice more affected by the distance than those related to good-colonisers, possibly due to differential success of transport and settlement.

SUBMITTER: Armenteros M

PROVIDER: S-EPMC7773714 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Diversity patterns of free-living nematode assemblages in seagrass beds from the Cuban archipelago (Caribbean Sea).

Armenteros Maickel M Rodríguez-García Patricia P Pérez-García José Andrés JA Gracia Adolfo A

Biodiversity data journal 20201223

Diversity patterns of free-living marine nematodes in tropical seagrass beds are understudied. Here, we describe the species richness and assemblage composition of nematodes in 13 seagrass sites covering the whole Cuban archipelago. Nematodes were collected from <i>Thalassia testudinum</i> seagrass beds and identified to species level. We provide a checklist of nematode species from seagrass beds. The species richness of nematode assemblages is high, with 215 species, 138 genus, 35 families, sev ...[more]

PMID: 33390760

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Project description:Background and aimsWorldwide, invasive species are spreading through marine systems at an unprecedented rate with both positive and negative consequences for ecosystems and the biological functioning of organisms. Human activities from shipping to habitat damage and modification are known vectors of spread, although biological interactions including epibiosis are increasingly recognized as potentially important to introduction into susceptible habitats.MethodsWe assessed a novel mechanism of spread - limpets as transporters of an invasive alga, Sargassum muticum, into beds of the seagrass Zostera marina - and the physiological impact of its invasion. The association of S. muticum with three limpet species and other habitats was assessed using intertidal surveys on rocky shores and snorkelling at two seagrass sites in the UK. A 4-year field study tested the effect of S. muticum on Z. marina shoot density, dry weight and phenolic compounds (caffeic and tannic acid) content, and a laboratory experiment tested the impact of S. muticum on nutrient partitioning (C/H/N/P/Si), photosynthetic efficiency (Fv/Fm) and growth of Z. marina.ResultsOn rocky shores 15 % of S. muticum occurrences were attached to the shells of live limpets. In seagrass beds 5 % of S. muticum occurrences were attached to the shells of dead limpets. The remainder were attached to rock, to cobblestones, to the seagrass matrix or embedded within the sand. Z. marina density and phenolics content was lower when S. muticum co-occurred with it. Over 3 years, photosynthetic responses of Z. marina to S. muticum were idiosyncratic, and S. muticum had no effect on nutrient partitioning in Z. marina.ConclusionsOur results show limpets support S. muticum as an epibiont and may act as a previously unreported transport mechanism introducing invaders into sensitive habitats. S. muticum reduced production of phenolics in Z. marina, which may weaken its defensive capabilities and facilitate proliferation of S. muticum. The effect of S. muticum on Z. marina photosynthesis requires further work but having no effect on the capacity of Z. marina to sequester nutrients suggests a degree of resilience to this invader.

Project description:BackgroundIdentifying species, particularly small metazoans, remains a daunting challenge and the phylum Nematoda is no exception. Typically, nematode species are differentiated based on morphometry and the presence or absence of certain characters. However, recent advances in artificial intelligence, particularly machine learning (ML) algorithms, offer promising solutions for automating species identification, mostly in taxonomically complex groups. By training ML models with extensive datasets of accurately identified specimens, the models can learn to recognize patterns in nematodes' morphological and morphometric features. This enables them to make precise identifications of newly encountered individuals. Implementing ML algorithms can improve the speed and accuracy of species identification and allow researchers to efficiently process vast amounts of data. Furthermore, it empowers non-taxonomists to make reliable identifications. The objective of this study is to evaluate the performance of ML algorithms in identifying species of free-living marine nematodes, focusing on two well-known genera: Acantholaimus Allgén, 1933 and Sabatieria Rouville, 1903.MethodsA total of 40 species of Acantholaimus and 60 species of Sabatieria were considered. The measurements and identifications were obtained from the original publications of species for both genera, this compilation included information regarding the presence or absence of specific characters, as well as morphometric data. To assess the performance of the species identification four ML algorithms were employed: Random Forest (RF), Stochastic Gradient Boosting (SGBoost), Support Vector Machine (SVM) with both linear and radial kernels, and K-nearest neighbor (KNN) algorithms.ResultsFor both genera, the random forest (RF) algorithm demonstrated the highest accuracy in correctly classifying specimens into their respective species, achieving an accuracy rate of 93% for Acantholaimus and 100% for Sabatieria, only a single individual from Acantholaimus of the test data was misclassified.ConclusionThese results highlight the overall effectiveness of ML algorithms in species identification. Moreover, it demonstrates that the identification of marine nematodes can be automated, optimizing biodiversity and ecological studies, as well as turning species identification more accessible, efficient, and scalable. Ultimately it will contribute to our understanding and conservation of biodiversity.

Dataset Information

Diversity patterns of free-living nematode assemblages in seagrass beds from the Cuban archipelago (Caribbean Sea).

Publications

Diversity patterns of free-living nematode assemblages in seagrass beds from the Cuban archipelago (Caribbean Sea).

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