Guidelines for species descriptions of free-living aquatic nematodes: characters, measurements and their presentation in taxonomic publications DOI Open Access
VADIM MOKIEVSKY, T.N. Bezerra, Wilfrida Decraemer

et al.

Zootaxa, Journal Year: 2024, Volume and Issue: 5543(2), P. 225 - 236

Published: Dec. 2, 2024

Free-living aquatic nematodes are abundant, diverse and of general environmental importance. However, knowledge species distributions both marine freshwater is sparse. Species distribution data crucial for evaluating impacts from human activities to conduct integrated nematode community assessments. Basic on taxonomy descriptions lacking many regions due decreasing taxonomic expertise, yet it essential biodiversity research building molecular sequence libraries the application methods such as DNA. In order encourage facilitate descriptive work this understudied group, we present here a framework description. We begin by providing brief overview nematology history, then provide suggestions microscopic that should be used list characters morphometric descriptions. Finally, briefly discuss common sequencing approaches commonly in literature.

Language: Английский

iMESc – an interactive machine learning app for environmental sciences DOI Creative Commons
Danilo Cândido Vieira, Fabiana S. Paula, Luciana Erika Yaginuma

et al.

Frontiers in Environmental Science, Journal Year: 2025, Volume and Issue: 13

Published: Jan. 31, 2025

As environmental sciences increasingly rely on complex datasets, machine learning (ML) has become crucial for identifying patterns and relationships. However, the integration of ML into workflows can pose challenges due to technical barriers or time-intensive nature coding. To address these issues, we developed iMESc , an interactive app designed streamline simplify data. Developed in R built Shiny platform, enables supervised unsupervised methods, along with tools data preprocessing, visualization, descriptive statistics, spatial analysis. The Datalist system ensures seamless transitions between analytical workflows, while “savepoints” feature enhances reproducibility by preserving analysis state. We demonstrate iMESc’s flexibility four applied a case study predicting nematode community structure based classical statistical approaches, Redundancy Analysis (RDA) Piecewise RDA (pwRDA), explained 30.7% 53%, respectively. SuperSOM model achieved 2 0.60 training 0.291 testing, across depth zones. Finally, hybrid combining SOM followed Random Forest returned accuracy 83.47% 80.77% test, Bathymetry, Chlorophyll, Coarse Sand as key predictive variables. IMESc permits customization plots saving guarantying reproducibility. bridges gap complexity algorithms need user-friendly interfaces research. By reducing burden coding, allows researchers focus scientific inquiry, improving both efficiency their analyses.

Language: Английский

Citations

1
Meiofauna investigation and taxonomic identification through imaging—a game of compromise DOI
Valentin Foulon, Abdesslam Benzinou, Kamal Nasreddine

et al.

Limnology and Oceanography Methods, Journal Year: 2025, Volume and Issue: unknown

Published: April 29, 2025

Abstract Imaging methods have developed rapidly in recent decades, opening new opportunities for taxonomy and biodiversity studies of marine organisms. In particular, the microscopic size range, which used to be challenging study due time‐consuming preparation observation steps, now benefits from high‐throughput quantitative imaging development fast high‐resolution microscopy approaches. Meiofauna, interstitial sediment animals ranging 20 μ m 1 mm, are important components ecosystems. These organisms can serve as bioindicators, group a whole holds immense potential discovery species. However, protocols studying meiobenthos highly time‐consuming, helps explain why this is understudied. We tested five techniques, low high resolution, that could accelerate hard‐bodied meiofauna studies, both ecology species description, address gap our understanding part life. Thus, two flow modalities (in line holographic classic optic microscopy), semi‐automated acquisition procedure, three‐dimensional (3D) fluorescence were used. examined compromises imaging, including throughput, data volume, evaluate using such techniques meiofaunal studies. For ecological surveys, benefit but resolution remains limiting factor. taxonomic 3D fluorescent added relevant information, considering time required acquisition. The motorized procedure purposes according versatility system.

Language: Английский

Citations

0
Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review DOI Creative Commons
Tymoteusz Miller, Grzegorz Michoński, Irmina Durlik

et al.

Biology, Journal Year: 2025, Volume and Issue: 14(5), P. 520 - 520

Published: May 8, 2025

Freshwater ecosystems are increasingly threatened by climate change and anthropogenic activities, necessitating innovative scalable monitoring solutions. Artificial intelligence (AI) has emerged as a transformative tool in aquatic biodiversity research, enabling automated species identification, predictive habitat modeling, conservation planning. This systematic review follows the PRISMA framework to analyze AI applications freshwater studies. Using structured literature search across Scopus, Web of Science, Google Scholar, we identified 312 relevant studies published between 2010 2024. categorizes into assessment, ecological risk evaluation, strategies. A bias assessment was conducted using QUADAS-2 RoB 2 frameworks, highlighting methodological challenges, such measurement inconsistencies model validation. The citation trends demonstrate exponential growth AI-driven with leading contributions from China, United States, India. Despite growing use this field, also reveals several persistent including limited data availability, regional imbalances, concerns related generalizability transparency. Our findings underscore AI’s potential revolutionizing but emphasize need for standardized methodologies, improved integration, interdisciplinary collaboration enhance insights efforts.

Language: Английский

Citations

0
Meiofauna as sentinels of beach ecosystems: A quantitative review of gaps and opportunities in beach meiofauna research. DOI Creative Commons
Alejandro Martínez,

Sören Kohler,

Marta García‐Cobo

et al.

Estuarine Coastal and Shelf Science, Journal Year: 2024, Volume and Issue: 313, P. 109092 - 109092

Published: Dec. 17, 2024

Language: Английский

Citations

2
DECIPHERING THE DEEP: MACHINE LEARNING APPROACHES TO UNDERSTANDING OCEANIC ECOSYSTEMS DOI
Tymoteusz Miller, Adrianna Łobodzińska,

Oliwia Kaczanowska

et al.

ГРААЛЬ НАУКИ, Journal Year: 2024, Volume and Issue: 36, P. 526 - 534

Published: Feb. 26, 2024

This paper presents a detailed exploration of the transformative role Machine Learning (ML) in oceanographic research, encapsulating paradigm shift towards more efficient and comprehensive analysis marine ecosystems. It delves into multifaceted applications ML, ranging from predictive modeling ocean currents to in-depth biodiversity deciphering complexities deep-sea ecosystems through advanced computer vision techniques. The discussion extends challenges opportunities that intertwine with integration AI ML oceanography, emphasizing need for robust data collection, interdisciplinary collaboration, ethical considerations. Through series case studies thematic discussions, this underscores profound potential revolutionize our understanding preservation oceanic ecosystems, setting new frontier future research conservation strategies realm oceanography.

Language: Английский

Citations

1
Multidecadal changes in coastal benthic species composition and ecosystem functioning occur independently of temperature‐driven community shifts DOI Creative Commons
Phoebe Armitage, Michael T. Burrows, James E. V. Rimmer

et al.

Global Change Biology, Journal Year: 2024, Volume and Issue: 30(8)

Published: Aug. 1, 2024

Rising global temperatures are often identified as the key driver impacting ecosystems and services they provide by affecting biodiversity structure function. A disproportionate amount of our understanding function is from short-term experimental studies static values indices, lacking ability to monitor long-term trends capture community dynamics. Here, we analyse a biennial dataset spanning 32 years macroinvertebrate benthic communities their functional response increasing temperatures. We monitored changes in species' thermal affinities examine warming-related shifts selecting mid-point temperature distribution range linking them traits. employed novel weighted metric using Biological Trait Analysis (BTA) gain better insights into ecological potential each species incorporating abundance body size subset traits that represent five ecosystem functions: bioturbation activity, sediment stability, nutrient recycling higher lower trophic production. Using indices (richness, Simpson's diversity vulnerability) Rao's Q redundancy), showed no significant change over time with narrow variation. However, show composition warming increases individuals, which altered functioning positively and/or non-linearly. Yet, when taxonomic groupings than were excluded analysis, there was only weak increase measured community-weighted average affinities, suggesting functions occur independently increase-related composition. Other environmental factors driving may be more important these subtidal macrobenthic communities. This challenges prevailing emphasis on primary climate emphasises necessity for comprehensive temporal dynamics complex systems.

Language: Английский

Citations

1
Emergent properties of free-living nematode assemblages exposed to multiple stresses DOI

Nilvea Ramalho Oliveira,

Giam Luca Altafim, Aline Vecchio Alves

et al.

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 912, P. 168790 - 168790

Published: Nov. 22, 2023

Language: Английский

Citations

3
Artificial Intelligence: Current and Future Role in Veterinary and Public Medicine DOI Open Access
Mohamed Amer, Aziza M. Amer,

khaked Mohamed El-bayoumi

et al.

Egyptian Journal of Veterinary Science, Journal Year: 2024, Volume and Issue: 0(0), P. 1 - 12

Published: Aug. 26, 2024

Artificial intelligence (AI) is revolutionizing many industries and medicine. This paper provides an overview of AI's current future role in Currently, AI used ways to improve healthcare including reducing costs, improving patient outcomes, increasing efficiency, early disease detection, diagnostics, medical imaging, drug discovery development, outbreak prediction modeling, surveillance monitoring, response, contact tracing applications such as proximity information, GPS data, vaccine distribution predictive analytics. These can potentially diagnosis accuracy, identify patients at risk for certain diseases, personalize treatment plans. For example, algorithms analyze images subtle abnormalities that human radiologists may miss. In the future, expected play a more important It has potential help physicians make informed decisions by analyzing large amounts data providing personalized recommendations. Additionally, AI-powered virtual assistants could manage chronic conditions, diabetes hypertension, real-time feedback guidance. However, there are also challenges widespread adoption One major concern perpetuate biases healthcare, diagnosis, histopathology, microbiota. security privacy data. Despite these challenges, benefits medicine highly significant. Electrode implantation microchips be option conditions. As technology continues advance, we will see leading better outcomes efficient delivery.

Language: Английский

Citations

0
Guidelines for species descriptions of free-living aquatic nematodes: characters, measurements and their presentation in taxonomic publications DOI Open Access
VADIM MOKIEVSKY, T.N. Bezerra, Wilfrida Decraemer

et al.

Zootaxa, Journal Year: 2024, Volume and Issue: 5543(2), P. 225 - 236

Published: Dec. 2, 2024

Free-living aquatic nematodes are abundant, diverse and of general environmental importance. However, knowledge species distributions both marine freshwater is sparse. Species distribution data crucial for evaluating impacts from human activities to conduct integrated nematode community assessments. Basic on taxonomy descriptions lacking many regions due decreasing taxonomic expertise, yet it essential biodiversity research building molecular sequence libraries the application methods such as DNA. In order encourage facilitate descriptive work this understudied group, we present here a framework description. We begin by providing brief overview nematology history, then provide suggestions microscopic that should be used list characters morphometric descriptions. Finally, briefly discuss common sequencing approaches commonly in literature.

Language: Английский

Citations

0