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Biodiversity

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Global intraspecific diversity of marine forests of brown macroalgae predicted by past climate conditions.

Communications biology
Global patterns of intraspecific genetic diversity are key to understanding evolutionary and ecological processes. However, insights into the distribution and drivers of genetic diversity remain limited, particularly for marine species. Here, we expl...

The potential for AI to revolutionize conservation: a horizon scan.

Trends in ecology & evolution
Artificial Intelligence (AI) is an emerging tool that could be leveraged to identify the effective conservation solutions demanded by the urgent biodiversity crisis. We present the results of our horizon scan of AI applications likely to significantl...

Making sense of fossils and artefacts: a review of best practices for the design of a successful workflow for machine learning-assisted citizen science projects.

PeerJ
Historically, the extensive involvement of citizen scientists in palaeontology and archaeology has resulted in many discoveries and insights. More recently, machine learning has emerged as a broadly applicable tool for analysing large datasets of fos...

Machine learning assessment of dredging impacts on the phytoplankton community on the Brazilian equatorial margin: A multivariate analysis.

Environmental pollution (Barking, Essex : 1987)
Dredging in estuarine systems significantly impacts phytoplankton communities, with suspended particulate matter (SPM) and dissolved aluminum (Al) serving as indicators of disturbance intensity. This study assessed the effects of dredging in the São ...

Exploring the impact of land use on bird diversity in high-density urban areas using explainable machine learning models.

Journal of environmental management
Amid rapid urbanization, land use shifts in cities globally have profound effects on ecosystems and biodiversity. Birds, as a crucial component of urban biodiversity, are highly sensitive to environmental changes and often serve as indicator species ...

Identifying the combined impact of human activities and natural factors on China's avian species richness using interpretable machine learning methods.

Journal of environmental management
With human activities-derived escalating climate change and rapid urbanization, avian species face significant survival challenges. Understanding the impact of human activities and environmental drivers on avian species richness is critical for effec...

Capillariid diversity in archaeological material from the New and the Old World: clustering and artificial intelligence approaches.

Parasites & vectors
BACKGROUND: Capillariid nematode eggs have been reported in archaeological material in both the New and the Old World, mainly in Europe and South America. They have been found in various types of samples, as coprolites, sediments from latrines, pits,...

A comparison of statistical methods for deriving occupancy estimates from machine learning outputs.

Scientific reports
The combination of autonomous recording units (ARUs) and machine learning enables scalable biodiversity monitoring. These data are often analysed using occupancy models, yet methods for integrating machine learning outputs with these models are rarel...

An Approach for Detecting Mangrove Areas and Mapping Species Using Multispectral Drone Imagery and Deep Learning.

Sensors (Basel, Switzerland)
Mangrove ecosystems are important in tropical and subtropical coastal zones, contributing to marine biodiversity and maintaining marine ecological balance. It is crucial to develop more efficient, intelligent, and accurate monitoring methods for mang...

Spatial and temporal classification and prediction of aspen probability in boreal forests using machine learning algorithms.

Environmental monitoring and assessment
Mapping and classifying the probability of occurrence of Populus tremula L. (aspen) in boreal forests is a complex task for sustainable forest management and biodiversity conservation. As a key broadleaved species in the taiga region, aspen supports ...