AIMC Topic: Biodiversity

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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 ...

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,...

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...

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...

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 ...

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 ...

Illuminating Entomological Dark Matter with DNA Barcodes in an Era of Insect Decline, Deep Learning, and Genomics.

Annual review of entomology
Most insects encountered in the field are initially entomological dark matter in that they cannot be identified to species while alive. This explains the enduring quest for efficient ways to identify collected specimens. Morphological tools came firs...

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...

Understanding ecosystem services of detailed forest and wetland types using remote sensing and deep learning techniques in Northern China.

Journal of environmental management
Spanning both temperate and sub-frigid zones, Northeast China boasts typical boreal forests and abundant wetland resources. Because of these attributes, the region is critically significant for global climate regulation, carbon sequestration, and bio...

Deep learning sharpens vistas on biodiversity mapping.

Proceedings of the National Academy of Sciences of the United States of America