AIMC Topic: Biodiversity

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A culture-independent approach, supervised machine learning, and the characterization of the microbial community composition of coastal areas across the Bay of Bengal and the Arabian Sea.

BMC microbiology May 10, 2024
BACKGROUND: Coastal areas are subject to various anthropogenic and natural influences. In this study, we investigated and compared the characteristics of two coastal regions, Andhra Pradesh (AP) and Goa (GA), focusing on pollution, anthropogenic acti...
Phylogeny Bays Seawater Salinity Sequence Analysis, DNA Supervised Machine Learning Microbiota Biodiversity India Bacteria DNA, Bacterial RNA, Ribosomal, 16S
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Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences May 6, 2024
Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and fastest developing sensor technologies, we describe a framework for automated, image-based monitoring of nocturnal ...
Artificial Intelligence Biodiversity Insecta Animals Image Processing, Computer-Assisted
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Automatedly identify dryland threatened species at large scale by using deep learning.

The Science of the total environment Jan 26, 2024
Dryland biodiversity is decreasing at an alarming rate. Advanced intelligent tools are urgently needed to rapidly, automatedly, and precisely detect dryland threatened species on a large scale for biological conservation. Here, we explored the perfor...
Biodiversity Endangered Species Neural Networks, Computer Animals Deep Learning China
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A gentle introduction to computer vision-based specimen classification in ecological datasets.

The Journal of animal ecology Jan 17, 2024
Classifying specimens is a critical component of ecological research, biodiversity monitoring and conservation. However, manual classification can be prohibitively time-consuming and expensive, limiting how much data a project can afford to process. ...
Neural Networks, Computer Machine Learning Biodiversity Computers Animals
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Deep learning in terrestrial conservation biology.

Biologia futura Jan 16, 2024
Biodiversity is being lost at an unprecedented rate on Earth. As a first step to more effectively combat this process we need efficient methods to monitor biodiversity changes. Recent technological advance can provide powerful tools (e.g. camera trap...
Biodiversity Artificial Intelligence Humans Biology Deep Learning Neural Networks, Computer
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Microscopic image recognition of diatoms based on deep learning.

Journal of phycology Nov 23, 2023
Diatoms are a crucial component in the study of aquatic ecosystems and ancient environmental records. However, traditional methods for identifying diatoms, such as morphological taxonomy and molecular detection, are costly, are time consuming, and ha...
Ecosystem Deep Learning Biodiversity Diatoms
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Using multilayer perceptron and similarity-weighted machine learning algorithms to reconstruct the past: A case study of the agricultural expansion on grasslands in the Uruguayan savannas.

Integrated environmental assessment and management Nov 17, 2023
Changes in land use and land cover (LULC) have significant implications for biodiversity, ecosystem functioning, and deforestation. Modeling LULC changes is crucial to understanding anthropogenic impacts on environmental conservation and ecosystem se...
Uruguay Grassland Biodiversity Conservation of Natural Resources Machine Learning Algorithms Agriculture Environmental Monitoring Neural Networks, Computer
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Deep learning with citizen science data enables estimation of species diversity and composition at continental extents.

Ecology Oct 23, 2023
Effective solutions to conserve biodiversity require accurate community- and species-level information at relevant, actionable scales and across entire species' distributions. However, data and methodological constraints have limited our ability to p...
Citizen Science Deep Learning Biodiversity
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Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests.

Nature communications Oct 17, 2023
Tropical forest recovery is fundamental to addressing the intertwined climate and biodiversity loss crises. While regenerating trees sequester carbon relatively quickly, the pace of biodiversity recovery remains contentious. Here, we use bioacoustics...
Tropical Climate Ecosystem Deep Learning Trees Biodiversity Animals Conservation of Natural Resources Forests
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Unsupervised machine learning for species delimitation, integrative taxonomy, and biodiversity conservation.

Molecular phylogenetics and evolution Oct 5, 2023
Integrative taxonomy, combining data from multiple axes of biologically relevant variation, is a major goal of systematics. Ideally, such taxonomies will derive from similarly integrative species-delimitation analyses. Yet, most current methods rely ...
Unsupervised Machine Learning Biodiversity Species Specificity Phenotype Humans Phylogeny
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