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Ecosystem

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Uncovering water conservation patterns in semi-arid regions through hydrological simulation and deep learning.

PloS one
Under the increasing pressure of global climate change, water conservation (WC) in semi-arid regions is experiencing unprecedented levels of stress. WC involves complex, nonlinear interactions among ecosystem components like vegetation, soil structur...

Ecological risks of PFAS in China's surface water: A machine learning approach.

Environment international
The persistence of per- and polyfluoroalkyl substances (PFAS) in surface water can pose risks to ecosystems, while due to data limitations, the occurrence, risks, and future trends of PFAS at large scales remain unknown. This study investigated the e...

Machine learning unveils large-scale impact of Posidonia oceanica on Mediterranean Sea water.

The Science of the total environment
Posidonia oceanica is a protected endemic seagrass of the Mediterranean Sea that fosters biodiversity, stores carbon, releases oxygen, and provides habitat to numerous sea organisms. Leveraging augmented research, we collected a comprehensive dataset...

Remote sensing estimation of aboveground biomass of different forest types in Xinjiang based on machine learning.

Scientific reports
Forest aboveground biomass (AGB) is a key indicator reflecting the function and quality of forest ecosystems, and accurate large-scale estimations of forest AGB are essential for effective forest ecosystem management. However, owing to limitations in...

Change analysis of surface water clarity in the Persian Gulf and the Oman Sea by remote sensing data and an interpretable deep learning model.

Environmental science and pollution research international
The health of an ecosystem and the quality of water can be determined by the clarity of the water. The Persian Gulf and Oman Sea have a unique ecosystem, and monitoring their water clarity is necessary for sustainable development. Here, various crite...

Exploring the response of bacterial community functions to microplastic features in lake ecosystems through interpretable machine learning.

Environmental research
Microplastics (MPs) are ubiquitous and have various characteristics. However, their impacts on bacterial community functions in lakes remain elusive. In this study, we identified 33 different MPs features including their abundance, shape, color, size...

aurora: a machine learning gwas tool for analyzing microbial habitat adaptation.

Genome biology
A primary goal of microbial genome-wide association studies is identifying genomic variants associated with a particular habitat. Existing tools fail to identify known causal variants if the analyzed trait shaped the phylogeny. Furthermore, due to in...

Machine learning-based habitat mapping of the invasive Prosopis juliflora in Sharjah, UAE.

Environmental monitoring and assessment
Prosopis juliflora, one of the most invasive trees, adversely affects the ecosystem and native plant communities in arid lands. This disrupts biodiversity and depletes water resources, posing significant ecological and economic challenges. Several at...

Forecasting deforestation and carbon loss across New Guinea using machine learning and cellular automata.

The Science of the total environment
The island of New Guinea harbors some of the world's most biologically diverse and highly endemic tropical ecosystems. Nevertheless, progressing land-use change in the region threatens their integrity, which will adversely affect their biodiversity a...

U-shaped deep learning networks for algal bloom detection using Sentinel-2 imagery: Exploring model performance and transferability.

Journal of environmental management
Inland water sources, such as lakes, support diverse ecosystems and provide essential services to human societies. However, these valuable resources are under increasing pressure from rapid climate changes and pollution resulting from human activitie...