AIMC Topic: Ecosystem

Clear Filters Showing 51 to 60 of 489 articles

Interpretable deep learning method to quantify the impact of extreme temperatures on vegetation productivity in China.

Scientific reports
As a key ecological parameter, NPP measures the photosynthetic efficiency of plants in capturing atmospheric carbon. With the warming of the climate, extreme temperature events are frequent, which has exerted a profound influence on NPP. Previous stu...

Identifying fish populations prone to abrupt shifts via dynamical footprint analysis.

Proceedings of the National Academy of Sciences of the United States of America
Fish population biomass fluctuates through time in ways that may be either gradual or abrupt. While abrupt shifts in fish population productivity have been shown to be common, they are rarely integrated into stock assessment or fishery management, in...

Impact of deep learning and post-processing algorithms performances on biodiversity metrics assessed on videos.

PloS one
Assessing the escalating biodiversity crisis, driven by climate change, habitat destruction, and exploitation, necessitates efficient monitoring strategies to assess species presence and abundance across diverse habitats. Video-based surveys using re...

Species richness is an important mediator of multifunctionality changes in Hobq desert shrub ecosystem.

Scientific reports
Studying the biodiversity and multifunctionality relationships of the Hobq Desert shrub ecosystem and its response to environmental factors is crucial for ecological restoration in the region. In this study, we examined variations in biodiversity and...

Wetland dynamics in the Indus River Delta: A Sentinel-2 and machine learning approach.

Journal of environmental management
Coastal wetlands of the Indus River Delta are vital ecological regions that have undergone significant transformations driven by anthropogenic activities and environmental stressors. This study assesses the dynamics of wetlands and reclamation in the...

Advancing fishery dependent and independent habitat assessments using automated image analysis: A fisheries management agency case study.

PloS one
Advances in artificial intelligence and machine learning have revolutionised data analysis, including in the field of marine and fisheries sciences. However, many fisheries agencies manage sensitive or proprietary data that cannot be shared externall...

A computational framework for inferring species dynamics and interactions with applications in microbiota ecology.

NPJ systems biology and applications
We present MBPert, a generic computational framework for inferring species interactions and predicting dynamics in time-evolving ecosystems from perturbation and time-series data. In this work, we contextualize the framework in microbial ecosystem mo...

Mapping hotspots and identifying drivers of lead bioaccumulation in Oryza sativa L. in tropical agroecosystems.

Journal of hazardous materials
Tropical rice systems exhibit high annual rates of heavy metal accumulation, requiring accurate identification of accumulation drivers in rice-growing ecosystems to ensure regional food security. Therefore, we collected 229 paired soil and rice sampl...

Creating interpretable deep learning models to identify species using environmental DNA sequences.

Scientific reports
Monitoring species' presence in an ecosystem is crucial for conservation and understanding habitat diversity, but can be expensive and time consuming. As a result, ecologists have begun using the DNA that animals naturally leave behind in water or so...

Neural network-based identification for scallops (Pecten maximus) in natural marine habitats.

PloS one
The Great Atlantic scallop, or King scallop (Pecten maximus), ranks third in value after mackerel and Nephrops in UK fisheries. Its landings have surged over recent decades, making it the UK's fastest-growing fishery. Scallop stock assessments, cruci...