AIMC Topic: Ecosystem

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Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape.

Nature communications
New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which ar...

Deep-Learning-Based Automated Tracking and Counting of Living Plankton in Natural Aquatic Environments.

Environmental science & technology
Plankton are widely distributed in the aquatic environment and serve as an indicator of water quality. Monitoring the spatiotemporal variation in plankton is an efficient approach to forewarning environmental risks. However, conventional microscopy c...

Prediction of microplastic abundance in surface water of the ocean and influencing factors based on ensemble learning.

Environmental pollution (Barking, Essex : 1987)
Microplastics are regarded as emergent contaminants posing a serious threat to the marine ecosystem. It is time-consuming and labor-intensive to determine the number of microplastics in different seas using traditional sampling and detection methods....

Jumping over fences: why field- and laboratory-based biomechanical studies can and should learn from each other.

The Journal of experimental biology
Locomotor biomechanics faces a core trade-off between laboratory-based and field-based studies. Laboratory conditions offer control over confounding factors, repeatability, and reduced technological challenges, but limit the diversity of animals and ...

Deep learning approach for prediction and classification of potable water.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry
Potable water, commonly known as drinking water, refers to water that is safe to drink and does not endanger human health. It must adhere to strict quality standards set by health organizations, be devoid of dangerous pollutants and chemicals, and me...

Will biomimetic robots be able to change a hivemind to guide honeybees' ecosystem services?

Bioinspiration & biomimetics
We study whether or not a group of biomimetic waggle dancing robots is able to significantly influence the swarm-intelligent decision making of a honeybee colony, e.g. to avoid foraging at dangerous food patches using a mathematical model. Our model ...

A robotic honeycomb for interaction with a honeybee colony.

Science robotics
Robotic technologies have shown the capability to interact with living organisms and even to form integrated mixed societies composed of living and artificial agents. Biocompatible robots, incorporating sensing and actuation capable of generating and...

Tritium: Its relevance, sources and impacts on non-human biota.

The Science of the total environment
Tritium (H) is a radioactive isotope of hydrogen that is abundantly released from nuclear industries. It is extremely mobile in the environment and in all biological systems, representing an increasing concern for the health of both humans and non-hu...

Social copying drives a tipping point for nonlinear population collapse.

Proceedings of the National Academy of Sciences of the United States of America
Sudden changes in populations are ubiquitous in ecological systems, especially under perturbations. The agents of global change may increase the frequency and severity of anthropogenic perturbations, but complex populations' responses hamper our unde...

Modeling Production-Living-Ecological Space for Chengdu, China: An Analytical Framework Based on Machine Learning with Automatic Parameterization of Environmental Elements.

International journal of environmental research and public health
Cities worldwide are facing the dual pressures of growing population and land expansion, leading to the intensification of conflicts in urban productive-living-ecological spaces (PLES). Therefore, the question of "how to dynamically judge the differe...