AIMC Topic: Biodiversity

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A machine learning approach to map the potential agroecological complexity in an indigenous community of Colombia.

Journal of environmental management
Agroecological systems are potential solutions to the environmental challenges of intensive agriculture. Indigenous communities, such as the Kamëntšá Biyá and Kamëntšá Inga from the Sibundoy Valley (SV) in Colombia, have their own ancient agroecologi...

Deep learning models map rapid plant species changes from citizen science and remote sensing data.

Proceedings of the National Academy of Sciences of the United States of America
Anthropogenic habitat destruction and climate change are reshaping the geographic distribution of plants worldwide. However, we are still unable to map species shifts at high spatial, temporal, and taxonomic resolution. Here, we develop a deep learni...

Deepdive: Leveraging Pre-trained Deep Learning for Deep-Sea ROV Biota Identification in the Great Barrier Reef.

Scientific data
Understanding and preserving the deep sea ecosystems is paramount for marine conservation efforts. Automated object (deep-sea biota) classification can enable the creation of detailed habitat maps that not only aid in biodiversity assessments but als...

Trait-mediated speciation and human-driven extinctions in proboscideans revealed by unsupervised Bayesian neural networks.

Science advances
Species life-history traits, paleoenvironment, and biotic interactions likely influence speciation and extinction rates, affecting species richness over time. Birth-death models inferring the impact of these factors typically assume monotonic relatio...

Sound evidence for biodiversity monitoring.

Science (New York, N.Y.)
Bioacoustics and artificial intelligence facilitate ecological studies of animal populations.

Innovative infrastructure to access Brazilian fungal diversity using deep learning.

PeerJ
In the present investigation, we employ a novel and meticulously structured database assembled by experts, encompassing macrofungi field-collected in Brazil, featuring upwards of 13,894 photographs representing 505 distinct species. The purpose of ut...

Enhancing microalgae classification accuracy in marine ecosystems through convolutional neural networks and support vector machines.

Marine pollution bulletin
Accurately classifying microalgae species is vital for monitoring marine ecosystems and managing the emergence of marine mucilage, which is crucial for monitoring mucilage phenomena in marine environments. Traditional methods have been inadequate due...

Species delimitation 4.0: integrative taxonomy meets artificial intelligence.

Trends in ecology & evolution
Although species are central units for biological research, recent findings in genomics are raising awareness that what we call species can be ill-founded entities due to solely morphology-based, regional species descriptions. This particularly appli...

Machine learning-based surrogate modelling of a robust, sustainable development goal (SDG)-compliant land-use future for Australia at high spatial resolution.

Journal of environmental management
We developed a high-resolution machine learning based surrogate model to identify a robust land-use future for Australia which meets multiple UN Sustainable Development Goals. We compared machine learning models with different architectures to pick t...

Leveraging AI to improve evidence synthesis in conservation.

Trends in ecology & evolution
Systematic evidence syntheses (systematic reviews and maps) summarize knowledge and are used to support decisions and policies in a variety of applied fields, from medicine and public health to biodiversity conservation. However, conducting these exe...