AIMC Topic: Biodiversity

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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
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...

Deep learning with citizen science data enables estimation of species diversity and composition at continental extents.

Ecology
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...

Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests.

Nature communications
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...

Unsupervised machine learning for species delimitation, integrative taxonomy, and biodiversity conservation.

Molecular phylogenetics and evolution
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 ...

New deep learning-based methods for visualizing ecosystem properties using environmental DNA metabarcoding data.

Molecular ecology resources
Environmental DNA (eDNA) metabarcoding provides an efficient approach for documenting biodiversity patterns in marine and terrestrial ecosystems. The complexity of these data prevents current methods from extracting and analyzing all the relevant eco...

NEAL: an open-source tool for audio annotation.

PeerJ
Passive acoustic monitoring is used widely in ecology, biodiversity, and conservation studies. Data sets collected via acoustic monitoring are often extremely large and built to be processed automatically using artificial intelligence and machine lea...

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...

Estimating resistance surfaces using gradient forest and allelic frequencies.

Molecular ecology resources
Understanding landscape connectivity has become a global priority for mitigating the impact of landscape fragmentation on biodiversity. Connectivity methods that use link-based methods traditionally rely on relating pairwise genetic distance between ...

A Fine-Grained Recognition Neural Network with High-Order Feature Maps via Graph-Based Embedding for Natural Bird Diversity Conservation.

International journal of environmental research and public health
The conservation of avian diversity plays a critical role in maintaining ecological balance and ecosystem function, as well as having a profound impact on human survival and livelihood. With species' continuous and rapid decline, information and inte...

Drone-assisted collection of environmental DNA from tree branches for biodiversity monitoring.

Science robotics
The protection and restoration of the biosphere is crucial for human resilience and well-being, but the scarcity of data on the status and distribution of biodiversity puts these efforts at risk. DNA released into the environment by organisms, i.e., ...