AIMC Topic: Biodiversity

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Multispecies deep learning using citizen science data produces more informative plant community models.

Nature communications
In the age of big data, scientific progress is fundamentally limited by our capacity to extract critical information. Here, we map fine-grained spatiotemporal distributions for thousands of species, using deep neural networks (DNNs) and ubiquitous ci...

Assessing the influence of landscape conservation and protected areas on social wellbeing using random forest machine learning.

Scientific reports
The urgency of interconnected social-ecological dilemmas such as rapid biodiversity loss, habitat loss and fragmentation, and the escalating climate crisis have led to increased calls for the protection of ecologically important areas of the planet. ...

DeepDive: estimating global biodiversity patterns through time using deep learning.

Nature communications
Understanding how biodiversity has changed through time is a central goal of evolutionary biology. However, estimates of past biodiversity are challenged by the inherent incompleteness of the fossil record, even when state-of-the-art statistical meth...

Automated identification of aquatic insects: A case study using deep learning and computer vision techniques.

The Science of the total environment
Deep learning techniques have recently found application in biodiversity research. Mayflies (Ephemeroptera), stoneflies (Plecoptera) and caddisflies (Trichoptera), often abbreviated as EPT, are frequently used for freshwater biomonitoring due to thei...

A culture-independent approach, supervised machine learning, and the characterization of the microbial community composition of coastal areas across the Bay of Bengal and the Arabian Sea.

BMC microbiology
BACKGROUND: Coastal areas are subject to various anthropogenic and natural influences. In this study, we investigated and compared the characteristics of two coastal regions, Andhra Pradesh (AP) and Goa (GA), focusing on pollution, anthropogenic acti...

Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and fastest developing sensor technologies, we describe a framework for automated, image-based monitoring of nocturnal ...

Automatedly identify dryland threatened species at large scale by using deep learning.

The Science of the total environment
Dryland biodiversity is decreasing at an alarming rate. Advanced intelligent tools are urgently needed to rapidly, automatedly, and precisely detect dryland threatened species on a large scale for biological conservation. Here, we explored the perfor...

A gentle introduction to computer vision-based specimen classification in ecological datasets.

The Journal of animal ecology
Classifying specimens is a critical component of ecological research, biodiversity monitoring and conservation. However, manual classification can be prohibitively time-consuming and expensive, limiting how much data a project can afford to process. ...

Deep learning in terrestrial conservation biology.

Biologia futura
Biodiversity is being lost at an unprecedented rate on Earth. As a first step to more effectively combat this process we need efficient methods to monitor biodiversity changes. Recent technological advance can provide powerful tools (e.g. camera trap...

Microscopic image recognition of diatoms based on deep learning.

Journal of phycology
Diatoms are a crucial component in the study of aquatic ecosystems and ancient environmental records. However, traditional methods for identifying diatoms, such as morphological taxonomy and molecular detection, are costly, are time consuming, and ha...