AIMC Topic: Biodiversity
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Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment.
PLoS computational biology
Apr 19, 2021
Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp ho...
Autonomous Marine Robot Based on AI Recognition for Permanent Surveillance in Marine Protected Areas.
Sensors (Basel, Switzerland)
Apr 10, 2021
The world's oceans are one of the most valuable sources of biodiversity and resources on the planet, although there are areas where the marine ecosystem is threatened by human activities. Marine protected areas (MPAs) are distinctive spaces protected...
Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences.
Scientific reports
Feb 25, 2021
Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains to be learned about the links between the microbiome and human health. The fusion of artificial inte...
A comparison of two remotely operated vehicle (ROV) survey methods used to estimate fish assemblages and densities around a California oil platform.
PloS one
Nov 10, 2020
Offshore oil and gas platforms have a finite life of production operations. Once production ceases, decommissioning options for the platform are assessed. The role that a platform's jacket plays as fish habitat can inform the decommissioning decision...
Automated conservation assessment of the orchid family with deep learning.
Conservation biology : the journal of the Society for Conservation Biology
Nov 9, 2020
International Union for Conservation of Nature (IUCN) Red List assessments are essential for prioritizing conservation needs but are resource intensive and therefore available only for a fraction of global species richness. Automated conservation ass...
Quantifying Tropical Plant Diversity Requires an Integrated Technological Approach.
Trends in ecology & evolution
Sep 7, 2020
Tropical biomes are the most diverse plant communities on Earth, and quantifying this diversity at large spatial scales is vital for many purposes. As macroecological approaches proliferate, the taxonomic uncertainties in species occurrence data are ...
Machine learning approaches identify male body size as the most accurate predictor of species richness.
BMC biology
Aug 28, 2020
BACKGROUND: A major challenge in biodiversity science is to understand the factors contributing to the variability of species richness -the number of different species in a community or region - among comparable taxonomic lineages. Multiple biotic an...
Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data.
Communications biology
Jul 15, 2020
The study of complex microbial communities typically entails high-throughput sequencing and downstream bioinformatics analyses. Here we expand and accelerate microbiota analysis by enabling cell type diversity quantification from multidimensional flo...
Mapping Atlantic rainforest degradation and regeneration history with indicator species using convolutional network.
PloS one
Feb 28, 2020
The Atlantic rainforest of Brazil is one of the global terrestrial hotspots of biodiversity. Despite having undergone large scale deforestation, forest cover has shown signs of increases in the last decades. Here, to understand the degradation and re...
Towards a global understanding of the drivers of marine and terrestrial biodiversity.
PloS one
Feb 5, 2020
Understanding the distribution of life's variety has driven naturalists and scientists for centuries, yet this has been constrained both by the available data and the models needed for their analysis. Here we compiled data for over 67,000 marine and ...
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