AIMC Topic: Ecology

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Uncovering Ecological Patterns with Convolutional Neural Networks.

Trends in ecology & evolution May 8, 2019
Using remotely sensed imagery to identify biophysical components across landscapes is an important avenue of investigation for ecologists studying ecosystem dynamics. With high-resolution remotely sensed imagery, algorithmic utilization of image cont...
Image Processing, Computer-Assisted Ecology Neural Networks, Computer Ecosystem
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Environmental metabolomics with data science for investigating ecosystem homeostasis.

Progress in nuclear magnetic resonance spectroscopy Nov 21, 2017
A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems,...
Ecosystem Biota Databases, Factual Metabolomics Humans Nuclear Magnetic Resonance, Biomolecular Animals Ecology Multivariate Analysis Machine Learning Homeostasis
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Predicting the Ecological Quality Status of Marine Environments from eDNA Metabarcoding Data Using Supervised Machine Learning.

Environmental science & technology Jul 25, 2017
Monitoring biodiversity is essential to assess the impacts of increasing anthropogenic activities in marine environments. Traditionally, marine biomonitoring involves the sorting and morphological identification of benthic macro-invertebrates, which ...
Environmental Monitoring Ecology DNA Barcoding, Taxonomic Foraminifera Supervised Machine Learning Biodiversity
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Next-Generation Global Biomonitoring: Large-scale, Automated Reconstruction of Ecological Networks.

Trends in ecology & evolution Mar 27, 2017
We foresee a new global-scale, ecological approach to biomonitoring emerging within the next decade that can detect ecosystem change accurately, cheaply, and generically. Next-generation sequencing of DNA sampled from the Earth's environments would p...
High-Throughput Nucleotide Sequencing Ecology DNA Biodiversity Machine Learning Environmental Monitoring Sequence Analysis, DNA Ecosystem
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Simulated herbivory does not constrain phenotypic plasticity to shade through ontogeny in a relict tree.

Plant biology (Stuttgart, Germany) Apr 7, 2016
Ecological limits to phenotypic plasticity (PP), induced by simultaneous biotic and abiotic factors, can prevent organisms from exhibiting optimal plasticity, and in turn lead to decreased fitness. Herbivory is an important biotic stressor and may li...
Herbivory Stress, Physiological Plant Shoots Ecology Biological Ontologies Seedlings Plant Roots Light Trees Models, Biological Phenotype Plant Leaves
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Typha latifolia (broadleaf cattail) as bioindicator of different types of pollution in aquatic ecosystems-application of self-organizing feature map (neural network).

Environmental science and pollution research international Apr 5, 2016
The contents of Cd, Cu, Fe, Mn, Ni, Pb, and Zn in leaves of Typha latifolia (broadleaf cattail), water and bottom sediment from 72 study sites designated in different regions of Poland were determined using atomic absorption spectrometry. The aim of ...
Environmental Monitoring Water Pollutants, Chemical Environmental Pollution Ecology Plant Leaves Neural Networks, Computer Metals Ecosystem Agriculture Typhaceae Poland Metals, Heavy Trace Elements Geologic Sediments
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Modeling of Beta Diversity in Tunisian Waters: Predictions Using Generalized Dissimilarity Modeling and Bioregionalisation Using Fuzzy Clustering.

PloS one Jul 6, 2015
Spatial patterns of beta diversity are a major focus of ecology. They can be especially valuable in conservation planning. In this study, we used a generalized dissimilarity modeling approach to analyze and predict the spatial patterns of beta divers...
Models, Theoretical Fuzzy Logic Biodiversity Mediterranean Sea Ecology Tunisia
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Process-Informed Neural Networks: A Hybrid Modelling Approach to Improve Predictive Performance and Inference of Neural Networks in Ecology and Beyond.

Ecology letters Nov 1, 2024
Despite deep learning being state of the art for data-driven model predictions, its application in ecology is currently subject to two important constraints: (i) deep-learning methods are powerful in data-rich regimes, but in ecology data are typical...
Models, Biological Forests Ecology Neural Networks, Computer Deep Learning
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Machine learning in molecular ecology.

Molecular ecology resources Nov 1, 2021
Machine Learning Ecology
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An ecologically motivated image dataset for deep learning yields better models of human vision.

Proceedings of the National Academy of Sciences of the United States of America Feb 23, 2021
Deep neural networks provide the current best models of visual information processing in the primate brain. Drawing on work from computer vision, the most commonly used networks are pretrained on data from the ImageNet Large Scale Visual Recognition ...
Visual Cortex Visual Perception Pattern Recognition, Visual Neural Networks, Computer Deep Learning Humans Models, Neurological Brain Mapping Ecology
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