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One Ecosystem 1: e8621
https://doi.org/10.3897/oneeco.1.e8621 (27 Jun 2016)
https://doi.org/10.3897/oneeco.1.e8621 (27 Jun 2016)
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Application of machine learning and acoustic predation tags to classify migration fate of Atlantic salmon smolts. Oecologia 198: 605.
DOI: 10.1007/s00442-022-05138-3
Survey of artificial intelligence approaches in the study of anthropogenic impacts on symbiotic organisms – a holistic view. Symbiosis 84: 271.
DOI: 10.1007/s13199-021-00778-0
Propuesta de evaluación para asignar tentativamente riesgo de extinción (ETRE): el caso de Peltogyne mexicana (Leguminosae). Botanical Sciences 96: 609.
DOI: 10.17129/botsci.1971
Progress in plant phenology modeling under global climate change. Science China Earth Sciences 63: 1237.
DOI: 10.1007/s11430-019-9622-2
Environmental influences shaping microbial communities in a low oxygen, highly stratified marine embayment. Aquatic Microbial Ecology 87: 185.
DOI: 10.3354/ame01978
Application of machine learning methods for paleoclimatic reconstructions from leaf traits. International Journal of Climatology 41: .
DOI: 10.1002/joc.6921
Spatial-Statistical Analysis of Landscape-Level Wildfire Rate of Spread. Remote Sensing 14: 3980.
DOI: 10.3390/rs14163980
Discovering divergence in the thermal physiology of intertidal crabs along latitudinal gradients using an integrated approach with machine learning. Journal of Thermal Biology 78: 140.
DOI: 10.1016/j.jtherbio.2018.09.016
LANDMark: an ensemble approach to the supervised selection of biomarkers in high-throughput sequencing data. BMC Bioinformatics 23: .
DOI: 10.1186/s12859-022-04631-z
Modeling Dynamic Processes in the Black Sea Pelagic Habitat—Causal Connections between Abiotic and Biotic Factors in Two Climate Change Scenarios. Sustainability 16: 1849.
DOI: 10.3390/su16051849
A review of machine learning and big data applications in addressing ecosystem service research gaps. Ecosystem Services 57: 101478.
DOI: 10.1016/j.ecoser.2022.101478
Predicting the abundances of aphids and their natural enemies in cereal crops: Machine-learning versus linear models. Biological Control 169: 104866.
DOI: 10.1016/j.biocontrol.2022.104866
Landscape Characteristics in Relation to Ecosystem Services Supply: The Case of a Mediterranean Forest on the Island of Cyprus. Forests 14: 1286.
DOI: 10.3390/f14071286
Machine learning is a powerful tool to study the effect of cancer on species and ecosystems. Methods in Ecology and Evolution 12: 2310.
DOI: 10.1111/2041-210X.13703
Machine-learning modeling on tree mortality and growth reduction of temperate forests with climatic and ecophysiological parameters. Ecological Modelling 483: 110456.
DOI: 10.1016/j.ecolmodel.2023.110456
Performances of machine learning algorithms for mapping fractional cover of an invasive plant species in a dryland ecosystem. Ecology and Evolution 9: 2562.
DOI: 10.1002/ece3.4919
Testing a Generalizable Machine Learning Workflow for Aquatic Invasive Species on Rainbow Trout (Oncorhynchus mykiss) in Northwest Montana. Frontiers in Big Data 4: .
DOI: 10.3389/fdata.2021.734990
Reinforcement and deep reinforcement learning for wireless Internet of Things: A survey. Computer Communications 178: 98.
DOI: 10.1016/j.comcom.2021.07.014
Optimizing WorldView-2, -3 cloud masking using machine learning approaches. Remote Sensing of Environment 284: 113332.
DOI: 10.1016/j.rse.2022.113332
Setting the stage for the machine intelligence era in marine science. ICES Journal of Marine Science 77: 1267.
DOI: 10.1093/icesjms/fsaa084
Historical and future spatially-explicit climate change impacts on mycorrhizal and saprotrophic macrofungal productivity in Mediterranean pine forests. Agricultural and Forest Meteorology 319: 108918.
DOI: 10.1016/j.agrformet.2022.108918
A Machine Learning approach to the assessment of the vulnerability of Posidonia oceanica meadows. Ecological Indicators 108: 105744.
DOI: 10.1016/j.ecolind.2019.105744
Ecology with artificial intelligence and machine learning in Asia: A historical perspective and emerging trends. Ecological Research 39: 5.
DOI: 10.1111/1440-1703.12425
The curse of the past – What can tile drain effluent tell us about arable field management?. Agriculture, Ecosystems & Environment 326: 107787.
DOI: 10.1016/j.agee.2021.107787
Not that kind of tree: Assessing the potential for decision tree–based plant identification using trait databases. Applications in Plant Sciences 8: .
DOI: 10.1002/aps3.11379
Application of deep learning in ecological resource research: Theories, methods, and challenges. Science China Earth Sciences 63: 1457.
DOI: 10.1007/s11430-019-9584-9
Performance of statistical and machine learning-based methods for predicting biogeographical patterns of fungal productivity in forest ecosystems. Forest Ecosystems 8: .
DOI: 10.1186/s40663-021-00297-w
Partial Decision Tree Forest: A Machine Learning Model for the Geosciences. Minerals 13: 800.
DOI: 10.3390/min13060800
An approach to multi-class imbalanced problem in ecology using machine learning. Ecological Informatics 71: 101822.
DOI: 10.1016/j.ecoinf.2022.101822
The unrealized potential of herbaria for global change biology. Ecological Monographs 88: 505.
DOI: 10.1002/ecm.1307
Counting using deep learning regression gives value to ecological surveys. Scientific Reports 11: .
DOI: 10.1038/s41598-021-02387-9
Data synthesis for crop variety evaluation. A review. Agronomy for Sustainable Development 40: .
DOI: 10.1007/s13593-020-00630-7
Coral reefs in transition: Temporal photoquadrat analyses and validation of underwater hyperspectral imaging for resource-efficient monitoring in Guam. PLOS ONE 19: e0299523.
DOI: 10.1371/journal.pone.0299523
Machine learning applied to lentic habitat use by spawning walleye demonstrates the benefits of considering multiple spatial scales in aquatic research. Canadian Journal of Fisheries and Aquatic Sciences 79: 1120.
DOI: 10.1139/cjfas-2021-0180
An Artificial Neural Network to Infer the Mediterranean 3D Chlorophyll-a and Temperature Fields from Remote Sensing Observations. Remote Sensing 12: 4123.
DOI: 10.3390/rs12244123
Machine learning identifies a strong association between warming and reduced primary productivity in an oligotrophic ocean gyre. Scientific Reports 10: .
DOI: 10.1038/s41598-020-59989-y
Machine Learning Classification and Accuracy Assessment from High-Resolution Images of Coastal Wetlands. Remote Sensing 13: 3669.
DOI: 10.3390/rs13183669
Annotating otoliths with a deep generative model. ICES Journal of Marine Science 81: 55.
DOI: 10.1093/icesjms/fsad170
Modeling Posidonia oceanica shoot density and rhizome primary production. Scientific Reports 10: .
DOI: 10.1038/s41598-020-73722-9
What drives MLOps adoption? An analysis using the TOE framework. Journal of Decision Systems : 1.
DOI: 10.1080/12460125.2023.2214306
A field-deployable water quality monitoring with machine learning-based smartphone colorimetry. Analytical Methods 14: 3458.
DOI: 10.1039/D2AY00785A
Apatite (U-Th-Sm)/He date dispersion: First insights from machine learning algorithms. Earth and Planetary Science Letters 554: 116655.
DOI: 10.1016/j.epsl.2020.116655
Machine learning-based prediction for grassland degradation using geographic, meteorological, plant and microbial data. Ecological Indicators 137: 108738.
DOI: 10.1016/j.ecolind.2022.108738
Functionally Assembled Terrestrial Ecosystem Simulator (FATES) for Hurricane Disturbance and Recovery. Journal of Advances in Modeling Earth Systems 16: .
DOI: 10.1029/2023MS003679
Predicting seasonal movements and distribution of the sperm whale using machine learning algorithms. Ecology and Evolution 11: 1432.
DOI: 10.1002/ece3.7154
A multi-model architecture based on Long Short-Term Memory neural networks for multi-step sea level forecasting. Future Generation Computer Systems 124: 1.
DOI: 10.1016/j.future.2021.05.008
Twitter-Based BIRGing: Big Data Analysis of English National Team Fans During the 2018 FIFA World Cup. Communication & Sport 8: 317.
DOI: 10.1177/2167479519834348
Machine Learning Model for Imbalanced Cholera Dataset in Tanzania. The Scientific World Journal 2019: 1.
DOI: 10.1155/2019/9397578
Detecting temporal changes in the temperature sensitivity of spring phenology with global warming: Application of machine learning in phenological model. Agricultural and Forest Meteorology 279: 107702.
DOI: 10.1016/j.agrformet.2019.107702
An overview of remote monitoring methods in biodiversity conservation. Environmental Science and Pollution Research 29: 80179.
DOI: 10.1007/s11356-022-23242-y
How to make use of unlabeled observations in species distribution modeling using point process models. Ecology and Evolution 11: 5220.
DOI: 10.1002/ece3.7411
Editors' Choice—Artificial Intelligence Based Mobile Application for Water Quality Monitoring. Journal of The Electrochemical Society 166: B3031.
DOI: 10.1149/2.0081909jes
A super SDM (species distribution model) ‘in the cloud’ for better habitat-association inference with a ‘big data’ application of the Great Gray Owl for Alaska. Scientific Reports 14: .
DOI: 10.1038/s41598-024-57588-9
Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach. Scientific Reports 14: .
DOI: 10.1038/s41598-023-50308-9
MACHINE LEARNING APPLICATIONS IN OCEANOGRAPHY. Aquatic Research : 161.
DOI: 10.3153/AR19014
Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods. Remote Sensing 15: 3344.
DOI: 10.3390/rs15133344
Toward the use of protists as bioindicators of multiple stresses in agricultural soils: A case study in vineyard ecosystems. Ecological Indicators 139: 108955.
DOI: 10.1016/j.ecolind.2022.108955
Landscape context importance for predicting forest transition success in central Panama. Landscape Ecology 38: 2307.
DOI: 10.1007/s10980-023-01694-y
Looking for reliable species distribution models for low‐density cetacean species: Compared effectiveness of SDMs for G. griseus, G. melas, Z. cavirostris in the Mediterranean Sea based on long‐term fixed‐transect data. Aquatic Conservation: Marine and Freshwater Ecosystems 34: .
DOI: 10.1002/aqc.4115
Using machine learning to identify the geographical drivers of Ceratitis capitata trap catch in an agricultural landscape. Computers and Electronics in Agriculture 162: 582.
DOI: 10.1016/j.compag.2019.05.008
Machine learning in landscape ecological analysis: a review of recent approaches. Landscape Ecology 37: 1227.
DOI: 10.1007/s10980-021-01366-9
Estimating ancient biogeographic patterns with statistical model discrimination. The Anatomical Record 306: 1880.
DOI: 10.1002/ar.25067
Comparison of methods for the automatic classification of forest habitat types in the Southern Alps—Application to ecological data from the French national forest inventory. Biodiversity and Conservation 31: 3257.
DOI: 10.1007/s10531-022-02487-6
Assessing the natural capital value of Posidonia oceanica meadows in the Italian seas by integrating Habitat Suitability and Environmental Accounting Models. ICES Journal of Marine Science 80: 739.
DOI: 10.1093/icesjms/fsac034
Effects of climate change on the distribution of Fusarium spp. in Italy. Science of The Total Environment 882: 163640.
DOI: 10.1016/j.scitotenv.2023.163640
Spatial biases of information influence global estimates of soil respiration: How can we improve global predictions?. Global Change Biology 27: 3923.
DOI: 10.1111/gcb.15666
Potential application of artificial neural networks for analyzing the occurrences of fish larvae and juveniles in an estuary in northern Vietnam. Aquatic Ecology 57: 813.
DOI: 10.1007/s10452-022-09959-5
A Machine Learning Approach to Predict Groundwater Levels in California Reveals Ecosystems at Risk. Frontiers in Earth Science 9: .
DOI: 10.3389/feart.2021.784499
Three-dimensional modelling using spatial regression machine learning and hydrogeological basement VES. Computers & Geosciences 156: 104907.
DOI: 10.1016/j.cageo.2021.104907
Data-Intensive Ecological Research Is Catalyzed by Open Science and Team Science. BioScience 68: 813.
DOI: 10.1093/biosci/biy097
Importance of machine learning for enhancing ecological studies using information-rich imagery. Endangered Species Research 39: 91.
DOI: 10.3354/esr00958
Designing Autonomy: Opportunities for New Wildness in the Anthropocene. Trends in Ecology & Evolution 32: 156.
DOI: 10.1016/j.tree.2016.12.004
A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation. Remote Sensing 14: 2066.
DOI: 10.3390/rs14092066
Against all odds: Harbor porpoises intensively use an anthropogenically modified estuary. Marine Mammal Science 38: 288.
DOI: 10.1111/mms.12858
Big Earth data analytics: a survey. Big Earth Data 3: 83.
DOI: 10.1080/20964471.2019.1611175
Application of machine-learning methods in forest ecology: recent progress and future challenges. Environmental Reviews 26: 339.
DOI: 10.1139/er-2018-0034
Assessing the impact of climate conditions on the distribution of mosquito species in Qatar. Frontiers in Public Health 10: .
DOI: 10.3389/fpubh.2022.970694
Unearthing trends in environmental science and engineering research: Insights from a probabilistic topic modeling literature analysis. Journal of Cleaner Production 317: 128322.
DOI: 10.1016/j.jclepro.2021.128322
Noninvasive Low‐cost Method to Identify Armadillos' Burrows: A Machine Learning Approach. Wildlife Society Bulletin 45: 396.
DOI: 10.1002/wsb.1222
Machine learning to detect marine animals in UAV imagery: effect of morphology, spacing, behaviour and habitat. Remote Sensing in Ecology and Conservation 7: 341.
DOI: 10.1002/rse2.205
Count Regression and Machine Learning Techniques for Zero-Inflated Overdispersed Count Data: Application to Ecological Data. Annals of Data Science : .
DOI: 10.1007/s40745-023-00464-6
Prediction of coastal vulnerability with machine learning techniques, Mediterranean coast of Tangier-Tetouan, Morocco. Estuarine, Coastal and Shelf Science 291: 108422.
DOI: 10.1016/j.ecss.2023.108422
Environmental drivers of phytoplankton taxonomic composition in an Antarctic fjord. Progress in Oceanography 183: 102295.
DOI: 10.1016/j.pocean.2020.102295
Cold-Water Coral Habitat Mapping: Trends and Developments in Acquisition and Processing Methods. Geosciences 11: 9.
DOI: 10.3390/geosciences11010009
A hierarchical path-segmentation movement ecology framework. Ecological Processes 11: .
DOI: 10.1186/s13717-022-00399-5
Statistically reinforced machine learning for nonlinear patterns and variable interactions. Ecosphere 8: .
DOI: 10.1002/ecs2.1976
Modelling tree diameter of less commonly planted tree species in New Zealand using a machine learning approach. Forestry: An International Journal of Forest Research 96: 87.
DOI: 10.1093/forestry/cpac037
Linking SVM based habitat model and evolutionary optimisation for managing environmental impacts of hydropower plants. River Research and Applications 39: 897.
DOI: 10.1002/rra.4121
Spatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by drones. Frontiers in Environmental Science 11: .
DOI: 10.3389/fenvs.2023.1083328
Using NDVI, climate data and machine learning to estimate yield in the Douro wine region. International Journal of Applied Earth Observation and Geoinformation 114: 103069.
DOI: 10.1016/j.jag.2022.103069
Biogeographic Patterns in Members of Globally Distributed and Dominant Taxa Found in Port Microbial Communities. mSphere 5: .
DOI: 10.1128/mSphere.00481-19
Socio-ecological determinants of multiple ecosystem services on the Mediterranean landscapes of the Ionian Islands (Greece). Ecological Modelling 422: 108994.
DOI: 10.1016/j.ecolmodel.2020.108994
Using machine learning to improve the estimate of U.S. background ozone. Atmospheric Environment 316: 120145.
DOI: 10.1016/j.atmosenv.2023.120145
Trends and hot spots of coastal science in Moroccan Atlantic coast: a bibliometric analysis. Environment, Development and Sustainability : .
DOI: 10.1007/s10668-023-03252-7
Synergistic use of Sentinel-2 and UAV-derived data for plant fractional cover distribution mapping of coastal meadows with digital elevation models. Biogeosciences 21: 1411.
DOI: 10.5194/bg-21-1411-2024
Using Unmanned Aerial Vehicle and LiDAR-Derived DEMs to Estimate Channels of Small Tributary Streams. Remote Sensing 13: 3380.
DOI: 10.3390/rs13173380
Robust and simplified machine learning identification of pitfall trap‐collected ground beetles at the continental scale. Ecology and Evolution 10: 13143.
DOI: 10.1002/ece3.6905
The use of radar and optical satellite imagery combined with advanced machine learning and metaheuristic optimization techniques to detect and quantify above ground biomass of intertidal seagrass in a New Zealand estuary. International Journal of Remote Sensing 42: 4712.
DOI: 10.1080/01431161.2021.1899335
Assessment of spatiotemporal dynamics of diurnal fog occurrence in subtropical montane cloud forests. Agricultural and Forest Meteorology 317: 108899.
DOI: 10.1016/j.agrformet.2022.108899
Classification of Ecological Data by Deep Learning. International Journal of Pattern Recognition and Artificial Intelligence 34: 2052010.
DOI: 10.1142/S0218001420520102
An Approach to Mapping Deforestation in Permanent Forest Reserve Using the Convolutional Neural Network and Sentinel-1 Synthetic Aperture Radar. 2021 Fifth International Conference on Information Retrieval and Knowledge Management (CAMP) : 59.
DOI: 10.1109/CAMP51653.2021.9498144
Artificial Intelligence for mussels farm quality assessment and prediction. 2022 IEEE International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea) : 33.
DOI: 10.1109/MetroSea55331.2022.9950875
Machine Learning for Ecology and Sustainable Natural Resource Management. Chapter 1: 3.
DOI: 10.1007/978-3-319-96978-7_1
Technologies and Innovation. Chapter 8: 98.
DOI: 10.1007/978-3-030-62015-8_8
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DOI: 10.1007/978-3-031-12946-9_1
Ecological Informatics. Chapter 17: 375.
DOI: 10.1007/978-3-319-59928-1_17
Digital Transformation for Sustainability. Chapter 18: 369.
DOI: 10.1007/978-3-031-15420-1_18
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