Cited by 724Advanced Neural Network ApplicationsWildlife Ecology and ConservationSmart Agriculture and AI

The DOI matches its Crossref record; 106 of the 106 references it cites were found in a registry or an index.

Applications for deep learning in ecology

Open access

Christin, Sylvain, Hervet, Éric, Lecomte, Nicolas·2019·Methods in Ecology and Evolution

DOI 10.1111/2041-210x.13256

Published version of a preprint

Open this paper in CoChat — free account Open PDF

Abstract

A lot of hype has recently been generated around deep learning, a novel group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning has revolutionized several research fields such as bioinformatics and medicine with its flexibility and ability to process large and complex datasets. As ecological datasets are becoming larger and more complex, we believe these methods can be useful to ecologists as well. In this paper, we review existing implementations and show that deep learning has been used successfully to identify species, classify animal behaviour and estimate biodiversity in large datasets like camera‐trap images, audio recordings and videos. We demonstrate that deep learning can be beneficial to most ecological disciplines, including applied contexts, such as management and conservation. We also identify common questions about how and when to use deep learning, such as what are the steps required to create a deep learning network, which tools are available to help, and what are the requirements in terms of data and computer power. We provide guidelines, recommendations and useful resources, including a reference flowchart to help ecologists get started with deep learning. We argue that at a time when automatic monitoring of populations and ecosystems generates a vast amount of data that cannot be effectively processed by humans anymore, deep learning could become a powerful reference tool for ecologists.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1111/2041-210x.13256

Cites

106 of 106 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Deep learning
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
Using Deep Learning for Image-Based Plant Disease Detection
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016) · Frontiers in Plant Science · doi:10.3389/fpls.2016.01419 · Cited by 5,712
Deep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968

Show all 102 references

Research works printed but not linked: 2

Christin S., 2019, Applications for deep learning in ecology, figshare
Goodfellow I., 2014, Advances in Neural Information Processing Systems 27, 2672

Sorted by citations

Items 201–300 of 723

Improving the Accuracy of Species Identification by Combining Deep Learning With Field Occurrence Records
Sun, J., Futahashi, R., & Yamanaka, T. (2021) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2021.762173 · Cited by 18
Deep inference of seabird dives from GPS-only records: Performance and generalization properties
Roy, A., Bertrand, S., & Fablet, R. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009890 · Cited by 20
Venomous animals in a changing world
Martínez, P. A., Gutiérrez, J. M., Olalla‐Tárraga, M. Á., & Amado, T. F. (2022) · Global Change Biology · doi:10.1111/gcb.16175 · Cited by 18
Effects of stand factors on tree growth of Chinese fir in the subtropics of China depends on climate conditions from predictions of a deep learning algorithm: A long-term spacing trial
Wang, Z., Zhang, X., Zhang, J., & Chhin, S. (2022) · Forest Ecology and Management · doi:10.1016/j.foreco.2022.120363 · Cited by 18
Micro-CT and deep learning: Modern techniques and applications in insect morphology and neuroscience
Jonsson, T. (2023) · Frontiers in Insect Science · doi:10.3389/finsc.2023.1016277 · Cited by 18
Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research
Hentati‐Sundberg, J., Olin, A. B., Reddy, S., Berglund, P., SVENSSON, E. I., Reddy, M. J., Kasarareni, S., Carlsen, A. A., Hanes, M., Kad, S., et al. (2023) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.329 · Cited by 18
Air Quality Modeling for Sustainable Clean Environment Using ANFIS and Machine Learning Approaches
Taylan, O., Alkabaa, A. S., Alamoudi, M., Basahel, A., Balubaid, M., Andejany, M., & Alidrisi, H. (2021) · Atmosphere · doi:10.3390/atmos12060713 · Cited by 18
A comparison of manual and automated detection of rusa deer (Rusa timorensis) from RPAS-derived thermal imagery
Sudholz, A., Denman, S., Pople, A., Brennan, M., Amos, M., & Hamilton, G. S. (2021) · Wildlife Research · doi:10.1071/wr20169 · Cited by 18
Writing mathematical ecology: A guide for authors and readers
Shoemaker, L. G., Walter, J. A., Gherardi, L., DeSiervo, M. H., & Wisnoski, N. I. (2021) · Ecosphere · doi:10.1002/ecs2.3701 · Cited by 18
Global Crop-Specific Fertilization Dataset from 1961–2019
Coello, F., Decorte, T., Janssens, I., Mortier, S., Sardans, J., Peñuelas, J., & Verdonck, T. (2024) · Scientific Data · doi:10.1038/s41597-024-04215-x · Cited by 20
Using generative adversarial networks ( GAN ) to simulate central‐place foraging trajectories
Roy, A., Bertrand, S., & Fablet, R. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13853 · Cited by 23
Non‐local effects of human activity on the spatial distribution of migratory wildlife in Serengeti National Park, Tanzania
Kavwele, C. M., Torney, C. J., Morrison, T. A., Fulford, S., Masolele, M. M., Masoy, J., & Hopcraft, J. G. C. (2022) · Ecological Solutions and Evidence · doi:10.1002/2688-8319.12159 · Cited by 16
imageseg: An R package for deep learning‐based image segmentation
Niedballa, J., Axtner, J., Döbert, T. F., Tilker, A., Nguyen, A., Wong, S. T., Fiderer, C., Heurich, M., & Wilting, A. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13984 · Cited by 18
Recognizability bias in citizen science photographs
Koch, W., Hogeweg, L., Nilsen, E. B., O’Hara, R. B., & Finstad, A. G. (2023) · Royal Society Open Science · doi:10.1098/rsos.221063 · Cited by 19
Deep learning in water protection of resources, environment, and ecology: achievement and challenges
Fu, X., Jiang, J., Wu, X., Huang, L., Han, R., Li, K., Liu, C., Roy, K., Chen, J., Mahmoud, N. T. A., et al. (2024) · Environmental Science and Pollution Research · doi:10.1007/s11356-024-31963-5 · Cited by 19
Going further with model verification and deep learning
Christin, S., Hervet, É., & Lecomte, N. (2020) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13494 · Cited by 17
Self-citation
Automated detection of Hainan gibbon calls for passive acoustic monitoringPreprint
Dufourq, E., Durbach, I. N., Hansford, J. P., Hoepfner, A., Ma, H., Bryant, J. V., Stender, C. S., Li, W., Liu, Z., Chen, Q., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.07.285502 · Cited by 17
Modeling fish habitat: model tuning, fit metrics, and applications
Brownscombe, J. W., Midwood, J. D., & Cooke, S. J. (2021) · Aquatic Sciences · doi:10.1007/s00027-021-00797-5 · Cited by 16
An automated approach for counting groups of flying animals applied to one of the world's largest bat colonies
Koger, B., Hurme, E., Costelloe, B. R., O’Mara, M. T., Wikelski, M. C., Kays, R., & Dechmann, D. K. N. (2023) · Ecosphere · doi:10.1002/ecs2.4590 · Cited by 16
Improving acoustic species identification using data augmentation within a deep learning framework
MacIsaac, J., Newson, S. E., Ashton‐Butt, A., Pearce, H., & Milner, B. P. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102851 · Cited by 19
A predictive flight-altitude model for avoiding future conflicts between an emblematic raptor and wind energy development in the Swiss Alps
Vignali, S., Loercher, F., Hegglin, D., Arlettaz, R., & Braunisch, V. (2022) · Royal Society Open Science · doi:10.1098/rsos.211041 · Cited by 16
Putting eagle rays on the map by coupling aerial video-surveys and deep learning
Desgarnier, L., Mouillot, D., Vigliola, L., Chaumont, M., & Mannocci, L. (2022) · Biological Conservation · doi:10.1016/j.biocon.2022.109494 · Cited by 15
Automating Bird Detection Based on Webcam Captured Images using Deep Learning
Mirugwe, A., Nyirenda, J., & Dufourq, E. (2022) · EPiC series in computing · doi:10.29007/9fr5 · Cited by 16
Expanding network ecology in freshwater ecosystems
Windsor, F. M. (2023) · Journal of Animal Ecology · doi:10.1111/1365-2656.13947 · Cited by 15
Optimized Small Waterbird Detection Method Using Surveillance Videos Based on YOLOv7
Lei, J., Shuhui, G., Rasool, M. A., Fan, R., Jia, Y., & Lei, G. (2023) · Animals · doi:10.3390/ani13121929 · Cited by 15
ECOGEN : Bird sounds generation using deep learning
Guei, A., Christin, S., Lecomte, N., & Hervet, É. (2023) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14239 · Cited by 15
Self-citation
Combining environmental DNA and remote sensing for efficient, fine-scale mapping of arthropod biodiversity
Li, Y., Devenish, C., Tosa, M. I., Luo, M., Bell, D. M., Lesmeister, D. B., Greenfield, P. A., Pichler, M., Levi, T., & Yu, D. W. (2024) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2023.0123 · Cited by 15
UAV equipped with infrared imaging for Cervidae monitoring: Improving detection accuracy by eliminating background information interference
Ma, G., Li, W., Bao, H., Roberts, N. J., Yang, L., Zhang, W., Yang, K., & Jiang, G. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102651 · Cited by 15
Deep learning image analysis for filamentous fungi taxonomic classification: Dealing with small datasets with class imbalance and hierarchical grouping
Stiller, S., Dueñas, J. F., Hempel, S., Rillig, M. C., & Ryo, M. (2024) · Biology Methods and Protocols · doi:10.1093/biomethods/bpae063 · Cited by 18
Exploring and exploiting the rice phytobiome to tackle climate change challenges
Khatibi, S. M. H., Dimaano, N. G., Veliz, E., Sundaresan, V., & Ali, J. (2024) · Plant Communications · doi:10.1016/j.xplc.2024.101078 · Cited by 16
Historical demography and climate driven distributional changes in a widespread Neotropical freshwater species with high economic importance
de Oliveira, E. A., Perez, M. F., Bertollo, L. A. C., Gestich, C. C., Ráb, P., Ezaz, T., de Souza, F. H. S., Viana, P. F., Feldberg, E., de Oliveira, E. H. C., et al. (2020) · Ecography · doi:10.1111/ecog.04874 · Cited by 15
Positive effects of projected climate change on post-disturbance forest regrowth rates in northeastern North American boreal forests
Danneyrolles, V., Boucher, Y., Fournier, R., & Valeria, O. (2023) · Environmental Research Letters · doi:10.1088/1748-9326/acb72a · Cited by 15
Near Real-Time Mapping of Tropical Forest Disturbance Using SAR and Semantic Segmentation in Google Earth Engine
Kilbride, J. B., Poortinga, A., Bhandari, B., Thwal, N. S., Quyen, N. H., Silverman, J., Tenneson, K. R., Bell, D. M., Gregory, M. J., Kennedy, R. E., et al. (2023) · Remote Sensing · doi:10.3390/rs15215223 · Cited by 15
Unlocking the Potential of Deep Learning for Migratory Waterbirds Monitoring Using Surveillance Video
Wu, E., Wang, H., Lu, H., Zhu, W., Jia, Y., Wen, L., Choi, C., Guo, H., Li, B., Sun, L., et al. (2022) · Remote Sensing · doi:10.3390/rs14030514 · Cited by 14
Historical and future spatially-explicit climate change impacts on mycorrhizal and saprotrophic macrofungal productivity in Mediterranean pine forests
Morera, A., de Aragón, J. M., Cáceres, M. D., Bonet, J. A., & de‐Miguel, S. (2022) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2022.108918 · Cited by 14
Computational Methods in Landscape Ecology
Hesselbarth, M. H. K., Nowosad, J., de Flamingh, A., Simpkins, C., Jung, M., Gerber, G., & Bosch, M. (2024) · Current Landscape Ecology Reports · doi:10.1007/s40823-024-00104-6 · Cited by 19
Developing a Southern Ocean Marine Ecosystem Model Ensemble to Assess Climate Risks and Uncertainties
Murphy, K. J., Fierro‐Arcos, D., Rohr, T. W., Green, D. B., Novaglio, C., Baker, K. B., Ortega‐Cisneros, K., Eddy, T. D., Harrison, C. S., Hill, S. L., et al. (2025) · Earth s Future · doi:10.1029/2024ef004849 · Cited by 14
Novel Applications of Technology for Advancing Tidal Marsh Ecology
Kimball, M. E., Connolly, R. M., Alford, S. B., Colombano, D. D., James, W. R., Kenworthy, M. D., Norris, G. S., Ollerhead, J., Ramsden, S., Rehage, J. S., et al. (2021) · Estuaries and Coasts · doi:10.1007/s12237-021-00939-w · Cited by 14
Synthesis and emerging frontiers in social-ecological systems research methods
Schlüter‬, M., Biggs, R., Clements, H. S., Vos, A. D., Maciejewski, K., & Preiser, R. (2021) · Routledge eBooks · doi:10.4324/9781003021339-40 · Cited by 16
Revisiting animal photo-identification using deep metric learning and network analysisPreprint
Miele, V., Dussert, G., Spataro, B., Chamaillé‐Jammes, S., Allainé, D., & Bonenfant, C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.25.007377 · Cited by 14
Machine learning for stone artifact identification: Distinguishing worked stone artifacts from natural clasts using deep neural networks
Emmitt, J., Masoud-Ansari, S., Phillipps, R., Middleton, S., Graydon, J., & Holdaway, S. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271582 · Cited by 17
Use of AI in conservation and for understanding climate change
Khaleel, M., Murtaza, N., Mueen, Q. H., Ahmad, S. A., & Qadri, S. F. I. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-24001-0.00013-0 · Cited by 14
Analysis of climate change impacts on the biogeographical patterns of species-specific productivity of socioeconomically important edible fungi in Mediterranean forest ecosystems
Morera, A., LeBlanc, H., de Aragón, J. M., Bonet, J. A., & de‐Miguel, S. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102557 · Cited by 16
“UDE DIATOMS in the Wild 2024”: a new image dataset of freshwater diatoms for training deep learning models
Venkataramanan, A., Kloster, M., Burfeid-Castellanos, A. M., Dani, M., Mayombo, N. A. S., Vidaković, D. P., Langenkämper, D., Tan, M., Pradalier, C., Nattkemper, T. W., et al. (2024) · GigaScience · doi:10.1093/gigascience/giae087 · Cited by 13
Six decades of soybean breeding in Ontario, Canada: a tradition of innovation
Yoosefzadeh-Najafabadi, M., & Rajcan, I. (2022) · Canadian Journal of Plant Science · doi:10.1139/cjps-2022-0183 · Cited by 13
Fraud Detection in Fintech Leveraging Machine Learning and Behavioral AnalyticsPreprint
Josyula, H. P. (2023) · Research Square · doi:10.21203/rs.3.rs-3548343/v1 · Cited by 18
Using large language models to extract plant functional traits from unstructured text
Domazetoski, V., Kreft, H., Bestová, H., Wieder, P., Koynov, R., Zarei, A., & Weigelt, P. (2025) · Applications in Plant Sciences · doi:10.1002/aps3.70011 · Cited by 23
Rookognise: Acoustic detection and identification of individual rooks in field recordings using multi-task neural networks
Martin, K., Adam, O., Obin, N., & Dufour, V. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101818 · Cited by 12
Deep Learning Methods for Animal Counting in Camera Trap Images
Wang, Y., Zhang, Y., Feng, Y., & Shang, Y. (2022) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai56018.2022.00143 · Cited by 12
Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections
Gillert, A., Resente, G., Anadon‐Rosell, A., Wilmking, M., & von Lukas, U. F. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01397 · Cited by 12
Multi-output deep learning models for enhanced reliability of simultaneous tree above- and below-ground biomass predictions in tropical forests of Vietnam
Huy, B., Truong, N. Q., Poudel, K. P., Temesgen, H., & Khiem, N. Q. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109080 · Cited by 15
Can machine-learning algorithms improve upon classical palaeoenvironmental reconstruction models?
Sun, P., Holden, P. B., & Birks, H. J. B. (2024) · Climate of the past · doi:10.5194/cp-20-2373-2024 · Cited by 12
Transformer Models improve the acoustic recognition of buzz-pollinating bee species
Ferreira, A. I. S., da Silva, N. F. F., Mesquita, F. N., Rosa, T. C., Buchmann, S. L., & Neto, J. N. M. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103010 · Cited by 14
A composite universal DNA signature for the tree of life
de Medeiros, B. A. S., Cai, L., Flynn, P. J., Yan, Y., Duan, X., Marinho, L. C., Anderson, C., & Davis, C. “. (2025) · Nature Ecology & Evolution · doi:10.1038/s41559-025-02752-1 · Cited by 19
Integrating AI models into ecological research workflows: The case of terrestrial bioacoustics
Kitzes, J. A., Chronister, L. M., Czarnecki, C., Fiss, C. J., Freeland‐Haynes, L., Goodman, B. D., Lapp, S., Lyon, R. P., Nossan, H., Rhinehart, T. A., et al. (2025) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.70133 · Cited by 18
Ecological state evaluation of lake ecosystems revisited: Latent variables with kSVM algorithm approach for assessment automatization and data comprehension
Chrobak, G. J., Kowalczyk, T., Fischer, T. B., Szewrański, S., Chrobak, K., & Kazak, J. K. (2021) · Ecological Indicators · doi:10.1016/j.ecolind.2021.107567 · Cited by 12
Classifying the activity states of small vertebrates using automated VHF telemetry
Gottwald, J., Royauté, R., Becker, M., Geitz, T., Höchst, J., Lampe, P., Leister, L., Lindner, K., Maier, J., Rösner, S., et al. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14037 · Cited by 12
Artificial intelligence-driven solutions for mitigating human–wildlife conflict in biodiversity hotspots
Ojija, F. S., Ogwu, M. C., Ally, J. S., John, J. P., Lameck, A. S., Felix, N., & Tekka, R. S. (2025) · Science Progress · doi:10.1177/00368504251394584 · Cited by 18
Deep Learning Based Real-Time Biodiversity Analysis Using Aerial Vehicles
Panigrahi, S., Maski, P., & Thondiyath, A. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-97672-9_36 · Cited by 11
Dominant Species-Physiognomy-Ecological (DSPE) System for the Classification of Plant Ecological Communities from Remote Sensing Images
Sharma, R. C. (2022) · Ecologies · doi:10.3390/ecologies3030025 · Cited by 11
RFIDeep: Unfolding the potential of deep learning for radio‐frequency identification
Bardon, G., Cristofari, R., Winterl, A., Barracho, T., Benoiste, M., Ceresa, C., Chatelain, N., Courtecuisse, J., Fernandes, F. A. N., Gauthier‐Clerc, M., et al. (2023) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14187 · Cited by 11
Self-citation
Species identification through deep learning and geometrical morphology in oaks (Quercus spp.): Pros and cons
Qi, M., Du, F., Guo, F., Yin, K., & Tang, J. (2024) · Ecology and Evolution · doi:10.1002/ece3.11032 · Cited by 18
Monitoring vegetation patterns and their drivers to infer resilience: Automated detection of vegetation and megaherbivores from drone imagery using deep learning
James, R. K., Daniels, F. M. J., Chauhan, A., Wicaksono, P., Hafizt, M., Harahap, S. D., & Christianen, M. J. A. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102580 · Cited by 11
A machine learning approach for protected species bycatch estimation
Long, C. A., Ahrens, R., Jones, T. T., & Siders, Z. A. (2024) · Frontiers in Marine Science · doi:10.3389/fmars.2024.1331292 · Cited by 11
UAV data and deep learning: efficient tools to map ant mounds and their ecological impact
Monsimet, J., Sjögersten, S., Sanders, N. J., Jonsson, M., Olofsson, J., & Siewert, M. (2024) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.400 · Cited by 11
Novel use of deep neural networks on photographic identification of epaulette sharks (Hemiscyllium ocellatum) across life stages
Lonati, M., Jahanbakht, M., Atkins, D., Bierwagen, S. L., Chin, A., Barnett, A., & Rummer, J. L. (2024) · Journal of Fish Biology · doi:10.1111/jfb.15887 · Cited by 14
A Power Management and Control System for Environmental Monitoring Devices
Balle, M., Xu, W., Darras, K., & Wanger, T. C. (2024) · IEEE Transactions on AgriFood Electronics · doi:10.1109/tafe.2024.3472493 · Cited by 13
DCGAN-based Cytology Image Augmentation for Cervical Cancer Cell Classification Using Transfer Learning
Wubineh, B. Z., Jeleń, Ł., & Rusiecki, A. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.02.206 · Cited by 11
Test set verification is an essential step in model building
Quinn, T. P., Le, V., & Cardilini, A. P. A. (2020) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13495 · Cited by 11
Localize Animal Sound Events Reliably (LASER): A New Software for Sound Localization in Zoos
Schneider, S., & Dierkes, P. W. (2021) · Journal of Zoological and Botanical Gardens · doi:10.3390/jzbg2020011 · Cited by 11
UAV-based hyperspectral images and monitoring of canopy tree diversity
Xu, Y., Zhang, C., Jiang, R., Wang, Z., Zhu, M., & Shen, G. (2021) · Biodiversity Science · doi:10.17520/biods.2021013 · Cited by 11
Toward the Definition of a Soundscape Ranking Index (SRI) in an Urban Park Using Machine Learning Techniques
Benocci, R., Afify, A., Potenza, A., Roman, H. E., & Zambon, G. (2023) · Sensors · doi:10.3390/s23104797 · Cited by 11
Windy events detection in big bioacoustics datasets using a pre-trained Convolutional Neural Network
Terranova, F., Betti, L., Ferrario, V., Friard, O., Ludynia, K., Petersen, G. S., Mathevon, N., Reby, D., & Favaro, L. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.174868 · Cited by 13
Transfer learning of species co-occurrence patterns between plant communities
Hirn, J., Veronica Sanz, B., Navarro, J. E. G., Goberna, M., Montesinos‐Navarro, A., Navarro‐Cano, J. A., Sánchez‐Martín, R., Valiente‐Banuet, A., & Verdú, M. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102826 · Cited by 10
Towards transparency in AI: Explainable bird species image classification for ecological research
Kumar, S. V. S., & Kondaveeti, H. K. (2024) · Ecological Indicators · doi:10.1016/j.ecolind.2024.112886 · Cited by 16
Artificial intelligence for biodiversity: Exploring the potential of recurrent neural networks in forecasting arthropod dynamics based on time series
Lhoumeau, S., Silva, J. P., & Borges, P. A. V. (2025) · Ecological Indicators · doi:10.1016/j.ecolind.2025.113119 · Cited by 10
Technological innovations for biodiversity monitoring and the design of agri-environmental schemes
Zavalloni, M., Targetti, S., & Viaggi, D. (2025) · Biological Conservation · doi:10.1016/j.biocon.2025.111069 · Cited by 10
Geographical Graph Attention Networks: Spatial Deep Learning Models for Spatial Prediction and Exploratory Spatial Data Analysis
Jiao, Z., & Tao, R. (2025) · Transactions in GIS · doi:10.1111/tgis.70029 · Cited by 14
Predicting Energy-Based CO2 Emissions in the United States Using Machine Learning: A Path Toward Mitigating Climate Change
Tian, L., Zhang, Z., He, Z., Yuan, C., Xie, Y., Zhang, K., & Jing, R. (2025) · Sustainability · doi:10.3390/su17072843 · Cited by 10
Hybrid modelling of leaf traits: Integrating neural networks with radiative transfer theory
Sun, P., van Bodegom, P. M., Timmermans, J., Liu, S., Wu, J., & Visser, M. D. (2025) · Remote Sensing of Environment · doi:10.1016/j.rse.2025.114958 · Cited by 10
Digital Data Mining
Venturelli, P., Skov, C., Audzijonytė, A., Belmaker, J., Cardoso, A. S., Gordon, L., Gundelund, C., Hyder, K., Jarić, I., Lennox, R. J., et al. (2025) · Fish & fisheries series/Fish and fisheries series (Print) · doi:10.1007/978-3-031-99739-6_15 · Cited by 10
Measuring inter-individual differences in behavioural types of gilthead seabreams in the laboratory using deep learning
Signaroli, M., Lana, A., Martorell‐Barceló, M., Sanllehi, J., Barcelo‐Serra, M., Aspillaga, E., Mulet, J., & Alós, J. (2022) · PeerJ · doi:10.7717/peerj.13396 · Cited by 10
Using pose estimation to identify regions and points on natural history specimens
He, Y., Cooney, C. R., Maddock, S., & Thomas, G. H. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010933 · Cited by 10
Rapid literature mapping on the recent use of machine learning for wildlife imagery
Nakagawa, S., Lagisz, M., Francis, R. J., Tam, J., Li, X., Elphinstone, A., Jordan, N. R., O’Brien, J. K., Pitcher, B. J., Sluys, M. V., et al. (2023) · Peer Community Journal · doi:10.24072/pcjournal.261 · Cited by 11
Optimising sampling of fish assemblages on intertidal reefs using remote underwater video
Erickson, K. R., Bugnot, A. B., & Figueira, W. F. (2023) · PeerJ · doi:10.7717/peerj.15426 · Cited by 10
An active learning framework and assessment of inter-annotator agreement facilitate automated recogniser development for vocalisations of a rare species, the southern black-throated finch (Poephila cincta cincta)
van Osta, J. M., Dreis, B., Meyer, E. A., Grogan, L. F., & Castley, J. G. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102233 · Cited by 12
Deep learning‐ and image processing‐based methods for automatic estimation of leaf herbivore damage
Wang, Z., Jiang, Y., Diallo, A. B., & Kembel, S. W. (2024) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14293 · Cited by 9
The use of suitability models and remote sensing to map forest suitability: a systematic review
Buthelezi, M. N. M., Lottering, R. T., Peerbhay, K. Y., & Mutanga, O. (2024) · Southern Forests a Journal of Forest Science · doi:10.2989/20702620.2024.2373749 · Cited by 8
Integration of Hyperspectral Imaging and Deep Learning for Sustainable Mangrove Management and Sustainable Development Goals Assessment
Ilamathi, P., & Chidambaram, S. (2025) · Wetlands · doi:10.1007/s13157-024-01887-4 · Cited by 11
Automatic detection, identification and counting of deep-water snappers on underwater baited video using deep learning
Baletaud, F., Villon, S., Gilbert, A., Côme, J.-M., Fiat, S., Iovan, C., & Vigliola, L. (2025) · Frontiers in Marine Science · doi:10.3389/fmars.2025.1476616 · Cited by 10
Dijital Medya Ortamında Rızanın Algoritmik İnşası: Eleştirel Algoritma Çalışmaları Perspektifinde Bir Tartışma
KARAKOÇ, E. (2024) · Yeni Medya Dergisi · doi:10.55609/yenimedya.1424585 · Cited by 16
Deep learning image segmentation reveals patterns of UV reflectance evolution in passerine birds
He, Y., Varley, Z. K., Nouri, L. O., Moody, C. J., Jardine, M. D., Maddock, S., Thomas, G. H., & Cooney, C. R. (2022) · Nature Communications · doi:10.1038/s41467-022-32586-5 · Cited by 10
iParasitology: Mining the Internet to Test Parasitological Hypotheses
Poulin, R., Bennett, J., Filion, A., Bhattarai, U. R., Chai, X., Dutra, D. D. A., Donlon, E. M. Y., Doherty, J., Jorge, F., Milotic, M., et al. (2021) · Trends in Parasitology · doi:10.1016/j.pt.2021.01.003 · Cited by 9
Partitioning variability in animal behavioral videos using semi-supervised variational autoencodersPreprint
Whiteway, M. R., Biderman, D., Friedman, Y., Dipoppa, M., Buchanan, E. K., Wu, A., Zhou, J., Bonacchi, N., Miska, N. J., Noel, J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.22.432309 · Cited by 9
Temporal and spatial variations in squid jigging catch efficiency in the Oyashio Extension region
Niu, Z., Chen, Z., Yu, W., Guo, H., Zhu, R., & Zhang, X.-M. (2024) · Fisheries Oceanography · doi:10.1111/fog.12692 · Cited by 8
A global review of landscape-scale analyses in bats reveals geographic and taxonomic biases and opportunities for novel research
Martínez‐Fonseca, J. G., Westeen, E. P., Wan, H. Y., Cushman, S. A., & Chambers, C. L. (2024) · Biological Conservation · doi:10.1016/j.biocon.2024.110829 · Cited by 8
Comparative assessment of automated and manual monitoring in comprehensive plant–pollinator communities
Serra‐Marin, P. E., Solé‐Ribalta, A., Lana, A., Borge‐Holthoefer, J., Hervías‐Parejo, S., & Traveset, A. (2025) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.70165 · Cited by 9
Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality
Simar, C., Colot, M., Cebolla, A.-M., Petieau, M., Cheron, G. A., & Bontempi, G. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1329411 · Cited by 9
Review on Detecting Pneumonia in Deep Learning
Sayeed, M. S., & Toh, J. J. (2024) · International Journal on Robotics Automation and Sciences · doi:10.33093/ijoras.2024.6.1.10 · Cited by 8
MEWC: A user-friendly AI workflow for customised wildlife-image classification
Brook, B. W., Buettel, J. C., van Lunteren, P., Rajmohan, P. P., & Aandahl, Z. (2025) · Peer Community Journal · doi:10.24072/pcjournal.565 · Cited by 12

Authors: Christin, S., Hervet, É., & Lecomte, N.
Affiliations: Université de Moncton (CA)
Corresponding author: Lecomte, Nicolas
ORCID: Christin, Sylvain
Hervet, Éric
Lecomte, Nicolas
Venue: Methods in Ecology and Evolution
Publisher: Wiley
Published: 2019-07-31
Volume: 10
Issue: 10
Pages: 1632-1644
ISSN: 2041-210X (ISSN-L)
DOI: 10.1111/2041-210x.13256
OpenAlex: W2954932437
Cited by: 724
References: 102
Citations by year: 2026: 121 · 2025: 119 · 2024: 114 · 2023: 108 · 2022: 123 · 2021: 84 · 2020: 49 · 2019: 5
FWCI: 29.20
Type: journal article
Language: en
Open access: green
doi.org
Licence: cc-by-nc-nd
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Methods in Ecology and Evolution (publishedVersion)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
Funders: Polar Knowledge Canada
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
Keywords: deep learning, ecological applications, species identification, animal behavior classification, biodiversity estimation, camera trap images, audio recordings, ecological monitoring, conservation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Applications for deep learning in ecology

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks