Situating Ecology as a Big-Data Science: Current Advances, Challenges, and Solutions

The FAIR Guiding Principles for scientific data management and stewardship

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423

The NIST definition of cloud computing

Mell, P., & Grance, T. (2011) · doi:10.6028/nist.sp.800-145 · Cited by 12,637

High-Resolution Global Maps of 21st-Century Forest Cover Change

Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., et al. (2013) · Science · doi:10.1126/science.1244693 · Cited by 11,704

Novel methods improve prediction of species’ distributions from occurrence data

Elith*, J., H. Graham*, C., P. Anderson, R., Dudík, M., Ferrier, S., Guisan, A., J. Hijmans, R., Huettmann, F., R. Leathwick, J., Lehmann, A., et al. (2006) · Ecography · doi:10.1111/j.2006.0906-7590.04596.x · Cited by 9,088

Untitled work

(2011) · Cited by 5,203

Jupyter Notebooks – a publishing format for reproducible computational workflows

Kluyver, T. A., Ragan-Kelley, B., Fernando, P., Granger, B., Bussonnier, M., Frederic, J., Kelley, K., Hamrick, J. B., Grout, J., Corlay, S., et al. (2016) · IOS Press eBooks · doi:10.3233/978-1-61499-649-1-87 · Cited by 3,517

XSEDE: Accelerating Scientific Discovery

Towns, J. W., Cockerill, T. M., Dahan, M., Foster, I. T., Gaither, K., Grimshaw, A. S., Hazlewood, V., Lathrop, S. A., Lifka, D. A., Peterson, G. D., et al. (2014) · Computing in Science & Engineering · doi:10.1109/mcse.2014.80 · Cited by 3,114

The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte CarloPreprint

Homan, M. D., & Gelman, A. (2014) · arXiv (Cornell University) · Cited by 3,078

Big data: A survey

Chen, M., Mao, S., & Liu, Y. (2014) · Mobile Networks and Applications · doi:10.1007/s11036-013-0489-0 · Cited by 2,913

The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte CarloPreprint

Hoffman, M. D., & Gelman, A. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1111.4246 · Cited by 2,815

Collating biodiversity occurrence data for conservation

Spear, D., van Wilgen, N. J., Rebelo, A. G., & Botha, J. M. (2023) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2023.1037282 · Cited by 15

An audacious approach to conservation

Ewers, R. M. (2024) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2024.07.003 · 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

What can we learn from 100,000 freshwater forecasts? A synthesis from the NEON Ecological Forecasting Challenge

Olsson, F., Carey, C. C., Boettiger, C., Harrison, G., Ladwig, R., Lapeyrolerie, M., Lewis, A. S. L., Lofton, M. E., Montealegre‐Mora, F., Rabaey, J. S., et al. (2025) · Ecological Applications · doi:10.1002/eap.70004 · Cited by 11

Collections Education: The Extended Specimen and Data Acumen

Monfils, A., Krimmel, E., Linton, D., Marsico, T. D., Morris, A. B., & Ruhfel, B. R. (2021) · BioScience · doi:10.1093/biosci/biab109 · Cited by 14

Operations research applicability in spatial conservation planning

Alagador, D., & Cerdeira, J. O. (2022) · Journal of Environmental Management · doi:10.1016/j.jenvman.2022.115172 · Cited by 17

Leveraging rangeland monitoring data for wildlife: From concept to practice

Pilliod, D. S., Beck, J. L., Duchardt, C. J., Rachlow, J. L., & Veblen, K. E. (2021) · Rangelands · doi:10.1016/j.rala.2021.09.005 · Cited by 14

Leveraging big data to uncover the eco-evolutionary factors shaping behavioural development

Ehlman, S. M., Scherer, U., Bierbach, D., Francisco, F. A., Laskowski, K. L., Krause, J., & Wolf, M. (2023) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2022.2115 · Cited by 16

Flying Laboratory of Imaging Systems: Fusion of Airborne Hyperspectral and Laser Scanning for Ecosystem Research

Hanuš, J., Slezák, L., Fabiánek, T., Fajmon, L. (., Hanousek, T., Janoutová, R., Kopkáně, D., Novotný, J., Pavelka, K., Pikl, M., et al. (2023) · Remote Sensing · doi:10.3390/rs15123130 · Cited by 14

North American pollen records provide evidence for macroscale ecological changes in the Anthropocene

Stegner, M. A., & Spanbauer, T. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2306815120 · Cited by 13

Can citizen science and social media images support the detection of new invasion sites? A deep learning test case with Cortaderia selloana

Cardoso, A. S., Pinto, E., Tabik, S., August, T., Roy, H. E., Correia, R. A., Vicente, J. R., & Vaz, A. S. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102602 · Cited by 13

annonex2embl : automatic preparation of annotated DNA sequences for bulk submissions to ENA

Gruenstaeudl, M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa209 · Cited by 14

Integration and harmonization of trait data from plant individuals across heterogeneous sources

Lenters, T. P., Henderson, A., Dracxler, C. M., Elias, G. A., Kamga, S. M., Couvreur, T. L. P., & Kissling, W. D. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2020.101206 · Cited by 13

Impediments to Understanding Seagrasses’ Response to Global Change

Rock, B. M., & Daru, B. H. (2021) · Frontiers in Marine Science · doi:10.3389/fmars.2021.608867 · Cited by 13

Addressing the need for interactive, efficient, and reproducible data processing in ecology with the datacleanr R package

Hurley, A., Peters, R. L., Pappas, C., Steger, D. N., & Heinrich, I. (2022) · PLoS ONE · doi:10.1371/journal.pone.0268426 · Cited by 13

Postdisaster Labor-Demand Surge in the US Highways, Roads, and Bridges Construction Sector

Pradhan, S., & Arneson, E. E. (2020) · Journal of Management in Engineering · doi:10.1061/(asce)me.1943-5479.0000869 · Cited by 13

Incentivising open ecological data using blockchain technology

Lewis, R. J., Marstein, K., & Grytnes, J. (2023) · Scientific Data · doi:10.1038/s41597-023-02496-2 · Cited by 13

Multi Layered Convolutional Neural Network in Classification of Different Tomato Diseases

Ferreria, R., Castillo, W. M., Nayga, D., Hermosura, L., Dioses, I. A. M., & Velasco, R. M. A. (2025) · IEEE International Conference on System Engineering and Technology (ICSET) · doi:10.1109/icset65917.2025.11283838 · Cited by 13

Audio tagging of avian dawn chorus recordings in California, Oregon and Washington

Weldy, M. J., Denton, T., Fleishman, A. B., Tolchin, J., McKown, M., Spaan, R. S., Ruff, Z. J., Jenkins, J. M. A., Betts, M. G., & Lesmeister, D. B. (2024) · Biodiversity Data Journal · doi:10.3897/bdj.12.e118315 · Cited by 14

From Slide Rule to Big Data: How Data Science is Changing Water Science and Engineering

Hering, J. G. (2019) · Journal of Environmental Engineering · doi:10.1061/(asce)ee.1943-7870.0001578 · Cited by 12

Forest responses to last‐millennium hydroclimate variability are governed by spatial variations in ecosystem sensitivity

Rollinson, C. R., Dawson, A., Raiho, A., Williams, J. W., Dietze, M. C., Hickler, T., Jackson, S. T., McLACHLAN, J. S., MOORE, D. J. P., Poulter, B., et al. (2020) · Ecology Letters · doi:10.1111/ele.13667 · Cited by 13

Learning to predict spatiotemporal movement dynamics from weather radar networks

Lippert, F., Kranstauber, B., Forré, P. D., & van Loon, E. E. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14007 · Cited by 14

Estimating causal effects with machine learning: A guide for ecologists

Arif, S. (2025) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.70191 · Cited by 13

Perspective: Improving the accuracy of plant phenology observations and land-cover and land-use detection by optical satellite remote-sensing in the Asian tropics

Nagai, S., Katsumata, C., Miura, T., Tsutsumida, N., Ichie, T., Kotani, A., Nakagawa, M., Khoon, K. L., Kobayashi, H., Kumagai, T., et al. (2023) · Frontiers in Forests and Global Change · doi:10.3389/ffgc.2023.1106723 · Cited by 11

New deep learning‐based methods for visualizing ecosystem properties using environmental DNA metabarcoding data

Lamperti, L., Sanchez, T., Si‐Moussi, S., Mouillot, D., Albouy, C. P., Flück, B., Bruno, M., Valentini, A., Pellissier, L., & Manel, S. (2023) · Molecular Ecology Resources · doi:10.1111/1755-0998.13861 · Cited by 18

Smart camera traps and computer vision improve detections of small fauna

Pestell, A. J. L., Rendall, A. R., Sinclair, R. D., Ritchie, E. G., Nguyen, D. T., Corva, D. M., Eichholtzer, A. C., Kouzani, A. Z., & Driscoll, D. A. (2025) · Ecosphere · doi:10.1002/ecs2.70220 · Cited by 12

Exploratory analysis of multivariate data: Applications of parallel coordinates in ecology

Alminagorta, O., Loewen, C. J. G., de Kerckhove, D. T., Jackson, D. A., & Chu, C. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101361 · Cited by 11

Incorporating management action suitability in conservation plans

Carter, Z. T., Hanson, J. O., Perry, G. L. W., & Russell, J. C. (2022) · Journal of Applied Ecology · doi:10.1111/1365-2664.14258 · Cited by 11

MOSAIC - A Unified Trait Database to Complement Structured Population Models

Bernard, C., Santos, G. S., Deere, J. A., Rodríguez‐Caro, R. C., Capdevila, P., Kusch, E., Gascoigne, S. J. L., Jackson, J., & Salguero‐Gómez, R. (2023) · Scientific Data · doi:10.1038/s41597-023-02070-w · Cited by 11

A digital twin for real-time biodiversity forecasting with citizen science data

Ovaskainen, O., Winter, S., Tikhonov, G., Lauha, P., Lehtiö, A., Nokelainen, O., Abrego, N., Aroluoma, A., Harrison, J. P., Heikkinen, M., et al. (2026) · Nature Ecology & Evolution · doi:10.1038/s41559-025-02966-3 · Cited by 13

Long‐term subtropical grassland plots take a long time to change: Replacement is more important than richness differences for beta diversity

Ward, D., Kirkman, K. P., & Morris, C. (2023) · Ecology and Evolution · doi:10.1002/ece3.10195 · Cited by 10

Shrinkage-based Bayesian variable selection for species distribution modelling in complex environments: An application to urban biodiversity

Dietzel, A., Moretti, M., & Cook, L. M. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102561 · Cited by 10

Spatial prediction of plant invasion using a hybrid of machine learning and geostatistical method

Shen, L., LaRue, E. A., Fei, S., & Zhang, H. (2024) · Ecology and Evolution · doi:10.1002/ece3.11605 · Cited by 11

Guidance framework to apply best practices in ecological data analysis: lessons learned from building Galaxy-Ecology

Royaux, C., Mihoub, J., Jossé, M., Pelletier, D., Norvez, O., Reecht, Y., Fouilloux, A., Rasche, H., Hiltemann, S., Batut, B., et al. (2025) · GigaScience · doi:10.1093/gigascience/giae122 · Cited by 10

The rise of big data in disease ecology

Doherty, J., Chai, X., Cope, L. E., Dutra, D. D. A., Milotic, M., Ni, S., Park, E., & Filion, A. (2021) · Trends in Parasitology · doi:10.1016/j.pt.2021.09.003 · Cited by 10

People, infrastructure, and data: A pathway to an inclusive and diverse ecological network of networks

SanClements, M. D., Record, S., Rose, K. C., Donnelly, A., Chong, S. S., Duffy, K. A., Hallmark, A. J., Heffernan, J. B., Liu, J., Mitchell, J. J., et al. (2022) · Ecosphere · doi:10.1002/ecs2.4262 · Cited by 10

EcoBank: A flexible database platform for sharing ecological data

Kim, H. W., Yoon, S., Kim, M., Shin, M., Yoon, H., & Kim, K. (2021) · Biodiversity Data Journal · doi:10.3897/bdj.9.e61866 · Cited by 10

A bird song detector for improving bird identification through deep learning: A case study from Doñana

Márquez‐Rodríguez, A., Mohedano-Muñoz, M. A., Marín-Jiménez, M. J., Santamaría-García, E., Bastianelli, G., Jordano, P., & Mendoza, I. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103254 · Cited by 12

Affirming Indigenous data sovereignty in collaborative wildlife conservation in the era of open data

Tattersall, E. R., Cardinal‐McTeague, W. M., Myers‐Smith, I. H., Jenkins, D. A., & Burton, A. C. (2025) · People and Nature · doi:10.1002/pan3.70161 · Cited by 9

Using large, open datasets to understand spatial and temporal patterns in lotic ecosystems: NEON case studies

Edmonds, J. W., King, K. B. S., Neely, M. B., Hensley, R. T., Goodman, K. J., & Cawley, K. M. (2022) · Ecosphere · doi:10.1002/ecs2.4102 · Cited by 9

Grand challenges in mathematical biology: Integrating multi-scale modeling and data

Eftimie, R. (2022) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2022.1010622 · Cited by 9

Extensive behavioral data contained within existing ecological datasets

Brehm, A. M., & Orrock, J. L. (2023) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2023.08.016 · Cited by 9

The need for mechanistic explanations in (seed) ecology

Pausas, J. G., Lamont, B. B., Keeley, J. E., & Bond, W. J. (2024) · New Phytologist · doi:10.1111/nph.19751 · Cited by 10

Efficient statistical inference methods for assessing changes in species’ populations using citizen science data

Dennis, E. B., Diana, A., Matechou, E., & Morgan, B. J. T. (2024) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1093/jrsssa/qnae105 · Cited by 9

Collaboration across Time and Space in the LTER Network

Huang, T.-Y., Downs, M. R., Ma, J., & Zhao, B. (2020) · BioScience · doi:10.1093/biosci/biaa014 · Cited by 9

You shall know a species by the company it keeps: Leveraging co‐occurrence data to improve ecological prediction

Siefert, A., Laughlin, D. C., & Sabatini, F. M. (2024) · Journal of Vegetation Science · doi:10.1111/jvs.13314 · Cited by 7

Human Supervision is Key to Achieving Accurate AI-assisted Wildlife Identifications in Camera Trap Images

Huebner, S. E., Palmer, M. S., & Packer, C. (2024) · Citizen Science Theory and Practice · doi:10.5334/cstp.752 · Cited by 8

A new data-driven paradigm for the study of avian migratory navigation

Demšar, U., Zein, B., & Long, J. (2025) · Movement Ecology · doi:10.1186/s40462-025-00543-8 · Cited by 9

Mineralogical, petrological and planetological heritage. The (Italian) story so far

Pratesi, G., & Franza, A. (2021) · RENDICONTI LINCEI · doi:10.1007/s12210-020-00970-2 · Cited by 8

Transitioning Machine Learning from Theory to Practice in Natural Resources Management

Saia, S. M., Nelson, N. G., Huseth, A. S., Grieger, K., & Reich, B. J. (2020) · Ecological Modelling · doi:10.1016/j.ecolmodel.2020.109257 · Cited by 8

Camtrap DP: An open standard for the FAIR exchange and archiving of camera trap dataPreprint

Bubnicki, J. W., Norton, B., Baskauf, S., Bruce, T., Cagnacci, F., Casaer, J., Churski, M., Cromsigt, J. P. G. M., Farra, S. D., Fiderer, C., et al. (2023) · doi:10.32942/x2bc8j · Cited by 8

stemflow: A Python Package for Adaptive Spatio-Temporal Exploratory Model

Chen, Y., Gu, Z., & Zhan, X. (2024) · The Journal of Open Source Software · doi:10.21105/joss.06158 · Cited by 8

WildDrone: autonomous drone technology for monitoring wildlife populations

Schultz, U. P., Afridi, S., Berthelot, C., Ngoc, D. N., Hlebowicz, K., Iannino, E., Laporte‐Devylder, L., Maalouf, G., May, G. B., Meier, K., et al. (2026) · Frontiers in Robotics and AI · doi:10.3389/frobt.2025.1695319 · Cited by 12

Temporal Memory Mechanisms and Biome‐Specific Drivers of Ecosystem Carbon Flux: Insights From Explainable Deep Learning Modeling

Ma, T., He, W., Fang, S., Xiao, J., Nguyen, N. T., Yang, H., Xu, P., Huang, C., Zhao, M., Liu, S., et al. (2026) · Global Change Biology · doi:10.1111/gcb.70722 · Cited by 7

Sentinel-2 for chlorophyll-a water quality monitoring: a review of validation evidence and application potential

Goodrich, S., Schaeffer, B. A., Meyers, K. L., Salls, W. B., King, T. V., Seegers, B. N., Cronin-Golomb, O., Demaree, D., & Reif, M. K. (2026) · International Journal of Remote Sensing · doi:10.1080/01431161.2026.2637851 · Cited by 7

Macrosystems EDDIE Teaching Modules Increase Students’ Ability to Define, Interpret, and Apply Concepts in Macrosystems Ecology

Hounshell, A. G., Farrell, K. J., & Carey, C. C. (2021) · Education Sciences · doi:10.3390/educsci11080382 · Cited by 7

Promises and Perils of Big Data: Philosophical Constraints on Chemical Ontologies

Duke, R., McCoy, R. E., Risko, C. M., & Bursten, J. R. S. (2024) · Journal of the American Chemical Society · doi:10.1021/jacs.3c11399 · Cited by 8

intSDM: An R Package for Building a Reproducible Workflow for the Field of Integrated Species Distribution Models

Mostert, P. S., Bjørkås, R., Bruls, A. J. H. M., Koch, W., Martin, E. C., & Perrin, S. W. (2025) · Ecology and Evolution · doi:10.1002/ece3.71029 · Cited by 7

Metagenomics studies in aquaculture systems: Big data analysis, bioinformatics, machine learning and quantum computing

Coskuner‐Weber, O., Alpsoy, S., Yolcu, O., Teber, E., de Marco, A., & Shumka, S. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2025.108444 · Cited by 7

Data science: A Review towards the Big Data Problems

Nasution, M. K. M., Sitompul, O. S., Elveny, M., & Syah, R. B. Y. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1898/1/012006 · Cited by 7

Mismatch between conservation status and climate change sensitivity leaves some anurans in the United States unprotected

DuBose, T. P., Moore, C. E., Silknetter, S., Benson, A. L., Alexander, T., O’Malley, G., & Mims, M. C. (2022) · Biological Conservation · doi:10.1016/j.biocon.2022.109866 · Cited by 7

Challenges and opportunities to build quantitative self-confidence in biologists

Cuddington, K. M., Abbott, K. C., Adler, F. R., Aydeniz, M., Dale, R., Gross, L. J., Hastings, A., Hobson, E. A., Karatayev, V. A., Killion, A. K., et al. (2023) · BioScience · doi:10.1093/biosci/biad015 · Cited by 7

Linking the Functional Traits of AustralianAcaciaSpecies to Their Geographic Distribution and Invasion Status

Martín‐Forés, I., Andrew, S. C., Guerin, G. R., & Gallagher Rachael, V. (2023) · CABI eBooks · doi:10.1079/9781800622197.0005 · Cited by 7

Coevolution of cyberinfrastructure development and scientific progress

Yu, Y., Ibarra, J. E., Kumar, K., & Chergarova, V. (2020) · Technovation · doi:10.1016/j.technovation.2020.102180 · Cited by 9

PyCLKDE: A big data‐enabled high‐performance computational framework for species habitat suitability modeling and mapping

Zhang, G. (2022) · Transactions in GIS · doi:10.1111/tgis.12901 · Cited by 7

Thermal Traits of Anurans Database for the Southeastern United States (TRAD): A Database of Thermal Trait Values for 40 Anuran Species

DuBose, T. P., Catalan, V., Moore, C. E., Farallo, V. R., Benson, A. L., Dade, J. L., Hopkins, W. A., & Mims, M. C. (2024) · Ichthyology & Herpetology · doi:10.1643/h2022102 · Cited by 6

Can internal range structure predict range shifts?

Gilbert, N. A., Kolbe, S. R., Eyster, H. N., & Grinde, A. R. (2024) · Journal of Animal Ecology · doi:10.1111/1365-2656.14168 · Cited by 6

Democratizing macroecology: Integrating unoccupied aerial systems with the National Ecological Observatory Network

Koontz, M. J., Scholl, V., Spiers, A. I., Cattau, M. E., Adler, J., McGlinchy, J., Goulden, T., Melbourne, B. A., & Balch, J. K. (2022) · Ecosphere · doi:10.1002/ecs2.4206 · Cited by 6

An integrative framework to assess the spatio-temporal impact of plant invasion on ecosystem functioning

Werner, C., Hellmann, C., & Große‐Stoltenberg, A. (2024) · NeoBiota · doi:10.3897/neobiota.94.126714 · Cited by 6

On Using Declarative Generation Rules To Deliver Linked Biodiversity Data

Akbar, Z., Kartika, Y. A., Saleh, D. R., Mustika, H. F., & Manik, L. P. (2020) · International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET) · doi:10.1109/icramet51080.2020.9298573 · Cited by 6

Evaluating the Quality of Ecoinformatics Data Derived From Commercial Agriculture: A Repeatability Analysis of Pest Density Estimates

Rosenheim, J. A. (2021) · Journal of Economic Entomology · doi:10.1093/jee/toab127 · Cited by 6

An Ontology-Driven Personalized Faceted Search for Exploring Knowledge Bases of Capsicum

Akbar, Z., Mustika, H. F., Rini, D. S., Manik, L. P., Indrawati, A., Fefirenta, A. D., & Djarwaningsih, T. (2021) · Future Internet · doi:10.3390/fi13070172 · Cited by 6

Contributions of Distribution Modelling to the Ecological Study of Psittaciformes

Ferrer-Paris, J. R., & Sánchez‐Mercado, A. (2021) · Diversity · doi:10.3390/d13120611 · Cited by 6

Twenty years of dynamic occupancy models: a review of applications and look to the future

Kelleher, S., Guillera‐Arroita, G., Elith, J., & Briscoe, N. J. (2025) · Ecography · doi:10.1002/ecog.07757 · Cited by 5

A deep learning approach to detect and identify live freshwater macroinvertebrates

Jaballah, S., Garcia, G. F., Martignac, F., Parisey, N., Jumel, S., Roussel, J., & Dézerald, O. (2023) · Aquatic Ecology · doi:10.1007/s10452-023-10053-7 · Cited by 5

Assessing foraging landscape quality in Quebec's commercial beekeeping through remote sensing, machine learning, and survival analysis

Vadnais, J., Pérez, L., & Coallier, N. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.124157 · Cited by 5

EfficientNet Convolutional Neural Network Approach in Classifying Multiple Tomato Diseases

Gonzales, K. K. C., & Dioses, I. A. M. (2024) · IEEE Conference on Systems, Process and Control · doi:10.1109/icspc63060.2024.10862516 · Cited by 5

LSDStrategy: A Lightweight Software-Driven Strategy for Addressing Big Data Variety of Multimedia Streaming

Khudhur, S. D., & Jeiad, H. A. (2022) · IEEE Access · doi:10.1109/access.2022.3215531 · Cited by 5

High performance machine learning models can fully automate labeling of camera trap images for ecological analysesPreprint

Whytock, R. C., Świeżewski, J., Zwerts, J. A., Bara‐Słupski, T., Pambo, A. F. K., Rogala, M., Bahaa‐el‐din, L., Boekee, K., Brittain, S., Cardoso, A. W., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.12.294538 · Cited by 5

Beyond Modeling: A Roadmap to Community Cyberinfrastructure for Ecological Data-Model IntegrationPreprint

Fer, I., Gardella, A. K., Shiklomanov, A., Serbin, S. P., Kauwe, M. G. D., Raiho, A., Johnston, M. R., Desai, A. R., Viskari, T., Quaife, T. L., et al. (2020) · Preprints.org · doi:10.20944/preprints202001.0176.v1 · Cited by 5

Informative priors can account for location uncertainty in stop-level analyses of the North American Breeding Bird Survey, allowing fine-scale ecological analyses

Burner, R. C., Kirschbaum, A. A., Hostetler, J. A., Ziolkowski, D. J., Anich, N. M., Turek, D., Striegel, E. D., & Niemuth, N. D. (2024) · Ornithological Applications · doi:10.1093/ornithapp/duae041 · Cited by 4

Drone Monitoring and Behavioral Analysis of White-Beaked Dolphins (Lagenorhynchus albirostris)

Lauridsen, D. G., Madsen, N., Pagh, S., Glarou, M., Pertoldi, C., & Rasmussen, M. H. (2025) · Drones · doi:10.3390/drones9090651 · Cited by 4

Synthesizing forecasts to inform decision‐making and advance ecological theory

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