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

Perspectives in machine learning for wildlife conservation

Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., Mathis, A., Mathis, M. W., van Langevelde, F., Burghardt, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-27980-y · Cited by 740

Towards the fully automated monitoring of ecological communities

Besson, M., Alison, J., Bjerge, K., Gorochowski, T. E., Høye, T. T., Jucker, T., Mann, H. M. R., & Clements, C. F. (2022) · Ecology Letters · doi:10.1111/ele.14123 · Cited by 304

The Movebank system for studying global animal movement and demography

Kays, R., Davidson, S. C., Berger, M., Bohrer, G., Fiedler, W., Flack, A., Hirt, J., Hahn, C., Gauggel, D., Russell, B., et al. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13767 · Cited by 233

Open Science principles for accelerating trait-based science across the Tree of Life

Gallagher, R. V., Falster, D. S., Maitner, B., Salguero‐Gómez, R., Vandvik, V., Pearse, W. D., Schneider, F. D., Kattge, J., Poelen, J. H., Madin, J. S., et al. (2020) · Nature Ecology & Evolution · doi:10.1038/s41559-020-1109-6 · Cited by 236

Wildlife Insights: A Platform to Maximize the Potential of Camera Trap and Other Passive Sensor Wildlife Data for the Planet

Ahumada, J., Fegraus, E., Birch, T., Flores, N., Kays, R., O’Brien, T. G., Palmer, J., Schuttler, S., Zhao, J. Y., Jetz, W., et al. (2019) · Environmental Conservation · doi:10.1017/s0376892919000298 · Cited by 215

Inferring microbiota functions from taxonomic genes: a review

Djemiel, C., Maron, P., Terrat, S., Dequiedt, S. S., Cottin, A., & Ranjard, L. (2022) · GigaScience · doi:10.1093/gigascience/giab090 · Cited by 174

Why don't we share data and code? Perceived barriers and benefits to public archiving practices

Gomes, D. G. E., Pottier, P., Crystal‐Ornelas, R., Hudgins, E. J., Foroughirad, V., Sánchez‐Reyes, L. L., Turba, R., Martinez, P. A., Moreau, D., Bertram, M. G., et al. (2022) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2022.1113 · Cited by 203

Harnessing Big Data to Support the Conservation and Rehabilitation of Mangrove Forests Globally

Worthington, T. A., Andradi‐Brown, D. A., Bhargava, R., Buelow, C. A., Bunting, P., Duncan, C., Fatoyinbo, T., Friess, D. A., Goldberg, L., Hilarides, L., et al. (2020) · One Earth · doi:10.1016/j.oneear.2020.04.018 · Cited by 153

Three Frontiers for the Future of Biodiversity Research Using Citizen Science Data

Callaghan, C. T., Poore, A. G. B., Mesaglio, T., Moles, A. T., Nakagawa, S., Roberts, C. J., Rowley, J. J. L., Vergés, A., Wilshire, J. H., & Cornwell, W. K. (2020) · BioScience · doi:10.1093/biosci/biaa131 · Cited by 151

Temporal β diversity—A macroecological perspective

Magurran, A. E., Dornelas, M., Moyes, F., & Henderson, P. A. (2019) · Global Ecology and Biogeography · doi:10.1111/geb.13026 · Cited by 139

A unifying framework for studying and managing climate-driven rates of ecological change

Williams, J. W., Ordonez, A., & Svenning, J.-C. (2020) · Nature Ecology & Evolution · doi:10.1038/s41559-020-01344-5 · Cited by 135

Harmonizing taxon names in biodiversity data: A review of tools, databases and best practices

Grenié, M., Berti, E., Carvajal‐Quintero, J. D., Dädlow, G. M. L., Sagouis, A., & Winter, M. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13802 · Cited by 130

Automated assessment reveals that the extinction risk of reptiles is widely underestimated across space and phylogeny

Caetano, G. H. D. O., Chapple, D. G., Grenyer, R., Raz, T., Rosenblatt, J., Tingley, R., Böhm, M., Meiri, S., & Roll, U. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001544 · Cited by 131

Circuitscape in julia: empowering dynamic approaches to connectivity assessment

Hall, K. R., Anantharaman, R., Landau, V. A., Clark, M., Dickson, B. G., Jones, A., Platt, J., Edelman, A., & Shah, V. B. (2021) · Land · doi:10.3390/land10030301 · Cited by 115

Machine learning in landscape ecological analysis: a review of recent approaches

Stupariu, M., Cushman, S. A., Pleșoianu, A., Pătru-Stupariu, I., & Fürst, C. (2021) · Landscape Ecology · doi:10.1007/s10980-021-01366-9 · Cited by 124

Born‐digital biodiversity data: Millions and billions

Kays, R., McShea, W. J., & Wikelski, M. C. (2020) · Diversity and Distributions · doi:10.1111/ddi.12993 · Cited by 110

To harness traits for ecology, let’s abandon ‘functionality’

Streit, R. P., & Bellwood, D. R. (2022) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2022.11.009 · Cited by 121

A survey of mobility-aware Multi-access Edge Computing: Challenges, use cases and future directions

Singh, R., Sukapuram, R., & Chakraborty, S. (2022) · Ad Hoc Networks · doi:10.1016/j.adhoc.2022.103044 · Cited by 107

Digital twins: dynamic model-data fusion for ecology

de Koning, K., Broekhuijsen, J., Kühn, I., Ovaskainen, O., Taubert, F., Endresen, D., Schigel, D., & Grimm, V. (2023) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2023.04.010 · Cited by 106

A review of machine learning and big data applications in addressing ecosystem service research gaps

Manley, K., Nyelele, C., & Egoh, B. N. (2022) · Ecosystem Services · doi:10.1016/j.ecoser.2022.101478 · Cited by 108

Bioacoustic time capsules: Using acoustic monitoring to document biodiversity

Sugai, L. S. M., & Llusia, D. (2018) · Ecological Indicators · doi:10.1016/j.ecolind.2018.12.021 · Cited by 93

Robust ecological analysis of camera trap data labelled by a machine learning model

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. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13576 · Cited by 106

Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration

Fer, I., Gardella, A. K., Shiklomanov, A., Campbell, E. E., Cowdery, E. M., Kauwe, M. G. D., Desai, A. R., Duveneck, M. J., Fisher, J. B., Haynes, K., et al. (2020) · Global Change Biology · doi:10.1111/gcb.15409 · Cited by 90

Big data applications: overview, challenges and future

Badshah, A., Daud, A. Y., Alharbey, R., Banjar, A., Bukhari, A. A., & Alshemaimri, B. K. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10938-5 · Cited by 117

A critical review of the data pipeline: how wastewater system operation flows from data to intelligence

Therrien, J.-D., Nicolaï, N., & Vanrolleghem, P. A. (2020) · Water Science & Technology · doi:10.2166/wst.2020.393 · Cited by 97

Remote sensing and modeling fusion for investigating the ecosystem water-carbon coupling processes

Sun, P., Wu, Y., Xiao, J., Hui, J., Hu, J., Zhao, F., Qiu, L., & Liu, S. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.134064 · Cited by 87

Global and national trends, gaps, and opportunities in documenting and monitoring species distributions

Oliver, R. Y., Meyer, C., Ranipeta, A., Winner, K., & Jetz, W. (2021) · PLoS Biology · doi:10.1371/journal.pbio.3001336 · Cited by 92

Incorporating physiology into species distribution models moderates the projected impact of warming on selected Mediterranean marine species

Gamliel, I., Buba, Y., Guy‐Haim, T., Garval, T., Willette, D. A., Rilov, G., & Belmaker, J. (2020) · Ecography · doi:10.1111/ecog.04423 · Cited by 78

The power of forecasts to advance ecological theory

Lewis, A. S. L., Rollinson, C. R., Allyn, A., Ashander, J., Brodie, S. J., Brookson, C. B., Collins, E. L., Dietze, M. C., Gallinat, A. S., Juvigny‐Khenafou, N. P. D., et al. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13955 · Cited by 87

fundiversity: a modular R package to compute functional diversity indices

Grenié, M., & Gruson, H. (2023) · Ecography · doi:10.1111/ecog.06585 · Cited by 76

Stressing over the Complexities of Multiple Stressors in Marine and Estuarine Systems

Glibert, P. M., Cai, W., Hall, E. R., Li, M., Main, K. L., Rose, K. A., Testa, J. M., & Vidyarathna, N. K. (2022) · Ocean-Land-Atmosphere Research · doi:10.34133/2022/9787258 · Cited by 75

Human vs. machine: Detecting wildlife in camera trap images

Leorna, S., & Brinkman, T. J. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101876 · Cited by 68

Data-Intensive Ecological Research Is Catalyzed by Open Science and Team Science

Cheruvelil, K. S., & Soranno, P. A. (2018) · BioScience · doi:10.1093/biosci/biy097 · Cited by 66

An Outlook for Deep Learning in Ecosystem Science

Perry, G. L. W., Seidl, R., Bellvé, A. M., & Rammer, W. (2022) · Ecosystems · doi:10.1007/s10021-022-00789-y · Cited by 72

Non‐native species drive biotic homogenization, but it depends on the realm, beta diversity facet and study design: a meta‐analytic systematic review

Petsch, D. K., Bertoncin, A. P. D. S., Ortega, J. C. G., & Thomaz, S. M. (2022) · Oikos · doi:10.1111/oik.08768 · Cited by 69

TaxoNERD: Deep neural models for the recognition of taxonomic entities in the ecological and evolutionary literature

Guillarme, N. L., & Thuiller, W. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13778 · Cited by 60

Advances in image acquisition and processing technologies transforming animal ecological studies

Nazir, S., & Kaleem, M. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101212 · Cited by 58

Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

Abdi, A. M., Brandt, M., Abel, C., & Fensholt, R. (2022) · Journal of Remote Sensing · doi:10.34133/2022/9835284 · Cited by 61

The Internet of Animals: what it is, what it could be

Kays, R., & Wikelski, M. C. (2023) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2023.04.007 · Cited by 58

‘Small Data’ for big insights in ecology

Todman, L., Bush, A., & Hood, A. S. C. (2023) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2023.01.015 · Cited by 51

A framework to integrate innovations in invasion science for proactive management

van Rees, C. B., Hand, B. K., Carter, S. C., Bargeron, C. T., Cline, T. J., Daniel, W. M., Ferrante, J. A., Gaddis, K. D., Hunter, M. E., Jarnevich, C. S., et al. (2022) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.12859 · Cited by 55

Ensembles of data-efficient vision transformers as a new paradigm for automated classification in ecology

Kyathanahally, S. P., Hardeman, T., Reyes, M., Merz, E., Bulas, T., Brun, P., Pomati, F., & Baity‐Jesi, M. (2022) · Scientific Reports · doi:10.1038/s41598-022-21910-0 · Cited by 47

Moving toward a new era of ecosystem science

Yu, G., Piao, S. L., Zhang, Y., Liu, L., Peng, J., & Niu, S. (2021) · Geography and sustainability · doi:10.1016/j.geosus.2021.06.004 · Cited by 50

Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects

Roy, D. B., Alison, J., August, T., Bélisle, M., Bjerge, K., Bowden, J. J., Bunsen, M. J., Cunha, F., Geissmann, Q., Goldmann, K., et al. (2024) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2023.0108 · Cited by 53

Animal-borne technologies in wildlife research and conservation

Rafiq, K., Pitcher, B. J., Cornelsen, K. A., Hansen, K. W., King, A. J., Appleby, R. G., Abrahms, B. L., & Jordan, N. R. (2021) · doi:10.1093/oso/9780198850243.003.0006 · Cited by 40

Building integrated plant health surveillance: a proactive research agenda for anticipating and mitigating disease and pest emergence

Soubeyrand, S., Estoup, A., Cruaud, A., Malembic-Maher, S., Meynard, C. N., Ravigné, V., Barbier, M., Barrès, B., Berthier, K., Boitard, S., et al. (2024) · CABI Agriculture and Bioscience · doi:10.1186/s43170-024-00273-8 · Cited by 51

Elephants and algorithms: a review of the current and future role of AI in elephant monitoring

Brickson, L. L., Zhang, L., Vollrath, F. W., Douglas‐Hamilton, I., & Titus, A. (2023) · Journal of The Royal Society Interface · doi:10.1098/rsif.2023.0367 · Cited by 55

Towards vibrant fish populations and sustainable fisheries that benefit all: learning from the last 30 years to inform the next 30 years

Cooke, S. J., Fulton, E. A., Sauer, W., Lynch, A. J., Link, J. S., Koning, A. A., Jena, J., Silva, L. G. M., King, A. J., Kelly, R., et al. (2023) · Reviews in Fish Biology and Fisheries · doi:10.1007/s11160-023-09765-8 · Cited by 51

Big data approaches to the spatial ecology and conservation of marine megafauna

Grémillet, D., Chevallier, D., & Guinet, C. (2022) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsac059 · Cited by 36

Evaluating the use of semi-structured crowdsourced data to quantify inequitable access to urban biodiversity: A case study with eBird

Grade, A. M., Chan, N. W., Gajbhiye, P., Perkins, D. J., & Warren, P. S. (2022) · PLoS ONE · doi:10.1371/journal.pone.0277223 · Cited by 35

Camtrap DP: an open standard for the FAIR exchange and archiving of camera trap data

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) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.374 · Cited by 50

Drones and AI-Driven Solutions for Wildlife Monitoring

Aliane, N. (2025) · Drones · doi:10.3390/drones9070455 · Cited by 39

Provoking a Cultural Shift in Data Quality

McCord, S. E., Webb, N. P., Zee, J. W. V., Burnett, S. H., Christensen, E. M., Courtright, E. M., Laney, C., Lunch, C. K., Maxwell, C. M., Karl, J. W., et al. (2021) · BioScience · doi:10.1093/biosci/biab020 · Cited by 33

MoveApps: a serverless no-code analysis platform for animal tracking data

Kölzsch, A., Davidson, S. C., Gauggel, D., Hahn, C., Hirt, J., Kays, R., Lang, I., Lohr, A., Russell, B., Scharf, A. K., et al. (2022) · Movement Ecology · doi:10.1186/s40462-022-00327-4 · Cited by 32

Exploring the landscape of automated species identification apps: Development, promise, and user appraisal

Truong, M.-X., & van der Wal, R. (2024) · BioScience · doi:10.1093/biosci/biae077 · Cited by 35

Four key challenges in the open‐data revolution

Salguero‐Gómez, R., Jackson, J., & Gascoigne, S. J. L. (2021) · Journal of Animal Ecology · doi:10.1111/1365-2656.13567 · Cited by 31

Artificial Intelligence-Assisted Environmental DNA Metabarcoding and High-Resolution Underwater Optical Imaging for Noninvasive and Innovative Marine Environmental Monitoring

Yang, J., Li, C., Lo, L. S. H., Zhang, X., Chen, Z., Gao, J., Clara, U., Dai, Z., Nakaoka, M., Yang, H., et al. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12101729 · Cited by 34

Advancing causal inference in ecology: Pathways for biodiversity change detection and attribution

Schrodt, F., Beck, M., Estopinan, J., Bowler, D. E., Fontaine, C., Gaüzère, P., Goury, R., Grenié, M., Martins, I. S., Morueta‐Holme, N., et al. (2025) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.70131 · Cited by 40

Changing the Nature of Quantitative Biology Education: Data Science as a Driver

Robeva, R., Jungck, J. R., & Gross, L. J. (2020) · Bulletin of Mathematical Biology · doi:10.1007/s11538-020-00785-0 · Cited by 29

Accelerating advances in landscape connectivity modelling with the ConScape library

Moorter, B. F. A. V., Kivimäki, I., Noack, A., Devooght, R., Panzacchi, M., Hall, K. R., Leleux, P., & Saerens, M. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13850 · Cited by 29

Why don’t we share data and code? Perceived barriers and benefits to public archiving practicesPreprint

Gomes, D. G. E., Pottier, P., Crystal‐Ornelas, R., Hudgins, E. J., Foroughirad, V., Sánchez‐Reyes, L. L., Turba, R., Martinez, P. A., Moreau, D., Bertram, M. G., et al. (2022) · doi:10.31222/osf.io/gaj43 · Cited by 29

Introductory overview: Recommendations for approaching scientific visualization with large environmental datasets

Kelleher, C. A., & Braswell, A. (2021) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2021.105113 · Cited by 30

Priorities for synthesis research in ecology and environmental science

Halpern, B. S., Boettiger, C., Dietze, M. C., Gephart, J. A., González, P., Grimm, N. B., Groffman, P. M., Gurevitch, J., Hobbie, S. E., Komatsu, K. J., et al. (2023) · Ecosphere · doi:10.1002/ecs2.4342 · Cited by 29

Effects of morphology and sediment permeability on coastal lagoons’ hydrological patterns

Meredith, W., Casamitjana, X., Quintana, X. D., & Menció, A. (2022) · Journal of Hydrology · doi:10.1016/j.jhydrol.2022.128259 · Cited by 27

AcousticIA, a deep neural network for multi-species fish detection using multiple models of acoustic cameras

Garcia, G. F., Corpetti, T., Nevoux, M., Beaulaton, L., & Martignac, F. (2023) · Aquatic Ecology · doi:10.1007/s10452-023-10004-2 · Cited by 36

Comparing the reliability of relative bird abundance indices from standardized surveys and community science data at finer resolutions

Feng, M.-L. E., & Che‐Castaldo, J. P. (2021) · PLoS ONE · doi:10.1371/journal.pone.0257226 · Cited by 26

Issues in calibrating models with multiple unbalanced constraints: the significance of systematic model and data errors

Cameron, D., Hartig, F., Minunno, F., Oberpriller, J., Reineking, B., Oijen, M. A. V., & Dietze, M. C. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14002 · Cited by 26

Uniting Experiments and Big Data to advance ecology and conservation

McCleery, R. A., Guralnick, R., Beatty, M. A., Belitz, M. W., Campbell, C. J., Idec, J. H., Jones, M. M., Kang, Y., Potash, A. D., & Fletcher, R. J. (2023) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2023.05.010 · Cited by 26

AI will change EA practice – but are we ready for it? A call for discussion based on developments in collecting and processing biodiversity data

Sandfort, R., Uhlhorn, B., Geißler, G., Lyhne, I., & Jiricka-Pürrer, A. (2024) · Impact Assessment and Project Appraisal · doi:10.1080/14615517.2024.2318684 · Cited by 24

Improper data practices erode the quality of global ecological databases and impede the progress of ecological research

Augustine, S. P., Bailey‐Marren, I., Charton, K. T., Kiel, N. G., & Peyton, M. (2024) · Global Change Biology · doi:10.1111/gcb.17116 · Cited by 27

AngleCam : Predicting the temporal variation of leaf angle distributions from image series with deep learning

Kattenborn, T., Richter, R., Guimarães‐Steinicke, C. R., Feilhauer, H., & Wirth, C. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13968 · Cited by 24

Ecology with artificial intelligence and machine learning in Asia: A historical perspective and emerging trends

Ryo, M. (2023) · Ecological Research · doi:10.1111/1440-1703.12425 · Cited by 23

Recommendations for quantitative uncertainty consideration in ecology and evolution

Simmonds, E. G., Adjei, K. P., Cretois, B., Dickel, L., González‐Gil, R., Laverick, J. H., Mandeville, C. P., Mandeville, E. G., Ovaskainen, O., Sicacha-Parada, J., et al. (2023) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2023.10.012 · Cited by 23

Deep learning with citizen science data enables estimation of species diversity and composition at continental extents

Davis, C. L., Bai, Y., Chen, D., Robinson, O. J., Ruiz‐Gutiérrez, V., Gomes, C. P., & Fink, D. (2023) · Ecology · doi:10.1002/ecy.4175 · Cited by 30

Connectivity and flow regime direct conservation priorities for pelagophil fishes

Mollenhauer, R., Brewer, S. K., Perkin, J. S., Swedberg, D. A., Wedgeworth, M., & Steffensmeier, Z. D. (2021) · Aquatic Conservation Marine and Freshwater Ecosystems · doi:10.1002/aqc.3631 · Cited by 23

Trait-Based Research on Rotifera: The Holy Grail or Just Messy?

Obertegger, U., & WALLACE, R. L. (2023) · Water · doi:10.3390/w15081459 · Cited by 22

Open Data Practices among Users of Primary Biodiversity Data

Mandeville, C. P., Koch, W., Nilsen, E. B., & Finstad, A. G. (2021) · BioScience · doi:10.1093/biosci/biab072 · Cited by 31

A gentle introduction to computer vision‐based specimen classification in ecological datasets

Blair, J. D., Gaynor, K. M., Palmer, M. S., & Marshall, K. E. (2024) · Journal of Animal Ecology · doi:10.1111/1365-2656.14042 · Cited by 23

Using explainable machine learning methods to evaluate vulnerability and restoration potential of ecosystem state transitions

Delaney, J. T., & Larson, D. M. (2023) · Conservation Biology · doi:10.1111/cobi.14203 · Cited by 21

A semi-automatic workflow to process images from small mammal camera traps

Böhner, H., Kleiven, E. F., Ims, R. A., & Soininen, E. M. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102150 · Cited by 21

Opinions of citizen scientists on open access to UK butterfly and moth occurrence data

Fox, R., Bourn, N. A. D., Dennis, E. B., Heafield, R. T., Maclean, I. M. D., & Wilson, R. J. (2019) · Biodiversity and Conservation · doi:10.1007/s10531-019-01824-6 · Cited by 21

Achieving higher standards in species distribution modeling by leveraging the diversity of available software

Kass, J. M., Smith, A. B., Warren, D. L., Vignali, S., Schmitt, S., Aiello‐Lammens, M. E., Arlé, E., Barbosa, A. M., Broennimann, O., Cobos, M. E., et al. (2024) · Ecography · doi:10.1111/ecog.07346 · Cited by 20

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Rivera‐Monroy, V. H., Zhao, X., Wang, H., & Xue, Z. G. (2022) · Forests · doi:10.3390/f13101638 · Cited by 20

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McCrea, R. S., King, R., Graham, L., & Börger, L. (2023) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14050 · Cited by 20

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Pijanowski, B. C., Rivas, F., Banerjee, S., Minghim, R., Lima, S. L., Bowers-Sword, R., Guzman, S. R., Revuelta-Acosta, J. D., Adeniji, A. E., Grimes, S. E., et al. (2024) · Current Landscape Ecology Reports · doi:10.1007/s40823-024-00101-9 · Cited by 20

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Bestelmeyer, B. T., McCord, S. E., Browning, D. M., Burkett, L. M., Elias, E. H., Estell, R. E., Herrick, J. E., James, D. K., Spiegal, S. A., Utsumi, S. A., et al. (2024) · Frontiers in Ecology and the Environment · doi:10.1002/fee.2736 · Cited by 19

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Levy, O., & Shahar, S. (2024) · Integrative and Comparative Biology · doi:10.1093/icb/icae127 · Cited by 19

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Gebler, D., Kolada, A., Pasztaleniec, A., & Szoszkiewicz, K. (2020) · Environmental Science and Pollution Research · doi:10.1007/s11356-020-10731-1 · Cited by 19

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Wohner, C., Peterseil, J., & Klug, H. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101708 · Cited by 18

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Li, D., Record, S., Sokol, E. R., Bitters, M. E., Chen, M. Y., Chung, Y. A., Helmus, M. R., Jaimes, R., Jansen, L., Jarzyna, M. A., et al. (2022) · Ecosphere · doi:10.1002/ecs2.4141 · Cited by 18

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Merow, C., Boyle, B. L., Enquist, B. J., Feng, X., Kass, J. M., Maitner, B., McGill, B. J., Owens, H. L., Park, D., Paz, A., et al. (2023) · Nature Ecology & Evolution · doi:10.1038/s41559-023-02008-w · Cited by 18

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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

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Richardson, L. E., Heenan, A., Delargy, A. J., Neubauer, P., Lecky, J., Gove, J. M., Green, M., Kindinger, T. L., Ingeman, K. E., & Williams, G. J. (2023) · Nature Ecology & Evolution · doi:10.1038/s41559-023-02201-x · Cited by 18

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Wegrzyn, J., Falk, T., Grau, E. S., Buehler, S. A., Ramnath, R., & Herndon, N. (2019) · Evolutionary Applications · doi:10.1111/eva.12860 · Cited by 17

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Supp, S. R., Bohrer, G., Fieberg, J., & Sorte, F. A. L. (2021) · Movement Ecology · doi:10.1186/s40462-021-00294-2 · Cited by 17

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Poger, D., Yen, L., & Braet, F. (2023) · Histochemistry and Cell Biology · doi:10.1007/s00418-023-02191-8 · Cited by 22

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Bimonte, S., Billaud, O., Fontaine, B., Martin, T., Flouvat, F., Hassan, A., Rouillier, N., & Sautot, L. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101231 · Cited by 18

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Manley, W., Tran, T., Prusinski, M. A., & Brisson, D. (2023) · Peer Community Journal · doi:10.24072/pcjournal.353 · Cited by 18

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Carter, S., van Rees, C. B., Hand, B. K., Muhlfeld, C. C., Luikart, G., & Kimball, J. S. (2021) · Frontiers in Big Data · doi:10.3389/fdata.2021.734990 · Cited by 19

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Aoki, L. R., Brisbin, M. M., Hounshell, A. G., Kincaid, D. W., Larson, E. I., Sansom, B. J., Shogren, A. J., Smith, R. S., & Sullivan‐Stack, J. (2022) · BioScience · doi:10.1093/biosci/biac020 · Cited by 17

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Hampton, S. E., Strasser, C. A., Tewksbury, J. J., Gram, W. K., Budden, A. E., Batcheller, A. L., Duke, C. S., & Porter, J. H. (2013) · Frontiers in Ecology and the Environment · doi:10.1890/120103 · Cited by 930

Ecoinformatics: supporting ecology as a data-intensive science

Michener, W. K., & Jones, M. B. (2012) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2011.11.016 · Cited by 458

Trends and challenges of ecosystem observations in the age of big data

Dai, S., & Zhao, B. (2016) · Biodiversity Science · doi:10.17520/biods.2015150 · Cited by 8

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Peters, D. P. C., & Okin, G. S. (2016) · Ecosystems · doi:10.1007/s10021-016-0072-1 · Cited by 23

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Todman, L., Bush, A., & Hood, A. S. C. (2023) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2023.01.015 · Cited by 51