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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Species Distribution Modeling for Machine Learning Practitioners: A Review
Sara Beery, Elijah Cole, Joseph Parker, et al.
(2021), pp. 329-348
Open Access | Times Cited: 45

Showing 1-25 of 45 citing articles:

Perspectives in machine learning for wildlife conservation
Devis Tuia, Benjamin Kellenberger, Sara Beery, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 407
Machine learning and deep learning—A review for ecologists
Maximilian Pichler, Florian Härtig
Methods in Ecology and Evolution (2023) Vol. 14, Iss. 4, pp. 994-1016
Open Access | Times Cited: 200
Mechanistic forecasts of species responses to climate change: The promise of biophysical ecology
Natalie J. Briscoe, Shane D. Morris, Paul D. Mathewson, et al.
Global Change Biology (2022) Vol. 29, Iss. 6, pp. 1451-1470
Open Access | Times Cited: 104
Top ten hazards to avoid when modeling species distributions: a didactic guide of assumptions, problems, and recommendations
Mariano Soley‐Guardia, Diego F. Alvarado‐Serrano, Robert P. Anderson
Ecography (2024) Vol. 2024, Iss. 4
Open Access | Times Cited: 38
Global range extension of bioclimatic zone of Bruguiera hainesii C.G.Rogers 1919 (Rhizophoraceae)
Mai Phuong Pham, Thi Thu Trang Hoang, Van Dien Pham, et al.
One Ecosystem (2025) Vol. 10
Open Access | Times Cited: 1
Eco-ISEA3H, a machine learning ready spatial database for ecometric and species distribution modeling
Michael Mechenich, Indrė Žliobaitė
Scientific Data (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 21
Conservation tools: the next generation of engineering–biology collaborations
Andrew Schulz, Cassie Shriver, Suzanne Stathatos, et al.
Journal of The Royal Society Interface (2023) Vol. 20, Iss. 205
Open Access | Times Cited: 13
Harnessing artificial intelligence to fill global shortfalls in biodiversity knowledge
Laura J. Pollock, Justin Kitzes, Sara Beery, et al.
(2025)
Closed Access
Study of ethnoveterinary medicine practices in a global hotspot of India using ecological modelling: why and where?
Kaushalendra Kumar Jha, Radhika Jha
(2025) Vol. 2, Iss. 1
Open Access
Tackling the problem of multi-class imbalanced classification on species distribution models using machine learning techniques
Bonelwa Sidumo
Research Square (Research Square) (2025)
Closed Access
Algorithms going wild – A review of machine learning techniques for terrestrial ecology
Cristina Cipriano, Sergio Noce, Simone Mereu, et al.
Ecological Modelling (2025) Vol. 506, pp. 111164-111164
Open Access
Modelling current and future suitable cultivation areas of cashew trees in Benin (West Africa) based on the major parasite and its parasitoid distribution under global climate warming
Coffi Fulgence Gbèwommindéa Dotonhoué, Adigla Appolinaire Wédjangnon, Gafarou Agoundé, et al.
Remote Sensing Applications Society and Environment (2025), pp. 101589-101589
Closed Access
Monitoring protected areas by integrating machine learning, remote sensing and citizen science
Thijs L. van der Plas, David G. Alexander, Michael J. O. Pocock
Ecological Solutions and Evidence (2025) Vol. 6, Iss. 2
Open Access
Generating Binary Species Range Maps
Filip Dorm, Christian Lange, Scott R. Loarie, et al.
Lecture notes in computer science (2025), pp. 1-17
Closed Access
Multi-scale and Multimodal Species Distribution Modeling
Nina van Tiel, Robin Zbinden, Emanuele Dalsasso, et al.
Lecture notes in computer science (2025), pp. 151-159
Closed Access
Impacts of base learners selection of heterogeneous ensemble for habitat suitability modeling
Omar El Alaoui, Ali Idri
Biodiversity and Conservation (2025)
Closed Access
Critical considerations for communicating environmental DNA science
Eric D. Stein, Christopher L. Jerde, Elizabeth Andruszkiewicz Allan, et al.
Environmental DNA (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 8
Predicting the current and future risk of ticks on livestock farms in Britain using random forest models
Katie Lihou, Richard Wall
Veterinary Parasitology (2022) Vol. 311, pp. 109806-109806
Open Access | Times Cited: 11
Opportunities for synthetic data in nature and climate finance
Nataliya Tkachenko
Frontiers in Artificial Intelligence (2024) Vol. 6
Open Access | Times Cited: 2
Modeling the importance of subsurface environmental variables in driving swordfish (Xiphias gladius) catchability in the Western Indian Ocean
Wei Tang, Xuefang Wang, Feng Wu, et al.
Fisheries Oceanography (2024) Vol. 33, Iss. 3
Closed Access | Times Cited: 2
Predicting the current habitat refugia of Himalayan Musk deer (Moschus chrysogaster) across Nepal
Bijaya Dhami, Nar Bahadur Chhetri, Bijaya Neupane, et al.
Ecology and Evolution (2024) Vol. 14, Iss. 2
Open Access | Times Cited: 2
Predicting species abundance using machine learning approach: a comparative assessment of random forest spatial variants and performance metrics
Ciza Arsène Mushagalusa, Adandé Belarmain Fandohan, Romain Glèlè Kakaï
Modeling Earth Systems and Environment (2024) Vol. 10, Iss. 4, pp. 5145-5171
Closed Access | Times Cited: 2
Identifying seasonal distribution patterns of fin whales across the Scotia Sea and the Antarctic Peninsula region using a novel approach combining habitat suitability models and ensemble learning methods
Sacha Viquerat, Claire M. Waluda, Amy S. Kennedy, et al.
Frontiers in Marine Science (2022) Vol. 9
Open Access | Times Cited: 10
Modelling the distribution of the Caucasian oak (Quercus macranthera) in Western Asia under future climate change scenarios
Nihal Kenar, Zaal Kikvidze
Botanica Serbica (2023) Vol. 47, Iss. 2, pp. 215-226
Open Access | Times Cited: 4
Biogeomorphological niche of a landform: Machine learning approaches reveal controls on the geographical distribution of Nitraria tangutorum nebkhas
Haochen Zhang, Shihan Li, Joseph A. Mason, et al.
Earth Surface Processes and Landforms (2024) Vol. 49, Iss. 5, pp. 1515-1529
Closed Access | Times Cited: 1
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