Situating Ecology as a Big-Data Science: Current Advances, Challenges, and Solutions
Abstract
Ecology has joined a world of big data. Two complementary frameworks define big data: data that exceed the analytical capacities of individuals or disciplines or the “Four Vs” axes of volume, variety, veracity, and velocity. Variety predominates in ecoinformatics and limits the scalability of ecological science. Volume varies widely. Ecological velocity is low but growing as data throughput and societal needs increase. Ecological big-data systems include in situ and remote sensors, community data resources, biodiversity databases, citizen science, and permanent stations. Technological solutions include the development of open code- and data-sharing platforms, flexible statistical models that can handle heterogeneous data and sources of uncertainty, and cloud-computing delivery of high-velocity computing to large-volume analytics. Cultural solutions include training targeted to early and current scientific workforce and strengthening collaborations among ecologists and data scientists. The broader goal is to maximize the power, scalability, and timeliness of ecological insights and forecasting.
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Central challenges to efficient data analysis are the sheer volume of data generated by modern collection methods and the heterogeneous nature of many ecological datasets, which preclude the use of simple automated analysis techniques.
“Central challenges to efficient data analysis are the sheer volume of data generated by modern collection methods and the heterogeneous nature of many ecological datasets, which preclude the use of simple automated analysis techniques26.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [26]
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‘Small Data’ for big insights in ecology
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Drones and AI-Driven Solutions for Wildlife Monitoring
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- Authors
- Farley, S. S., Dawson, A., Goring, S. J., & Williams, J. W.
- Affiliations
- University of Wisconsin–Madison (US)
- Mount Royal University (CA)
- Venue
- BioScience
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-07-18
- Volume
- 68
- Issue
- 8
- Pages
- 563-576
- ISSN
- 0006-3568 (ISSN-L)
- 1525-3244
- OpenAlex
- W2883471235
- Cited by
- 331
- References
- 143
- Citations by year
- 2026: 36 · 2025: 65 · 2024: 50 · 2023: 52 · 2022: 53 · 2021: 38 · 2020: 27 · 2019: 8 · 2018: 2
- FWCI
- 26.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- BioScience (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Physical Sciences › Earth and Planetary Sciences › Geology › Environmental Monitoring and Data Management
- Keywords
- ecoinformatics, data scalability, remote sensing, citizen science, biodiversity databases, open data sharing, cloud computing, uncertainty quantification, ecological forecasting, data science education, interdisciplinary collaboration
About these checks
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