ReviewNature ecology & evolution2025
Generative AI as a tool to accelerate the field of ecology.
Review in Nature ecology & evolution, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Thinking Outside the Black Box: Seven Guidelines for GenAI Use in the Ecological Sciences.Ecology and evolution · 2026Article
- Citizen science platforms must mitigate against the threat of generative AI.Nature ecology & evolution · 2026Article
- The application of intelligent generation model for international discourse of grand canal culture based on artificial intelligence and BPNN model.Scientific reports · 2026Article
- Steering open-source AI to accelerate the sustainable development goals.Nature communications · 2026Article
- Art as a source of historical biodiversity data.iScience · 2026Review
- FAIR digital twins for biodiversity: enabling data, model, and workflow integration.npj biodiversity · 2026Article
- Predicting oil contamination in water using machine learning on microbial compositions.PloS one · 2026Article
- Spotlight on our AI policies.Nature ecology & evolution · 2025Article
- Towards a digital planetary health perspective: generative AI and the digital determinants of health.Health promotion international · 2025Article
- Insect Decline in the Anthropocene: Historical Parallels and Emerging Monitoring Tools.Insects · 2025Review
- AI carves out a niche in ecology and conservation research.Nature methods · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The emergence of generative artificial intelligence (AI) models specializing in the generation of new data with the statistical patterns and properties of the data upon which the models were trained has profoundly influenced a range of academic disciplines, industry and public discourse. Combined with the vast amounts of diverse data now available to ecologists, from genetic sequences to remotely sensed animal tracks, generative AI presents enormous potential applications within ecology. Here we draw upon a range of fields to discuss unique potential applications in which generative AI could accelerate the field of ecology, including augmenting data-scarce datasets, extending observations of ecological patterns and increasing the accessibility of ecological data. We also highlight key challenges, risks and considerations when using generative AI within ecology, such as privacy risks, model biases and environmental effects. Ultimately, the future of generative AI in ecology lies in the development of robust interdisciplinary collaborations between ecologists and computer scientists. Such partnerships will be important for embedding ecological knowledge within AI, leading to more ecologically meaningful and relevant models. This will be critical for leveraging the power of generative AI to drive ecological insights into species across the globe.
Indexed as
Identifiers
39880986What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.