Towards ‘digital ecology’ : Advances in integrating artificial intelligence from data generation to ecological insight

dc.contributor.authorTuia, Devis
dc.contributor.authorBeery, Sara
dc.contributor.authorCostelloe, Blair R.
dc.contributor.authorOliver, Ruth Y.
dc.contributor.authorLecomte, Nicolas
dc.date.accessioned2026-02-11T10:58:21Z
dc.date.available2026-02-11T10:58:21Z
dc.date.issued2026-02
dc.description.abstract1. Ecology and artificial intelligence (AI) are becoming increasingly intertwined. Originally, the intersection between the two disciplines was driven by a critical need for AI to help process rapidly growing volumes of ecological data. Early applications primarily entailed applying AI methods to automate relatively basic tasks, such as detecting blank images from camera traps. However, researchers in both disciplines are beginning to recognize the potential for transformative advances when AI is fully integrated into ecological research and conservation practice. 2. This special feature presents research at the cutting edge of the AI–ecology interface, focusing on work that advances the state of both fields beyond proof‐of‐concept to true interdisciplinary insight. 3. The papers in this collection reveal a maturing field that balances technical advancement with ecological relevance. They address both methodological challenges and the critical need for meaningful integration between computer science innovations and fundamental ecological questions. 4. As a whole, this collection demonstrates the potential for AI to enhance both fundamental ecological understanding and applied conservation efforts, as well as to bridge the gap between scientific discovery and policy implementation. The special feature underscores the importance of genuine interdisciplinary collaboration in developing technologies that not only showcase technical prowess, but also address pressing ecological challenges and support evidence‐based decision‐making in biodiversity conservation.
dc.description.versionpublisheddeu
dc.identifier.doi10.1111/2041-210x.70243
dc.identifier.ppn1963111680
dc.identifier.urihttps://kops.uni-konstanz.de/handle/123456789/76157
dc.language.isoeng
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectartificial intelligence
dc.subjectconservation
dc.subject.ddc570
dc.titleTowards ‘digital ecology’ : Advances in integrating artificial intelligence from data generation to ecological insighteng
dc.typeJOURNAL_ARTICLE
dspace.entity.typePublication
kops.citation.bibtex
@article{Tuia2026-02Towar-76157,
  title={Towards ‘digital ecology’ : Advances in integrating artificial intelligence from data generation to ecological insight},
  year={2026},
  doi={10.1111/2041-210x.70243},
  number={2},
  volume={17},
  issn={2041-210X},
  journal={Methods in Ecology and Evolution},
  pages={222--227},
  author={Tuia, Devis and Beery, Sara and Costelloe, Blair R. and Oliver, Ruth Y. and Lecomte, Nicolas}
}
kops.citation.iso690TUIA, Devis, Sara BEERY, Blair R. COSTELLOE, Ruth Y. OLIVER, Nicolas LECOMTE, 2026. Towards ‘digital ecology’ : Advances in integrating artificial intelligence from data generation to ecological insight. In: Methods in Ecology and Evolution. Wiley. 2026, 17(2), S. 222-227. ISSN 2041-210X. eISSN 2041-210X. Verfügbar unter: doi: 10.1111/2041-210x.70243deu
kops.citation.iso690TUIA, Devis, Sara BEERY, Blair R. COSTELLOE, Ruth Y. OLIVER, Nicolas LECOMTE, 2026. Towards ‘digital ecology’ : Advances in integrating artificial intelligence from data generation to ecological insight. In: Methods in Ecology and Evolution. Wiley. 2026, 17(2), pp. 222-227. ISSN 2041-210X. eISSN 2041-210X. Available under: doi: 10.1111/2041-210x.70243eng
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2. This special feature presents research at the cutting edge of the AI–ecology interface, focusing on work that advances the state of both fields beyond proof‐of‐concept to true interdisciplinary insight.
3. The papers in this collection reveal a maturing field that balances technical advancement with ecological relevance. They address both methodological challenges and the critical need for meaningful integration between computer science innovations and fundamental ecological questions. 
4. As a whole, this collection demonstrates the potential for AI to enhance both fundamental ecological understanding and applied conservation efforts, as well as to bridge the gap between scientific discovery and policy implementation. The special feature underscores the importance of genuine interdisciplinary collaboration in developing technologies that not only showcase technical prowess, but also address pressing ecological challenges and support evidence‐based decision‐making in biodiversity conservation.</dcterms:abstract>
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kops.sourcefieldMethods in Ecology and Evolution. Wiley. 2026, <b>17</b>(2), S. 222-227. ISSN 2041-210X. eISSN 2041-210X. Verfügbar unter: doi: 10.1111/2041-210x.70243deu
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kops.sourcefield.plainMethods in Ecology and Evolution. Wiley. 2026, 17(2), pp. 222-227. ISSN 2041-210X. eISSN 2041-210X. Available under: doi: 10.1111/2041-210x.70243eng
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