Aplicación de inteligencia artificial generativa en biología y gestión ambiental para la conservación sostenible

Ricardo Manuel Candanedo Yau; Belkis Ichelle Camaño Lasso

doi:10.48204/j.mesoamericana.v28n2.a11302

Submitted September 21, 2026

Published 2026-09-21

Artículos

Vol. 28 No. 2 (2026): Mesoamericana

Application of generative artificial intelligence in biology and environmental management for sustainable conservation

Ricardo Manuel Candanedo Yau ⁺⁻
Belkis Ichelle Camaño Lasso⁺⁻

DOI https://doi.org/10.48204/j.mesoamericana.v28n2.a11302

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DOI: 10.48204/j.mesoamericana.v28n2.a11302

Published: 2026-09-21

How to Cite

Candanedo Yau , R. M. and Camaño Lasso, B. I. (2026) “Application of generative artificial intelligence in biology and environmental management for sustainable conservation”, Mesoamericana, 28(2), pp. 17–30. doi: 10.48204/j.mesoamericana.v28n2.a11302.

Abstract

This research analyzed the use of generative artificial intelligence in biology and environmental management, with an emphasis on its contribution to the sustainable conservation of ecosystems. The study was developed using a qualitative approach, through a descriptive documentary research based on a systematic review of recent scientific literature related to generative models, environmental monitoring, and biological data analysis. Initially, 96 publications were in indexed databases; subsequently, after applying criteria of thematic relevance, currency, and direct relationship to ecological sustainability, 50 documents were selected for the final analysis. The findings showed that artificial intelligence-based tools optimize the processing of ecological information, facilitate the prediction of environmental changes, and strengthen decision-making in conservation programs. It was also observed that these technologies facilitate the automation of scientific processes and the development of predictive models applied to biodiversity protection. Overall, the results showed that the integration of generative artificial intelligence into environmental management represents a technological alternative with broad potential to support sustainable strategies in the face of current ecological challenges. It was concluded that its responsible implementation can contribute to strengthening environmental sustainability and preserving natural resources.

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References

Abrahms, B., Beery, S., Palmer, M. S., Harchaoui, Z., & Rafiq, K. (2025). Generative AI as a tool to accelerate the field of ecology. Nature Ecology & Evolution, 9, 378–385. https://doi.org/10.1038/s41559-024-02623-1
Ahmed, N., Rahman, T., & Islam, M. (2023). Remote sensing and AI for ecosystem conservation. Remote Sensing Applications: Society and Environment, 31, 100963. https://doi.org/10.1016/j.rsase.2023.100963
Anderson, C., White, J., & Moore, P. (2021). Artificial intelligence technologies for sustainable environmental management. Environmental Research Letters, 16(12), 124038. https://doi.org/10.1088/1748-9326/ac3d91
Brown, T., Harris, E., & Clark, D. (2022). AI-assisted environmental management and sustainable ecosystem planning. Journal of Environmental Management, 318, 115601. https://doi.org/10.1016/j.jenvman.2022.115601
Chen, Y., Wu, L., & Fang, Z. (2024). Generative AI and environmental decision support systems. Environmental Impact Assessment Review, 106, 107435. https://doi.org/10.1016/j.eiar.2024.107435
Dillon, S. C., Provost, L., & Tritinger, A. (2026). AI-driven insights on coastal management: A comparative discussion of the use of generative chatbots and natural infrastructure. Frontiers in Ecology and Evolution, 14, 1636308. https://doi.org/10.3389/fevo.2026.1636308
Félix, R., Correia, F., Pedroso-Roussado, C., & Nunes, N. J. (2026). Exploring a biocentric LLM-based assistant in environmental decision-making with more-than-human representation of the Tagus Estuary. Discover Sustainability, 7, 241. https://doi.org/10.1007/s43621-025-02474-1
Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2023). Generative AI. Business & Information Systems Engineering, 66(1), 111–126. https://doi.org/10.1007/s12599-023-00834-7
González, R., Pérez, M., & Salas, C. (2021). Digital transformation and sustainability in environmental research. Sustainability, 13(21), 11987. https://doi.org/10.3390/su132111987
Guerrero-Casado, J., Murillo-Jiménez, T., Carpio, A. J., Tortosa, F. S., & Serrano-Rodríguez, R. (2026). Threats to conservation from artificial-intelligence-generated wildlife images and videos. Conservation Biology, 40(1), e70138. https://doi.org/10.1111/cobi.70138
Hernández, J., Morales, A., & Castillo, P. (2023). Intelligent environmental monitoring systems based on artificial intelligence. Sensors, 23(11), 5120. https://doi.org/10.3390/s23115120
Islam, S., Koivula, H., Andrew, C., Lopez Gordillo, J., Weiland, C., & Schigel, D. (2026). FAIR digital twins for biodiversity: Enabling data, model, and workflow integration. npj Biodiversity, 5, 5. https://doi.org/10.1038/s44185-025-00116-3
Johnson, P., Green, R., & Evans, T. (2023). Artificial intelligence in conservation science: Challenges and opportunities. Biological Conservation, 285, 110210. https://doi.org/10.1016/j.biocon.2023.110210
Kong, L., Kim, C. W., Choo, D., & Tambe, M. (2026). Generative AI for social impact. arXiv. https://arxiv.org/abs/2601.04238
Kong, L., Wang, H., Emogor, C. A., Börsch-Supan, V., Xu, L., & Tambe, M. (2025). Generative AI against poaching: Latent composite flow matching for wildlife conservation. arXiv. https://arxiv.org/abs/2508.14342
Kumar, R., Singh, A., & Verma, P. (2025). Machine learning approaches for wildlife conservation and habitat monitoring. Ecological Modelling, 487, 110512. https://doi.org/10.1016/j.ecolmodel.2025.110512
Lee, J., Kim, S., & Park, H. (2024). Ethical implications of artificial intelligence in environmental sciences. AI & Society, 39(4), 1467–1481. https://doi.org/10.1007/s00146-024-01821-5
Li, W., Zhou, Y., & Chen, X. (2022). Machine learning applications in environmental risk assessment and ecosystem conservation. Science of the Total Environment, 806, 150674. https://doi.org/10.1016/j.scitotenv.2021.150674
Martínez, H., Ortega, L., & Núñez, P. (2025). Sustainable environmental governance supported by artificial intelligence systems. Sustainability, 17(3), 1455. https://doi.org/10.3390/su17031455
Patel, S., Kumar, V., & Singh, D. (2025). Generative models for ecological forecasting and biodiversity analysis. Environmental Modelling & Software, 183, 106201. https://doi.org/10.1016/j.envsoft.2025.106201
Peterson, A., Miller, G., & Davis, K. (2025). Artificial intelligence for ecological resilience and sustainability planning. Ecological Indicators, 169, 112345. https://doi.org/10.1016/j.ecolind.2025.112345
Ramírez, D., López, J., & Martínez, E. (2022). Smart technologies for biodiversity preservation and ecological sustainability. Journal of Cleaner Production, 370, 133501. https://doi.org/10.1016/j.jclepro.2022.133501
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Rodríguez, F., Sánchez, V., & Gómez, A. (2026). Generative artificial intelligence and ecological sustainability: Emerging trends in environmental conservation. Environmental Advances, 18, 100621. https://doi.org/10.1016/j.envadv.2026.100621
Silva, A., Ferreira, L., & Costa, R. (2024). AI-driven biodiversity monitoring using computer vision techniques. Ecological Informatics, 79, 102318. https://doi.org/10.1016/j.ecoinf.2024.102318
Silva, M., Torres, J., & Andrade, F. (2024). Artificial intelligence and climate change prediction in environmental systems. Sustainable Computing: Informatics and Systems, 41, 100987. https://doi.org/10.1016/j.suscom.2024.100987
Tuia, D., Beery, S., Costelloe, B. R., & Oliver, R. Y. (2026). Towards digital ecology: Advances in integrating artificial intelligence from data generation to ecological insight. Methods in Ecology and Evolution, 17(2), 222–227. https://doi.org/10.1111/2041-210X.70243
Vuilliomenet, A., Jones, K E., & Wilson, D. (2026). Future of Edge AI in biodiversity monitoring. arXiv. https://arxiv.org/abs/2602.13496
Wang, Y., Li, X., & Chen, H. (2024). Artificial intelligence applications in biodiversity conservation and ecological monitoring. Ecological Informatics, 82, 102445. https://doi.org/10.1016/j.ecoinf.2024.102445
Zhang, L., Zhao, P., & Liu, Q. (2023). Deep learning and environmental sustainability: Emerging applications in ecosystem management. Environmental Science and Pollution Research, 30(14), 39821–39839. https://doi.org/10.1007/s11356-023-25684-7

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[1] Abrahms, B., Beery, S., Palmer, M. S., Harchaoui, Z., & Rafiq, K. (2025). Generative AI as a tool to accelerate the field of ecology. Nature Ecology & Evolution, 9, 378–385. https://doi.org/10.1038/s41559-024-02623-1

[2] Ahmed, N., Rahman, T., & Islam, M. (2023). Remote sensing and AI for ecosystem conservation. Remote Sensing Applications: Society and Environment, 31, 100963. https://doi.org/10.1016/j.rsase.2023.100963

[3] Anderson, C., White, J., & Moore, P. (2021). Artificial intelligence technologies for sustainable environmental management. Environmental Research Letters, 16(12), 124038. https://doi.org/10.1088/1748-9326/ac3d91

[4] Brown, T., Harris, E., & Clark, D. (2022). AI-assisted environmental management and sustainable ecosystem planning. Journal of Environmental Management, 318, 115601. https://doi.org/10.1016/j.jenvman.2022.115601

[5] Chen, Y., Wu, L., & Fang, Z. (2024). Generative AI and environmental decision support systems. Environmental Impact Assessment Review, 106, 107435. https://doi.org/10.1016/j.eiar.2024.107435

[6] Dillon, S. C., Provost, L., & Tritinger, A. (2026). AI-driven insights on coastal management: A comparative discussion of the use of generative chatbots and natural infrastructure. Frontiers in Ecology and Evolution, 14, 1636308. https://doi.org/10.3389/fevo.2026.1636308

[7] Félix, R., Correia, F., Pedroso-Roussado, C., & Nunes, N. J. (2026). Exploring a biocentric LLM-based assistant in environmental decision-making with more-than-human representation of the Tagus Estuary. Discover Sustainability, 7, 241. https://doi.org/10.1007/s43621-025-02474-1

[8] Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2023). Generative AI. Business & Information Systems Engineering, 66(1), 111–126. https://doi.org/10.1007/s12599-023-00834-7

[9] González, R., Pérez, M., & Salas, C. (2021). Digital transformation and sustainability in environmental research. Sustainability, 13(21), 11987. https://doi.org/10.3390/su132111987

[10] Guerrero-Casado, J., Murillo-Jiménez, T., Carpio, A. J., Tortosa, F. S., & Serrano-Rodríguez, R. (2026). Threats to conservation from artificial-intelligence-generated wildlife images and videos. Conservation Biology, 40(1), e70138. https://doi.org/10.1111/cobi.70138

[11] Hernández, J., Morales, A., & Castillo, P. (2023). Intelligent environmental monitoring systems based on artificial intelligence. Sensors, 23(11), 5120. https://doi.org/10.3390/s23115120

[12] Islam, S., Koivula, H., Andrew, C., Lopez Gordillo, J., Weiland, C., & Schigel, D. (2026). FAIR digital twins for biodiversity: Enabling data, model, and workflow integration. npj Biodiversity, 5, 5. https://doi.org/10.1038/s44185-025-00116-3

[13] Johnson, P., Green, R., & Evans, T. (2023). Artificial intelligence in conservation science: Challenges and opportunities. Biological Conservation, 285, 110210. https://doi.org/10.1016/j.biocon.2023.110210

[14] Kong, L., Kim, C. W., Choo, D., & Tambe, M. (2026). Generative AI for social impact. arXiv. https://arxiv.org/abs/2601.04238

[15] Kong, L., Wang, H., Emogor, C. A., Börsch-Supan, V., Xu, L., & Tambe, M. (2025). Generative AI against poaching: Latent composite flow matching for wildlife conservation. arXiv. https://arxiv.org/abs/2508.14342

[16] Kumar, R., Singh, A., & Verma, P. (2025). Machine learning approaches for wildlife conservation and habitat monitoring. Ecological Modelling, 487, 110512. https://doi.org/10.1016/j.ecolmodel.2025.110512

[17] Lee, J., Kim, S., & Park, H. (2024). Ethical implications of artificial intelligence in environmental sciences. AI & Society, 39(4), 1467–1481. https://doi.org/10.1007/s00146-024-01821-5

[18] Li, W., Zhou, Y., & Chen, X. (2022). Machine learning applications in environmental risk assessment and ecosystem conservation. Science of the Total Environment, 806, 150674. https://doi.org/10.1016/j.scitotenv.2021.150674

[19] Martínez, H., Ortega, L., & Núñez, P. (2025). Sustainable environmental governance supported by artificial intelligence systems. Sustainability, 17(3), 1455. https://doi.org/10.3390/su17031455

[20] Patel, S., Kumar, V., & Singh, D. (2025). Generative models for ecological forecasting and biodiversity analysis. Environmental Modelling & Software, 183, 106201. https://doi.org/10.1016/j.envsoft.2025.106201

[21] Peterson, A., Miller, G., & Davis, K. (2025). Artificial intelligence for ecological resilience and sustainability planning. Ecological Indicators, 169, 112345. https://doi.org/10.1016/j.ecolind.2025.112345

[22] Ramírez, D., López, J., & Martínez, E. (2022). Smart technologies for biodiversity preservation and ecological sustainability. Journal of Cleaner Production, 370, 133501. https://doi.org/10.1016/j.jclepro.2022.133501

[23] Reuters. (2026, 13 de enero). How AI is helping to bring nature into the boardroom. Reuters Sustainability. https://www.reuters.com/sustainability/land-use-biodiversity/how-ai-is-helping-bring-nature-into-boardroom--ecmii-2026-01-13/

[24] Rodríguez, F., Sánchez, V., & Gómez, A. (2026). Generative artificial intelligence and ecological sustainability: Emerging trends in environmental conservation. Environmental Advances, 18, 100621. https://doi.org/10.1016/j.envadv.2026.100621

[25] Silva, A., Ferreira, L., & Costa, R. (2024). AI-driven biodiversity monitoring using computer vision techniques. Ecological Informatics, 79, 102318. https://doi.org/10.1016/j.ecoinf.2024.102318

[26] Silva, M., Torres, J., & Andrade, F. (2024). Artificial intelligence and climate change prediction in environmental systems. Sustainable Computing: Informatics and Systems, 41, 100987. https://doi.org/10.1016/j.suscom.2024.100987

[27] Tuia, D., Beery, S., Costelloe, B. R., & Oliver, R. Y. (2026). Towards digital ecology: Advances in integrating artificial intelligence from data generation to ecological insight. Methods in Ecology and Evolution, 17(2), 222–227. https://doi.org/10.1111/2041-210X.70243

[28] Vuilliomenet, A., Jones, K E., & Wilson, D. (2026). Future of Edge AI in biodiversity monitoring. arXiv. https://arxiv.org/abs/2602.13496

[29] Wang, Y., Li, X., & Chen, H. (2024). Artificial intelligence applications in biodiversity conservation and ecological monitoring. Ecological Informatics, 82, 102445. https://doi.org/10.1016/j.ecoinf.2024.102445

[30] Zhang, L., Zhao, P., & Liu, Q. (2023). Deep learning and environmental sustainability: Emerging applications in ecosystem management. Environmental Science and Pollution Research, 30(14), 39821–39839. https://doi.org/10.1007/s11356-023-25684-7

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