IMPACT OF AI IN WILDLIFE

Authors

  • Ms. Priya Harit
  • Arjun K

DOI:

https://doi.org/10.53555/ephijse.v11i1.292

Keywords:

effectiveness, artificial intelligence, technologies

Abstract

This research investigates the effectiveness of artificial intelligence (AI) technologies in enhancing wildlife conservation efforts, specifically in the face of critical challenges such as diminishing biodiversity and habitat loss. By analyzing quantitative data on species population trends, habitat utilization, and the deployment outcomes of AI applications, the study reveals that AI significantly improves monitoring accuracy and operational efficiency in conservation tasks. Key findings indicate that AI-driven methods, such as remote sensing and predictive analytics, lead to a measurable increase in species protection rates and habitat restoration success. The significance of these findings extends to the broader context of ecological health, emphasizing that healthy wildlife populations are essential not only for preserving ecosystems but also for maintaining the planetary health that underpins human well-being. Moreover, this research underscores the potential impact of AI in wildlife conservation as a model for adopting innovative technologies in the healthcare sector, where data-driven approaches can enhance patient outcomes, streamline resource allocation, and enable proactive health management. By bridging the fields of wildlife conservation and healthcare, this study advocates for a multidisciplinary approach to address global challenges, illustrating that technological advancements in one area can provide valuable insights and methodologies for another, ultimately fostering a more sustainable future for both wildlife and human health.

Author Biographies

Ms. Priya Harit

Lecturer IHM Chennai

Arjun K

IHM, Chennai

References

Paul Fergus, C. Chalmers, Steven N. Longmore, Serge A. Wich (2024) Harnessing Artificial Intelligence for Wildlife Conservation. Volume(abs/2409.10523). ArXiv. doi: https://www.semanticscholar.org/paper/9fbccff79228d44c6c4a8386426d3f6701d604ec

Suvarna Sathe, Mahesh Randhave, Manasi Sadhale, Aditi Joshi, Amit Khare, Nilesh Upadhye (2024) Assessing Wildlife Tourism's Role in Conservation Awareness Using Artificial Intelligence and Data Science for Sustainable Tourism Development. 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). doi: https://www.semanticscholar.org/paper/e19498600918f157938628334e456c42c258890c

Sathish Samiappan, B. Krishnan, Damion Dehart, Landon R. Jones, Jared A. Elmore, Kristine O. Evans, Raymond B. Iglay (2024) Aerial Wildlife Image Repository for animal monitoring with drones in the age of artificial intelligence. Volume(2024). Database: The Journal of Biological Databases and Curation. doi: https://www.semanticscholar.org/paper/3232d4eea82e897f69d6e289d39a4bf56d2afe00

Yasmin Rahmati (2024) Artificial Intelligence for Sustainable Urban Biodiversity: A Framework for Monitoring and Conservation. Volume(abs/2501.14766). ArXiv. doi: https://www.semanticscholar.org/paper/22765e2208648fe1da4e54f52b18ca6d6f63c93d

Tinao Petso, R. Jamisola (2023) Wildlife conservation using drones and artificial intelligence in Africa. Volume(8). Science Robotics. doi: https://www.semanticscholar.org/paper/a5b7b3cdb3f1979448ce085a8e36aa68f53d4749

Paul R. Krausman (2023) Managing artificial intelligence. Volume(87). Journal of Wildlife Management. doi: https://doi.org/10.1002/jwmg.22492

Gyandeep Chaudhary (2023) Environmental Sustainability: Can Artificial Intelligence be an Enabler for SDGs?. Volume(22), 1411-1420. Nature Environment and Pollution Technology. doi: https://doi.org/10.46488/nept.2023.v22i03.027

Gabriele Mirra, Alexander Holland, Stanislav Roudavski, Jasper S. Wijnands, Alberto Pugnale (2022) An Artificial Intelligence Agent That Synthesises Visual Abstractions of Natural Forms to Support the Design of Human-Made Habitat Structures. Volume(10). Frontiers in Ecology and Evolution. doi: https://doi.org/10.3389/fevo.2022.806453

Devis Tuia, Benjamin Kellenberger, Sara Beery, Blair R. Costelloe, Silvia Zuffi, Benjamin Risse, Alexander Mathis, et al. (2022) Perspectives in machine learning for wildlife conservation. Volume(13). Nature Communications. doi: https://doi.org/10.1038/s41467-022-27980-y

Raina K. Plowright, Jamie K. Reaser, Harvey Locke, Stephen Woodley, Jonathan A. Patz, Daniel J. Becker, Gabriel Oppler, et al. (2021) Land use-induced spillover: a call to action to safeguard environmental, animal, and human health. Volume(5), e237-e245. The Lancet Planetary Health. doi: https://doi.org/10.1016/s2542-5196(21)00031-0

Toph Allen, Kris A. Murray, Carlos Zambrana‐Torrelio, Stephen S. Morse, Carlo Rondinini, Moreno Di Marco, Nathan Breit, et al. (2017) Global hotspots and correlates of emerging zoonotic diseases. Volume(8). Nature Communications. doi: https://doi.org/10.1038/s41467-017-00923-8

Nathan Bennett, Robin Roth, Sarah C. Klain, Kai M. A. Chan, Patrick Christie, Douglas A. Clark, Georgina Cullman, et al. (2016) Conservation social science: Understanding and integrating human dimensions to improve conservation. Volume(205), 93-108. Biological Conservation. doi: https://doi.org/10.1016/j.biocon.2016.10.006

Antoine Guisan, Reid Tingley, John B. Baumgartner, Ilona Naujokaitis‐Lewis, Patricia Sutcliffe, Ayesha Tulloch, Tracey J. Regan, et al. (2013) Predicting species distributions for conservation decisions. Volume(16), 1424-1435. Ecology Letters. doi: https://doi.org/10.1111/ele.12189

Céline Bellard, Cléo Bertelsmeier, Paul Leadley, Wilfried Thuiller, Franck Courchamp (2012) Impacts of climate change on the future of biodiversity. Volume(15), 365-377. Ecology Letters. doi: https://doi.org/10.1111/j.1461-0248.2011.01736.x

Janis L. Dickinson, Benjamin Zuckerberg, David N. Bonter (2010) Citizen Science as an Ecological Research Tool: Challenges and Benefits. Volume(41), 149-172. Annual Review of Ecology Evolution and Systematics. doi: https://doi.org/10.1146/annurev-ecolsys-102209-144636

Yogesh K. Dwivedi, Laurie Hughes, Arpan Kumar Kar, Abdullah M. Baabdullah, Purva Grover, Roba Abbas, Daniela Andreini, et al. (2021) Climate change and COP26: Are digital technologies and information management part of the problem or the solution? An editorial reflection and call to action. Volume(63), 102456-102456. International Journal of Information Management. doi: https://doi.org/10.1016/j.ijinfomgt.2021.102456

Patrick Farcy, Dominique Durand, Guillaume Charria, S. J. Painting, Timo Tamminen, Kate Collingridge, Antoine Grémare, et al. (2019) Toward a European Coastal Observing Network to Provide Better Answers to Science and to Societal Challenges; The JERICO Research Infrastructure. Volume(6). Frontiers in Marine Science. doi: https://doi.org/10.3389/fmars.2019.00529

Ove Hoegh‐Guldberg, Emma Kennedy, Hawthorne L. Beyer, Caleb McClennen, Hugh P. Possingham (2018) Securing a Long-term Future for Coral Reefs. Volume(33), 936-944. Trends in Ecology & Evolution. doi: https://doi.org/10.1016/j.tree.2018.09.006

Mohammad Sadegh Norouzzadeh, Anh‐Tu Nguyen, Margaret Kosmala, Alexandra Swanson, Meredith S. Palmer, Craig Packer, Jeff Clune (2018) Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Volume(115). Proceedings of the National Academy of Sciences. doi: https://doi.org/10.1073/pnas.1719367115

Khaled Telli, Okba Kraa, Yassine Himeur, Abdelmalik Ouamane, Mohamed Boumehraz, Shadi Atalla, Wathiq Mansoor (2023) A Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs). Volume(11), 400-400. Systems. doi: https://doi.org/10.3390/systems11080400

FIGUREKyle Wiggers (2019). Google’s AI can identify wildlife from trap-camera footage with up to 98.6% accuracy. *VentureBeat*. Retrieved from https://venturebeat.com/ai/googles-ai-can-identify-wildlife-from-trap-camera-footage-with-up-to-98-6-accuracy/*Note.* Adapted from Google’s AI can identify wildlife from trap-camera footage with up to 98.6% accuracy, by Kyle Wiggers, 2019, VentureBeat. Retrieved from https://venturebeat.com/ai/googles-ai-can-identify-wildlife-from-trap-camera-footage-with-up-to-98-6-accuracy/.Methodology for the Monitoring and Control of the Alterations Related to Biodeterioration and Physical-Chemical Processes Produced on the Paintings on the Ceiling of the Polychrome Hall at Altamira (2025). Methodology for the Monitoring and Control of the Alterations Related to Biodeterioration and Physical-Chemical Processes Produced on the Paintings on the Ceiling of the Polychrome Hall at Altamira. **. Retrieved from https://www.mdpi.com/2673-7159/4/4/41*Note.* Adapted from Methodology for the Monitoring and Control of the Alterations Related to Biodeterioration and Physical-Chemical Processes Produced on the Paintings on the Ceiling of the Polychrome Hall at Altamira, by Methodology for the Monitoring and Control of the Alterations Related to Biodeterioration and Physical-Chemical Processes Produced on the Paintings on the Ceiling of the Polychrome Hall at Altamira, 2025. Retrieved from https://www.mdpi.com/2673-7159/4/4/41.Reported Press (2025). Google SpeciesNet: Can AI Identify Wildlife?. **. Retrieved from https://www.reportedpress.com/google-speciesnet-can-ai-identify-wildlife/*Note.* Adapted from Google SpeciesNet: Can AI Identify Wildlife?, by Reported Press, 2025. Retrieved from https://www.reportedpress.com/google-speciesnet-can-ai-identify-wildlife/.

TABLE. . **. Retrieved from https://apnews.com/article/9e863fa6c873ecbf8441b33272ccfed2*Note.* , 2025. Retrieved from https://apnews.com/article/9e863fa6c873ecbf8441b33272ccfed2. . **. Retrieved from https://www.fisheries.noaa.gov/new-england-mid-atlantic/science-data/using-artificial-intelligence-study-protected-species*Note.* , 2025. Retrieved from https://www.fisheries.noaa.gov/new-england-mid-atlantic/science-data/using-artificial-intelligence-study-protected-species.Yu-Juan Zhang, Zeyu Luo, Yawen Sun, Junhao Liu, Zongqing Chen (2023). From beasts to bytes: Revolutionizing zoological research with artificial intelligence. *Editorial Office of Zoological Research, Kunming Institute of Zoology, Chinese Academy of Sciences*. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC10802096/*Note.* Adapted from From beasts to bytes: Revolutionizing zoological research with artificial intelligence, by Yu-Juan Zhang, Zeyu Luo, Yawen Sun, Junhao Liu, Zongqing Chen, 2023, Editorial Office of Zoological Research, Kunming Institute of Zoology, Chinese Academy of Sciences, Zoological Research, Vol 44, Issue 6, p. 1115-1131. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC10802096/.

TABLE. . **. Retrieved from https://apnews.com/article/9e863fa6c873ecbf8441b33272ccfed2*Note.* , 2025. Retrieved from https://apnews.com/article/9e863fa6c873ecbf8441b33272ccfed2.Yu-Juan Zhang, Zeyu Luo, Yawen Sun, Junhao Liu, Zongqing Chen (2023). From beasts to bytes: Revolutionizing zoological research with artificial intelligence. *Editorial Office of Zoological Research, Kunming Institute of Zoology, Chinese Academy of Sciences*. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC10802096/*Note.* Adapted from From beasts to bytes: Revolutionizing zoological research with artificial intelligence, by Yu-Juan Zhang, Zeyu Luo, Yawen Sun, Junhao Liu, Zongqing Chen, 2023, Editorial Office of Zoological Research, Kunming Institute of Zoology, Chinese Academy of Sciences, Zoological Research, Vol 44, Issue 6, p. 1115-1131. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC10802096/.Devis Tuia, Benjamin Kellenberger, Sara Beery, Blair R Costelloe, Silvia Zuffi, Benjamin Risse, Alexander Mathis, Mackenzie W Mathis, Frank van Langevelde, Tilo Burghardt, Roland Kays, Holger Klinck, Martin Wikelski, Iain D Couzin, Grant van Horn, Margaret C Crofoot, Charles V Stewart, Tanya Berger-Wolf (2022). Perspectives in machine learning for wildlife conservation. *Nature Communications*. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC8828720/*Note.* Adapted from Perspectives in machine learning for wildlife conservation, by Devis Tuia, Benjamin Kellenberger, Sara Beery, Blair R Costelloe, Silvia Zuffi, Benjamin Risse, Alexander Mathis, Mackenzie W Mathis, Frank van Langevelde, Tilo Burghardt, Roland Kays, Holger Klinck, Martin Wikelski, Iain D Couzin, Grant van Horn, Margaret C Crofoot, Charles V Stewart, Tanya Berger-Wolf, 2022, Nature Communications, Nature Communications, Vol 13, p. 792. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC8828720/.

TABLE. . **. Retrieved from https://apnews.com/article/ab1d38583c4102185a494e465358963e*Note.* , 2025. Retrieved from https://apnews.com/article/ab1d38583c4102185a494e465358963e.Lucy Colback (2025). How we can use AI to create a better society. *Financial Times*. Retrieved from https://www.ft.com/content/33ed8ad0-f8ad-42ed-983a-54d5b9eb2d27*Note.* Adapted from How we can use AI to create a better society, by Lucy Colback, 2025, Financial Times. Retrieved from https://www.ft.com/content/33ed8ad0-f8ad-42ed-983a-54d5b9eb2d27.Methodology for the Monitoring and Control of the Alterations Related to Biodeterioration and Physical-Chemical Processes Produced on the Paintings on the Ceiling of the Polychrome Hall at Altamira (2025). Methodology for the Monitoring and Control of the Alterations Related to Biodeterioration and Physical-Chemical Processes Produced on the Paintings on the Ceiling of the Polychrome Hall at Altamira. **. Retrieved from https://www.mdpi.com/2673-7159/4/4/41*Note.* Adapted from Methodology for the Monitoring and Control of the Alterations Related to Biodeterioration and Physical-Chemical Processes Produced on the Paintings on the Ceiling of the Polychrome Hall at Altamira, by Methodology for the Monitoring and Control of the Alterations Related to Biodeterioration and Physical-Chemical Processes Produced on the Paintings on the Ceiling of the Polychrome Hall at Altamira, 2025. Retrieved from https://www.mdpi.com/2673-7159/4/4/41.

TABLEMethodology for the Monitoring and Control of the Alterations Related to Biodeterioration and Physical-Chemical Processes Produced on the Paintings on the Ceiling of the Polychrome Hall at Altamira (2025). Methodology for the Monitoring and Control of the Alterations Related to Biodeterioration and Physical-Chemical Processes Produced on the Paintings on the Ceiling of the Polychrome Hall at Altamira. **. Retrieved from https://www.mdpi.com/2673-7159/4/4/41*Note.* Adapted from Methodology for the Monitoring and Control of the Alterations Related to Biodeterioration and Physical-Chemical Processes Produced on the Paintings on the Ceiling of the Polychrome Hall at Altamira, by Methodology for the Monitoring and Control of the Alterations Related to Biodeterioration and Physical-Chemical Processes Produced on the Paintings on the Ceiling of the Polychrome Hall at Altamira, 2025. Retrieved from https://www.mdpi.com/2673-7159/4/4/41.

Wildlife Observations via Night Vision Technology [FIGURE]. (2025). Retrieved from https://images.squarespace-cdn.com/content/v1/611f8cb96f2ba92ecac45339/b587aa7e-e629-457b-817a-368bee40e53f/Capture+d%E2%80%99e%CC%81cran+2023-08-24+a%CC%80+12.03.1

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Published

2025-03-29