ReviewScience progress
Artificial intelligence-driven solutions for mitigating human-wildlife conflict in biodiversity hotspots.
Review in Science progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Deep Learning-Based Monitoring of European Hamster Activity.Ecology and evolution · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biodiversity hotspots are biologically rich yet highly threatened regions that play a critical role in global conservation but often serve as epicentres of human-wildlife conflict (HWC). HWC poses major conservation and development challenges, undermining both human livelihoods and wildlife protection efforts. Artificial intelligence (AI) offers transformative tools for mitigating HWC by enhancing monitoring, prediction, and decision support. Through systematic searches of peer-reviewed and grey literature, this review analyzed 105 studies (1990-2025) from 163 screened sources, revealing that AI improved HWC monitoring (65%), predictive accuracy (47%), and community engagement (39%). AI-driven technologies such as machine learning, deep learning, and computer vision enable conservationists to process large datasets, automate species identification, and make real-time decisions. Integrated platforms like Earth Ranger and the Spatial Monitoring and Reporting Tool (SMART) use AI to manage data from rangers, camera traps, drones, and patrol logs, providing situational awareness and strategic planning tools. Furthermore, remote sensing, Geographic Information Systems (GIS), and participatory data integration offer multi-layered insights for mapping HWC zones, tracking wildlife movement, and modelling species distribution. This review highlights the application of AI in conflict detection, community engagement, and decision support while addressing challenges, limitations, and ethical concerns. It also underscores the importance of policies and future research to integrate AI with local knowledge systems, participatory governance, and adaptive conservation strategies. Overall, AI advancements are transforming HWC surveillance and enabling more proactive, equitable, and sustainable biodiversity conservation efforts worldwide.
Indexed as
Identifiers
What 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.