Although artificial intelligence (AI) offers a powerful solution to the bottleneck in processing camera-trap imagery, ecological researchers have long struggled with the practical application of such tools. The complexity of development, training, and deploying deep learning models remains a significant barrier for many conservationists, ecologists, and citizen scientists who lack formal training in computer science. While platforms like Wildlife Insights (Ahumada et al. 2020), MegaDetector (Beery et al. 2019), and tools such as ClassifyMe (Falzon et al. 2020) have laid critical groundwork in AI-assisted wildlife monitoring, these solutions either remain opaque, lack customisability, or are often locked behind commercial or infrastructural limitations. Others, like Sherlock (Penn et al., 2024), while powerful, are not always deployable without significant local expertise. A notable example of a more open and collaborative approach is the DeepFaune initiative, which provides a free, high-performance tool for the automatic classification of European wildlife in camera-trap images, highlighting the growing importance of locally relevant, user-friendly AI solutions developed through broad partnerships (Rigoudy et al. 2022).

It is in this context that Brook et al. (2025) makes a compelling and timely contribution. The authors present an elegant, open-source AI pipeline—MEWC (Mega-Efficient Wildlife Classifier)—that bridges the gap between high-performance image classification and usability by non-specialists. Combining deep learning advances with user-friendly software engineering, MEWC enables users to detect, classify, and manage wildlife imagery without advanced coding skills or reliance on high-cost third-party infrastructures.

What makes MEWC particularly impactful is its modular and accessible architecture. Built on Docker containers, the system can run seamlessly across different operating systems, cloud services, and local machines. From image detection using MegaDetector to classifier training via EfficientNet or Vision Transformers, the pipeline maintains a careful balance between technical flexibility and operational simplicity. This design empowers ecologists to train their own species-specific classifiers and maintain full control over their data—an essential feature given the increasing scrutiny around data sovereignty and privacy.

The practical implications are impressive. The case study provided—focused on Tasmanian wildlife—demonstrates not only high accuracy (up to 99.6%) but also remarkable scalability, with models trainable even on mid-range desktops. Integration with community tools like Camelot (Hendry and Mann 2018)and AddaxAI (Lunteren 2023) further enhances its utility, allowing rapid expert validation and facilitating downstream analyses.

Yet the article does not shy away from discussing the limitations. As with any supervised system, MEWC’s performance is only as good as the training data provided. Class imbalances, rare species, or subtle morphological traits can challenge even the best classifiers. Moreover, the authors caution that pre-trained models may not generalise well across regions with different fauna, requiring careful curation and expert tagging for local deployments.

One particularly exciting future direction briefly mentioned—and worth highlighting—is MEWC’s potential application to behavioural and cognitive ecology (Sueur et al. 2013; Battesti et al. 2015; Grampp et al. 2019). Studies in these domains underscore the need for scalable tools to quantify social dynamics in real time. By assisting with individual identification and the detection of postures or spatial configurations, MEWC could significantly enhance the throughput, reproducibility, and objectivity of such research.

This opens the door to even richer applications. Behavioural ecologists might use MEWC for fine-grained detection tasks such as individual grooming interactions, kin proximity analysis, or identification of tool-use sequences in wild primates. Similarly, for within-species classification (e.g. sex, reproductive state, or disease symptoms), MEWC's modular backbone and compatibility with transfer learning architectures like EfficientNet or ViT make it a suitable candidate for expansion (Ferreira et al. 2020; Clapham et al. 2022).

In conclusion (Brook et al. 2025) have delivered more than a tool—they've designed an ecosystem. MEWC lowers the technical barrier to AI in ecology, promotes open science, and enables tailored workflows for a wide variety of conservation, research, and educational contexts. For anyone interested in democratising ecological AI and reclaiming control over wildlife-monitoring data, this article and its associated software are essential resources.

References

Ahumada JA, Fegraus E, Birch T, et al (2020) Wildlife Insights: A Platform to Maximize the Potential of Camera Trap and Other Passive Sensor Wildlife Data for the Planet. Environmental Conservation 47:1–6. https://doi.org/10.1017/S0376892919000298

Battesti M, Pasquaretta C, Moreno C, et al (2015) Ecology of information: social transmission dynamics within groups of non-social insects. Proceedings of the Royal Society of London B: Biological Sciences 282:20142480. https://doi.org/10.1098/rspb.2014.2480

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Barry W. Brook, Jessie C. Buettel, Peter van Lunteren, Prakash P. Rajmohan, R. Zach Aandahl (2025) MEWC: A user-friendly AI workflow for customised wildlife-image classification. EcoEvoRxiv, ver.3 peer-reviewed and recommended by PCI Ecology https://doi.org/10.32942/X2ZW3D

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Hendry H, Mann C (2018) Camelot—intuitive software for camera-trap data management. Oryx 52:15–15. https://doi.org/10.1017/S0030605317001818

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