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This study presents an initial model for bark beetle identification, serving as a foundational step toward developing a fully functional and practical identification tool. Bark beetles are known for extensive damage to forests globally, as well as for uniform and homoplastic morphology which poses identification challenges. Utilizing a MaxViT-based deep learning backbone which utilizes local and global attention to classify bark beetles down to the genus level from images containing multiple beetles. The methodology involves a process of image collection, preparation, and model training, leveraging pre-classified beetle species to ensure accuracy and reliability. The model's F1 score estimates of 0.99 and 1.0 indicates a strong ability to accurately classify genera in the collected data, including those previously unknown to the model. This makes it a valuable first step towards building a tool for applications in forest management and ecological research. While the current model distinguishes among 12 genera, further refinement and additional data will be necessary to achieve reliable species-level identification, which is particularly important for detecting new invasive species. Despite the controlled conditions of image collection and potential challenges in real-world application, this study provides the first model capable of identifying the bark beetle genera, and by far the largest training set of images for any comparable insect group. We also designed a function that reports if a species appears to be unknown. Further research is suggested to enhance the model's generalization capabilities and scalability, emphasizing the integration of advanced machine learning techniques for improved species classification and the detection of invasive or undescribed species. 

This protocol describes the different methods to collect and preserve bark and ambrosia beetles, detailing collecting tools, recording relevant data, and optimizing step-by-step methods to extract beetles from twigs, branches, bark, and trunks. It elaborates on trapping techniques, tools, lures, baits, and beetle preservation. The main rule of manual collecting is to not attempt to pry the insect out of the wood or bark, but instead, remove the wood/bark away from the beetle: gently and systematically. The main rule of trapping is that there is no general attractant; instead, attractants and traps should reflect the ecology of the targeted beetle taxa. 

One of the main threats to forests in the Anthropocene are novel or altered interactions among trees, insects and fungi. To critically assess the contemporary research on bark beetles, their associated fungi, and their relationships with trees, the international Bark Beetle Mycobiome research coordination network has been formed. The network comprises 22 researchers from 17 institutions. This forward-looking review summarizes the group’s assessment of the current status of the bark beetle mycobiome research field and priorities for its advancement. Priorities include data mobility and standards, the adoption of new technologies for the study of these symbioses, reconciliation of conflicting paradigms, and practices for robust inference of symbiosis and tree epidemiology. The Net work proposes contemporary communication strategies to interact with the global community of researchers studying symbioses and natural resource managers. We conclude with a call to the broader scientific community to participate in the network and contribute their perspectives.