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Abstract Tropical elevation gradients support highly diverse assemblages, but competing hypotheses suggest either peak species richness in lowland rainforests or at mid‐elevations. We investigated scolytine beetles—phloem, ambrosia and seed‐feeding beetles—along a tropical elevational gradient in Papua New Guinea.Highly standardised sampling from 200 to 3700 m above sea level (asl) identified areas of highest and lowest species richness, abundance and other biodiversity variables.Using passive flight intercept traps at eight elevations from 200 to 3500 m asl, we collected over 9600 specimens representing 215 species. Despite extensive sampling, species accumulation curves suggest that diversity was not fully exhausted.Scolytine species richness followed a unimodal distribution, peaking between 700 and 1200 m asl, supporting prior findings of highest diversity at low‐to‐mid elevations.Alternative models, such as a monotonous decrease from lowlands to higher elevations and a mid‐elevation maximum, showed lesser fit to our data. Abundance is greatest at the lowest sites, driven by a few extremely abundant species. The turnover rate—beta diversity between elevation steps—is greatest between the highest elevations.Among dominant tribes—Dryocoetini, Xyleborini and Cryphalini—species richness peaked between 700 and 2200 m asl. Taxon‐specific analyses revealed distinct patterns:Euwallaceaspp. abundance uniformly declined with elevation, while other genera were driven by dominant species at different elevations.Coccotrypesand phloem‐feedingCryphalushave undergone evolutionary radiations in New Guinea, with many species still undescribed. Species not yet known to science are most likely to be found at lower and middle elevations, where overall diversity is highest. 

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.