Here we revisit and update the state of knowledge on the Sphingidae fauna of Mexico. Our aims were to: (1) document the species diversity of Mexican Sphingidae; (2) generate spatial distribution models for sphingid species; and (3) discuss distributional patterns and the conservation implications for key taxa that, due to their life-history characteristics and restricted distributions, may require conservation actions. We compiled a total of 9,985 records representing 202 taxa (185 species and 17 subspecies) in 49 genera and three subfamilies-Macroglossinae, Sphinginae, and Smerinthinae. We report Perigonia odileae Haxaire & Melichar, 2025, and Perigonia pallida Rothschild & Jordan 1903, as new Sphingidae records for the Mexican realm. Species distribution models indicated that most sphingid genera in Mexico have a predominantly Neotropical biogeographic affinity: 12 genera (24%) exhibit a Neotropical distribution, seven (14%) are exclusively Nearctic, eight (16%) occur in both regions, and 22 (45%) are largely Neotropical but extend marginally into the Nearctic. We discussed taxonomic and distributional considerations for taxa that represent confirmed species (based on barcoded specimens in BOLD), along with species records representing significant expansions into the Mexican realm. Maintaining a network of threatened ecotopes-such as cloud forests and tropical rainforests-will be crucial for preserving sphingid populations and for promoting population (re)colonization processes.
Thrips parvispinus (Thysanoptera: Thripidae: Karny) is an invasive pest that harms peppers and ornamentals. It has not been reported as present in Southern California but has a history of establishing in regions prior to official detection. We surveyed retail garden stores in San Diego and Orange Counties to determine if T. parvispinus was present in California but undetected, and found it on Capsicum, Dipladenia, Mandevilla, and Gardenia species across a year-long period. Our work demonstrates that T. parvispinus can spread to new areas undetected on nursery stock and validates sampling retail stores as a low-effort early detection method that can be used by researchers and regulators to find T. parvispinus on nursery stock.
The differentiation of workers into supplementary reproductives (ergatoids) is an important component of reproductive plasticity in termites, but the behavioral and physiological changes accompanying this transition remain incompletely understood. We used Reticulitermes chinensis (Snyder) to examine allogrooming, locomotor behavior, egg production, enzyme activity, and gene expression during the worker-to-ergatoid transition. The first ergatoids appeared within 2 days after orphaning, and female ergatoids outnumbered male ergatoids. Ergatoids that survived to the end of the observation period differentiated earlier than those that did not. Compared with workers, ergatoids received more grooming, gave less grooming, and showed lower locomotor velocity, shorter distance traveled, and reduced moving time. Total egg number was positively associated with the body mass of the largest female ergatoid and with the number of surviving workers, but not with the number of female ergatoid reproductives. Catalase (CAT), superoxide dismutase (SOD), and acid phosphatase (ACP) activities were higher in ergatoids than in workers. Based on biological replicate means, gene-expression analyses showed higher CAT expression in both ergatoid sexes, higher SOD expression in female ergatoids, and a female-biased ACP expression pattern. These results describe behavioral and physiological changes associated with reproductive transition in laboratory groups of R. chinensis.
Anthonomus eugenii Cano, the pepper weevil, is a devastating pest of peppers (Capsicum spp.). A. eugenii is a pest in both field and greenhouse pepper production across North America, with field production generally impacted in Central America, Mexico, the southern United States, and Hawaii, and greenhouse production being impacted in the northern United States and Canada. The life cycle of A. eugenii primarily occurs inside a pepper fruit, protecting these stages from contact-based insecticidal sprays. Only the adult stage is susceptible to chemical management strategies. However, these strategies are expensive to growers and have negative impacts on agroecosystems, presenting a critical need for new management strategies such as initial oviposition deterrence. Female A. eugenii produce an oviposition-deterrent pheromone (ODP) after egg-laying, indicating to other females the pepper has been occupied. This study evaluated the initial oviposition deterrence of kaolin clay in combination with dye and a previously characterized A. eugenii ODP. Results indicated that peppers treated with kaolin clay alone deterred oviposition, but a concentration of 10% ODP + kaolin clay further reduced oviposition events. When peppers were treated with red-dyed kaolin clay, the rate of oviposition was also decreased as compared to kaolin clay alone. Further investigations of dyed and ODP-infused kaolin clay should be conducted under greenhouse and field conditions to evaluate their effectiveness against initial oviposition in traditional agricultural settings.
Machine learning is transforming chemical ecology by accelerating the discovery and deployment of semiochemical-based tools for precision pest management. These advances are particularly important in the face of climate change, pesticide resistance, and the growing need for sustainable agricultural intensification. This review synthesizes how machine learning can be applied across the semiochemical discovery and implementation pipeline, from chemical signal detection to field deployment and decision support for integrated pest management. We review major machine learning approaches and demonstrate how they extract biologically relevant information from high-dimensional chemical, electrophysiological, behavioral, sensor, and field datasets. These methods accelerate semiochemical discovery, prioritize candidate compounds, optimize formulations and deployment strategies, and support adaptive pest management under dynamic environmental conditions. We further examine the integration of sensor-based technologies, explainable machine learning, and optimization algorithms to improve pest detection, monitoring, and intervention. Current challenges include data scarcity, heterogeneous datasets, the lack of benchmark resources, limited field validation, and the underrepresentation of tropical agroecosystems and smallholder farming systems. We propose practical recommendations for developing reusable datasets that integrate volatile profiles, electrophysiology, insect behavior, and management outcomes to improve model robustness and reproducibility. Central to this review is a 4-stage framework comprising detection, design, deployment, and decision support that connects machine learning with chemical ecology and provides a practical roadmap for translating computational advances into targeted, efficient, and sustainable pest management.
