Timothy L. Warren

Long-distance movement in insects has crucial impacts on agriculture, human health, and biodiversity. Although it was long assumed that only large, specialist insects had the navigation capacity to support long-distance dispersal, recent studies have demonstrated that smaller insects, such as the tiny fruit fly Drosophila melanogaster, can maintain extended, straight paths while flying or walking. This raises the question of whether other Drosophila species possess the navigation capacity to support extended dispersal. Resolving this question is particularly important for Drosophila suzukii (spotted-wing drosophila), a potent pest species that causes enormous damage worldwide to ripe fruit and berries. Spotted-wing drosophila has been thought to lack a capacity for long-distance dispersal, as prior studies have estimated maximal daily dispersal distances of less than 90 m. We developed a system to continuously track the flight trajectories of magnetically tethered D. suzukii relative to a discrete, overhead LED that mimicked the sun. We found that flies maintained remarkably straight flight headings that varied unpredictably across individuals. Male and female D. suzukii exhibited a similar navigation capacity; both sexes responded to rotation of a discrete sun stimulus with compensatory turns to maintain a stable relative heading. Our results suggest that D. suzukii has an underappreciated ability for stable flight navigation, which could potentially enable rapid, radial dispersal, contributing to the pest species' invasiveness and reliable, annual re-establishment in seasonally intolerable climates. Our findings highlight the importance of developing area-wide, regional strategies to manage dipteran pest species such as D. suzukii.

Widespread declines in insect diversity and abundance underscore an urgent need for standardized, nonlethal monitoring methods for important pollinators such as bumble bees ( Bombus spp.). Camera traps are widely used for nonintrusive, continuous surveys of large animals but have not yet been extensively adopted for monitoring diurnal insects. Automated camera traps could improve current insect survey methods, which often rely on lethal traps or in‐person observation. Here we developed an open‐source, low‐cost camera trap for monitoring wild insects and evaluated its performance relative to established sampling approaches. We used a low‐power microcomputer to collect time‐lapse images on colored platforms that served as non‐rewarding visual lures. We compared the performance of whole‐image and tiled deep‐learning models for automated detection of small insects in images captured by the traps. We found that tiled inference—which performs detection inside subframes of the original image—significantly improved accuracy, even outperforming human reviewers. Bumble bee visitation was highest on platforms with a repeating fluorescent bullseye pattern. Via continuous monitoring, the camera traps recorded bumble bee visits during all daylight hours over 18 days. From the recorded images, expert human taxonomists identified six Bombus species, yielding community composition and diversity estimates comparable to those obtained by hand netting. We simulated the effect of deploying variable numbers of cameras at sites with two levels of diversity. These simulations suggested that adding cameras substantially increased sampling completeness, particularly in species‐rich communities. By pairing low‐cost, semi‐automated camera traps with deep‐learning image analysis, our work provides a foundation for scalable, nonlethal studies of bumble bee diversity and activity patterns.

Long-distance movement in insects has crucial impacts on agriculture, human health, and biodiversity. Although it was long assumed that only large, specialist insects had the navigation capacity to support long-distance dispersal, recent studies have demonstrated that smaller insects, such as the tiny fruit fly Drosophila melanogaster , can maintain extended, straight paths while flying or walking. This raises the question of whether other Drosophila species possess the navigation capacity to support extended dispersal. Resolving this question is particularly important for Drosophila suzukii (spotted-wing drosophila), a potent pest species that causes enormous damage worldwide to ripe fruit and berries. Spotted-wing drosophila has been thought to lack a capacity for long-distance dispersal, as prior studies have estimated maximal daily dispersal distances of less than 90 m. We developed a system to continuously track the flight trajectories of magnetically tethered D. suzukii relative to a discrete, overhead LED that mimicked the sun. We found that flies maintained remarkably straight flight headings that varied unpredictably across individuals. Male and female D. suzukii exhibited a similar navigation capacity; both sexes responded to rotation of a discrete sun stimulus with compensatory turns to maintain a stable relative heading. Our results suggest that D. suzukii has an underappreciated capacity for rapid, radial dispersal, which could exceed 250 m in 15 min. This capacity may contribute to the pest species’ invasiveness and its reliable, annual re-establishment in seasonally intolerable climates. Our findings highlight the importance of developing area-wide, regional strategies to manage dipteran pest species such as D. suzukii .

Widespread declines in insect diversity and abundance underscore an urgent need for standardized, nonlethal monitoring methods for important pollinators such as bumble bees (Bombus spp.). Camera traps are widely used for non-intrusive, continuous surveys of large animals but have not yet been extensively adopted for monitoring insects. An automated camera trap system could improve current insect survey methods, which often rely on lethal traps or in-person observation. We developed an open-source, low-cost camera trap for monitoring wild insects and evaluated its performance relative to established sampling approaches. The system used a low-power microcomputer to collect time-lapse images on colored platforms. We trained whole-image and tiled deep-learning object detection models to detect insects in images captured by the traps. We found that tiled inference models significantly improved detection accuracy and outperformed human review. Bumble bee visitation was highest on platforms featuring a fluorescent bullseye pattern; adding a fluorescent coat to blue platforms increased visitation modestly. With continuous monitoring, the cameras recorded bumble bee visits during all daylight hours. Over 18 days, camera traps recorded six Bombus species, yielding community composition and diversity estimates comparable to those obtained by hand netting and blue vane traps. Using our observed data, we simulated the effect of deploying variable numbers of cameras at sites with distinct levels of diversity. Adding cameras substantially increased sampling completeness, particularly in species-rich communities. Our findings demonstrate that low-cost, automated camera traps paired with deep-learning image analysis can enable scalable, nonlethal studies of bumble bee diversity and behavior. Our work establishes a foundation for monitoring other diurnal insect communities.

Mason bees, subgenus Osmia Panzer (Hymenoptera: Megachilidae), are economically and ecologically significant pollinators. In eastern North America, the rapid spread of 2 non-native species from Asia, Osmia cornifrons Radoszkowski and Osmia taurus Smith, has coincided with declines in native Osmia populations, raising concern about the effects of further exotic arrivals. Here we investigate the recent establishment in British Columbia, Canada of the European orchard bee, Osmia cornuta Latreille, previously thought to be limited to Europe and its periphery. We document O. cornuta records ranging more than 170 km, including sightings of live adults and the discovery of a multigenerational nest with hundreds of cocoons. We tested whether these cocoons could be discriminated from other Osmia species by training a machine learning classifier on features extracted from images. The best performing model could not reliably discriminate cocoons by species, raising the possibility O. cornuta could be inadvertently intermingled in future commercial shipments. Recent occurrence records of O. cornifrons and O. taurus were spatially isolated, suggesting ongoing anthropogenic dispersal of these species. We predicted the suitability of North American habitats for O. cornuta by estimating its native climate niche. This analysis indicated broad regions of the Pacific Northwest and eastern North America contain potentially suitable habitat. Our findings document the establishment of O. cornuta in North America and the potential for its expansion. Our study demonstrates the utility of accessible biodiversity data archives and public observation programs in tracking non-native species spread and highlights the need for future monitoring of exotic Osmia.

Mason bees, subgenus Osmia Panzer (Hymenoptera: Megachilidae), are economically and ecologically significant pollinators. In eastern North America, the rapid spread of two non-native species from Asia, O. cornifrons Radoszkowski and O. taurus Smith, has coincided with declines in native Osmia populations, raising concern about the effects of further exotic invasions. Here we investigate the recent establishment in British Columbia, Canada of the European orchard bee, O. cornuta Latreille, previously thought to be limited to Europe and its periphery. We document O. cornuta records ranging over 170 km, including sightings of live adults and the discovery of a multigenerational nest with hundreds of cocoons. We tested whether these O. cornuta cocoons could be discriminated from other Osmia species by training a machine learning classifier on features extracted from images. The best performing model could not reliably discriminate cocoons by species, raising the possibility O. cornuta could be inadvertently intermingled in future commercial shipments. Spatially isolated records of O. cornifrons and O. taurus further suggest ongoing anthropogenic dispersal of these species. To determine environmentally suitable regions for O. cornuta to spread in North America, we estimated its climate niche using native-range occurrence data. This analysis indicated broad regions of the Pacific Northwest and eastern North America contain potentially suitable habitat. Together, our findings document the establishment of O. cornuta in North America and the potential for it to spread broadly. Our study demonstrates the utility of accessible biodiversity data archives and public observation programs in tracking biological invasions and highlights the need for future monitoring of exotic Osmia .

Complex skills like speech and dance are composed of ordered sequences of simpler elements, but the neuronal basis for the syntactic ordering of actions is poorly understood. Birdsong is a learned vocal behavior composed of syntactically ordered syllables, controlled in part by the songbird premotor nucleus HVC (proper name). Here, we test whether one of HVC’s recurrent inputs, mMAN (medial magnocellular nucleus of the anterior nidopallium), contributes to sequencing in adult male Bengalese finches (Lonchura striata domestica). Bengalese finch song includes several patterns: (1) chunks, comprising stereotyped syllable sequences; (2) branch points, where a given syllable can be followed probabilistically by multiple syllables; and (3) repeat phrases, where individual syllables are repeated variable numbers of times. We found that following bilateral lesions of mMAN, acoustic structure of syllables remained largely intact, but sequencing became more variable, as evidenced by ‘breaks’ in previously stereotyped chunks, increased uncertainty at branch points, and increased variability in repeat numbers. Our results show that mMAN contributes to the variable sequencing of vocal elements in Bengalese finch song and demonstrate the influence of recurrent projections to HVC. Furthermore, they highlight the utility of species with complex syntax in investigating neuronal control of ordered sequences.

Complex motor skills like speech and dance are composed of ordered sequences of simpler elements, but the neuronal basis for syntactic ordering of individual actions into sequences is poorly understood. Birdsong is a learned vocal behavior composed of syntactically ordered sequences of individual syllables. Activity in song premotor nucleus HVC (proper name) has been linked to the control of syllable sequencing, but sequencing may also be affected by its recurrent inputs. We here test the contribution of one of HVC’s inputs, mMAN (medial magnocellular nucleus of the anterior nidopallium), to the variable songs of adult male Bengalese finches (Lonchura striata domestica). The syntax of Bengalese song includes several patterns: 1) chunks, where syllables follow stereotypical order 2) branch points, where a given syllable can be followed by two or more different syllables in a probabilistic manner and 3) repeat phrases, where an individual syllable is repeated a variable number of times. We found that after bilateral lesions of mMAN, the acoustic structure of syllables remained largely intact, but sequencing became more variable for each of these patterns, seen by ‘breaks’ in previously stereotyped chunks, increased uncertainty at branch points and increased variability of repeat numbers. This increase in sequencing variability highlights the potential importance of regions projecting to HVC in the ordering of vocal elements. Previous studies on zebra finches found little effect of mMAN lesions on their relatively stereotyped adult song. In contrast, our results suggest that mMAN contributes to sequencing the variable songs of Bengalese finches and highlight the utility of species with more complex song syntax in investigating neuronal control of ordered motor sequences.

Complex behaviors depend on the coordinated activity of neural ensembles in interconnected brain areas. The behavioral function of such coordination, often measured as co-fluctuations in neural activity across areas, is poorly understood. One hypothesis is that rapidly varying co-fluctuations may be a signature of moment-by-moment task-relevant influences of one area on another. We tested this possibility for error-corrective adaptation of birdsong, a form of motor learning which has been hypothesized to depend on the top-down influence of a higher-order area, LMAN (lateral magnocellular nucleus of the anterior nidopallium), in shaping moment-by-moment output from a primary motor area, RA (robust nucleus of the arcopallium). In paired recordings of LMAN and RA in singing birds, we discovered a neural signature of a top-down influence of LMAN on RA, quantified as an LMAN-leading co-fluctuation in activity between these areas. During learning, this co-fluctuation strengthened in a premotor temporal window linked to the specific movement, sequential context, and acoustic modification associated with learning. Moreover, transient perturbation of LMAN activity specifically within this premotor window caused rapid occlusion of pitch modifications, consistent with LMAN conveying a temporally localized motor-biasing signal. Combined, our results reveal a dynamic top-down influence of LMAN on RA that varies on the rapid timescale of individual movements and is flexibly linked to contexts associated with learning. This finding indicates that inter-area co-fluctuations can be a signature of dynamic top-down influences that support complex behavior and its adaptation.

Complex motor skills like speech and dance are composed of ordered sequences of simpler elements, but the neuronal basis for syntactic ordering of individual actions into sequences is poorly understood. Birdsong is a learned vocal behavior composed of syntactically ordered sequences of individual syllables. Activity in song premotor nucleus HVC (proper name) has been linked to the control of syllable sequencing, but sequencing may also be affected by its recurrent inputs. We here test the contribution of one of HVC’s inputs, mMAN ( medial magnocellular nucleus of the anterior nidopallium ), to the variable songs of adult male Bengalese finches ( Lonchura striata domestica ). The syntax of Bengalese song includes several patterns: 1) chunks , where syllables follow stereotypical order 2) branch points , where a given syllable can be followed by two or more different syllables in a probabilistic manner and 3) repeat phrases , where an individual syllable is repeated a variable number of times. We found that after bilateral lesions of mMAN, the acoustic structure of syllables remained largely intact, but sequencing became more variable for each of these patterns, seen by ‘breaks’ in previously stereotyped chunks, increased uncertainty at branch points and increased variability of repeat numbers. This increase in sequencing variability highlights the potential importance of regions projecting to HVC in the ordering of vocal elements. Previous studies on zebra finches found little effect of mMAN lesions on their relatively stereotyped adult song. In contrast, our results suggest that mMAN contributes to sequencing the variable songs of Bengalese finches and highlight the utility of species with more complex song syntax in investigating neuronal control of ordered motor sequences.