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Garland, C.

Publications and source records attributed to Garland, C..

3 recordsLinked to original sources

Sensitive and easily democratized environmental DNA methods for low-risk surveillance of at-risk bumble bees

Terrestrial environmental DNA (eDNA) techniques have been proposed as a means of sensitive, non-lethal pollinator monitoring. To date, however, no studies have provided evidence that eDNA methods can achieve detection densities on par with traditional pollinator surveys. Using a large-scale dataset of eDNA and corresponding net surveys, we show that eDNA methods enable sensitive, species-level characterization of whole bumble bee communities, including rare and critically endangered species such as the rusty pathed bumble bee (RPBB; Bombus affinis). All species present in netting surveys were detected within eDNA surveys, apart from two rare species in the socially parasitic subgenus Psithyrus (cuckoo bumble bees). Further, for rare non-parasitic species, eDNA methods exhibited similar sensitivity relative to traditional netting. Relative to flower eDNA samples, sequenced field negative controls resulted in significantly lower rates of Bombus detection, and these detections were likely attributable to high rates of background eDNA on environmental surfaces. Lastly, we found that eDNA-based frequency of detection across replicate surveys was strongly associated with net-based measures of abundance across site visits. We conclude that the method is cost-effective and highly scalable for semi-quantitative characterization of at-risk bumble bee communities, providing a new approach for improving our understanding of species habitat associations.

ecology↗

Scalable environmental DNA methods reveal associations between landscape-scale forest habitat and insect richness

O_LIWhile aquatic environmental DNA (eDNA) methods have reached relative maturity, terrestrial eDNA methods are nascent and have yet to reach widespread use. Field-ready applications require eDNA survey methods where samples are easy to collect by inexperienced practitioners, easy to transport between the field and lab, and easy to process thereafter. Here, we demonstrate methods that satisfy these requirements and show strong potential for characterizing diverse terrestrial eDNA samples collected from flower and leaf surfaces. C_LIO_LIWe used novel methods to collect and process 236 flower eDNA samples and 21 leaf surface eDNA samples, obtaining 2,228 Arthropoda eDNA detections spanning 175 families using amplicon sequencing of two genetic markers. C_LIO_LIDetected taxa were diverse and included numerous groups of conservation concern, such as bees (Hymenoptera; Anthophila, 32 genera spanning 5 families) and Lepidoptera (209 genera from 21 families). Data reveal strong associations between insect community richness and remotely sensed measures of forest habitat, providing a quantitative perspective of relevance to insect conservation. C_LIO_LIIt is increasingly clear that a variety of organisms readily disperse eDNA throughout the environment, supporting the notion that eDNA will be a powerful tool for characterizing species distributions and monitoring at-risk species. However, we conclude that researchers seeking to characterize fine-scale habitat associations or plant-pollinator interactions using eDNA will need to carefully design studies with appropriate field controls, such as the leaf surface eDNA samples collected here. C_LI

ecology↗

Fractal Structure of Human and Primate Social Networks Optimizes Information Flow

Primate and human social groups exhibit a fractal structure that has a very limited range of preferred layer sizes, with groups of 5, 15, 50 and (in humans) 150 and 500 predominating. This same fractal distribution is also observed in the distribution of species mean group sizes in primates. Here we demonstrate that this preferential numbering arises because of the critical nature of dynamic self-organization within complex social networks. We calculate the size dependence of the scaling properties of complex social network models and argue that this aggregate behaviour exhibits a form of collective intelligence. Direct calculation establishes that the complexity of social networks as measured by their scaling behaviour is non-monotonic, peaking globally around 150 with a secondary peak at 500 and tertiary peaks centred on 15 and 50, thereby providing a theory-based rationale for the fractal layering of primate and human social groups.

animal behavior and cognition↗