High-throughput monitoring of wild bee diversity and abundance via mitogenomics

1. Bee populations and other pollinators face multiple, synergistically acting threats, which have led to population declines, loss of local species richness and pollination services, and extinctions. However, our understanding of the degree, distribution and causes of declines is patchy, in part due to inadequate monitoring systems, with the challenge of taxonomic identification posing a major logistical barrier. Pollinator conservation would benefit from a high-throughput identification pipeline. 2. We show that the metagenomic mining and resequencing of mitochondrial genomes (mitogenomics) can be applied successfully to bulk samples of wild bees. We assembled the mitogenomes of 48 UK bee species and then shotgun-sequenced total DNA extracted from 204 whole bees that had been collected in 10 pan-trap samples from farms in England and been identified morphologically to 33 species. Each sample data set was mapped against the 48 reference mitogenomes. 3. The morphological and mitogenomic data sets were highly congruent. Out of 63 total species detections in the morphological data set, the mitogenomic data set made 59 correct detections (93�7% detection rate) and detected six more species (putative false positives). Direct inspection and an analysis with species-specific primers suggested that these putative false positives were most likely due to incorrect morphological IDs. Read frequency significantly predicted species biomass frequency (R2 = 24�9%). Species lists, biomass frequencies, extrapolated species richness and community structure were recovered with less error than in a metabarcoding pipeline. 4. Mitogenomics automates the onerous task of taxonomic identification, even for cryptic species, allowing the tracking of changes in species richness and istributions. A mitogenomic pipeline should thus be able to contain costs, maintain consistently high-quality data over long time series, incorporate retrospective taxonomic revisions and provide an auditable evidence trail. Mitogenomic data sets also provide estimates of species counts within samples and thus have potential for tracking population trajectories.

Impact of a Fram Strait cyclone on ice edge, drift, divergence, and concentration: Possibilities and limits of an observational analysis

This study aims at the determination of a Fram Strait cyclone track and of the cyclone’s impact on ice edge, drift, divergence, and concentration. A 24 h period on 13–14 March 2002 framed by two RADARSAT images is analyzed. Data are included from autonomous ice buoys, a research vessel, Special Sensor Microwave Imager (SSM/I) and QuikSCAT satellite, and the operational European Centre for Medium-Range Weather Forecasts (ECMWF) model. During this 24 h period the cyclone moved northward along the western ice edge in the Fram Strait, crossed the northern ice edge, made a left-turn loop with 150 km diameter over the sea ice, and returned to the northern ice edge. The ECMWF analysis places the cyclone track 100 km too far west over the sea ice, a deviation which is too large for representative sea ice simulations. On the east side of the northward moving cyclone, the ice edge was pushed northward by 55 km because of strong winds. On the rear side, the ice edge advanced toward the open water but by a smaller distance because of weaker winds there. The ice drift pattern as calculated from the ice buoys and the two RADARSAT images is cyclonically curved around the center of the cyclone loop. Ice drift divergence shows a spatial pattern with divergence in the loop center and a zone of convergence around. Ice concentration changes as retrieved from SSM/I data follow the divergence pattern such that sea ice concentration increased in areas of divergence and decreased in areas of convergence.