In cereals, a common salinity tolerance mechanism is to limit accumulation of Na(+) in the shoot. In a cross between the barley variety Barque-73 (Hordeum vulgare ssp. vulgare) and the accession CPI-71284 of wild barley (H. vulgare ssp. spontaneum), the HvNax3 locus on chromosome 7H was found to determine a ~10-25 % difference in leaf Na(+) accumulation in seedlings grown in saline hydroponics, with the beneficial exclusion trait originating from the wild parent. The Na(+) exclusion allele was also associated with a 13-21 % increase in shoot fresh weight. The HvNax3 locus was delimited to a 0.4 cM genetic interval, where it cosegregated with the HVP10 gene for vacuolar H(+)-pyrophosphatase (V-PPase). Sequencing revealed that the mapping parents encoded identical HVP10 proteins, but salinity-induced mRNA expression of HVP10 was higher in CPI-71284 than in Barque-73, in both roots and shoots. By contrast, the expression of several other genes predicted by comparative mapping to be located in the HvNax3 interval was similar in the two parent lines. Previous work demonstrated roles for V-PPase in ion transport and salinity tolerance. We therefore considered transcription levels of HVP10 to be a possible basis for variation in shoot Na(+) accumulation and biomass production controlled by the HvNax3 locus under saline conditions. Potential mechanisms linking HVP10 expression patterns to the observed phenotypes are discussed.

Alpine plant–pollinator communities play an important role in the functioning of alpine ecosystems, which are highly threatened by climate change. However, we still have a poor understanding of how environmental factors and spatiotemporal variability shape these communities. Here, we investigate what drives structure and beta diversity in a plant–pollinator metacommunity from the Australian alpine region using two approaches: pollen DNA metabarcoding (MB) and observations. Individual pollinators often carry pollen from multiple plant species, and therefore we expected MB to reveal a more diverse and complex network structure. We used two gene regions (ITS2 and trnL) to identify plant species present in the pollen loads of 154 insect pollinator specimens from three alpine habitats and construct MB networks, and compared them to networks based on observations alone. We compared species and interaction turnover across space for both types of networks, and evaluated their differences for plant phylogenetic diversity and beta diversity. We found significant structural differences between the two types of networks; notably, MB networks were much less specialized but more diverse than observation networks, with MB detecting many cryptic plant species. Both approaches revealed that alpine pollination networks are very generalized, but we estimated a high spatial turnover of plant species (0.79) and interaction rewiring (0.6) as well as high plant phylogenetic diversity (0.68) driven by habitat differences based on the larger diversity of plant species and species interactions detected with MB. Overall, our findings show that habitat and microclimatic heterogeneity drives diversity and fine‐scale spatial turnover of alpine plant–pollinator networks.

Monitoring biodiversity is a growing and pressing challenge, particularly as climate change threatens species with extinction and leads to widespread shifts in plant distribution and phenology. Tracking changes via ground vegetation surveys is costly and time‐consuming, hence monitoring of complex and heterogenous communities remains an ongoing challenge. Molecular DNA methods are rapidly being developed to provide fast and reproducible results for environmental monitoring, including diet and ecosystem assessments. Here, we used DNA metabarcoding of pollen foraged by European honey bees (Apis mellifera) to investigate their floral resource use in an urban reserve. We collected three different pollen samples from hives: individual bees, raw honey and pollen traps, and identified plants using two metabarcoding markers (ITS2 and trnL). We then compared the results to a ground vegetation survey of surrounding flowering taxa. Pollen DNA metabarcoding detected 74 taxa (48.6% identified to species) across all pollen sources, compared to 44 taxa recorded by the survey (93% identified to species). Within the metabarcoding results, we identified 25% of the genera and 9% of the species found during the survey, with three of the top 10 flowering genera represented. While honey was the most taxon‐rich pollen source (mean = 8.5, SD = 3.5), followed by honey bees (mean = 5.8, SD = 6.1) and pollen traps (mean = 4.2, SD = 1.7), combining the results of six individual bees could detect similar taxa numbers to honey, while 20 bees were required to detect as many taxa as the survey. We demonstrate how DNA metabarcoding of the pollen foraged by honey bees can detect more flowering taxa than traditional survey methods, and how different pollen sources and genetic markers affect the level of detection of plant taxa. The foraging choices of honey bees matched few species detected by the vegetation survey, therefore pollen metabarcoding is recommended as a complementary approach to ground surveys. Rigorous validation and stringent filtering of metabarcoding results were also required to exclude potential false positives. Altogether, this molecular approach can be used to augment vegetation surveys, while tracking the floral resources used by bees.