20 research outputs found

    Nightside clouds and disequilibrium chemistry on the hot Jupiter WASP-43b

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    Hot Jupiters are among the best-studied exoplanets, but it is still poorly understood how their chemical composition and cloud properties vary with longitude. Theoretical models predict that clouds may condense on the nightside and that molecular abundances can be driven out of equilibrium by zonal winds. Here we report a phase-resolved emission spectrum of the hot Jupiter WASP-43b measured from 5-12 μm with JWST's Mid-Infrared Instrument (MIRI). The spectra reveal a large day-night temperature contrast (with average brightness temperatures of 1524±35 and 863±23 Kelvin, respectively) and evidence for water absorption at all orbital phases. Comparisons with three-dimensional atmospheric models show that both the phase curve shape and emission spectra strongly suggest the presence of nightside clouds which become optically thick to thermal emission at pressures greater than ~100 mbar. The dayside is consistent with a cloudless atmosphere above the mid-infrared photosphere. Contrary to expectations from equilibrium chemistry but consistent with disequilibrium kinetics models, methane is not detected on the nightside (2σ upper limit of 1-6 parts per million, depending on model assumptions)

    Emory Bee Foraging Sequences

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    This dataset contains bee foraging sequences from a laboratory foraging enclosure at Emory University in Atlanta, GA. This dataset contains information for eight bees (numbered from one to eight) foraging on 32 artificial flowers (numbered from one to 32). Each row represents one flower visit, and the rows are sorted chronologically for each bee individual. In column one, "bee_num" represents the number of the bee individual. In column two "flower_id" represents the ID number of the flower visited, and in column three "source" represents the source of the data (Emory University)

    Quantitative and qualitative assessment of pollen DNA metabarcoding using constructed species mixtures

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    © 2018 John Wiley & Sons Ltd Pollen DNA metabarcoding—marker-based genetic identification of potentially mixed-species pollen samples—has applications across a variety of fields. While basic species-level pollen identification using standard DNA barcode markers is established, the extent to which metabarcoding (a) correctly assigns species identities to mixes (qualitative matching) and (b) generates sequence reads proportionally to their relative abundance in a sample (quantitative matching) is unclear, as these have not been assessed relative to known standards. We tested the quantitative and qualitative robustness of metabarcoding in constructed pollen mixtures varying in species richness (1–9 species), taxonomic relatedness (within genera to across class) and rarity (5%–100% of grains), using Illumina MiSeq with the markers rbcL and ITS2. Qualitatively, species composition determinations were largely correct, but false positives and negatives occurred. False negatives were typically driven by lack of a barcode gap or rarity in a sample. Species richness and taxonomic relatedness, however, did not strongly impact correct determinations. False positives were likely driven by contamination, chimeric sequences and/or misidentification by the bioinformatics pipeline. Quantitatively, the proportion of reads for each species was only weakly correlated with its relative abundance, in contrast to suggestions from some other studies. Quantitative mismatches are not correctable by consistent scaling factors, but instead are context-dependent on the other species present in a sample. Together, our results show that metabarcoding is largely robust for determining pollen presence/absence but that sequence reads should not be used to infer relative abundance of pollen grains

    Applying Pollen DNA Metabarcoding to the Study of Plant-Pollinator Interactions

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    © 2017 Bell et al. Published by the Botanical Society of America. • Premise of the study: To study pollination networks in a changing environment, we need accurate, high-throughput methods. Previous studies have shown that more highly resolved networks can be constructed by studying pollen loads taken from bees, relative to field observations. DNA metabarcoding potentially allows for faster and finer-scale taxonomic resolution of pollen compared to traditional approaches (e.g., light microscopy), but has not been applied to pollination networks. • Methods: We sampled pollen from 38 bee species collected in Florida from sites differing in forest management. We isolated DNA from pollen mixtures and sequenced rbcL and ITS2 gene regions from all mixtures in a single run on the Illumina MiSeq platform. We identified species from sequence data using comprehensive rbcL and ITS2 databases. • Results: We successfully built a proof-of-concept quantitative pollination network using pollen metabarcoding. • Discussion: Our work underscores that pollen metabarcoding is not quantitative but that quantitative networks can be constructed based on the number of interacting individuals. Due to the frequency of contamination and false positive reads, isolation and PCR negative controls should be used in every reaction. DNA metabarcoding has advantages in efficiency and resolution over microscopic identification of pollen, and we expect that it will have broad utility for future studies of plant-pollinator interactions

    Applying Pollen DNA Metabarcoding to the Study of Plant–Pollinator Interactions

    No full text
    © 2017 Bell et al. Published by the Botanical Society of America. • Premise of the study: To study pollination networks in a changing environment, we need accurate, high-throughput methods. Previous studies have shown that more highly resolved networks can be constructed by studying pollen loads taken from bees, relative to field observations. DNA metabarcoding potentially allows for faster and finer-scale taxonomic resolution of pollen compared to traditional approaches (e.g., light microscopy), but has not been applied to pollination networks. • Methods: We sampled pollen from 38 bee species collected in Florida from sites differing in forest management. We isolated DNA from pollen mixtures and sequenced rbcL and ITS2 gene regions from all mixtures in a single run on the Illumina MiSeq platform. We identified species from sequence data using comprehensive rbcL and ITS2 databases. • Results: We successfully built a proof-of-concept quantitative pollination network using pollen metabarcoding. • Discussion: Our work underscores that pollen metabarcoding is not quantitative but that quantitative networks can be constructed based on the number of interacting individuals. Due to the frequency of contamination and false positive reads, isolation and PCR negative controls should be used in every reaction. DNA metabarcoding has advantages in efficiency and resolution over microscopic identification of pollen, and we expect that it will have broad utility for future studies of plant-pollinator interactions
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