Morgane Philippe

How Effective Are DNA Barcodes in the Identification of African Rainforest Trees

Background DNA barcoding of rain forest trees could potentially help biologists identify species and discover new ones. However, DNA barcodes cannot always distinguish between closely related species, and the size and completeness of barcode databases are key parameters for their successful application. We test the ability of rbcL, matK and trnH-psbA plastid DNA markers to identify rain forest trees at two sites in Atlantic central Africa under the assumption that a database is exhaustive in terms of species content, but not necessarily in terms of haplotype diversity within species. Methodology/Principal Findings We assess the accuracy of identification to species or genus using a genetic distance matrix between samples either based on a global multiple sequence alignment (GD) or on a basic local alignment search tool (BLAST). Where a local database is available (within a 50 ha plot), barcoding was generally reliable for genus identification (95–100% success), but less for species identification (71–88%). Using a single marker, best results for species identification were obtained with trnH-psbA. There was a significant decrease of barcoding success in species-rich clades. When the local database was used to identify the genus of trees from another region and did include all genera from the query individuals but not all species, genus identification success decreased to 84–90%. The GD method performed best but a global multiple sequence alignment is not applicable on trnH-psbA. Conclusions/Significance Barcoding is a useful tool to assign unidentified African rain forest trees to a genus, but identification to a species is less reliable, especially in species-rich clades, even using an exhaustive local database. Combining two markers improves the accuracy of species identification but it would only marginally improve genus identification. Finally, we highlight some limitations of the BLAST algorithm as currently implemented and suggest possible improvements for barcoding applications.

Massive subsurface ice formed by refreezing of ice-shelf melt ponds

Surface melt ponds form intermittently on several Antarctic ice shelves. Although implicated in ice-shelf break up, the consequences of such ponding for ice formation and ice-shelf structure have not been evaluated. Here we report the discovery of a massive subsurface ice layer, at least 16 km across, several kilometres long and tens of metres deep, located in an area of intense melting and intermittent ponding on Larsen C Ice Shelf, Antarctica. We combine borehole optical televiewer logging and radar measurements with remote sensing and firn modelling to investigate the layer, found to be ∼10 °C warmer and ∼170 kg m−3 denser than anticipated in the absence of ponding and hitherto used in models of ice-shelf fracture and flow. Surface ponding and ice layers such as the one we report are likely to form on a wider range of Antarctic ice shelves in response to climatic warming in forthcoming decades.

Comparing the potential of MEG and EEG to uncover brain tracking of speech temporal envelope

&NA; During connected speech listening, brain activity tracks speech rhythmicity at delta (˜0.5 Hz) and theta (4–8 Hz) frequencies. Here, we compared the potential of magnetoencephalography (MEG) and high‐density electroencephalography (EEG) to uncover such speech brain tracking. Ten healthy right‐handed adults listened to two different 5‐min audio recordings, either without noise or mixed with a cocktail‐party noise of equal loudness. Their brain activity was simultaneously recorded with MEG and EEG. We quantified speech brain tracking channel‐by‐channel using coherence, and with all channels at once by speech temporal envelope reconstruction accuracy. In both conditions, speech brain tracking was significant at delta and theta frequencies and peaked in the temporal regions with both modalities (MEG and EEG). However, in the absence of noise, speech brain tracking estimated from MEG data was significantly higher than that obtained from EEG. Furthemore, to uncover significant speech brain tracking, recordings needed to be ˜3 times longer in EEG than MEG, depending on the frequency considered (delta or theta) and the estimation method. In the presence of noise, both EEG and MEG recordings replicated the previous finding that speech brain tracking at delta frequencies is stronger with attended speech (i.e., the sound subjects are attending to) than with the global sound (i.e., the attended speech and the noise combined). Other previously reported MEG findings were replicated based on MEG but not EEG recordings: 1) speech brain tracking at theta frequencies is stronger with attended speech than with the global sound, 2) speech brain tracking at delta frequencies is stronger in noiseless than noisy conditions, and 3) when noise is added, speech brain tracking at delta frequencies dampens less in the left hemisphere than in the right hemisphere. Finally, sources of speech brain tracking reconstructed from EEG data were systematically deeper and more posterior than those derived from MEG. The present study demonstrates that speech brain tracking is better seen with MEG than EEG. Quantitatively, EEG recordings need to be ˜3 times longer than MEG recordings to uncover significant speech brain tracking. As a consequence, MEG appears more suited than EEG to pinpoint subtle effects related to speech brain tracking in a given recording time. HighlightsSpeech brain tracking was analysed from simultaneous MEG and EEG data.Uncovering speech brain tracking requires 3 times shorter MEG than EEG recordings.Some previous MEG findings were replicated with MEG but not with EEG.