Streptococcus castoreus sp nov., isolated from a beaver (Castor fiber)

A previously undescribed, Gram-positive, catalase-negative, Streptococcus-like organism originating from a European beaver (Castor fiber) was subjected to a taxonomic study. The organism displayed beta-haemolytic activity and gave a positive reaction with Lancefield group A antisera. Based on the results of biochemical testing, the organism was tentatively identified as a member of the genus Streptococcus, but it did not correspond phenotypically to any recognized species of this genus. Comparative 16S rRNA gene sequencing studies confirmed this assignment, with the bacterium forming a hitherto unknown subline within the genus. Sequence divergence values of greater than 3% from other reference streptococcal species, however, demonstrated that the unidentified coccus-shaped organism represents a hitherto unknown species. Based on phenotypic and molecular phylogenetic evidence, it is therefore proposed that the unknown organism from a beaver be classified as a novel species, Streptococcus castoreus sp. nov. The type strain is M605815/03/2(T) (=CCUG 48115(T) = CIP 108205(T)).

Preliminary leaf area index estimates from airborne small footprint full-waveform LiDAR data

This study has compared preliminary estimates of effective leaf area index (LAI) derived from fish-eye lens photographs to those estimated from airborne full-waveform small-footprint LiDAR data for a forest dataset in Australia. The full-waveform data was decomposed and optimized using a trust-region-reflective algorithm to extract denser point clouds. LAI LiDAR estimates were derived in two ways (1) from the probability of discrete pulses reaching the ground without being intercepted (point method) and (2) from raw waveform canopy height profile processing adapted to small-footprint laser altimetry (waveform method) accounting for reflectance ratio between vegetation and ground. The best results, that matched hemispherical photography estimates, were achieved for the waveform method with a study area-adjusted reflectance ratio of 0.4 (RMSE of 0.15 and 0.03 at plot and site level, respectively). The point method generally overestimated, whereas the waveform method with an arbitrary reflectance ratio of 0.5 underestimated the fish-eye lens LAI estimates.

Daily thin-ice thickness maps from modis thermal infrared imagery

Accurate knowledge of ice-production rates within the marginal ice zones of the Arctic Ocean requires monitoring of the thin-ice distribution within polynyas. The thickness of the ice layer controls the heat loss and hence the new-ice formation. An established thinice algorithm using high-resolution MODIS data allows deriving the ice-thickness distribution within polynyas. The average uncertainty is ±4.7 cm for ice thicknesses below 0.2 m. In this study, the ice-thickness distributions within the Laptev Sea polynya for the two winter seasons 2007/08 and 2008/09 are calculated. Then, a new method is applied to determine a daily MODIS thin-ice product.