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An undescribed bacterium capable of clearing tannic acid‐protein complexes has been isolated from ruminal contents of feral goats browsing tannin‐rich Acacia species. The bacterium is a Gram‐positive facultative anaerobe, characterized as a Streptococcus, but DNA‐DNA hybridization and 16S rDNA sequencing show that it is distinct from the common ruminal species Strep. bovis. We propose the name Streptococcus caprinus for this species. The type strain is Strep. caprinus 2.3, Australian Collection of Microorganisms (ACM) 3969. The bacterium grows in media containing at least 2.5% w/v tannic acid or condensed tannin and produces zones of clearing around colonies on nutrient agar plates with added tannic acid. Streptococcus caprinus is not a major inhabitant of domestic livestock, but is found in feral goats browsing tannin‐rich Acacia species, at a population of up to 2 times 106 cfu ml‐1 of rumen fluid.

Hyperspectral sensing, measuring reflectance over visible to shortwave infrared wavelengths, has enabled the classification and mapping of vegetation at a range of taxonomic scales, often down to the species level. Classification with hyperspectral measurements, acquired by narrow band spectroradiometers or imaging sensors, has generally required some form of spectral feature selection to reduce the dimensionality of the data to a level suitable for the construction of a classification model. Despite the large number of hyperspectral plant classification studies, an in-depth review of feature selection methods and resultant waveband selections has not yet been performed. Here, we present a review of the last 22 years of hyperspectral vegetation classification literature that evaluates the overall waveband selection frequency, waveband selection frequency variation by taxonomic, structural, or functional group, and the influence of feature selection choice by comparing such methods as stepwise discriminant analysis (SDA), support vector machines (SVM), and random forests (RF). This review determined that all characteristics of hyperspectral plant studies influence the wavebands selected for classification. This includes the taxonomic, structural, and functional groups of the target samples, the methods, and scale at which hyperspectral measurements are recorded, as well as the feature selection method used. Furthermore, these influences do not appear to be consistent. Moreover, the considerable variability in waveband selection caused by the feature selectors effectively masks the analysis of any variability between studies related to plant groupings. Additionally, questions are raised about the suitability of SDA as a feature selection method, with it producing waveband selections at odds with the other feature selectors. Caution is recommended when choosing a feature selector for hyperspectral plant classification: We recommend multiple methods being performed. The resultant sets of selected spectral features can either be evaluated individually by multiple classification models or combined as an ensemble for evaluation by a single classifier. Additionally, we suggest caution when relying upon waveband recommendations from the literature to guide waveband selections or classifications for new plant discrimination applications, as such recommendations appear to be weakly generalizable between studies.

11Green vegetation (GV), nonphotosynthetic vegetation (NPV), and soil are important 12 ground cover components in terrestrial ecosystems worldwide. There are many good 13 methods for observing the dynamics of GV with optical remote sensing, but there are 14 fewer good methods for observing the dynamics of NPV and soil. Given the difficulty of 15 remotely deriving information on NPV and soil, the purpose of this study is to evaluate 16 several methods for the retrieval of information on fractional cover of GV, NPV, and 17 soil using 500-m MODIS nadir BRDF-adjusted reflectance (NBAR) data. In particular, 18 three spectral mixture analysis (SMA) techniques are evaluated: simple SMA, multiple-19 endmember SMA (MESMA), and relative SMA (RSMA). In situ cover data from 20 agricultural fields in Southern Australia are used as the basis for comparison. RSMA 21 provides an index of fractional cover of GV, NPV, and soil, so a method for converting 22 these to absolute fractional cover estimates is also described and evaluated. All 23 methods displayed statistically significant correlations with in situ data. All methods 24 proved equally capable at predicting the dynamics of GV. MESMA predicted NPV 25 dynamics best. RSMA predicted dynamics of soil best. The method for converting RSMA 26

Measuring Mangrove Biomass With Drones estimates can be made far more quickly and over extensive areas when compared to traditional data collection techniques and, with improved accuracy through further model-calibration, have the potential to be a powerful tool for mangrove biomass and carbon storage estimation.