Sentence examples for identifying taxa from inspiring English sources

For identifying taxa present at 1% or greater, we recommend a subsampling size of 250 random reads and a sampling size of 2,500 reads for identifying taxa present at 0.1% (see Additional file 2: Table S1b-c).

Methods of positioning our plots, measuring vegetation cover, and identifying taxa were described by Marler and del Moral.

The resulting information is essential for identifying misclassifications in the taxonomic classification of other sequences (as outlined earlier) and identifying taxa that were missed in the taxonomic classification process.

The phylum Proteobacteria represented 38% of 299 identified taxa.

A total of 21 exotic herbs were found, accounting for 11.3% of all identified taxa.

We identified taxa that are characteristic of paleoecological change, such as Moraceae, Ficus and Asteraceae, among others.

Incorporating visuals into trees may draw attention to the organisms, help users to recognize and identify taxa, and assist visitors in connecting labeled synapomorphies with visible morphological characteristics.

It should be noted that the use of multiple primer sets biases the number of sequences belonging to the identified taxa.

The identified taxa include representatives of Xenarthra (Eremotherium laurillardi, cf. Glossotherium sp., Pachyarmatherium brasiliense, Glyptodontinae, Propraopus sulcatus, Pampatherium humboldti, and Holmesina paulacoutoi), Notoungulata (Toxodontidae), Proboscidea, Perissodactyla (Tapirus terrestris and Equus (Amerhippus) neogaeus), and Cetartiodactyla (Camelidae).

Most of the identified taxa belong to the extended multi-layered broadleaved mesophytic forest that occupied the lowlands of the New Jersey hinterland and reflects the zonal vegetation during time of deposition.

Linear discriminant analysis (LDA) effect size (LEfSe) employed the factorial Kruskal Wallis sum-rank test (α = 0.05) to identify taxa with significant differential abundances between categories (using one-against-all comparisons), followed by LDA to estimate the effect size of each differentially abundant feature (logarithmic LDA score >2.0).