Phenolic Compounds as Unambiguous Chemical Markers for the Identification of Keystone Plant Species in the Bale Mountains, Ethiopia
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Doi: 10.5281/zenodo.3247691
Description: Figure S1: Comparison of Accuracy (A) and F1 Score (B) of Support Vector Machine (SVM, blue), Random Forest (RF, red) and Recursive Partitioning (RP, grey) algorithm based on the level of relative phenols, in relation to the ratio of tested/total number of samples (n=47) that has been split into test and training datasets, each model has been computed 5 times, the bars indicate the standard derivation between the prediction results of the models; Figure S2: Principal Component Analysis, PCA based on the relative phenol abundance in 47 leaf and twig samples from the Bale Mountains, shown are the first two principal components (PC1 and PC2). Dark red arrows indicate the direction of each vector of feature; Figure S3: Cross-validation of the model used; Table S1: Sum of weighted mean of phenolic compounds of each dominant plant species.
Lemma, B.; Grehl, C.; Zech, M.; Mekonnen, B.; Zech, W.; Nemomissa, S.; Bekele, T.; Glaser, B. Phenolic Compounds as Unambiguous Chemical Markers for the Identification of Keystone Plant Species in the Bale Mountains, Ethiopia. Plants 2019, 8, 228. https://doi.org/10.3390/plants8070228
Lemma B, Grehl C, Zech M, Mekonnen B, Zech W, Nemomissa S, Bekele T, Glaser B. Phenolic Compounds as Unambiguous Chemical Markers for the Identification of Keystone Plant Species in the Bale Mountains, Ethiopia. Plants. 2019; 8(7):228. https://doi.org/10.3390/plants8070228
Chicago/Turabian StyleLemma, Bruk, Claudius Grehl, Michael Zech, Betelhem Mekonnen, Wolfgang Zech, Sileshi Nemomissa, Tamrat Bekele, and Bruno Glaser. 2019. "Phenolic Compounds as Unambiguous Chemical Markers for the Identification of Keystone Plant Species in the Bale Mountains, Ethiopia" Plants 8, no. 7: 228. https://doi.org/10.3390/plants8070228