Search Results (312)

Development of site index models for shrubby species in arid ecosystems remains a challenge, due to the absence of dominant height–age relationships and the complexity of ecological drivers in these environments. In this study, a multivariate approach to classify site quality for Agave lechuguilla Torr, a wild non-timber species of ecological and economic importance in northern Mexico, was performed. Data were collected from 112 sampling plots where the abundance, height, basal diameter, and crown diameter for the A. lechuguilla plants were measured. Sites were grouped into three site index categories (low, medium, and high) using the Importance Value Index (IVI). Afterward a classical discriminant analysis (CDA) was applied to derive linear functions capable of classifying new sites into these predefined categories. Statistical assumptions of multivariate normality, homogeneity of covariance matrices, and low multicollinearity were met. The discriminant functions showed high classification accuracy (95.54%), with full correct classification of low and high site index categories. Additional validation through MANOVA and principal component analysis (PCA) confirmed the separation of groups and the ecological coherence of the selected variables. This approach provides a simple, practical, and replicable model for assessing shrubland site quality using field measurable features. It also offers a tool for sustainable harvesting and conservation of A. lechuguilla. Full article

The Ichubamba Yasepan wetlands, in the Andean páramos of Ecuador, suffer heavy metal contamination due to anthropogenic activities and volcanic ash from Sangay, impacting biodiversity and ecosystem services. This quasi-experimental study evaluated the bioaccumulation and tolerance of metals in high Andean species through stratified random sampling and linear transects in two altitudinal ranges. Concentrations of Cr, Pb, Hg, As, and Fe in water and the tissues of eight dominant plant species were analyzed using atomic absorption spectrophotometry, calculating bioaccumulation indices (BAIs) and applying principal component analysis (PCA), clustering, and linear discriminant analysis (LDA). Twenty-five species from 14 families were identified, predominantly Poaceae and Cyperaceae, with Calamagrostis intermedia as the most relevant (IVI = 12.74). The water exceeded regulatory limits for As, Cr, Fe, and Pb, indicating severe contamination. Carex bonplandii showed a high BAI for Cr (47.8), Taraxacum officinale and Plantago australis for Pb, and Lachemilla orbiculata for Hg, while Fe was widely accumulated. The LDA highlighted differences based on As and Pb, suggesting physiological adaptations. Pollution threatens biodiversity and human health, but C. bonplandii and L. orbiculata have phytoremediation potential. Full article

Unconventional food plants (UFPs) are increasingly valued for their nutritional composition and bioactive potential. This study proposes a comprehensive characterization of the chemical and bioactive properties of Pereskia aculeata Miller (Cactaceae) (PA); Xanthosoma sagittifolium (L.) Schott (Araceae) (XS); Stachys byzantina K. Koch (Lamiaceae) (SB); and inflorescences from three cultivars of Musa acuminata (Musaceae) var. Dwarf Cavendish, var. BRS Platina, and var. BRS Conquista (MAD, MAP, and MAC), including the assessment of physical, nutritional, phytochemical, and biological parameters. Notably, detailed phenolic profiles were established for these species, many of which are poorly documented in the literature. XS was characterized by a unique abundance of C-glycosylated flavones, especially apigenin and luteolin derivatives, rarely described for this species. SB exhibited high levels of phenylethanoid glycosides, particularly verbascoside and its isomers (up to 21.32 mg/g extract), while PA was rich in O-glycosylated flavonols such as quercetin, kaempferol, and isorhamnetin derivatives. Nutritionally, XS had the highest protein content (16.3 g/100 g dw), while SB showed remarkable dietary fiber content (59.8 g/100 g). Banana inflorescences presented high fiber (up to 66.5 g/100 g) and lipid levels (up to 7.35 g/100 g). Regarding bioactivity, PA showed the highest DPPH radical scavenging activity (95.21%) and SB the highest reducing power in the FRAP assay (4085.90 µM TE/g). Cellular antioxidant activity exceeded 2000% in most samples, except for SB. Cytotoxic and anti-inflammatory activities were generally low, with only SB showing moderate effects against Caco-2 and AGS cell lines. SB and PA demonstrated the strongest antimicrobial activity, particularly against Yersinia enterocolitica, methicillin-resistant Staphylococcus aureus (MRSA), and Enterococcus faecalis, with minimum inhibitory concentrations ranging from 0.156 to 0.625 mg/mL. Linear discriminant analysis revealed distinctive chemical patterns among the species, with organic acids (e.g., oxalic up to 7.53 g/100 g) and fatty acids (e.g., linolenic acid up to 52.38%) as key discriminant variables. Overall, the study underscores the nutritional and functional relevance of these underutilized plants and contributes rare quantitative data to the scientific literature regarding their phenolic signatures. Full article

The spatial distribution of ephemeral and perennial dryland plant species is increasingly modified and restricted by ever-changing climates and development expansion. At the interface of biodiversity conservation and developmental planning in desert landscapes is the growing need for adaptable tools in identifying and monitoring these ecologically fragile plant assemblages, habitats, and, often, heritage sites. This study evaluates usage of Sentinel-2 time series composite imagery to discriminate vegetation assemblages in a hyper-arid landscape. Spatial predictor spaces were compared to classify different vegetation communities: spectral components (PCs), vegetation indices (VIs), and their combination. Further, the uncertainty in discriminating field-verified vegetation assemblages is assessed using Shannon entropy and intensity analysis. Lastly, the intensity analysis helped to decipher and quantify class transitions between maps from different spatial predictors. We mapped plant assemblages in 2022 from combined PCs and VIs at an overall accuracy of 82.71% (95% CI: 81.08, 84.28). A high overall accuracy did not directly translate to high class prediction probabilities. Prediction by spectral components, with comparably lower accuracy (80.32, 95% CI: 78.60, 81.96), showed lower class uncertainty. Class disagreement or transition between classification models was mainly contributed by class exchange (a component of spatial allocation) and less so from quantity disagreement. Different artefacts of vegetation classes are associated with the predictor space—spectral components versus vegetation indices. This study contributes insights into using feature extraction (VIs) versus feature selection (PCs) for pixel-based classification of plant assemblages. Emphasising the ecologically sensitive vegetation in desert landscapes, the study contributes uncertainty considerations in translating optical satellite imagery to vegetation maps of arid landscapes. These are perceived to inform and support vegetation map creation and interpretation for operational management and conservation of plant biodiversity and habitats in such landscapes. Full article

As one component of a study to improve Medicago spp. germplasm in eastern Australia, fifteen phenotypic and agronomic attributes were recorded for 4715 plants grown from the seed of 90 accessions of the widely naturalized pasture legume Medicago polymorpha from 90 sites in eight regions of inland New South Wales. The species expressed wide polymorphism. However, many leaflet attributes were associated with specific climate and soil characteristics, which varied from east to west across the collection zone. Discriminant analysis showed that accessions from the four most northern (summer dominant rainfall) and western (arid–semiarid) regions (Group A) differed from accessions from the most southern, temperate (winter dominant rainfall) and eastern (higher rainfall) regions (Group B). Group A flowered earlier and had shorter pod spines. Group B had lower plant vigor. Regions from which Group A accessions were collected had higher soil pH, lower winter rainfall, and higher minimum winter temperature than Group B regions. The diversity in the population, particularly the difference in flowering times among accessions collected from drier, warmer regions and those from more mesic, cooler regions, and the wide variation in flowering time measured among plants grown from accessions within all collection regions, is likely to ensure the long-term persistence of M. polymorpha in a changing climate. Elite lines were subsequently identified and lodged in National and International Genebanks for future research. Full article

Olive trees are generally considered a species well-adapted to drought, but the impact of water shortage is of critical importance on olive production. For this reason, developing tolerant cultivars could be an effective strategy to mitigate the impact of drought in the future. Characterizing drought stress tolerance in olive is a complex task due to the numerous traits involved in this response. In this study, plant growth, pressure–volume curves, gas-exchange and chlorophyll fluorescence traits, and stomata characteristics were monitored in nine cultivars to assess the effects of mild and severe drought stress conditions induced by withholding water for 7 and 21 days, respectively, and were compared to a well-watered control treatment. The plant materials evaluated included traditional cultivars, as well as new developed cultivars suited for high-density hedgerow olive orchards or resistant to verticillium wilt. Significant differences between cultivars were observed for most evaluated traits, with more pronounced differences under severe drought conditions. A multivariate analysis of the complete dataset recorded throughout the evaluation period allowed for the identification of promising cultivars under stress conditions (‘Sikitita’, ‘Sikitita-2’, and ‘Martina’) as well as highly discriminative traits that could serve as key selection parameters in future breeding programs. Full article

Background: Drought stress is a major abiotic factor limiting Brassica juncea productivity, resulting in significant yield reductions. Plant Growth-Promoting Rhizobacteria (PGPR) have shown potential in enhancing drought tolerance; however, the metabolomic changes associated with their effects remain largely unexplored. This study examines the metabolic changes induced by a PGPR consortium (Enterobacter hormaechei, Pantoea dispersa, and Acinetobacter sp.) in two contrasting genotypes B. juncea (L.) Czern. ‘RH 725’ (drought tolerant) and B. juncea (L.) Czern. ‘RH-749’ (drought sensitive for drought tolerance, under both control and drought conditions. Methods: Metabolite profiling was conducted using gas chromatography-mass spectrometry (GC-MS) to identify compounds that accumulated differentially across treatments. We applied multivariate statistical methods, such as Partial Least Squares Discriminant Analysis (PLS-DA), hierarchical clustering, and pathway enrichment analysis, to explore metabolic reprogramming. Results: Drought stress induced significant changes in metabolite profile, particularly increasing the levels of osmoprotectants such as trehalose, glucose, sucrose, proline, and valine. Additionally, alterations in organic acids (malic acid and citric acid) and fatty acids (oleic acid and linoleic acid) were observed. PGPR inoculation further amplified these metabolic responses to enhance the osmotic regulation, reactive oxygen species (ROS) detoxification, and carbon-nitrogen metabolism, with RH-725 displaying a stronger adaptive response. Pathway enrichment analysis revealed that PGPR treatment significantly influenced metabolic pathways related to starch and sucrose metabolism, galactose metabolism, and amino acid biosynthesis, which play critical roles in drought adaptation. Conclusion: These findings provide insights into how PGPR contributes to stress resilience in B. juncea by modulating key biochemical pathways. This study provides new molecular insights into the known effect of PGPR for mitigating drought stress in oilseed crops. Full article

Structural characterization of natural products in complex herbal extracts remains a major challenge in phytochemical analysis. In this study, we present a novel post-acquisition data-processing strategy—key ion diagnostics–neutral loss filtering (KID-NLF)—combined with ultra-high-performance liquid chromatography–quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS) for systematic profiling of the medicinal plant Terminalia chebula. The strategy consists of four main steps. First, untargeted data are acquired in negative electrospray ionization (ESI⁻) mode. Second, a genus-specific diagnostic ion database is constructed by leveraging characteristic fragment ions (e.g., gallic acid, chebuloyl, and HHDP groups) and conserved substructures. Third, MS/MS data are high-resolution filtered using key ion diagnostics and neutral loss patterns (302 Da for HHDP; 320 Da for chebuloyl). Finally, structures are elucidated via detailed spectral analysis. The methanol extract of T. chebula was separated on a C18 column using a gradient of acetonitrile and 0.1% aqueous formic acid within 33 min. This separation enabled detection of 164 compounds, of which 47 were reported for the first time. Based on fragmentation pathways and diagnostic ions (e.g., m/z 169 for gallic acid, m/z 301 for ellagic acid, and neutral losses of 152, 302, and 320 Da), the compounds were classified into three major groups: gallic acid derivatives, ellagitannins (containing HHDP, chebuloyl, or neochebuloyl moieties), and triterpenoid glycosides. KID-NLF overcomes key limitations of conventional workflows—namely, isomer discrimination and detection of low-abundance compounds—by exploiting genus-specific structural signatures. This strategy demonstrates high efficiency in resolving complex polyphenolic and triterpenoid profiles and enables rapid annotation of both known and novel metabolites. This study highlights KID-NLF as a robust framework for phytochemical analysis in species with high chemical complexity. It also paves the way for applications in quality control, drug discovery, and mechanistic studies of medicinal plants. Full article

Low-cost unmanned aerial vehicle (UAV) visible light remote sensing provides new opportunities for plant community monitoring, but its practical deployment in different ecosystems is still limited by the lack of standardized vegetation index (VI) optimization for multi-species coverage extraction. This study developed a universal method integrating four VIs—Excess Green Index (EXG), Visible Band Difference Vegetation Index (VDVI), Red-Green Ratio Index (RGRI), and Red-Green-Blue Vegetation Index (RGBVI)—to bridge UAV imagery with plant communities. By combining spectral separability analysis with machine learning (SVM), we established dynamic thresholds applicable to crops, trees, and shrubs, achieving cross-species compatibility without multispectral data. The results showed that all VIs achieved robust vegetation/non-vegetation discrimination (Kappa > 0.84), with VDVI being more suitable for distinguishing vegetation from non-vegetation. The overall classification accuracy for different vegetation types exceeded 92.68%, indicating that the accuracy is considerable. Crop coverage extraction showed a minimum segmentation error of 0.63, significantly lower than that of other vegetation types. These advances enable high-resolution vegetation monitoring, supporting biodiversity assessment and ecosystem service quantification. Our research findings track the impact of plant communities on the ecological environment and promote the application of UAVs in ecological restoration and precision agriculture. Full article

Flower closure is a widespread yet understudied trait that may serve multiple functions in the success of plant reproduction. In this study, we investigated the role of flower closure in protecting pollen from rain-induced loss in lesser celandine (Ficaria verna Huds., 1762), an early-flowering species vulnerable to spring rains. Through simulated and natural rain experiments, we found that the flowers that were prevented from closing retained significantly fewer pollen grains compared to the control flowers. This demonstrates that flower closure effectively protects pollen from rain-induced flushing, thus enhancing reproductive success. Furthermore, flowers that were prevented from closing had fewer viable pollen grains than control flowers. We propose that the evolution of petal movement in F. verna was primarily driven by pressures favoring petal movement that protected pollen, with secondary contributions from herbivore avoidance. Flowers are unable to discriminate between low luminosity caused by cloudy weather and night, thus responding to both. Future studies should explore the relative importance of primary and secondary evolutionary drivers of flower closure across species, particularly in early-flowering plants facing complex environmental challenges. Full article

Background/Objectives: Plant-derived secondary metabolites have long contributed to the discovery of novel therapeutic agents, especially in the treatment of parasitic and infectious diseases in developing countries. Metabolomics provides a systems-level approach to understanding plant biochemistry, enabling the discovery of secondary metabolites with pharmacological relevance. Taraxacum kok-saghyz (TKS), widely known for its rubber-producing capabilities, remains underexplored as a medicinal plant. Given the well-established therapeutic properties of Taraxacum officinale and the emerging pharmacological profiles of related species, this study investigates the metabolic composition of TKS roots and leaves to uncover bioactive compounds with antioxidant, anti-inflammatory, or hepatoprotective potential. Methods: Widely targeted metabolomics was conducted on 10-month-old field-grown Kultevar™ TKS plants using ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS). Samples were hand-harvested and preserved on dry ice to maintain biochemical integrity. Metabolite identification and classification were performed using the MWDB and KEGG databases. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to evaluate metabolic variation between tissues. Results: A total of 1813 metabolites were identified, including flavonoids, alkaloids, lipids, amino acids, and phenolic compounds. Differential analysis revealed 964 significantly altered metabolites—609 downregulated and 355 upregulated in roots relative to leaves. Multivariate analysis confirmed clear tissue-specific metabolic profiles. KEGG pathway enrichment highlighted the involvement of flavonoid biosynthesis, amino acid metabolism, and lipid metabolism pathways, suggesting bioactive potential. This study presents the first comprehensive metabolic profile of TKS, highlighting its potential value beyond rubber production. The detection of numerous therapeutic secondary metabolites supports its promise as a pharmaceutical and nutraceutical resource. Further functional validation of identified compounds is warranted. Full article

The taxonomic identification of plant species is traditionally based on morphological traits, the use of which may create difficulties in cases of close-related species showing great morphological variability. In such cases, the use of DNA markers for species identification and delimitation can be of great help. Himantoglossum W.D.J.Koch (Orchidaceae) is a genus with notable morphological variability, comprising the clade hircinum-caprinum (Himantoglossum s.s.) with nine taxa, from which H. jankae, H. hircinum, H. montis-tauri, H. caprinum and H. samariense have being reported in Greece. However, a previous morphological study of Himantoglossum s.s. from all over Greece could not verify the presence of these reported species, but of only one highly diverse taxon throughout the country. Here, we studied the genetic variation and differentiation of Himantoglossum s.s. populations from the entire distribution of the genus in Greece employing ISSR markers, to further elucidate the taxonomic status of Himantoglossum s.s. in Greece. High genetic variation was revealed, both in the populations of the “core” distribution and in the peripheral/marginal ones, pointing to their evolutionary potential. This variation is mainly attributed to differences within the populations and, to a lesser extent, among them. No differentiation of the populations proposed to belong to a different taxon was found and no species-specific markers were identified that may discriminate the above populations from the rest. In addition, two cpDNA and one nDNA fragments (accD, psbA-trnH and ITS2, respectively) were sequenced in a number of individuals representative of the whole dataset. All three fragments were conserved, showing restricted polymorphism and having no correlation to the populations or to the taxa of Himantoglossum s.s. in Greece. Overall, the high genetic variation of the populations of Himantoglossum s.s. in Greece, especially of the peripheral/marginal ones, is a valuable asset towards their conservation. Full article

This study aims to utilize hyperspectral imaging technology combined with machine learning methods for the authenticity identification and classification of Anoectochilus roxburghii and its counterfeit species. Hyperspectral data were collected from the front and back leaves of nine species of Goldthread and two counterfeit species (Bloodleaf and Spotted-leaf), followed by classification using a variety of machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Linear Discriminant Analysis (LDA), and Convolutional Neural Networks (CNN). The experimental results demonstrated that the SVM model achieved 100% classification accuracy for distinguishing Goldthread from its counterfeit species, effectively capturing the spectral differences between the front and back leaves. In contrast, traditional machine learning models showed varied performance, with SVM proving superior due to its ability to handle high-dimensional feature spaces. The introduction of a multi-view spectral fusion CNN model, which integrates spectral data from both the front and back leaves, further enhanced classification accuracy, achieving a perfect classification rate of 100%. This approach highlights the potential of hyperspectral imaging and machine learning in plant authenticity identification and provides a new perspective for the detection of counterfeit species. Full article

Most fir species in the Mediterranean have small to medium-sized distributions, are often endemic and endangered, and are mainly found in relict areas, except for Abies alba. The IUCN Red List of Threatened Species identified Abies nebrodensis as the rarest conifer in the world, with only 30 adult trees remaining. Additionally, Abies pinsapo is threatened and limited to five fragmented locations in Spain and Morocco. This study aimed to characterize the seed terpene profiles of Mediterranean Abies species, such as A. nebrodensis, A. pinsapo, and A. alba, since morphological results showed minimal variation among the Abies populations examined. Terpenes were extracted using n-heptane and then analyzed by GC-MS. The chemical composition revealed the dominance of limonene and α-pinene as the main monoterpenes in all the species, while A. nebrodensis reported the considerable presence of germacrene D-4-ol and selina-6-en-4-ol as sesquiterpenes. The relative contents of most of the terpenes were significantly different among the species, and subsequent statistical multivariate analysis showed clear discrimination among three distinct groups. These results confirmed the suitability of the terpene profile as a potential tool to study chemotaxonomic differences between species from the same family. Moreover, the compounds identified can be interesting for further studies on plant defense against biotic stress to reduce the risk of species extinction caused by pests and diseases. Full article

Epipactis helleborine (L.) Crantz is considered a challenging and phenotypically difficult species to identify due to its wide range of morphological variability. This variability is mainly observed in the perianth parts but also extends to the gynostemium structure, which has so far been considered one of the most useful diagnostic characteristics. As a result, a simple graphic illustrating the structural pattern of gynostemium morphology has appeared in 10 different forms in available European taxonomic keys, which significantly complicates the identification of this species. A total of 122 flowers of E. helleborine were collected from four natural populations in the Lower Silesia region (Poland) between 2017 and 2019 and analysed for gynostemium morphological variation. Geometric morphometric analyses, including Procrustes ANOVA, PCA, and CVA, were used to examine gynostemium shape, with statistical tests assessing variation in size and stigma inclination angle among populations, individual plants (ramets), and years of research. Statistical analysis revealed significant positive correlations between gynostemium width and height, with significant variation in size and angle of stigma inclination, primarily driven by population, while ramet and year of research had a lesser impact. Geometric morphometric analyses indicated significant population-level variation in gynostemium shape, with principal component analysis identifying the ventral view as the most informative for discriminating these differences. The first two principal components explained the major shape variation, and canonical variate analysis confirmed that this view is most important for species identification. Full article