Near Infrared Spectroscopy Facilitates Rapid Identification of Both Young and Mature Amazonian Tree Species

Insights into the Taxonomically Challenging Hexaploid Alpine Shrub Willows of Salix Sections Phylicifoliae and Nigricantes (Salicaceae)

The complex genomic composition of allopolyploid plants leads to morphologically diverse species. The traditional taxonomical treatment of the medium-sized, hexaploid shrub willows distributed in the Alps is difficult based on their variable morphological characters. In this study, RAD sequencing data, infrared-spectroscopy, and morphometric data are used to analyze the phylogenetic relationships of the hexaploid species of the sections Nigricantes and Phylicifoliae in a phylogenetic framework of 45 Eurasian Salix species. Both sections comprise local endemics as well as widespread species. Based on the molecular data, the described morphological species appeared as monophyletic lineages (except for S. phylicifolia s.str. and S. bicolor, which are intermingled). Both sections Phylicifoliae and Nigricantes are polyphyletic. Infrared-spectroscopy mostly confirmed the differentiation of hexaploid alpine species. The morphometric data confirmed the molecular results and supported the inclusion of S. bicolor into S. phylicifolia s.l., whereas the alpine endemic S. hegetschweileri is distinct and closely related to species of the section Nigricantes. The genomic structure and co-ancestry analyses of the hexaploid species revealed a geographical pattern for widespread S. myrsinifolia, separating the Scandinavian from the alpine populations. The newly described S. kaptarae is tetraploid and is grouped within S. cinerea. Our data reveal that both sections Phylicifoliae and Nigricantes need to be redefined.

Infrared spectroscopy for ploidy estimation: An example in two species of Veronica using fresh and herbarium specimens

PREMISE

Polyploidy has become a central factor in plant evolutionary biological research in recent decades. Methods such as flow cytometry have revealed the widespread occurrence of polyploidy; however, its inference relies on expensive lab equipment and is largely restricted to fresh or recently dried material.

METHODS

Here, we assess the applicability of infrared spectroscopy to infer ploidy in two related species of Veronica (Plantaginaceae). Infrared spectroscopy relies on differences in the absorbance of tissues, which could be affected by primary and secondary metabolites related to polyploidy. We sampled 33 living plants from the greenhouse and 74 herbarium specimens with ploidy known through flow cytometrical measurements and analyzed the resulting spectra using discriminant analysis of principal components (DAPC) and neural network (NNET) classifiers.

RESULTS

Living material of both species combined was classified with 70% (DAPC) to 75% (NNET) accuracy, whereas herbarium material was classified with 84% (DAPC) to 85% (NNET) accuracy. Analyzing both species separately resulted in less clear results.

DISCUSSION

Infrared spectroscopy is quite reliable but is not a certain method for assessing intraspecific ploidy level differences in two species of Veronica. More accurate inferences rely on large training data sets and herbarium material. This study demonstrates an important way to expand the field of polyploid research to herbaria.

Classification and Prediction by Pigment Content in Lettuce (Lactuca sativa L.) Varieties Using Machine Learning and ATR-FTIR Spectroscopy

Green or purple lettuce varieties produce many secondary metabolites, such as chlorophylls, carotenoids, anthocyanins, flavonoids, and phenolic compounds, which is an emergent search in the field of biomolecule research. The main objective of this study was to use multivariate and machine learning algorithms on Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR)-based spectra to classify, predict, and categorize chemometric attributes. The cluster heatmap showed the highest efficiency in grouping similar lettuce varieties based on pigment profiles. The relationship among pigments was more significant than the absolute contents. Other results allow classification based on ATR-FTIR fingerprints of inflections associated with structural and chemical components present in lettuce, obtaining high accuracy and precision (>97%) by using principal component analysis and discriminant analysis (PCA-LDA)-associated linear LDA and SVM machine learning algorithms. In addition, PLSR models were capable of predicting Chla, Chlb, Chla+b, Car, AnC, Flv, and Phe contents, with R2P and RPDP values considered very good (0.81−0.88) for Car, Anc, and Flv and excellent (0.91−0.93) for Phe. According to the RPDP metric, the models were considered excellent (>2.10) for all variables estimated. Thus, this research shows the potential of machine learning solutions for ATR-FTIR spectroscopy analysis to classify, estimate, and characterize the biomolecules associated with secondary metabolites in lettuce.

A taxonomic account of Myrcia (Myrtaceae) at the sites of the Biological Dynamics of Forest Fragments Project, Amazonas, Brazil

Abstract Myrcia is the sole genus of the Myrciinae, one of the nine subtribes of Myrteae (Myrtaceae). The Amazon forest holds about one-quarter of the Brazilian species of Myrcia, but the genus is still understudied in this whole region. In this context, this study presents a floristic survey of Myrcia in the permanent plots of the Biological Dynamics of Forest Fragments Project (BDFFP), in Amazonas state, Brazil. The genus is represented by 36 species in the study area, comprehending 32% of its total richness in the Brazilian Amazon forest, with 19 of them endemic to this domain. Myrcia neospeciosa is reported as a new occurrence for Amazonas state and M. grandis is recorded for the first time from upland terra firme forests on clayish soils. Myrcia cuspidata, a species with calyptrate flowers, is classified under Myrcia sect. Aulomyrcia, representing the second taxon of the genus with this feature removed from Myrcia sect. Calyptranthes. Finally, morphological aspects of the infra-generic categories of Myrcia are reported more overlapping than previously thought. A map containing the location of the study area, an identification key, descriptions, comments, and figures are provided.

Identification of Amaranthus Species Using Visible-Near-Infrared (Vis-NIR) Spectroscopy and Machine Learning Methods

The feasibility of rapid and non-destructive classification of six different Amaranthus species was investigated using visible-near-infrared (Vis-NIR) spectra coupled with chemometric approaches. The focus of this research would be to use a handheld spectrometer in the field to classify six Amaranthus sp. in different geographical regions of South Korea. Spectra were obtained from the adaxial side of the leaves at 1.5 nm intervals in the Vis-NIR spectral range between 400 and 1075 nm. The obtained spectra were assessed with four different preprocessing methods in order to detect the optimum preprocessing method with high classification accuracy. Preprocessed spectra of six Amaranthus sp. were used as input for the machine learning-based chemometric analysis. All the classification results were validated using cross-validation to produce robust estimates of classification accuracies. The different combinations of preprocessing and modeling were shown to have a classification accuracy of between 71% and 99.7% after the cross-validation. The combination of Savitzky-Golay preprocessing and Support vector machine showed a maximum mean classification accuracy of 99.7% for the discrimination of Amaranthus sp. Considering the high number of spectra involved in this study, the growth stage of the plants, varying measurement locations, and the scanning position of leaves on the plant are all important. We conclude that Vis-NIR spectroscopy, in combination with appropriate preprocessing and machine learning methods, may be used in the field to effectively classify Amaranthus sp. for the effective management of the weedy species and/or for monitoring their food applications.

Spectroscopic and Molecular Methods to Differentiate Gender in Immature Date Palm (Phoenix dactylifera L.)

Phoenix dactylifera (date palm) is a well-known nutritious and economically important fruit tree found in arid regions of the Middle East and North Africa. Being diploid, it has extremely high divergence in gender, where sex differentiation in immature date palms (Phoenix dactylifera L.) has remained an enigma in recent years. Herein, new robust infrared (near-infrared reflectance spectroscopy (NIRS) and Fourier transform infrared attenuated total reflectance (FTIR/ATR)) and nuclear magnetic resonance (NMR) spectroscopy methods coupled with extensive chemometric analysis were used to identify the sex differentiation in immature date palm leaves. NIRS/FTIR reflectance and 1H-NMR profiling suggested that the signals of monosaccharides (glucose and fructose) and/or disaccharides (maltose and sucrose) play key roles in sex differentiation. The three kinds of spectroscopic data were clearly differentiated among known and unknown male and female leaves via principal component and partial least square discriminant analyses. Furthermore, sex-specific genes and molecular markers obtained from the lower halves of LG12 chromosomes showed enhanced transcript accumulation of mPdIRDP52, mPdIRDP50, and PDK101 in females compared with in males. The phylogeny showed that the mPdIRD033, mPdIRD031, and mPdCIR032 markers formed distinctive clades with more than 70% similarity in gender differentiation. The three robust analyses provide an alternative tool to differentiate sex in date palm trees, which offers a solution to the long-standing challenge of dioecism and could enhance in situ tree propagation programs.

A taxonomic account of Myrcia (Myrtaceae) at the sites of the Biological Dynamics of Forest Fragments Project, Amazonas, Brazil

Abstract Myrcia is the sole genus of the Myrciinae, one of the nine subtribes of Myrteae (Myrtaceae). The Amazon forest holds about one-quarter of the Brazilian species of Myrcia, but the genus is still understudied in this whole region. In this context, this study presents a floristic survey of Myrcia in the permanent plots of the Biological Dynamics of Forest Fragments Project (BDFFP), in Amazonas state, Brazil. The genus is represented by 36 species in the study area, comprehending 32% of its total richness in the Brazilian Amazon forest, with 19 of them endemic to this domain. Myrcia neospeciosa is reported as a new occurrence for Amazonas state and M. grandis is recorded for the first time from upland terra firme forests on clayish soils. Myrcia cuspidata, a species with calyptrate flowers, is classified under Myrcia sect. Aulomyrcia, representing the second taxon of the genus with this feature removed from Myrcia sect. Calyptranthes. Finally, morphological aspects of the infra-generic categories of Myrcia are reported more overlapping than previously thought. A map containing the location of the study area, an identification key, descriptions, comments, and figures are provided.

Quantifying Tropical Plant Diversity Requires an Integrated Technological Approach

Tropical biomes are the most diverse plant communities on Earth, and quantifying this diversity at large spatial scales is vital for many purposes. As macroecological approaches proliferate, the taxonomic uncertainties in species occurrence data are easily neglected and can lead to spurious findings in downstream analyses. Here, we argue that technological approaches offer potential solutions, but there is no single silver bullet to resolve uncertainty in plant biodiversity quantification. Instead, we propose the use of artificial intelligence (AI) approaches to build a data-driven framework that integrates several data sources - including spectroscopy, DNA sequences, image recognition, and morphological data. Such a framework would provide a foundation for improving species identification in macroecological analyses while simultaneously improving the taxonomic process of species delimitation.

Endogenous phytohormones of frankincense producing Boswellia sacra tree populations

Boswellia sacra, an endemic tree to Oman, is exposed to man-made incisions for commercial level frankincense production, whereas unsustainable harvesting may lead to population decline. In this case, assessment of endogenous phytohormones (gibberellic acid (GA), indole-acetic acid (IAA), salicylic acid (SA) and kinetin) can help to understand population health and growth dynamics. Hence, it was aimed to devise a robust method using Near-Infrared spectroscopy (NIRS) coupled with multivariate methods for phytohormone analysis of thirteen different populations of B. sacra. NIRS data was recorded in absorption mode (10000-4000 cm-1) to build partial least squares regression model (calibration set 70%). Model was externally cross validated (30%) as a test set to check their prediction ability before the application to quantify the unknown amount of phytohormones in thirteen different populations of B. sacra. The results showed that phytohormonal contents varied significantly, showing a trend of SA>GA/IAA>kinetin across different populations. SA and GA contents were significantly higher in Pop13 (Hasik), followed by Pop2 (Dowkah)-an extreme end of B. sacra tree cover in Dhofar region. A similar trend in the concentration of phytohormones was found when the samples from 13 populations were subjected to advance liquid chromatography mass spectrophotometer and gas chromatograph with selected ion monitor analysis. The current analysis provides alternative tool to assess plant health, which could be important to in situ propagation of tree population as well as monitoring tree population growth dynamics.

Near-infrared spectrometry allows fast and extensive predictions of functional traits from dry leaves and branches

The linking of individual functional traits to ecosystem processes is the basis for making generalizations in ecology, but the measurement of individual values is laborious and time consuming, preventing large-scale trait mapping. Also, in hyper-diverse systems, errors occur because identification is difficult, and species level values ignore intra-specific variation. To allow extensive trait mapping at the individual level, we evaluated the potential of Fourrier-Transformed Near Infra-Red Spectrometry (FT-NIR) to adequately describe 14 traits that are key for plant carbon, water, and nutrient balance. FT-NIR absorption spectra (1,000-2,500 nm) were obtained from dry leaves and branches of 1,324 trees of 432 species from a hyper-diverse Amazonian forest. FT-NIR spectra were related to measured traits for the same plants using partial least squares regressions. A further 80 plants were collected from a different site to evaluate model applicability across sites. Relative prediction error (RMSE_rel ) was calculated as the percentage of the trait value range represented by the final model RMSE. The key traits used in most functional trait studies; specific leaf area, leaf dry matter content, wood density and wood dry matter content can be well predicted by the model (R²  = 0.69-0.78, RMSE_rel  = 9-11%), while leaf density, xylem proportion, bark density and bark dry matter content can be moderately well predicted (R²  = 0.53-0.61, RMSE_rel  = 14-17%). Community-weighted means of all traits were well estimated with NIR, as did the shape of the frequency distribution of the community values for the above key traits. The model developed at the core site provided good estimations of the key traits of a different site. An evaluation of the sampling effort indicated that 400 or less individuals may be sufficient for establishing a good local model. We conclude that FT-NIR is an easy, fast and cheap method for the large-scale estimation of individual plant traits that was previously impossible. The ability to use dry intact leaves and branches unlocks the potential for using herbarium material to estimate functional traits; thus advancing our knowledge of community and ecosystem functioning from local to global scales.

Chemocoding as an identification tool where morphological- and DNA-based methods fall short: Inga as a case study

The need for species identification and taxonomic discovery has led to the development of innovative technologies for large-scale plant identification. DNA barcoding has been useful, but fails to distinguish among many species in species-rich plant genera, particularly in tropical regions. Here, we show that chemical fingerprinting, or 'chemocoding', has great potential for plant identification in challenging tropical biomes. Using untargeted metabolomics in combination with multivariate analysis, we constructed species-level fingerprints, which we define as chemocoding. We evaluated the utility of chemocoding with species that were defined morphologically and subject to next-generation DNA sequencing in the diverse and recently radiated neotropical genus Inga (Leguminosae), both at single study sites and across broad geographic scales. Our results show that chemocoding is a robust method for distinguishing morphologically similar species at a single site and for identifying widespread species across continental-scale ranges. Given that species are the fundamental unit of analysis for conservation and biodiversity research, the development of accurate identification methods is essential. We suggest that chemocoding will be a valuable additional source of data for a quick identification of plants, especially for groups where other methods fall short.

Convergence in relationships between leaf traits, spectra and age across diverse canopy environments and two contrasting tropical forests

Leaf age structures the phenology and development of plants, as well as the evolution of leaf traits over life histories. However, a general method for efficiently estimating leaf age across forests and canopy environments is lacking. Here, we explored the potential for a statistical model, previously developed for Peruvian sunlit leaves, to consistently predict leaf ages from leaf reflectance spectra across two contrasting forests in Peru and Brazil and across diverse canopy environments. The model performed well for independent Brazilian sunlit and shade canopy leaves (R²  = 0.75-0.78), suggesting that canopy leaves (and their associated spectra) follow constrained developmental trajectories even in contrasting forests. The model did not perform as well for mid-canopy and understory leaves (R²  = 0.27-0.29), because leaves in different environments have distinct traits and trait developmental trajectories. When we accounted for distinct environment-trait linkages - either by explicitly including traits and environments in the model, or, even better, by re-parameterizing the spectra-only model to implicitly capture distinct trait-trajectories in different environments - we achieved a more general model that well-predicted leaf age across forests and environments (R²  = 0.79). Fundamental rules, linked to leaf environments, constrain the development of leaf traits and allow for general prediction of leaf age from spectra across species, sites and canopy environments.

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.

Maximising Synergy among Tropical Plant Systematists, Ecologists, and Evolutionary Biologists

Closer collaboration among ecologists, systematists, and evolutionary biologists working in tropical forests, centred on studies within long-term permanent plots, would be highly beneficial for their respective fields. With a key unifying theme of the importance of vouchered collection and precise identification of species, especially rare ones, we identify four priority areas where improving links between these communities could achieve significant progress in biodiversity and conservation science: (i) increasing the pace of species discovery; (ii) documenting species turnover across space and time; (iii) improving models of ecosystem change; and (iv) understanding the evolutionary assembly of communities and biomes.

The discovery of the Amazonian tree flora with an updated checklist of all known tree taxa

Amazonia is the most biodiverse rainforest on Earth, and the debate over how many tree species grow there remains contentious. Here we provide a checklist of all tree species collected to date, and describe spatial and temporal trends in data accumulation. We report 530,025 unique collections of trees in Amazonia, dating between 1707 and 2015, for a total of 11,676 species in 1225 genera and 140 families. These figures support recent estimates of 16,000 total Amazonian tree species based on ecological plot data from the Amazonian Tree Diversity Network. Botanical collection in Amazonia is characterized by three major peaks, centred around 1840, 1920, and 1980, which are associated with flora projects and the establishment of inventory plots. Most collections were made in the 20th century. The number of collections has increased exponentially, but shows a slowdown in the last two decades. We find that a species' range size is a better predictor of the number of times it has been collected than the species' estimated basin-wide population size. Finding, describing, and documenting the distribution of the remaining species will require coordinated efforts at under-collected sites.