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Integrating morphological and molecular data in plant taxonomy
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Synopsis
Artificial intelligence (AI) is poised to revolutionize many aspects of science, including the study of evolutionary morphology. While classical AI methods such as principal component analysis and cluster analysis have been commonplace in the study of evolutionary morphology for decades, recent years have seen increasing application of deep learning to ecology and evolutionary biology. As digitized specimen databases become increasingly prevalent and openly available, AI is offering vast new potential to circumvent long-standing barriers to rapid, big data analysis of phenotypes. Here, we review the current state of AI methods available for the study of evolutionary morphology, which are most developed in the area of data acquisition and processing. We introduce the main available AI techniques, categorizing them into 3 stages based on their order of appearance: (1) machine learning, (2) deep learning, and (3) the most recent advancements in large-scale models and multimodal learning. Next, we present case studies of existing approaches using AI for evolutionary morphology, including image capture and segmentation, feature recognition, morphometrics, and phylogenetics. We then discuss the prospectus for near-term advances in specific areas of inquiry within this field, including the potential of new AI methods that have not yet been applied to the study of morphological evolution. In particular, we note key areas where AI remains underutilized and could be used to enhance studies of evolutionary morphology. This combination of current methods and potential developments has the capacity to transform the evolutionary analysis of the organismal phenotype into evolutionary phenomics, leading to an era of “big data” that aligns the study of phenotypes with genomics and other areas of bioinformatics.
In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these 2 fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we lay out a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., genome-wide association studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur analytically and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate by showing how a standard GWAS technique—including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model—can mitigate spurious correlations in phylogenetic analyses. As a case study, we re-examine an analysis testing for coevolution of expression levels between genes across a fungal phylogeny and show that including eigenvectors of the covariance matrix as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.
Computer-aided analysis of proteins or nucleic acids seems like a matter of course nowadays; however, the history of Bioinformatics and Computational Biology is quite recent. The advent of high-throughput sequencing has led to the production of “big data”, which has also affected the field of virology. The collaboration between the communities of bioinformaticians and virologists already started a few decades ago and it was strongly enhanced by the recent SARS-CoV-2 pandemics. In this article, which is the first in a series on how bioinformatics can enhance virus research, we show that highly useful information is retrievable from selected general and dedicated databases. Indeed, an enormous amount of information—both in terms of nucleotide/protein sequences and their annotation—is deposited in the general databases of international organisations participating in the International Nucleotide Sequence Database Collaboration (INSDC). However, more and more virus-specific databases have been established and are progressively enriched with the contents and features reported in this article. Since viruses are intracellular obligate parasites, a special focus is given to host-pathogen protein-protein interaction databases. Finally, we illustrate several phylogenetic and phylodynamic tools, combining information on algorithms and features with practical information on how to use them and case studies that validate their usefulness. Databases and tools for functional inference will be covered in the next article of this series: Bioinformatics goes viral: II. Sequence-based and structure-based functional analyses for boosting virus research.
The rapid advancement of high-throughput technologies, particularly next-generation sequencing (NGS), has revolutionized cancer research by enabling the investigation of genetic variations such as SNPs, copy number variations, gene expression, and protein levels. These technologies have elevated the significance of precision oncology, creating a demand for biomarker identification and validation. This review explores the complex interplay of oncology, cancer biology, and bioinformatics tools, highlighting the challenges in statistical learning, experimental validation, data processing, and quality control that underpin this transformative field. This review outlines the methodologies and applications of bioinformatics tools in cancer genomics research, encompassing tools for data structuring, pathway analysis, network analysis, tools for analyzing biomarker signatures, somatic variant interpretation, genomic data analysis, and visualization tools. Open-source tools and repositories like The Cancer Genome Atlas (TCGA), Genomic Data Commons (GDC), cBioPortal, UCSC Genome Browser, Array Express, and Gene Expression Omnibus (GEO) have emerged to streamline cancer omics data analysis. Bioinformatics has significantly impacted cancer research, uncovering novel biomarkers, driver mutations, oncogenic pathways, and therapeutic targets. Integrating multi-omics data, network analysis, and advanced ML will be pivotal in future biomarker discovery and patient prognosis prediction.
Although species are central units for biological research, recent findings in genomics are raising awareness that what we call species can be ill-founded entities due to solely morphology-based, regional species descriptions. This particularly applies to groups characterized by intricate evolutionary processes such as hybridization, polyploidy, or asexuality. Here, challenges of current integrative taxonomy (genetics/genomics + morphology + ecology, etc.) become apparent: different favored species concepts, lack of universal characters/markers, missing appropriate analytical tools for intricate evolutionary processes, and highly subjective ranking and fusion of datasets. Now, integrative taxonomy combined with artificial intelligence under a unified species concept can enable automated feature learning and data integration, and thus reduce subjectivity in species delimitation. This approach will likely accelerate revising and unraveling eukaryotic biodiversity.
The structural features of vegetative organs have been used in the identification of species de Miconia, Melastomataceae, but the data available are inconsistent because they are commonly subject to environmental variations. In the present work we have attempted to point out floral characters that can be employed in the taxonomy of the genus. Floral buds and flowers were obtained from herbarium vouchers, and plants that occur in natural environments of Brazilian state parks, embedded in historesin, and analyzed in light microscope. Flowers consist of perianth with homogeneous parenchymatous mesophyll, the inferior or semi-inferior ovary with collenchyma and/or parenchyma, anatropous or hemi-anatropous ovules, a single strand of transmitting tissue in the style, hypanthium with collenchyma and parenchyma, and poricidal anthers. Associated floral characters have value at the specific level, viz. perigynous hypanthium structure, filament structure, anther type, ovary position and number of carpels/locules, placentation, wall of the inferior ovary, vascular supply of the ovary, ovule type, and style structure. These structural characteristics of the flower can be useful at the specific and generic level.
Simple Summary
Next-generation sequencing (NGS) is a powerful tool used in genomics research. NGS can sequence millions of DNA fragments at once, providing detailed information about the structure of genomes, genetic variations, gene activity, and changes in gene behavior. Recent advancements have focused on faster and more accurate sequencing, reduced costs, and improved data analysis. These advancements hold great promise for unlocking new insights into genomics and improving our understanding of diseases and personalized healthcare. This review article provides an overview of NGS technology and its impact on various areas of research, such as clinical genomics, cancer, infectious diseases, and the study of the microbiome.
Abstract
The advent of next-generation sequencing (NGS) has brought about a paradigm shift in genomics research, offering unparalleled capabilities for analyzing DNA and RNA molecules in a high-throughput and cost-effective manner. This transformative technology has swiftly propelled genomics advancements across diverse domains. NGS allows for the rapid sequencing of millions of DNA fragments simultaneously, providing comprehensive insights into genome structure, genetic variations, gene expression profiles, and epigenetic modifications. The versatility of NGS platforms has expanded the scope of genomics research, facilitating studies on rare genetic diseases, cancer genomics, microbiome analysis, infectious diseases, and population genetics. Moreover, NGS has enabled the development of targeted therapies, precision medicine approaches, and improved diagnostic methods. This review provides an insightful overview of the current trends and recent advancements in NGS technology, highlighting its potential impact on diverse areas of genomic research. Moreover, the review delves into the challenges encountered and future directions of NGS technology, including endeavors to enhance the accuracy and sensitivity of sequencing data, the development of novel algorithms for data analysis, and the pursuit of more efficient, scalable, and cost-effective solutions that lie ahead.
Biodiversity changes, such as decline in species richness and biotic homogenization, can have grave consequences for ecosystem functionality. Careful investigation of biodiversity–ecosystem multifunctionality linkages with due consideration of conceptual and technical challenges is required to make the knowledge practically useful in managing social–ecological systems. In this paper, we introduced different methods to assess perspectives regarding the issue of diversity‐multifunctionality, including a possible multifunctional redundancy/uniqueness, and the influences of the number and identity of functions on multifunctionality. In particular, we aimed to align methods with detecting the mechanisms underpinning diversity‐multifunctional relationships that are free from statistical biases. Based on a set of novel methods that excluded analytical biases resulting from differences in the number and identities of multiple functions considered, we found that a substantial portion of species disproportionately supported ecosystem functions and that the diversity effects on multifunctionality were more markedly observed when more functions were considered. These results jointly emphasize that individual species are, to some extent, both functionally unique as well as redundant, highlighting the complexity and necessity for managed assemblages to retain high levels of diversity. We also observed that the relative magnitude of uniqueness or redundancy can differ between species and functions and therefore should be defined in a multifunctional context. We further found that only a small subset of species was identified as significantly less important, especially at low levels of multifunctionality. Taken together, given the low level of multifunctional redundancy we identified, we stress that unraveling the hierarchical roles of biodiversity at different levels, such as individual species and their assemblages, should be a high research priority, in both theory and practice.
DNA barcoding is a powerful taxonomic tool to identify and discover species. DNA barcoding utilizes one or more standardized short DNA regions for taxon identification. With the emergence of new sequencing techniques, such as Next-generation sequencing (NGS), ONT MinION nanopore sequencing, and Pac Bio sequencing, DNA barcoding has become more accurate, fast, and reliable. Rapid species identification by DNA barcodes has been used in a variety of fields, including forensic science, control of the food supply chain, and disease understanding. The Consortium for Barcode of Life (CBOL) presents various working groups to identify the universal barcode gene, such as COI in metazoans; rbcL, matK, and ITS in plants; ITS in fungi; 16S rRNA gene in bacteria and archaea, and creating a reference DNA barcode library. In this article, an attempt has been made to analyze the various proposed DNA barcode for different organisms, strengths & limitations, recent advancements in DNA barcoding, and methods to speed up the DNA barcode reference library construction. This study concludes that constructing a reference library with high species coverage would be a major step toward identifying species by DNA barcodes. This can be achieved in a short period of time by using advanced sequencing and data analysis methods.
There is currently international interest in applying DNA barcoding as a tool for plant species discrimination and identification. In this study, we evaluated the utility of four candidate plant DNA barcoding regions [rbcL, matK, trnL-F, and internal transcribed spacer (ITS)] in seven genera of Gramineae including Agropyron, Bromus, Elymus, Elytrigia, Festuca, Leymus, and Lolium. Fourteen accessions were analyzed, and matK and ITS showed the highest species, subspecies, and variety discriminatory power, each resolving 11 accessions. Species discrimination using rbcL and trnL-F was lower, resolving 7 and 8 accessions, respectively. Subspecies and variety discrimination using rbcL and trnL-F could not identify 4 accessions of Agropyron. A technical system can be established using the proposed DNA barcode to rapidly and reliably identify the seven genera of Gramineae. This study serves as a “useful reference” for identifying the genetic diversity of grass germplasm resources. DNA barcoding can be utilized to uncover the relatives of different species within the same family or between different families. It can also be used to determine the related groups of important herbage, turfgrass, and crops and provide crucial background information for discovering excellent genes and improving existing crop varieties.
The use of next-generation sequencing (NGS) techniques for variant detection has become increasingly important in clinical research and in clinical practice in oncology. Many cancer patients are currently being treated in clinical practice or in clinical trials with drugs directed against specific genomic alterations. In this scenario, the development of reliable and reproducible bioinformatics tools is essential to derive information on the molecular characteristics of each patient’s tumor from the NGS data. The development of bioinformatics pipelines based on the use of machine learning and statistical methods is even more relevant for the determination of complex biomarkers. In this review, we describe some important technologies, computational algorithms and models that can be applied to NGS data from Whole Genome to Targeted Sequencing, to address the problem of finding complex cancer-associated biomarkers. In addition, we explore the future perspectives and challenges faced by bioinformatics for precision medicine both at a molecular and clinical level, with a focus on an emerging complex biomarker such as homologous recombination deficiency (HRD).
Environmental DNA (eDNA) analysis has recently transformed and modernized biodiversity monitoring. The accurate detection, and to some extent quantification, of organisms (individuals/populations/communities) in environmental samples is galvanizing eDNA as a successful cost and time-efficient biomonitoring technique. Currently, eDNA’s application to plants remains more limited in implementation and scope compared to animals and microorganisms. Thus, this review evaluates the development of eDNA-based methods for (vascular) plants, comparing its performance and power of detection with that of traditional methods, to critically evaluate and advise best practices needed for innovating plant biomonitoring. Recent advancements, standardization, and field applications of eDNA-based methods have provided enough scope to utilize it in conservation biology for numerous organisms. eDNA also has considerable potential for plants, where successful detection of invasive, endangered and rare species, and community-level interpretations have provided proof-of-concept. Monitoring methods using eDNA were found to be equal or more effective than traditional methods, however species detection increased when both the methods were coupled. Additionally, eDNA methods were found to be effective in studying species interactions, community dynamics, and even effects of anthropogenic pressure. Currently, elimination of potential obstacles (e.g., lack of relevant DNA reference libraries for plants) and the development of user-friendly protocols would greatly contribute to comprehensive eDNA-based plant monitoring programs. This is particularly needed in the data-depauperate tropics and for some less-concern plant groups. We further advocate it may be valuable to couple traditional methods with eDNA approaches, as the former is often cheaper and methodologically more straightforward, while the latter offers a non-destructive approach with the ability to identify plants in situations where morphological identification is difficult or impossible. Furthermore, in order to make a global platform for eDNA, governmental and academic-industrial collaborations are essential to make eDNA surveys a broadly adopted and implemented, rapid, cost-effective, and non-invasive plant monitoring approach.
Species concepts have long provided a source of debate among biologists. These lively debates have been important for reaching consensus on how to communicate across scientific disciplines and for advancing innovative strategies to study evolution, population biology, ecology, natural history, and disease epidemiology. Species concepts are also important for evaluating variability and diversity among communities, understanding biogeographical distributions, and identifying causal agents of disease across animal and plant hosts. While there have been many attempts to address the concept of species in the fungi, there are several concepts that have made taxonomic delimitation especially challenging. In this review we discuss these major challenges and describe methodological approaches that show promise for resolving ambiguity in fungal taxonomy by improving discrimination of genetic and functional traits. We highlight the relevance of eco-evolutionary theory used in conjunction with integrative taxonomy approaches to improve the understanding of interactions between environment, ecology, and evolution that give rise to distinct species boundaries. Beyond recent advances in genomic and phenomic methods, bioinformatics tools and modeling approaches enable researchers to test hypothesis and expand our knowledge of fungal biodiversity. Looking to the future, the pairing of integrative taxonomy approaches with multi-locus genomic sequencing and phenomic techniques, such as transcriptomics and proteomics, holds great potential to resolve many unknowns in fungal taxonomic classification.
Hybridization and polyploidization are important processes for plant evolution. However, classification of hybrid or polyploid species has been notoriously difficult because of the complexity of processes and different evolutionary scenarios that do not fit with classical species concepts. Polyploid complexes are formed via combinations of allopolyploidy, autopolyploidy and homoploid hybridization with persisting sexual reproduction, resulting in many discrete lineages that have been classified as species. Polyploid complexes with facultative apomixis result in complicated net-work like clusters, or rarely in agamospecies. Various case studies illustrate the problems that apply to traditional species concepts to hybrids and polyploids. Conceptual progress can be made if lineage formation is accepted as an inevitable consequence of meiotic sex, which is established already in the first eukaryotes as a DNA restoration tool. The turnaround of the viewpoint that sex forms species as lineages helps to overcome traditional thinking of species as “units”. Lineage formation and self-sustainability is the prerequisite for speciation and can also be applied to hybrids and polyploids. Species delimitation is aided by the improved recognition of lineages via various novel -omics methods, by understanding meiosis functions, and by recognizing functional phenotypes by considering morphological-physiological-ecological adaptations.
About 50 y ago, Crow and Kimura [ An Introduction to Population Genetics Theory (1970)] and Ohta and Kimura [ Genet. Res. 22, 201–204 (1973)] laid the foundations of conservation genetics by predicting the relationship between population size and genetic marker diversity. This work sparked an enormous research effort investigating the importance of population dynamics, in particular small population size, for population mean performance, population viability, and evolutionary potential. In light of a recent perspective [J. C. Teixeira, C. D. Huber, Proc. Natl. Acad. Sci. U.S.A. 118, 10 (2021)] that challenges some fundamental assumptions in conservation genetics, it is timely to summarize what the field has achieved, what robust patterns have emerged, and worthwhile future research directions. We consider theory and methodological breakthroughs that have helped management, and we outline some fundamental and applied challenges for conservation genetics.
Cryptic species comprise two or more taxa that are grounded under a single name because they are more-or-less indistinguishable morphologically. These species are potentially important for detailed assessments of biodiversity, but there now appear to be many more cryptic species than previously estimated. One taxonomic group likely to contain many cryptic species is Dicranopteris, a genus of forked ferns that occurs commonly along roadsides in Asia. The genus has a complex taxonomical history, and D. linearis has been particularly challenging with many intra-specific taxa dubiously erected to accommodate morphological variation that lacks clear discontinuities. To resolve species boundaries within Dicranopteris, we applied a molecular phylogenetic approach as complementary to morphology. Specifically, we used five chloroplast gene regions (rbcL, atpB, rps4, matK, and trnL-trnF) to generate a well-resolved phylogeny based on 37 samples representing 13 taxa of Dicranopteris, spanning the major distributional area in Asia. The results showed that Dicranopteris consists of ten highly supported clades, and D. linearis is polyphyletic, suggesting cryptic diversity within the species. Further through morphological comparison, we certainly erected Dicranopteris austrosinensis Y.H. Yan & Z.Y. Wei sp. nov. and Dicranopteris baliensis Y.H. Yan & Z.Y. Wei sp. nov. as distinct species and proposed five new combinations. We also inferred that the extant diversity of the genus Dicranopteris may result from relatively recent diversification in the Miocene based on divergence time dating. Overall, our study not only provided additional insights on the Gleicheniaceae tree of life, but also served as a case of integrating molecular and morphological approaches to elucidate cryptic diversity in taxonomically difficult groups.
Background
DNA markers improved the productivity and accuracy of classical plant breeding by means of marker-assisted selection (MAS). The enormous number of quantitative trait loci (QTLs) mapping read for different plant species have given a plenitude of molecular marker-gene associations.
Main body of the abstract
In this review, we have discussed the positive aspects of molecular marker-assisted selection and its precise applications in plant breeding programmes. Molecular marker-assisted selection has considerably shortened the time for new crop varieties to be brought to the market. To explore the information about DNA markers, many reviews have been published in the last few decades; all these reviews were intended by plant breeders to obtain information on molecular genetics. In this review, we intended to be a synopsis of recent developments of DNA markers and their application in plant breeding programmes and devoted to early breeders with little or no knowledge about the DNA markers. The progress made in molecular plant breeding, plant genetics, genomics selection, and editing of genome contributed to the comprehensive understanding of DNA markers and provides several proofs on the genetic diversity available in crop plants and greatly complemented plant breeding devices.
Short conclusion
MAS has revolutionized the process of plant breeding with acceleration and accuracy, which is continuously empowering plant breeders around the world.
Global biodiversity loss is a profound consequence of human activity. Disturbingly, biodiversity loss is greater than realized because of the unknown number of undocumented species. Conservation fundamentally relies on taxonomic recognition of species, but only a fraction of biodiversity is described. Here, we provide a new quantitative approach for prioritizing rigorous taxonomic research for conservation. We implement this approach in a highly diverse vertebrate group—Australian lizards and snakes. Of 870 species assessed, we identified 282 (32.4%) with taxonomic uncertainty, of which 17.6% likely comprise undescribed species of conservation concern. We identify 24 species in need of immediate taxonomic attention to facilitate conservation. Using a broadly applicable return-on-investment framework, we demonstrate the importance of prioritizing the fundamental work of identifying species before they are lost.
Aim
How environmental factors drive plant distribution across the globe is one of the most fundamental questions in ecology. Nevertheless, the relative importance of different environmental factors in driving plant distributions across spatial scales and among plant groups is not clear. This study aims to disentangle how plant–environment relationships vary with latitude and among plant taxa.
Location
Global.
Time period
Present day.
Main taxa
Plant taxa including angiosperms, gymnosperms, pteridophytes and bryophytes.
Methods
We obtained global distribution occurrence data of mass plant groups (625 families, 6,221 genera, and 54,101 species) from the Global Biodiversity Information Facility (GBIF) database. We used random forest method to quantify the relative effects of 15 environmental factors (including climate, soil and topography) on plant distributions at global and regional scales (divided into different latitude zones). We also used phylogenetic generalized least squares (PGLS) models to investigate the relationships between environmental variables and geographical range size, latitudinal range and limits of plants at the global scale.
Results
Our analyses revealed the primacy of the effects of climatic variability (temperature seasonality and isothermality) on plant distribution at the global scale. The relative contributions of temperature seasonality and isothermality peaked in tropical areas, whereas solar radiation and annual mean temperature had stronger influence in high‐latitude areas. Wide‐range plant groups tended to occur in areas with higher temperature variability (isothermality and temperature seasonality) and flatter terrain (low slope). Both climate variability and extreme influenced geographical range size, latitudinal range and limits of plants.
Main conclusions
Our study highlights the significance of climate variability for global plant distributions. Environmental effects upon plant distributions vary across latitudes. The findings imply that environmental factors affecting plant distributions change with geographical scales, suggesting that different geographical and ecological processes should be integrated to explain multi‐scale distribution patterns.
Biodiversity is under threat worldwide. Over the past decade, the field of population genomics has developed across nonmodel organisms, and the results of this research have begun to be applied in conservation and management of wildlife species. Genomics tools can provide precise estimates of basic features of wildlife populations, such as effective population size, inbreeding, demographic history and population structure, that are critical for conservation efforts. Moreover, population genomics studies can identify particular genetic loci and variants responsible for inbreeding depression or adaptation to changing environments, allowing for conservation efforts to estimate the capacity of populations to evolve and adapt in response to environmental change and to manage for adaptive variation. While connections from basic research to applied wildlife conservation have been slow to develop, these connections are increasingly strengthening. Here we review the primary areas in which population genomics approaches can be applied to wildlife conservation and management, highlight examples of how they have been used, and provide recommendations for building on the progress that has been made in this field.
Plant functional traits are the features (morphological, physiological, phenological) that represent ecological strategies and determine how plants respond to environmental factors, affect other trophic levels and influence ecosystem properties. Variation in plant functional traits, and trait syndromes, has proven useful for tackling many important ecological questions at a range of scales, giving rise to a demand for standardised ways to measure ecologically meaningful plant traits. This line of research has been among the most fruitful avenues for understanding ecological and evolutionary patterns and processes. It also has the potential both to build a predictive set of local, regional and global relationships between plants and environment and to quantify a wide range of natural and human-driven processes, including changes in biodiversity, the impacts of species invasions, alterations in biogeochemical processes and vegetation–atmosphere interactions. The importance of these topics dictates the urgent need for more and better data, and increases the value of standardised protocols for quantifying trait variation of different species, in particular for traits with power to predict plant- and ecosystem-level processes, and for traits that can be measured relatively easily. Updated and expanded from the widely used previous version, this handbook retains the focus on clearly presented, widely applicable, step-by-step recipes, with a minimum of text on theory, and not only includes updated methods for the traits previously covered, but also introduces many new protocols for further traits. This new handbook has a better balance between whole-plant traits, leaf traits, root and stem traits and regenerative traits, and puts particular emphasis on traits important for predicting species' effects on key ecosystem properties. We hope this new handbook becomes a standard companion in local and global efforts to learn about the responses and impacts of different plant species with respect to environmental changes in the present, past and future.
This review provides a synthesis of current knowledge on the morphological and functional traits of testate amoebae, a polyphyletic group of protists commonly used as proxies of past hydrological changes in paleoecological investigations from peatland, lake sediment and soil archives. A trait-based approach to understanding testate amoebae ecology and paleoecology has gained in popularity in recent years, with research showing that morphological characteristics provide complementary information to the commonly used environmental inferences based on testate amoeba (morpho-)species data. We provide a broad overview of testate amoeba morphological and functional traits and trait-environment relationships in the context of ecology, evolution, genetics, biogeography, and paleoecology. As examples we report upon previous ecological and paleoecological studies that used trait-based approaches, and describe key testate amoebae traits that can be used to improve the interpretation of environmental studies. We also highlight knowledge gaps and speculate on potential future directions for the application of trait-based approaches in testate amoeba research.
Morphological interpretation is the standard in diagnosing myelodysplastic syndrome (MDS), but it has limitations, such as varying reliability in pathologic evaluation and lack of integration with genetic data. Somatic events shape morphologic features, but the complexity of morphologic and genetic changes make clear associations challenging. This article interrogates novel clinical subtypes of MDS using a machine learning technique devised to identify patterns of co-occurrence among morphologic features and genomic events. We sequenced 1,079 MDS patients and analyzed bone marrow morphological alterations and other clinical features. A total of 1,929 somatic mutations were identified. Five distinct morphologic profiles with unique clinical characteristics were defined. 77% of higher-risk patients clustered in profile-1. All lower-risk patients clustered into the remaining 4 profiles: profile-2 was characterized by pancytopenia, profile-3 by monocytosis, profile-4 by elevated megakaryocytes, and profile-5 by erythroid dysplasia. These profiles could also separate patients with different prognosis. Lower-risk MDS patients were classified into eight genetic signatures (e.g. signature-A had TET2 mutations, signature-B had both TET2 and SRSF2 mutations and signature-G had SF3B1 mutations) demonstrating association with specific morphologic profiles. Six morphologic profiles/genetic signatures' associations were confirmed in a separate analysis of an independent cohort. Our study demonstrates that non-random or even pathognomonic relationships between morphology and genotype to define clinical features can be identified. This is the first comprehensive implementation of machine learning algorithms to elucidate potential intrinsic interdependencies among genetic lesions, morphologies, and clinical prognostic in attributes of MDS.
Cryptic bryophyte species exhibit a decoupling in the degree of morphological and molecular divergence, as a result of different processes, from recent divergence to stasis. Here a body of cryptic species literature comprising 110 papers published between 2000 and the end of 2018 is reviewed. Most studies of cryptic species focused on northern hemispheric taxa, but we do not yet have sufficient studies to assess whether a geographic bias in the distribution of cryptic species exists, and we don't know how many cryptic bryophyte species there might be globally. Fully two-thirds of all studies on cryptic bryophyte species rested their claims of morphological crypsis on previous taxonomic investigations, without revision of morphology to confirm cryptic species status. There is more than one kind of morphological crypsis, and while quantification of morphological patterns can contribute to our understanding of crypsis this is a commonly neglected component. Hybridisation is possibly an under-appreciated contributor to cryptic species, but inference of hybridization has been limited by study design. Opportunities exist in the application of geometric morphometric methods and next generation sequencing technologies to overcome intrinsic limitations in traditional morphological and molecular data sources. The usage of 'cryptic species' as a tool to flag instances where traditional species concepts are deficient devalues the term, and a distinction between genuine crypsis and business as usual revision of species circumscription should be re-established and maintained.
Current divergent selection may promote floral trait differentiation among conspecific populations in flowering plants. However, whether this applies to complex traits such as colour or scents has been little studied, even though these traits often vary within species. In this study, we compared floral colour and odour as well as selective pressures imposed upon these traits among seven populations belonging to three subspecies of the widespread, generalist orchid Anacamptis coriophora . Colour was characterised using calibrated photographs and scents were sampled using dynamic headspace extraction and analysed using gas chromatography‐mass spectrometry. We then quantified phenotypic selection exerted on these traits by regressing fruit set values on floral trait values. We showed that the three studied subspecies were characterised by different floral colour and odour, with one of the two predominant floral volatiles emitted by each subspecies being taxon‐specific. Plant size was positively correlated with fruit set in most populations, while we found no apparent link between floral colour and female reproductive success. We detected positive selection on several taxon‐specific compounds in A. coriophora subsp. fragrans , whereas no selection was found on floral volatiles of A. coriophora subsp. coriophora and A. coriophora subsp. martrinii . This study is one of the first to document variation in phenotypic selection exerted on floral scents among conspecific populations. Our results suggest that selection could contribute to ongoing chemical divergence among A. coriophora subspecies.
Multiple large-scale restoration strategies are emerging globally to counteract ecosystem degradation and biodiversity loss. However, restoration often remains insufficient to offset that loss. To address this challenge, we propose to focus restoration science on the long-term (centuries to millennia) re-assembly of degraded ecosystem complexity integrating interaction network and evolutionary potential approaches. This approach provides insights into eco-evolutionary feedbacks determining the structure, functioning, and stability of recovering ecosystems. Eco-evolutionary feedbacks may help understand changes in the adaptive potential after disturbance of metacommunity hub species with core structural and functional roles for their use in restoration. Those changes can be studied combining a restoration genomics approach based on whole genome sequencing with replicated space-for-time substitutions linking changes in genetic variation to functions or traits relevant to the establishment of evolutionarily resilient communities. This approach may set the knowledge basis for future tools to accelerate the restoration of ecosystems able to adapt to ongoing global changes.
Plant species detection aims at the automatic identification of plants. Although a lot of aspects like leaf, flowers, fruits, seeds could contribute to the decision, but leaf features are the most significant. As a plant leaf is always more accessible as compared to other parts of the plants, it is obvious to study it for plant identification. The present paper introduced a novel plant species classifier based on the extraction of morphological features using a Multilayer Perceptron with Adaboosting. The proposed framework comprises pre-processing, feature extraction, feature selection, and classification. Initially, some pre-processing techniques are used to set up a leaf image for the feature extraction process. Various morphological features, i.e., centroid, major axis length, minor axis length, solidity, perimeter, and orientation are extracted from the digital images of various categories of leaves. Different classifiers, i.e., k-NN, Decision Tree and Multilayer perceptron are employed to test the accuracy of the algorithm. AdaBoost methodology is explored for improving the precision rate of the proposed system. Experimental results are obtained on a public dataset (FLAVIA) downloaded from http://flavia.sourceforge.net/. A precision rate of 95.42% has been achieved using the proposed machine learning classifier, which outperformed the state-of-the-art algorithms.
Our world is in the midst of unprecedented change—climate shifts and sustained, widespread habitat degradation have led to dramatic declines in biodiversity rivaling historical extinction events. At the same time, new approaches to publishing and integrating previously disconnected data resources promise to help provide the evidence needed for more efficient and effective conservation and management. Stakeholders have invested considerable resources to contribute to online databases of species occurrences. However, estimates suggest that only 10% of biocollections are available in digital form. The biocollections community must therefore continue to promote digitization efforts, which in part requires demonstrating compelling applications of the data. Our overarching goal is therefore to determine trends in use of mobilized species occurrence data since 2010, as online systems have grown and now provide over one billion records. To do this, we characterized 501 papers that use openly accessible biodiversity databases. Our standardized tagging protocol was based on key topics of interest, including: database(s) used, taxa addressed, general uses of data, other data types linked to species occurrence data, and data quality issues addressed. We found that the most common uses of online biodiversity databases have been to estimate species distribution and richness, to outline data compilation and publication, and to assist in developing species checklists or describing new species. Only 69% of papers in our dataset addressed one or more aspects of data quality, which is low considering common errors and biases known to exist in opportunistic datasets. Globally, we find that biodiversity databases are still in the initial stages of data compilation. Novel and integrative applications are restricted to certain taxonomic groups and regions with higher numbers of quality records. Continued data digitization, publication, enhancement, and quality control efforts are necessary to make biodiversity science more efficient and relevant in our fast-changing environment.
Hybridization and convergent evolution are phenomena of broad interest in evolutionary biology, but their occurrence poses challenges for reconstructing evolutionary affinities among affected taxa. Sticklebacks in the genus Pungitius are a case in point: evolutionary relationships and taxonomic validity of different species and populations in this circumpolarly distributed species complex remain contentious due to convergent evolution of traits regarded as diagnostic in their taxonomy, and possibly also due to frequent hybridization among taxa. To clarify the evolutionary relationships among different Pungitius species and populations globally, as well as to study prevalence and extent of introgression among recognized species, genomic datasets of both reference genome‐anchored SNPs and de novo assembled RAD‐tag loci were constructed with RAD‐seq data. Both datasets yielded topologically identical and well‐supported species trees. Incongruence between nuclear and mitochondrial DNA‐based trees was found and suggested possibly frequent hybridization and mitogenome capture during the evolution of Pungitius sticklebacks. Further analyses revealed evidence for frequent nuclear genetic introgression among Pungitius species, although the estimated proportions of autosomal introgression were low. Apart from providing evidence for frequent hybridization, the results challenge earlier mitochondrial and morphology‐based hypotheses about the number of species and their affinities in this genus: at least seven extant species can be recognized on the basis of genetic data. The results also shed new light on the biogeographic history of the Pungitius‐complex, including suggestion of several trans‐Arctic invasions of Europe from the Northern Pacific. The well‐resolved phylogeny should facilitate the utility of this genus as a model system for future comparative evolutionary studies. This article is protected by copyright. All rights reserved.
The Arabian Peninsula is known to have a comprehensive and rich endowment of unique and genetically diverse plant genetic resources. Analysis and conservation of biological diversity is a crucial issue to the whole Arabian Peninsula. The rapid and accurate delimitation and identification of a species is crucial to genetic diversity analysis and the first critical step in the assessment of distribution, population abundance and threats related to a particular target species. During the last two decades, classical strategies of evaluating genetic variability, such as morphology and physiology, have been greatly complemented by phylogenetic, taxonomic, genetic diversity and breeding research molecular studies. At present, initiatives are taking place around the world to generate DNA barcode libraries for vascular plant flora and to make these data available in order to better understand, conserve and utilize biodiversity. The number of herbarium collection-based plant evolutionary genetics and genomics studies being conducted has been increasing worldwide. The herbaria provide a rich resource of already preserved and identified material, and these as well as freshly collected samples from the wild can be used for creating a reference DNA barcode library for the vascular plant flora of a region. This review discusses the main molecular and genomic techniques used in plant identification and biodiversity analysis. Hence, we highlight studies emphasizing various molecular techniques undertaken during the last 10 years to study the plant biodiversity of the Arabian Peninsula. Special emphasis on the role of DNA barcoding as a powerful tool for plant biodiversity analysis is provided, along with the crucial role of herbaria in creating a DNA barcode library.
The first two decades of the twenty-first century have seen a rapid rise in the mobilization of digital biodiversity data. This has thrust natural history museums into the forefront of biodiversity research, underscoring their central role in the modern scientific enterprise. The advent of mobilization initiatives such as the United States National Science Foundation's Advancing Digitization of Biodiversity Collections (ADBC), Australia's Atlas of Living Australia (ALA), Mexico's National Commission for the Knowledge and Use of Biodiversity (CONABIO), Brazil's Centro de Referência em Informação (CRIA) and China's National Specimen Information Infrastructure (NSII) has led to a rapid rise in data aggregators and an exponential increase in digital data for scientific research and arguably provide the best evidence of where species live. The international Global Biodiversity Information Facility (GBIF) now serves about 131 million museum specimen records, and Integrated Digitized Biocollections (iDigBio) in the USA has amassed more than 115 million. These resources expose collections to a wider audience of researchers, provide the best biodiversity data in the modern era outside of nature itself and ensure the primacy of specimen-based research. Here, we provide a brief history of worldwide data mobilization, their impact on biodiversity research, challenges for ensuring data quality, their contribution to scientific publications and evidence of the rising profiles of natural history collections.
This article is part of the theme issue ‘Biological collections for understanding biodiversity in the Anthropocene’.
Mosses (division Bryophyta) are characterized by the dominance of haploid, poikilohydric gametophytes, and relatively persistent sporophytes that are dependent on the gametophyte generation. Because they are poikilohydric, tend to be desiccation tolerant, and have primarily ectohydric water uptake mechanisms, the ecological requirements of mosses tend to differ from those of vascular plants. We review recent research on phylogenetic relationships and morphological and ecological diversity among mosses. Ancestral character state reconstructions illustrate the evolution of habitat preferences and the relationships between these and morphological variation. These reconstructions reveal the convergent evolution of both epiphytic and aquatic habitat preferences, as well as several reversals from epiphytic to other terrestrial habitats. Morphological character states connected to ectohydry, such as lack of water-conducting stem central strands, may be more prone to adaption driven by environmental conditions, while connections between endohydry and environmental conditions remain ambiguous and require further study. The distribution of the most elaborate endohydric water-conducting structures may be phylogenetically determined rather than resulting from adaptation to habitats. Among the early diverging lineages in the Bryophyta, shifts to cladocarpy may be connected to epiphytic lifestyles. We discuss ecological drivers of aspects of plant architecture including acrocarpous, pleurocarpous and cladocarpous perichaetial positions, and sporophytic reductions, the latter being common in dry, frequently disturbed terrestrial environments and in epiphytic habitats, but also occasionally found in aquatic habitats.
High-throughput sequencing methods have facilitated obtaining large amounts of data from degraded DNA, thus resulting in a dramatic increase in destructive sampling requests to museums. Because the tissues taken from museum specimens as sources of DNA are destroyed during analysis, consideration of the costs and benefits of loss of valuable specimen material relative to knowledge gained is required for any project utilizing destructive sampling. Variation exists in the preservation of DNA in historical specimens due to specimen age and type of museum preparation, among other factors. Thus, it is important to assess DNA yield and quality from different sources of museum specimens when considering the needs of a particular molecular project. We compared DNA derived from several common sources of museum specimens including bone, claw, skin, and soft tissue adherent to skeletal preparations. To account for differences in preparation type and therefore specimen preservation, we tested the performance of samples representing 3 taxonomic groups: mephitids, rodents, and marsupials. We also compared yields from 2 commonly used DNA extraction techniques. DNA quality was assessed by comparing average fragment size, concentration, and copy number of template DNA (for mitochondrial and nuclear markers) in genomic DNA extracts, as well as mitochondrial genome sequence coverage resulting from shotgun sequencing. We show that DNA quality derived from historic museum samples differs depending on specimen and sample type; however, all samples yielded high mitochondrial copy number except the skin and nail from the tanned specimen. Overall, claw samples produced the greatest number of high-quality sequencing reads with the least amount of bacterial contamination. We also found that high DNA concentrations did not necessarily result in high percentages of on-target reads; in fact, the samples that yielded the highest DNA quantities also had the highest amount of exogenous bacterial DNA. Our results indicate that most historical tissue types can be suitable for next-generation sequencing approaches, therefore providing multiple options for natural history collection staff and researchers when considering destructive sampling requests.
Biology is a science that explores life. It produces a wide range of data due to its descriptive nature, covering everything from an organism’s size and state to its genetic makeup. The increasing use of gene sequencing and biotechnological techniques has led to a remarkable surge in biological data. This has emphasized the importance of effectively managing, processing, and analyzing this data. To address these challenges, scientists developed bioinformatics—an interdisciplinary field that combines biology, computer science, mathematics, statistics, information science, and engineering to analyze complex biological data sets. Bioinformatics leverages computational methods for in silico analysis of biological questions, facilitating deeper insights and discoveries. The integration of bioinformatics, big data technologies, cloud computing, machine learning, and AI is revolutionizing our understanding of biology and medicine, presenting new opportunities to address global health challenges such as infectious diseases and pandemics. This book chapter examines the evolution of bioinformatics as a multidisciplinary field, highlighting key resources, web tools, and its applications across various domains.
Plants are primary producers and a food source for many heterotrophic phytophagous organisms. They are affected by different biotic and abiotic environmental stress. Insects, fungi, bacteria, viruses, nematodes, and other pests are biotic factors that significantly reduce crop productivity. Naturally, plants protect themselves from pest attacks by developing different morphological, structural, and biochemical defense mechanisms. However, our understanding of these defensive mechanisms is still limited. Hence, the objective of this paper is to review the mechanism of plant resistance to insects, weeds, and pathogens to know the relevant defense or resistance mechanisms of plants against pests. Many morphological characteristics contribute to plant resistance to insect pests. These include trichomes, surface waxes and hardness of plant tissues, thickening of cell walls and cuticles, the rapid proliferation of tissues, anatomical changes in plant organs, and color and shape of plant parts. The chemical composition of the host plant affects the behavior and adaptation of the herbivore and the host plant. These chemicals can be physiological inhibitors or nutritional deficiencies. Secondary metabolites are compounds that decrease the palatability of the plant tissues in which they are produced but have no effect on a plant's regular growth and development. Plants defend themselves against pathogens by a combination of weapons termed host resistance which is a structural and biochemical defense mechanism and they also defend from weeds by producing allelochemicals. Thus, plants have developed multiple resistance mechanisms to protect against pests. These resistance mechanisms could be an important tool for pest management by reducing the dose of chemicals used in pest control, resulting in a minimal effect of the chemicals on the environment. Also, these resistance mechanisms are compatible with other control methods that act as one of the components of integrated pest management methods to reduce the damage caused by pests.
Introduction: myelodysplastic syndromes (MDS) are a heterogeneous group of hematological diseases. Morphologic bone marrow (BM) examination, cytogenetic analysis and molecular techniques are indispensable for accurate diagnosis and disease classification. BM constitutes the most suitable sample for molecular studies, however, peripheral blood (PB) can be obtained by minimally invasive venipuncture and would be ideal for comprehensive sequential monitoring of MDS over time. Aim: to assess whether targeted deep sequencing (TDS) allows the detection of somatic variants from diagnosis and follow-up PB samples and if the molecular profiles are comparable to those from BM samples.
Methods: preliminary results include the study of 32 low risk patients (28 MDS and 4 chronic myelomonocytic leukemia, CMML). At least one sample, either BM or PB, was obtained at the moment of diagnosis and whenever possible, paired samples obtained the same day were collected. Over the course of the disease only PB samples were obtained. Libraries were prepared for all available samples using DNA from whole BM or whole PB using a custom hybridization-probe based panel (KAPA HyperCap, Roche), including selected exons of 50 myeloid-related genes.
TDS was performed on Illumina MiSeq instrument at a mean coverage of 1000x. Data were analyzed using local bioinformatic pipeline. Only those variants with ≥100x locus coverage and ≥25 reads for the alternative allele (quality criteria) were considered for downstream analysis. Variants were filtered according to variant type, location, read depth (>100x) and population frequency (<1%).
Results: patients´ characteristics are described in table 1. The combination of cytogenetics and TDS identified genetic alterations in 29/32 patients (91%). TDS allowed the identification of somatic variants in 27/32 patients (85%), being TET2 (n=23, 29%), SF3B1 (n=12, 15%) and ASXL1 (n=8, 10%) the most frequently mutated genes.
Considering molecular information, restratification of IPSS-R to IPSS-M risk groups of MDS patients (n=28) was performed (Table 1). As previously described (Sauta et al, J Clin Oncol, 2023), we performed a five-to-five comparison of IPSS-R and IPSS-M patients' distribution (merging moderate low and moderate high to moderate in IPSS-M). This resulted in the restratification of 36% of MDS patients (10/28) of which 6% (n=2) were downstaged and 25% (n=8) were upstaged.
Suitability of PB samples for NGS analysis was evaluated in 21 patients in which paired BM and PB samples were obtained at diagnosis. Overall, 65 variants were found, and 63 of them were detectable in both BM and PB samples. Despite the other two variants were also detected in PB samples at low variant allele frequency (VAF) values ( TET2 and NRAS, VAF=1,5% and 0,99%, respectively), they strictly did not meet our quality criteria (≥25 reads for the alternative allele). Nevertheless, a strong correlation was identified between VAF values for the individual variants detected in BM vs. PB (Figure 1).
As part of this project, molecular profile during patients' follow-up is being studied using PB samples. To date, at least one follow-up sample of each patient was studied. The mean time between diagnosis and the first follow-up sample was 28 months (range 6-84 months). After this time, and despite molecular profiles are heterogeneous, variations could be detected in up to 72% of patients (23/32) using PB samples. The most commonly detected variation was an increasing in VAF values (9/23; defined by a difference >5% in relation to the VAF detected in the previous sample), followed by a new variant detection + increasing in VAF values (7/23), new variant detection only (6/23) and a single case where simultaneous decreasing VAF and disappearance of subclones was detected (single patient receiving treatment with hypomethylating agent).
Conclusion: molecular testing has become increasingly important for accurate diagnosis and risk assessment in MDS and CMML patients, specially with the new classifications and prognostic scoring system. Here we demonstrate that the molecular analysis from PB cells constitutes an appropriate alternative for the detection and quantification of somatic variants in MDS patients at diagnosis and during disease course.
Funding: Bristol Myers Squibb (PI-13279-IIT), FEHH and 2021 SGR 00560 (GRC).
Marine and freshwater biodiversity is under threat from both natural and manmade causes. Biological monitoring is currently a top priority for biodiversity protection. Given present limitations, traditional biological monitoring methods may not achieve the proposed monitoring aims. Environmental DNA metabarcoding technology reflects species information by capturing and extracting DNA from environmental samples, using molecular biology techniques to sequence and analyze the DNA, and comparing the obtained information with existing reference libraries to obtain species identification. However, its practical application has highlighted several limitations. This paper summarizes the main steps in the environmental application of eDNA metabarcoding technology in aquatic ecosystems, including the discovery of unknown species, the detection of invasive species, and evaluations of biodiversity. At present, with the rapid development of big data and artificial intelligence, certain advanced technologies and devices can be combined with environmental DNA metabarcoding technology to promote further development of aquatic species monitoring and management.
Remote sensing has transformed the monitoring of life on Earth by revealing spatial and temporal dimensions of biological diversity through structural, compositional and functional measurements of ecosystems. Yet, many aspects of Earth’s biodiversity are not directly quantified by reflected or emitted photons. Inclusive integration of remote sensing with field-based ecology and evolution is needed to fully understand and preserve Earth’s biodiversity. In this Perspective, we argue that multiple data types are necessary for almost all draft targets set by the Convention on Biological Diversity. We examine five key topics in biodiversity science that can be advanced by integrating remote sensing with in situ data collection from field sampling, experiments and laboratory studies to benefit conservation. Lowering the barriers for bringing these approaches together will require global-scale collaboration.
Advances in quantitative biomarker development have accelerated new forms of data-driven insights for patients with cancer. However, most approaches are limited to a single mode of data, leaving integrated approaches across modalities relatively underdeveloped. Multimodal integration of advanced molecular diagnostics, radiological and histological imaging, and codified clinical data presents opportunities to advance precision oncology beyond genomics and standard molecular techniques. However, most medical datasets are still too sparse to be useful for the training of modern machine learning techniques, and significant challenges remain before this is remedied. Combined efforts of data engineering, computational methods for analysis of heterogeneous data and instantiation of synergistic data models in biomedical research are required for success. In this Perspective, we offer our opinions on synthesizing complementary modalities of data with emerging multimodal artificial intelligence methods. Advancing along this direction will result in a reimagined class of multimodal biomarkers to propel the field of precision oncology in the coming decade. This Perspective proposes that data from multiple modalities, including molecular diagnostics, radiological and histological imaging and codified clinical data, should be integrated by multimodal machine learning models to advance the prognosis and treatment management of patients with cancer.
Numerous restoration programs have been launched worldwide in recent years, but the effectiveness of such programs for biodiversity conservation remains unclear. Additionally, priority areas of restoration need to be identified in a region where resources are limited. Habitat availability combines habitat amounts with interpatch connectivity, governing whether a landscape can shelter biological populations in the long term. Consequently, restoration efforts should focus on enhancing habitat availability to promote biological flows. In this study, we proposed a multi-scale approach to evaluate the effectiveness of restoration program and to set priority areas for forest restoration. The evaluations of effectiveness highlighted the contributions of forest cover change to habitat availability. The restoration priorities of landscapes were ranked by three indicators which are also tightly associated with habitat availability: relative urgency of intervention, efficiency of restoration, and connectivity importance for broader-scale biotic flows. We applied the approach in the Eastern Himalaya region of China, a global biodiversity conservation hotspot. Forest cover has been increasing by 3.4% in this region since 2000 due to the implementation of the Grain-for-Green Program, but not all the new forest areas were available as species habitat and/or stepping stones. By the method we developed, 14% of the areas are ranked as the high restoration priorities aiming to enhance available habitat for multiple species with different dispersal abilities. Our approach can significantly improve the outcome of a restoration program. It is flexible and can aid policy makers in optimizing restoration efforts given the biodiversity conservation goals and available resources.
Being able to identify plant species is an important factor for understanding biodiversity and its change due to natural and anthropogenic drivers.
We discuss the freely available Flora Incognita app for Android, iOS and Harmony OS devices that allows users to interactively identify plant species and capture their observations. Specifically developed deep learning algorithms, trained on an extensive repository of plant observations, classify plant images with yet unprecedented accuracy. By using this technology in a context‐adaptive and interactive identification process, users are now able to reliably identify plants regardless of their botanical knowledge level.
Users benefit from an intuitive interface and supplementary educational materials. The captured observations in combination with their metadata provide a rich resource for researching, monitoring and understanding plant diversity.
Mobile applications such as Flora Incognita stimulate the successful interplay of citizen science, conservation and education.
Taxonomy is the science that explores, describes, names, and classifies all organisms. In this introductory chapter, we highlight the major historical steps in the elaboration of this science, which provides baseline data for all fields of biology and plays a vital role for society but is also an independent, complex, and sound hypothesis-driven scientific discipline.
In a first part, we underline that plant taxonomy is one of the earliest scientific disciplines that emerged thousands of years ago, even before the important contributions of the Greeks and Romans (e.g., Theophrastus, Pliny the Elder, and Dioscorides). In the fifteenth–sixteenth centuries, plant taxonomy benefited from the Great Navigations, the invention of the printing press, the creation of botanic gardens, and the use of the drying technique to preserve plant specimens. In parallel with the growing body of morpho-anatomical data, subsequent major steps in the history of plant taxonomy include the emergence of the concept of natural classification, the adoption of the binomial naming system (with the major role of Linnaeus) and other universal rules for the naming of plants, the formulation of the principle of subordination of characters, and the advent of the evolutionary thought. More recently, the cladistic theory (initiated by Hennig) and the rapid advances in DNA technologies allowed to infer phylogenies and to propose true natural, genealogy-based classifications.
In a second part, we put the emphasis on the challenges that plant taxonomy faces nowadays. The still very incomplete taxonomic knowledge of the worldwide flora (the so-called taxonomic impediment) is seriously hampering conservation efforts that are especially crucial as biodiversity has entered its sixth extinction crisis. It appears mainly due to insufficient funding, lack of taxonomic expertise, and lack of communication and coordination. We then review recent initiatives to overcome these limitations and to anticipate how taxonomy should and could evolve. In particular, the use of molecular data has been era-splitting for taxonomy and may allow an accelerated pace of species discovery. We examine both strengths and limitations of such techniques in comparison to morphology-based investigations, we give broad recommendations on the use of molecular tools for plant taxonomy, and we highlight the need for an integrative taxonomy based on evidence from multiple sources.
- Morphologically cryptic taxa have proved to be a long-standing challenge for taxonomists. Lineages that show strong genomic structuring across the landscape but are phenotypically similar pose a conundrum, with traditional morphological analyses of these cryptic lineages struggling to keep up with species delimitation advances. Micro X-ray computed tomography (CT) combined with geometric morphometric analyses provides a promising avenue for identification of morphologically cryptic taxa, given its ability to detect subtle differences in anatomical structures. This approach, however, has yet to be used in combination with genomic data in a comparative analytical framework to distinguish cryptic taxa. We present an integrative approach incorporating genomic and geometric morphometric evidence to assess the species delimitation of grassland earless dragons (Tympanocryptis spp.) in north-eastern Australia. Using mitochondrial and nuclear genes (ND2 and RAG1, respectively), along with >8500 SNPs (nuclear single nucleotide polymorphisms), we assess the evolutionary independence of target lineages and several closely related species. We then integrate phylogenomic data with osteological cranial variation between lineages using landmark-based analyses of three-dimensional CT models. High levels of genomic differentiation between the three target lineages were uncovered, also supported by significant osteological differences. By incorporating multiple lines of evidence we provide strong support that there are three undescribed cryptic lineages of Tympanocryptis in north-eastern Australia that warrant taxonomic review. Our approach demonstrates the successful application of CT with integrative taxonomic approaches for cryptic species delimitation, which is broadly applicable across vertebrates containing morphologically similar yet genetically distinct lineages. Additionally, we provide a review of recent integrative taxonomic approaches for cryptic species delimitation, and an assessment of how our approach can value-add to taxonomic research.