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Societal Impact Statement Biological samples and their associated information are an essential resource used by scientists, governments, policymakers, practitioners and communities to ensure that biodiversity can be appropriately protected and sustainably used. Yet, considering the enormous task of documenting the vast numbers of as‐yet‐unknown plant and fungal species, greater international coordination for biological collecting and recording is necessary, built on equitable collecting practices and standards. Here, we propose five commitments to accelerate and enhance scientific knowledge of plant and fungal diversity, while increasing collaboration, benefit sharing and efficiency. Summary Almost all life depends on plants and fungi, making knowledge of their diversity and distribution—primarily derived from biological collections—fundamental to national and international conservation, restoration and sustainable use commitments. However, it is estimated that some 15% of all plant species and over 90% of all fungal species have not yet been scientifically described, hampering our ability to assess and demonstrate the impact of efforts to halt biodiversity loss. In addition, organisations and researchers around the world lack a concerted strategy for increasing complementarity and avoiding overlap in botanical and mycological research, particularly in relation to the collection of specimens. We here present the 2030 Declaration on Scientific Plant and Fungal Collecting, summarising a commitment towards such a necessary strategy. Its components were identified from discussions during and after a series of four workshops and plenary discussions at the 2023 State of the World's Plants and Fungi symposium convened by the Royal Botanic Gardens, Kew, and were then consolidated into the present form by the authors. The Declaration was subsequently opened up for endorsement by signatories. Collectively, we agree on a set of five commitments for cataloguing the world's flora and funga, designed to maximise efficiency, facilitate knowledge exchange and promote equitable collaborations: (1) use evidence‐based collection strategies; (2) strengthen local capacity; (3) collaborate across taxa and disciplines; (4) collect for the future; and (5) share the benefits. This Declaration is a first step towards increased global and regional coordination of scientific collecting efforts.

Abstract Trait datasets are at the basis of a large share of ecology and evolutionary research, being used to infer ancestral morphologies, quantify species extinction risks, or evaluate the functional diversity of biological communities. These datasets, however, are often plagued by missing data, for instance, due to incomplete sampling, limited data and resource availability. Several imputation methods exist to predict missing values and recent studies have explored their performance for continuous traits in biological datasets. However, less is known about the accuracy of these methods for categorical traits. Here we explore the performance of different imputation methods on categorical biological traits combining phylogenetic comparative methods, machine learning and deep learning models. To this end, we develop an open‐source R package, to impute trait data while integrating a simulation framework to evaluate their performance on synthetic datasets. We run a range of simulations under different missing rates, mechanisms, biases and evolutionary models. We propose an integration between phylogenetic comparative methods and machine learning imputation, and an ensemble approach, in which selected imputation methods are combined. Our simulations show that this approach provides the most robust and accurate predictions. We applied our imputation pipeline to an incomplete trait dataset of 1015 elasmobranch species (i.e. sharks, rays and skates) and found a high imputation accuracy of the predictions based on an expert‐based assessment of the missing traits. Overall, our R package facilitates the comparison of multiple imputation methods and allows robust predictions of missing trait values. Our study highlights the benefits of coupling phylogenetic evolutionary models with machine learning inference to augment incomplete biological datasets.

Abstract Understanding how biodiversity has changed through time is a central goal of evolutionary biology. However, estimates of past biodiversity are challenged by the inherent incompleteness of the fossil record, even when state-of-the-art statistical methods are applied to adjust estimates while correcting for sampling biases. Here we develop an approach based on stochastic simulations of biodiversity and a deep learning model to infer richness at global or regional scales through time while incorporating spatial, temporal and taxonomic sampling variation. Our method outperforms alternative approaches across simulated datasets, especially at large spatial scales, providing robust palaeodiversity estimates under a wide range of preservation scenarios. We apply our method on two empirical datasets of different taxonomic and temporal scope: the Permian-Triassic record of marine animals and the Cenozoic evolution of proboscideans. Our estimates provide a revised quantitative assessment of two mass extinctions in the marine record and reveal rapid diversification of proboscideans following their expansion out of Africa and a >70% diversity drop in the Pleistocene.

Abstract Elasmobranchs (sharks, rays and skates) are among the most threatened marine vertebrates, yet their global functional diversity remains largely unknown. Here, we use a trait dataset of >1000 species to assess elasmobranch functional diversity and compare it against other previously studied biodiversity facets (taxonomic and phylogenetic), to identify species- and spatial- conservation priorities. We show that threatened species encompass the full extent of functional space and disproportionately include functionally distinct species. Applying the conservation metric FUSE (Functionally Unique, Specialised, and Endangered) reveals that most top-ranking species differ from the top Evolutionarily Distinct and Globally Endangered (EDGE) list. Spatial analyses further show that elasmobranch functional richness is concentrated along continental shelves and around oceanic islands, with 18 distinguishable hotspots. These hotspots only marginally overlap with those of other biodiversity facets, reflecting a distinct spatial fingerprint of functional diversity. Elasmobranch biodiversity facets converge with fishing pressure along the coast of China, which emerges as a critical frontier in conservation. Meanwhile, several components of elasmobranch functional diversity fall in high seas and/or outside the global network of marine protected areas. Overall, our results highlight acute vulnerability of the world’s elasmobranchs’ functional diversity and reveal global priorities for elasmobranch functional biodiversity previously overlooked.

Societal Impact Statement The development and implementation of conservation actions that address urgent threats to biodiversity across the globe require large amounts of historic and current data. Machine learning approaches offer the tools to process, analyse and interpret large data sets, giving insights into trends and guiding evidence‐based allocation of limited resources to maximise positive biodiversity outcomes. Here we describe how data acquired from remote sensing, citizen science and other monitoring approaches could feed in near‐real time into an early warning system for biodiversity that integrates automated red‐listing of species with the identification of priority areas for conservation. Summary Application of machine learning approaches is aiding biodiversity conservation and research at a time of rapid global change. Two emerging topics and their data requirements are presented. First, to identify areas of priority protection for preventing biodiversity loss, reinforcement learning is used by training models that take into account human disturbance and climate change under recurrent monitoring schemes. Second, neural networks are used to approximate classification of species into Red List categories of the International Union for Conservation of Nature, offering the possibility of real‐time re‐classification after events such as widespread fires and deforestation. We discuss how the identification of areas and species most at risk could be integrated into an ‘early warning system’ based on climatic monitoring, remotely sensed land‐use changes and near‐real time biological and threat data from citizen science initiatives. Such system would help guide actions to prevent biodiversity loss at the speed required for effective conservation.

Abstract What controls species diversity and diversification is one of the major questions in evolutionary biology and paleontology. Previous studies have addressed this issue based on various plant and animal groups, geographic regions, and time intervals. However, as most previous research focused on terrestrial or marine ecosystems, our understanding of the controls on diversification of biota (and particularly invertebrates) in freshwater environments in deep time is still limited. Here, we infer diversification rates of North American freshwater gastropods from the Late Triassic to the Pleistocene and explore potential links between shifts in speciation and extinction and major changes in paleogeography, climate, and biotic interactions. We found that variation in the speciation rate is best explained by changes in continental fragmentation, with rate shifts coinciding with major paleogeographic reorganizations in the Mesozoic, in particular the retreat of the Sundance Sea and subsequent development of the Bighorn wetland and the advance of the Western Interior Seaway. Climatic events in the Cenozoic (Middle Eocene Climate Optimum, Miocene Climate Optimum) variably coincide with shifts in speciation and extinction as well, but no significant long-term association could be detected. Similarly, no influence of diversity dependence was found across the entire time frame of ~ 214 Myr. Our results indicate that short-term climatic events and paleogeographic changes are relevant to the diversification of continental freshwater biota, while long-term trends have limited effect.

The expansion timing and dynamics of open vegetation are disputed. Here, the authors present a model of paleovegetation changes in North America, showing open vegetation beginning around 23 million years ago and accelerating at 5 million years ago to become the most prominent natural vegetation type in North America today.

Global diversity trends in the fossil record vary regionally through time and space, affecting our ability to interpret macroevolutionary history. Here, the authors propose a method to eliminate spatial sampling bias, estimate origination and extinction rates, and generate diversity estimates, applying this method to the Late Permian to Early Jurassic marine fossil record.

Plants are a rich source of bioactive compounds and a number of plant-derived antiplasmodial compounds have been developed into pharmaceutical drugs for the prevention and treatment of malaria, a major public health challenge. However, identifying plants with antiplasmodial potential can be time-consuming and costly. One approach for selecting plants to investigate is based on ethnobotanical knowledge which, though having provided some major successes, is restricted to a relatively small group of plant species. Machine learning, incorporating ethnobotanical and plant trait data, provides a promising approach to improve the identification of antiplasmodial plants and accelerate the search for new plant-derived antiplasmodial compounds. In this paper we present a novel dataset on antiplasmodial activity for three flowering plant families – Apocynaceae, Loganiaceae and Rubiaceae (together comprising c. 21,100 species) – and demonstrate the ability of machine learning algorithms to predict the antiplasmodial potential of plant species. We evaluate the predictive capability of a variety of algorithms – Support Vector Machines, Logistic Regression, Gradient Boosted Trees and Bayesian Neural Networks – and compare these to two ethnobotanical selection approaches – based on usage as an antimalarial and general usage as a medicine. We evaluate the approaches using the given data and when the given samples are reweighted to correct for sampling biases. In both evaluation settings each of the machine learning models have a higher precision than the ethnobotanical approaches. In the bias-corrected scenario, the Support Vector classifier performs best – attaining a mean precision of 0.67 compared to the best performing ethnobotanical approach with a mean precision of 0.46. We also use the bias correction method and the Support Vector classifier to estimate the potential of plants to provide novel antiplasmodial compounds. We estimate that 7677 species in Apocynaceae, Loganiaceae and Rubiaceae warrant further investigation and that at least 1300 active antiplasmodial species are highly unlikely to be investigated by conventional approaches. While traditional and Indigenous knowledge remains vital to our understanding of people-plant relationships and an invaluable source of information, these results indicate a vast and relatively untapped source in the search for new plant-derived antiplasmodial compounds.

The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value and threat considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence of species in unsampled areas deemed suitable by environmental niche models. Here, we present a deep learning approach that directly estimates species richness, skipping the step of estimating individual species ranges. We train a neural network model based on species lists from inventory plots, which provide ground truth data for supervised machine learning. The model learns to predict species richness based on spatially associated variables, including climatic and geographic predictors, as well as counts of available species records from online databases. We assess the empirical utility of our approach by producing independently verifiable maps of alpha, beta, and gamma plant diversity at high spatial resolutions for Australia, a continent with highly heterogeneous diversity patterns. Our deep learning framework provides a powerful and flexible new approach for estimating biodiversity patterns, constituting a step forward toward automated biodiversity assessments.

Trees are fundamental for Earth’s biodiversity as primary producers and ecosystem engineers and are responsible for many of nature’s contributions to people. Yet, many tree species at present are threatened with extinction by human activities. Accurate identification of threatened tree species is necessary to quantify the current biodiversity crisis and to prioritize conservation efforts. However, the most comprehensive dataset of tree species extinction risk—the Red List of the International Union for the Conservation of Nature (IUCN RL)—lacks assessments for a substantial number of known tree species. The RL is based on a time-consuming expert-based assessment process, which hampers the inclusion of less-known species and the continued updating of extinction risk assessments. In this study, we used a computational pipeline to approximate RL extinction risk assessments for more than 21,000 tree species (leading to an overall assessment of 89% of all known tree species) using a supervised learning approach trained based on available IUCN RL assessments. We harvested the occurrence data for tree species worldwide from online databases, which we used with other publicly available data to design features characterizing the species’ geographic range, biome and climatic affinities, and exposure to human footprint. We trained deep neural network models to predict their conservation status, based on these features. We estimated 43% of the assessed tree species to be threatened with extinction and found taxonomic and geographic heterogeneities in the distribution of threatened species. The results are consistent with the recent estimates by the Global Tree Assessment initiative, indicating that our approach provides robust and time-efficient approximations of species’ IUCN RL extinction risk assessments.

Reconstructions of evolutionary history from molecular vs. fossil data are often in conflict. Here, the authors show that discrepancies in speciation and extinction rates can arise from differing assumptions about underlying processes and present a model that reconciles fossil and molecular evidence.

Abstract Combining palaeontological and neontological data offers a unique opportunity to investigate the relative roles of biotic and abiotic controls of species diversification, and the importance of origination versus extinction in driving evolutionary dynamics. Ferns comprise a major terrestrial plant radiation with an extensive evolutionary history providing a wealth of modern and fossil data for modelling environmental drivers of diversification. Here we develop a novel Bayesian model to simultaneously estimate correlations between diversification dynamics and multiple environmental trajectories. We estimate the impact of different factors on fern diversification over the past 400 million years by analysing a comprehensive dataset of fossil occurrences and complement these findings by analysing a large molecular phylogeny. We show that origination and extinction rates are governed by fundamentally different processes: originations depend on within-group diversity but are largely unaffected by environmental changes, whereas extinctions are strongly affected by external factors such as climate and geology. Our results indicate that the prime driver of fern diversity dynamics is environmentally driven extinction, with origination being an opportunistic response to diminishing ecospace occupancy.

Questions about the evolution of material culture are widespread in the humanities and social sciences. Statistical modeling of long-term changes in material culture is less common due to a lack of appropriate frameworks. Our goal is to close this gap and provide robust statistical methods for examining changes in the diversity of material culture. We provide an open-source and quantitative workflow for estimating rates of origination, extinction, and preservation, as well as identifying key shift points in the diversification histories of material culture. We demonstrate our approach using two distinct kinds of data: age ranges for the production of American car models, and radiocarbon dates associated with archaeological cultures of the European Neolithic. Our approach improves on existing frameworks by disentangling the relative contributions of origination and extinction to diversification. Our method also permits rigorous statistical testing of competing hypotheses to explain changes in diversity. Finally, we stress the value of a flexible approach that can be applied to data in various forms; this flexibility allows scholars to explore commonalities between forms of material culture and ask questions about the general properties of cultural change.

The pleiotropic drug resistance (PDR) transporter Pdr11p is expressed under anaerobic growth conditions at the plasma membrane of the yeast Saccharomyces cerevisiae, where it facilitates the uptake of exogenous sterols. Members of the fungal PDR family contain six conserved cysteines in their extracellular loops (ECL). For the functional analysis of these cysteine residues in Pdr11p, we generated a series of single cysteine-to-serine mutants. All mutant proteins expressed well and displayed robust ATPase activity upon purification. Mass-spectrometry analysis identified two cysteine residues (C582 and C603) in ECL3 forming a disulfide bond. Further characterization by cell-based assays showed that all mutants are compromised in facilitating sterol uptake, protein stability, and trafficking to the plasma membrane. Our data highlight the fundamental importance of all six extracellular cysteine residues for the functional integrity of Pdr11p and provide new structural insights into the PDR family of transporters.

The unparalleled biodiversity found in the American tropics (the Neotropics) has attracted the attention of naturalists for centuries. Despite major advances in recent years in our understanding of the origin and diversification of many Neotropical taxa and biotic regions, many questions remain to be answered. Additional biological and geological data are still needed, as well as methodological advances that are capable of bridging these research fields. In this review, aimed primarily at advanced students and early-career scientists, we introduce the concept of “trans-disciplinary biogeography,” which refers to the integration of data from multiple areas of research in biology (e.g., community ecology, phylogeography, systematics, historical biogeography) and Earth and the physical sciences (e.g., geology, climatology, palaeontology), as a means to reconstruct the giant puzzle of Neotropical biodiversity and evolution in space and time. We caution against extrapolating results derived from the study of one or a few taxa to convey general scenarios of Neotropical evolution and landscape formation. We urge more coordination and integration of data and ideas among disciplines, transcending their traditional boundaries, as a basis for advancing tomorrow’s ground-breaking research. Our review highlights the great opportunities for studying the Neotropical biota to understand the evolution of life.

Nitraria is a halophytic taxon (i.e., adapted to saline environments) that belongs to the plant family Nitrariaceae and is distributed from the Mediterranean, across Asia into the south-eastern tip of Australia. This taxon is thought to have originated in Asia during the Paleogene (66–23 Ma), alongside the proto-Paratethys epicontinental sea. The evolutionary history of Nitraria might hold important clues on the links between climatic and biotic evolution but limited taxonomic documentation of this taxon has thus far hindered this line of research. Here we investigate if the pollen morphology and the chemical composition of the pollen wall are informative of the evolutionary history of Nitraria and could explain if origination along the proto-Paratethys and dispersal to the Tibetan Plateau was simultaneous or a secondary process. To answer these questions, we applied a novel approach consisting of a combination of Fourier Transform Infrared spectroscopy (FTIR), to determine the chemical composition of the pollen wall, and pollen morphological analyses using Light Microscopy (LM) and Scanning Electron Microscopy (SEM). We analysed our data using ordinations (principal components analysis and non-metric multidimensional scaling), and directly mapped it on the Nitrariaceae phylogeny to produce a phylomorphospace and a phylochemospace. Our LM, SEM and FTIR analyses show clear morphological and chemical differences between the sister groups Peganum and Nitraria. Differences in the morphological and chemical characteristics of highland species (Nitraria schoberi, N. sphaerocarpa, N. sibirica and N. tangutorum) and lowland species (Nitraria billardierei and N. retusa) are very subtle, with phylogenetic history appearing to be a more important control on Nitraria pollen than local environmental conditions. Our approach shows a compelling consistency between the chemical and morphological characteristics of the eight studied Nitrariaceae species, and these traits are in agreement with the phylogenetic tree. Taken together, this demonstrates how novel methods for studying fossil pollen can facilitate the evolutionary investigation of living and extinct taxa, and the environments they represent.

The exceptional increase in molecular DNA sequence data in open repositories is mirrored by an ever-growing interest among evolutionary biologists to harvest and use those data for phylogenetic inference. Many quality issues, however, are known and the sheer amount and complexity of data available can pose considerable barriers to their usefulness. A key issue in this domain is the high frequency of sequence mislabeling encountered when searching for suitable sequences for phylogenetic analysis. These issues include, among others, the incorrect identification of sequenced species, non-standardized and ambiguous sequence annotation, and the inadvertent addition of paralogous sequences by users. Taken together, these issues likely add considerable noise, error or bias to phylogenetic inference, a risk that is likely to increase with the size of phylogenies or the molecular datasets used to generate them. Here we present a software package, phylotaR that bypasses the above issues by using instead an alignment search tool to identify orthologous sequences. Our package builds on the framework of its predecessor, PhyLoTa, by providing a modular pipeline for identifying overlapping sequence clusters using up-to-date GenBank data and providing new features, improvements and tools. We demonstrate and test our pipeline’s effectiveness by presenting trees generated from phylotaR clusters for two large taxonomic clades: Palms and primates. Given the versatility of this package, we hope that it will become a standard tool for any research aiming to use GenBank data for phylogenetic analysis.

Sterols are crucial lipid components that regulate membrane permeability and fluidity and are the precursors of bioactive steroids. The plant sterols exist as three major forms, free sterols, steryl glycosides and steryl esters. The storage of steryl esters in lipid droplets has been shown to contribute to cellular sterol homeostasis. To further document cellular aspects of sterol biosynthesis in plants, we addressed the question of the subcellular localization of the enzymes implicated in the final steps of the post-squalene biosynthetic pathway. In order to create a clear localization map of steroidogenic enzymes in cells, the coding regions of Δ(7)-sterol-C(5)-desaturase (STE1/DWARF7), Δ(24)-sterol-Δ(24)-reductase (DIMINUTO/DWARF1) and Δ(5,7)-sterol-Δ(7)-reductase (DWARF5) were fused to the yellow fluorescent protein (YFP) and transformed into Arabidopsis thaliana mutant lines deficient in the corresponding enzymes. All fusion proteins were found to localize in the endoplasmic reticulum in functionally complemented plants. The results show that both Δ(5,7)-sterol-Δ(7)-reductase and Δ(24)-sterol-Δ(24)-reductase are in addition localized to the plasma membrane, whereas Δ(7)-sterol-C(5)-desaturase was clearly detected in lipid particles. These findings raise new challenging questions about the spatial and dynamic cellular organization of sterol biosynthesis in plants.

Current phylogenetic comparative methods modeling quantitative trait evolution generally assume that, during speciation, phenotypes are inherited identically between the two daughter species. This, however, neglects the fact that species consist of a set of individuals, each bearing its own trait value. Indeed, because descendent populations after speciation are samples of a parent population, we can expect their mean phenotypes to randomly differ from one another potentially generating a "jump" of mean phenotypes due to asymmetrical trait inheritance at cladogenesis. Here, we aim to clarify the effect of asymmetrical trait inheritance at speciation on macroevolutionary analyses, focusing on model testing and parameter estimation using some of the most common models of quantitative trait evolution. We developed an individual-based simulation framework in which the evolution of phenotypes is determined by trait changes at the individual level accumulating across generations, and cladogenesis occurs then by separation of subsets of the individuals into new lineages. Through simulations, we assess the magnitude of phenotypic jumps at cladogenesis under different modes of trait inheritance at speciation. We show that even small jumps can strongly alter both the results of model selection and parameter estimations, potentially affecting the biological interpretation of the estimated mode of evolution of a trait. Our results call for caution when interpreting analyses of trait evolution, while highlighting the importance of testing a wide range of alternative models. In the light of our findings, we propose that future methodological advances in comparative methods should more explicitly model the intraspecific variability around species mean phenotypes and how it is inherited at speciation., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society of Systematic Biologists.)