Islands are key for protecting the world's plant endemism.

Islands are renowned as evolutionary laboratories and support many species that are not found elsewhere1,2. Islands are also of great conservation concern, with many of their endemic species currently threatened or extinct3. Here we present a standardized checklist of all known vascular plants that occur on islands and document their geographical and phylogenetic distribution and conservation risk. Our analyses of 304,103 plant species reveal that 94,052 species (31%) are native to islands, which constitute 5.3% of the global landmass4. Of these, 63,280 are island endemic species, which represent 21% of global plant diversity. Three-quarters of these are restricted to large or isolated islands. Compared with the world flora, island endemics are non-randomly distributed within the tree of life, with a total of 1,005 billion years of unique phylogenetic history with 17 families and 1,702 genera being entirely endemic to islands. Of all vascular plants assigned International Union for Conservation of Nature conservation categories5, 22% are island endemics. Among these endemic species, 51% are threatened, and 55% of all documented global extinctions have occurred on islands. We find that of all single-island endemic species, only 6% occur on islands meeting the United Nations 30×30 conservation target. Urgent measures including habitat restoration, invasive species removal and ex situ programmes are needed to protect the world's island flora. Our checklist quantifies the uniqueness of island life, provides a basis for future studies of island floras, and highlights the urgent need to take actions for conserving them.

Climatic stability and geological history shape global centers of neo- and paleoendemism in seed plants.

Assessing the distribution of geographically restricted and evolutionarily unique species and their underlying drivers is key to understanding biogeographical processes and critical for global conservation prioritization. Here, we quantified the geographic distribution and drivers of phylogenetic endemism for ~320,000 seed plants worldwide and identified centers and drivers of evolutionarily young (neoendemism) and evolutionarily old endemism (paleoendemism). Tropical and subtropical islands as well as tropical mountain regions displayed the world's highest phylogenetic endemism. Most tropical rainforest regions emerged as centers of paleoendemism, while most Mediterranean-climate regions showed high neoendemism. Centers where high neo- and paleoendemism coincide emerged on some oceanic and continental fragment islands, in Mediterranean-climate regions and parts of the Irano-Turanian floristic region. Global variation in phylogenetic endemism was well explained by a combination of past and present environmental factors (79.8 to 87.7% of variance explained) and most strongly related to environmental heterogeneity. Also, warm and wet climates, geographic isolation, and long-term climatic stability emerged as key drivers of phylogenetic endemism. Neo- and paleoendemism were jointly explained by climatic and geological history. Long-term climatic stability promoted the persistence of paleoendemics, while the isolation of oceanic islands and their unique geological histories promoted neoendemism. Mountainous regions promoted both neo- and paleoendemism, reflecting both diversification and persistence over time. Our study provides insights into the evolutionary underpinnings of biogeographical patterns in seed plants and identifies the areas on Earth with the highest evolutionary and biogeographical uniqueness-key information for setting global conservation priorities.

Global models and predictions of plant diversity based on advanced machine learning techniques.

Despite the paramount role of plant diversity for ecosystem functioning, biogeochemical cycles, and human welfare, knowledge of its global distribution is still incomplete, hampering basic research and biodiversity conservation. Here, we used machine learning (random forests, extreme gradient boosting, and neural networks) and conventional statistical methods (generalized linear models and generalized additive models) to test environment-related hypotheses of broad-scale vascular plant diversity gradients and to model and predict species richness and phylogenetic richness worldwide. To this end, we used 830 regional plant inventories including c. 300 000 species and predictors of past and present environmental conditions. Machine learning showed a superior performance, explaining up to 80.9% of species richness and 83.3% of phylogenetic richness, illustrating the great potential of such techniques for disentangling complex and interacting associations between the environment and plant diversity. Current climate and environmental heterogeneity emerged as the primary drivers, while past environmental conditions left only small but detectable imprints on plant diversity. Finally, we combined predictions from multiple modeling techniques (ensemble predictions) to reveal global patterns and centers of plant diversity at multiple resolutions down to 7774 km2 . Our predictive maps provide accurate estimates of global plant diversity available at grain sizes relevant for conservation and macroecology.

Trait ecology of startup plants.

Startup plants include seedlings and basal and epicormic resprouts. It has long been held that startups have different strategies from adult plants, but theory for what trait differences to expect is limited and not yet quantitatively tested. Three applicable concepts are analogous to human startup firms, R-shift, and trait-growth theory. All three suggest startups should be built with lower construction costs than established plants. This appears to be almost always true in terms of leaf mass per area (LMA), though many comparisons are complicated by the startups growing in lower light. Trait-growth theory predicts LMA should increase progressively with height or total leaf area, driven by higher conductive-pathway costs associated with each unit leaf area, and by greater reward from slowing leaf turnover. Basal resprouts often have somewhat higher LMA than seedlings, but possibly this is simply because they are larger. A number of eminently testable questions are identified. Prospects are good for a theoretically cogent and field-tested body of knowledge about plant startups.

Biodiversity data integration-the significance of data resolution and domain.

Recent years have seen an explosion in the availability of biodiversity data describing the distribution, function, and evolutionary history of life on earth. Integrating these heterogeneous data remains a challenge due to large variations in observational scales, collection purposes, and terminologies. Here, we conceptualize widely used biodiversity data types according to their domain (what aspect of biodiversity is described?) and informational resolution (how specific is the description?). Applying this framework to major data providers in biodiversity research reveals a strong focus on the disaggregated end of the data spectrum, whereas aggregated data types remain largely underutilized. We discuss the implications of this imbalance for the scope and representativeness of current macroecological research and highlight the synergies arising from a tighter integration of biodiversity data across domains and resolutions. We lay out effective strategies for data collection, mobilization, imputation, and sharing and summarize existing frameworks for scalable and integrative biodiversity research. Finally, we use two case studies to demonstrate how the explicit consideration of data domain and resolution helps to identify biases and gaps in global data sets and achieve unprecedented taxonomic and geographical data coverage in macroecological analyses.

Diminishing returns among lamina fresh and dry mass, surface area, and petiole fresh mass among nine Lauraceae species.

PREMISE: The phenomenon called "diminishing returns" refers to a scaling relationship between lamina mass (M) vs. lamina area (A) in many species, i.e., M ∝ Aα>1 , where α is the scaling exponent exceeding unity. Prior studies have focused on the scaling relationships between lamina dry mass (DM) and A, or between fresh mass (FM) and A. However, the scaling between petiole mass and M and A has seldom been investigated. Here, we examine the scaling relationships among FM, DM, A, and petiole fresh mass (PFM). METHODS: For each of 3268 leaves from nine Lauraceae species, FM, DM, A, and PFM were measured, and their scaling relationships were fitted using reduced major axis regression protocols. The bootstrap percentile method was used to test the significance of the difference in α-values between any two species. RESULTS: The phenomenon of diminishing returns was verified between FM vs. A and DM vs. A. The FM vs. A scaling relationship was statistically more robust than the DM vs. A scaling relationship based on bivariate regression r2 -values. Diminishing returns were also observed for the PFM vs. FM and PFM vs. A scaling relationships. The PFM vs. FM scaling relationship was statistically more robust than the PFM vs. A scaling relationship. CONCLUSIONS: "Diminishing returns" was confirmed among the FM, DM, A, and PFM scaling relationships. The data collectively indicate that the petiole scales mechanically more strongly with lamina mass than with area, suggesting that static (self) loading takes precedence over dynamic (wind) loading.

Motivating data contributions via a distinct career currency.

If collecting research data is perceived as poorly rewarded compared to data synthesis and analysis, this can slow overall research progress via two effects. People who have already collected data may be slow to make it openly accessible. Also, researchers may reallocate effort from collecting fresh data to synthesizing and analysing data already accessible. Here, we advocate for a second career currency in the form of data contributions statements embedded within applications for jobs, promotions and research grants. This workable step forward would provide for peer opinion to operate across thousands of selection and promotion committees and granting panels. In this way, fair valuation of data contributions relative to publications could emerge.

Leaf size estimation based on leaf length, width and shape.

BACKGROUND AND AIMS: Leaf size has considerable ecological relevance, making it desirable to obtain leaf size estimations for as many species worldwide as possible. Current global databases, such as TRY, contain leaf size data for ~30 000 species, which is only ~8% of known species worldwide. Yet, taxonomic descriptions exist for the large majority of the remainder. Here we propose a simple method to exploit information on leaf length, width and shape from species descriptions to robustly estimate leaf areas, thus closing this considerable knowledge gap for this important plant functional trait. METHODS: Using a global dataset of all major leaf shapes measured on 3125 leaves from 780 taxa, we quantified scaling functions that estimate leaf size as a product of leaf length, width and a leaf shape-specific correction factor. We validated our method by comparing leaf size estimates with those obtained from image recognition software and compared our approach with the widely used correction factor of 2/3. KEY RESULTS: Correction factors ranged from 0.39 for highly dissected, lobed leaves to 0.79 for oblate leaves. Leaf size estimation using leaf shape-specific correction factors was more accurate and precise than estimates obtained from the correction factor of 2/3. CONCLUSION: Our method presents a tractable solution to accurately estimate leaf size when only information on leaf length, width and shape is available or when labour and time constraints prevent usage of image recognition software. We see promise in applying our method to data from species descriptions (including from fossils), databases, field work and on herbarium vouchers, especially when non-destructive in situ measurements are needed.

Nondestructive estimation of leaf area for 15 species of vines with different leaf shapes.

PREMISE: The nondestructive measurement of leaf area is important for expediting data acquisition in the field. The Montgomery equation (ME) assumes that leaf area (A) is a proportional function of the product of leaf length (L) and width (W), i.e., A = cLW, where c is called the Montgomery parameter. The ME has been successfully applied to calculate the surface area of many broad-leaved species with simple leaf shapes. However, whether this equation is valid for more complex leaf shapes has not been verified. METHODS: Leaf A, L, and W were measured directly for each of 5601 leaves of 15 vine species, and ME and three other models were used to fit the data. All four models were compared based on their root mean square errors (RMSEs) to determine whether ME provided the best fit. RESULTS: The ME was a reliable method for estimating the A of all 15 species. In addition, the numerical values of 13 of the 15 values of c fell within a previously predicted numerical range (i.e., between 1/2 and π/4). The data show that the numerical values of c are largely affected by the value of W/L, the concavity of the leaf base, and the number of lobes on the lamina. CONCLUSIONS: The Montgomery parameter can reflect the influence of leaf shape on leaf-area calculations and can serve as an important tool for nondestructive measurements of leaf area for many broad-leaved species and for the investigation of leaf morphology.

Leaf-age and petiole biomass play significant roles in leaf scaling theory.

Foliage leaves are essential for plant survival and growth, and how plants allocate biomass to their leaves reveals their economic and ecological strategies. Prior studies have shown that leaf-age significantly influences leaf biomass allocation patterns. However, unravelling the effects of ontogeny on partitioning biomass remains a challenge because it is confounded by the effects of environmental factors. Here, we aim to elucidate whether leaf-age affects the allocation to the lamina and petiole by examining leaves of known age growing in the same general environmental context. We sampled 2698 Photinia serratifolia leaves developing in the same environment from April to November 2021, representing eight leaf-ages (n > 300 for each leaf-age). Petiole and lamina biomass, and lamina area were measured to evaluate the scaling relationships using reduced major axis regression protocols. The bootstrap percentile method was used to determine the differences in scaling exponents among the different leaf-ages. ANOVA with Tukey's HSD was used to compare the ratios of petiole and lamina biomass to lamina area across the leaf-ages. Correlation tests were used to determine if exponents, intercepts, and ratios differed significantly across the different leaf-ages. The data indicated that (i) the ratio of petiole and lamina biomass to lamina area and the scaling exponent of lamina biomass versus lamina area correlate positively with leaf-age, and (ii) the scaling exponent of petiole biomass versus lamina area correlates negatively with leaf-age. Leaf maturation process involves an inverse proportional allocation between lamina and petiole biomass for expanding photosynthetic area. This phenomenon underscores the effect of leaf-age on biomass allocation and the importance of adopting an ontogenetic perspective when entertaining plant scaling theories and unravelling the principles governing shifts in biomass allocation throughout the leaf lifespan.

Comparison of Two Simplified Versions of the Gielis Equation for Describing the Shape of Bamboo Leaves.

Bamboo is an important component in subtropical and tropical forest communities. The plant has characteristic long lanceolate leaves with parallel venation. Prior studies have shown that the leaf shapes of this plant group can be well described by a simplified version (referred to as SGE-1) of the Gielis equation, a polar coordinate equation extended from the superellipse equation. SGE-1 with only two model parameters is less complex than the original Gielis equation with six parameters. Previous studies have seldom tested whether other simplified versions of the Gielis equation are superior to SGE-1 in fitting empirical leaf shape data. In the present study, we compared a three-parameter Gielis equation (referred to as SGE-2) with the two-parameter SGE-1 using the leaf boundary coordinate data of six bamboo species within the same genus that have representative long lanceolate leaves, with >300 leaves for each species. We sampled 2000 data points at approximately equidistant locations on the boundary of each leaf, and estimated the parameters for the two models. The root−mean−square error (RMSE) between the observed and predicted radii from the polar point to data points on the boundary of each leaf was used as a measure of the model goodness of fit, and the mean percent error between the RMSEs from fitting SGE-1 and SGE-2 was used to examine whether the introduction of an additional parameter in SGE-1 remarkably improves the model's fitting. We found that the RMSE value of SGE-2 was always smaller than that of SGE-1. The mean percent errors among the two models ranged from 7.5% to 20% across the six species. These results indicate that SGE-2 is superior to SGE-1 and should be used in fitting leaf shapes. We argue that the results of the current study can be potentially extended to other lanceolate leaf shapes.

A nondestructive method of calculating the wing area of insects.

Most insects engage in winged flight. Wing loading, that is, the ratio of body mass to total wing area, has been demonstrated to reflect flight maneuverability. High maneuverability is an important survival trait, allowing insects to escape natural enemies and to compete for mates. In some ecological field experiments, there is a need to calculate the wing area of insects without killing them. However, fast, nondestructive estimation of wing area for insects is not available based on past work. The Montgomery equation (ME), which assumes a proportional relationship between leaf area and the product of leaf length and width, is frequently used to calculate leaf area of plants, in crops with entire linear, lanceolate leaves. Recently, the ME was proved to apply to leaves with more complex shapes from plants that do not have any needle leaves. Given that the wings of insects are similar in shape to broad leaves, we tested the validity of the ME approach in calculating the wing area of insects using three species of cicadas common in eastern China. We compared the actual area of the cicadas' wings with the estimates provided by six potential models used for wing area calculation, and we found that the ME performed best, based on the trade-off between model structure and goodness of fit. At the species level, the estimates for the proportionality coefficients of ME for three cicada species were 0.686, 0.693, and 0.715, respectively. There was a significant difference in the proportionality coefficients between any two species. Our method provides a simple and powerful approach for the nondestructive estimation of insect wing area, which is also valuable in quantifying wing morphological features of insects. The present study provides a nondestructive approach to estimating the wing area of insects, allowing them to be used in mark and recapture experiments.

Diminishing returns: A comparison between fresh mass vs. area and dry mass vs. area in deciduous species.

"Diminishing returns" in leaf economics occurs when increases in lamina mass (M), which can either be represented by lamina dry mass (DM) or fresh mass (FM), fail to produce proportional increases in leaf surface area (A), such that the scaling exponent (α) for the M vs. A scaling relationship exceeds unity (i.e., α > 1.0). Prior studies have shown that FM vs. A is better than DM vs A in assessing diminishing returns in evergreen species. However, the superiority of FM vs. A over DM vs. A has been less well examined for deciduous species. Here, we applied reduced major axis protocols to test whether FM vs. A is better than DM vs. A to describe the M vs. A scaling relationship, using a total of 4271 leaves from ten deciduous and two evergreen tree species in the Fagaceae and Ulmaceae for comparison. The significance of the difference between the scaling exponents of FM vs. A and DM vs. A was tested using the bootstrap percentile method. Further, we tested the non-linearity of the FM (DM) vs. A data on a log-log scale using ordinary least squares. We found that (i) the majority of scaling exponents of FM vs. A and DM vs. A were >1 thereby confirming diminishing returns for all 12 species, (ii) FM vs. A was more robust than DM vs. A to identify the M vs. A scaling relationship, (iii) the non-linearity of the allometric model was significant for both DM vs. A and FM vs. A., and (iv) the evergreen species of Fagaceae had significantly higher DM and FM per unit area than other deciduous species. In summary, FM vs. A is a more reliable measure than DM vs. A when dealing with diminishing returns, and deciduous species tend to invest less biomass in unit leaf light harvesting area than evergreen species.

'biogeom': An R package for simulating and fitting natural shapes.

Many natural objects exhibit radial or axial symmetry in a single plane. However, a universal tool for simulating and fitting the shapes of such objects is lacking. Herein, we present an R package called 'biogeom' that simulates and fits many shapes found in nature. The package incorporates novel universal parametric equations that generate the profiles of bird eggs, flowers, linear and lanceolate leaves, seeds, starfish, and tree-rings, and three growth-rate equations that generate the profiles of ovate leaves and the ontogenetic growth curves of animals and plants. 'biogeom' includes several empirical datasets comprising the boundary coordinates of bird eggs, fruits, lanceolate and ovate leaves, tree rings, seeds, and sea stars. The package can also be applied to other kinds of natural shapes similar to those in the datasets. In addition, the package includes sigmoid curves derived from the three growth-rate equations, which can be used to model animal and plant growth trajectories and predict the times associated with maximum growth rate. 'biogeom' can quantify the intra- or interspecific similarity of natural outlines, and it provides quantitative information of shape and ontogenetic modification of shape with important ecological and evolutionary implications for the growth and form of the living world.

A roadmap to plant functional island biogeography.

Island biogeography is the study of the spatio-temporal distribution of species, communities, assemblages or ecosystems on islands and other isolated habitats. Island diversity is structured by five classes of process: dispersal, establishment, biotic interactions, extinction and evolution. Classical approaches in island biogeography focused on species richness as the deterministic outcome of these processes. This has proved fruitful, but species traits can potentially offer new biological insights into the processes by which island life assembles and why some species perform better at colonising and persisting on islands. Functional traits refer to morphological and phenological characteristics of an organism or species that can be linked to its ecological strategy and that scale up from individual plants to properties of communities and ecosystems. A baseline hypothesis is for traits and ecological strategies of island species to show similar patterns as a matched mainland environment. However, strong dispersal, environmental and biotic-interaction filters as well as stochasticity associated with insularity modify this baseline. Clades that do colonise often embark on distinct ecological and evolutionary pathways, some because of distinctive evolutionary forces on islands, and some because of the opportunities offered by freedom from competitors or herbivores or the absence of mutualists. Functional traits are expected to be shaped by these processes. Here, we review and discuss the potential for integrating functional traits into island biogeography. While we focus on plants, the general considerations and concepts may be extended to other groups of organisms. We evaluate how functional traits on islands relate to core principles of species dispersal, establishment, extinction, reproduction, biotic interactions, evolution and conservation. We formulate existing knowledge as 33 working hypotheses. Some of these are grounded on firm empirical evidence, others provide opportunities for future research. We organise our hypotheses under five overarching sections. Section A focuses on plant functional traits enabling species dispersal to islands. Section B discusses how traits help to predict species establishment, successional trajectories and natural extinctions on islands. Section C reviews how traits indicate species biotic interactions and reproduction strategies and which traits promote intra-island dispersal. Section D discusses how evolution on islands leads to predictable changes in trait values and which traits are most susceptible to change. Section E debates how functional ecology can be used to study multiple drivers of global change on islands and to formulate effective conservation measures. Islands have a justified reputation as research models. They illuminate the forces operating within mainland communities by showing what happens when those forces are released or changed. We believe that the lens of functional ecology can shed more light on these forces than research approaches that do not consider functional differences among species.

Phosphorus fertilization is eradicating the niche of northern Eurasia's threatened plant species.

The greater bioavailability of nitrogen (N), phosphorus (P) and potassium (K) in the Anthropocene has strongly impacted terrestrial plant communities. In northwest Europe, because high N deposition is considered the main driver of plant diversity loss, European Union (EU) legislation to reduce N deposition is expected to promote plant species recovery. However, this expectation is simplistic: it ignores the role of other macronutrients. Analysing the relationship between plant species pools and species stoichiometric niches along nutrient gradients across northern Eurasia's herbaceous ecosystems, we found that both absolute and relative P availability are more critical than N or K availability. This result is consistent with stoichiometric niche theory, and with findings from studies of hyperdiverse forests and shrublands at lower latitudes. We show that ecosystems with low absolute and relative P availability harbour a unique set of threatened species that have narrower nutrient-based niche widths than non-threatened species. Such ecosystems represent a conservation priority, but may be further threatened by latent effects of relative P enrichment arising from reduction of N availability without simultaneous reduction of P. The narrow focus of EU legislation on reducing N, but not P, may therefore inadvertently increase the threat to many of Europe's already threatened plant species. An EU Phosphate Directive is needed.

Reducing Pesticides and Increasing Crop Diversification Offer Ecological and Economic Benefits for Farmers-A Case Study in Cambodian Rice Fields.

Rice production is often associated with high pesticide input. To improve farmers' practice, sustainable management approaches are urgently needed, such as ecological engineering (EE), which aims at enhancing beneficial arthropods while reducing pesticides. Here, we implemented and tested EE in Cambodian rice fields by comparing: (i) fields not treated with pesticides (control); (ii) fields not treated with pesticides but with non-rice crops planted in the surrounding (EE); and (iii) conventionally farmed fields using pesticides (CR). Using benefit-cost analysis, we compared the economic value of each treatment. The non-rice crops preferred by men and women farmers as well as farmers' willingness to implement EE were assessed using surveys. We sampled arthropod abundance and richness in rice fields and bunds during two seasons. During the dry season, we compared EE and CR among three Cambodian provinces. During the wet season, we specifically assessed the differences in EE, control and CR in arthropod abundance and rice yield in one province. While withholding from using pesticides did not result in a decrease in yield in EE and control treatments, parasitoid abundance was higher in both treatments during the wet season. The benefit-cost ratio was highest for EE and control treatments. Pesticides were likely the main driver causing low arthropod abundance, without any benefit towards increased rice yield. The proper implementation of EE coupled with farmers' knowledge of ecologically based pest management is a promising solution towards sustainable rice production.

A new dataset on plant occurrences on small islands, including species abundances and functional traits across different spatial scales.

BACKGROUND: We introduce a new dataset of woody plants on 60 small tropical islands located in the Raja Ampat archipelago in Indonesia. The dataset includes incidence, abundance and functional trait data for 57 species. All islands were sampled using a standardised transect and plot design providing detailed information on plant occurrences at different spatial scales ranging from the local (plot and transect scale) to the island scale. In addition, the dataset includes information on key plant functional traits linked to species dispersal, resource acquisition and competitive strategies. The dataset can be used to address ecological questions connected to the species-area relationship and community assembly processes on small islands and in isolated habitats. NEW INFORMATION: The dataset yields detailed information on plant community structure and links incidence, abundance and functional trait data at different spatial scales. Furthermore, this is the first plant-island dataset for the Raja Ampat archipelago, a remote and poorly studied region, and provides important new information on species occurrences.

Leaf-IT: An Android application for measuring leaf area.

The use of plant functional traits has become increasingly popular in ecological studies because plant functional traits help to understand key ecological processes in plant species and communities. This also includes changes in diversity, inter- and intraspecific interactions, and relationships of species at different spatiotemporal scales. Leaf traits are among the most important traits as they describe key dimensions of a plant's life history strategy. Further, leaf area is a key parameter with relevance for other traits such as specific leaf area, which in turn correlates with leaf chemical composition, photosynthetic rate, leaf longevity, and carbon investment. Measuring leaf area usually involves the use of scanners and commercial software and can be difficult under field conditions. We present Leaf-IT, a new smartphone application for measuring leaf area and other trait-related areas. Leaf-IT is free, designed for scientific purposes, and runs on Android 4 or higher. We tested the precision and accuracy using objects with standardized area and compared the area measurements of real leaves with the well-established, commercial software WinFOLIA using the Altman-Bland method. Area measurements of standardized objects show that Leaf-IT measures area with high accuracy and precision. Area measurements with Leaf-IT of real leaves are comparable to those of WinFOLIA. Leaf-IT is an easy-to-use application running on a wide range of smartphones. That increases the portability and use of Leaf-IT and makes it possible to measure leaf area under field conditions typical for remote locations. Its high accuracy and precision are similar to WinFOLIA. Currently, its main limitation is margin detection of damaged leaves or complex leaf morphologies.