Informing trait-based ecology by assessing remotely sensed functional diversity across a broad tropical temperature gradient

Plant height as an indicator for alpine carbon sequestration and ecosystem response to warming

Growing evidence indicates that plant community structure and traits have changed under climate warming, especially in cold or high-elevation regions. However, the impact of these warming-induced changes on ecosystem carbon sequestration remains unclear. Using a warming experiment on the high-elevation Qinghai-Tibetan Plateau, we found that warming not only increased plant species height but also altered species composition, collectively resulting in a taller plant community associated with increased net ecosystem productivity (NEP). Along a 1,500 km transect on the Plateau, taller plant community promoted NEP and soil carbon through associated chlorophyll content and other photosynthetic traits at the community level. Overall, plant community height as a dominant trait is associated with species composition and regulates ecosystem C sequestration in the high-elevation biome. This trait-based association provides new insights into predicting the direction, magnitude and sensitivity of ecosystem C fluxes in response to climate warming.

The functional diversity-productivity relationship of woody plants is climatically sensitive

Plot-scale experiments indicate that functional diversity (FD) plays a pivotal role in sustaining ecosystem functions such as net primary productivity (NPP). However, the relationships between functional diversity and NPP across larger scale under varying climatic conditions are sparsely studied, despite its significance for understanding forest-atmosphere interactions and informing policy development. Hence, we examine the relationships of community-weighted mean (CWM) and functional dispersion (FDis) of woody plant traits on NPP across China and if such relationships are modulated by climatic conditions at the national scale. Using comprehensive datasets of distribution, functional traits, and productivity for 9120 Chinese woody plant species, we evaluated the distribution pattern of community-weighted mean and functional dispersion (including three orthogonal trait indicators: plant size, leaf morphology, and flower duration) and its relationships with NPP. Finally, we tested the effects of climatic conditions on community-weighted mean/functional dispersion-NPP relationships. We first found overall functional diversity-NPP relationships, but also that the magnitude of these relationships was sensitive to climate, with plant size community-weighted mean promoting NPP in warm regions and plant size functional dispersion promoting NPP in wet regions. Second, warm and wet conditions indirectly increased NPP by its positive effects on community-weighted mean or functional dispersion, particularly through mean plant size and leaf morphology. Our study provides comprehensive evidence for the relationships between functional diversity and NPP under varying climates at a large scale. Importantly, our results indicate a broadening significance of multidimensional plant functional traits for woody vegetation NPP in response to rising temperatures and wetter climates. Restoration, reforestation actions and natural capital accounting need to carefully consider not only community-weighted mean and functional dispersion but also their interactions with climate, to predict how functional diversity may promote ecosystem functioning under future climatic conditions.

HAVOC: Small-scale histomic mapping of cancer biodiversity across large tissue distances using deep neural networks

Intratumoral heterogeneity can wreak havoc on current precision medicine strategies because of challenges in sufficient sampling of geographically separated areas of biodiversity distributed across centimeter-scale tumor distances. To address this gap, we developed a deep learning pipeline that leverages histomorphologic fingerprints of tissue to create "Histomic Atlases of Variation Of Cancers" (HAVOC). Using a number of objective molecular readouts, we demonstrate that HAVOC can define regional cancer boundaries with distinct biology. Using larger tumor specimens, we show that HAVOC can map biodiversity even across multiple tissue sections. By guiding profiling of 19 partitions across six high-grade gliomas, HAVOC revealed that distinct differentiation states can often coexist and be regionally distributed within these tumors. Last, to highlight generalizability, we benchmark HAVOC on additional tumor types. Together, we establish HAVOC as a versatile tool to generate small-scale maps of tissue heterogeneity and guide regional deployment of molecular resources to relevant biodiverse niches.

Prediction of spatial distribution characteristics of ecosystem functions based on a minimum data set of functional traits of desert plants

The relationship between plant functional traits and ecosystem function is a hot topic in current ecological research, and community-level traits based on individual plant functional traits play important roles in ecosystem function. In temperate desert ecosystems, which functional trait to use to predict ecosystem function is an important scientific question. In this study, the minimum data sets of functional traits of woody (wMDS) and herbaceous (hMDS) plants were constructed and used to predict the spatial distribution of C, N, and P cycling in ecosystems. The results showed that the wMDS included plant height, specific leaf area, leaf dry weight, leaf water content, diameter at breast height (DBH), leaf width, and leaf thickness, and the hMDS included plant height, specific leaf area, leaf fresh weight, leaf length, and leaf width. The linear regression results based on the cross-validations (FTEI_{W - L}, FTEI_{A - L}, FTEI_{W - NL}, and FTEI_{A - NL}) for the MDS and TDS (total data set) showed that the R² (coefficients of determination) for wMDS were 0.29, 0.34, 0.75, and 0.57, respectively, and those for hMDS were 0.82, 0.75, 0.76, and 0.68, respectively, proving that the MDSs can replace the TDS in predicting ecosystem function. Then, the MDSs were used to predict the C, N, and P cycling in the ecosystem. The results showed that non-linear models RF and BPNN were able to predict the spatial distributions of C, N and P cycling, and the distributions showed inconsistent patterns between different life forms under moisture restrictions. The C, N, and P cycling showed strong spatial autocorrelation and were mainly influenced by structural factors. Based on the non-linear models, the MDSs can be used to accurately predict the C, N, and P cycling, and the predicted values of woody plant functional traits visualized by regression kriging were closer to the kriging results based on raw values. This study provides a new perspective for exploring the relationship between biodiversity and ecosystem function.

Predicting leaf traits across functional groups using reflectance spectroscopy

Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits, but it remains unclear whether general trait-spectra models can yield accurate estimates across functional groups and ecosystems. We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 103 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems, mainly in eastern Canada. We used partial least-squares regression (PLSR) to build empirical models for estimating traits from spectra. Within the dataset, our PLSR models predicted traits such as leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy (R²  > 0.85; %RMSE < 10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy (R²  = 0.55-0.85; %RMSE = 12.7-19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits such as LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. We provide models that produce fast, reliable estimates of several functional traits from leaf spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.

Estimating Community-Level Plant Functional Traits in a Species-Rich Alpine Meadow Using UAV Image Spectroscopy

Plant functional traits at the community level (plant community traits hereafter) are commonly used in trait-based ecology for the study of vegetation–environment relationships. Previous studies have shown that a variety of plant functional traits at the species or community level can be successfully retrieved by airborne or spaceborne imaging spectrometer in homogeneous, species-poor ecosystems. However, findings from these studies may not apply to heterogeneous, species-rich ecosystems. Here, we aim to determine whether unmanned aerial vehicle (UAV)-based hyperspectral imaging could adequately estimate plant community traits in a species-rich alpine meadow ecosystem on the Qinghai–Tibet Plateau. To achieve this, we compared the performance of four non-parametric regression models, i.e., partial least square regression (PLSR), the generic algorithm integrated with the PLSR (GA-PLSR), random forest (RF) and extreme gradient boosting (XGBoost) for the retrieval of 10 plant community traits using visible and near-infrared (450–950 nm) UAV hyperspectral imaging. Our results show that chlorophyll a, chlorophyll b, carotenoid content, starch content, specific leaf area and leaf thickness were estimated with good accuracies, with the highest R2 values between 0.64 (nRMSE = 0.16) and 0.83 (nRMSE = 0.11). Meanwhile, the estimation accuracies for nitrogen content, phosphorus content, plant height and leaf dry matter content were relatively low, with the highest R2 varying from 0.3 (nRMSE = 0.24) to 0.54 (nRMSE = 0.20). Among the four tested algorithms, the GA-PLSR produced the highest accuracy, followed by PLSR and XGBoost, and RF showed the poorest performance. Overall, our study demonstrates that UAV-based visible and near-infrared hyperspectral imaging has the potential to accurately estimate multiple plant community traits for the natural grassland ecosystem at a fine scale.

Species-level tree crown maps improve predictions of tree recruit abundance in a tropical landscape

Predicting forest recovery at landscape scales will aid forest restoration efforts. The first step in successful forest recovery is tree recruitment. Forecasts of tree recruit abundance, derived from the landscape-scale distribution of seed sources (i.e., adult trees), could assist efforts to identify sites with high potential for natural regeneration. However, previous work revealed wide variation in the effect of seed sources on seedling abundance, from positive to no effect. We quantified the relationship between adult tree seed sources and tree recruits and predicted where natural recruitment would occur in a fragmented, tropical, agricultural landscape. We integrated species-specific tree crown maps generated from hyperspectral imagery and property ownership data with field data on the spatial distribution of tree recruits from five species. We then developed hierarchical Bayesian models to predict landscape-scale recruit abundance. Our models revealed that species-specific maps of tree crowns improved recruit abundance predictions. Conspecific crown area had a much stronger impact on recruitment abundance (8.00% increase in recruit abundance when conspecific tree density increases from zero to one tree; 95% credible interval (CI): 0.80% to 11.57%) than heterospecific crown area (0.03% increase with the addition of a single heterospecific tree, 95% CI: -0.60% to 0.68%). Individual property ownership was also an important predictor of recruit abundance: The best performing model had varying effects of conspecific and heterospecific crown area on recruit abundance, depending on individual property ownership. We demonstrate how novel remote sensing approaches and cadastral data can be used to generate high-resolution and landscape-level maps of tree recruit abundance. Spatial models parameterized with field, cadastral, and remote sensing data are poised to assist decision support for forest landscape restoration.

Functional Diversity and Its Influencing Factors in a Subtropical Forest Community in China

Functional diversity is considered a key link between ecosystem functions and biodiversity, and forms the basis for making community diversity conservation strategies. Here, we chose a subtropical forest community in China as the research object, which is unique in that other regions of the world at the same latitude have almost no vegetation cover. We measured 17 functional traits of 100 plant species and calculated seven different functional diversity indices, based on functional richness, evenness, and divergence. We found that most functional diversity and species diversity indices significantly differed with plant habit. There was a significant positive correlation among functional richness indices. However, functional divergence indices, multidimensional functional divergence (FDiv), and Rao’s quadratic entropy index (RaoQ) were significantly negatively correlated, and RaoQ and functional divergence indices (FDis) were uncorrelated. The correlations between three types (richness, evenness, and divergence) of functional diversity indices and three species diversity indices were different. Lineage regression results generally showed that three functional richness indices (Average distance of functional traits (MFAD), Functional volume (FRic) and Posteriori functional group richness (FGR)) were increased with three species diversity indices (species richness (S), Shannon-Wiener index (H) and Pielou index (E)). The functional evenness index (FEve) decreased with species richness (S), Shannon-Wiener index (H) and increased with species evenness (Pielou index (E)), but the change trends were small. All three types of functional diversity indices declined with altitude, although altitude had a weak influence on them. Other environmental factors affected the functional diversity of the community. Here, soil total phosphorus (TP) was the most critical environmental factor and the convex had the least effect on functional diversity in our subtropical forest community. These results will contribute to our understanding of functional diversity in subtropical forests, and provide a basis for biodiversity conservation in this region.

Linking Land Use and Plant Functional Diversity Patterns in Sabah, Borneo, through Large-Scale Spatially Continuous Sentinel-2 Inference

Global biodiversity losses erode the functioning of our vital ecosystems. Functional diversity is increasingly recognized as a critical link between biodiversity and ecosystem functioning. Satellite earth observation was proposed to address the current absence of information on large-scale continuous patterns of plant functional diversity. This study demonstrates the inference and spatial mapping of functional diversity metrics through satellite remote sensing over a large key biodiversity region (Sabah, Malaysian Borneo, ~53,000 km2) and compares the derived estimates across a land-use gradient as an initial qualitative assessment to test the potential merits of the approach. Functional traits (leaf water content, chlorophyll-a and -b, and leaf area index) were estimated from Sentinel-2 spectral reflectance using a pre-trained neural network on radiative transfer modeling simulations. Multivariate functional diversity metrics were calculated, including functional richness, divergence, and evenness. Spatial patterns of functional diversity were related to land-use data distinguishing intact forest, logged forest, and oil palm plantations. Spatial patterns of satellite remotely sensed functional diversity are significantly related to differences in land use. Intact forests, as well as logged forests, featured consistently higher functional diversity compared to oil palm plantations. Differences were profound for functional divergence, whereas functional richness exhibited relatively large variances within land-use classes. By linking large-scale patterns of functional diversity as derived from satellite remote sensing to land-use information, this study indicated initial responsiveness to broad human disturbance gradients over large geographical and spatially contiguous extents. Despite uncertainties about the accuracy of the spatial patterns, this study provides a coherent early application of satellite-derived functional diversity toward further validation of its responsiveness across ecological gradients.

Landscape analyses using eDNA metabarcoding and Earth observation predict community biodiversity in California

Ecosystems globally are under threat from ongoing anthropogenic environmental change. Effective conservation management requires more thorough biodiversity surveys that can reveal system-level patterns and that can be applied rapidly across space and time. Using modern ecological models and community science, we integrate environmental DNA and Earth observations to produce a time snapshot of regional biodiversity patterns and provide multi-scalar community-level characterization. We collected 278 samples in spring 2017 from coastal, shrub, and lowland forest sites in California, a complex ecosystem and biodiversity hotspot. We recovered 16,118 taxonomic entries from eDNA analyses and compiled associated traditional observations and environmental data to assess how well they predicted alpha, beta, and zeta diversity. We found that local habitat classification was diagnostic of community composition and distinct communities and organisms in different kingdoms are predicted by different environmental variables. Nonetheless, gradient forest models of 915 families recovered by eDNA analysis and using BIOCLIM variables, Sentinel-2 satellite data, human impact, and topographical features as predictors, explained 35% of the variance in community turnover. Elevation, sand percentage, and photosynthetic activities (NDVI32) were the top predictors. In addition to this signal of environmental filtering, we found a positive relationship between environmentally predicted families and their numbers of biotic interactions, suggesting environmental change could have a disproportionate effect on community networks. Together, these analyses show that coupling eDNA with environmental predictors including remote sensing data has capacity to test proposed Essential Biodiversity Variables and create new landscape biodiversity baselines that span the tree of life.

Landscape analyses using eDNA metabarcoding and Earth observation predict community biodiversity in California

Ecosystems globally are under threat from ongoing anthropogenic environmental change. Effective conservation management requires more thorough biodiversity surveys that can reveal system-level patterns and that can be applied rapidly across space and time. Using modern ecological models and community science, we integrate environmental DNA and Earth observations to produce a time snapshot of regional biodiversity patterns and provide multi-scalar community-level characterization. We collected 278 samples in spring 2017 from coastal, shrub, and lowland forest sites in California, a complex ecosystem and biodiversity hotspot. We recovered 16,118 taxonomic entries from eDNA analyses and compiled associated traditional observations and environmental data to assess how well they predicted alpha, beta, and zeta diversity. We found that local habitat classification was diagnostic of community composition and distinct communities and organisms in different kingdoms are predicted by different environmental variables. Nonetheless, gradient forest models of 915 families recovered by eDNA analysis and using BIOCLIM variables, Sentinel-2 satellite data, human impact, and topographical features as predictors, explained 35% of the variance in community turnover. Elevation, sand percentage, and photosynthetic activities (NDVI32) were the top predictors. In addition to this signal of environmental filtering, we found a positive relationship between environmentally predicted families and their numbers of biotic interactions, suggesting environmental change could have a disproportionate effect on community networks. Together, these analyses show that coupling eDNA with environmental predictors including remote sensing data has capacity to test proposed Essential Biodiversity Variables and create new landscape biodiversity baselines that span the tree of life.

The Potential of Mapping Grassland Plant Diversity with the Links among Spectral Diversity, Functional Trait Diversity, and Species Diversity

Mapping biodiversity is essential for assessing conservation and ecosystem services in global terrestrial ecosystems. Compared with remotely sensed mapping of forest biodiversity, that of grassland plant diversity has been less studied, because of the small size of individual grass species and the inherent difficulty in identifying these species. The technological advances in unmanned aerial vehicle (UAV)-based or proximal imaging spectroscopy with high spatial resolution provide new approaches for mapping and assessing grassland plant diversity based on spectral diversity and functional trait diversity. However, relatively few studies have explored the relationships among spectral diversity, remote-sensing-estimated functional trait diversity, and species diversity in grassland ecosystems. In this study, we examined the links among spectral diversity, functional trait diversity, and species diversity in a semi-arid grassland monoculture experimental site. The results showed that (1) different grassland plant species harbored different functional traits or trait combinations (functional trait diversity), leading to different spectral patterns (spectral diversity). (2) The spectral diversity of grassland plant species increased gradually from the visible (VIR, 400–700 nm) to the near-infrared (NIR, 700–1100 nm) region, and to the short-wave infrared (SWIR, 1100–2400 nm) region. (3) As the species richness increased, the functional traits and spectral diversity increased in a nonlinear manner, finally tending to saturate. (4) Grassland plant species diversity could be accurately predicted using hyperspectral data (R2 = 0.73, p < 0.001) and remotely sensed functional traits (R2 = 0.66, p < 0.001) using cluster algorithms. This will enhance our understanding of the effect of biodiversity on ecosystem functions and support regional grassland biodiversity conservation.

Remotely sensed between-individual functional trait variation in a temperate forest

Trait-based ecology holds the promise to explain how plant communities work, for example, how functional diversity may support community productivity. However, so far it has been difficult to combine field-based approaches assessing traits at the level of plant individuals with limited spatial coverage and approaches using remote sensing (RS) with complete spatial coverage but assessing traits at the level of vegetation pixels rather than individuals. By delineating all individual-tree crowns within a temperate forest site and then assigning RS-derived trait measures to these trees, we combine the two approaches, allowing us to use general linear models to estimate the influence of taxonomic or environmental variation on between- and within-species variation across contiguous space.We used airborne imaging spectroscopy and laser scanning to collect individual-tree RS data from a mixed conifer-angiosperm forest on a mountain slope extending over 5.5 ha and covering large environmental gradients in elevation as well as light and soil conditions. We derived three biochemical (leaf chlorophyll, carotenoids, and water content) and three architectural traits (plant area index, foliage-height diversity, and canopy height), which had previously been used to characterize plant function, from the RS data. We then quantified the contributions of taxonomic and environmental variation and their interaction to trait variation and partitioned the remaining within-species trait variation into smaller-scale spatial and residual variation. We also investigated the correlation between functional trait and phylogenetic distances at the between-species level. The forest consisted of 13 tree species of which eight occurred in sufficient abundance for quantitative analysis.On average, taxonomic variation between species accounted for more than 15% of trait variation in biochemical traits but only around 5% (still highly significant) in architectural traits. Biochemical trait distances among species also showed a stronger correlation with phylogenetic distances than did architectural trait distances. Light and soil conditions together with elevation explained slightly more variation than taxonomy across all traits, but in particular increased plant area index (light) and reduced canopy height (elevation). Except for foliage-height diversity, all traits were affected by significant interactions between taxonomic and environmental variation, the different responses of the eight species to the within-site environmental gradients potentially contributing to the coexistence of the eight abundant species.We conclude that with high-resolution RS data it is possible to delineate individual-tree crowns within a forest and thus assess functional traits derived from RS data at individual level. With this precondition fulfilled, it is then possible to apply tools commonly used in field-based trait ecology to partition trait variation among individuals into taxonomic and potentially even genetic variation, environmental variation, and interactions between the two. The method proposed here presents a promising way of assessing individual-based trait information with complete spatial coverage and thus allowing analysis of functional diversity at different scales. This information can help to better understand processes shaping community structure, productivity, and stability of forests.

A methodological roadmap to quantify animal-vectored spatial ecosystem subsidies

Energy, nutrients and organisms move over landscapes, connecting ecosystems across space and time. Meta-ecosystem theory investigates the emerging properties of local ecosystems coupled spatially by these movements of organisms and matter, by explicitly tracking exchanges of multiple substances across ecosystem borders. To date, meta-ecosystem research has focused mostly on abiotic flows-neglecting biotic nutrient flows. However, recent work has indicated animals act as spatial nutrient vectors when they transport nutrients across landscapes in the form of excreta, egesta and their own bodies. Partly due to its high level of abstraction, there are few empirical tests of meta-ecosystem theory. Furthermore, while animals may be viewed as important mediators of ecosystem functions, better integration of tools is needed to develop predictive insights of their relative roles and impacts on diverse ecosystems. We present a methodological roadmap that explains how to do such integration by discussing how to combine insights from movement, foraging and ecosystem ecology to develop a coherent understanding of animal-vectored nutrient transport on meta-ecosystems processes. We discuss how the slate of newly developed technologies and methods-tracking devices, mechanistic movement models, diet reconstruction techniques and remote sensing-that when integrated have the potential to advance the quantification of animal-vectored nutrient flows and increase the predictive power of meta-ecosystem theory. We demonstrate that by integrating novel and established tools of animal ecology, ecosystem ecology and remote sensing, we can begin to identify and quantify animal-mediated nutrient translocation by large animals. We also provide conceptual examples that show how our proposed integration of methodologies can help investigate ecosystem impacts of large animal movement. We conclude by describing practical advancements to understanding cross-ecosystem contributions of animals on the move. Understanding the mechanisms by which animals shape ecosystem dynamics is important for ongoing conservation, rewilding and restoration initiatives around the world, and for developing more accurate models of ecosystem nutrient budgets. Our roadmap will enable ecologists to better qualify and quantify animal-mediated nutrient translocation for animals on the move.

A Remote Sensing Approach to Understanding Patterns of Secondary Succession in Tropical Forest

Monitoring biodiversity on a global scale is a major challenge for biodiversity conservation. Field assessments commonly used to assess patterns of biodiversity and habitat condition are costly, challenging, and restricted to small spatial scales. As ecosystems face increasing anthropogenic pressures, it is important that we find ways to assess patterns of biodiversity more efficiently. Remote sensing has the potential to support understanding of landscape-level ecological processes. In this study, we considered cacao agroforests at different stages of secondary succession, and primary forest in the Northern Range of Trinidad, West Indies. We assessed changes in tree biodiversity over succession using both field data, and data derived from remote sensing. We then evaluated the strengths and limitations of each method, exploring the potential for expanding field data by using remote sensing techniques to investigate landscape-level patterns of forest condition and regeneration. Remote sensing and field data provided different insights into tree species compositional changes, and patterns of alpha- and beta-diversity. The results highlight the potential of remote sensing for detecting patterns of compositional change in forests, and for expanding on field data in order to better understand landscape-level patterns of forest diversity.

Exploring the Impact of Noise on Hybrid Inversion of PROSAIL RTM on Sentinel-2 Data

Remote sensing (RS) of biophysical variables plays a vital role in providing the information necessary for understanding spatio-temporal dynamics in ecosystems. The hybrid approach to retrieve biophysical variables from RS by combining Machine Learning (ML) algorithms with surrogate data generated by Radiative Transfer Models (RTM). The susceptibility of the ill-posed solutions to noise currently constrains further application of hybrid approaches. Here, we explored how noise affects the performance of ML algorithms for biophysical trait retrieval. We focused on synthetic Sentinel-2 (S2) data generated using the PROSAIL RTM and four commonly applied ML algorithms: Gaussian Processes (GPR), Random Forests (RFR), and Artificial Neural Networks (ANN) and Multi-task Neural Networks (MTN). After identifying which biophysical variables can be retrieved from S2 using a Global Sensitivity Analysis, we evaluated the performance loss of each algorithm using the Mean Absolute Percentage Error (MAPE) with increasing noise levels. We found that, for S2 data, Carotenoid concentrations are uniquely dependent on band 2, Chlorophyll is almost exclusively dependent on the visible ranges, and Leaf Area Index, water, and dry matter contents are mostly dependent on infrared bands. Without added noise, GPR was the best algorithm (<0.05%), followed by the MTN (<3%) and ANN (<5%), with the RFR performing very poorly (<50%). The addition of noise critically affected the performance of all algorithms (>20%) even at low levels of added noise (≈5%). Overall, both neural networks performed significantly better than GPR and RFR when noise was added with the MTN being slightly better when compared to the ANN. Our results imply that the performance of the commonly used algorithms in hybrid-RTM inversion are pervasively sensitive to noise. The implication is that more advanced models or approaches are necessary to minimize the impact of noise to improve near real-time and accurate RS monitoring of biophysical trait retrieval.

Ecological convergence of secondary phytochemicals along elevational gradients

Biologists still strive to identify the ecological and evolutionary drivers of phytochemical variation that mediate biotic interactions. We hypothesized that plant species growing at sites characterized by high herbivore pressure would converge to produce highly toxic blends of secondary metabolites, independent of phylogenetic constraints. To address the role of shared evolutionary history and ecological niches in driving variation in plant phytochemistry, we combined targeted metabolomics with insect herbivore bioassays and with a set of growth-related traits of several Cardamine species growing along the entire elevational gradient of the Alps. We observed that Cardamine phytochemical profiles grouped according to previously established growth form categorizations within specific abiotic conditions, independently of phylogenetic relationship. We also showed that novel indices summarizing functional phytochemical diversity better explain plant resistance against chewing and sap-feeding herbivores than classic diversity indices. We conclude that multiple functional axes of phytochemical diversity should be integrated with the functional axis of plant growth forms to study phenotypic convergence along large-scale ecological gradients.