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Agroforestry systems (AFS) are important for biodiversity conservation outside protected areas. The presence of shade trees in AFS form structurally complex habitats that provide food for many species. Habitat complexity is considered an essential biodiversity variable and its characterization is now possible using remote sensing techniques, including 3D point clouds derived from images obtained with unmanned aerial vehicles (UAVs). However, studies evaluating the relationship between canopy structure and variables derived from 3D clouds are rare for AFS, especially for the tropical Andes. Here, we show how six important variables of canopy structure can be predicted across a canopy structure gradient from AFS with cacao and coffee to a natural forest using characteristics extracted from the 3D point clouds and multiple linear regression. For leaf area index the best model obtained an R² of 0.82 with a relative RMSE = 24%, for canopy cover an R² of 0.81 and relative RMSE = 13%, for above-ground biomass (AGB) an R² of 0.81 and relative RMSE = 10%, the density of shade trees was predicted with an R² of 0.66 and relative RMSE = 34%, the mean height and the standard deviation of height in the canopy obtained an R² of 0.82 and 0.79 respectively, and relative RMSE of 18% for both. The approach presented in this study allows an accurate characterization of the canopy structure of AFS using UAVs, which can be useful for assessing above-ground biomass and biodiversity in tropical agricultural landscapes to monitor sustainable management practices and derive payments for ecosystem services.

En años recientes, R se ha vuelto una herramienta indispensable para el análisis de datos en muchas áreas de las ciencias básicas, aplicadas y en la industria. En lugar de aprender múltiples herramientas, los estudiantes pueden utilizar un entorno único para una gran diversidad de métodos de análisis de datos. Más que un lenguaje de programación, R ofrece nuevas vías para la enseñanza de la estadística, debido a que los problemas de análisis de datos pueden ser desarrollados paso a paso y analizados de manera transparente y reproducible, p.ej. usando R Markdown. Esto permite un aprendizaje interactivo y orientado a resolver problemas, combinando la teoría y la práctica.

In the Colombian Andes, agroforestry is a traditional form of agriculture, characterized by a heterogeneous and often diversified composition of trees and crops. This form of land use provides important ecosystem services, such as carbon sequestration, reduction of soil erosion and the maintenance of biodiversity by providing a structural complex habitat. Satellite remote sensing is widely used for studying land use patterns and forest cover, however the discrimination between agroforestry systems and forests is still a challenge, especially in heterogeneous landscapes and in rough terrain. Here, we aim to advance the remote sensing of agroforestry systems using field measurements of vegetation structure in combination with Sentinel-2 images. We use spectral and textural variables derived from Sentinel-2 imagery to predict above ground biomass (AGB), leaf area index (LAI) and canopy closure (CC). The relationship between predicted and observed values obtained from Random Forest regression models showed good fits: for AGB with an R2 = 0.92 and relative RMSE = 34%; for LAI with an R2 = 0.91 and relative RMSE = 19%; and for CC an R2 = 0.89 and relative RMSE = 9%. This allowed us to map these important ecosystem variables at landscape scale and establish empirical thresholds, with which a discrimination of agroforestry systems from forests was possible with an accuracy of 94%. Our results suggest that the relationship between vegetation structure and the spectral information obtained by Sentinel-2 can contribute to the detection and characterization of agroforestry systems and thus help quantifying the ecosystem services and biodiversity conservation potential provided by this type of tropical agriculture.

Plant functional traits can predict community assembly and ecosystem functioning and are thus widely used in global models of vegetation dynamics and land–climate feedbacks. Still, we lack a global understanding of how land and climate affect plant traits. A previous global analysis of six traits observed two main axes of variation: (1) size variation at the organ and plant level and (2) leaf economics balancing leaf persistence against plant growth potential. The orthogonality of these two axes suggests they are differently influenced by environmental drivers. We find that these axes persist in a global dataset of 17 traits across more than 20,000 species. We find a dominant joint effect of climate and soil on trait variation. Additional independent climate effects are also observed across most traits, whereas independent soil effects are almost exclusively observed for economics traits. Variation in size traits correlates well with a latitudinal gradient related to water or energy limitation. In contrast, variation in economics traits is better explained by interactions of climate with soil fertility. These findings have the potential to improve our understanding of biodiversity patterns and our predictions of climate change impacts on biogeochemical cycles., Nature Ecology & Evolution, 6 (1), ISSN:2397-334X

1. Canopy structure is an important driver of the energy budget of grassland ecosystem and is, at the same time, altered by plant diversity. Diverse plant communities typically have taller and more densely packed canopies than less diverse communities. With this, they absorb more radiation, have a higher transpiring leaf surface and are better coupled to the atmosphere which leads to cooler canopy surfaces. However, whether plant diversity generally translates into a cooling potential remains unclear and lacks empirical evidence. Here, we assessed how functional identity, functional diversity, and species richness of grassland communities in the Jena Experiment predict the mean and variation of plant surface temperature mediated via effects of canopy structure. 2. Using terrestrial laser scanning, we estimated canopy structure describing metrics of vertical structure (mean height, LAI), the distribution (evenness), and the highest allocation (center of gravity) of biomass along height strata. As metrics of horizontal structure, we considered community stand gaps, canopy surface variation and emergent flowers. We measured surface temperature with a thermal camera. We used SEM models to predict biodiversity effects on the surface temperature during two seasonal peaks of biomass. 3. Before the first cut in May, herb‐dominated communities directly promoted lower leaf surface temperatures. However, communities with lower center of gravity (mostly herb‐dominated) also increased canopy surface temperatures compared with grass‐dominated communities with higher biomass stored in the top canopy. Grass‐dominated communities showed a smaller variation of surface temperatures, which was also positively affected by species richness via an increase in mean height. In August, mean surface temperature decreased with increasing community clumpiness and LAI. The variation of surface temperature was greater in herb‐dominated than in grass‐dominated communities and increased with plant species richness (direct effects). 4. Synthesis: The mean and variation of canopy surface temperature were driven by differences in functional group composition (herbs‐ vs. grass dominance) and to a lesser extent by plant diversity. These effects were partly mediated the metrics of canopy structure but also by direct effects unrelated to the structural metrics considered., Journal of Ecology, 109 (5), ISSN:0022-0477

SummaryBackground and AimsCarbohydrate reserves play a vital role in plant survival during periods of negative carbon balance. Considering active storage of reserves, there is a trade-off between carbon allocation to growth and to reserves and defense. A resulting hypothesis is that allocation to reserves exhibits a coordinated variation with functional traits associated with the ‘fast-slow’ plant economics spectrum.MethodsWe tested the relationship between non-structural carbohydrates (NSC) of tree organs and functional traits using 61 angiosperm tree species from temperate and tropical forests with phylogenetic hierarchical Bayesian models.Key ResultsOur results provide evidence that NSC concentrations in woody organs and plant functional traits are largely decoupled, meaning that species’ resilience is unrelated to their position on the ‘fast-slow’ plant economics spectrum. In contrast, we found that variation between NSC concentrations in leaves and the fast-slow continuum was coordinated, as species with higher leaf NSC had traits values associated with resource conservative species such as lower SLA, lower Amax, and high wood density. We did not detect an influence of leaf habit on the variation of NSC concentrations in tree organs.ConclusionsEfforts to predict the response of ecosystems to global change will need to integrate a suite of plant traits, such as NSC concentrations in woody organs, that are independent of the ‘fast-slow’ spectrum and that capture how species respond to a broad range of global change factors.

Habitat fragmentation, the transformation of interconnected large habitats into several small patches, is generally considered to have a negative influence on biodiversity. Management policies should therefore favour strategies that minimize fragmentation. We studied amphibian diversity and compositional patterns in a complex montane agroforestry matrix of north-eastern Colombia to assess whether small-scale agroforestry is a viable way of reconciling crop production, farming, and biodiversity conservation. A total of five different land-use systems, including natural cloud forest, were compared. We used standardized visual and acoustic transect sampling routines in combination with field-based habitat assessments to establish amphibian assemblage data sets and corresponding habitat templets. Data sets were analysed using classical species richness and diversity statistics in combination with multivariate procedures to address compositional aspects. PER(mutational)MANOVA models were applied to identify environmental and land-use type-specific drivers of shifts in assemblage composition. Results show that agricultural management has a strong influence on amphibian species richness and diversity. With the exception of high intensity cattle pasture, diversity was generally highest in agriculturally used sites particularly in shaded plantations. Species composition differed markedly between different land-use types, indicating that there was not a single best land-use type that supported all species. This highlights the need for a landscape approach to biodiversity conservation in fragmented forests and consequently requires refocusing on conservation in management units as well as in the matrix. In human modified montane forest landscapes small-scale agriculture that generates landscape heterogeneity should therefore be promoted to effectively safeguard biodiversity.

With a growing human population facing multiple global change drivers (i.e. climate change and land management change), the future of food security is of major importance. Sustainable agriculture is therefore key to ensure food supply and food security under future climatic conditions. Forage provision (composed of forage quantity and forage quality) is an important ecosystem service of grasslands for dairy production. However, monitoring forage quality in semi-natural species-rich grasslands is rarely done due to the inherent complexity in determining forage quality, high variability within natural systems and financial and workload restrictions. Here, we i) demonstrate the ability of visible-near-infrared spectroscopy (vis-NIRS) to predict forage quality of bulk samples of species-rich montane pastures and ii) show its potential to reveal effects of two key global change drivers, climate change and land management, on forage quality. Spectral information and chemometrics allowed us to predict three (ash, fat and protein) out of four analyzed forage quality parameters with high accuracy. Land management intensity strongly influenced species-rich grasslands’ protein and fat content, whereas altered climatic conditions influenced ash and fat content. High management intensity increased protein content of high- and mid-elevation pastures by 22 % and 30 % and fat content by 19 % and 20 % respectively. Though forage quality was improved by intensive land management, extensive land management generally revealed sufficient forage quality for livestock. Vis-NIRS provides a rapid, cost-efficient and high-throughput technique to analyze forage quality, revealing effects of global change drivers on forage quality of grasslands. This approach will help to support stakeholders assure optimal nutrition feeding of livestock and achieve steps towards sustainable agriculture.

Biodiversity often enhances ecosystem functioning likely due to multiple, often temporarily separated drivers. Yet, most studies are based on one or two snapshot measurements per year. We estimated productivity using bi-weekly estimates of high-resolution canopy height in 2 years with terrestrial laser scanning (TLS) in a grassland diversity experiment. We measured how different facets of plant diversity (functional dispersion [FDis], functional identity [PCA species scores], and species richness [SR]) predict aboveground biomass over time. We found strong intra- and inter-annual variability in the relative importance of different mechanisms underlying the diversity effects on mean canopy height, i.e., resource partitioning (via FDis) and identity effects (via species scores), respectively. TLS is a promising tool to quantify community development non-destructively and to unravel the temporal dynamics of biodiversity-ecosystem functioning mechanisms. Our results show that harvesting at estimated peak biomass—as done in most grassland experiments—may miss important variation in underlying mechanisms driving cumulative biomass production.

The allocation of non-structural carbohydrates (NSCs) to reserves constitutes an important physiological mechanism associated with tree growth and survival. However, procedures for measuring NSC in plant tissue are expensive and time-consuming. Near-infrared spectroscopy (NIRS) is a high-throughput technology that has the potential to infer the concentration of organic constituents for a large number of samples in a rapid and inexpensive way based on empirical calibrations with chemical analysis. The main objectives of this study were (i) to develop a general NSC concentration calibration that integrates various forms of variation such as tree species and tissue types and (ii) to identify characteristic spectral regions associated with NSC molecules. In total, 180 samples from different tree organs (root, stem, branch, leaf) belonging to 73 tree species from tropical and temperate biomes were analysed. Statistical relationships between NSC concentration and NIRS spectra were assessed using partial least squares regression (PLSR) and a variable selection procedure (competitive adaptive reweighted sampling, CARS), in order to identify key wavelengths. Parsimonious and accurate calibration models were obtained for total NSC (r2 of 0·91, RMSE of 1·34% in external validation), followed by starch (r2 = 0·85 and RMSE = 1·20%) and sugars (r2 = 0·82 and RMSE = 1·10%). Key wavelengths coincided among these models and were mainly located in the 1740-1800, 2100-2300 and 2410-2490 nm spectral regions. This study demonstrates the ability of general calibration model to infer NSC concentrations across species and tissue types in a rapid and cost-effective way. The estimation of NSC in plants using NIRS therefore serves as a tool for functional biodiversity research, in particular for the study of the growth-survival trade-off and its implications in response to changing environmental conditions, including growth limitation and mortality. © 2015 British Ecological Society.

Aim Projections of future climate change suggest that regional climates may evolve to states that are unlike any climate regime found on Earth today. These climates will impose novel constraints on plant species, and are likely to give rise to plant associations that are compositionally unlike any found on Earth today. Here, we explore whether the geographical distribution of previously mapped no-analogue climates corresponds to the geographical distribution of simulated no-analogue vegetation under scenarios of global warming. Location Global landmasses. Methods We used JeDi, a process-based vegetation model that accounts for ecophysiological trade-offs in plant growth and survival,to identify the assembly of plant functional types into no-analogue associations under scenarios of global warming.We compared the geographical distribution of these no-analogue vegetation types with those of no-analogue climates derived from seasonal temperature andprecipitation.Tobetterunderstandtheclimaticcausesthatleadtono-analogue vegetation, we performed a set of JeDi simulation experiments and compared them, as well as selected climate indices, with the geographical distribution of no-analogue vegetation. Finally, we explored the changes in plant characteristics leading to no-analogue vegetation composition. Results In our model simulations, a no-analogue vegetation type emerged in Northern Eurasia due to the interacting effects of rising temperatures and the prolongation of the growing season, combined with stable patterns in the seasonal insolation cycle. Future tropical biomes experiencing novel temperature and precipitation regimes however, resemble contemporary vegetation despite significant losses of plant diversity. Main conclusions Our modelling study shows how no-analogue vegetation can emerge in response to novel climates produced by rising temperatures and stable insolation, while also suggesting that no-analogue climates do not necessarily lead to no-analogue vegetation types. This result underlines the importance of considering plant diversity and the need to integrate ecophysiological knowledge through process-orientated models when projecting future vegetation.

After more than 50-years of armed conflict, Colombia is now transitioning to a more stable social and political climate due to a series of peace agreements between the government and different armed groups. Consequences of these socio-economic and political changes on ecosystems are largely uncertain, but there is growing concern about derived increases in environmental degradation. Here, we review the capacity of Colombia to monitor the state of its ecosystems and their rate of change over time. We found several important programs currently set in place by different institutions as well as by independent groups of scientists that address different aspects of environmental monitoring. However, most of the current initiatives could be improved in terms of data coverage, quality and access, and could be better articulated among each other. We propose a set of activities that would increase the capacity of Colombia to monitor its ecosystems, provide useful information to policy makers, and facilitate scientific research. These include: 1) the establishment of a national center for ecological synthesis that focuses on analyzing existing information; 2) the establishment of an ecological observatory system that collects new information, integrates remote sensing products, and produces near real-time products on key ecological variables; and 3) the creation of new platforms for dialog and action within existing scientific and policy groups. © 2017 The Authors

Plant traits – the morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs – determine how primary producers respond to environmental factors, affect other trophic levels, influence ecosystem processes and services and provide a link from species richness to ecosystem functional diversity. Trait data thus represent the raw material for a wide range of research from evolutionary biology, community and functional ecology to biogeography. Here we present the global database initiative named TRY, which has united a wide range of the plant trait research community worldwide and gained an unprecedented buy-in of trait data: so far 93 trait databases have been contributed. The data repository currently contains almost three million trait entries for 69 000 out of the world's 300 000 plant species, with a focus on 52 groups of traits characterizing the vegetative and regeneration stages of the plant life cycle, including growth, dispersal, establishment and persistence. A first data analysis shows that most plant traits are approximately log-normally distributed, with widely differing ranges of variation across traits. Most trait variation is between species (interspecific), but significant intraspecific variation is also documented, up to 40% of the overall variation. Plant functional types (PFTs), as commonly used in vegetation models, capture a substantial fraction of the observed variation – but for several traits most variation occurs within PFTs, up to 75% of the overall variation. In the context of vegetation models these traits would better be represented by state variables rather than fixed parameter values. The improved availability of plant trait data in the unified global database is expected to support a paradigm shift from species to trait-based ecology, offer new opportunities for synthetic plant trait research and enable a more realistic and empirically grounded representation of terrestrial vegetation in Earth system models.

Aim: Two of the oldest observations in plant geography are the increase in plant diversity from the poles towards the tropics and the global geographic distribution of vegetation physiognomy (biomes). The objective of this paper is to use a process-based vegetation model to evaluate the relationship between modelled and observed global patterns of plant diversity and the geographic distribution of biomes. Location: The global terrestrial biosphere. Methods: We implemented and tested a novel vegetation model aimed at identifying strategies that enable plants to grow and reproduce within particular climatic conditions across the globe. Our model simulates plant survival according to the fundamental ecophysiological processes of water uptake, photosynthesis, reproduction and phenology. We evaluated the survival of an ensemble of 10,000 plant growth strategies across the range of global climatic conditions. For the simulated regional plant assemblages we quantified functional richness, functional diversity and functional identity. Results: A strong relationship was found (correlation coefficient of 0.75) between the modelled and the observed plant diversity. Our approach demonstrates that plant functional dissimilarity increases and then saturates with increasing plant diversity. Six of the major Earth biomes were reproduced by clustering grid cells according to their functional identity (mean functional traits of a regional plant assemblage). These biome clusters were in fair agreement with two other global vegetation schemes: a satellite image classification and a biogeography model (kappa statistics around 0.4). Main conclusions: Our model reproduces the observed global patterns of plant diversity and vegetation physiognomy from the number and identity of simulated plant growth strategies. These plant growth strategies emerge from the first principles of climatic constraints and plant functional trade-offs. Our study makes important contributions to furthering the understanding of how climate affects patterns of plant diversity and vegetation physiognomy from a process-based rather than a phenomenological perspective.

Earth is home to a remarkable diversity of plant forms and life histories, yet comparatively few essential trait combinationshave proved evolutionarily viable in today?s terrestrial biosphere. By analysing worldwide variation in six major traitscritical to growth, survival and reproduction within the largest sample of vascular plant species ever compiled, we foundthat occupancy of six-dimensional trait space is strongly concentrated, indicating coordination and trade-offs. Threequartersof trait variation is captured in a two-dimensional global spectrum of plant form and function. One majordimension within this plane reflects the size of whole plants and their parts; the other represents the leaf economicsspectrum, which balances leaf construction costs against growth potential. The global plant trait spectrum provides abackdrop for elucidating constraints on evolution, for functionally qualifying species and ecosystems, and for improvingmodels that predict future vegetation based on continuous variation in plant form and function. Fil: Díaz, Sandra Myrna. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; Argentina Fil: Kattge, Jens. Max Planck Institute for Biogeochemistry; Alemania Fil: Cornelissen, Johannes H. C.. Vrije Universiteit; Países Bajos Fil: Wright, Ian J.. Macquarie University; Australia Fil: Lavorel, Sandra. Université Grenoble Alpes; Francia Fil: Dray, Stéphane. Université Lyon; Francia Fil: Reu, Björn. University of Leipzig; Francia Fil: Kleyer, Michael. University of Oldenburg; Alemania Fil: Wirth, Christian. Max Planck Institute for Biogeochemistry; Alemania Fil: Prentice, I. Colin. Macquarie University; Australia Fil: Garnier, Eric. Université Paul-Valéry Montpellier; Francia Fil: Bönisch, Gerhard. Max Planck Institute for Biogeochemistry; Alemania Fil: Westoby, Mark. Macquarie University; Australia Fil: Poorter, Hendrik. Forschungszentrum Jülich GmbH; Alemania Fil: Reich, Peter B.. University of Minnesota; Estados Unidos. University of Western Sydney; Australia Fil: Moles, Angela T.. University of New South Wales; Australia Fil: Dickie, John. The Royal Botanic Gardens Kew; Reino Unido Fil: Gillison, Andrew N.. Center for Biodiversity Management; Australia Fil: Zanne, Amy E.. George Washington University; Estados Unidos. Missouri Botanical Garden; Estados Unidos Fil: Chave, Jérôme. Université Paul Sabatier; Francia Fil: Wright, S. Joseph. Smithsonian Tropical Research Institute; Estados Unidos Fil: Sheremetev, Serge N.. Komarov Botanical Institute; Rusia Fil: Jactel, Hervé. Université de Bordeaux; Francia Fil: Baraloto, Christopher. Florida International University; Estados Unidos. Ecologie des Forêts de Guyane; Guayana Francesa Fil: Cerabolini, Bruno. University of Insubria; Italia Fil: Pierce, Simon. University of Milan; Italia Fil: Shipley, Bill. Université de Sherbrooke; Canadá Fil: Kirkup, Donald. The Royal Botanic Gardens Kew; Reino Unido Fil: Casanoves, Fernando. Centro Agronómico Tropical de Investigación y Enseñanza; Costa Rica Fil: Joswig, Julia S.. Max Planck Institute for Biogeochemistry; Alemania Fil: Günther, Angela. Max Planck Institute for Biogeochemistry; Alemania Fil: Falczuk, Valeria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; Argentina Fil: Rüger, Nadja. German Centre for Integrative Biodiversity Research; Alemania Fil: Mahecha, Miguel D.. Max Planck Institute for Biogeochemistry; Alemania Fil: Gorne, Lucas Damián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; Argentina

A species’ climate niche summarizes the observed climatic conditions at its range limits. This information can be used to predict range shifts of species under climate change, but it does not explain why they occur under a given climate or are absent from another. Functional traits associated with the climate niche, however, allow for such an explanation. We show that key plant functional traits predict the climate ranges of North American trees and discuss the underlying filter mechanisms that define “no-go areas” for specific trait expressions. This approach replaces species idiosyncrasy by the generality of traits, puts biogeography on more functional grounds, and yields products that will serve the improvement of next generation global vegetation models. Using functional traits to explain species’ range limits is a promising approach in functional biogeography. It replaces the idiosyncrasy of species-specific climate ranges with a generic trait-based predictive framework. In addition, it has the potential to shed light on specific filter mechanisms creating large-scale vegetation patterns. However, its application to a continental flora, spanning large climate gradients, has been hampered by a lack of trait data. Here, we explore whether five key plant functional traits (seed mass, wood density, specific leaf area (SLA), maximum height, and longevity of a tree)—indicative of life history, mechanical, and physiological adaptations—explain the climate ranges of 250 North American tree species distributed from the boreal to the subtropics. Although the relationship between traits and the median climate across a species range is weak, quantile regressions revealed strong effects on range limits. Wood density and seed mass were strongly related to the lower but not upper temperature range limits of species. Maximum height affects the species range limits in both dry and humid climates, whereas SLA and longevity do not show clear relationships. These results allow the definition and delineation of climatic “no-go areas” for North American tree species based on key traits. As some of these key traits serve as important parameters in recent vegetation models, the implementation of trait-based climatic constraints has the potential to predict both range shifts and ecosystem consequences on a more functional basis. Moreover, for future trait-based vegetation models our results provide a benchmark for model evaluation.

The adaptation of plant species to their biotic and abiotic environment is manifested in their traits. Suites of correlated functional traits may reflect fundamental tradeoffs and general plant strategies and hence represent trait spectra along which plant species can vary according to their respective strategies. However, the functional interpretation of these trait spectra requires the inspection of their relation to plant performance. We employed principle coordinate analysis (PCoA) to quantify fundamental whole-plant trait spectra based on 23 traits for 305 North American woody species that span boreal to subtropical climates. We related the major axes of PCoA to five measures of plant performance (i.e., growth rate, and tolerance to drought, shade, water-logging and fire) for all species and separately for gymnosperms and angiosperms. Across all species a unified gymnosperm-angiosperm trait spectrum (wood density, seed mass, rooting habit) is identified, which is correlated with drought tolerance. Apart from this, leaf type and specific leaf area (SLA) strongly separate gymnosperms from angiosperms. For gymnosperms, one trait spectrum emerges (seed mass, rooting habit), which is positively correlated with drought tolerance and inversely with shade tolerance, reflecting a tradeoff between these two strategies due to opposing trait characteristics. Angiosperms are functionally more diverse. The trait spectra related to drought tolerance and shade tolerance are decoupled and three distinct strategies emerge: high drought tolerance (low SLA, dense wood, heavy seeds, taproot), high shade tolerance (high SLA, shallow roots, high toxicity, opposite arranged leaves), and fast growth/stress intolerance (large maximum heights, soft wood, light seeds, high seed spread rate). In summary, our approach reveals that complex suits of traits and potential tradeoffs underlie fundamental performance strategies in forests. Studies relying on small sets of plant traits may not be able to reveal such underlying strategies. Read More: http://www.esajournals.org/doi/abs/10.1890/ES13-00143.1