Future global productivity will be affected by plant trait response to climate

Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset

Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for > 700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R2 (0.12-0.49, 0.15-0.42, and 0.25-0.56) and increased NRMSE (3.58-18.24%, 6.27-11.55%, and 7.0-33.12%) compared with nonspatial random cross-validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.

Plant size traits are key contributors in the spatial variation of net primary productivity across terrestrial biomes in China

Understanding the spatial variability of ecosystem functions is an important step forward in predicting changes in ecosystems under global transformations. Plant functional traits are important drivers of ecosystem functions such as net primary productivity (NPP). Although trait-based approaches have advanced rapidly, the extent to which specific plant functional traits are linked to the spatial diversity of NPP at a regional scale remains uncertain. Here, we used structural equation models (SEMs) to disentangle the relative effects of abiotic variables (i.e., climate, soil, nitrogen deposition, and human footprint) and biotic variables (i.e., plant functional traits and community structure) on the spatial variation of NPP across China and its eight biomes. Additionally, we investigated the indirect influence of climate and soil on the spatial variation of NPP by directly affecting plant functional traits. Abiotic and biotic variables collectively explained 62.6 % of the spatial differences of NPP within China, and 28.0 %-69.4 % across the eight distinct biomes. The most important abiotic factors, temperature and precipitation, had positive effects for NPP spatial variation. Interestingly, plant functional traits associated with the size of plant organs (i.e., plant height, leaf area, seed mass, and wood density) were the primary biotic drivers, and their positive effects were independent of biome type. Incorporating plant functional traits improved predictions of NPP by 6.7 %-50.2 %, except for the alpine tundra on the Qinghai-Tibet Plateau. Our study identifies the principal factors regulating NPP spatial variation and highlights the importance of plant size traits in predictions of NPP variation at a large scale. These results provide new insights for involving plant size traits in carbon process models.

Impacts of climate timescale on the stability of trait-environment relationships

Predictive relationships between plant traits and environmental factors can be derived at global and regional scales, informing efforts to reorient ecological models around functional traits. However, in a changing climate, the environmental variables used as predictors in such relationships are far from stationary. This could yield errors in trait-environment model predictions if timescale is not accounted for. Here, the timescale dependence of trait-environment relationships is investigated by regressing in situ trait measurements of specific leaf area, leaf nitrogen content, and wood density on local climate characteristics summarized across several increasingly long timescales. We identify contrasting responses of leaf and wood traits to climate timescale. Leaf traits are best predicted by recent climate timescales, while wood density is a longer term memory trait. The use of sub-optimal climate timescales reduces the accuracy of the resulting trait-environment relationships. This study concludes that plant traits respond to climate conditions on the timescale of tissue lifespans rather than long-term climate normals, even at large spatial scales where multiple ecological and physiological mechanisms drive trait change. Thus, determining trait-environment relationships with temporally relevant climate variables may be critical for predicting trait change in a nonstationary climate system.

Global prediction of gross primary productivity under future climate change

The ecosystem gross primary productivity (GPP) is crucial to land-atmosphere carbon exchanges, and changes in global GPP as well as its influencing factors have been well studied in recent years. However, identifying the spatio-temporal variations of global GPP under future climate changes is still a challenging issue. This study aims to develop data-driven approach for predicting the global GPP as well as its monthly and annual variations up to the year 2100 under changing climate. Specifically, Catboost was employed to examine the potential relationship between the GPP and environmental factors, with climate variables, CO2 concentration and terrain attributes being selected as environmental factors. The predicted monthly and annual GPP from Coupled Model Intercomparison Project phase 6 (CMIP6) under future SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios were analyzed. The results indicate that the global GPP is predicted to increase under the future climate change in the 21st century. The annual GPP is expected to be 115.122 Pg C, 116.537 Pg C, 117.626 Pg C, and 120.097 Pg C in 2100 under four future scenarios, and the predicted monthly GPP shows seasonal difference. Meanwhile, GPP tends to increase in the northern mid-high latitude regions and decrease in the equatorial regions. For the climate zones form Köppen-Geiger classification, the arid, cold, and polar zones present increased GPP, while GPP in the tropical zone will decrease in the future. Moreover, the high importance of climate variables in GPP prediction illustrates that the future climate change is the main driver of the global GPP dynamics. This study provides a basis for predicting how global GPP responds to future climate change in the coming decades, which contribute to understanding the interactions between vegetation and climate.

Future changes in key plant traits across Central Europe vary with biogeographical status, woodiness, and habitat type

Many plant traits covary with environmental gradients, reflecting shifts in adaptive strategies and thus informing about potential consequences of future environmental change for vegetation and ecosystem functioning. Yet, the evidence of trait-environment relationships (TERs) remains too heterogeneous for reliable predictions, partially due to insufficient consideration of trait syndromes specific to certain growth forms and habitats. Moreover, it is still unclear whether non-native and native plants' traits align similarly along environmental gradients, limiting our ability to assess the impacts of future plant invasions. Using a Bayesian multilevel modelling framework, we assess TERs for native and non-native woody and herbaceous plants across six broad habitat types in Central Europe at a resolution of c. 130 km2 and use them to project trait change under future environmental change scenarios until 2081-2100. We model TERs between three key plant traits (maximum height, Hmax; specific leaf area, SLA; seed mass, SM) and individual environmental factors (7 climate variables and % urban land cover) and estimate trait change summed across all environmental effects. We also quantify the change in the average trait difference between native and non-native plants. Our models depict multiple TERs, with important differences attributed to biogeographical status and woodiness within and across habitat types. The overall magnitude of trait change is projected to be greater for non-native than native taxa and to increase under more extreme scenarios. Native woody plant assemblages may generally experience a future increase across all three traits, whereas woody non-natives may decline in Hmax and increase in SLA and SM. Herbaceous Hmax is estimated to increase and SLA to decrease in most habitats. The obtained trait projections highlight conditions of competitive advantage of non-native plants over natives and vice versa and can serve as starting points for projecting future changes in ecosystem functions and services.

Data-driven identification of major axes of functional variation in bacteria

The discovery of major axes of correlated functional variation among species and habitats has revealed the fundamental trade-offs structuring both functional and taxonomic diversity in eukaryotes such as plants. Whether such functional axes exist in the bacterial realm and whether they could explain bacterial taxonomic turnover among ecosystems remains unknown. Here, we use a data-driven approach to leverage global genomic and metagenomic datasets to reveal the existence of major axes of functional variation explaining both evolutionary differentiation within Bacteria and their ecological sorting across diverse habitats. We show that metagenomic variation among bacterial communities from various ecosystems is structured along a few axes of correlated functional pathways. Similar clusters of traits explained phylogenetic trait variation among >16,000 bacterial genomes, suggesting that functional turnover among bacterial communities from distinct habitats does not only result from the differential filtering of similar functions among communities, but also from phylogenetic correlations among these functions. Concordantly, functional pathways associated with trait clusters that were most important for defining functional turnover among bacterial communities were also those that had the highest phylogenetic signal in the bacterial genomic phylogeny. This study overall underlines the important role of evolutionary history in shaping contemporary distributions of bacteria across ecosystems.

Tropical Trees Will Need to Acclimate to Rising Temperatures-But Can They?

For tropical forests to survive anthropogenic global warming, trees will need to avoid rising temperatures through range shifts and "species migrations" or tolerate the newly emerging conditions through adaptation and/or acclimation. In this literature review, we synthesize the available knowledge to show that although many tropical tree species are shifting their distributions to higher, cooler elevations, the rates of these migrations are too slow to offset ongoing changes in temperatures, especially in lowland tropical rainforests where thermal gradients are shallow or nonexistent. We also show that the rapidity and severity of global warming make it unlikely that tropical tree species can adapt (with some possible exceptions). We argue that the best hope for tropical tree species to avoid becoming "committed to extinction" is individual-level acclimation. Although several new methods are being used to test for acclimation, we unfortunately still do not know if tropical tree species can acclimate, how acclimation abilities vary between species, or what factors may prevent or facilitate acclimation. Until all of these questions are answered, our ability to predict the fate of tropical species and tropical forests-and the many services that they provide to humanity-remains critically impaired.

Predicting the Responses of Functional Leaf Traits to Global Warming: An In Situ Temperature Manipulation Design Using Iris pumila L

Phenotypic plasticity is widely acknowledged as one of the most common solutions for coping with novel environmental conditions following climate change. However, it is less known whether the current amounts of trait plasticity, which is sufficient for matching with the contemporary climate, will be adequate when global temperatures exceed historical levels. We addressed this issue by exploring the responses of functional and structural leaf traits in Iris pumila clonal individuals to experimentally increased temperatures (~1.5 °C) using an open top chamber (OTC) design. We determined the phenotypic values of the specific leaf area, leaf dry matter content, specific leaf water content, and leaf thickness in the leaves sampled from the same clone inside and outside of the OTC deployed on it, over seasons and years within two natural populations. We analyzed the data using a repeated multivariate analysis of variance, which primarily focusses on the profiles (reaction norms (RNs)) of a variable gathered from the same individual at several different time points. We found that the mean RNs of all analyzed traits were parallel regardless of experienced temperatures, but differed in the level and the shape. The populations RNs were similar as well. As the amount of plasticity in the analyzed leaf trait was adequate for coping with elevated temperatures inside the OTCs, we predict that it will be also sufficient for responding to increased temperatures if they exceed the 1.5 °C target.

Analyzing trait-climate relationships within and among taxa using machine learning and herbarium specimens

PREMISE

Continental-scale leaf trait studies can help explain how plants survive in different environments, but large data sets are costly to assemble at this scale. Automating the measurement of digitized herbarium collections could rapidly expand the data available to such studies. We used machine learning to identify and measure leaves from existing, digitized herbarium specimens. The process was developed, validated, and applied to analyses of relationships between leaf size and climate within and among species for two genera: Syzygium (Myrtaceae) and Ficus (Moraceae).

METHODS

Convolutional neural network (CNN) models were used to detect and measure complete leaves in images. Predictions of a model trained with a set of 35 randomly selected images and a second model trained with 35 user-selected images were compared using a set of 50 labeled validation images. The validated models were then applied to 1227 Syzygium and 2595 Ficus specimens digitized by the National Herbarium of New South Wales, Australia. Leaf area measurements were made for each genus and used to examine links between leaf size and climate.

RESULTS

The user-selected training method for Syzygium found more leaves (9347 vs. 8423) using fewer training masks (218 vs. 225), and found leaves with a greater range of sizes than the random image training method. Within each genus, leaf size was positively associated with temperature and rainfall, consistent with previous observations. However, within species, the associations between leaf size and environmental variables were weaker.

CONCLUSIONS

CNNs detected and measured leaves with levels of accuracy useful for trait extraction and analysis and illustrate the potential for machine learning of herbarium specimens to massively increase global leaf trait data sets. Within-species relationships were weak, suggesting that population history and gene flow have a strong effect at this level. Herbarium specimens and machine learning could expand sampling of trait data within many species, offering new insights into trait evolution.

Changing plant functional diversity over the last 12,000 years provides perspectives for tracking future changes in vegetation communities

Plant communities are largely reshaped by climate and the environment over millennia, providing a powerful tool for understanding their response to future climates. Using a globally applicable functional palaeocological approach, we provide a deeper understanding of fossil pollen-inferred long-term response of vegetation to past climatic disturbances based on changes in functional trait composition. Specifically, we show how and why the ecological strategies exhibited by vegetation have changed through time by linking observations of plant traits to multiple pollen records from southeast Australia to reconstruct past functional diversity (FD, the value and the range of species traits that influence ecosystem functioning). The drivers of FD changes were assessed quantitatively by comparing FD reconstructions to independent records of past climates. During the last 12,000 years, peaks in FD were associated with both dry and wet climates in southeast Australia, with shifts in leaf traits particularly pronounced under wet conditions. Continentality determined the degree of stability maintained by high FD, with the greatest seen on the mainland. We expect projected frequent drier conditions in southeast Australia over coming decades to drive changes in vegetation community functioning and productivity mirroring the functional palaeocological record, particularly in western Tasmania and western southeast mainland.

Citizen science plant observations encode global trait patterns

Global maps of plant functional traits are essential for studying the dynamics of the terrestrial biosphere, yet the spatial distribution of trait measurements remains sparse. With the increasing popularity of species identification apps, citizen scientists contribute to growing vegetation data collections. The question emerges whether such opportunistic citizen science data can help map plant functional traits globally. Here we show that we can map global trait patterns by complementing vascular plant observations from the global citizen science project iNaturalist with measurements from the plant trait database TRY. We evaluate these maps using sPlotOpen, a global collection of vegetation plot data. Our results show high correlations between the iNaturalist- and sPlotOpen-based maps of up to 0.69 (r) and higher correlations than to previously published trait maps. As citizen science data collections continue to grow, we can expect them to play a significant role in further improving maps of plant functional traits.

The future of tundra carbon storage in Greenland - Sensitivity to climate and plant trait changes

The continuous change in observed key indicators such as increasing nitrogen deposition, temperatures and precipitation will have marked but uncertain consequences for the ecosystem carbon (C) sink-source functioning of the Arctic. Here, we use multiple in-situ data streams measured by the Greenland Ecosystem Monitoring programme in tight connection with the Soil-Plant-Atmosphere model and climate projections from the high-resolution HIRHAM5 regional model. We apply this modelling framework with focus on two climatically different tundra sites in Greenland (Zackenberg and Kobbefjord) to assess how sensitive the net C uptake will expectedly be under warmer and wetter conditions across the 21st century and pin down the relative contribution to the overall C sink strength from climate versus plant trait variability. Our results suggest that temperatures (5-7.7 °C), total precipitation (19-110 %) and vapour pressure deficit will increase (32-36 %), while shortwave radiation will decline (6-9 %) at both sites by 2100 under the RCP8.5 scenario. Such a combined effect will, on average, intensify the net C uptake by 9-10 g C m^-2 year^-1 at both sites towards the end of 2100, but Zackenberg is expected to have more than twice the C sink strength capacity of Kobbefjord. Our sensitivity analysis not only reveals that plant traits are the most sensitive parameters controlling the net C exchange in both sites at the beginning and end of the century, but also that the projected increase in the net C uptake will likely be similarly influenced by future changes in climate and existing local nutrient conditions. A series of experiments forcing realistic changes in plant nitrogen status at both sites corroborates this hypothesis. This work proves the unique synergy between monitoring data and numerical models to assist robust model calibration/validation and narrow uncertainty ranges and ultimately produce more reliable C cycle projections in understudied regions such as Greenland.

High Atmospheric CO2 Concentration Mitigates Drought Effects on Acanthostyles buniifolius an Important Grassland Weed in South America

The differential growth and yield response of plant species to rising carbon dioxide concentrations and climatic change may alter species diversity within biomes. The Pampa Biome in South America is an important grassland biome of agronomic and environmental importance. Acanthostyles buniifolius (Chirca) is one of the most important weeds in natural pasture areas widely distributed in southern South America and can adversely affect livestock production. The current study was designed to identify possible responses of Chirca to CO₂ concentration ([CO₂]) and drought that would indicate higher adaptation and potential proliferation within the Pampa Biome. Chirca plants were cultivated at two CO₂ concentrations (400 (a[CO₂]) and 700 (e[CO2]) µmol mol^-1) and two water conditions (under water restriction-15% of the pot capacity; and plants without water restriction-pot capacity). Besides growth parameters, we also determined water potential (ѱw), relative water contents (RWC), proline, glycine betaine, total soluble sugars, hydrogen peroxide, lipid peroxidation, superoxide dismutase (SOD), ascorbate peroxidase (APX) activity, chlorophyll A and B, carotenoids and root dry mass (RDM). Plants exposed to e[CO₂] are more efficient in water use and have a greater increase in root dry mass, enabling greater adaptation to climate-induced droughts. Among the biochemical changes observed in the plants under drought stress, the accumulation of proline, glycine betaine, and total soluble sugars were the most evident mechanisms allowing plants to tolerate drought stress by osmotic adjustment.

Response of plant functional traits to nitrogen enrichment under climate change: A meta-analysis

Soil nitrogen (N) supply is essential in influencing plant functional traits and regulating plant morphological and physiological performances. The effects of N on plants can be altered by complex environmental changes. However, conflicting results have been reported on the co-effects of N and climatic variables on plant performance, which may be attributed to differences in experiment setting and approach, e.g., ecosystem, duration, plant type, and fertilizer form. To elucidate the general response of plant performance to increasing soil N availability under climate change, a global meta-analysis was conducted to synthesize 380 publications studying interactions of N enrichment and four climatic variables (e.g., elevated atmospheric CO₂ (eCO₂), drought, precipitation, and warming) on performance-related traits (e.g., size, nutrient, and fitness). Results showed that N enrichment increased shoot and root size, nutrient, and fitness of terrestrial plants. The synergistic interactions of N × eCO₂ and antagonistic interactions of N × drought were found on plant overall performance (mainly on plant size), indicating that the N effects can be aggregated by eCO₂ and mitigated by drought. The co-effects of N and climatic variables on plant overall performance rely on experiment approach, duration, ecosystem type, or plant functional type. Synergistic interactions of N × eCO₂ and antagonistic interactions of N × drought, N × precipitation, and N × warming on plant overall performance were found mainly in greenhouse experiments and short-term experiments (duration ≤ one year), but not in the field or longer-term experiments. The results highlighted that N effects on plant performance were not isolated, but can be modified by climate changes. These findings can improve the future modeling predictions of plant performance under complex climate change and provide a fundamental basis for N management strategies to optimize plant performance in production, N nutrient, and reproduction while enabling sustainability of plant production systems.

Plant functional traits play the second fiddle to plant functional types in explaining peatland CO2 and CH4 gas exchange

Peatlands constitute a significant soil carbon (C) store, yet the C gas flux components show distinct spatial variation both between and within peatlands. Determining the controls on this variability could aid in our understanding of the response of peatlands to global changes. In this study, we assess the usefulness of different vegetation related parameters to explain spatial variation in peatland C gas flux components. We hypothesise that spatial variation is best explained by trait-based indices (similarly to other terrestrial ecosystems), and that the impact of soil physicochemical properties, such as nitrogen (N) content or water level, can be manifested through the traits. Furthermore, we expect that the spatial variability associated with each of the C gas flux components can be explained by a distinct set of traits. To address our aim, we used a successional peatland chronosequence from wet meadows to a bog, along which all variables were recorded with similar methods and under similar climatic conditions. We observed spatial variability with all measured gas fluxes, with carbon dioxide (CO₂) fluxes showing significant variability between sites, while within site variability was more important for methane (CH₄) fluxes. As expected, our results show that the impacts of physicochemical conditions were directed via vegetation. However, the cover of functional plant types that capture multiple traits proved to be more powerful in explaining gas flux variability compared to functional trait-based indices. Our findings imply that for future gas flux modelling purposes, rather than attempting to use individual traits - as is the ongoing trend in ecology - it might be more useful to refine plant functional groupings and ensure they are based on a set of plant traits relevant for the studied ecosystem process. This could be facilitated by the collation of a large data set of traits measured from peatlands.

The association between rainforest disturbance and recovery, tree community composition, and community traits in the Yangambi area in the Democratic Republic of the Congo

Despite their key role in biodiversity conservation, forests in the Congo Basin are increasingly threatened by human activities, but it remains challenging to assess the impact of forest degradation under a more or less intact canopy. Likewise, forest recovery following agricultural abandonment remains poorly understood in the Congo Basin. Here, we surveyed 125 vegetation quadrats across 25 forest inventory plots in the Yangambi area. We aimed to find associations between both selective logging and forest recovery, and a range of forest community and tree community trait characteristics, as compared to reference undisturbed old-growth forest. We found that plots in undisturbed old-growth forest harboured both more tree individuals and tree species with a higher wood density as compared to plots in disturbed old-growth forest. In addition, their tree community composition was significantly different, whereas species diversity recovered since relatively recent agricultural abandonment (< 60 years), community composition and forest structure remained significantly different from the plots in undisturbed old-growth forest. Our study provides some insights into the rate of forest recovery in the Congo basin after agricultural abandonment and highlights the need of proper conservation of the remaining relatively undisturbed old-growth forests. Finally, we stress the need for more extensive vegetation surveys in the Congo Basin to further unravel the effects of anthropogenic disturbance.

Rocks and Vegetation Cover Improve Body Condition of Desert Lizards During Both Summer and Winter

Microhabitats provide ecological and physiological benefits to animals, sheltering them from predation and extreme temperatures and offering an additional supply of water and food. However, most studies have assumed no energetic costs of searching for microhabitats or moving between them, or considered how the availability of microhabitats may affect the energy reserves of animals and how such effects may differ between seasons. To fill these gaps, we studied how the body condition of lizards is affected by microhabitat availability in the extreme environment of the Judean Desert. In particular, we quantified how vegetation and rock cover in the vicinity of these lizards affect their body condition during summer and winter. First, we used aerial imagery to map the vegetation/rock cover at two study sites. Next, we collected 68 adult lizards and examined how their body condition varies across seasons and availability of vegetation and rock cover. In addition, we examined how vegetation and rock cover may differ in their effective distance (i.e, the distance that best explains body condition of lizards). We found that lizards body condition was better if they were collected closer to a higher availability of vegetation or rocks. However, while close proximity (within 10 m) was the best predictor for the positive effect of rocks, a greater distance (up to 90 m) was the best predictor for the effect of the vegetation cover. Moreover, the positive effect of vegetation was 12-fold higher than the effect of rocks. Interestingly, although the lizards' body condition during winter was poorer than during summer, the positive effects of rock and vegetation cover remained constant between the seasons. This similarity of benefits across seasons suggests that shaded microhabitats have important additional ecological roles regardless of climate, and that they may provide thermoregulatory benefits in winter too. We also found a synergic effect of vegetation and rock cover on the lizards' body condition, suggesting that their roles are complementary rather than overlapping. Our research has revealed the importance of shade- and shelter-providing microhabitats in both summer and winter. We suggest that proximity to microhabitat diversity may contribute to better body condition in lizards; or, alternatively, facilitates competition and attracts lizards with better body condition. Comprehending the complex interactions between animals and different microhabitats is critical for developing better conservation plans, understanding the risks of climate change, and suggesting mitigation strategies.

Biodiversity mediates ecosystem sensitivity to climate variability

A rich body of evidence from local-scale experiments and observational studies has revealed stabilizing effects of biodiversity on ecosystem functioning. However, whether these effects emerge across entire regions and continents remains largely overlooked. Here we combine data on the distribution of more than 57,500 plant species and remote-sensing observations throughout the entire Western Hemisphere to investigate the role of multiple facets of plant diversity (species richness, phylogenetic diversity, and functional diversity) in mediating the sensitivity of ecosystems to climate variability at the regional-scale over the past 20 years. We show that, across multiple biomes, regions of greater plant diversity exhibit lower sensitivity (more stable over time) to temperature variability at the interannual and seasonal-scales. While these areas can display lower sensitivity to interannual variability in precipitation, they emerge as highly sensitive to precipitation seasonality. Conserving landscapes of greater diversity may help stabilize ecosystem functioning under climate change, possibly securing the continuous provisions of productivity-related ecosystem service to people.

With the help of spatial autoregressive models, the relationship between multiple facets of plant biodiversity and ecosystem sensitivity to climate variability is explored on a landscape-scale.

Changes in tree functional composition across topographic gradients and through time in a tropical montane forest

Understanding variation in tree functional traits along topographic gradients and through time provides insights into the processes that will shape community composition and determine ecosystem functioning. In montane environments, complex topography is known to affect forest structure and composition, yet its role in determining trait composition, indices on community climatic tolerances, and responses to changing environmental conditions has not been fully explored. This study investigates how functional trait composition (characterized as community-weighted moments) and community climatic indices vary for the tree community as a whole and for its separate demographic components (i.e., dying, surviving, recruiting trees) over eight years in a topographically complex tropical Andean forest in southern Ecuador. We identified a strong influence of topography on functional composition and on species' climatic optima, such that communities at lower topographic positions were dominated by acquisitive species adapted to both warmer and wetter conditions compared to communities at upper topographic positions which were dominated by conservative cold adapted species, possibly due to differences in soil conditions and hydrology. Forest functional and climatic composition remained stable through time; and we found limited evidence for trait-based responses to environmental change among demographic groups. Our findings confirm that fine-scale environmental conditions are a critical factor structuring plant communities in tropical forests, and suggest that slow environmental warming and community-based processes may promote short-term community functional stability. This study highlights the need to explore how diverse aspects of community trait composition vary in tropical montane forests, and to further investigate thresholds of forest response to environmental change.

Spatiotemporal Variation in Gross Primary Productivity and Their Responses to Climate in the Great Lakes Region of Sub-Saharan Africa during 2001–2020

The impacts of climate on spatiotemporal variations of eco-physiological and bio-physical factors have been widely explored in previous research, especially in dry areas. However, the understanding of gross primary productivity (GPP) variations and its interactions with climate in humid and semi-humid areas remains unclear. Based on hyperspectral satellite remotely sensed vegetation phenology processes and related indices and the re-analysed climate datasets, we investigated the seasonal and inter-annual variability of GPP by using different light-use efficiency (LUE) models including the Carnegie-Ames-Stanford Approaches (CASA) model, vegetation photosynthesis models (VPMChl and VPMCanopy) and Moderate Resolution Imaging Spectroradiometer (MODIS) GPP products (MOD17A2H) during 2001–2020 over the Great Lakes region of Sub-Saharan Africa (GLR-SSA). The models’ validation against the in situ GPP-based upscaled observations (GPP-EC) indicated that these three models can explain 82%, 79% and 80% of GPP variations with root mean square error (RMSE) values of 5.7, 8.82 and 10.12 g C·m−2·yr−1, respectively. The spatiotemporal variations of GPP showed that the GLR-SSA experienced: (i) high GPP values during December-May; (ii) high annual GPP increase during 2002–2003, 2011–2013 and 2015–2016 and annual decreasing with a marked alternation in other years; (iii) evergreen broadleaf forests having the highest GPP values while grasslands and croplands showing lower GPP values. The spatial correlation between GPP and climate factors indicated 60% relative correlation between precipitation and GPP and 65% correction between surface air temperature and GPP. The results also showed high GPP values under wet conditions (in rainy seasons and humid areas) that significantly fell by the rise of dry conditions (in long dry season and arid areas). Therefore, these results showed that climate factors have potential impact on GPP variability in this region. However, these findings may provide a better understanding of climate implications on GPP variability in the GLR-SSA and other tropical climate zones.

Prediction of conserved and variable heat and cold stress response in maize using cis-regulatory information

Changes in gene expression are important for responses to abiotic stress. Transcriptome profiling of heat- or cold-stressed maize genotypes identifies many changes in transcript abundance. We used comparisons of expression responses in multiple genotypes to identify alleles with variable responses to heat or cold stress and to distinguish examples of cis- or trans-regulatory variation for stress-responsive expression changes. We used motifs enriched near the transcription start sites (TSSs) for thermal stress-responsive genes to develop predictive models of gene expression responses. Prediction accuracies can be improved by focusing only on motifs within unmethylated regions near the TSS and vary for genes with different dynamic responses to stress. Models trained on expression responses in a single genotype and promoter sequences provided lower performance when applied to other genotypes but this could be improved by using models trained on data from all three genotypes tested. The analysis of genes with cis-regulatory variation provides evidence for structural variants that result in presence/absence of transcription factor binding sites in creating variable responses. This study provides insights into cis-regulatory motifs for heat- and cold-responsive gene expression and defines a framework for developing models to predict expression responses across multiple genotypes.

Deep learning and citizen science enable automated plant trait predictions from photographs

Plant functional traits ('traits') are essential for assessing biodiversity and ecosystem processes, but cumbersome to measure. To facilitate trait measurements, we test if traits can be predicted through visible morphological features by coupling heterogeneous photographs from citizen science (iNaturalist) with trait observations (TRY database) through Convolutional Neural Networks (CNN). Our results show that image features suffice to predict several traits representing the main axes of plant functioning. The accuracy is enhanced when using CNN ensembles and incorporating prior knowledge on trait plasticity and climate. Our results suggest that these models generalise across growth forms, taxa and biomes around the globe. We highlight the applicability of this approach by producing global trait maps that reflect known macroecological patterns. These findings demonstrate the potential of Big Data derived from professional and citizen science in concert with CNN as powerful tools for an efficient and automated assessment of Earth's plant functional diversity.

Functional leaf traits indicate phylogenetic signals in forests across an elevational gradient in the central Himalaya

Traits are the primary attributes that distinguish a species niche. Species and higher taxa are part of a structured phylogeny, and variation in plant traits depends on lineage as well as on environmental conditions. Therefore, it is crucial to investigate linkages between taxonomic identity, shared ancestry, and environment for understanding the variation in leaf traits. We investigated the evolutionary relationships, based on multiple gene sequences among 26 plant species sampled along an elevational gradient from 650 to 3600 m a.s.l. in the central Himalaya. We tested for the phylogenetic signal based on three different measures in 10 leaf traits having a significant association with the resource acquisition-conservation trade-offs axis and influencing plant growth, development, and ecological performance. We further assessed the role of elevation and growth forms as the potential drivers of leaf traits variation while controlling for phylogeny. 5 out of 10 leaf traits showed significant phylogenetic signal. Plant species clustered more often by growth forms at the tips of the phylogeny indicating multiple instances of independent evolution. Evergreen taxa showed niche separation with deciduous and incorporated larger trait variation. Trait variations were guided by both growth forms and elevation when accounted for phylogeny. Growth form has a higher contribution to trait variation compared to elevation. Trade-offs were detected between resource conservation and resource acquisition machinery traits (that would maximise carbon gain), differing between growth forms and along elevation.

Heterogeneous Impact of Water Warming on Exotic and Native Submerged and Emergent Plants in Outdoor Mesocosms

Some aquatic plants present high biomass production with serious consequences on ecosystem functioning. Such mass development can be favored by environmental factors. Temperature increases are expected to modify individual species responses that could shape future communities. We explored the impact of rising water temperature on the growth, phenology, and metabolism of six macrophytes belonging to two biogeographic origins (exotic, native) and two growth forms (submerged, emergent). From June to October, they were exposed to ambient temperatures and a 3 °C warming in outdoor mesocosms. Percent cover and canopy height were favored by warmer water for the exotic emergent Ludwigia hexapetala. Warming did not modify total final biomass for any of the species but led to a decrease in total soluble sugars for all, possibly indicating changes in carbon allocation. Three emergent species presented lower flavonol and anthocyanin contents under increased temperatures, suggesting lower investment in defense mechanisms and mitigation of the stress generated by autumn temperatures. Finally, the 3 °C warming extended and shortened flowering period for L. hexapetala and Myosotis scorpioides, respectively. The changes generated by increased temperature in outdoor conditions were heterogeneous and varied depending on species but not on species biogeographic origin or growth form. Results suggest that climate warming could favor the invasiveness of L. hexapetala and impact the structure and composition of aquatic plants communities.

Simultaneous inversion of concentrations of POC and its endmembers in lakes: A novel remote sensing strategy

Data on the concentration of particulate organic carbon (POC) and its endmembers provide a basis for the characterisation of lake biogeochemical cycles. Here, a novel remote sensing strategy (the SC_POC algorithm) was developed to determine total POC concentrations, as well as terrestrial and endogenous POC concentrations in lakes. This strategy provides a successful example for the combination of isotope tracer and remote sensing technology. First, we obtained the terrestrial and endogenous POC concentration at the sampling point based on isotope tracing technology. Afterwards, we established a relationship between the phytoplankton absorption coefficient and the endogenous POC concentration (C_end), and applied a semi-analytical algorithm to invert the C_end value. Finally, the POC source ratio model and C_end value were combined to obtain the POC concentration (C_POC) and terrestrial POC (C_ter). The results of synchronisation verification based on ocean and land colour instrument (OLCI) images show that the SC_POC algorithm has high C_end, C_ter, and C_POC inversion accuracy, with MAPE values of 26.07%, 30.43%, and 42.28%, respectively. In fact, the SC_POC algorithm not only improved the accuracy of lake POC mapping, but also fills the gap of optical retrieval of POC endmember concentrations. Additionally, data from the OLCI images indicated that the studied lakes were dominated by external POC. However, because of the greater contribution of algal blooms to POC, this dominant advantage weakens in summer, although the terrestrial organic carbon carried by rainfall runoff also affects lake POC composition. Different POC sources have different ecological roles in lakes, and the superior POC end-element estimation capability of the SC_POC algorithm can not only be used as a supplement to traditional tracing methods, but also provides accurate spatial data for lake management.

Diversity of parental environments increases phenotypic variation in Arabidopsis populations more than genetic diversity but similarly affects productivity

BACKGROUND AND AIMS

The observed positive diversity effect on ecosystem functioning has rarely been assessed in terms of intraspecific trait variability within populations. Intraspecific phenotypic variability could stem both from underlying genetic diversity and from plasticity in response to environmental cues. The latter might derive from modifications to a plant's epigenome and potentially last multiple generations in response to previous environmental conditions. We experimentally disentangled the role of genetic diversity and diversity of parental environments on population productivity, resistance against environmental fluctuations and intraspecific phenotypic variation.

METHODS

A glasshouse experiment was conducted in which different types of Arabidopsis thaliana populations were established: one population type with differing levels of genetic diversity and another type, genetically identical, but with varying diversity levels of the parental environments (parents grown in the same or different environments). The latter population type was further combined, or not, with experimental demethylation to reduce the potential epigenetic diversity produced by the diversity of parental environments. Furthermore, all populations were each grown under different environmental conditions (control, fertilization and waterlogging). Mortality, productivity and trait variability were measured in each population.

KEY RESULTS

Parental environments triggered phenotypic modifications in the offspring, which translated into more functionally diverse populations when offspring from parents grown under different conditions were brought together in mixtures. In general, neither the increase in genetic diversity nor the increase in diversity of parental environments had a remarkable effect on productivity or resistance to environmental fluctuations. However, when the epigenetic variation was reduced via demethylation, mixtures were less productive than monocultures (i.e. negative net diversity effect), caused by the reduction of phenotypic differences between different parental origins.

CONCLUSIONS

A diversity of environmental parental origins within a population could ameliorate the negative effect of competition between coexisting individuals by increasing intraspecific phenotypic variation. A diversity of parental environments could thus have comparable effects to genetic diversity. Disentangling the effect of genetic diversity and that of parental environments appears to be an important step in understanding the effect of intraspecific trait variability on coexistence and ecosystem functioning.

BII-Implementation: The causes and consequences of plant biodiversity across scales in a rapidly changing world

The proposed Biology Integration Institute will bring together two major research institutions in the Upper Midwest—the University of Minnesota (UMN) and University of Wisconsin-Madison (UW)—to investigate the causes and consequences of plant biodiversity across scales in a rapidly changing world—from genes and molecules within cells and tissues to communities, ecosystems, landscapes and the biosphere. The Institute focuses on plant biodiversity, defined broadly to encompass the heterogeneity within life that occurs from the smallest to the largest biological scales. A premise of the Institute is that life is envisioned as occurring at different scales nested within several contrasting conceptions of biological hierarchies, defined by the separate but related fields of physiology, evolutionary biology and ecology. The Institute will emphasize the use of ‘spectral biology’—detection of biological properties based on the interaction of light energy with matter—and process-oriented predictive models to investigate the processes by which biological components at one scale give rise to emergent properties at higher scales. Through an iterative process that harnesses cutting edge technologies to observe a suite of carefully designed empirical systems—including the National Ecological Observatory Network (NEON) and some of the world’s longest running and state-of-the-art global change experiments—the Institute will advance biological understanding and theory of the causes and consequences of changes in biodiversity and at the interface of plant physiology, ecology and evolution.

INTELLECTUAL MERIT

The Institute brings together a diverse, gender-balanced and highly productive team with significant leadership experience that spans biological disciplines and career stages and is poised to integrate biology in new ways. Together, the team will harness the potential of spectral biology, experiments, observations and synthetic modeling in a manner never before possible to transform understanding of how variation within and among biological scales drives plant and ecosystem responses to global change over diurnal, seasonal and millennial time scales. In doing so, it will use and advance state-of-the-art theory. The institute team posits that the designed projects will unearth transformative understanding and biological rules at each of the various scales that will enable an unprecedented capacity to discern the linkages between physiological, ecological and evolutionary processes in relation to the multi-dimensional nature of biodiversity in this time of massive planetary change. A strength of the proposed Institute is that it leverages prior federal investments in research and formalizes partnerships with foreign institutions heavily invested in related biodiversity research. Most of the planned projects leverage existing research initiatives, infrastructure, working groups, experiments, training programs, and public outreach infrastructure, all of which are already highly synergistic and collaborative, and will bring together members of the overall research and training team.

BROADER IMPACTS

A central goal of the proposed Institute is to train the next generation of diverse integrative biologists. Post-doctoral, graduate student and undergraduate trainees, recruited from non-traditional and underrepresented groups, including through formal engagement with Native American communities, will receive a range of mentoring and training opportunities. Annual summer training workshops will be offered at UMN and UW as well as training experiences with the Global Change and Biodiversity Research Priority Program (URPP-GCB) at the University of Zurich (UZH) and through the Canadian Airborne Biodiversity Observatory (CABO). The Institute will engage diverse K-12 audiences, the general public and Native American communities through Market Science modules, Minute Earth videos, a museum exhibit and public engagement and educational activities through the Bell Museum of Natural History, the Cedar Creek Ecosystem Science Reserve (CCESR) and the Wisconsin Tribal Conservation Association.

Current and future impacts of drought and ozone stress on Northern Hemisphere forests

Rising ozone (O₃ ) concentrations, coupled with an increase in drought frequency due to climate change, pose a threat to plant growth and productivity which could negatively affect carbon sequestration capacity of Northern Hemisphere (NH) forests. Using long-term observations of O₃ mixing ratios and soil water content (SWC), we implemented empirical drought and O₃ stress parameterizations in a coupled stomatal conductance-photosynthesis model to assess their impacts on plant gas exchange at three FLUXNET sites: Castelporziano, Blodgett and Hyytiälä. Model performance was evaluated by comparing model estimates of gross primary productivity (GPP) and latent heat fluxes (LE) against present-day observations. CMIP5 GCM model output data were then used to investigate the potential impact of the two stressors on forests by the middle (2041-2050) and end (2091-2100) of the 21st century. We found drought stress was the more significant as it reduced model overestimation of GPP and LE by ~11%-25% compared to 1%-11% from O₃ stress. However, the best model fit to observations at all the study sites was obtained with O₃ and drought stress combined, such that the two stressors counteract the impact of each other. With the inclusion of drought and O₃ stress, GPP at CPZ, BLO and HYY is projected to increase by 7%, 5% and 8%, respectively, by mid-century and by 14%, 11% and 14% by 2091-2100 as atmospheric CO₂ increases. Estimates were up to 21% and 4% higher when drought and O₃ stress were neglected respectively. Drought stress will have a substantial impact on plant gas exchange and productivity, off-setting and possibly negating CO₂ fertilization gains in future, suggesting projected increases in the frequency and severity of droughts in the NH will play a significant role in forest productivity and carbon budgets in future.

Radial Growth Responses to Climate of Pinus yunnanensis at Low Elevations of the Hengduan Mountains, China

The relationship between climate and forest is critical to understanding the influence of future climate change on terrestrial ecosystems. Research on trees at high elevations has uncovered the relationship in the Hengduan Mountains region, a critical biodiversity hotspot area in southwestern China. The relationship for the area at low elevations below 2800 m a.s.l. in the region remains unclear. In this study, we developed tree ring width chronologies of Pinus yunnanensis Franch. at five sites with elevations of 1170–1725 m in this area. Monthly precipitation, relative humidity, maximum/mean/minimum air temperature and the standardized precipitation evapotranspiration index (SPEI), a drought indicator with a multi-timescale, were used to investigate the radial growth-climate relationship. Results show that the growth of P. yunnanensis at different sites has a similar response pattern to climate variation. Relative humidity, precipitation, and air temperature in the dry season, especially in its last month (May), are critical to the radial growth of trees. Supplemental precipitation amounts and reduced mean or maximum air temperature can promote tree growth. The high correlations between chronologies and SPEI indicate that the radial growth of trees at the low elevations of the region is significantly limited by the moisture availability. Precipitation in the last month of the previous wet season determines the drought regime in the following dry seasons. In spite of some differences in the magnitudes of correlations in the low-elevation area of the Hengduan Mountains region, chronologies generally matched well with each other at different elevations, and the differences are not evident with the change in elevation.

Identification of the Roles of Climate Factors, Engineering Construction, and Agricultural Practices in Vegetation Dynamics in the Lhasa River Basin, Tibetan Plateau

Understanding vegetation dynamics is necessary to address potential ecological threats and develop sustainable ecosystem management at high altitudes. In this study, we revealed the spatiotemporal characteristics of vegetation growth in the Lhasa River Basin using net primary productivity (NPP) and normalized difference vegetation index (NDVI) during the period of 2000–2005. The roles of climatic factors and specific anthropogenic activities in vegetation dynamics were also identified, including positive or negative effects and the degree of impact. The results indicated that the interannual series of NPP and NDVI in the whole basin both had a continuous increasing trend from 102 to 128 gC m−2 yr−1 and from 0.417 to 0.489 (p < 0.05), respectively. The strongest advanced trends (>2 gC m−2 yr−1 or >0.005 yr−1) were detected in mainly the southeastern and northeastern regions. Vegetation dynamics were not detected in 10% of the basin. Only 20% of vegetation dynamics were driven by climatic conditions, and precipitation was the controlling climatic factor determining vegetation growth. Accordingly, anthropogenic activities made a great difference in vegetation coverage, accounting for about 70%. The construction of urbanization and reservoir led to vegetation degradation, but the farmland practices contributed the vegetation growth. Reservoir construction had an adverse impact on vegetation within 6 km of the river, and the direct damage to vegetation was within 1 km. The impacts of urbanization were more serious than that of reservoir construction. Urban sprawl had an adverse impact on vegetation within a 6 km distance from the surrounding river and resulted in the degradation of vegetation, especially within a 3 km range. Intensive fertilization and guaranteed irrigation improved the cropland ecosystem conditions, creating a favorable effect on the accumulation of crop organic matter in a range of 5 km, with an NPP trend value of 1.2 gC m−2 yr−1. The highly intensive grazing activity forced ecological environmental pressures such that the correlation between livestock numbers and vegetation growth trend was significantly linear negative.

Assessing the reliability of predicted plant trait distributions at the global scale

AIM

Predictions of plant traits over space and time are increasingly used to improve our understanding of plant community responses to global environmental change. A necessary step forward is to assess the reliability of global trait predictions. In this study, we predict community mean plant traits at the global scale and present a systematic evaluation of their reliability in terms of the accuracy of the models, ecological realism and various sources of uncertainty.

LOCATION

Global.

TIME PERIOD

Present.

MAJOR TAXA STUDIED

Vascular plants.

METHODS

We predicted global distributions of community mean specific leaf area, leaf nitrogen concentration, plant height and wood density with an ensemble modelling approach based on georeferenced, locally measured trait data representative of the plant community. We assessed the predictive performance of the models, the plausibility of predicted trait combinations, the influence of data quality, and the uncertainty across geographical space attributed to spatial extrapolation and diverging model predictions.

RESULTS

Ensemble predictions of community mean plant height, specific leaf area and wood density resulted in ecologically plausible trait-environment relationships and trait-trait combinations. Leaf nitrogen concentration, however, could not be predicted reliably. The ensemble approach was better at predicting community trait means than any of the individual modelling techniques, which varied greatly in predictive performance and led to divergent predictions, mostly in African deserts and the Arctic, where predictions were also extrapolated. High data quality (i.e., including intraspecific variability and a representative species sample) increased model performance by 28%.

MAIN CONCLUSIONS

Plant community traits can be predicted reliably at the global scale when using an ensemble approach and high-quality data for traits that mostly respond to large-scale environmental factors. We recommend applying ensemble forecasting to account for model uncertainty, using representative trait data, and more routinely assessing the reliability of trait predictions.

Leaf size of woody dicots predicts ecosystem primary productivity

A key challenge in ecology is to understand the relationships between organismal traits and ecosystem processes. Here, with a novel dataset of leaf length and width for 10 480 woody dicots in China and 2374 in North America, we show that the variation in community mean leaf size is highly correlated with the variation in climate and ecosystem primary productivity, independent of plant life form. These relationships likely reflect how natural selection modifies leaf size across varying climates in conjunction with how climate influences canopy total leaf area. We find that the leaf size-primary productivity functions based on the Chinese dataset can predict productivity in North America and vice-versa. In addition to advancing understanding of the relationship between a climate-driven trait and ecosystem functioning, our findings suggest that leaf size can also be a promising tool in palaeoecology for scaling from fossil leaves to palaeo-primary productivity of woody ecosystems.

Trait integration and functional differentiation among co-existing plant species

PREMISE

Determining which traits characterize strategies of coexisting species is important to developing trait-based models of plant communities. First, global dimensions may not exist locally. Second, the degree to which traits and trait spectra constitute independent dimensions of functional variation at various scales continues to be refined. Finally, traits may be associated with existing categorical groupings.

METHODS

We assessed trait integration and differentiation across 57 forest understory plant species in Douglas-fir forests of western Oregon, United States. We combined measurements for a range of traits with literature-based estimates of seed mass and species groupings. We used network analysis and nonmetric multidimensional scaling ordination (NMS) to determine the degree of integration.

RESULTS

We observed a strong leaf economics spectrum (LES) integrated with stem but not root traits. However, stem traits and intrinsic water-use efficiency integrated LES and root traits. Network analyses indicated a modest grouping of a priori trait dimensions. NMS indicated that multivariate differences among species were related primarily to (1) rooting depth and plant height vs. specific root length, (2) the LES, and (3) leaf size vs. seed mass. These differences were related to species groupings associated with growth and life form, leaf lifespan and seed dispersal mechanisms.

CONCLUSIONS

The strategies of coexisting understory plant species could not be reduced to a single dimension. Yet, species can be characterized efficiently and effectively for trait-based studies of plant communities by measuring four common traits: plant height, specific leaf area, leaf size, and seed mass.

Trait integration and functional differentiation among co-existing plant species

PREMISE

Determining which traits characterize strategies of coexisting species is important to developing trait-based models of plant communities. First, global dimensions may not exist locally. Second, the degree to which traits and trait spectra constitute independent dimensions of functional variation at various scales continues to be refined. Finally, traits may be associated with existing categorical groupings.

METHODS

We assessed trait integration and differentiation across 57 forest understory plant species in Douglas-fir forests of western Oregon, United States. We combined measurements for a range of traits with literature-based estimates of seed mass and species groupings. We used network analysis and nonmetric multidimensional scaling ordination (NMS) to determine the degree of integration.

RESULTS

We observed a strong leaf economics spectrum (LES) integrated with stem but not root traits. However, stem traits and intrinsic water-use efficiency integrated LES and root traits. Network analyses indicated a modest grouping of a priori trait dimensions. NMS indicated that multivariate differences among species were related primarily to (1) rooting depth and plant height vs. specific root length, (2) the LES, and (3) leaf size vs. seed mass. These differences were related to species groupings associated with growth and life form, leaf lifespan and seed dispersal mechanisms.

CONCLUSIONS

The strategies of coexisting understory plant species could not be reduced to a single dimension. Yet, species can be characterized efficiently and effectively for trait-based studies of plant communities by measuring four common traits: plant height, specific leaf area, leaf size, and seed mass.

Human eutrophication drives biogeographic salt marsh productivity patterns in China

Salt marshes are important natural carbon sinks with a large capacity to absorb exogenous nutrient inputs. The effects of nutrients on biogeographic productivity patterns, however, have been poorly explored in salt marshes. We conducted field surveys to examine how complex environments affect productivity of two common salt marsh plants, invasive Spartina alterniflora and native Phragmites australis, along an 18,000-km latitudinal gradient on the Chinese coastline. We harvested peak aboveground biomass as a proxy for productivity, and measured leaf functional traits (e.g., leaf area, specific leaf area [SLA], leaf nitrogen [N] and phosphorus [P]), soil nutrients (dissolved inorganic N [DIN] and available P [AP]), and salinity. We compiled data on mean annual temperature (MAT) and exogenous nutrients (both N and P). Then, we examined how these abiotic factors affect salt marsh productivity using both linear mixed effect models and structural equation modeling. Using a trait-based approach, we also examined how salt marsh productivity responds to changing environments across latitude. Exogenous nutrients (both N and P), compared with temperature and other variables (e.g., DIN, AP, salinity), were the dominant factors in explaining the biogeographic productivity patterns of both S. alterniflora and P. australis. Leaf size-related traits (e.g., leaf area), rather than leaf economic traits (e.g., SLA, leaf N and P), can be used to indicate the positive effects of exogenous nutrients on the productivity of these two species. Our results demonstrated that human eutrophication surpassed temperature as the major driver of biogeographic salt marsh productivity pattern, challenging current models in which biogeographic productivity pattern is primarily controlled by temperature. Our findings have potential broad implications for the management of S. alterniflora, which is a global invader, as it has benefited from coastal eutrophication. Furthermore, exogenous nutrient availability and leaf size need to be integrated into earth system models that are used to predict global plant productivity in salt marshes.

Plant functional traits differ in adaptability and are predicted to be differentially affected by climate change

Climate change is testing the resilience of forests worldwide pushing physiological tolerance to climatic extremes. Plant functional traits have been shown to be adapted to climate and have evolved patterns of trait correlations (similar patterns of distribution) and coordinations (mechanistic trade-off). We predicted that traits would differentiate between populations associated with climatic gradients, suggestive of adaptive variation, and correlated traits would adapt to future climate scenarios in similar ways.We measured genetically determined trait variation and described patterns of correlation for seven traits: photochemical reflectance index (PRI), normalized difference vegetation index (NDVI), leaf size (LS), specific leaf area (SLA), δ13C (integrated water-use efficiency, WUE), nitrogen concentration (NCONC), and wood density (WD). All measures were conducted in an experimental plantation on 960 trees sourced from 12 populations of a key forest canopy species in southwestern Australia.Significant differences were found between populations for all traits. Narrow-sense heritability was significant for five traits (0.15-0.21), indicating that natural selection can drive differentiation; however, SLA (0.08) and PRI (0.11) were not significantly heritable. Generalized additive models predicted trait values across the landscape for current and future climatic conditions (>90% variance). The percent change differed markedly among traits between current and future predictions (differing as little as 1.5% (δ13C) or as much as 30% (PRI)). Some trait correlations were predicted to break down in the future (SLA:NCONC, δ13C:PRI, and NCONC:WD).Synthesis: Our results suggest that traits have contrasting genotypic patterns and will be subjected to different climate selection pressures, which may lower the working optimum for functional traits. Further, traits are independently associated with different climate factors, indicating that some trait correlations may be disrupted in the future. Genetic constraints and trait correlations may limit the ability for functional traits to adapt to climate change.

Diverse photosynthetic capacity of global ecosystems mapped by satellite chlorophyll fluorescence measurements

Photosynthetic capacity is often quantified by the Rubisco-limited photosynthetic capacity (i.e. maximum carboxylation rate, V_cmax). It is a key plant functional trait that is widely used in Earth System Models for simulation of the global carbon and water cycles. Measuring V_cmax is time-consuming and laborious; therefore, the spatiotemporal distribution of V_cmax is still poorly understood due to limited measurements of V_cmax. In this study, we used a data assimilation approach to map the spatial variation of V_cmax for global terrestrial ecosystems from a 11-year-long satellite-observed solar-induced chlorophyll fluorescence (SIF) record. In this SIF-derived V_cmax map, the mean V_cmax value for each plant function type (PFT) is found to be comparable to a widely used N-derived V_cmax dataset by Kattge et al. (2009). The gradient of V_cmax along PFTs is clearly revealed even without land cover information as an input. Large seasonal and spatial variations of V_cmax are found within each PFT, especially for diverse crop rotation systems. The distribution of major crop belts, characterized with high V_cmax values, is highlighted in this V_cmax map. Legume plants are characterized with high V_cmax values. This V_cmax map also clearly illustrates the emerging soybean revolution in South America where V_cmax is the highest among the world. The gradient of V_cmax in Amazon is found to follow the transition of soil types with different soil N and P contents. This study suggests that satellite-observed SIF is powerful in deriving the important plant functional trait, i.e. V_cmax, for global climate change studies.

Magnitude of urban heat islands largely explained by climate and population

Urban heat islands (UHIs) exacerbate the risk of heat-related mortality associated with global climate change. The intensity of UHIs varies with population size and mean annual precipitation, but a unifying explanation for this variation is lacking, and there are no geographically targeted guidelines for heat mitigation. Here we analyse summertime differences between urban and rural surface temperatures (ΔT_s) worldwide and find a nonlinear increase in ΔT_s with precipitation that is controlled by water or energy limitations on evapotranspiration and that modulates the scaling of ΔT_s with city size. We introduce a coarse-grained model that links population, background climate, and UHI intensity, and show that urban-rural differences in evapotranspiration and convection efficiency are the main determinants of warming. The direct implication of these nonlinearities is that mitigation strategies aimed at increasing green cover and albedo are more efficient in dry regions, whereas the challenge of cooling tropical cities will require innovative solutions.

Trait-based plant ecology a flawed tool in climate studies? The leaf traits of wild olive that pattern with climate are not those routinely measured

Climate-related studies have generally focussed upon physiologically well-defined 'mechanistic' traits rather than 'functional' ones relating indirectly to resource capture. Nevertheless, field responses to climate are likely to typically include both 'mechanistic' specialization to climatic extremes and 'functional' strategies that optimize resource acquisition during less climatically-severe periods. Here, this hypothesis was tested. Seventeen traits (six 'functional', six 'mechanistic' and five 'intermediate') were measured from 19 populations of oleaster (wild olive) along a climatic gradient in Morocco. Principal components analysis of the trait dataset identified size and the 'worldwide leaf economics spectrum' as PCA axes 1 and 2. However, contrary to our prediction, these axes, and commonly-measured 'functional' traits, were little correlated with climate. Instead, PCA 3, perhaps relating to water-use and succulence, together stomatal density, specific leaf water content and leaf shape, patterned with altitude, aridity, rainfall and temperature. We concluded that, at least for slow-growing species, such as oleaster, 'mechanistic' traits are key to identifying mechanisms of climatic restriction. Meaningful collaboration between 'mechanistic' and 'functional' disciplines provides the best way of improving our understanding of the global impacts of climate change on species distribution and performance.

Drier tropical forests are susceptible to functional changes in response to a long-term drought

Climatic changes have profound effects on the distribution of biodiversity, but untangling the links between climatic change and ecosystem functioning is challenging, particularly in high diversity systems such as tropical forests. Tropical forests may also show different responses to a changing climate, with baseline climatic conditions potentially inducing differences in the strength and timing of responses to droughts. Trait-based approaches provide an opportunity to link functional composition, ecosystem function and environmental changes. We demonstrate the power of such approaches by presenting a novel analysis of long-term responses of different tropical forest to climatic changes along a rainfall gradient. We explore how key ecosystem's biogeochemical properties have shifted over time as a consequence of multi-decadal drying. Notably, we find that drier tropical forests have increased their deciduous species abundance and generally changed more functionally than forests growing in wetter conditions, suggesting an enhanced ability to adapt ecologically to a drying environment.

Climate shapes and shifts functional biodiversity in forests worldwide

Much ecological research aims to explain how climate impacts biodiversity and ecosystem-level processes through functional traits that link environment with individual performance. However, the specific climatic drivers of functional diversity across space and time remain unclear due largely to limitations in the availability of paired trait and climate data. We compile and analyze a global forest dataset using a method based on abundance-weighted trait moments to assess how climate influences the shapes of whole-community trait distributions. Our approach combines abundance-weighted metrics with diverse climate factors to produce a comprehensive catalog of trait-climate relationships that differ dramatically-27% of significant results change in sign and 71% disagree on sign, significance, or both-from traditional species-weighted methods. We find that (i) functional diversity generally declines with increasing latitude and elevation, (ii) temperature variability and vapor pressure are the strongest drivers of geographic shifts in functional composition and ecological strategies, and (iii) functional composition may currently be shifting over time due to rapid climate warming. Our analysis demonstrates that climate strongly governs functional diversity and provides essential information needed to predict how biodiversity and ecosystem function will respond to climate change.

Global Estimation of Biophysical Variables from Google Earth Engine Platform

This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC), and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using global and climate data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets allowing the global estimation of biophysical variables at unprecedented timeliness. We combine a massive global compilation of leaf trait measurements (TRY), which is the baseline for more realistic leaf parametrization for the considered RTM, with large amounts of remote sensing data ingested by GEE. Moreover, the proposed retrieval chain includes the estimation of both FVC and CWC, which are not operationally produced for the MODIS sensor. The derived global estimates are validated over the BELMANIP2.1 sites network by means of an inter-comparison with the MODIS LAI/FAPAR product available in GEE. Overall, the retrieval chain exhibits great consistency with the reference MODIS product (R2 = 0.87, RMSE = 0.54 m2/m2 and ME = 0.03 m2/m2 in the case of LAI, and R2 = 0.92, RMSE = 0.09 and ME = 0.05 in the case of FAPAR). The analysis of the results by land cover type shows the lowest correlations between our retrievals and the MODIS reference estimates (R2 = 0.42 and R2 = 0.41 for LAI and FAPAR, respectively) for evergreen broadleaf forests. These discrepancies could be attributed mainly to different product definitions according to the literature. The provided results proof that GEE is a suitable high performance processing tool for global biophysical variable retrieval for a wide range of applications.