Tropical tree mortality has increased with rising atmospheric water stress.

Evidence exists that tree mortality is accelerating in some regions of the tropics1,2, with profound consequences for the future of the tropical carbon sink and the global anthropogenic carbon budget left to limit peak global warming below 2 °C. However, the mechanisms that may be driving such mortality changes and whether particular species are especially vulnerable remain unclear3-8. Here we analyse a 49-year record of tree dynamics from 24 old-growth forest plots encompassing a broad climatic gradient across the Australian moist tropics and find that annual tree mortality risk has, on average, doubled across all plots and species over the last 35 years, indicating a potential halving in life expectancy and carbon residence time. Associated losses in biomass were not offset by gains from growth and recruitment. Plots in less moist local climates presented higher average mortality risk, but local mean climate did not predict the pace of temporal increase in mortality risk. Species varied in the trajectories of their mortality risk, with the highest average risk found nearer to the upper end of the atmospheric vapour pressure deficit niches of species. A long-term increase in vapour pressure deficit was evident across the region, suggesting that thresholds involving atmospheric water stress, driven by global warming, may be a primary cause of increasing tree mortality in moist tropical forests.

Functional susceptibility of tropical forests to climate change.

Tropical forests are some of the most biodiverse ecosystems in the world, yet their functioning is threatened by anthropogenic disturbances and climate change. Global actions to conserve tropical forests could be enhanced by having local knowledge on the forests' functional diversity and functional redundancy as proxies for their capacity to respond to global environmental change. Here we create estimates of plant functional diversity and redundancy across the tropics by combining a dataset of 16 morphological, chemical and photosynthetic plant traits sampled from 2,461 individual trees from 74 sites distributed across four continents together with local climate data for the past half century. Our findings suggest a strong link between climate and functional diversity and redundancy with the three trait groups responding similarly across the tropics and climate gradient. We show that drier tropical forests are overall less functionally diverse than wetter forests and that functional redundancy declines with increasing soil water and vapour pressure deficits. Areas with high functional diversity and high functional redundancy tend to better maintain ecosystem functioning, such as aboveground biomass, after extreme weather events. Our predictions suggest that the lower functional diversity and lower functional redundancy of drier tropical forests, in comparison with wetter forests, may leave them more at risk of shifting towards alternative states in face of further declines in water availability across tropical regions.

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

Spatially continuous data on functional diversity will improve our ability to predict global change impacts on ecosystem properties. We applied methods that combine imaging spectroscopy and foliar traits to estimate remotely sensed functional diversity in tropical forests across an Amazon-to-Andes elevation gradient (215 to 3537 m). We evaluated the scale dependency of community assembly processes and examined whether tropical forest productivity could be predicted by remotely sensed functional diversity. Functional richness of the community decreased with increasing elevation. Scale-dependent signals of trait convergence, consistent with environmental filtering, play an important role in explaining the range of trait variation within each site and along elevation. Single- and multitrait remotely sensed measures of functional diversity were important predictors of variation in rates of net and gross primary productivity. Our findings highlight the potential of remotely sensed functional diversity to inform trait-based ecology and trait diversity-ecosystem function linkages in hyperdiverse tropical forests.

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.

Covariance of Sun and Shade Leaf Traits Along a Tropical Forest Elevation Gradient.

Foliar trait adaptation to sun and shade has been extensively studied in the context of photosynthetic performance of plants, focusing on nitrogen allocation, light capture and use via chlorophyll pigments and leaf morphology; however, less is known about the potential sun-shade dichotomy of other functionally important foliar traits. In this study, we measured 19 traits in paired sun and shade leaves along a 3,500-m elevation gradient in southern Peru to test whether the traits differ with canopy position, and to assess if relative differences vary with species composition and/or environmental filters. We found significant sun-shade differences in leaf mass per area (LMA), photosynthetic pigments (Chl ab and Car), and δ13C. Sun-shade offsets among these traits remained constant with elevation, soil substrates, and species compositional changes. However, other foliar traits related to structure and chemical defense, and those defining general metabolic processes, did not differ with canopy position. Our results suggest that whole-canopy function is captured in many traits of sun leaves; however, photosynthesis-related traits must be scaled based on canopy light extinction. These findings show that top-of-canopy measurements of foliar chemistry from spectral remote sensing approaches map directly to whole-canopy foliar traits including shaded leaves that cannot be directly observed from above.

Solar radiation and functional traits explain the decline of forest primary productivity along a tropical elevation gradient.

One of the major challenges in ecology is to understand how ecosystems respond to changes in environmental conditions, and how taxonomic and functional diversity mediate these changes. In this study, we use a trait-spectra and individual-based model, to analyse variation in forest primary productivity along a 3.3 km elevation gradient in the Amazon-Andes. The model accurately predicted the magnitude and trends in forest productivity with elevation, with solar radiation and plant functional traits (leaf dry mass per area, leaf nitrogen and phosphorus concentration, and wood density) collectively accounting for productivity variation. Remarkably, explicit representation of temperature variation with elevation was not required to achieve accurate predictions of forest productivity, as trait variation driven by species turnover appears to capture the effect of temperature. Our semi-mechanistic model suggests that spatial variation in traits can potentially be used to estimate spatial variation in productivity at the landscape scale.

Scale dependence of canopy trait distributions along a tropical forest elevation gradient.

Average responses of forest foliar traits to elevation are well understood, but far less is known about trait distributional responses to elevation at multiple ecological scales. This limits our understanding of the ecological scales at which trait variation occurs in response to environmental drivers and change. We analyzed and compared multiple canopy foliar trait distributions using field sampling and airborne imaging spectroscopy along an Andes-to-Amazon elevation gradient. Field-estimated traits were generated from three community-weighting methods, and remotely sensed estimates of traits were made at three scales defined by sampling grain size and ecological extent. Field and remote sensing approaches revealed increases in average leaf mass per unit area (LMA), water, nonstructural carbohydrates (NSCs) and polyphenols with increasing elevation. Foliar nutrients and photosynthetic pigments displayed little to no elevation trend. Sample weighting approaches had little impact on field-estimated trait responses to elevation. Plot representativeness of trait distributions at landscape scales decreased with increasing elevation. Remote sensing indicated elevation-dependent increases in trait variance and distributional skew. Multiscale invariance of LMA, leaf water and NSC mark these traits as candidates for tracking forest responses to changing climate. Trait-based ecological studies can be greatly enhanced with multiscale studies made possible by imaging spectroscopy.

Variation in leaf wettability traits along a tropical montane elevation gradient.

Leaf wetting is often considered to have negative effects on plant function, such that wet environments may select for leaves with certain leaf surface, morphological, and architectural traits that reduce leaf wettability. However, there is growing recognition that leaf wetting can have positive effects. We measured variation in two traits, leaf drip tips and leaf water repellency, in a series of nine tropical forest communities occurring along a 3300-m elevation gradient in southern Peru. To extend this climatic gradient, we also assembled published leaf water repellency values from 17 additional sites. We then tested hypotheses for how these traits should vary as a function of climate. Contrary to expectations, we found that the proportion of species with drip tips did not increase with increasing precipitation. Instead, drip tips increased with increasing temperature. Moreover, leaf water repellency was very low in our sites and the global analysis indicated high repellency only in sites with low precipitation and temperatures. Our findings suggest that drip tips and repellency may not solely reflect the negative effects of wetting on plant function. Understanding the drivers of leaf wettability traits can provide insight into the effects of leaf wetting on plant, community, and ecosystem function.

Examining variation in the leaf mass per area of dominant species across two contrasting tropical gradients in light of community assembly.

Understanding variation in key functional traits across gradients in high diversity systems and the ecology of community changes along gradients in these systems is crucial in light of conservation and climate change. We examined inter- and intraspecific variation in leaf mass per area (LMA) of sun and shade leaves along a 3330-m elevation gradient in Peru, and in sun leaves across a forest-savanna vegetation gradient in Brazil. We also compared LMA variance ratios (T-statistics metrics) to null models to explore internal (i.e., abiotic) and environmental filtering on community structure along the gradients. Community-weighted LMA increased with decreasing forest cover in Brazil, likely due to increased light availability and water stress, and increased with elevation in Peru, consistent with the leaf economic spectrum strategy expected in colder, less productive environments. A very high species turnover was observed along both environmental gradients, and consequently, the first source of variation in LMA was species turnover. Variation in LMA at the genus or family levels was greater in Peru than in Brazil. Using dominant trees to examine possible filters on community assembly, we found that in Brazil, internal filtering was strongest in the forest, while environmental filtering was observed in the dry savanna. In Peru, internal filtering was observed along 80% of the gradient, perhaps due to variation in taxa or interspecific competition. Environmental filtering was observed at cloud zone edges and in lowlands, possibly due to water and nutrient availability, respectively. These results related to variation in LMA indicate that biodiversity in species rich tropical assemblages may be structured by differential niche-based processes. In the future, specific mechanisms generating these patterns of variation in leaf functional traits across tropical environmental gradients should be explored.

Quantifying ecological memory in plant and ecosystem processes.

The role of time in ecology has a long history of investigation, but ecologists have largely restricted their attention to the influence of concurrent abiotic conditions on rates and magnitudes of important ecological processes. Recently, however, ecologists have improved their understanding of ecological processes by explicitly considering the effects of antecedent conditions. To broadly help in studying the role of time, we evaluate the length, temporal pattern, and strength of memory with respect to the influence of antecedent conditions on current ecological dynamics. We developed the stochastic antecedent modelling (SAM) framework as a flexible analytic approach for evaluating exogenous and endogenous process components of memory in a system of interest. We designed SAM to be useful in revealing novel insights promoting further study, illustrated in four examples with different degrees of complexity and varying time scales: stomatal conductance, soil respiration, ecosystem productivity, and tree growth. Models with antecedent effects explained an additional 18-28% of response variation compared to models without antecedent effects. Moreover, SAM also enabled identification of potential mechanisms that underlie components of memory, thus revealing temporal properties that are not apparent from traditional treatments of ecological time-series data and facilitating new hypothesis generation and additional research.

Inclusion of vein traits improves predictive power for the leaf economic spectrum: a response to Sack et al. (2013).

Our model for the worldwide leaf economics spectrum (LES) based on venation networks (Blonder et al., 2011, 2013) was strongly criticized by Sack et al. (2013) in this journal. Here, we show that the majority of criticisms by Sack et al. are based on mathematical and conceptual misunderstandings. Using empirical data from both our original study as well as others in the literature, we show support for our original hypothesis, that venation networks provide predictive power and conceptual unification for the LES. In an effort to reconcile differing viewpoints related to the role of leaf venation traits for the LES, we highlight several lines of further investigation.

Quantifying the timescales over which exogenous and endogenous conditions affect soil respiration.

Understanding how exogenous and endogenous factors and above-ground-below-ground linkages modulate carbon dynamics is difficult because of the influences of antecedent conditions. For example, there are variable lags between above-ground assimilation and below-ground efflux, and the duration of antecedent periods are often arbitrarily assigned. Nonetheless, developing models linking above- and below-ground processes is crucial for estimating current and future carbon dynamics. We collected data on leaf-level photosynthesis (Asat ) and soil respiration (Rsoil ) in different microhabitats (under shrubs vs under bunchgrasses) in the Sonoran Desert. We evaluated timescales over which endogenous and exogenous factors control Rsoil by analyzing data in the context of a semimechanistic temperature-response model of Rsoil that incorporated effects of antecedent exogenous (soil water) and endogenous (Asat ) conditions. For both microhabitats, antecedent soil water and Asat significantly affected Rsoil , but Rsoil under shrubs was more sensitive to Asat than that under bunchgrasses. Photosynthetic rates 1 and 3 d before the Rsoil measurement were most important in determining current-day Rsoil under bunchgrasses and shrubs, respectively, indicating a significant lag effect. Endogenous and exogenous controls are critical drivers of Rsoil , but the relative importance and the timescale over which each factor affects Rsoil depends on above-ground vegetation and ecosystem structure characteristics.

An empirical assessment of tree branching networks and implications for plant allometric scaling models.

Several theories predict whole-tree function on the basis of allometric scaling relationships assumed to emerge from traits of branching networks. To test this key assumption, and more generally, to explore patterns of external architecture within and across trees, we measure branch traits (radii/lengths) and calculate scaling exponents from five functionally divergent species. Consistent with leading theories, including metabolic scaling theory, branching is area preserving and statistically self-similar within trees. However, differences among scaling exponents calculated at node- and whole-tree levels challenge the assumption of an optimised, symmetrically branching tree. Furthermore, scaling exponents estimated for branch length change across branching orders, and exponents for scaling metabolic rate with plant size (or number of terminal tips) significantly differ from theoretical predictions. These findings, along with variability in the scaling of branch radii being less than for branch lengths, suggest extending current scaling theories to include asymmetrical branching and differential selective pressures in plant architectures.

Allometric convergence in savanna trees and implications for the use of plant scaling models in variable ecosystems.

Theoretical models of allometric scaling provide frameworks for understanding and predicting how and why the morphology and function of organisms vary with scale. It remains unclear, however, if the predictions of 'universal' scaling models for vascular plants hold across diverse species in variable environments. Phenomena such as competition and disturbance may drive allometric scaling relationships away from theoretical predictions based on an optimized tree. Here, we use a hierarchical Bayesian approach to calculate tree-specific, species-specific, and 'global' (i.e. interspecific) scaling exponents for several allometric relationships using tree- and branch-level data harvested from three savanna sites across a rainfall gradient in Mali, West Africa. We use these exponents to provide a rigorous test of three plant scaling models (Metabolic Scaling Theory (MST), Geometric Similarity, and Stress Similarity) in savanna systems. For the allometric relationships we evaluated (diameter vs. length, aboveground mass, stem mass, and leaf mass) the empirically calculated exponents broadly overlapped among species from diverse environments, except for the scaling exponents for length, which increased with tree cover and density. When we compare empirical scaling exponents to the theoretical predictions from the three models we find MST predictions are most consistent with our observed allometries. In those situations where observations are inconsistent with MST we find that departure from theory corresponds with expected tradeoffs related to disturbance and competitive interactions. We hypothesize savanna trees have greater length-scaling exponents than predicted by MST due to an evolutionary tradeoff between fire escape and optimization of mechanical stability and internal resource transport. Future research on the drivers of systematic allometric variation could reconcile the differences between observed scaling relationships in variable ecosystems and those predicted by ideal models such as MST.

Differential daytime and night-time stomatal behavior in plants from North American deserts.

Night-time stomatal conductance (g(night)) occurs in many ecosystems, but the g(night) response to environmental drivers is relatively unknown, especially in deserts. Here, we conducted a Bayesian analysis of stomatal conductance (g) (N=5013) from 16 species in the Sonoran, Chihuahuan, Mojave and Great Basin Deserts (North America). We partitioned daytime g (g(day)) and g(night) responses by describing g as a mixture of two extreme (dark vs high light) behaviors. Significant g(night) was observed across 15 species, and the g(night) and g(day) behavior differed according to species, functional type and desert. The transition between extreme behaviors was determined by light environment, with the transition behavior differing between functional types and deserts. Sonoran and Chihuahuan C(4) grasses were more sensitive to vapor pressure difference (D) at night and soil water potential (Ψ(soil)) during the day, Great Basin C(3) shrubs were highly sensitive to D and Ψ(soil) during the day, and Mojave C(3) shrubs were equally sensitive to D and Ψ(soil) during the day and night. Species were split between the exhibition of isohydric or anisohydric behavior during the day. Three species switched from anisohydric to isohydric behavior at night. Such behavior, combined with differential D, Ψ(soil) and light responses, suggests that different mechanisms underlie g(day) and g(night) regulation.

Greater seasonal carbon gain across a broad temperature range contributes to the invasive potential of Phalaris arundinacea (Poaceae; reed canary grass) over the native sedge Carex stricta (Cyperaceae).

PREMISE OF THE STUDY: Most invasive plants grow faster and produce more biomass than the species that they displace, but physiological mechanisms leading to invasive success are poorly understood. To foster novel control approaches, our goal was to determine whether the grass Phalaris arundinacea possessed superior physiological strategies that contributed to its success over native sedges. METHODS: Data for spring, summer, and autumn diel gas-exchange, leaf morphology, and nitrogen content for plants of P. arundinacea and Carex stricta in water-saturated, drained, and periodically flooded sites in northern Indiana, USA, were compared with similar data for plants in a greenhouse. KEY RESULTS: Phalaris arundinacea had higher maximum CO(2) assimilation (A) across a broad range of temperatures, greater summer/autumn net carbon gain, higher water use efficiencies, larger leaf areas per shoot, and higher specific leaf areas than did C. stricta. Species differences in gas-exchange data were similar in the greenhouse. However, long-term flooding reduced A for P. arundinacea. Greater declines in leaf A and nitrogen content from July to October compared to P. arundinacea were suggestive of earlier leaf senescence for C. stricta. CONCLUSIONS: We propose that superior daily and seasonal carbon gain, especially during rhizome carbohydrate storage in the summer and autumn, contribute to the success of invasive P. arundinacea over C. stricta. This advantage may be enhanced by frequent summer/autumn heat waves. The poor performance of P. arundinacea during long-term flooding is consistent with C. stricta's dominance in water-saturated soil, implying that water management strategies could be crucial to controlling P. arundinacea.

Venation networks and the origin of the leaf economics spectrum.

The leaf economics spectrum describes biome-invariant scaling functions for leaf functional traits that relate to global primary productivity and nutrient cycling. Here, we develop a comprehensive framework for the origin of this leaf economics spectrum based on venation-mediated economic strategies. We define a standardized set of traits - density, distance and loopiness - that provides a common language for the study of venation. We develop a novel quantitative model that uses these venation traits to model leaf-level physiology, and show that selection to optimize the venation network predicts the mean global trait-trait scaling relationships across 2548 species. Furthermore, using empirical venation data for 25 plant species, we test our model by predicting four key leaf functional traits related to leaf economics: net carbon assimilation rate, life span, leaf mass per area ratio and nitrogen content. Together, these results indicate that selection on venation geometry is a fundamental basis for understanding the diversity of leaf form and function, and the carbon balance of leaves. The model and associated predictions have broad implications for integrating venation network geometry with pattern and process in ecophysiology, ecology and palaeobotany.