Vegetation demographics in Earth System Models: A review of progress and priorities

Improving plant allometry by fusing forest models and remote sensing

Allometry determines how tree shape and function scale with each other, related through size. Allometric relationships help scale processes from the individual to the global scale and constitute a core component of vegetation models. Allometric relationships have been expected to emerge from optimisation theory, yet this does not suitably predict empirical data. Here we argue that the fusion of high-resolution data, such as those derived from airborne laser scanning, with individual-based forest modelling offers insight into how plant size contributes to large-scale biogeochemical processes. We review the challenges in allometric scaling, how they can be tackled by advances in data-model fusion, and how individual-based models can serve as data integrators for dynamic global vegetation models.

The influence of canopy radiation parameter uncertainty on model projections of terrestrial carbon and energy cycling

Reducing uncertainties in Earth System Model predictions requires carefully evaluating core model processes. Here we examined how canopy radiative transfer model (RTM) parameter uncertainties, in combination with canopy structure, affect terrestrial carbon and energy projections in a demographic land-surface model, the Ecosystem Demography model (ED2). Our analyses focused on temperate deciduous forests and tested canopies of varying structural complexity. The results showed a strong sensitivity of tree productivity, albedo, and energy balance projections to RTM parameters. Impacts of radiative parameter uncertainty on stand-level canopy net primary productivity ranged from ~2 to > 20% and was most sensitive to canopy clumping and leaf reflectance/transmittance in the visible spectrum (~400–750 nm). ED2 canopy albedo varied by ~1 to ~10% and was most sensitive to near-infrared reflectance (~800–1200 nm). Bowen ratio, in turn, was most sensitive to wood optical properties parameterization; this was much larger than expected based on literature, suggesting model instabilities. In vertically and spatially complex canopies the model response to RTM parameterization may show an apparent reduced sensitivity when compared to simpler canopies, masking much larger changes occurring within the canopy. Our findings highlight both the importance of constraining canopy RTM parameters in models and valuating how the canopy structure responds to those parameter values. Finally, we advocate for more model evaluation, similar to this study, to highlight possible issues with model behavior or process representations, particularly models with demographic representations, and identify potential ways to inform and constrain model predictions.

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.

Clothing the Emperor: Dynamic Root-Shoot Allocation Trajectories in Relation to Whole-Plant Growth Rate and in Response to Temperature

We quantified how root-shoot biomass allocation and whole-plant growth rate co-varied ontogenetically in contrasting species in response to cooling. Seven grass and four forb species were grown for 56 days in hydroponics. Growth was measured repeatedly before and after day/night temperatures were reduced at 28 days from 20 °C/15 °C to 10 °C/5 °C; controls remained unchanged. Sigmoid trajectories of root and shoot growth were reconstructed from the experimental data to derive continuous whole-plant relative growth rates (RGRs) and root mass fractions (RMFs). Root mass fractions in cooled plants generally increased, but this originated from unexpected and previously uncharacterised differences in response among species. Root mass fraction and RGR co-trajectories were idiosyncratic in controls and cooled plants. The RGR-RMF co-trajectories responded to cooling in grasses, but not forbs. The RMF responses of stress-tolerant grasses were predictably weak but projected to eventually out-respond faster-growing species. Sigmoid growth constrains biomass allocation. Only when neither root nor shoot biomass is near-maximal can biomass allocation respond to environmental drivers. Near maximum size, plants cannot adjust RMF, which then reflects net above- and belowground productivities. Ontogenetic biomass allocations are not equivalent to those based on interspecific surveys, especially in mature vegetation. This reinforces the importance of measuring temporal growth dynamics, and not relying on "snapshot" comparisons to infer the functional significance of root-shoot allocation.

Spatial patterning among savanna trees in high-resolution, spatially extensive data

In savannas, predicting how vegetation varies is a longstanding challenge. Spatial patterning in vegetation may structure that variability, mediated by spatial interactions, including competition and facilitation. Here, we use unique high-resolution, spatially extensive data of tree distributions in an African savanna, derived from airborne Light Detection and Ranging (LiDAR), to examine tree-clustering patterns. We show that tree cluster sizes were governed by power laws over two to three orders of magnitude in spatial scale and that the parameters on their distributions were invariant with respect to underlying environment. Concluding that some universal process governs spatial patterns in tree distributions may be premature. However, we can say that, although the tree layer may look unpredictable locally, at scales relevant to prediction in, e.g., global vegetation models, vegetation is instead strongly structured by regular statistical distributions.

A heuristic classification of woody plants based on contrasting shade and drought strategies

Woody plants vary in their adaptations to drought and shade. For a better prediction of vegetation responses to drought and shade within dynamic global vegetation models, it is critical to group species into functional types with similar adaptations. One of the key challenges is that the adaptations are generally determined by a large number of plant traits that may not be available for a large number of species. In this study, we present two heuristic woody plant groups that were separated using cluster analysis in a three-dimensional trait-environment space based on three key metrics for each species: mean xylem embolism resistance, shade tolerance and habitat aridity. The two heuristic groups separate these species into tolerators and avoiders. The tolerators either rely on their high embolism resistance to tolerate drought in arid habitats (e.g., Juniperus and Prunus) or rely on high shade tolerance to withstand shaded conditions in wet habitats (e.g., Picea, Abies and Acer). In contrast, all avoiders have low embolism resistance and low shade tolerance. In arid habitats, avoiders tend to minimize catastrophic embolism (e.g., most Pinus species) while in wet habitats, they may survive despite low shade tolerance (e.g., Betula, Populus, Alnus and Salix). Because our approach links traits to the environmental conditions, we expect it could be a promising framework for predicting changes in species composition, and therefore ecosystem function, under changing environmental conditions.

Seasonal and drought-related changes in leaf area profiles depend on height and light environment in an Amazon forest

Seasonal dynamics in the vertical distribution of leaf area index (LAI) may impact the seasonality of forest productivity in Amazonian forests. However, until recently, fine-scale observations critical to revealing ecological mechanisms underlying these changes have been lacking. To investigate fine-scale variation in leaf area with seasonality and drought we conducted monthly ground-based LiDAR surveys over 4 yr at an Amazon forest site. We analysed temporal changes in vertically structured LAI along axes of both canopy height and light environments. Upper canopy LAI increased during the dry season, whereas lower canopy LAI decreased. The low canopy decrease was driven by highly illuminated leaves of smaller trees in gaps. By contrast, understory LAI increased concurrently with the upper canopy. Hence, tree phenological strategies were stratified by height and light environments. Trends were amplified during a 2015-2016 severe El Niño drought. Leaf area low in the canopy exhibited behaviour consistent with water limitation. Leaf loss from short trees in high light during drought may be associated with strategies to tolerate limited access to deep soil water and stressful leaf environments. Vertically and environmentally structured phenological processes suggest a critical role of canopy structural heterogeneity in seasonal changes in Amazon ecosystem function.

Modelling water fluxes in plants: from tissues to biosphere

Contents Summary 1207 I. Introduction 1207 II. A brief history of modelling plant water fluxes 1208 III. Main components of plant water transport models 1208 IV. Stand-scale water fluxes and coupling to climate and soil 1213 V. Water fluxes in terrestrial biosphere models and feedbacks to community dynamics 1215 VI. Outstanding challenges in modelling water fluxes in the soil-plant-atmosphere continuum 1217 Acknowledgements 1218 References 1218 SUMMARY: Models of plant water fluxes have evolved from studies focussed on understanding the detailed structure and functioning of specific components of the soil-plant-atmosphere (SPA) continuum to architectures often incorporated inside eco-hydrological and terrestrial biosphere (TB) model schemes. We review here the historical evolution of this field, examine the basic structure of a simplified individual-based model of plant water transport, highlight selected applications for specific ecological problems and conclude by examining outstanding issues requiring further improvements in modelling vegetation water fluxes. We particularly emphasise issues related to the scaling from tissue-level traits to individual-based predictions of water transport, the representation of nonlinear and hysteretic behaviour in soil-xylem hydraulics and the need to incorporate knowledge of hydraulics within broader frameworks of plant ecological strategies and their consequences for predicting community demography and dynamics.

Approaching the potential of model-data comparisons of global land carbon storage

Carbon storage dynamics in vegetation and soil are determined by the balance of carbon influx and turnover. Estimates of these opposing fluxes differ markedly among different empirical datasets and models leading to uncertainty and divergent trends. To trace the origin of such discrepancies through time and across major biomes and climatic regions, we used a model-data fusion framework. The framework emulates carbon cycling and its component processes in a global dynamic ecosystem model, LPJ-GUESS, and preserves the model-simulated pools and fluxes in space and time. Thus, it allows us to replace simulated carbon influx and turnover with estimates derived from empirical data, bringing together the strength of the model in representing processes, with the richness of observational data informing the estimations. The resulting vegetation and soil carbon storage and global land carbon fluxes were compared to independent empirical datasets. Results show model-data agreement comparable to, or even better than, the agreement between independent empirical datasets. This suggests that only marginal improvement in land carbon cycle simulations can be gained from comparisons of models with current-generation datasets on vegetation and soil carbon. Consequently, we recommend that model skill should be assessed relative to reference data uncertainty in future model evaluation studies.

How are nitrogen availability, fine-root mass, and nitrogen uptake related empirically? Implications for models and theory

Understanding the effects of global change in terrestrial communities requires an understanding of how limiting resources interact with plant traits to affect productivity. Here, we focus on nitrogen and ask whether plant community nitrogen uptake rate is determined (a) by nitrogen availability alone or (b) by the product of nitrogen availability and fine-root mass. Surprisingly, this is not empirically resolved. We performed controlled microcosm experiments and reanalyzed published pot experiments and field data to determine the relationship between community-level nitrogen uptake rate, nitrogen availability, and fine-root mass for 46 unique combinations of species, nitrogen levels, and growing conditions. We found that plant community nitrogen uptake rate was unaffected by fine-root mass in 63% of cases and saturated with fine-root mass in 29% of cases (92% in total). In contrast, plant community nitrogen uptake rate was clearly affected by nitrogen availability. The results support the idea that although plants may over-proliferate fine roots for individual-level competition, it comes without an increase in community-level nitrogen uptake. The results have implications for the mechanisms included in coupled carbon-nitrogen terrestrial biosphere models (CN-TBMs) and are consistent with CN-TBMs that operate above the individual scale and omit fine-root mass in equations of nitrogen uptake rate but inconsistent with the majority of CN-TBMs, which operate above the individual scale and include fine-root mass in equations of nitrogen uptake rate. For the much smaller number of CN-TBMs that explicitly model individual-based belowground competition for nitrogen, the results suggest that the relative (not absolute) fine-root mass of competing individuals should be included in the equations that determine individual-level nitrogen uptake rates. By providing empirical data to support the assumptions used in CN-TBMs, we put their global climate change predictions on firmer ground.

Plant traits inform predictions of tundra responses to global change

Contents Summary 1742 I. Introduction 1742 II. The global context of tundra trait variation 1743 III. The current state of knowledge on trait change in the tundra biome 1744 IV. The links between traits and ecosystem functions 1744 V. Future priorities for tundra trait research 1746 VI. Conclusions 1746 References 1747 SUMMARY: In the rapidly warming tundra biome, plant traits provide an essential link between ongoing vegetation change and feedbacks to key ecosystem functions. However, only recently have comprehensive trait data been compiled for tundra species and sites, allowing us to assess key elements of functional responses to global change. In this review, we summarize trait-based research in tundra ecosystems, with a focus on three components: plant trait variation and how it compares with global patterns; shifts in community-level traits in response to environmental change; and the use of traits to understand and predict ecosystem function. Quantifying patterns and trends in plant traits will allow us to better project the consequences of environmental change for the ecology and functioning of tundra ecosystems.

Greater stem growth, woody allocation, and aboveground biomass in Paleotropical forests than in Neotropical forests

Forest dynamics and tree species composition vary substantially between Paleotropical and Neotropical forests, but these broad biogeographic regions are treated uniformly in many land models. To assess whether these regional differences translate into variation in productivity and carbon (C) storage, we compiled a database of climate, tree stem growth, litterfall, aboveground net primary production (ANPP), and aboveground biomass across tropical rainforest sites spanning 33 countries throughout Central and South America, Asia, and Australasia, but excluding Africa due to a paucity of available data. Though the sum of litterfall and stem growth (ANPP) did not differ between regions, both stem growth and the ratio of stem growth to litterfall were higher in Paleotropical forests compared to Neotropical forests across the full observed range of ANPP. Greater C allocation to woody growth likely explains the much larger aboveground biomass estimates in Paleotropical forests (~29%, or ~80 Mg DW/ha, greater than in the Neotropics). Climate was similar in Paleo- and Neotropical forests, thus the observed differences in C likely reflect differences in the evolutionary history of species and forest structure and function between regions. Our analysis suggests that Paleotropical forests, which can be dominated by tall-statured Dipterocarpaceae species, may be disproportionate hotspots for aboveground C storage. Land models typically treat these distinct tropical forests with differential structures as a single functional unit, but our findings suggest that this may overlook critical biogeographic variation in C storage potential among regions.

A Forest Model Intercomparison Framework and Application at Two Temperate Forests Along the East Coast of the United States

State-of-the-art forest models are often complex, analytically intractable, and computationally expensive, due to the explicit representation of detailed biogeochemical and ecological processes. Different models often produce distinct results while predictions from the same model vary with parameter values. In this project, we developed a rigorous quantitative approach for conducting model intercomparisons and assessing model performance. We have applied our original methodology to compare two forest biogeochemistry models, the Perfect Plasticity Approximation with Simple Biogeochemistry (PPA-SiBGC) and Landscape Disturbance and Succession with Net Ecosystem Carbon and Nitrogen (LANDIS-II NECN). We simulated past-decade conditions at flux tower sites located within Harvard Forest, MA, USA (HF-EMS) and Jones Ecological Research Center, GA, USA (JERC-RD). We mined field data available from both sites to perform model parameterization, validation, and intercomparison. We assessed model performance using the following time-series metrics: Net ecosystem exchange, aboveground net primary production, aboveground biomass, C, and N, belowground biomass, C, and N, soil respiration, and species total biomass and relative abundance. We also assessed static observations of soil organic C and N, and concluded with an assessment of general model usability, performance, and transferability. Despite substantial differences in design, both models achieved good accuracy across the range of pool metrics. While LANDIS-II NECN showed better fidelity to interannual NEE fluxes, PPA-SiBGC indicated better overall performance for both sites across the 11 temporal and two static metrics tested (HF-EMS R 2 ¯ = 0.73 , + 0.07 , R M S E ¯ = 4.68 , − 9.96 ; JERC-RD R 2 ¯ = 0.73 , + 0.01 , R M S E ¯ = 2.18 , − 1.64 ). To facilitate further testing of forest models at the two sites, we provide pre-processed datasets and original software written in the R language of statistical computing. In addition to model intercomparisons, our approach may be employed to test modifications to forest models and their sensitivity to different parameterizations.

Crown damage and the mortality of tropical trees

What causes individual tree death in tropical forests remains a major gap in our understanding of the biology of tropical trees and leads to significant uncertainty in predicting global carbon cycle dynamics. We measured individual characteristics (diameter at breast height, wood density, growth rate, crown illumination and crown form) and environmental conditions (soil fertility and habitat suitability) for 26 425 trees ≥ 10 cm diameter at breast height belonging to 416 species in a 52-ha plot in Lambir Hills National Park, Malaysia. We used structural equation models to investigate the relationships among the different factors and tree mortality. Crown form (a proxy for mechanical damage and other stresses) and prior growth were the two most important factors related to mortality. The effect of all variables on mortality (except habitat suitability) was substantially greater than expected by chance. Tree death is the result of interactions between factors, including direct and indirect effects. Crown form/damage and prior growth mediated most of the effect of tree size, wood density, fertility and habitat suitability on mortality. Large-scale assessment of crown form or status may result in improved prediction of individual tree death at the landscape scale.

Moisture pulse-reserve in the soil-plant continuum observed across biomes

The degree to which individual pulses of available water drive plant activity across diverse biomes and climates is not well understood. It has previously only been investigated in a few dryland locations. Here, plant water uptake following pulses of surface soil moisture, an indicator for the pulse-reserve hypothesis, is investigated across South America, Africa and Australia with satellite-based estimates of surface soil and canopy water content. Our findings show that this behaviour is widespread: occurring over half of the vegetated landscapes. We estimate spatially varying soil moisture thresholds at which plant water uptake ceases, noting dependence on soil texture and proximity to the wilting point. The soil type and biome-dependent soil moisture threshold and the plant soil water uptake patterns at the scale of Earth system models allow a unique opportunity to test and improve model parameterization of vegetation function under water limitation.

Traits drive global wood decomposition rates more than climate

Wood decomposition is a major component of the global carbon cycle. Decomposition rates vary across climate gradients, which is thought to reflect the effects of temperature and moisture on the metabolic kinetics of decomposers. However, decomposition rates also vary with wood traits, which may reflect the influence of stoichiometry on decomposer metabolism as well as geometry relating the surface areas that decomposers colonize with the volumes they consume. In this paper, we combined metabolic and geometric scaling theories to formalize hypotheses regarding the drivers of wood decomposition rates, and assessed these hypotheses using a global compilation of data on climate, wood traits, and wood decomposition rates. Our results are consistent with predictions from both metabolic and geometric scaling theories. Approximately half of the global variation in decomposition rates was explained by wood traits (nitrogen content and diameter), whereas only a fifth was explained by climate variables (air temperature, precipitation, and relative humidity). These results indicate that global variation in wood decomposition rates is best explained by stoichiometric and geometric wood traits. Our findings suggest that inclusion of wood traits in global carbon cycle models can improve predictions of carbon fluxes from wood decomposition.

A stomatal control model based on optimization of carbon gain versus hydraulic risk predicts aspen sapling responses to drought

Empirical models of plant drought responses rely on parameters that are difficult to specify a priori. We test a trait- and process-based model to predict environmental responses from an optimization of carbon gain vs hydraulic risk. We applied four drought treatments to aspen (Populus tremuloides) saplings in a research garden. First we tested the optimization algorithm by using predawn xylem pressure as an input. We then tested the full model which calculates root-zone water budget and xylem pressure hourly throughout the growing season. The optimization algorithm performed well when run from measured predawn pressures. The per cent mean absolute error (MAE) averaged 27.7% for midday xylem pressure, transpiration, net assimilation, leaf temperature, sapflow, diffusive conductance and soil-canopy hydraulic conductance. Average MAE was 31.2% for the same observations when the full model was run from irrigation and rain data. Saplings that died were projected to exceed 85% loss in soil-canopy hydraulic conductance, whereas surviving plants never reached this threshold. The model fit was equivalent to that of an empirical model, but with the advantage that all inputs are specific traits. Prediction is empowered because knowing these traits allows knowing the response to climatic stress.

A Wood Biology Agenda to Support Global Vegetation Modelling

Realistic forecasting of forest responses to climate change critically depends on key advancements in global vegetation modelling. Compared with traditional 'big-leaf' models that simulate forest stands, 'next-generation' vegetation models aim to track carbon-, light-, water-, and nutrient-limited growth of individual trees. Wood biology can play an important role in delivering the required knowledge at tissue-to-individual levels, at minute-to-century scales and for model parameterization and benchmarking. We propose a wood biology research agenda that contributes to filling six knowledge gaps: sink versus source limitation, drivers of intra-annual growth, drought impacts, functional wood traits, dynamic biomass allocation, and nutrient cycling. Executing this agenda will expedite model development and increase the ability of models to forecast global change impact on forest dynamics.

Semi-Automated Delineation of Stands in an Even-Age Dominated Forest: A LiDAR-GEOBIA Two-Stage Evaluation Strategy

Regional scale maps of homogeneous forest stands are valued by forest managers and are of interest for landscape and ecological modelling. Research focused on stand delineation has substantially increased in the last decade thanks to the development of Geographic Object Based Image Analysis (GEOBIA). Nevertheless, studies focused on even-age dominated forests are still few and the proposed approaches are often heuristic, local, or lacking objective evaluation protocols. In this study, we present a two-stage evaluation strategy combining both unsupervised and supervised evaluation methods for semi-automatic delineation of forest stands at regional scales using Light Detection and Ranging (LiDAR) raster summary metrics. The methodology is demonstrated on two contiguous LiDAR datasets covering more than 54,000 ha in central Idaho, where clearcuts were a common harvesting method during the twentieth century. Results show good delineation of even-aged forests and demonstrate the ability of LiDAR to discriminate stands harvested more than 50 years ago, that are generally challenging to discriminate with optical data. The two-stage strategy reduces the reference data required within the supervised evaluation and increases the scope of a reliable semi-automatic delineation to larger areas. This is an objective and straightforward approach that could potentially be replicated and adapted to address other study needs.

Modelling tropical forest responses to drought and El Niño with a stomatal optimization model based on xylem hydraulics

The current generation of dynamic global vegetation models (DGVMs) lacks a mechanistic representation of vegetation responses to soil drought, impairing their ability to accurately predict Earth system responses to future climate scenarios and climatic anomalies, such as El Niño events. We propose a simple numerical approach to model plant responses to drought coupling stomatal optimality theory and plant hydraulics that can be used in dynamic global vegetation models (DGVMs). The model is validated against stand-scale forest transpiration (E) observations from a long-term soil drought experiment and used to predict the response of three Amazonian forest sites to climatic anomalies during the twentieth century. We show that our stomatal optimization model produces realistic stomatal responses to environmental conditions and can accurately simulate how tropical forest E responds to seasonal, and even long-term soil drought. Our model predicts a stronger cumulative effect of climatic anomalies in Amazon forest sites exposed to soil drought during El Niño years than can be captured by alternative empirical drought representation schemes. The contrasting responses between our model and empirical drought factors highlight the utility of hydraulically-based stomatal optimization models to represent vegetation responses to drought and climatic anomalies in DGVMs.This article is part of a discussion meeting issue 'The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.

Thinning Can Reduce Losses in Carbon Use Efficiency and Carbon Stocks in Managed Forests Under Warmer Climate

Forest carbon use efficiency (CUE, the ratio of net to gross primary productivity) represents the fraction of photosynthesis that is not used for plant respiration. Although important, it is often neglected in climate change impact analyses. Here we assess the potential impact of thinning on projected carbon cycle dynamics and implications for forest CUE and its components (i.e., gross and net primary productivity and plant respiration), as well as on forest biomass production. Using a detailed process-based forest ecosystem model forced by climate outputs of five Earth System Models under four representative climate scenarios, we investigate the sensitivity of the projected future changes in the autotrophic carbon budget of three representative European forests. We focus on changes in CUE and carbon stocks as a result of warming, rising atmospheric CO2 concentration, and forest thinning. Results show that autotrophic carbon sequestration decreases with forest development, and the decrease is faster with warming and in unthinned forests. This suggests that the combined impacts of climate change and changing CO2 concentrations lead the forests to grow faster, mature earlier, and also die younger. In addition, we show that under future climate conditions, forest thinning could mitigate the decrease in CUE, increase carbon allocation into more recalcitrant woody pools, and reduce physiological-climate-induced mortality risks. Altogether, our results show that thinning can improve the efficacy of forest-based mitigation strategies and should be carefully considered within a portfolio of mitigation options.

Water stress limits transpiration and growth of European larch up to the lower subalpine belt in an inner-alpine dry valley

Climate change will further constrain water availability in dry inner-alpine environments and affect water relations and growth conditions in mountain forests, including the widespread larch forests. To estimate the effects of climate conditions on water balance and growth, variation in sap flow and stem radius of European larch was measured for 3 yr along an elevation transect from 1070 to 2250 m above sea level (asl) in an inner-alpine dry valley in South Tyrol/Italy. Additionally, long-term climate-growth relations were derived from tree cores. Sap flow and radial growth were reduced in dry periods up to an elevation of 1715 m, leading to maximum annual growth at 2000 m. In a wet year no growth difference between elevations was observed. Long-term tree ring data showed a positive growth response to precipitation up to 1715 m and to temperature only above 2000 m. Our results demonstrate that reduced water availability and higher atmospheric water demand limit larch at low elevation within dry Alpine regions. This indicates a general upward shift of this species' elevational amplitude upon climate change, and respective negative effects on future silvicultural use and ecosystem services at lower elevations in the European Alps.

Topography and neighborhood crowding can interact to shape species growth and distribution in a diverse Amazonian forest

Abiotic constraints and biotic interactions act simultaneously to shape communities. However, these community assembly mechanisms are often studied independently, which can limit understanding of how they interact to affect species dynamics and distributions. We develop a hierarchical Bayesian neighborhood modeling approach to quantify the simultaneous effects of topography and crowding by neighbors on the growth of 124,704 individual stems ≥1 cm DBH for 1,047 tropical tree species in a 25-ha mapped rainforest plot in Amazonian Ecuador. We build multi-level regression models to evaluate how four key functional traits (specific leaf area, maximum tree size, wood specific gravity and seed mass) mediate tree growth response to topography and neighborhood crowding. Tree growth is faster in valleys than on ridges and is reduced by neighborhood crowding. Topography and crowding interact to influence tree growth in ~10% of the species. Specific leaf area, maximum tree size and seed mass are associated with growth responses to topography, but not with responses to neighborhood crowding or with the interaction between topography and crowding. In sum, our study reveals that topography and neighborhood crowding each influence tree growth in tropical forests, but act largely independently in shaping species distributions. While traits were associated with species response to topography, their role in species response to neighborhood crowding was less clear, which suggests that trait effects on neighborhood dynamics may depend on the direction (negative/positive) and degree of symmetry of biotic interactions. Our study emphasizes the importance of simultaneously assessing the individual and interactive role of multiple mechanisms in shaping species dynamics in high diversity tropical systems.

Climate sensitive size-dependent survival in tropical trees

Survival rates of large trees determine forest biomass dynamics. Survival rates of small trees have been linked to mechanisms that maintain biodiversity across tropical forests. How species survival rates change with size offers insight into the links between biodiversity and ecosystem function across tropical forests. We tested patterns of size-dependent tree survival across the tropics using data from 1,781 species and over 2 million individuals to assess whether tropical forests can be characterized by size-dependent life-history survival strategies. We found that species were classifiable into four 'survival modes' that explain life-history variation that shapes carbon cycling and the relative abundance within forests. Frequently collected functional traits, such as wood density, leaf mass per area and seed mass, were not generally predictive of the survival modes of species. Mean annual temperature and cumulative water deficit predicted the proportion of biomass of survival modes, indicating important links between evolutionary strategies, climate and carbon cycling. The application of survival modes in demographic simulations predicted biomass change across forest sites. Our results reveal globally identifiable size-dependent survival strategies that differ across diverse systems in a consistent way. The abundance of survival modes and interaction with climate ultimately determine forest structure, carbon storage in biomass and future forest trajectories.

Ecosystem heterogeneity and diversity mitigate Amazon forest resilience to frequent extreme droughts

The impact of increases in drought frequency on the Amazon forest's composition, structure and functioning remain uncertain. We used a process- and individual-based ecosystem model (ED2) to quantify the forest's vulnerability to increased drought recurrence. We generated meteorologically realistic, drier-than-observed rainfall scenarios for two Amazon forest sites, Paracou (wetter) and Tapajós (drier), to evaluate the impacts of more frequent droughts on forest biomass, structure and composition. The wet site was insensitive to the tested scenarios, whereas at the dry site biomass declined when average rainfall reduction exceeded 15%, due to high mortality of large-sized evergreen trees. Biomass losses persisted when year-long drought recurrence was shorter than 2-7 yr, depending upon soil texture and leaf phenology. From the site-level scenario results, we developed regionally applicable metrics to quantify the Amazon forest's climatological proximity to rainfall regimes likely to cause biomass loss > 20% in 50 yr according to ED2 predictions. Nearly 25% (1.8 million km² ) of the Amazon forests could experience frequent droughts and biomass loss if mean annual rainfall or interannual variability changed by 2σ. At least 10% of the high-emission climate projections (CMIP5/RCP8.5 models) predict critically dry regimes over 25% of the Amazon forest area by 2100.

Variation in hydroclimate sustains tropical forest biomass and promotes functional diversity

The fate of tropical forests under climate change is unclear as a result, in part, of the uncertainty in projected changes in precipitation and in the ability of vegetation models to capture the effects of drought-induced mortality on aboveground biomass (AGB). We evaluated the ability of a terrestrial biosphere model with demography and hydrodynamics (Ecosystem Demography, ED2-hydro) to simulate AGB and mortality of four tropical tree plant functional types (PFTs) that operate along light- and water-use axes. Model predictions were compared with observations of canopy trees at Barro Colorado Island (BCI), Panama. We then assessed the implications of eight hypothetical precipitation scenarios, including increased annual precipitation, reduced inter-annual variation, El Niño-related droughts and drier wet or dry seasons, on AGB and functional diversity of the model forest. When forced with observed meteorology, ED2-hydro predictions capture multiple BCI benchmarks. ED2-hydro predicts that AGB will be sustained under lower rainfall via shifts in the functional composition of the forest, except under the drier dry-season scenario. These results support the hypothesis that inter-annual variation in mean and seasonal precipitation promotes the coexistence of functionally diverse PFTs because of the relative differences in mortality rates. If the hydroclimate becomes chronically drier or wetter, functional evenness related to drought tolerance may decline.

Plant Hydraulic Trait Covariation: A Global Meta-Analysis to Reduce Degrees of Freedom in Trait-Based Hydrologic Models

Current vegetation modeling strategies use broad categorizations of plants to estimate transpiration and biomass functions. A significant source of model error stems from vegetation categorizations that are mostly taxonomical with no basis in plant hydraulic strategy and response to changing environmental conditions. Here, we compile hydraulic traits from 355 species around the world to determine trait covariations in order to represent hydraulic strategies. Simple and stepwise regression analyses demonstrate the interconnectedness of multiple vegetative hydraulic traits, specifically, traits defining hydraulic conductivity and vulnerability to embolism with wood density and isohydricity. Drought sensitivity is strongly (Adjusted R2 = 0.52, p < 0.02) predicted by a stepwise linear model combining rooting depth, wood density, and isohydricity. Drought tolerance increased with increasing wood density and anisohydric response, but with decreasing rooting depth. The unexpected response to rooting depth may be due to other tradeoffs within the hydraulic system. Rooting depth was able to be predicted from sapwood specific conductivity and the water potential at 50% loss of conductivity. Interestingly, the influences of biome or growth form do not increase the accuracy of the drought tolerance model and were able to be omitted. Multiple regression analysis revealed 3D trait spaces and tradeoff axes along which species’ hydraulic strategies can be analyzed. These numerical trait spaces can reduce the necessary input to and parameterization of plant hydraulics modules, while increasing the physical representativeness of such simulations.

Uncertainty Quantification of Extratropical Forest Biomass in CMIP5 Models over the Northern Hemisphere

Simplified representations of processes influencing forest biomass in Earth system models (ESMs) contribute to large uncertainty in projections. We evaluate forest biomass from eight ESMs outputs archived in the Coupled Model Intercomparison Project Phase 5 (CMIP5) using the biomass data synthesized from radar remote sensing and ground-based observations across northern extratropical latitudes. ESMs exhibit large biases in the forest distribution, forest fraction, and mass of carbon pools that contribute to uncertainty in forest total biomass (biases range from −20 Pg C to 135 Pg C). Forest total biomass is primarily positively correlated with precipitation variations, with surface temperature becoming equally important at higher latitudes, in both simulations and observations. Relatively small differences in forest biomass between the pre-industrial period and the contemporary period indicate uncertainties in forest biomass were introduced in the pre-industrial model equilibration (spin-up), suggesting parametric or structural model differences are a larger source of uncertainty than differences in transient responses. Our findings emphasize the importance of improved (1) models of carbon allocation to biomass compartments, (2) distribution of vegetation types in models, and (3) reproduction of pre-industrial vegetation conditions, in order to reduce the uncertainty in forest biomass simulated by ESMs.

Shifts in the dynamics of productivity signal ecosystem state transitions at the biome-scale

Understanding ecosystem dynamics and predicting directional changes in ecosystem in response to global changes are ongoing challenges in ecology. Here we present a framework that links productivity dynamics and ecosystem state transitions based on a spatially continuous dataset of aboveground net primary productivity (ANPP) from the temperate grassland of China. Across a regional precipitation gradient, we quantified spatial patterns in ANPP dynamics (variability, asymmetry and sensitivity to rainfall) and related these to transitions from desert to semi-arid to mesic steppe. We show that these three indices of ANPP dynamics displayed distinct spatial patterns, with peaks signalling transitions between grassland types. Thus, monitoring shifts in ANPP dynamics has the potential for predicting ecosystem state transitions in the future. Current ecosystem models fail to capture these dynamics, highlighting the need to incorporate more nuanced ecological controls of productivity in models to forecast future ecosystem shifts.

The Calibration and Use of Capacitance Sensors to Monitor Stem Water Content in Trees

Water transport and storage through the soil-plant-atmosphere continuum is critical to the terrestrial water cycle, and has become a major research focus area. Biomass capacitance plays an integral role in the avoidance of hydraulic impairment to transpiration. However, high temporal resolution measurements of dynamic changes in the hydraulic capacitance of large trees are rare. Here, we present procedures for the calibration and use of capacitance sensors, typically used to monitor soil water content, to measure the volumetric water content in trees in the field. Frequency domain reflectometry-style observations are sensitive to the density of the media being studied. Therefore, it is necessary to perform species-specific calibrations to convert from the sensor-reported values of dielectric permittivity to volumetric water content. Calibration is performed on a harvested branch or stem cut into segments that are dried or re-hydrated to produce a full range of water contents used to generate a best-fit regression with sensor observations. Sensors are inserted into calibration segments or installed in trees after pre-drilling holes to a tolerance fit using a fabricated template to ensure proper drill alignment. Special care is taken to ensure that sensor tines make good contact with the surrounding media, while allowing them to be inserted without excessive force. Volumetric water content dynamics observed via the presented methodology align with sap flow measurements recorded using thermal dissipation techniques and environmental forcing data. Biomass water content data can be used to observe the onset of water stress, drought response and recovery, and has the potential to be applied to the calibration and evaluation of new plant-level hydrodynamics models, as well as to the partitioning of remotely sensed moisture products into above- and belowground components.