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Biomass allocation in plants is the foundation for understanding dynamics in ecosystem carbon balance, species competition, and plant-environment interactions. However, existing work on plant allometry has mainly focused on trees, with fewer studies having developed allometric equations for grasses. Grasses with different life histories can vary in their carbon investment by prioritizing the growth of specific organs to survive, outcompete co-occurring plants, and ensure population persistence. Further, because grasses are important fuels for wildfire, the lack of grass allocation data adds uncertainty to process-based models that relate plant physiology to wildfire dynamics. To fill this gap, we conducted a greenhouse experiment with 11 common California grasses varying in photosynthetic pathway and growth form. We measured plant sizes and harvested above- and belowground biomass throughout the life cycle of annual species, while for the establishment stage of perennial grasses to quantify allometric relationships for leaf, stem, and root biomass, as well as plant height and canopy area. We used basal diameter as a reference measure of plant size. Overall, basal diameter is the best predictor for leaf and stem biomass, height, and canopy area. Including height as another predictor can improve model accuracy in predicting leaf and stem biomass and canopy area. Fine root biomass is a function of leaf biomass alone. Species vary in their allometric relationships, with most variation occurring for plant height, canopy area, and stem biomass. We further explored potential trade-offs in biomass allocation across species between leaf and fine root, leaf and stem, and allocation to reproduction. Consistent with our expectation, we found that fast-growing plants allocated a greater fraction to reproduction. Additionally, plant height and specific leaf area negatively influenced the leaf-to-stem ratio. However, contrary to our hypothesis, there were no differences in root-to-leaf ratio between perennial and annual or C4 and C3 plants. Our study provides species-specific and functional-type-specific allometry equations for both above- and belowground organs of 11 common California grass species, enabling nondestructive biomass assessment in California grasslands. These allometric relationships and trade-offs in carbon allocation across species can improve ecosystem model predictions of grassland species interactions and environmental responses through differences in morphology.

We present a representation of nitrogen and phosphorus cycling in the Functionally Assembled Terrestrial Ecosystem Simulator, a demographic vegetation model within the Energy Exascale Earth System land model. This representation is modular, and designed to allow testing of multiple hypothetical approaches for carbon-nutrient coupling in plants. Novel model hypotheses introduced in this work include, (a) the controls on plant acquisition of aqueous mineralized nutrients in the soil and (b) fairly straight forward methods of allocating nutrients to specific plant organs and their losses through live plant turnover as well as litter fluxes generated through plant mortality. This combines the new with pre-existing hypotheses (such as nitrogen fixation and soil decomposition) into a system that can accommodate plant-soil dynamics for a large number of size- and functional-type-resolved plant cohorts within a time-since-disturbance-resolved ecosystem. Root uptake of nutrients is governed by fine root biomass, and plants vary in their fine root biomass allocation in order to balance carbon and nutrient limitations to growth. We test the sensitivity of the model to a wide range of parameter variations and structural representations, and in the context of observations at Barro Colorado Island, Panama. A key model prediction is that plants in the high-light-availability canopy positions allocate more carbon to fine roots than plants in low-light understory environments, given the widely different carbon versus nutrient constraints of these two niches within a given ecosystem. This model provides a basis for exploring carbon-nutrient coupling with vegetation demography within Earth system models.

Bacteria on and inside leaves can influence forest tree health and resilience. The distribution and limits of a tree species' range can be influenced by various factors, with biological interactions among the most significant. We investigated the processes shaping the bacterial needle community across the species distribution of limber pine, a widespread Western conifer inhabiting a range of extreme habitats. We tested four hypotheses: (i) Needle community structure varies across sites, with site-specific factors more important to microbial assembly than host species selection; (ii) dispersal limitation structures foliar communities across the range of limber pine; (iii) the relative significance of dispersal and selection differs across sites in the tree species range; and (iv) needle age structures bacterial communities. We characterized needle communities from the needle surface and tissue of limber pine and co-occurring conifers across 16 sites in the limber pine distribution. Our findings confirmed that site characteristics shape the assembly of bacterial communities across the host species range and showed that these patterns are not driven by dispersal limitation. Furthermore, the strength of selection by the host varied by site, possibly due to differences in available microbes. Our study, by focusing on trees in their natural setting, reveals real needle bacterial dynamics in forests, which is key to understanding the balance between stochastic and deterministic processes in shaping forest tree-microbe interactions. Such understanding will be necessary to predict or manipulate these interactions to support forest ecosystem productivity or assist plant migration and adaptation in the face of global change.

Purpose: Due to a rapidly changing climate, voluntary carbon markets are gaining momentum and should be leveraged to improve and expand tropical sustainable forest management plans, limiting carbon emissions and enhancing critical carbon sinks. By sequestering more carbon than any other terrestrial ecosystem — ~1 Pg C yr−1 — tropical forests provide crucial natural climate solutions and opportunities in the evolving voluntary carbon market. Methods: Here, we argue that some issues with the current sustainable management of tropical forests can be addressed using carbon-focused sustainable forest management (SFM + C) to leverage financial resources for tropical forest carbon storage and sequestration. We suggest an extended harvest cycle in SFM + C and calculate an associated potential increase in aboveground carbon stocks of commercial timber of 1.26 Mg C ha−1 after each cycle in the Brazilian Amazon. Results: The additional carbon storage due to a longer harvest cycle can generate carbon credits worth 152.6 (SD 9.2) US dollars per hectare in 40 years. Considering an average cost of 180 BRL per m3 of commercial timber delivered to the sawmill, an SFM + C plan with a 40-year cycle could generate 28.7% (SD 2.5) more profit than 35-year cycles by combining timber and carbon revenues. Conclusion: A robust carbon price could incentivize the further extension of harvest cycles, providing a monetary return that offsets the opportunity cost intrinsic to harvesting under longer cycles. Finally, we highlight research needs to support tropical SFM + C, which can be part of a global collective effort to limit global warming to below 2 °C above pre-industrial levels.

While tropical cyclone regimes are shifting with climate change, the mechanisms underpinning the resistance (ability to withstand disturbance-induced change) and resilience (capacity to return to pre-disturbance reference) of tropical forest litterfall to cyclones remain largely unexplored pantropically. Single-site studies in Australia and Hawaii suggest that litterfall on low-phosphorus (P) soils is more resistant and less resilient to cyclones. We conducted a meta-analysis to investigate the pantropical importance of total soil P in mediating forest litterfall resistance and resilience to 22 tropical cyclones. We evaluated cyclone-induced and post-cyclone litterfall mass (g/m2 /day), and P and nitrogen (N) fluxes (mg/m2 /day) and concentrations (mg/g), all indicators of ecosystem function and essential for nutrient cycling. Across 73 case studies in Australia, Guadeloupe, Hawaii, Mexico, Puerto Rico, and Taiwan, total litterfall mass flux increased from ~2.5 ± 0.3 to 22.5 ± 3 g/m2 /day due to cyclones, with large variation among studies. Litterfall P and N fluxes post-cyclone represented ~5% and 10% of the average annual fluxes, respectively. Post-cyclone leaf litterfall N and P concentrations were 21.6 ± 1.2% and 58.6 ± 2.3% higher than pre-cyclone means. Mixed-effects models determined that soil P negatively moderated the pantropical litterfall resistance to cyclones, with a 100 mg P/kg increase in soil P corresponding to a 32% to 38% decrease in resistance. Based on 33% of the resistance case studies, total litterfall mass flux reached pre-disturbance levels within one-year post-disturbance. A GAMM indicated that soil P, gale wind duration and time post-cyclone jointly moderate the short-term resilience of total litterfall, with the nature of the relationship between resilience and soil P contingent on time and wind duration. Across pantropical forests observed to date, our results indicate that litterfall resistance and resilience in the face of intensifying cyclones will be partially determined by total soil P.

Earth system models must predict forest responses to global change in order to simulate future global climate, hydrology, and ecosystem dynamics. These models are increasingly adopting vegetation demographic approaches that explicitly represent tree growth, mortality, and recruitment, enabling advances in the projection of forest vulnerability and resilience, as well as evaluation with field data. To date, simulation of regeneration processes has received far less attention than simulation of processes that affect growth and mortality, in spite of their critical role maintaining forest structure, facilitating turnover in forest composition over space and time, enabling recovery from disturbance, and regulating climate-driven range shifts. Our critical review of regeneration process representations within current Earth system vegetation demographic models reveals the need to improve parameter values and algorithms for reproductive allocation, dispersal, seed survival and germination, environmental filtering in the seedling layer, and tree regeneration strategies adapted to wind, fire, and anthropogenic disturbance regimes. These improvements require synthesis of existing data, specific field data-collection protocols, and novel model algorithms compatible with global-scale simulations. Vegetation demographic models offer the opportunity to more fully integrate ecological understanding into Earth system prediction; regeneration processes need to be a critical part of the effort.

Vegetation demographic models (VDMs) endeavor to predict how global forests will respond to climate change. This requires simulating which trees, if any, are able to recruit under changing environmental conditions. We present a new recruitment scheme for VDMs in which functional-type-specific recruitment rates are sensitive to light, soil moisture and the productivity of reproductive trees. We evaluate the scheme by predicting tree recruitment for four tropical tree functional types under varying meteorology and canopy structure at Barro Colorado Island, Panama. We compare predictions to those of a current VDM, quantitative observations and ecological expectations. We find that the scheme improves the magnitude and rank order of recruitment rates among functional types and captures recruitment limitations in response to variable understory light, soil moisture and precipitation regimes. Our results indicate that adopting this framework will improve VDM capacity to predict functional-type-specific tree recruitment in response to climate change, thereby improving predictions of future forest distribution, composition and function.

Deep-water access is arguably the most effective, but under-studied, mechanism that plants employ to survive during drought. Vulnerability to embolism and hydraulic safety margins can predict mortality risk at given levels of dehydration, but deep-water access may delay plant dehydration. Here, we tested the role of deep-water access in enabling survival within a diverse tropical forest community in Panama using a novel data-model approach. We inversely estimated the effective rooting depth (ERD, as the average depth of water extraction), for 29 canopy species by linking diameter growth dynamics (1990-2015) to vapor pressure deficit, water potentials in the whole-soil column, and leaf hydraulic vulnerability curves. We validated ERD estimates against existing isotopic data of potential water-access depths. Across species, deeper ERD was associated with higher maximum stem hydraulic conductivity, greater vulnerability to xylem embolism, narrower safety margins, and lower mortality rates during extreme droughts over 35 years (1981-2015) among evergreen species. Species exposure to water stress declined with deeper ERD indicating that trees compensate for water stress-related mortality risk through deep-water access. The role of deep-water access in mitigating mortality of hydraulically-vulnerable trees has important implications for our predictive understanding of forest dynamics under current and future climates.

Plant community composition influences carbon, water, and energy fluxes at regional to global scales. Vegetation demographic models (VDMs) allow investigation of the effects of changing climate and disturbance regimes on vegetation composition and fluxes. Such investigation requires that the models can accurately resolve these feedbacks to simulate realistic composition. Vegetation in VDMs is composed of plant functional types (PFTs), which are specified according to plant traits. Defining PFTs is challenging due to large variability in trait observations within and between plant types and a lack of understanding of model sensitivity to these traits. Here we present an approach for developing PFT parameterizations that are connected to the underlying ecological processes determining forest composition in the mixed-conifer forest of the Sierra Nevada of California, USA. We constrain multiple relative trait values between PFTs, as opposed to randomly sampling within the range of observations. An ensemble of PFT parameterizations are then filtered based on emergent forest properties meeting observation-based ecological criteria under alternate disturbance scenarios. A small ensemble of alternate PFT parameterizations is identified that produces plausible forest composition and demonstrates variability in response to disturbance frequency and regional environmental variation. Retaining multiple PFT parameterizations allows us to quantify the uncertainty in forest responses due to variability in trait observations. Vegetation composition is a key emergent outcome from VDMs and our methodology provides a foundation for robust PFT parameterization across ecosystems.

Vegetation tolerance to drought depends on an array of site-specific environmental and plant physiological factors. This tolerance is poorly understood for many forest types despite its importance for predicting and managing vegetation stress. We analyzed the relationships between precipitation variability and forest die-off in California's Sierra Nevada and introduce a new measure of drought tolerance that emphasizes plant access to subsurface moisture buffers. We applied this metric to California's severe 2012-2015 drought, and show that it predicted the patterns of tree mortality. We then examined future climate scenarios, and found that the probability of droughts that lead to widespread die-off increases threefold by the end of the 21st century. Our analysis shows that tree mortality in the Sierra Nevada will likely accelerate in the coming decades and that forests in the Central and Northern Sierra Nevada that largely escaped mortality in 2012-2015 are vulnerable to die-off.

There is large uncertainty whether Amazon forests will remain a carbon sink as atmospheric CO2 increases. Hence, we simulated an old-growth tropical forest using six versions of four terrestrial models differing in scale of vegetation structure and representation of biogeochemical (BGC) cycling, all driven with CO2 forcing from the preindustrial period to 2100. The models were benchmarked against tree inventory and eddy covariance data from a Brazilian site for present-day predictions. All models predicted positive vegetation growth that outpaced mortality, leading to continual increases in present-day biomass accumulation. Notably, the two vegetation demographic models (VDMs) (ED2 and ELM-FATES) always predicted positive stem diameter growth in all size classes. The field data, however, indicated that a quarter of canopy trees didn't grow over the 15-year period, and while high interannual variation existed, biomass change was near neutral. With a doubling of CO2, three of the four models predicted an appreciable biomass sink (0.77 to 1.24 Mg ha−1 year−1). ELMv1-ECA, the only model used here that includes phosphorus constraints, predicted the lowest biomass sink relative to initial biomass stocks (+21%), lower than the other BGC model, CLM5 (+48%). Models projections differed primarily through variations in nutrient constraints, then carbon allocation, initial biomass, and density-dependent mortality. The VDM's performance was similar or better than the BGC models run in carbon-only mode, suggesting that nutrient competition in VDMs will improve predictions. We demonstrate that VDMs are comparable to nondemographic (i.e., “big-leaf”) models but also include finer scale demography and competition that can be evaluated against field observations.

Plant functional traits determine vegetation responses to environmental variation, but variation in trait values is large, even within a single site. Likewise, uncertainty in how these traits map to Earth system feedbacks is large. We use a vegetation demographic model (VDM), the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), to explore parameter sensitivity of model predictions, and comparison to observations, at a tropical forest site: Barro Colorado Island in Panama. We define a single 12-dimensional distribution of plant trait variation, derived primarily from observations in Panama, and define plant functional types (PFTs) as random draws from this distribution. We compare several model ensembles, where individual ensemble members vary only in the plant traits that define PFTs, and separate ensembles differ from each other based on either model structural assumptions or non-trait, ecosystem-level parameters, which include (a) the number of competing PFTs present in any simulation and (b) parameters that govern disturbance and height-based light competition. While singlePFT simulations are roughly consistent with observations of productivity at Barro Colorado Island, increasing the number of competing PFTs strongly shifts model predictions towards higher productivity and biomass forests. Different ecosystem variables show greater sensitivity than others to the number of competing PFTs, with the predictions that are most dominated by large trees, such as biomass, being the most sensitive. Changing disturbance and height-sorting parameters, i.e., the rules of competitive trait filtering, shifts regimes of dominance or coexistence between early-and late-successional PFTs in the model. Increases to the extent or severity of disturbance, or to the degree of determinism in height-based light competition, all act to shift the community towards early-successional PFTs. In turn, these shifts in competitive outcomes alter predictions of ecosystem states and fluxes, with more early-successional-dominated forests having lower biomass. It is thus crucial to differentiate between plant traits, which are under competitive pressure in VDMs, from those model parameters that are not and to better understand the relationships between these two types of model parameters to quantify sources of uncertainty in VDMs.

Vegetation plays an important role in regulating global carbon cycles and is a key component of the Earth system models (ESMs) that aim to project Earth's future climate. In the last decade, the vegetation component within ESMs has witnessed great progress from simple "big-leaf" approaches to demographically structured approaches, which have a better representation of plant size, canopy structure, and disturbances. These demographically structured vegetation models typically have a large number of input parameters, and sensitivity analysis is needed to quantify the impact of each parameter on the model outputs for a better understanding of model behavior. In this study, we conducted a comprehensive sensitivity analysis to diagnose the Community Land Model coupled to the Functionally Assembled Terrestrial Simulator, or CLM4.5(FATES). Specifically, we quantified the first- and second-order sensitivities of the model parameters to outputs that represent simulated growth and mortality as well as carbon fluxes and stocks for a tropical site with an extent of 1×1°. While the photosynthetic capacity parameter (Vc;max25) is found to be important for simulated carbon stocks and fluxes, we also show the importance of carbon storage and allometry parameters, which determine survival and growth strategies within the model. The parameter sensitivity changes with different sizes of trees and climate conditions. The results of this study highlight the importance of understanding the dynamics of the next generation of demographically enabled vegetation models within ESMs to improve model parameterization and structure for better model fidelity.

Climate change has the potential to affect crop prices and price volatility. However, the economic models used in prior assessments largely do not include known, automatic, stabilizing factors. Crop storage can stabilize prices and U.S. crop policy tends to provide support that moves opposite prices. We quantify effects of circa 2050 climate forcing on the inter-annual variability of U.S. Corn Belt corn and soybean yields using statistical crop models and climate scenarios from regional and global climate models. Climate change generally reduces mean yields and increases the inter-annual variability of yields in the Midwestern U.S. Using these yield impacts and an economic model with automatic market stabilizers, we find only modest increases in price volatility. Although individual producers and states are negatively affected by the yield reductions, the aggregate effect for all corn and soybean producer returns can be positive because of price increases. Moreover, agricultural policies based on price levels or revenue variation offset some of the impacts of market variation on farm income. Our results differ from other recent results and temper concerns that increasing climate instability necessarily translates to greater uncertainty about agricultural commodity uses, including as food and biofuels, in the near future.

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.

Background and aims: Forest tree microbiomes are important to forest dynamics, diversity, and ecosystem processes. Mature limber pines (Pinus flexilis) host a core microbiome of acetic acid bacteria in their foliage, but the bacterial endophyte community structure, variation, and assembly across tree ontogeny is unknown. The aims of this study were to test if the core microbiome observed in adult P. flexilis is established at the seedling stage, if seedlings host different endophyte communities in root and shoot tissues, and how environmental factors structure seedling endophyte communities. Methods: The 16S rRNA gene was sequenced to characterize the bacterial endophyte communities in roots and shoots of P. flexilis seedlings grown in plots at three elevations at Niwot Ridge, Colorado, subjected to experimental treatments (watering and heating). The data was compared to previously sequenced endophyte communities from adult tree foliage sampled in the same year and location. Results: Seedling shoots hosted a different core microbiome than adult tree foliage and were dominated by a few OTUs in the family Oxalobacteraceae, identical or closely related to strains with antifungal activity. Shoot and root communities significantly differed from each other but shared major OTUs. Watering but not warming restructured the seedling endophyte communities. Conclusions: The results suggest differences in assembly and ecological function across conifer life stages. Seedlings may recruit endophytes to protect against fungi under increased soil moisture.

Conifers are the dominant plant species throughout the high latitude boreal forests as well as some lower latitude temperate forests of North America, Europe, and Asia. As such, they play an integral economic and ecological role across much of the world. This study focused on the characterization of needle transcriptomes from four ecologically important and understudied North American white pines within the Pinus subgenus Strobus The populations of many Strobus species are challenged by native and introduced pathogens, native insects, and abiotic factors. RNA from the needles of western white pine (Pinus monticola), limber pine (Pinus flexilis), whitebark pine (Pinus albicaulis), and sugar pine (Pinus lambertiana) was sampled, Illumina short read sequenced, and de novo assembled. The assembled transcripts and their subsequent structural and functional annotations were processed through custom pipelines to contend with the challenges of non-model organism transcriptome validation. Orthologous gene family analysis of over 58,000 translated transcripts, implemented through Tribe-MCL, estimated the shared and unique gene space among the four species. This revealed 2025 conserved gene families, of which 408 were aligned to estimate levels of divergence and reveal patterns of selection. Specific candidate genes previously associated with drought tolerance and white pine blister rust resistance in conifers were investigated.

Predictions of upslope range shifts for tree species with warming are based on assumptions of moisture stress at lower elevation limits and low‐temperature stress at high‐elevation limits. However, recent studies have shown that warming can reduce tree seedling establishment across the entire gradient from subalpine forest to alpine via moisture limitation. Warming effects also vary with species, potentially resulting in community shifts in high‐elevation forests. We examined the growth and physiology underlying effects of warming on seedling demographic patterns. We evaluated dry mass (DM), root length, allocation above‐ and below‐ground, and relative growth rate (RGR) of whole seedlings, and their ability to avoid or endure water stress via water‐use efficiency and resisting turgor loss, for Pinus flexilis, Picea engelmannii and Pinus contorta seeded below, at and above tree line in experimentally warmed, watered and control plots in the Rocky Mountains, USA. We expected that growth and allocation responses to warming would relate to moisture status and that variation in drought tolerance traits would explain species differences in survival rates. Across treatments and elevations, seedlings of all species had weak turgor‐loss resistance, and growth was marginal with negative RGR in the first growth phase (−0.01 to −0.04 g g⁻¹ day⁻¹). Growth was correlated with soil moisture, particularly in the relatively small‐seeded P. contorta and P. engelmannii. Pinus flexilis, known to have the highest survivorship, attained the greatest DM and longest root but was also the slowest growing and most water‐use efficient. This was likely due to its greater reliance on seed reserves. Seedlings developed 15% less total DM, 25% less root DM and 11% shorter roots in heated compared with unheated plots. Higher temperatures slightly increased DM, root length and RGR where soils were wettest, but more strongly decreased these variables under drier conditions. Synthesis. The surprising heat inhibition of tree seedling establishment at the cold edge of forests appears to have a physiological basis: newly germinated seedlings have poor moisture stress tolerance, which appears related to marginal initial growth and heavy reliance on seed reserves. Variation in these attributes among tree species at tree line helps explain their different climate responses.

The spatial patterning of alpine plant communities is strongly influenced by the variation in physical factors such as temperature and moisture, which are strongly affected by snow depth and snowmelt patterns. Earlier snowmelt timing and greater soil-moisture limitations may favor wide-ranging species adapted to a broader set of ecohydrological conditions than alpine-restricted species. We asked how plant community composition, phenology, plant water relations, and photosynthetic gas exchange of alpine-restricted and wide-ranging species differ in their responses to a ca. 40-day snowmelt gradient in the Colorado Rocky Mountains (Lewisia pygmaea, Sibbaldia procumbens, and Hymenoxys grandiflora were alpine-restricted and Artemisia scopulorum, Carex rupestris, and Geum rossii were wide-ranging species). As hypothesized, species richness and foliar cover increased with earlier snowmelt, due to a greater abundance of wide-ranging species present in earlier melting plots. Flowering initiation occurred earlier with earlier snowmelt for 12 out of 19 species analyzed, while flowering duration was shortened with later snowmelt for six species (all but one were wide-ranging species). We observed >50% declines in net photosynthesis from July to September as soil moisture and plant water potentials declined. Early-season stomatal conductance was higher in wide-ranging species, indicating a more competitive strategy for water acquisition when soil moisture is high. Even so, there were no associated differences in photosynthesis or transpiration, suggesting no strong differences between these groups in physiology. Our findings reveal that plant species with different ranges (alpine-restricted vs. wide-ranging) could have differential phenological and physiological responses to snowmelt timing and associated soil moisture dry-down, and that alpine-restricted species' performance is more sensitive to snowmelt. As a result, alpine-restricted species may serve as better indicator species than their wide-ranging heterospecifics. Overall, alpine community composition and peak % cover are strongly structured by spatio-temporal patterns in snowmelt timing. Thus, near-term, community-wide changes (or variation) in phenology and physiology in response to shifts in snowmelt timing or rates of soil dry down are likely to be contingent on the legacy of past climate on community structure.

Understanding how climate warming will affect the demographic rates of different ecotypes is critical to predicting shifts in species distributions. Here, we present results from a common garden, climate change experiment in which we measured seedling recruitment of lodgepole pine, a widespread North American conifer that is also planted globally. Seeds from a low-elevation provenance had more than three-fold greater recruitment to their third year than seeds from a high-elevation provenance across sites within and above its native elevation range and across climate manipulations. Heating halved recruitment to the third year of both low- and high-elevation seed sources across the elevation gradient, while watering more than doubled recruitment, alleviating some of the negative effects of heating. Demographic models based on recruitment data from the climate manipulations and long-term observations of adult populations revealed that heating could effectively halt modeled upslope range expansion except when combined with watering. Simulating fire and rapid postfire forest recovery at lower elevations accelerated lodgepole pine expansion into the alpine, but did not alter final abundance rankings among climate scenarios. Regardless of climate scenario, greater recruitment of low-elevation seeds compensated for longer dispersal distances to treeline, assuming colonization was allowed to proceed over multiple centuries. Our results show that ecotypes from lower elevations within a species' range could enhance recruitment and facilitate upslope range shifts with climate change.

Metadata describe the ancillary information needed for data preservation and independent interpretation, comparison across heterogeneous datasets, and quality assessment and quality control (QA/QC). Environmental observations are vastly diverse in type and structure, can be taken across a wide range of spatiotemporal scales in a variety of measurement settings and approaches, and saved in multiple formats. Thus, well-organized, consistent metadata are required to produce usable data products from diverse environmental observations collected across field sites. However, existing metadata reporting protocols do not support the complex data synthesis and model-data integration needs of interdisciplinary earth system research. We developed a metadata reporting framework (FRAMES) to enable management and synthesis of observational data that are essential in advancing a predictive understanding of earth systems. FRAMES utilizes best practices for data and metadata organization enabling consistent data reporting and compatibility with a variety of standardized data protocols. We used an iterative scientist-centered design process to develop FRAMES, resulting in a data reporting format that incorporates existing field practices to maximize data-entry efficiency. Thus, FRAMES has a modular organization that streamlines metadata reporting and can be expanded to incorporate additional data types. With FRAMES's multi-scale measurement position hierarchy, data can be reported at observed spatial resolutions and then easily aggregated and linked across measurement types to support model-data integration. FRAMES is in early use by both data originators (persons generating data) and consumers (persons using data and metadata). In this paper, we describe FRAMES, identify lessons learned, and discuss areas of future development.

Mountain meadows have high biodiversity and help regulate stream water release following the snowmelt pulse. However, many meadows are experiencing woody plant encroachment, threatening these ecosystem services. While there have been field surveys of individual meadows and remote sensing-based landscape-scale studies of encroachment, what is missing is a broad-scale, ground-based study to understand common regional drivers, especially at high elevations, where land management has often played a less direct role. With this study, we ask: What are the climate and landscape conditions conducive to woody plant encroachment at the landscape scale, and how has historical climate variation affected tree recruitment in subalpine meadows over time? We measured density of encroaching trees across 340 subalpine meadows in the central Sierra Nevada, California, USA, and used generalized additive models (GAMs) to determine the relationship between landscape-scale patterns of encroachment and meadow environmental properties. We determined ages of trees in 30 survey meadows, used observed climate and GAMs to model the relationship between timing of recruitment and climate since the early 1900s, and extrapolated recruitment patterns into the future using downscaled climate scenarios. Encroachment was high among meadows with lodgepole pine (Pinus contorta Douglas ex Loudon var. murrayana (Balf.) Engelm.) in the immediate vicinity, at lower elevations, with physical conditions favoring strong soil drying, and with maximum temperatures above or below average. Climatic conditions during the year of germination were unimportant, with tree recruitment instead depending on a 3-yr seed production period prior to germination and a 6-yr seedling establishment period following germination. Recruitment was high when the seed production period had high snowpack, and when the seedling establishment period had warm summer maximum temperatures, high summer precipitation, and high snowpack. Applying our temporal model to downscaled output from four global climate models indicated that the average meadow will shift to forest by the end of the 21st century. Sierra Nevada meadow encroachment by conifers is ubiquitous and associated with climate conditions increasingly favorable for tree recruitment, which will lead to substantial changes in subalpine meadows and the ecosystem services they provide.

Species distribution shifts in response to climate change require that recruitment increase beyond current range boundaries. For trees with long life spans, the importance of climate‐sensitive seedling establishment to the pace of range shifts has not been demonstrated quantitatively. Using spatially explicit, stochastic population models combined with data from long‐term forest surveys, we explored whether the climate‐sensitivity of recruitment observed in climate manipulation experiments was sufficient to alter populations and elevation ranges of two widely distributed, high‐elevation North American conifers. Empirically observed, warming‐driven declines in recruitment led to rapid modelled population declines at the low‐elevation, ‘warm edge’ of subalpine forest and slow emergence of populations beyond the high‐elevation, ‘cool edge’. Because population declines in the forest occurred much faster than population emergence in the alpine, we observed range contraction for both species. For Engelmann spruce, this contraction was permanent over the modelled time horizon, even in the presence of increased moisture. For limber pine, lower sensitivity to warming may facilitate persistence at low elevations – especially in the presence of increased moisture – and rapid establishment above tree line, and, ultimately, expansion into the alpine. Synthesis. Assuming 21st century warming and no additional moisture, population dynamics in high‐elevation forests led to transient range contractions for limber pine and potentially permanent range contractions for Engelmann spruce. Thus, limitations to seedling recruitment with warming can constrain the pace of subalpine tree range shifts.

Land-atmosphere interactions are important to climate prediction, but the underlying effects of surface forcing of the atmosphere are not well understood. In the U.S. Southern Great Plains, grassland/pasture and winter wheat are the dominant land covers but have distinct growing periods that may differently influence land-atmosphere coupling during spring and summer. Variables that influence surface flux partitioning can change seasonally, depending on the state of local vegetation. Here we use surface observations from multiple sites in the U.S. Department of Energy Atmospheric Radiation Measurement Southern Great Plains Climate Research Facility and statistical modeling at a paired grassland/agricultural site within this facility to quantify land cover influence on surface energy balance and variables controlling evaporative fraction (latent heat flux normalized by the sum of sensible and latent heat fluxes). We demonstrate that the radiative balance and evaporative fraction are closely related to green leaf area at both winter wheat and grassland/pasture sites and that the early summer harvest of winter wheat abruptly shifts the relationship between evaporative fraction and surface state variables. Prior to harvest, evaporative fraction of winter wheat is strongly influenced by leaf area and soil-atmosphere temperature differences. After harvest, variations in soil moisture have a stronger effect on evaporative fraction. This is in contrast with grassland/pasture sites, where variation in green leaf area has a large influence on evaporative fraction throughout spring and summer, and changes in soil-atmosphere temperature difference and soil moisture are of relatively minor importance.

Scientists estimate that U.S. Corn Belt crop yields will increase or decrease, on average, and become more variable with climate change. Corn and soybeanfarming dominates this region, but studies typically do not assess the joint impact of new distributions of corn and soybean yields on markets. We use a structural economic model with projections of climate-driven yield changes to simulate these effects. Our findings suggest that a narrow focus on a single crop in this key growing region risks underestimating the impact on price distributions and average crop receipts, and can lead to incorrect signs on estimated impacts.

Climate niche models project that subalpine forest ranges will extend upslope with climate warming. These projections assume that the climate suitable for adult trees will be adequate for forest regeneration, ignoring climate requirements for seedling recruitment, a potential demographic bottleneck. Moreover, local genetic adaptation is expected to facilitate range expansion, with tree populations at the upper forest edge providing the seed best adapted to the alpine. Here, we test these expectations using a novel combination of common gardens, seeded with two widely distributed subalpine conifers, and climate manipulations replicated at three elevations. Infrared heaters raised temperatures in heated plots, but raised temperatures more in the forest than at or above treeline because strong winds at high elevation reduced heating efficiency. Watering increased season-average soil moisture similarly across sites. Contrary to expectations, warming reduced Engelmann spruce recruitment at and above treeline, as well as in the forest. Warming reduced limber pine first-year recruitment in the forest, but had no net effect on fourth-year recruitment at any site. Watering during the snow-free season alleviated some negative effects of warming, indicating that warming exacerbated water limitations. Contrary to expectations of local adaptation, low-elevation seeds of both species initially recruited more strongly than high-elevation seeds across the elevation gradient, although the low-provenance advantage diminished by the fourth year for Engelmann spruce, likely due to small sample sizes. High- and low-elevation provenances responded similarly to warming across sites for Engelmann spruce, but differently for limber pine. In the context of increasing tree mortality, lower recruitment at all elevations with warming, combined with lower quality, high-provenance seed being most available for colonizing the alpine, portends range contraction for Engelmann spruce. The lower sensitivity of limber pine to warming indicates a potential for this species to become more important in subalpine forest communities in the coming centuries.

Winter wheat is a staple crop for global food security, and is the dominant vegetation cover for a significant fraction of Earth's croplands. As such, it plays an important role in carbon cycling and land-atmosphere interactions in these key regions. Accurate simulation of winter wheat growth is not only crucial for future yield prediction under a changing climate, but also for accurately predicting the energy and water cycles for winter wheat dominated regions. We modified the winter wheat model in the Community Land Model (CLM) to better simulate winter wheat leaf area index, latent heat flux, net ecosystem exchange of CO2, and grain yield. These included schemes to represent vernalization as well as frost tolerance and damage. We calibrated three key parameters (minimum planting temperature, maximum crop growth days, and initial value of leaf carbon allocation coefficient) and modified the grain carbon allocation algorithm for simulations at the US Southern Great Plains ARM site (US-ARM), and validated the model performance at eight additional sites across North America. We found that the new winter wheat model improved the prediction of monthly variation in leaf area index, reduced latent heat flux, and net ecosystem exchange root mean square error (RMSE) by 41 and 35% during the spring growing season. The model accurately simulated the interannual variation in yield at the USARM site, but underestimated yield at sites and in regions (northwestern and southeastern US) with historically greater yields by 35 %.

Windthrows are a recurrent disturbance in Amazonia and are an important driver of forest dynamics and carbon storage. In this study, we present for the first time the seasonal and interannual variability of windthrows, focusing on Central Amazonia, and discuss the potential meteorological factors associated with this variability. Landsat images over the 1998-2010 time period were used to detect the occurrence of windthrows, which were identified based on their spectral characteristics and shape. Here, we found that windthrows occurred every year but were more frequent between September and February. Organized convective activity associated with multicell storms embedded in mesoscale convective systems, such as northerly squall lines (that move from northeast to southwest) and southerly squall lines (that move from southwest to northeast) can cause windthrows. We also found that southerly squall lines occurred more frequently than their previously reported ~50 year interval. At the interannual scale, we did not find an association between El Niño-Southern Oscillation (ENSO) and windthrows.

Biases in land-atmosphere coupling in climate models can contribute to climate prediction biases, but land models are rarely evaluated in the context of this coupling. We tested land-atmosphere coupling and explored effects of land surface parameterizations on climate prediction in a single-column version of the National Center for Atmospheric Research Community Earth System Model (CESM1.2.2) and an off-line Community Land Model (CLM4.5). The correlation between leaf area index (LAI) and surface evaporative fraction (ratio of latent to total turbulent heat flux) was substantially underpredicted compared to observations in the U.S. Southern Great Plains, while the correlation between soil moisture and evaporative fraction was overpredicted by CLM4.5. To estimate the impacts of these errors on climate prediction, we modified CLM4.5 by prescribing observed LAI, increasing soil resistance to evaporation, increasing minimum stomatal conductance, and increasing leaf reflectance. The modifications improved the predicted soil moisture-evaporative fraction (EF) and LAI-EF correlations in off-line CLM4.5 and reduced the root-mean-square error in summer 2 m air temperature and precipitation in the coupled model. The modifications had the largest effect on prediction during a drought in summer 2006, when a warm bias in daytime 2 m air temperature was reduced from +6°C to a smaller cold bias of -1.3°C, and a corresponding dry bias in precipitation was reduced from -111 mm to -23 mm. The role of vegetation in droughts and heat waves is underpredicted in CESM1.2.2, and improvements in land surface models can improve prediction of climate extremes.

Gross primary productivity (GPP) has been reported to increase with the fraction of diffuse solar radiation, for a given total irradiance. The correlation between GPP and diffuse radiation suggests effects of diffuse radiation on canopy light-use efficiency, but potentially confounding effects of vegetation phenology have not been fully explored. We applied several approaches to control for phenology, using 8 years of eddy-covariance measurements of winter wheat in the U.S. Southern Great Plains. The apparent enhancement of daily GPP due to diffuse radiation was reduced from 260% to 75%, after subsampling over the peak growing season or by subtracting a 15 day moving average of GPP, suggesting a role of phenology. The diffuse radiation effect was further reduced to 22% after normalizing GPP by a spectral reflectance index to account for phenological variations in leaf area index LAI and canopy photosynthetic capacity. Canopy photosynthetic capacity covaries with diffuse fraction at a given solar irradiance at this site because both factors are dependent on day of year or solar zenith angle. Using a two-leaf Sun-shaded canopy radiative transfer model, we confirmed that the effects of phenological variations in photosynthetic capacity can appear qualitatively similar to the effects of diffuse radiation on GPP and therefore can be difficult to distinguish using observations. The importance of controlling for phenology when inferring diffuse radiation effects on GPP raises new challenges and opportunities for using radiation measurements to improve carbon cycle models.

Climate change is expected to alter primary production and community composition in alpine ecosystems, but the direction and magnitude of change is debated. Warmer, wetter growing seasons may increase productivity; however, in the absence of additional precipitation, increased temperatures may decrease soil moisture, thereby diminishing any positive effect of warming. Since plant species show individual responses to environmental change, responses may depend on community composition and vary across life form or functional groups. We warmed an alpine plant community at Niwot Ridge, Colorado continuously for four years to test whether warming increases or decreases productivity of life form groups and the whole community. We provided supplemental water to a subset of plots to alleviate the drying effect of warming. We measured annual above-ground productivity and soil temperature and moisture, from which we calculated soil degree days and adequate soil moisture days. Using an information-theoretic approach, we observed that positive productivity responses to warming at the community level occur only when warming is combined with supplemental watering; otherwise we observed decreased productivity. Watering also increased community productivity in the absence of warming. Forbs accounted for the majority of the productivity at the site and drove the contingent community response to warming, while cushions drove the generally positive response to watering and graminoids muted the community response. Warming advanced snowmelt and increased soil degree days, while watering increased adequate soil moisture days. Heated and watered plots had more adequate soil moisture days than heated plots. Overall, measured changes in soil temperature and moisture in response to treatments were consistent with expected productivity responses. We found that available soil moisture largely determines the responses of this forb-dominated alpine community to simulated climate warming.

Coniferous forest nitrogen (N) budgets indicate unknown sources of N. A consistent association between limber pine (Pinus flexilis) and potential N2 -fixing acetic acid bacteria (AAB) indicates that native foliar endophytes may supply subalpine forests with N. To assess whether the P. flexilis-AAB association is consistent across years, we re-sampled P. flexilis twigs at Niwot Ridge, CO and characterized needle endophyte communities via 16S rRNA Illumina sequencing. To investigate whether endophytes have access to foliar N2 , we incubated twigs with (13) N2 -enriched air and imaged radioisotope distribution in needles, the first experiment of its kind using (13) N. We used the acetylene reduction assay to test for nitrogenase activity within P. flexilis twigs four times from June to September. We found evidence for N2 fixation in P. flexilis foliage. N2 diffused readily into needles and nitrogenase activity was positive across sampling dates. We estimate that this association could provide 6.8-13.6 μg N m(-2)  d(-1) to P. flexilis stands. AAB dominated the P. flexilis needle endophyte community. We propose that foliar endophytes represent a low-cost, evolutionarily stable N2 -fixing strategy for long-lived conifers. This novel source of biological N2 fixation has fundamental implications for understanding forest N budgets.

© 2015 Springer-Verlag Berlin Heidelberg In this study, we coupled Version 4.0 of the Community Land Model that includes crop growth and management (CLM4crop) into the Weather Research and Forecasting (WRF) model Version 3.3 to better represent interactions between climate and agriculture. We evaluated the performance of the coupled model (WRF3.3-CLM4crop) by comparing simulated crop growth and surface climate to multiple observational datasets across the continental United States. The results showed that although the model with dynamic crop growth overestimated leaf area index (LAI) and growing season length, interannual variability in peak LAI was improved relative to a model with prescribed crop LAI and growth period, which has no environmental sensitivity. Adding irrigation largely improved daily minimum temperature but the RMSE is still higher over irrigated land than non-irrigated land. Improvements in climate variables were limited by an overall model dry bias. However, with addition of an irrigation scheme, soil moisture and surface energy flux partitioning were largely improved at irrigated sites. Irrigation effects were sensitive to crop growth: the case with prescribed crop growth underestimated irrigation water use and effects on temperature and overestimated soil evaporation relative to the case with dynamic crop growth in moderately irrigated regions. We conclude that studies examining irrigation effects on weather and climate using coupled climate–land surface models should include dynamic crop growth and realistic irrigation schemes to better capture land surface effects in agricultural regions.

Studies that model the effect of climate change on terrestrial ecosystems often use climate projections from downscaled global climate models (GCMs). These simulations are generally too coarse to capture patterns of fine-scale climate variation, such as the sharp coastal energy and moisture gradients associated with wind-driven upwelling of cold water. Coastal upwelling may limit future increases in coastal temperatures, compromising GCMs' ability to provide realistic scenarios of future climate in these coastal ecosystems. Taking advantage of naturally occurring variability in the high-resolution historic climatic record, we developed multiple fine-scale scenarios of California climate that maintain coherent relationships between regional climate and coastal upwelling. We compared these scenarios against coarse resolution GCM projections at a regional scale to evaluate their temporal equivalency. We used these historically based scenarios to estimate potential suitable habitat for coast redwood (Sequoia sempervirens D. Don) under 'normal' combinations of temperature and precipitation, and under anomalous combinations representative of potential future climates. We found that a scenario of warmer temperature with historically normal precipitation is equivalent to climate projected by GCMs for California by 2020-2030 and that under these conditions, climatically suitable habitat for coast redwood significantly contracts at the southern end of its current range. Our results suggest that historical climate data provide a high-resolution alternative to downscaled GCM outputs for near-term ecological forecasts. This method may be particularly useful in other regions where local climate is strongly influenced by ocean-atmosphere dynamics that are not represented by coarse-scale GCMs.

Climate change is altering plant species distributions globally, and warming is expected to promote uphill shifts in mountain trees. However, at many cold-edge range limits, such as alpine treelines in the western United States, tree establishment may be colimited by low temperature and low moisture, making recruitment patterns with warming difficult to predict. We measured response functions linking carbon (C) assimilation and temperature- and moisture-related microclimatic factors for limber pine (Pinus flexilis) seedlings growing in a heating × watering experiment within and above the alpine treeline. We then extrapolated these response functions using observed microclimate conditions to estimate the net effects of warming and associated soil drying on C assimilation across an entire growing season. Moisture and temperature limitations were each estimated to reduce potential growing season C gain from a theoretical upper limit by 15-30% (c. 50% combined). Warming above current treeline conditions provided relatively little benefit to modeled net assimilation, whereas assimilation was sensitive to either wetter or drier conditions. Summer precipitation may be at least as important as temperature in constraining C gain by establishing subalpine trees at and above current alpine treelines as seasonally dry subalpine and alpine ecosystems continue to warm.

Survival of tree seedlings at high elevations has been shown to be limited by thermal constraints on carbon balance, but it is unknown if carbon relations also limit seedling survival at lower elevations, where water relations may be more important. We measured and modeled carbon fluxes and water relations in first-year Pinus flexilis seedlings in garden plots just beyond the warm edge of their natural range, and compared these with dry-mass gain and survival across two summers. We hypothesized that mortality in these seedlings would be associated with declines in water relations, more so than with carbon-balance limitations. Rather than gradual declines in survivorship across growing seasons, we observed sharp, large-scale mortality episodes that occurred once volumetric soil-moisture content dropped below 10%. By this point, seedling water potentials had decreased below -5 MPa, seedling hydraulic conductivity had decreased by 90% and seedling hydraulic resistance had increased by >900%. Additionally, non-structural carbohydrates accumulated in aboveground tissues at the end of both summers, suggesting impairments in phloem-transport from needles to roots. This resulted in low carbohydrate concentrations in roots, which likely impaired root growth and water uptake at the time of critically low soil moisture. While photosynthesis and respiration on a leaf area basis remained high until critical hydraulic thresholds were exceeded, modeled seedling gross primary productivity declined steadily throughout the summers. At the time of mortality, modeled productivity was insufficient to support seedling biomass-gain rates, metabolism and secondary costs. Thus the large-scale mortality events that we observed near the end of each summer were most directly linked with acute, episodic declines in plant hydraulic function that were linked with important changes in whole-seedling carbon relations.

Effects of infrared heaters on snow accumulation, snowmelt, and snow–atmosphere energy exchange were examined at Niwot Ridge, Colorado (CO) and compared to a naturally warmer, but otherwise similar subalpine site in the Valles Caldera National Preserve, New Mexico (NM). Observed snow accumulation was 30% lower on average and snow melted out 16 days earlier in the heated plots compared to the controls. Soil temperature during snowmelt was 3°C greater on average and soil moisture was 4% lower on average in heated plots compared to controls. In NM, snow accumulation was 23% lower, snow melted 23 days earlier, soil temperature was 0.6°C greater, and soil moisture was 13% lower on average relative to CO controls. In order to estimate differences in energy and mass balance fluxes at the snow–atmosphere interface in control versus warmer plots, the 1-D, physically based snowmelt model, SNOWPACK, was used. Model results indicated that heaters alter radiative, turbulent and mass fluxes by amounts comparable to the differences between CO and NM fluxes. The proportion of the energy flux associated with latent heat exchange during snowmelt was 9–27% of the total energy flux in heated models and 19–22% of NM models compared to 3–7% in control models. Thus, sublimation loss to the atmosphere was greater in both experimentally and naturally warmer cases relative to the control case. We conclude that IR heaters can provide alterations to the timing and magnitude of snow accumulation and snowmelt consistent with conditions observed at a warmer analog site and with climate and hydrology model projections. Impacts of IR heating on energy partitioning and sublimation should be considered when designing manipulations of the snowpack, as reductions in snowmelt water may alter biological or ecological processes.

Forestry – including afforestation (the planting of trees on land where they have not recently existed), reforestation, avoided deforestation, and forest management – can lead to increased sequestration of atmospheric carbon dioxide and has therefore been proposed as a strategy to mitigate climate change. However, forestry also influences land-surface properties, including albedo (the fraction of incident sunlight reflected back to space), surface roughness, and evapotranspiration, all of which affect the amount and forms of energy transfer to the atmosphere. In some circumstances, these biophysical feedbacks can result in local climate warming, thereby counteracting the effects of carbon sequestration on global mean temperature and reducing or eliminating the net value of climate-change mitigation projects. Here, we review published and emerging research that suggests ways in which forestry projects can counteract the consequences associated with biophysical interactions, and highlight knowledge gaps in managing forests for climate protection. We also outline several ways in which biophysical effects can be incorporated into frameworks that use the maintenance of forests as a climate protection strategy.

Many serious ecosystem consequences of climate change will take decades or even centuries to emerge. Long-term ecological responses to global change are strongly regulated by slow processes, such as changes in species composition, carbon dynamics in soil and by long-lived plants, and accumulation of nutrient capitals. Understanding and predicting these processes require experiments on decadal time scales. But decadal experiments by themselves may not be adequate because many of the slow processes have characteristic time scales much longer than experiments can be maintained. This article promotes a coordinated approach that combines long-term, large-scale global change experiments with process studies and modeling. Long-term global change manipulative experiments, especially in high-priority ecosystems such as tropical forests and high-latitude regions, are essential to maximize information gain concerning future states of the earth system. The long-term experiments should be conducted in tandem with complementary process studies, such as those using model ecosystems, species replacements, laboratory incubations, isotope tracers, and greenhouse facilities. Models are essential to assimilate data from long-term experiments and process studies together with information from long-term observations, surveys, and space-for-time studies along environmental and biological gradients. Future research programs with coordinated long-term experiments, process studies, and modeling have the potential to be the most effective strategy to gain the best information on long-term ecosystem dynamics in response to global change.