Reduced global plant respiration due to the acclimation of leaf dark respiration coupled with photosynthesis.

Leaf dark respiration (Rd ) acclimates to environmental changes. However, the magnitude, controls and time scales of acclimation remain unclear and are inconsistently treated in ecosystem models. We hypothesized that Rd and Rubisco carboxylation capacity (Vcmax ) at 25°C (Rd,25 , Vcmax,25 ) are coordinated so that Rd,25 variations support Vcmax,25 at a level allowing full light use, with Vcmax,25 reflecting daytime conditions (for photosynthesis), and Rd,25 /Vcmax,25 reflecting night-time conditions (for starch degradation and sucrose export). We tested this hypothesis temporally using a 5-yr warming experiment, and spatially using an extensive field-measurement data set. We compared the results to three published alternatives: Rd,25 declines linearly with daily average prior temperature; Rd at average prior night temperatures tends towards a constant value; and Rd,25 /Vcmax,25 is constant. Our hypothesis accounted for more variation in observed Rd,25 over time (R2 = 0.74) and space (R2 = 0.68) than the alternatives. Night-time temperature dominated the seasonal time-course of Rd , with an apparent response time scale of c. 2 wk. Vcmax dominated the spatial patterns. Our acclimation hypothesis results in a smaller increase in global Rd in response to rising CO2 and warming than is projected by the two of three alternative hypotheses, and by current models.

Soil nitrogen fertilization reduces relative leaf nitrogen allocation to photosynthesis.

The connection between soil nitrogen availability, leaf nitrogen, and photosynthetic capacity is not perfectly understood. Because these three components tend to be positively related over large spatial scales, some posit that soil nitrogen positively drives leaf nitrogen, which positively drives photosynthetic capacity. Alternatively, others posit that photosynthetic capacity is primarily driven by above-ground conditions. Here, we examined the physiological responses of a non-nitrogen-fixing plant (Gossypium hirsutum) and a nitrogen-fixing plant (Glycine max) in a fully factorial combination of light by soil nitrogen availability to help reconcile these competing hypotheses. Soil nitrogen stimulated leaf nitrogen in both species, but the relative proportion of leaf nitrogen used for photosynthetic processes was reduced under elevated soil nitrogen in all light availability treatments due to greater increases in leaf nitrogen content than chlorophyll and leaf biochemical process rates. Leaf nitrogen content and biochemical process rates in G. hirsutum were more responsive to changes in soil nitrogen than those in G. max, probably due to strong G. max investments in root nodulation under low soil nitrogen. Nonetheless, whole-plant growth was significantly enhanced by increased soil nitrogen in both species. Light availability consistently increased relative leaf nitrogen allocation to leaf photosynthesis and whole-plant growth, a pattern that was similar between species. These results suggest that the leaf nitrogen-photosynthesis relationship varies under different soil nitrogen levels and that these species preferentially allocated more nitrogen to plant growth and non-photosynthetic leaf processes, rather than photosynthesis, as soil nitrogen increased.

Rising CO2 and warming reduce global canopy demand for nitrogen.

Nitrogen (N) limitation has been considered as a constraint on terrestrial carbon uptake in response to rising CO2 and climate change. By extension, it has been suggested that declining carboxylation capacity (Vcmax ) and leaf N content in enhanced-CO2 experiments and satellite records signify increasing N limitation of primary production. We predicted Vcmax using the coordination hypothesis and estimated changes in leaf-level photosynthetic N for 1982-2016 assuming proportionality with leaf-level Vcmax at 25°C. The whole-canopy photosynthetic N was derived using satellite-based leaf area index (LAI) data and an empirical extinction coefficient for Vcmax , and converted to annual N demand using estimated leaf turnover times. The predicted spatial pattern of Vcmax shares key features with an independent reconstruction from remotely sensed leaf chlorophyll content. Predicted leaf photosynthetic N declined by 0.27% yr-1 , while observed leaf (total) N declined by 0.2-0.25% yr-1 . Predicted global canopy N (and N demand) declined from 1996 onwards, despite increasing LAI. Leaf-level responses to rising CO2 , and to a lesser extent temperature, may have reduced the canopy requirement for N by more than rising LAI has increased it. This finding provides an alternative explanation for declining leaf N that does not depend on increasing N limitation.

Daytime warming triggers tree growth decline in the Northern Hemisphere.

Global warming has been linked to declines in tree growth. However, it is unclear how the asymmetry in daytime and nighttime warming influences this response. Here, we use 2947 residual tree-ring width chronologies covering 32 species at 2493 sites, between 1901 and 2018, across the Northern Hemisphere, to analyze the effects of daytime and nighttime temperatures, precipitation, and drought stress on the radial growth of trees. We show that drought stress was primarily triggered by daytime rather than nighttime warming. The radial growth of trees was more sensitive to drought stress in warm regions than in cold regions, especially for angiosperms. Our study provides robust evidence that daytime warming is the primary driver of the observed declines in forest productivity related to drought stress and that daytime and nighttime warming should be considered separately when modelling forest-climate interactions and feedbacks in a future, warmer world.

Increasing the spatial and temporal impact of ecological research: A roadmap for integrating a novel terrestrial process into an Earth system model.

Terrestrial ecosystems regulate Earth's climate through water, energy, and biogeochemical transformations. Despite a key role in regulating the Earth system, terrestrial ecology has historically been underrepresented in the Earth system models (ESMs) that are used to understand and project global environmental change. Ecology and Earth system modeling must be integrated for scientists to fully comprehend the role of ecological systems in driving and responding to global change. Ecological insights can improve ESM realism and reduce process uncertainty, while ESMs offer ecologists an opportunity to broadly test ecological theory and increase the impact of their work by scaling concepts through time and space. Despite this mutualism, meaningfully integrating the two remains a persistent challenge, in part because of logistical obstacles in translating processes into mathematical formulas and identifying ways to integrate new theories and code into large, complex model structures. To help overcome this interdisciplinary challenge, we present a framework consisting of a series of interconnected stages for integrating a new ecological process or insight into an ESM. First, we highlight the multiple ways that ecological observations and modeling iteratively strengthen one another, dispelling the illusion that the ecologist's role ends with initial provision of data. Second, we show that many valuable insights, products, and theoretical developments are produced through sustained interdisciplinary collaborations between empiricists and modelers, regardless of eventual inclusion of a process in an ESM. Finally, we provide concrete actions and resources to facilitate learning and collaboration at every stage of data-model integration. This framework will create synergies that will transform our understanding of ecology within the Earth system, ultimately improving our understanding of global environmental change, and broadening the impact of ecological research.

Invasion-induced root-fungal disruptions alter plant water and nitrogen economies.

Despite widespread evidence that biological invasion influences both the biotic and abiotic soil environments, the extent to which these two pathways underpin the effects of invasion on plant traits and performance remains unknown. Leveraging a long-term (14-year) field experiment, we show that an allelochemical-producing invader affects plants through biotic mechanisms, altering the soil fungal community composition, with no apparent shifts in soil nutrient availability. Changes in belowground fungal communities resulted in high costs of nutrient uptake for native perennials and a shift in plant traits linked to their water and nutrient use efficiencies. Some plants in the invaded community compensate for the disruption of nutritional symbionts and reduced nutrient provisioning by sanctioning more nitrogen to photosynthesis and expending more water, which demonstrates a trade-off in trait investment. For the first time, we show that the disruption of belowground nutritional symbionts can drive plants towards alternative regions of their trait space in order to maintain water and nutrient economics.

Eco-evolutionary optimality as a means to improve vegetation and land-surface models.

Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generates parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change.

Root mass carbon costs to acquire nitrogen are determined by nitrogen and light availability in two species with different nitrogen acquisition strategies.

Plant nitrogen acquisition requires carbon to be allocated belowground to build roots and sustain microbial associations. This carbon cost to acquire nitrogen varies by nitrogen acquisition strategy; however, the degree to which these costs vary due to nitrogen availability or demand has not been well tested under controlled conditions. We grew a species capable of forming associations with nitrogen-fixing bacteria (Glycine max) and a species not capable of forming such associations (Gossypium hirsutum) under four soil nitrogen levels to manipulate nitrogen availability and four light levels to manipulate nitrogen demand in a full-factorial greenhouse experiment. We quantified carbon costs to acquire nitrogen as the ratio of total root carbon to whole-plant nitrogen within each treatment combination. In both species, light availability increased carbon costs due to a larger increase in root carbon than whole-plant nitrogen, while nitrogen fertilization generally decreased carbon costs due to a larger increase in whole-plant nitrogen than root carbon. Nodulation data indicated that G. max shifted relative carbon allocation from nitrogen fixation to direct uptake with increased nitrogen fertilization. These findings suggest that carbon costs to acquire nitrogen are modified by changes in light and nitrogen availability in species with and without associations with nitrogen-fixing bacteria.

Contrasting temporal variations in responses of leaf unfolding to daytime and nighttime warming.

Earlier spring phenological events have been widely reported in plants under global warming. Recent studies reported a slowdown in the warming-induced advanced spring phenology in temperate regions. However, previous research mainly focused on daily mean temperature, thus neglecting the asymmetric phenological responses to daytime and nighttime temperature. Using long-term records of leaf unfolding in eight deciduous species at 1300 sites across central Europe, we assessed and compared the effects of daytime temperature, nighttime temperature, and photoperiod on leaf unfolding during 1951-1980 and 1981-2013. Although leaf unfolding was advanced by daytime warming during 1951-2013, the advancing responses of leaf unfolding significantly decreased from 1951-1980 to 1981-2013 due to a lower accumulation of chilling units by daytime warming. Nighttime warming delayed leaf unfolding during 1951-1980 but advanced it during 1981-2013 due to a higher accumulation of chilling units by nighttime warming. In contrast, critical daylength and plasticity of leaf unfolding dates remained unchanged between 1951 and 2013. Our study provided evidence that daytime warming instead of nighttime warming accounts for the slowdown in the advancing spring phenology and implied that nighttime warming-induced earlier spring phenology may be buffering the slowdown of the advanced spring phenology by daytime warming. The response of spring phenology to nighttime temperature may override that to daytime temperature under the actual trends in global warming.

When and where soil is important to modify the carbon and water economy of leaves.

Photosynthetic 'least-cost' theory posits that the optimal trait combination for a given environment is that where the summed costs of photosynthetic water and nutrient acquisition/use are minimised. The effects of soil water and nutrient availability on photosynthesis should be stronger as climate-related costs for both resources increase. Two independent datasets of photosynthetic traits, Globamax (1509 species, 288 sites) and Glob13C (3645 species, 594 sites), were used to quantify biophysical and biochemical limitations of photosynthesis and the key variable Ci /Ca (CO2 drawdown during photosynthesis). Climate and soil variables were associated with both datasets. The biochemical photosynthetic capacity was higher on alkaline soils. This effect was strongest at more arid sites, where water unit-costs are presumably higher. Higher values of soil silt and depth increased Ci /Ca , likely by providing greater H2 O supply, alleviating biophysical photosynthetic limitation when soil water is scarce. Climate is important in controlling the optimal balance of H2 O and N costs for photosynthesis, but soil properties change these costs, both directly and indirectly. In total, soil properties modify the climate-demand driven predictions of Ci /Ca by up to 30% at a global scale.

Mechanisms underlying leaf photosynthetic acclimation to warming and elevated CO2 as inferred from least-cost optimality theory.

The mechanisms responsible for photosynthetic acclimation are not well understood, effectively limiting predictability under future conditions. Least-cost optimality theory can be used to predict the acclimation of photosynthetic capacity based on the assumption that plants maximize carbon uptake while minimizing the associated costs. Here, we use this theory as a null model in combination with multiple datasets of C3 plant photosynthetic traits to elucidate the mechanisms underlying photosynthetic acclimation to elevated temperature and carbon dioxide (CO2 ). The model-data comparison showed that leaves decrease the ratio of the maximum rate of electron transport to the maximum rate of Rubisco carboxylation (Jmax /Vcmax ) under higher temperatures. The comparison also indicated that resources used for Rubisco and electron transport are reduced under both elevated temperature and CO2 . Finally, our analysis suggested that plants underinvest in electron transport relative to carboxylation under elevated CO2 , limiting potential leaf-level photosynthesis under future CO2 concentrations. Altogether, our results show that acclimation to temperature and CO2 is primarily related to resource conservation at the leaf level. Under future, warmer, high CO2 conditions, plants are therefore likely to use less nutrients for leaf-level photosynthesis, which may impact whole-plant to ecosystem functioning.

Acclimation of leaf respiration consistent with optimal photosynthetic capacity.

Plant respiration is an important contributor to the proposed positive global carbon-cycle feedback to climate change. However, as a major component, leaf mitochondrial ('dark') respiration (Rd ) differs among species adapted to contrasting environments and is known to acclimate to sustained changes in temperature. No accepted theory explains these phenomena or predicts its magnitude. Here we propose that the acclimation of Rd follows an optimal behaviour related to the need to maintain long-term average photosynthetic capacity (Vcmax ) so that available environmental resources can be most efficiently used for photosynthesis. To test this hypothesis, we extend photosynthetic co-ordination theory to predict the acclimation of Rd to growth temperature via a link to Vcmax , and compare predictions to a global set of measurements from 112 sites spanning all terrestrial biomes. This extended co-ordination theory predicts that field-measured Rd and Vcmax accessed at growth temperature (Rd,tg and Vcmax,tg ) should increase by 3.7% and 5.5% per degree increase in growth temperature. These acclimated responses to growth temperature are less steep than the corresponding instantaneous responses, which increase 8.1% and 9.9% per degree of measurement temperature for Rd and Vcmax respectively. Data-fitted responses proof indistinguishable from the values predicted by our theory, and smaller than the instantaneous responses. Theory and data are also shown to agree that the basal rates of both Rd and Vcmax assessed at 25°C (Rd,25 and Vcmax,25 ) decline by ~4.4% per degree increase in growth temperature. These results provide a parsimonious general theory for Rd acclimation to temperature that is simpler-and potentially more reliable-than the plant functional type-based leaf respiration schemes currently employed in most ecosystem and land-surface models.

Global photosynthetic capacity is optimized to the environment.

Earth system models (ESMs) use photosynthetic capacity, indexed by the maximum Rubisco carboxylation rate (Vcmax ), to simulate carbon assimilation and typically rely on empirical estimates, including an assumed dependence on leaf nitrogen determined from soil fertility. In contrast, new theory, based on biochemical coordination and co-optimization of carboxylation and water costs for photosynthesis, suggests that optimal Vcmax can be predicted from climate alone, irrespective of soil fertility. Here, we develop this theory and find it captures 64% of observed variability in a global, field-measured Vcmax dataset for C3 plants. Soil fertility indices explained substantially less variation (32%). These results indicate that environmentally regulated biophysical constraints and light availability are the first-order drivers of global photosynthetic capacity. Through acclimation and adaptation, plants efficiently utilize resources at the leaf level, thus maximizing potential resource use for growth and reproduction. Our theory offers a robust strategy for dynamically predicting photosynthetic capacity in ESMs.

Targeted mutation detection in breast cancer using MammaSeq™.

BACKGROUND: Breast cancer is the most common invasive cancer among women worldwide. Next-generation sequencing (NGS) has revolutionized the study of cancer across research labs around the globe; however, genomic testing in clinical settings remains limited. Advances in sequencing reliability, pipeline analysis, accumulation of relevant data, and the reduction of costs are rapidly increasing the feasibility of NGS-based clinical decision making. METHODS: We report the development of MammaSeq, a breast cancer-specific NGS panel, targeting 79 genes and 1369 mutations, optimized for use in primary and metastatic breast cancer. To validate the panel, 46 solid tumors and 14 plasma circulating tumor DNA (ctDNA) samples were sequenced to a mean depth of 2311× and 1820×, respectively. Variants were called using Ion Torrent Suite 4.0 and annotated with cravat CHASM. CNVKit was used to call copy number variants in the solid tumor cohort. The oncoKB Precision Oncology Database was used to identify clinically actionable variants. Droplet digital PCR was used to validate select ctDNA mutations. RESULTS: In cohorts of 46 solid tumors and 14 ctDNA samples from patients with advanced breast cancer, we identified 592 and 43 protein-coding mutations. Mutations per sample in the solid tumor cohort ranged from 1 to 128 (median 3), and the ctDNA cohort ranged from 0 to 26 (median 2.5). Copy number analysis in the solid tumor cohort identified 46 amplifications and 35 deletions. We identified 26 clinically actionable variants (levels 1-3) annotated by OncoKB, distributed across 20 out of 46 cases (40%), in the solid tumor cohort. Allele frequencies of ESR1 and FOXA1 mutations correlated with CA.27.29 levels in patient-matched blood draws. CONCLUSIONS: In solid tumor biopsies and ctDNA, MammaSeq detects clinically actionable mutations (OncoKB levels 1-3) in 22/46 (48%) solid tumors and in 4/14 (29%) of ctDNA samples. MammaSeq is a targeted panel suitable for clinically actionable mutation detection in breast cancer.

Acclimation and adaptation components of the temperature dependence of plant photosynthesis at the global scale.

The temperature response of photosynthesis is one of the key factors determining predicted responses to warming in global vegetation models (GVMs). The response may vary geographically, owing to genetic adaptation to climate, and temporally, as a result of acclimation to changes in ambient temperature. Our goal was to develop a robust quantitative global model representing acclimation and adaptation of photosynthetic temperature responses. We quantified and modelled key mechanisms responsible for photosynthetic temperature acclimation and adaptation using a global dataset of photosynthetic CO2 response curves, including data from 141 C3 species from tropical rainforest to Arctic tundra. We separated temperature acclimation and adaptation processes by considering seasonal and common-garden datasets, respectively. The observed global variation in the temperature optimum of photosynthesis was primarily explained by biochemical limitations to photosynthesis, rather than stomatal conductance or respiration. We found acclimation to growth temperature to be a stronger driver of this variation than adaptation to temperature at climate of origin. We developed a summary model to represent photosynthetic temperature responses and showed that it predicted the observed global variation in optimal temperatures with high accuracy. This novel algorithm should enable improved prediction of the function of global ecosystems in a warming climate.

Drivers of leaf carbon exchange capacity across biomes at the continental scale.

Realistic representations of plant carbon exchange processes are necessary to reliably simulate biosphere-atmosphere feedbacks. These processes are known to vary over time and space, though the drivers of the underlying rates are still widely debated in the literature. Here, we measured leaf carbon exchange in >500 individuals of 98 species from the Neotropics to high boreal biomes to determine the drivers of photosynthetic and dark respiration capacity. Covariate abiotic (long- and short-term climate) and biotic (plant type, plant size, ontogeny, water status) data were used to explore significant drivers of temperature-standardized leaf carbon exchange rates. Using model selection, we found the previous week's temperature and soil moisture at the time of measurement to be a better predictor of photosynthetic capacity than long-term climate, with the combination of high recent temperatures and low soil moisture tending to decrease photosynthetic capacity. Non-trees (annual and perennials) tended to have greater photosynthetic capacity than trees, and, within trees, adults tended to have greater photosynthetic capacity than juveniles, possibly as a result of differences in light availability. Dark respiration capacity was less responsive to the assessed drivers than photosynthetic capacity, with rates best predicted by multi-year average site temperature alone. Our results suggest that, across large spatial scales, photosynthetic capacity quickly adjusts to changing environmental conditions, namely light, temperature, and soil moisture. Respiratory capacity is more conservative and most responsive to longer-term conditions. Our results provide a framework for incorporating these processes into large-scale models and a data set to benchmark such models.

LCE: leaf carbon exchange data set for tropical, temperate, and boreal species of North and Central America.

Leaf canopy carbon exchange processes, such as photosynthesis and respiration, are substantial components of the global carbon cycle. Climate models base their simulations of photosynthesis and respiration on an empirical understanding of the underlying biochemical processes, and the responses of those processes to environmental drivers. As such, data spanning large spatial scales are needed to evaluate and parameterize these models. Here, we present data on four important biochemical parameters defining leaf carbon exchange processes from 626 individuals of 98 species at 12 North and Central American sites spanning ~53° of latitude. The four parameters are the maximum rate of Rubisco carboxylation (Vcmax ), the maximum rate of electron transport for the regeneration of Ribulose-1,5,-bisphosphate (Jmax ), the maximum rate of phosphoenolpyruvate carboxylase carboxylation (Vpmax ), and leaf dark respiration (Rd ). The raw net photosynthesis by intercellular CO2 (A/Ci ) data used to calculate Vcmax , Jmax , and Vpmax rates are also presented. Data were gathered on the same leaf of each individual (one leaf per individual), allowing for the examination of each parameter relative to others. Additionally, the data set contains a number of covariates for the plants measured. Covariate data include (1) leaf-level traits (leaf mass, leaf area, leaf nitrogen and carbon content, predawn leaf water potential), (2) plant-level traits (plant height for herbaceous individuals and diameter at breast height for trees), (3) soil moisture at the time of measurement, (4) air temperature from nearby weather stations for the day of measurement and each of the 90 d prior to measurement, and (5) climate data (growing season mean temperature, precipitation, photosynthetically active radiation, vapor pressure deficit, and aridity index). We hope that the data will be useful for obtaining greater understanding of the abiotic and biotic determinants of these important biochemical parameters and for evaluating and improving large-scale models of leaf carbon exchange.

Short-term acclimation to warmer temperatures accelerates leaf carbon exchange processes across plant types.

While temperature responses of photosynthesis and plant respiration are known to acclimate over time in many species, few studies have been designed to directly compare process-level differences in acclimation capacity among plant types. We assessed short-term (7 day) temperature acclimation of the maximum rate of Rubisco carboxylation (Vcmax ), the maximum rate of electron transport (Jmax ), the maximum rate of phosphoenolpyruvate carboxylase carboxylation (Vpmax ), and foliar dark respiration (Rd ) in 22 plant species that varied in lifespan (annual and perennial), photosynthetic pathway (C3 and C4 ), and climate of origin (tropical and nontropical) grown under fertilized, well-watered conditions. In general, acclimation to warmer temperatures increased the rate of each process. The relative increase in different photosynthetic processes varied by plant type, with C3 species tending to preferentially accelerate CO2 -limited photosynthetic processes and respiration and C4 species tending to preferentially accelerate light-limited photosynthetic processes under warmer conditions. Rd acclimation to warmer temperatures caused a reduction in temperature sensitivity that resulted in slower rates at high leaf temperatures. Rd acclimation was similar across plant types. These results suggest that temperature acclimation of the biochemical processes that underlie plant carbon exchange is common across different plant types, but that acclimation to warmer temperatures tends to have a relatively greater positive effect on the processes most limiting to carbon assimilation, which differ by plant type. The acclimation responses observed here suggest that warmer conditions should lead to increased rates of carbon assimilation when water and nutrients are not limiting.

Mapping the global distribution of C4 vegetation using observations and optimality theory.

Plants with the C4 photosynthesis pathway typically respond to climate change differently from more common C3-type plants, due to their distinct anatomical and biochemical characteristics. These different responses are expected to drive changes in global C4 and C3 vegetation distributions. However, current C4 vegetation distribution models may not predict this response as they do not capture multiple interacting factors and often lack observational constraints. Here, we used global observations of plant photosynthetic pathways, satellite remote sensing, and photosynthetic optimality theory to produce an observation-constrained global map of C4 vegetation. We find that global C4 vegetation coverage decreased from 17.7% to 17.1% of the land surface during 2001 to 2019. This was the net result of a reduction in C4 natural grass cover due to elevated CO2 favoring C3-type photosynthesis, and an increase in C4 crop cover, mainly from corn (maize) expansion. Using an emergent constraint approach, we estimated that C4 vegetation contributed 19.5% of global photosynthetic carbon assimilation, a value within the range of previous estimates (18-23%) but higher than the ensemble mean of dynamic global vegetation models (14 ± 13%; mean ± one standard deviation). Our study sheds insight on the critical and underappreciated role of C4 plants in the contemporary global carbon cycle.