Robotic versus laparoscopic versus open nephrectomy for live kidney donors.

BACKGROUND: Waiting lists for kidney transplantation continue to grow. Live kidney donation significantly reduces waiting times and improves long-term outcomes for recipients. Major disincentives to potential kidney donors are the pain and morbidity associated with surgery. This is an update of a review published in 2011. OBJECTIVES: To assess the benefits and harms of open donor nephrectomy (ODN), laparoscopic donor nephrectomy (LDN), hand-assisted LDN (HALDN) and robotic donor nephrectomy (RDN) as appropriate surgical techniques for live kidney donors. SEARCH METHODS: We contacted the Information Specialist and searched the Cochrane Kidney and Transplant Register of Studies up to 31 March 2024 using search terms relevant to this review. Studies in the Register are identified through searches of CENTRAL, MEDLINE, and EMBASE, conference proceedings, the International Clinical Trials Registry Platform (ICTRP) Search Portal, and ClinicalTrials.gov. SELECTION CRITERIA: Randomised controlled trials (RCTs) comparing LDN with ODN, HALDN, or RDN were included. DATA COLLECTION AND ANALYSIS: Two review authors independently screened titles and abstracts for eligibility, assessed study quality, and extracted data. We contacted study authors for additional information where necessary. Summary estimates of effect were obtained using a random-effects model, and results were expressed as risk ratios (RR) and their 95% confidence intervals (CI) for dichotomous outcomes and mean difference (MD) or standardised mean difference (SMD) and 95% CI for continuous outcomes. Confidence in the evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. MAIN RESULTS: Thirteen studies randomising 1280 live kidney donors to ODN, LDN, HALDN, or RDN were included. All studies were assessed as having a low or unclear risk of bias for selection bias. Five studies had a high risk of bias for blinding. Seven studies randomised 815 live kidney donors to LDN or ODN. LDN was associated with reduced analgesia use (high certainty evidence) and shorter hospital stay, a longer procedure and longer warm ischaemia time (moderate certainty evidence). There were no overall differences in blood loss, perioperative complications, or need for operations (low or very low certainty evidence). Three studies randomised 270 live kidney donors to LDN or HALDN. There were no differences between HALDN and LDN for analgesia requirement, hospital stay (high certainty evidence), duration of procedure (moderate certainty evidence), blood loss, perioperative complications, or reoperations (low certainty evidence). The evidence for warm ischaemia time was very uncertain due to high heterogeneity. One study randomised 50 live kidney donors to retroperitoneal ODN or HALDN and reported less pain and analgesia requirements with ODN. It found decreased blood loss and duration of the procedure with HALDN. No differences were found in perioperative complications, reoperations, hospital stay, or primary warm ischaemia time. One study randomised 45 live kidney donors to LDN or RDN and reported a longer warm ischaemia time with RDN but no differences in analgesia requirement, duration of procedure, blood loss, perioperative complications, reoperations, or hospital stay. One study randomised 100 live kidney donors to two variations of LDN and reported no differences in hospital stay, duration of procedure, conversion rates, primary warm ischaemia times, or complications (not meta-analysed). The conversion rates to ODN were 6/587 (1.02%) in LDN, 1/160 (0.63%) in HALDN, and 0/15 in RDN. Graft outcomes were rarely or selectively reported across the studies. There were no differences between LDN and ODN for early graft loss, delayed graft function, acute rejection, ureteric complications, kidney function or one-year graft loss. In a meta-regression analysis between LDN and ODN, moderate certainty evidence on procedure duration changed significantly in favour of LDN over time (yearly reduction = 7.12 min, 95% CI 2.56 to 11.67; P = 0.0022). Differences in very low certainty evidence on perioperative complications also changed significantly in favour of LDN over time (yearly change in LnRR = 0.107, 95% CI 0.022 to 0.192; P = 0.014). Various different combinations of techniques were used in each study, resulting in heterogeneity among the results. AUTHORS' CONCLUSIONS: LDN is associated with less pain compared to ODN and has comparable pain to HALDN and RDN. HALDN is comparable to LDN in all outcomes except warm ischaemia time, which may be associated with a reduction. One study reported kidneys obtained during RDN had greater warm ischaemia times. Complications and occurrences of perioperative events needing further intervention were equivalent between all methods.

Seasonal trends in leaf-level photosynthetic capacity and water use efficiency in a North American Eastern deciduous forest and their impact on canopy-scale gas exchange.

Vegetative transpiration (E) and photosynthetic carbon assimilation (A) are known to be seasonally dynamic, with changes in their ratio determining the marginal water use efficiency (WUE). Despite an understanding that stomata play a mechanistic role in regulating WUE, it is still unclear how stomatal and nonstomatal processes influence change in WUE over the course of the growing season. As a result, limited understanding of the primary physiological drivers of seasonal dynamics of canopy WUE remains one of the largest uncertainties in earth system model projections of carbon and water exchange in temperate deciduous forest ecosystems. We investigated seasonal patterns in leaf-level physiological, hydraulic, and anatomical properties, including the seasonal progress of the stomatal slope parameter (g1 ; inversely proportional to WUE) and the maximum carboxylation rate (Vcmax ). Vcmax and g1 were seasonally variable; however, their patterns were not temporally synchronized. g1 generally showed an increasing trend until late in the season, while Vcmax peaked during the midsummer months. Seasonal progression of Vcmax was primarily driven by changes in leaf structural, and anatomical characteristics, while seasonal changes in g1 were most strongly related to changes in Vcmax and leaf hydraulics. Using a seasonally variable Vcmax and g1 to parameterize a canopy-scale gas exchange model increased seasonally aggregated A and E by 3% and 16%, respectively.

Short-term variation in leaf-level water use efficiency in a tropical forest.

The representation of stomatal regulation of transpiration and CO2 assimilation is key to forecasting terrestrial ecosystem responses to global change. Given its importance in determining the relationship between forest productivity and climate, accurate and mechanistic model representation of the relationship between stomatal conductance (gs ) and assimilation is crucial. We assess possible physiological and mechanistic controls on the estimation of the g1 (stomatal slope, inversely proportional to water use efficiency) and g0 (stomatal intercept) parameters, using diurnal gas exchange surveys and leaf-level response curves of six tropical broadleaf evergreen tree species. g1 estimated from ex situ response curves averaged 50% less than g1 estimated from survey data. While g0 and g1 varied between leaves of different phenological stages, the trend was not consistent among species. We identified a diurnal trend associated with g1 and g0 that significantly improved model projections of diurnal trends in transpiration. The accuracy of modeled gs can be improved by accounting for variation in stomatal behavior across diurnal periods, and between measurement approaches, rather than focusing on phenological variation in stomatal behavior. Additional investigation into the primary mechanisms responsible for diurnal variation in g1 will be required to account for this phenomenon in land-surface models.

The effect of the vertical gradients of photosynthetic parameters on the CO2 assimilation and transpiration of a Panamanian tropical forest.

Terrestrial biosphere models (TBMs) include the representation of vertical gradients in leaf traits associated with modeling photosynthesis, respiration, and stomatal conductance. However, model assumptions associated with these gradients have not been tested in complex tropical forest canopies. We compared TBM representation of the vertical gradients of key leaf traits with measurements made in a tropical forest in Panama and then quantified the impact of the observed gradients on simulated canopy-scale CO2 and water fluxes. Comparison between observed and TBM trait gradients showed divergence that impacted canopy-scale simulations of water vapor and CO2 exchange. Notably, the ratio between the dark respiration rate and the maximum carboxylation rate was lower near the ground than at the top-of-canopy, leaf-level water-use efficiency was markedly higher at the top-of-canopy, and the decrease in maximum carboxylation rate from the top-of-canopy to the ground was less than TBM assumptions. The representation of the gradients of leaf traits in TBMs is typically derived from measurements made within-individual plants, or, for some traits, assumed constant due to a lack of experimental data. Our work shows that these assumptions are not representative of the trait gradients observed in species-rich, complex tropical forests.

Wood-density has no effect on stomatal control of leaf-level water use efficiency in an Amazonian forest.

Forest disturbances increase the proportion of fast-growing tree species compared to slow-growing ones. To understand their relative capacity for carbon uptake and their vulnerability to climate change, and to represent those differences in Earth system models, it is necessary to characterise the physiological differences in their leaf-level control of water use efficiency and carbon assimilation. We used wood density as a proxy for the fast-slow growth spectrum and tested the assumption that trees with a low wood density (LWD) have a lower water-use efficiency than trees with a high wood density (HWD). We selected 5 LWD tree species and 5 HWD tree species growing in the same location in an Amazonian tropical forest and measured in situ steady-state gas exchange on top-of-canopy leaves with parallel sampling and measurement of leaf mass area and leaf nitrogen content. We found that LWD species invested more nitrogen in photosynthetic capacity than HWD species, had higher photosynthetic rates and higher stomatal conductance. However, contrary to expectations, we showed that the stomatal control of the balance between transpiration and carbon assimilation was similar in LWD and HWD species and that they had the same dark respiration rates.

Leaves as bottlenecks: The contribution of tree leaves to hydraulic resistance within the soil-plant-atmosphere continuum.

Within vascular plants, the partitioning of hydraulic resistance along the soil-to-leaf continuum affects transpiration and its response to environmental conditions. In trees, the fractional contribution of leaf hydraulic resistance (Rleaf ) to total soil-to-leaf hydraulic resistance (Rtotal ), or fRleaf (=Rleaf /Rtotal ), is thought to be large, but this has not been tested comprehensively. We compiled a multibiome data set of fRleaf using new and previously published measurements of pressure differences within trees in situ. Across 80 samples, fRleaf averaged 0.51 (95% confidence interval [CI] = 0.46-0.57) and it declined with tree height. We also used the allometric relationship between field-based measurements of soil-to-leaf hydraulic conductance and laboratory-based measurements of leaf hydraulic conductance to compute the average fRleaf for 19 tree samples, which was 0.40 (95% CI = 0.29-0.56). The in situ technique produces a more accurate descriptor of fRleaf because it accounts for dynamic leaf hydraulic conductance. Both approaches demonstrate the outsized role of leaves in controlling tree hydrodynamics. A larger fRleaf may help stems from loss of hydraulic conductance. Thus, the decline in fRleaf with tree height would contribute to greater drought vulnerability in taller trees and potentially to their observed disproportionate drought mortality.

Infection after ureteroscopy for ureteric stones: analysis of 71 305 cases in the Hospital Episode Statistics database.

OBJECTIVES: To investigate the burden of infectious complications following ureteroscopy (URS) for ureteric stones on a national level in England using data from the Hospital Episodes Statistics (HES) data warehouse. MATERIALS AND METHODS: A retrospective cohort was identified and followed up in HES during the period April 2013 to March 2020 for all procedure codes relating to ureteroscopic stone treatment (M27.1, M27.2, M27.3). Treatment episodes relating to the first URS ('index ureteroscopy') for each patient were further analysed. All subsequent admissions within 30 days were also captured. The primary outcome was diagnosis of urinary tract infection (UTI; including all codes relating to a UTI/sepsis within the first 30 days of index URS). Secondary outcomes were critical care attendance, attendance at the accident and emergency department (A&E) within 30 days, and mortality. RESULTS: A total of 71 305 index ureteroscopies were eligible for analysis. The median age was 55 years, and 81% of procedures were elective and 45% were undertaken as day-cases. At the time of index URS, 16% of patients had diabetes, 0.5% had coexisting neurological disease and 40% had an existing stent/nephrostomy. Overall, 6.8% of the cohort (n = 4822) had a diagnosis of UTI within 30 days of index URS (3.9% immediately after surgery). A total of 339 patients (0.5%) required an unplanned stay in critical care during their index URS admission; 8833 patients (12%) attended A&E within 30 days. Overall mortality was 0.18% (60 in-hospital, 65 within 30 days); 40 deaths (0.056%) included infection as a contributing cause of death. CONCLUSION: We present the largest series evaluating infectious complications after ureteroscopic stone treatment. The procedure is safe, with low inpatient infective complication and critical care admission rates.

Reducing model uncertainty of climate change impacts on high latitude carbon assimilation.

The Arctic-Boreal Region (ABR) has a large impact on global vegetation-atmosphere interactions and is experiencing markedly greater warming than the rest of the planet, a trend that is projected to continue with anticipated future emissions of CO2 . The ABR is a significant source of uncertainty in estimates of carbon uptake in terrestrial biosphere models such that reducing this uncertainty is critical for more accurately estimating global carbon cycling and understanding the response of the region to global change. Process representation and parameterization associated with gross primary productivity (GPP) drives a large amount of this model uncertainty, particularly within the next 50 years, where the response of existing vegetation to climate change will dominate estimates of GPP for the region. Here we review our current understanding and model representation of GPP in northern latitudes, focusing on vegetation composition, phenology, and physiology, and consider how climate change alters these three components. We highlight challenges in the ABR for predicting GPP, but also focus on the unique opportunities for advancing knowledge and model representation, particularly through the combination of remote sensing and traditional boots-on-the-ground science.

An improved representation of the relationship between photosynthesis and stomatal conductance leads to more stable estimation of conductance parameters and improves the goodness-of-fit across diverse data sets.

Stomata play a central role in surface-atmosphere exchange by controlling the flux of water and CO2 between the leaf and the atmosphere. Representation of stomatal conductance (gsw ) is therefore an essential component of models that seek to simulate water and CO2 exchange in plants and ecosystems. For given environmental conditions at the leaf surface (CO2 concentration and vapor pressure deficit or relative humidity), models typically assume a linear relationship between gsw and photosynthetic CO2 assimilation (A). However, measurement of leaf-level gsw response curves to changes in A are rare, particularly in the tropics, resulting in only limited data to evaluate this key assumption. Here, we measured the response of gsw and A to irradiance in six tropical species at different leaf phenological stages. We showed that the relationship between gsw and A was not linear, challenging the key assumption upon which optimality theory is based-that the marginal cost of water gain is constant. Our data showed that increasing A resulted in a small increase in gsw at low irradiance, but a much larger increase at high irradiance. We reformulated the popular Unified Stomatal Optimization (USO) model to account for this phenomenon and to enable consistent estimation of the key conductance parameters g0 and g1 . Our modification of the USO model improved the goodness-of-fit and reduced bias, enabling robust estimation of conductance parameters at any irradiance. In addition, our modification revealed previously undetectable relationships between the stomatal slope parameter g1 and other leaf traits. We also observed nonlinear behavior between A and gsw in independent data sets that included data collected from attached and detached leaves, and from plants grown at elevated CO2 concentration. We propose that this empirical modification of the USO model can improve the measurement of gsw parameters and the estimation of plant and ecosystem-scale water and CO2  fluxes.

Ten-year survival outcomes after radical nephroureterectomy with a risk-stratified approach using prior diagnostic ureteroscopy: a single-institution observational retrospective cohort study.

OBJECTIVES: To evaluate the long-term oncological outcomes of patients with upper tract urothelial carcinoma (UTUC) undergoing radical nephroureterectomy (RNU) and the impact of diagnostic ureteroscopy (URS) on survival outcomes. MATERIALS AND METHODS: A retrospective analysis of all consecutive patients undergoing RNU for suspected UTUC at a UK tertiary referral centre from a prospectively maintained database was conducted. The primary outcome measures were 5- and 10-year cancer-specific survival (CSS). The secondary outcomes were: overall survival (OS), recurrence-free survival (RFS), impact of prior diagnostic URS on OS, CSS and intravesical RFS (intravesical-RFS), and predictors of intravesical recurrence. Statistical analysis was performed in R using the 'survminer' and 'survival' packages. The Kaplan-Meier method was used to calculate survival functions and these were expressed in graphical form. Uni-/multivariate survival analyses were performed using the Cox proportional hazard regression model. Statistical significance in this study was set at P < 0.05. RESULTS: A total of 422 patients underwent RNU with confirmed UTUC. The median (interquartile range) follow-up of patients with confirmed UTUC was 9.2 (5.6-12.7) years. The 5- and 10-year CSS rates were 70.5% (95% confidence interval [CI] 65.9-74.9) and 67.1% (95% CI 62.4-71.6), respectively. OS (HR 1.04 [95% CI 0.78-1.38]; P = 0.46) and CSS (HR 0.96 [95% CI 0.68-1.34]; P = 0.81) were similar in the diagnostic URS and the direct RNU cohorts. intravesical RFS was superior for the direct RNU cohort (HR 1.94 [95% CI 1.19-3.17]; P = 0.008). In multivariate analysis, prior URS, T2 stage, proximal ureter tumour and bladder cancer history were predictors of metachronous bladder recurrence. CONCLUSION: This single-centre retrospective cohort study reports the long-term oncological outcomes of RNU with a median follow-up of 9.2 years, serving as a reference standard in counselling patients undergoing RNU. Stage and grade of the RNU specimen were the only two studied factors that appeared to adversely impact long-term CSS and OS. Our results suggest that the risk of intravesical recurrence is increased nearly twofold in patients who have undergone diagnostic URS prior to RNU. Prior URS, however, does not appear to adversely impact long-term CSS and OS. The authors suggest that a risk-stratified approach be adopted, wherein diagnostic URS is offered only in equivocal cases.

Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types.

Leaf trait relationships are widely used to predict ecosystem function in terrestrial biosphere models (TBMs), in which leaf maximum carboxylation capacity (Vc,max ), an important trait for modelling photosynthesis, can be inferred from other easier-to-measure traits. However, whether trait-Vc,max relationships are robust across different forest types remains unclear. Here we used measurements of leaf traits, including one morphological trait (leaf mass per area), three biochemical traits (leaf water content, area-based leaf nitrogen content, and leaf chlorophyll content), one physiological trait (Vc,max ), as well as leaf reflectance spectra, and explored their relationships within and across three contrasting forest types in China. We found weak and forest type-specific relationships between Vc,max and the four morphological and biochemical traits (R2 ≤ 0.15), indicated by significantly changing slopes and intercepts across forest types. By contrast, reflectance spectroscopy effectively collapsed the differences in the trait-Vc,max relationships across three forest biomes into a single robust model for Vc,max (R2 = 0.77), and also accurately estimated the four traits (R2 = 0.75-0.94). These findings challenge the traditional use of the empirical trait-Vc,max relationships in TBMs for estimating terrestrial plant photosynthesis, but also highlight spectroscopy as an efficient alternative for characterising Vc,max and multitrait variability, with critical insights into ecosystem modelling and functional trait ecology.

Source:sink imbalance detected with leaf- and canopy-level spectroscopy in a field-grown crop.

The finely tuned balance between sources and sinks determines plant resource partitioning and regulates growth and development. Understanding and measuring metabolic indicators of source or sink limitation forms a vital part of global efforts to increase crop yield for future food security. We measured metabolic profiles of Cucurbita pepo (zucchini) grown in the field under carbon sink limitation and control conditions. We demonstrate that these profiles can be measured non-destructively using hyperspectral reflectance at both leaf and canopy scales. Total non-structural carbohydrates (TNC) increased 82% in sink-limited plants; leaf mass per unit area (LMA) increased 38% and free amino acids increased 22%. Partial least-squares regression (PLSR) models link these measured functional traits with reflectance data, enabling high-throughput estimation of traits comprising the sink limitation response. Leaf- and canopy-scale models for TNC had R2 values of 0.93 and 0.64 and %RMSE of 13 and 38%, respectively. For LMA, R2 values were 0.91 and 0.60 and %RMSE 7 and 14%; for free amino acids, R2 was 0.53 and 0.21 with %RMSE 20 and 26%. Remote sensing can enable accurate, rapid detection of sink limitation in the field at the leaf and canopy scale, greatly expanding our ability to understand and measure metabolic responses to stress.

Detection of the metabolic response to drought stress using hyperspectral reflectance.

Drought is the most important limitation on crop yield. Understanding and detecting drought stress in crops is vital for improving water use efficiency through effective breeding and management. Leaf reflectance spectroscopy offers a rapid, non-destructive alternative to traditional techniques for measuring plant traits involved in a drought response. We measured drought stress in six glasshouse-grown agronomic species using physiological, biochemical, and spectral data. In contrast to physiological traits, leaf metabolite concentrations revealed drought stress before it was visible to the naked eye. We used full-spectrum leaf reflectance data to predict metabolite concentrations using partial least-squares regression, with validation R2 values of 0.49-0.87. We show for the first time that spectroscopy may be used for the quantitative estimation of proline and abscisic acid, demonstrating the first use of hyperspectral data to detect a phytohormone. We used linear discriminant analysis and partial least squares discriminant analysis to differentiate between watered plants and those subjected to drought based on measured traits (accuracy: 71%) and raw spectral data (66%). Finally, we validated our glasshouse-developed models in an independent field trial. We demonstrate that spectroscopy can detect drought stress via underlying biochemical changes, before visual differences occur, representing a powerful advance for measuring limitations on yield.

A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression.

Partial least squares regression (PLSR) modelling is a statistical technique for correlating datasets, and involves the fitting of a linear regression between two matrices. One application of PLSR enables leaf traits to be estimated from hyperspectral optical reflectance data, facilitating rapid, high-throughput, non-destructive plant phenotyping. This technique is of interest and importance in a wide range of contexts including crop breeding and ecosystem monitoring. The lack of a consensus in the literature on how to perform PLSR means that interpreting model results can be challenging, applying existing models to novel datasets can be impossible, and unknown or undisclosed assumptions can lead to incorrect or spurious predictions. We address this lack of consensus by proposing best practices for using PLSR to predict plant traits from leaf-level hyperspectral data, including a discussion of when PLSR is applicable, and recommendations for data collection. We provide a tutorial to demonstrate how to develop a PLSR model, in the form of an R script accompanying this manuscript. This practical guide will assist all those interpreting and using PLSR models to predict leaf traits from spectral data, and advocates for a unified approach to using PLSR for predicting traits from spectra in the plant sciences.

Multi-hypothesis comparison of Farquhar and Collatz photosynthesis models reveals the unexpected influence of empirical assumptions at leaf and global scales.

Mechanistic photosynthesis models are at the heart of terrestrial biosphere models (TBMs) simulating the daily, monthly, annual and decadal rhythms of carbon assimilation (A). These models are founded on robust mathematical hypotheses that describe how A responds to changes in light and atmospheric CO2 concentration. Two predominant photosynthesis models are in common usage: Farquhar (FvCB) and Collatz (CBGB). However, a detailed quantitative comparison of these two models has never been undertaken. In this study, we unify the FvCB and CBGB models to a common parameter set and use novel multi-hypothesis methods (that account for both hypothesis and parameter variability) for process-level sensitivity analysis. These models represent three key biological processes: carboxylation, electron transport, triose phosphate use (TPU) and an additional model process: limiting-rate selection. Each of the four processes comprises 1-3 alternative hypotheses giving 12 possible individual models with a total of 14 parameters. To broaden inference, TBM simulations were run and novel, high-resolution photosynthesis measurements were made. We show that parameters associated with carboxylation are the most influential parameters but also reveal the surprising and marked dominance of the limiting-rate selection process (accounting for 57% of the variation in A vs. 22% for carboxylation). The limiting-rate selection assumption proposed by CBGB smooths the transition between limiting rates and always reduces A below the minimum of all potentially limiting rates, by up to 25%, effectively imposing a fourth limitation on A. Evaluation of the CBGB smoothing function in three TBMs demonstrated a reduction in global A by 4%-10%, equivalent to 50%-160% of current annual fossil fuel emissions. This analysis reveals a surprising and previously unquantified influence of a process that has been integral to many TBMs for decades, highlighting the value of multi-hypothesis methods.

Stimulation of isoprene emissions and electron transport rates as key mechanisms of thermal tolerance in the tropical species Vismia guianensis.

Tropical forests absorb large amounts of atmospheric CO2 through photosynthesis, but high surface temperatures suppress this absorption while promoting isoprene emissions. While mechanistic isoprene emission models predict a tight coupling to photosynthetic electron transport (ETR) as a function of temperature, direct field observations of this phenomenon are lacking in the tropics and are necessary to assess the impact of a warming climate on global isoprene emissions. Here we demonstrate that in the early successional species Vismia guianensis in the central Amazon, ETR rates increased with temperature in concert with isoprene emissions, even as stomatal conductance (gs ) and net photosynthetic carbon fixation (Pn ) declined. We observed the highest temperatures of continually increasing isoprene emissions yet reported (50°C). While Pn showed an optimum value of 32.6 ± 0.4°C, isoprene emissions, ETR, and the oxidation state of PSII reaction centers (qL ) increased with leaf temperature with strong linear correlations for ETR (Æ¿ = 0.98) and qL (Æ¿ = 0.99) with leaf isoprene emissions. In contrast, other photoprotective mechanisms, such as non-photochemical quenching, were not activated at elevated temperatures. Inhibition of isoprenoid biosynthesis repressed Pn at high temperatures through a mechanism that was independent of stomatal closure. While extreme warming will decrease gs and Pn in tropical species, our observations support a thermal tolerance mechanism where the maintenance of high photosynthetic capacity under extreme warming is assisted by the simultaneous stimulation of ETR and metabolic pathways that consume the direct products of ETR including photorespiration and the biosynthesis of thermoprotective isoprenoids. Our results confirm that models which link isoprene emissions to the rate of ETR hold true in tropical species and provide necessary "ground-truthing" for simulations of the large predicted increases in tropical isoprene emissions with climate warming.

The response of stomatal conductance to seasonal drought in tropical forests.

Stomata regulate CO2 uptake for photosynthesis and water loss through transpiration. The approaches used to represent stomatal conductance (gs ) in models vary. In particular, current understanding of drivers of the variation in a key parameter in those models, the slope parameter (i.e. a measure of intrinsic plant water-use-efficiency), is still limited, particularly in the tropics. Here we collected diurnal measurements of leaf gas exchange and leaf water potential (Ψleaf ), and a suite of plant traits from the upper canopy of 15 tropical trees in two contrasting Panamanian forests throughout the dry season of the 2016 El Niño. The plant traits included wood density, leaf-mass-per-area (LMA), leaf carboxylation capacity (Vc,max ), leaf water content, the degree of isohydry, and predawn Ψleaf . We first investigated how the choice of four commonly used leaf-level gs models with and without the inclusion of Ψleaf as an additional predictor variable influence the ability to predict gs , and then explored the abiotic (i.e. month, site-month interaction) and biotic (i.e. tree-species-specific characteristics) drivers of slope parameter variation. Our results show that the inclusion of Ψleaf did not improve model performance and that the models that represent the response of gs to vapor pressure deficit performed better than corresponding models that respond to relative humidity. Within each gs model, we found large variation in the slope parameter, and this variation was attributable to the biotic driver, rather than abiotic drivers. We further investigated potential relationships between the slope parameter and the six available plant traits mentioned above, and found that only one trait, LMA, had a significant correlation with the slope parameter (R2  = 0.66, n = 15), highlighting a potential path towards improved model parameterization. This study advances understanding of gs dynamics over seasonal drought, and identifies a practical, trait-based approach to improve modeling of carbon and water exchange in tropical forests.

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.

Terrestrial biosphere models may overestimate Arctic CO2 assimilation if they do not account for decreased quantum yield and convexity at low temperature.

How terrestrial biosphere models (TBMs) represent leaf photosynthesis and its sensitivity to temperature are two critical components of understanding and predicting the response of the Arctic carbon cycle to global change. We measured the effect of temperature on the response of photosynthesis to irradiance in six Arctic plant species and determined the quantum yield of CO2 fixation ( ϕCO2 ) and the convexity factor (θ). We also determined leaf absorptance (α) from measured reflectance to calculate ϕCO2 on an absorbed light basis ( ϕCO2.a ) and enabled comparison with nine TBMs. The mean ϕCO2.a was 0.045 mol CO2  mol-1 absorbed quanta at 25°C and closely agreed with the mean TBM parameterisation (0.044), but as temperature decreased measured ϕCO2.a diverged from TBMs. At 5°C measured ϕCO2.a was markedly reduced (0.025) and 60% lower than TBM estimates. The θ also showed a significant reduction between 25°C and 5°C. At 5°C θ was 38% lower than the common model parameterisation of 0.7. These data show that TBMs are not accounting for observed reductions in ϕCO2.a and θ that can occur at low temperature. Ignoring these reductions in ϕCO2.a and θ could lead to a marked (45%) overestimation of CO2 assimilation at subsaturating irradiance and low temperature.

From the Arctic to the tropics: multibiome prediction of leaf mass per area using leaf reflectance.

Leaf mass per area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long-standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth's biomes, an efficient, globally generalizable approach to predict LMA is still lacking. We explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 to 393 g m-2 . Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad- and needleleaf species, and upper- and lower-canopy (i.e. sun and shade) growth environments. Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age and canopy position from diverse biomes. Our model captures LMA variability with high accuracy and low error (R2  = 0.89; root mean square error (RMSE) = 15.45 g m-2 ). Our finding highlights the fact that the leaf economics spectrum is mirrored by the leaf optical spectrum, paving the way for this technology to predict the diversity of LMA in ecosystems across global biomes.