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The perception of greater impact via new sinks, as opposed to through avoided emissions, has already led some large investors to focus on sink-related projects. This is a flawed perception when applied universally and carries a risk that effective routes to mitigation through avoiding emissions are side-lined. In reality, both emissions avoidance and emissions removal are needed, and both can be a cost-effective means of delivering mitigation.

Sugarcane production supports the livelihoods of millions of small‐scale farmers in developing countries, and the bioenergy needs of millions of consumers. Yet, future sugarcane yields remain uncertain due to differences in climate projections, and because the sensitivity of sugarcane ecophysiology to individual climate drivers (i.e. temperature, precipitation, shortwave radiation, VPD and CO2) and their interactions is largely unresolved. Here we ask: how sensitive is sugarcane yield to future climate change, including climate extremes, and what are its key climate drivers? We combine the Soil‐Plant‐Atmosphere model with detailed time‐series measurements from experimental plots in Guangxi, China, and São Paulo State, Brazil. We first calibrated and validated modelled carbon and water cycling against field flux and biometric data. Second, we simulated sugarcane growth under the historical climate (1980–2018), and six future climate projections (2015–2100). We computed the ‘yield‐effect’ of each climate driver by generating synthetic climate forcings in which the driver time series was alternated to that of the historical median. In Guangxi, median yield and yield lows (i.e. lower decile) were relatively insensitive to forecast climate change. In São Paulo, median yield and yield lows decreased under all future climates projections (urn:x-wiley:17571693:media:gcbb12797:gcbb12797-math-0001 = −4% and −12% respectively). At Guangxi, where moisture stress was low, radiation was the principal driver of yield variability (yield‐effect urn:x-wiley:17571693:media:gcbb12797:gcbb12797-math-0002 = −1.2%). Conversely, high moisture stress at São Paulo raised yield sensitivity to temperature (yield‐effect urn:x-wiley:17571693:media:gcbb12797:gcbb12797-math-0003 = −7.9%). In contrast, a number of other modelling studies report a positive effect of increased temperatures on sugarcane yield. We ascribe the disparity between model predictions to the representation of key phenological processes, including the link between leaf ageing and thermal time, and the role of ageing in driving leaf senescence. We highlight climate sensitivity of phenological processes as a key focus for future research efforts.

BACKGROUND Patients with renal insufficiency have poor short-term outcomes after transcatheter aortic valve replacement (TAVR). METHODS Retrospective chart review identified 575 consecutive patients not on hemodialysis who underwent TAVR between September 2014 and January 2017. Outcomes were defined by VARC-2 criteria. Primary outcome of all-cause mortality was evaluated at a median follow-up of 811 days (interquartile range 125-1,151). RESULTS Preprocedural glomerular filtration rate (GFR) was ≥60 ml/min in 51.7%, 30-60 ml/min in 42.1%, and

Forecasts of tropical ecosystem C cycling diverge among models due to differences in simulation of internal processes such as turnover, or transit times, of carbon pools. Estimates of these processes for the recent past are needed to test model representations, and so build confidence in model forecasts within and across biomes. Here, we evaluate carbon cycle process representation in two land surface models [Joint UK Land Environment Simulator (JULES) and Integrated Model of Land Surface Processes (INLAND)] for the period 2001–10 across Brazilian biomes. Model outputs are evaluated using the ILAMB system. Probabilistic benchmarking data were created using the carbon data model framework that assimilates observational times series of leaf area index and maps of woody biomass and soil C. New custom uncertainty metrics assess if models are within benchmark uncertainties. Simulations are better in homogeneous areas of vegetation type, and are less robust at ecotones between biomes, likely due to disturbance effects and parameter errors. Gross biosphere-atmosphere fluxes are robustly modelled across Brazil. However, benchmark uncertainty is too high on net ecosystem exchange to provide an accurate evaluation of the models. The LSMs have significant differences in internal carbon allocation and the dynamics of the different C pools. JULES models dead C stocks more accurately while living C stocks are best resolved for INLAND. JULES' over-estimate of the C wood pool results from over-estimation of both inputs to wood and the transit time of wood. INLAND's under-estimate of dead C stocks arises from an under-estimate of the transit time of dead organic matter. The models are better at simulating annual averages than seasonal variation of fluxes. Analyses of monthly net C exchanges show that INLAND correctly simulates seasonality, but over-estimates amplitudes, whereas JULES correctly simulates the annual amplitudes, but is out of phase with the benchmark.

Potential land–climate feedbacks in subarctic regions, where rapid warming is driving forest expansion into the tundra, may be mediated by differences in transpiration of different plant functional types. Here, we assess the environmental controls of overstorey transpiration and its relevance for ecosystem evapotranspiration in subarctic deciduous woodlands. We measured overstorey transpiration of mountain birch canopies and ecosystem evapotranspiration in two locations in northern Fennoscandia, having dense (Abisko) and sparse (Kevo) overstories. For Kevo, we also upscale chamber‐measured understorey evapotranspiration from shrubs and lichen using a detailed land cover map. Subdaily evaporative fluxes were not affected by soil moisture and showed similar controls by vapour pressure deficit and radiation across sites. At the daily timescale, increases in evaporative demand led to proportionally higher contributions of overstorey transpiration to ecosystem evapotranspiration. For the entire growing season, the overstorey transpired 33% of ecosystem evapotranspiration in Abisko and only 16% in Kevo. At this latter site, the understorey had a higher leaf area index and contributed more to ecosystem evapotranspiration compared with the overstorey birch canopy. In Abisko, growing season evapotranspiration was 27% higher than precipitation, consistent with a gradual soil moisture depletion over the summer. Our results show that overstorey canopy transpiration in subarctic deciduous woodlands is not the dominant evaporative flux. However, given the observed environmental sensitivity of evapotranspiration components, the role of deciduous trees in driving ecosystem evapotranspiration may increase with the predicted increases in tree cover and evaporative demand across subarctic regions.

Tree phenology mediates land-atmosphere mass and energy exchange and is a determinant of ecosystem structure and function. In the dry tropics, including African savannas, many trees grow new leaves during the dry season – weeks or months before the rains typically start. This syndrome of pre-rain green-up has long been recognised at small scales, but the high spatial and interspecific variability in leaf phenology has precluded regional generalisationsWe use remote sensing data to show that this precocious phenology is ubiquitous across the woodlands and savannas of southern tropical Africa.In 70% of the study area, green-up preceded rain onset by > 20 days (42% >40 days). All the main vegetation formations exhibit pre-rain green-up, by as much as 53±18 days (in the wet miombo). Green-up shows low interannual variability (SD between years = 11 days), and high spatial variability (>100 days)These results are consistent with a high degree of local phenological adaptation, and an insolation trigger of green-up. Tree-tree competition and niche separation may explain the ubiquity of this precocious phenology. The ubiquity of pre-rain green-up described here challenges existing model representations and suggests resistance (but not necessarily resilience) to the delay in rain onset predicted under climate change.

Background and objective Measuring adherence to inhaled asthma treatment is a key priority for asthma care. The aim of this study was to determine the relationship between self-report and actual medication use as measured by electronic monitoring for single and combination inhaled corticosteroid (ICS) and long-acting beta-agonist (LABA) metered-dose inhaler therapy. Methods In this retrospective analysis from a previously completed prospective 24-week randomized, controlled trial of single or combination ICS/LABA therapy, medication use in the week prior to study visits determined by self-report questionnaire and electronic monitoring was compared. One hundred eleven participants received 125 µg fluticasone dipropionate and 25 µg salmeterol, two actuations twice daily, by either separate or combination ICS/LABA inhalers. Paired data for self-report and electronic monitoring were analysed. Measurement of agreement was by Bland–Altman-like plots by visit with calculation of limits of agreement. Results For single and combination ICS/LABA therapy, self-report consistently overestimated actual inhaler use assessed by electronic monitoring by a mean of 2.2–8.4 inhalations over a 1-week period, with limits of agreement ranging from ±15.8 to 25.6 inhalations. Participants who underused their inhalers tended to overreport their use, while those who overused tended to underreport their medication use. The greater the degree of underuse, the greater the magnitude of overreport, and likewise, the greater the degree of overuse, the greater the magnitude of underreport. Conclusions Self-report is inaccurate in measuring actual use of inhaled asthma treatment with patients who underuse their maintenance therapy overreporting their use and those who overuse their therapy underreporting their use.

Environmental controls on carbon dynamics operate at a range of interacting scales from the leaf to landscape. The key questions of this study addressed the influence of water and nitrogen (N) availability on Pinus palustris (Mill.) physiology and primary productivity across leaf and canopy scales, linking the soil-plant-atmosphere (SPA) model to leaf and stand-scale flux and leaf trait/canopy data. We present previously unreported ecophysiological parameters (e.g. V(cmax) and J(max)) for P. palustris and the first modelled estimates of its annual gross primary productivity (GPP) across xeric and mesic sites and under extreme drought. Annual mesic site P. palustris GPP was ∼23% greater than at the xeric site. However, at the leaf level, xeric trees had higher net photosynthetic rates, and water and light use efficiency. At the canopy scale, GPP was limited by light interception (canopy level), but co-limited by nitrogen and water at the leaf level. Contrary to expectations, the impacts of an intense growing season drought were greater at the mesic site. Modelling indicated a 10% greater decrease in mesic GPP compared with the xeric site. Xeric P. palustris trees exhibited drought-tolerant behaviour that contrasted with mesic trees' drought-avoidance behaviour.

Summary 1. Arctic ecosystems are experiencing rapid climate change, which could result in positive feedbacks on climate warming if ecosystem carbon (C) loss exceeds C uptake through plant growth. Bryophytes (mosses, liverworts and hornworts) are important components of Arctic vegetation, but are currently not well represented in terrestrial C models; in particular, seasonal patterns in bryophyte C metabolism compared to vascular plant vegetation are poorly understood. 2. Our objective was to quantify land-surface CO2 fluxes for common sub-Arctic bryophyte patches (dominated by Polytrichum piliferum and Sphagnum fuscum) in spring (March–May) and during the summer growing season (June–August) and to develop a simple model of bryophyte gross primary productivity fluxes (PB). We use the model to explore the key environmental controls over PB for P. piliferum and S. fuscum and compare seasonal patterns of productivity with those of typical vascular plant communities at the same site. 3. The modelled total gross primary productivity (ΣPB) over 1 year (March – November) for P. piliferum was c. 360 g C m−2 ground and for S. fuscum c. 112 g C m−2 ground, c. 90% and 30% of total gross primary productivity for typical vascular plant communities (ΣPV) over the same year. In spring (March–May), when vascular plant leaves are not fully developed, ΣPB for P. piliferum was 3 × ΣPV. 4. Model sensitivity analysis indicated that bryophyte turf water content does not significantly affect (March–November) ΣPB for P. piliferum and S. fuscum, at least for periods without sustained lack of precipitation. However, we find that seasonal changes in bryophyte photosynthetic capacity are important in determining ΣPB for both bryophyte species. 5. Our study implies that models of C dynamics in the Arctic must include a bryophyte component if they are intended to predict the effects of changes in the timing of the growing season, or of changes in vegetation composition, on Arctic C balance.

The planetary boundary layer (PBL) provides an important link between the scales and processes resolved by global atmospheric sampling/modeling and site-based flux measurements. The PBL is in direct contact with the land surface, both driving and responding to ecosystem processes. Measurements within the PBL (e. g., by radiosondes, aircraft profiles, and flask measurements) have a footprint, and thus an integrating scale, on the order of; similar to 1-100 km. We use the coupled atmosphere-biosphere model (CAB) and a Bayesian data assimilation framework to investigate the amount of biosphere process information that can be inferred from PBL measurements. We investigate the information content of PBL measurements in a two-stage study. First, we demonstrate consistency between the coupled model (CAB) and measurements, by comparing the model to eddy covariance flux tower measurements (i.e., water and carbon fluxes) and also PBL scalar profile measurements (i.e., water, carbon dioxide, and temperature) from Canadian boreal forest. Second, we use the CAB model in a set of Bayesian inversions experiments using synthetic data for a single day. In the synthetic experiment, leaf area and respiration were relatively well constrained, whereas surface albedo and plant hydraulic conductance were only moderately constrained. Finally, the abilities of the PBL profiles and the eddy covariance data to constrain the parameters were largely similar and only slightly lower than the combination of both observations. Publisher PDF

A soil–plant–atmosphere model was used to estimate gross primary productivity (GPP) and evapotranspiration (ET) of a tropical savanna in Australia. This paper describes model modifications required to simulate the substantial C4 grass understory together with C3 trees. The model was further improved to include a seasonal distribution of leaf area and foliar nitrogen through 10 canopy layers. Model outputs were compared with a 5-year eddy covariance dataset. Adding the C4 photosynthesis component improved the model efficiency and root-mean-squared error (RMSE) for total ecosystem GPP by better emulating annual peaks and troughs in GPP across wet and dry seasons. The C4 photosynthesis component had minimal impact on modelled values of ET. Outputs of GPP from the modified model agreed well with measured values, explaining between 79% and 90% of the variance and having a low RMSE (0.003–0.281 g C m � 2 day � 1 ). Approximately, 40% of total annual GPP was contributed by C4 grasses. Total (trees and grasses) wet season GPP was approximately 75–80% of total annual GPP. Light-use efficiency (LUE) was largest for the wet season and smallest in the dry season and C4 LUE was larger than that of the trees. A sensitivity analysis of GPP revealed that daily GPP was most sensitive to changes in leaf area index (LAI) and foliar nitrogen (Nf) and relatively insensitive to changes in maximum carboxylation rate (Vcmax), maximum electron transport rate (Jmax) and minimum leaf water potential (cmin). The modified model was also able to represent daily and seasonal patterns in ET, (explaining 68–81% of variance) with a low RMSE (0.038–0.19 mm day � 1 ). Current values of Nf, LAI and other parameters appear to be colimiting for maximizing GPP. By manipulating LAI and soil moisture content inputs, we show that modelled GPP is limited by light interception rather than water availability at this site.

Quantifying ecosystem carbon stocks is vital for understanding the relationship between changes in land use and carbon dioxide emissions. Here, we estimate carbon stocks in an area of miombo woodland in Mozambique, by identifying the major carbon stocks and their variability. Data on the biomass of tree stems and roots, saplings, and soil carbon stocks are reported and compared with other savannas systems around the globe. A new allometric relationship between stem diameter and tree stem and root biomass is presented, based on the destructive harvest of 29 trees. These allometrics are combined with an inventory of 12,733 trees on 58 plots over an area of 27ha. Ecosystem carbon stocks totaled 110tC/ha, with 76tC/ha in the soil carbon pool (to 50cm depth), 21.2tC/ha in tree stem biomass, 8.5tC/ha in tree coarse root biomass, and 3.6tC/ha in total sapling biomass. Plot-level tree root:stem (R:S) ratio varied from 0.27 to 0.58, with a mean of 0.42, slightly higher than the mean reported for 18 other savanna sites with comparable aboveground biomass (R:S=0.35). Tree biomass (stem1root) ranged from 3.1 to 86.5tC/ha, but the mean (32.1tC/ha) was well constrained (95% CI 28‐36.6). In contrast, soil carbon stocks were almost uniformly distributed and varied from 32 to 133tC/ha. Soil carbon stocks are thus the major uncertainty in the carbon storage of these woodlands. Soil texture explained 53 percent of the variation in soil carbon content, but only 13 percent of the variation in woody carbon stocks. The history of disturbance (fire, elephants, logging/charcoal production, and shifting cultivation) is likely to decouple changes in woody carbon stocks from soil carbon stocks, mediated by tree‐grass interactions.

Miombo woodlands are the largest savanna in the world and dominate southern Africa. They are strongly influenced by anthropogenic fires and support the livelihoods of over 100 million people. Managing the fire regime of these flammable systems is difficult, but crucial for sustaining biodiversity, ecosystem services, and carbon stocks. Fire intensity is more easily manipulated than fire frequency, because suppression is expensive and ineffective. However, there are important issues relating fire intensity to impacts on woody vegetation that need to be understood to inform management approaches. Such impacts include the links between fire intensity, tree top-kill, resprouting, and regrowth rates. Here we present results from a fire experiment in Mozambican miombo; the results of a 50-year fire experiment in Zimbabwean miombo; and observations of forest structure at a dry-forest site in Mozambique. We synthesize these data with a process-based gap model of stem growth, regeneration, and mortality; this model explicitly considers the effect of different frequencies and intensities of fire. We use the model, tested against the field data, to explore the sensitivity of woodland tree populations and biomass to fire intensity and frequency.The fire experiments show that large (>5 cm dbh) stems are vulnerable to fire, with top-kill rates of up to 12% in intense fires. In contrast to idealized physical representations of tree mortality, stems of >10 cm dbh did not gain further protection from fire with increasing dbh. Resprouting was very common and not obviously linked to fire intensity. The modeling showed that miombo tree populations and biomass are very sensitive to fire intensity, offering opportunities for effective management. At any achievable fire return interval (

*The effects of drought on the Amazon rainforest are potentially large but remain poorly understood. Here, carbon (C) cycling after 5 yr of a large-scale through-fall exclusion (TFE) experiment excluding about 50% of incident rainfall from an eastern Amazon rainforest was compared with a nearby control plot. *Principal C stocks and fluxes were intensively measured in 2005. Additional minor components were either quantified in later site measurements or derived from the available literature. *Total ecosystem respiration (R(eco)) and total plant C expenditure (PCE, the sum of net primary productivity (NPP) and autotrophic respiration (R(auto))), were elevated on the TFE plot relative to the control. The increase in PCE and R(eco) was mainly caused by a rise in R(auto) from foliage and roots. Heterotrophic respiration did not differ substantially between plots. NPP was 2.4 +/- 1.4 t C ha(-1) yr(-1) lower on the TFE than the control. Ecosystem carbon use efficiency, the proportion of PCE invested in NPP, was lower in the TFE plot (0.24 +/- 0.04) than in the control (0.32 +/- 0.04). *Drought caused by the TFE treatment appeared to drive fundamental shifts in ecosystem C cycling with potentially important consequences for long-term forest C storage.

P>1. The Amazon region may experience increasing moisture limitation over this century. Leaf dark respiration (R) is a key component of the Amazon rain forest carbon (C) cycle, but relatively little is known about its sensitivity to drought. 2. Here, we present measurements of R standardized to 25 degrees C and leaf morphology from different canopy heights over 5 years at a rain forest subject to a large-scale through-fall reduction (TFR) experiment, and nearby, unmodified Control forest, at the Caxiuana reserve in the eastern Amazon. 3. In all five post-treatment measurement campaigns, mean R at 25 degrees C was elevated in the TFR forest compared to the Control forest experiencing normal rainfall. After 5 years of the TFR treatment, R per unit leaf area and mass had increased by 65% and 42%, respectively, relative to pre-treatment means. In contrast, leaf area index (L) in the TFR forest was consistently lower than the Control, falling by 23% compared to the pre-treatment mean, largely because of a decline in specific leaf area (S). 4. The consistent and significant effects of the TFR treatment on R, L and S suggest that severe drought events in the Amazon, of the kind that may occur more frequently in future, could cause a substantial increase in canopy carbon dioxide emissions from this ecosystem to the atmosphere.

Summary 1. Leaf area index (LAI) is an emergent property of vascular plants closely linked to primary production and surface energy balance. LAI can vary by an order of magnitude among Arctic tundra communities and is closely associated with plant functional type. 2. We examined topographic controls on vegetation type and LAI distribution at two different scales in an Arctic tundra ecosystem in northern Sweden. ‘Micro-scale’ measurements were made at 0.2-m resolution over a 40 m × 40 m domain, while ‘macro-scale’ data were collected at approximately 10-m resolution over a 500 m × 500 m domain. Tundra LAI varied from 0.1‐3.6 at the micro-scale resolution, and from 0.1‐1.6 at the macro-scale resolution. 3. The correlation between dominant vascular species and LAI at the micro-scale ( r 2 = 0.40) was greater than the correlation between dominant vegetation and LAI at the macro-scale ( r 2 = 0.14). At the macro-scale, LAI was better explained by topographic parameters and spatial autocorrelation (pseudo r 2 = 0.32) than it was at the micro-scale ( r 2 = 0.16). Exposure and elevation were significantly but weakly correlated with LAI at the micro-scale, while on the macro-scale the most significant explanatory topographic variable was elevation ( r 2 = 0.12). 4. The distribution of plant communities at both scales was significantly associated with topography. Shrub communities, dominated by Betula nana , were associated with low elevation sites at both scales, while more exposed and/or high elevation sites were dominated by cryptogams. 5. Synthesis. Dominant vegetation, topography and LAI were linked at both scales of investigation but, for explaining LAI, topography became more important and dominant vegetation less important at the coarser scale. The explanatory power of dominant species/functional type for LAI variation was weaker at coarser scales, because communities often contained more than one functional type at 10 m resolution. The data suggest that remotely sensed topography can be combined with remotely sensed optical measurements to generate a useful tool for LAI mapping in Arctic environments.

Monitoring and understanding global change requires a detailed focus on upscaling, the process for extrapolating from the site-specific scale to the smallest scale resolved in regional or global models or earth observing systems. Leaf area index (LAI) is one of the most sensitive determinants of plant production and can vary by an order of magnitude over short distances. The landscape distribution of LAI is generally determined by remote sensing of surface reflectance (e.g. normalized difference vegetation index, NDVI) but the mismatch in scales between ground and satellite measurements complicates LAI upscaling. Here, we describe a series of measurements to quantify the spatial distribution of LAI in a sub-Arctic landscape and then describe the upscaling process and its associated errors. Working from a fine-scale harvest LAI?NDVI relationship, we collected NDVI data over a 500 m × 500 m catchment in the Swedish Arctic, at resolutions from 0.2 to 9.0 m in a nested sampling design. NDVI scaled linearly, so that NDVI at any scale was a simple average of multiple NDVI measurements taken at finer scales. The LAI?NDVI relationship was scale invariant from 1.5 to 9.0 m resolution. Thus, a single exponential LAI?NDVI relationship was valid at all these scales, with similar prediction errors. Vegetation patches were of a scale of 0.5 m and at measurement scales coarser than this, there was a sharp drop in LAI variance. Landsat NDVI data for the study catchment correlated significantly, but poorly, with ground-based measurements. A variety of techniques were used to construct LAI maps, including interpolation by inverse distance weighting, ordinary Kriging, External Drift Kriging using Landsat data, and direct estimation from a Landsat NDVI?LAI calibration. All methods produced similar LAI estimates and overall errors. However, Kriging approaches also generated maps of LAI estimation error based on semivariograms. The spatial variability of this Arctic landscape was such that local measurements assimilated by Kriging approaches had a limited spatial influence. Over scales >50 m, interpolation error was of similar magnitude to the error in the Landsat NDVI calibration. The characterisation of LAI spatial error in this study is a key step towards developing spatio-temporal data assimilation systems for assessing C cycling in terrestrial ecosystems by combining models with field and remotely sensed data.

Warmer and drier climates over Eastern Amazonia have been predicted as a component of climate change during the next 50–100 years. It remains unclear what effect such changes will have on forest–atmosphere exchange of carbon dioxide (CO2) and water, but the cumulative effect is anticipated to produce climatic feedback at both regional and global scales. To allow more detailed study of forest responses to soil drying, a simulated soil drought or ‘throughfall exclusion’ (TFE) experiment was established at a rain forest site in Eastern Amazonia, Brazil, for which time-series sap flow and soil moisture data were obtained. The experiment excluded 50% of the throughfall from the soil. Sap flow data from the forest plot experiencing normal rainfall showed no limitation of transpiration throughout the two monitored dry seasons. Conversely, data from the TFE showed large dry season declines in transpiration, with tree water use restricted to 20% of that in the control plot at the peak of both dry seasons. The results were examined to evaluate the paradigm that the restriction on transpiration in the dry season was caused by limitation of soil-to-root water transport, driven by low soil water potential and high soil-to-root hydraulic resistance. This paradigm, embedded in the soil–plant–atmosphere (SPA) model and driven using on-site measurements, provided a good explanation (R2 > 0.69) of the magnitude and timing of changes in sap flow and soil moisture. This model-data correspondence represents a substantial improvement compared with other ecosystem models of drought stress tested in Amazonia. Inclusion of deeper rooting should lead to lower sensitivity to drought than the majority of existing models. Modelled annual GPP declined by 13–14% in response to the treatment, compared with estimated declines in transpiration of 30–40%.

1. Arctic landscapes are characterized by extreme vegetation patchiness, often with sharply defined borders between very different vegetation types. This patchiness makes it difficult to predict landscape-level C balance and its change in response to environment. 2. Here we develop a model of net CO2 flux by arctic landscapes that is independent of vegetation composition, using instead a measure of leaf area derived from NDVI (normalized-difference vegetation index). 3. Using the light response of CO2 flux (net ecosystem exchange, NEE) measured in a wide range of vegetation in arctic Alaska and Sweden, we exercise the model using various data subsets for parameter estimation and tests of predictions. 4. Overall, the model consistently explains similar to 80% of the variance in NEE knowing only the estimated leaf area index (LAI), photosynthetically active photon flux density (PPFD) and air temperature. 5. Model parameters derived from measurements made in one site or vegetation type can be used to predict NEE in other sites or vegetation types with acceptable accuracy and precision. Further improvements in model prediction may come from incorporating an estimate of moss area in addition to LAI, and from using vegetation-specific estimates of LAI. 6. The success of this model at predicting NEE independent of any information on species composition indicates a high level of convergence in canopy structure and function in the arctic landscape.

Background and objectives: The diagnosis, assessment and management of a wide range of respiratory diseases rely on accurate interpretation of lung function tests through the use of reference equations to generate predicted values. This paper ascertains the suitability of reference equations currently used in New Zealand through comparison with newly derived equations from the Wellington Respiratory Survey, and discusses the relevance of the findings to the Asia Pacific region. Methods: A survey of lung function testing facilities determined the reference equations in common usage. Pulmonary function test results from healthy, lifelong non-smoking subjects (n = 180) were expressed as percentage predicted values, with comparisons made between the currently used and Wellington Respiratory Survey reference equations. Differences in disease severity classification in subjects with COPD (n = 46) and asthma (n = 61) were determined, using the different reference equations. Results: Currently used equations significantly underpredict measured values for FEV1, PEF, TLC and RV by up to 20%. Severity classification of COPD and asthma based on per cent predicted FEV1 was substantially altered by the choice of reference equation. Conclusion: Many reference equations in current usage in New Zealand are no longer suitable for use. The applicability of reference equations used in other populations and countries within the Asia Pacific region requires further investigation. We recommend that up-to-date reference equations are derived and implemented if those currently used are shown to be unsatisfactory.

This study evaluates a novel method for extracting roots from soil samples and applies it to estimate standing crop root mass (+/- confidence intervals) in an eastern Amazon rainforest. Roots were manually extracted from soil cores over a period of 40 min, which was split into 10 min time intervals. The pattern of cumulative extraction over time was used to predict root extraction beyond 40 min. A maximum-likelihood approach was used to calculate confidence intervals. The temporal prediction method added 21-32% to initial estimates of standing crop root mass. According to predictions, complete manual root extraction from 18 samples would have taken c. 239 h, compared with 12 h using the prediction method. Uncertainties (percentage difference between mean, and 10th and 90th percentiles) introduced by the prediction method were small (12-15%), compared with uncertainties caused by spatial variation in root mass (72-191%, for nine samples per plot surveyed). This method provides a way of increasing the number of root samples processed per unit time, without compromising measurement accuracy.

1 The arctic environment is highly heterogeneous in terms of plant distribution and productivity. If we are to make regional scale predictions of carbon exchange it is necessary to find robust relationships that can simplify this variability. One such potential relationship is that of leaf area to photosynthetic CO2 flux at the canopy scale. 2 In this paper we assess the effectiveness of canopy leaf area in explaining variation in gross primary productivity (GPP): (i) across different vegetation types; (ii) at various stages of leaf development; and (iii) under enhanced nutrient availability. To do this we measure net CO2 flux light response curves with a 1 × 1 m chamber, and calculate GPP at a photosynthetic photon flux density (PPFD) of 600 µmol m2 s1. 3 At a subarctic site in Sweden, we report 10-fold variation in GPP among natural vegetation types with leaf area index (LAI) values of 0.05?2.31 m2 m2. At a site of similar latitude in Alaska we document substantially elevated rates of GPP in fertilized vegetation. 4 We can explain 80% of the observed variation in GPP in natural vegetation (including vegetation measured before deciduous leaf bud burst) by leaf area alone, when leaf area is predicted from measurements of normalized difference vegetation index (NDVI). 5 In fertilized vegetation the relative increase in leaf area between control and fertilized treatments exceeds the relative increase in GPP. This suggests that higher leaf area causes increased self-shading, or that lower leaf nitrogen per unit leaf area causes a reduction in the rate of photosynthesis. 6 The results of this study indicate that canopy leaf area is an excellent predictor of GPP in diverse low arctic tundra, across a wide range of plant functional types.

Climate modelling studies predict that the rain forests of the Eastern Amazon basin are likely to experience reduc- tions in rainfall of up to 50% over the next 50-100 years. Efforts to predict the effects of changing climate, especially drought stress, on forest gas exchange are currently limited by uncertainty about the mechanism that controls stomatal closure in response to low soil moisture. At a through-fall exclusion experiment in Eastern Amazonia where water was experimentally excluded from the soil, we tested the hypothesis that plants are isohydric, that is, when water is scarce, the stomata act to prevent leaf water potential from dropping below a critical threshold level. We made diurnal measurements of leaf water potential ( Ψ l ), stomatal con- ductance ( g s ), sap flow and stem water potential ( Ψ stem ) in the wet and dry seasons. We compared the data with the predictions of the soil-plant-atmosphere (SPA) model, which embeds the isohydric hypothesis within its stomatal conductance algorithm. The model inputs for meteorology, leaf area index (LAI), soil water potential and soil-to-leaf hydraulic resistance ( R ) were altered between seasons in accordance with measured values. No optimization param- eters were used to adjust the model. This 'mechanistic' model of stomatal function was able to explain the individ- ual tree-level seasonal changes in water relations ( r 2 = 0.85, 0.90 and 0.58 for Ψ l , sap flow and g s , respectively). The model indicated that the measured increase in R was the dominant cause of restricted water use during the dry sea- son, resulting in a modelled restriction of sap flow four times greater than that caused by reduced soil water poten- tial. Higher resistance during the dry season resulted from an increase in below-ground resistance (including root and soil-to-root resistance) to water flow.

There are two broad approaches to quantifying landscape C dynamics - by measuring changes in C stocks over time, or by measuring fluxes of C directly. However, these data may be patchy, and have gaps or biases. An alternative approach to generating C budgets has been to use process-based models, constructed to simulate the key processes involved in C exchange. However, the process of model building is arguably subjective, and parameters may be poorly defined. This paper demonstrates why data assimilation (DA) techniques - which combine stock and flux observations with a dynamic model - improve estimates of, and provide insights into, ecosystem carbon (C) exchanges. We use an ensemble Kalman filter (EnKF) to link a series of measurements with a simple box model of C transformations. Measurements were collected at a young ponderosa pine stand in central Oregon over a 3-year period, and include eddy flux and soil C02 efflux data, litterfall collections, stem surveys, root and soil cores, and leaf area index data. The simple C model is a mass balance model with nine unknown parameters, tracking changes in C storage among five pools; foliar, wood and fine root pools in vegetation, and also fresh litter and soil organic matter (SOM) plus coarse woody debris pools. We nested the EnKF within an optimization routine to generate estimates from the data of the unknown parameters and the five initial conditions for the pools. The efficacy of the DA process can be judged by comparing the probability distributions of estimates produced with the EnKF analysis vs. those produced with reduced data or model alone. Using the model alone, estimated net ecosystem exchange of C (NEE)= -251 f 197g Cm-2 over the 3 years, compared with an estimate of -419 f 29gCm-2 when all observations were assimilated into the model. The uncertainty on daily measurements of NEE via eddy fluxes was estimated at 0.5gCm-2 day-1, but the uncertainty on assimilated estimates averaged 0.47 g Cm-2 day-1, and only exceeded 0.5gC m-2 day-1 on days where neither eddy flux nor soil efflux data were available. In generating C budgets, the assimilation process reduced the uncertainties associated with using data or model alone and the forecasts of NEE were statistically unbiased estimates. The results of the analysis emphasize the importance of time series as constraints. Occasional, rare measurements of stocks have limited use in constraining the estimates of other components of the C cycle. Long time series are particularly crucial for improving the analysis of pools with long time constants, such as SOM, woody biomass, and woody debris. Long-running forest stem surveys, and tree ring data, offer a rich resource that could be assimilated to provide an important constraint on C cycling of slow pools. For extending estimates of NEE across regions, DA can play a further important role, by assimilating remote-sensing data into the analysis of C cycles. We show, via sensitivity analysis, how assimilating an estimate of photosynthesis - which might be provided indirectly by remotely sensed data - improves the analysis of NEE.

Models represent our primary method for integration of small-scale, process-level phenomena into a comprehensive description of forest-stand or ecosystem function. They also represent a key method for testing hypotheses about the response of forest ecosystems to multiple changing environmental conditions. This paper describes the evaluation of 13 stand-level models varying in their spatial, mechanistic, and temporal complexity for their ability to capture intra- and interannual components of the water and carbon cycle for an upland, oak-dominated forest of eastern Tennessee. Comparisons between model simulations and observations were conducted for hourly, daily, and annual time steps. Data for the comparisons were obtained from a wide range of methods including: eddy covariance, sapflow, chamber-based soil respiration, biometric estimates of stand-level net primary production and growth, and soil water content by time or frequency domain reflectometry. Response surfaces of carbon and water flux as a function of environmental drivers, and a variety of goodness-of-fit statistics (bias, absolute bias, and model efficiency) were used to judge model performance.A single model did not consistently perform the best at all time steps or for all variables considered. Intermodel comparisons showed good agreement for water cycle fluxes, but considerable disagreement among models for predicted carbon fluxes. The mean of all model outputs, however, was nearly always the best fit to the observations. Not surprisingly, models missing key forest components or processes, such as roots or modeled soil water content, were unable to provide accurate predictions of ecosystem responses to short-term drought phenomenon. Nevertheless, an inability to correctly capture short-term physiological processes under drought was not necessarily an indicator of poor annual water and carbon budget simulations. This is possible because droughts in the subject ecosystem were of short duration and therefore had a small cumulative impact. Models using hourly time steps and detailed mechanistic processes, and having a realistic spatial representation of the forest ecosystem provided the best predictions of observed data. Predictive ability of all models deteriorated under drought conditions, suggesting that further work is needed to evaluate and improve ecosystem model performance under unusual conditions, such as drought, that are a common focus of environmental change discussions.Read More: http://www.esajournals.org/doi/abs/10.1890/03-4049

Long-term ecosystem-level experiments, in which the environment is manipulated in a controlled manner, are important tools to predict the responses of ecosystem functioning and composition to future global change. We present the results of a meta-analysis performed on the results of long-term ecosystem-level experiments near Toolik Lake, Alaska, and Abisko, Sweden. We quantified aboveground biomass responses of different arctic and subarctic ecosystems to experimental fertilization, warming and shading. We not only analysed the general patterns but also the differences in responsiveness between sites and regions. Aboveground plant biomass showed a broad similarity of responses in both locations, and also showed some important differences. In both locations, aboveground plant biomass, particularly the biomass of deciduous and graminoid plants, responded most strongly to nutrient addition. The biomass of mosses and lichens decreased in both locations as the biomass of vascular plants increased. An important difference between the two regions was the smaller positive aboveground biomass response of deciduous shrubs in Abisko as compared with Toolik Lake. Whereas in Toolik Lake Betula nana increased its dominance and replaced many of the other plant types, in Abisko all vascular plant types increased in abundance without major shifts in relative abundance. The differences between the responses of the dominant vegetation types of the Toolik Lake region, i.e. tussock tundra systems, and that of the Abisko region, i.e. heath systems, may have important implications for ecosystem development under expected patterns of global change. However, there were also large site-specific differences within each region. Several potential mechanistic explanations for the differences between sites and regions are discussed. The response patterns show the need for analyses of joint data sets from many regions and sites, in order to uncover common responses to changes in climate across large arctic regions from regional or local responses.

1 A field-experiment was used to determine how plant species might retain dominance in an arctic ecosystem receiving added nutrients. We both measured and modelled the above-ground and below-ground biomass allocation and root morphology of non-acidic tussock tundra near Toolik Lake, Alaska, after 4 years of fertilization with nitrogen and phosphorus. 2 Compared with control plots, the fertilized plots showed significant increases in overall root weight ratio, and root biomass, root length and root nitrogen concentration in the upper soil layers. There was a strong trend towards relatively more biomass below ground. 3 We constructed an individual teleonomic (i.e. optimality) plant allocation and growth model, and a competition model in which two plants grow and compete for the limiting resources. 4 The individual plant model predicted a strong decrease in root weight ratio with increased nutrient availability, contrary to the results obtained in the field. 5 The increased investment in roots in the fertilized plots found in the field could be explained in the competition model in terms of luxury consumption of nutrients (i.e. the absorbance of nutrients in excess of the immediate plant growth requirements). For slow-growing species with relatively low phenological and physiological plasticity it can be advantageous to increase relative investment into root growth and root activity. This increased investment can limit nutrient availability to other fast-growing species and, thereby, preclude the successful invasion of these species. 6 These results have implications for the transient response of communities and ecosystems to global change.

We present a linked model of plant productivity, plant phenology, snowmelt and soil thaw in order to estimate interannual variability of arctic plant phenology and its effects on plant productivity. The model is tested using 8 years of soil temperature data, and three years of bud break data of Betula nana . Because the factors that trigger the end of the growing season of arctic vegetation are less well known than those of the start of the growing season, three hypotheses were formulated and tested for their effects on productivity and its sensitivity to climate change; the hypothesised factors determining the end of the growing season were frost, photoperiod and periodic constraints. The performance of the soil thermal model was good; both the onset of soil thaw in spring and the initiation of freezing in autumn were predicted correctly in most cases. The phenology model predicted the bud break date of Betula nana closely for the three different years. The soil thaw model predicted similar growing season start dates under current climate as the models based on sum of temperatures, but it made significantly different predictions under climate change scenarios, probably because of the non-linear interactions between snowmelt and soil thaw. The uncertainty about the driving factors for the end of the growing season, in turn, resulted in uncertainty in the interannual variability of the simulated annual gross primary productivity (GPP). The interannual variability ranged from - 25 to + 26% of the mean annual GPP for the frost hypothesis, from - 20 to + 20% for the photoperiod hypothesis and only from - 7 to + 7% for the periodic hypothesis. The different hypotheses also resulted in different sensitivity to climate change, with the frost hypothesis resulting in 30% higher annual GPP values than the periodic hypothesis when air temperatures were increased by 3 degreesC.

We describe a scaling protocol that combines two hierarchically linked mod- els with field surveys, spatially distributed weather data, and remotely sensed images to generate daily predictions of gross primary production (GPP) for a 9200-km 2 arctic wa- tershed. A detailed process-based model of vegetation-atmosphere interactions, which has been tested in a variety of arctic ecosystems against independent hourly gas exchange data, forms the base of the hierarchy. This detailed model was used to construct a second and simpler, ''big-leaf'' model, which was calibrated for arctic conditions and which required many fewer parameters and input data. For landscape forcing data, we derived spatiotem- poral data on weather conditions (maximum and minimum temperature, and irradiance) from weather stations throughout the watershed. Spatiotemporal descriptions of the biotic constraints on production, chiefly leaf area index (LAI) and total foliar nitrogen ( Nf), were derived from field surveys, a land cover database, and normalized difference vegetation index (NDVI) data acquired from satellites. The scaling hierarchy avoided propagation of error via a compensation process, though the procedures involved still introduced uncer- tainty into daily GPP predictions averaging 16% of the growing season daily mean. The construction of the spatiotemporal temperature and irradiance fields introduced uncertainty of 1-2% at spatial and temporal resolutions of 1 km 2 and one day, respectively. The greatest uncertainty was introduced by assignment of LAI across the region, because of the highly heterogeneous landscape and the high sensitivity of production to changes in LAI at the low levels found in the Arctic. Uncertainty in vegetation properties introduced an uncertainty of 615% in basin GPP predictions, a value commensurate with basin net ecosystem pro- duction (NEP). In conclusion, improved characterization of vegetation via remote sensing is required before any bottom-up approach to carbon budgeting can reduce uncertainty to a reasonable level.

Relationships between tree size and physiological processes such as transpiration may have important implications for plant and ecosystem function, but as yet are poorly understood. We used a process-based model of the soil‐plant‐ atmosphere continuum to investigate patterns of wholetree sap flow in ponderosa pine trees of different size and age (36 m and ~ ~ ~ 250 years versus 13 m and 10‐50 years) over a developing summer drought. We examined three different hypothetical controls on hydraulic resistance, and found that size-related differences in sap flow could be best explained by absolute differences in plant resistance related to path length (hypothesis 1) rather than through different dynamic relationships between plant resistance and leaf water potential (hypothesis 2), or alterations in rates of cumulative inducement and repair of cavitation (hypothesis 3). Reductions in sap flow over time could be best explained by rising soil‐root resistance (hypothesis 1), rather than by a combination of rising plant and soil‐root resistance (hypothesis 2), or by rising plant resistance alone (hypothesis 3). Comparing hourly predictions with observed sap flow, we found that a direct relationship between plant resistance and leaf water potential (hypothesis 2) led to unrealistic bimodal patterns of sap flow within a day. Explaining seasonal reduction in sap flow purely through rising plant resistance (hypothesis 3) was effective but failed to explain the observed decline in pre-dawn leaf water potential for small trees. Thus, hypothesis 1 was best corroborated. A sensitivity analysis revealed a significant difference in the response to drought-relieving rains; precipitation induced a strong recovery in sap flow in the hypothetical case of limiting soil‐root resistance (hypothesis 1), and an insignificant response in the case of limiting plant resistance (hypothesis 3). Longer term monitoring and manipulation experiments are thus likely to resolve the uncertainties in hydraulic constraints on plant function.

Assessments of carbon (C) fluxes in the Arctic require detailed data on both how and why these fluxes vary across the landscape. Such assessments are complicated because tundra vegetation has diverse structure and function at both local and regional scales. To investigate this diversity, the Arctic Flux Study has used the eddy covariance technique to generate ecosystem CO2-exchange data along a transect in northern Alaska. We use an extant process-based model of the soil‐plant‐atmosphere continuum to make independent predictions of gross photosynthesis and foliar respiration at 9 of the sites along the transect, using data on local canopy structure and meteorology. We make two key assumptions: (i) soil respiration is constant throughout the flux measurement period, so that the diurnal cycle in CO2 exchange is driven by canopy processes only (except at two sites where a soil respiration‐temperature relationship was indicated in the data); and (ii) mosses and lichens play an insignificant role in ecosystem C exchange, even though in some locations their live biomass exceeds 300 g m ‐2 . We found that even with these assumptions the model could explain much of the dynamics of net ecosystem production (NEP) at sites with widely differing vegetation structure and moss/lichen cover. Errors were mostly associated with the predictions of maximum NEP; the likely cause of such discrepancies was (i) a mismatch between vegetation sampled for characterizing the canopy structure and that contained within the footprint of the eddy covariance flux measurements, or (ii) an increase in daytime soil and root respiration. Thus the model results tended to falsify our first assumption but not our second. We also note evidence for an actual reduction in NEP caused by water stress on warm, dry days at some sites. The model‐flux comparison also suggests that photosynthesis may be less sensitive to low temperatures than leaf-level gas-exchange measurements have indicated.

Summary We conducted ecosystem carbon and water vapour exchange studies in an old-growth Pinus ponderosa forest in the Pacific North-west region of the United States. The canopy is heterogeneous, with tall multiaged trees and an open, clumped canopy with low leaf area. Carbon assimilation can occur throughout relatively mild winters, although night frosts can temporarily halt the process and physiological factors limit its efficiency. In contrast, carbon assimilation is often limited in the ‘growing season’ by stomatal closure associated with high evaporative demand (D) and soil water deficits. All of these factors present a challenge to effectively modelling ecosystem processes. Our objective was to generate an understanding of the controls on ecosystem processes across seasonal and annual cycles from a combination of fine-scale process modelling, ecophysiological measurements, and carbon and water vapour fluxes measured by the eddy covariance method. Flux measurements showed that 50% and 70% of the annual carbon uptake occurred outside the ‘growing season’ (defined as bud break to senescence, ∼ days 125–275) in 1996 and 1997. On a daily basis in summer, net ecosystem productivity (NEP) was low when D and soil water deficits were large. Whole ecosystem water vapour fluxes (LE) increased from spring to summer (1.0–1.9 mm d−1) as conducting leaf area increased by 30% and as evaporative demand increased, while evaporation from the soil surface became a smaller portion of total LE as soil water deficits increased. The models underestimated soil evaporation, particularly following rain. In the SPA model, varying the temperature optimum for photosynthesis seasonally resulted in overestimation of carbon uptake in winter and spring, showing that in coniferous forests, assumptions about temperature optima are clearly important. Daily estimates of soil surface CO2 flux from measurements and site meteorological data demonstrated that modelling of soil CO2 flux based on an Arrhenius-type equation in CANPOND overestimated CO2 respired from the soil during drought and when temperatures were low.

Summary 1 Arctic terrestrial ecosystems, which are important components of the global carbon (C) cycle, are expected to undergo considerable future climate change. However, predicting arctic C budgets is complex because the landscape is highly diverse and plant biomass very variable. 2 We investigated the diversity of vegetation at 14 sites along an arctic transect in northern Alaska, including sites on wet coastal tundra, tussock tundra, heath and shrub tundra. We applied these data to a model of the soil–plant–atmosphere (SPA) continuum, to investigate how variations in vegetation structure might affect landscape patterns of gross primary productivity (GPP). 3 Although the dominant species varied, there was a highly significant relationship for vascular plants between the leaf area index (LAI) and total foliar nitrogen (N; g m–2 ground area) at each site. 4 A modelled response surface of GPP to changes in both N and LAI indicated that for tundra plants there is an almost constant ratio between the sensitivity of productivity to the two variables. 5 The conservative nature of canopy LAI–N ratios simplifies the task of generating regional predictions of C assimilation by vascular plants (for which LAI and N are key requirements) because measurements of LAI can be used to infer N. 6 Spatial and temporal patterns of LAI and N must be determined if the photosynthetic component of regional C budgets is to be modelled accurately for diverse arctic ecosystems. Patterns of solar irradiance, together with air temperature and soil moisture constraints, are also influential but less important. The contribution of bryophytes must be accounted for as well.

Naeem ( Conservation Biology 12: 39–45) formalized the relationships among species diversity, functional groups, species redundancy, and ecosystem reliability. Although we applaud his efforts and agree with many of his conclusions, we disagree with some fundamental assumptions used in the derivation of Naeem’s equation 2. These assumptions affect some of the conclusions concerning the role of redundancy. We discuss two possible interpretations of Naeem’s equation. Under the first interpretation, the equation relates to the probability that a species is present at a particular time t . Under the second interpretation, the equation relates to the probability that a species is present at least once during the interval of time between time 0 and time t . Under this second interpretation we also disagree with the application of Naeem’s equation 4. In his equation 2, Naeem relates “the probability that the i th species in the j th functional group is present by time t ” to the probability that the initial population survives until time t and the probability of colonization by time t , as follows

Tropical rain forests account for a significant fraction of global net primary productivity, and are important latent energy (LE) sources, affecting extra-tropical atmospheric circulation. The influence of environmental factors on these fluxes has until recently been poorly understood, largely due to a paucity of data, but in recent years the amount of available data has been increased greatly by use of eddy covariance techniques. In this paper we examine the factors that control daily and seasonal carbon (C) and LE fluxes, by comparing a detailed model of the soil‐plant‐atmosphere continuum against a unique long-term data-set collected using eddy covariance at an undisturbed rain forest site north of Manaus, Brazil. Our initial application of the model was parametrized with simple measurements of canopy structure, and driven with local meteorological data. It made effective predictions of C and LE exchange during the wet season, but dry season predictions were overestimates in both cases. Sensitivity analyses indicated that the best explanation for this behaviour was a seasonal change in soil and root hydraulic resistances ( Rb). An optimization routine was then used to estimate the increase in Rb during the dry season that would be required to explain the reduced dry season fluxes. The local soil, a clay latosol, is typical of much of Amazonia, having very low available water and low hydraulic conductivity. We conclude that an increase in soil‐root hydraulic resistance in the dry season introduces a significant seasonal cycle to carbon and water fluxes from this tropical forest. Furthermore, our model structure appears to be an effective tool for regional and temporal scaling of C and LE fluxes, with primary data requirements being regional and temporal information on meteorology, leaf area index (LAI), foliar N, critical leaf water potentials, and plant and soil hydraulic characteristics.

Our goal was to construct a simple, highly aggregated model, driven by easily available data sets, that accurately predicted terrestrial gross primary productivity (GPP; carboxylation plus oxygenation) in diverse environments and ecosystems. Our starting point was a fine-scale, multilayer model of half-hourly canopy processes that has been parametrized for Harvard Forest, Massachusetts. Over varied growing season conditions, this fine-scale model predicted hourly carbon and latent energy fluxes that were in good agreement with data from eddy covariance studies. Using an heuristic process, we derived a simple aggregated set of equations operating on cumulative or average values of the most sensitive driving variables (leaf area index, mean foliar N concentration, canopy height, average daily temperature and temperature range, atmospheric transmittance, latitude, day of year, atmospheric CO2 concentration, and an index of soil moisture). We calibrated the aggregated model to provide estimates of GPP similar to those of the fine-scale model across a wide range of these driving variables. Our calibration across this broad range of conditions captured 96% of fine-scale model behavior, but was computationally many orders of magnitude faster. We then tested the assumptions we had made in generating the aggregated model by applying it in different ecosystems. Using the same parameter values derived for Harvard Forest, the aggregated model made sound predictions of GPP for wet-sedge tundra in the Arctic under a variety of experimental manipulations, and also for a range of forest types across the OTTER (Oregon Transect Ecosystem Research) transect in Oregon, running from coastal Sitka spruce to high-plateau mountain juniper.

Our objective is to describe a multi-layer model of C 3canopy processes that effectively simulates hourly CO 2 and latent energy(LE) fluxes in a mixed deciduous Quercus-Acer (oak-maple) stand in central Massachusetts, USA . The key hypothesis governing the biological component of the model is that stomatal conductance (g s) is varied so that daily carbon uptake per unit of foliar nitrogen is maximized within the limitations of canopy water availability . The hydraulic system is modelled as an analogue to simple electrical circuits in parallel, including a separate soil hydraulic resistance, plant resistance and plant capacitance for each canopy layer . Stomatal opening is initially controlled to conserve plant water stores and delay the onset of water stress . Stomatal closure at a threshold minimum leaf water potential prevents xylem cavitation and controls the maximum rate of water flux through the hydraulic system . We show a strong correlation between predicted hourly CO 2 exchange rate (r2= 0.86) andLE (r2= 0.87) with independent whole-forest measurements made by the eddy correlation method during the summer of 1992. Our theoretical derivation shows that observed relationships between CO 2assimilation andLE flux can be explained on the basis of stomatal behaviour optimizing carbon gain, and provides an explicit link between canopy structure, soil properties, atmospheric conditions and stomatal conductance .