Regional and seasonal patterns of litterfall in tropical South America

The production of aboveground soft tissue represents an important share of total net primary production in tropical rain forests. Here we draw from a large number of published and unpublished datasets (n=81 sites) to assess the determinants of litterfall variation across South American tropical forests. We show that across old-growth tropical rainforests, litterfall averages 8.61±1.91 Mg ha−1 yr−1 (mean ± standard deviation, in dry mass units). Secondary forests have a lower annual litterfall than old-growth tropical forests with a mean of 8.01±3.41 Mg ha−1 yr−1. Annual litterfall shows no significant variation with total annual rainfall, either globally or within forest types. It does not vary consistently with soil type, except in the poorest soils (white sand soils), where litterfall is significantly lower than in other soil types (5.42±1.91 Mg ha−1 yr−1). We also study the determinants of litterfall seasonality, and find that it does not depend on annual rainfall or on soil type. However, litterfall seasonality is significantly positively correlated with rainfall seasonality. Finally, we assess how much carbon is stored in reproductive organs relative to photosynthetic organs. Mean leaf fall is 5.74±1.83 Mg ha−1 yr−1 (71% of total litterfall). Mean allocation into reproductive organs is 0.69±0.40 Mg ha−1 yr−1 (9% of total litterfall). The investment into reproductive organs divided by leaf litterfall increases with soil fertility, suggesting that on poor soils, the allocation to photosynthetic organs is prioritized over that to reproduction. Finally, we discuss the ecological and biogeochemical implications of these result

Matching the phenology of net ecosystem exchange and vegetation indices estimated with MODIS and FLUXNET in-situ observations

Shifts in ecosystem phenology play an important role in the definition of inter-annual variability of net ecosystem carbon uptake. A good estimate at the global scale of ecosystem phenology, mainly that of photosynthesis or gross primary productivity (GPP), may be provided by vegetation indices derived from MODIS satellite image data. However, the relationship between the start date of a growing (or greening) season (SGS) when derived from different vegetation indices (VI's), and the starting day of carbon uptake is not well elucidated. Additionally, the validation of existing phenology data with in-situ measurements is largely missing. We have investigated the possibility to use different VI's to predict the starting day of the growing season for 28 FLUXNET sites as well as MODIS data. This analysis included main plant functional types (PFT's). Of all VI's taken into account in this paper, the NDVI (Normalized Difference Vegetation Index) shows the highest correlation coefficient for the relationship between the starting day of the growing season as observed with MODIS and in-situ observations. However, MODIS observations elicit a 20-21 days earlier SGS date compared to in-situ observations. The prediction for the NEE start of the growing season diverges when using different VI's, and seems to depend on the amplitude for carbon and VI and on PFT. The optimal VI for estimation of a SGS date was PFT-specific - for example the WRDVI for cropland, but the MODIS NDVI performed best when applied as an estimator for Net Ecosystem Exchange and when considering all PFT's pooled

Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites:TL-LUE Parameterization and Validation

Light use efficiency (LUE) models are widely used to simulate gross primary production (GPP). However, the treatment of the plant canopy as a big leaf by these models can introduce large uncertainties in simulated GPP. Recently, a two-leaf light use efficiency (TL-LUE) model was developed to simulate GPP separately for sunlit and shaded leaves and has been shown to outperform the big-leaf MOD17 model at six FLUX sites in China. In this study we investigated the performance of the TL-LUE model for a wider range of biomes. For this we optimized the parameters and tested the TL-LUE model using data from 98 FLUXNET sites which are distributed across the globe. The results showed that the TL-LUE model performed in general better than the MOD17 model in simulating 8 day GPP. Optimized maximum light use efficiency of shaded leaves (εmsh) was 2.63 to 4.59 times that of sunlit leaves (εmsu). Generally, the relationships of εmsh and εmsu with εmax were well described by linear equations, indicating the existence of general patterns across biomes. GPP simulated by the TL-LUE model was much less sensitive to biases in the photosynthetically active radiation (PAR) input than the MOD17 model. The results of this study suggest that the proposed TL-LUE model has the potential for simulating regional and global GPP of terrestrial ecosystems, and it is more robust with regard to usual biases in input data than existing approaches which neglect the bimodal within-canopy distribution of PAR

Ecosystem transpiration and evaporation : Insights from three water flux partitioning methods across FLUXNET sites

We apply and compare three widely applicable methods for estimating ecosystem transpiration (T) from eddy covariance (EC) data across 251 FLUXNET sites globally. All three methods are based on the coupled water and carbon relationship, but they differ in assumptions and parameterizations. Intercomparison of the three dailyTestimates shows high correlation among methods (Rbetween .89 and .94), but a spread in magnitudes ofT/ET (evapotranspiration) from 45% to 77%. When compared at six sites with concurrent EC and sap flow measurements, all three EC-basedTestimates show higher correlation to sap flow-basedTthan EC-based ET. The partitioning methods show expected tendencies ofT/ET increasing with dryness (vapor pressure deficit and days since rain) and with leaf area index (LAI). Analysis of 140 sites with high-quality estimates for at least two continuous years shows thatT/ET variability was 1.6 times higher across sites than across years. Spatial variability ofT/ET was primarily driven by vegetation and soil characteristics (e.g., crop or grass designation, minimum annual LAI, soil coarse fragment volume) rather than climatic variables such as mean/standard deviation of temperature or precipitation. Overall,TandT/ET patterns are plausible and qualitatively consistent among the different water flux partitioning methods implying a significant advance made for estimating and understandingTglobally, while the magnitudes remain uncertain. Our results represent the first extensive EC data-based estimates of ecosystemTpermitting a data-driven perspective on the role of plants' water use for global water and carbon cycling in a changing climate.Peer reviewe

Ecosystem transpiration and evaporation: Insights from three water flux partitioning methods across FLUXNET sites

We apply and compare three widely applicable methods for estimating ecosystem transpiration (T) from eddy covariance (EC) data across 251 FLUXNET sites globally. All three methods are based on the coupled water and carbon relationship, but they differ in assumptions and parameterizations. Intercomparison of the three daily T estimates shows high correlation among methods (R between .89 and .94), but a spread in magnitudes of T/ET (evapotranspiration) from 45% to 77%. When compared at six sites with concurrent EC and sap flow measurements, all three EC‐based T estimates show higher correlation to sap flow‐based T than EC‐based ET. The partitioning methods show expected tendencies of T/ET increasing with dryness (vapor pressure deficit and days since rain) and with leaf area index (LAI). Analysis of 140 sites with high‐quality estimates for at least two continuous years shows that T/ET variability was 1.6 times higher across sites than across years. Spatial variability of T/ET was primarily driven by vegetation and soil characteristics (e.g., crop or grass designation, minimum annual LAI, soil coarse fragment volume) rather than climatic variables such as mean/standard deviation of temperature or precipitation. Overall, T and T/ET patterns are plausible and qualitatively consistent among the different water flux partitioning methods implying a significant advance made for estimating and understanding T globally, while the magnitudes remain uncertain. Our results represent the first extensive EC data‐based estimates of ecosystem T permitting a data‐driven perspective on the role of plants’ water use for global water and carbon cycling in a changing climate

Water Availability Is the Main Climate Driver of Neotropical Tree Growth

• Climate models for the coming century predict rainfall reduction in the Amazonian region, including change in water availability for tropical rainforests. Here, we test the extent to which climate variables related to water regime, temperature and irradiance shape the growth trajectories of neotropical trees. • We developed a diameter growth model explicitly designed to work with asynchronous climate and growth data. Growth trajectories of 205 individual trees from 54 neotropical species censused every 2 months over a 4-year period were used to rank 9 climate variables and find the best predictive model. • About 9% of the individual variation in tree growth was imputable to the seasonal variation of climate. Relative extractable water was the main predictor and alone explained more than 60% of the climate effect on tree growth, i.e. 5.4% of the individual variation in tree growth. Furthermore, the global annual tree growth was more dependent on the diameter increment at the onset of the rain season than on the duration of dry season. • The best predictive model included 3 climate variables: relative extractable water, minimum temperature and irradiance. The root mean squared error of prediction (0.035 mm.d–1) was slightly above the mean value of the growth (0.026 mm.d–1). • Amongst climate variables, we highlight the predominant role of water availability in determining seasonal variation in tree growth of neotropical forest trees and the need to include these relationships in forest simulators to test, in silico, the impact of different climate scenarios on the future dynamics of the rainforest

Branch xylem density variations across Amazonia

International audienceMeasurements of branch xylem density, Dx, were made for 1466 trees representing 503 species, sampled from 80 sites across the Amazon basin. Measured values ranged from 240 kg m?3 for a Brosimum parinarioides from Tapajos in West Pará, Brazil to 1130 kg m?3 for an Aiouea sp. from Caxiuana, Central Pará, Brazil. Analysis of variance showed significant differences in average Dx across the sample plots as well as significant differences between families, genera and species. A partitioning of the total variance in the dataset showed that geographic location and plot accounted for 33% of the variation with species identity accounting for an additional 27%; the remaining "residual" 40% of the variance accounted for by tree to tree (within species) variation. Variations in plot means, were, however, hardly accountable at all by differences in species composition. Rather, it would seem that variations of xylem density at plot level must be explained by the effects of soils and/or climate. This conclusion is supported by the observation that the xylem density of the more widely distributed species varied systematically from plot to plot. Thus, as well as having a genetic component branch xylem density is a plastic trait that, for any given species, varies according to where the tree is growing and in a predictable manner. Exceptions to this general rule may be some pioneers belonging to Pourouma and Miconia and some species within the genera Brosimum, Rinorea and Trichillia which seem to be more constrained in terms of this plasticity than most species sampled as part of this study

Temperature thresholds of ecosystem respiration at a global scale.

Ecosystem respiration is a major component of the global terrestrial carbon cycle and is strongly influenced by temperature. The global extent of the temperature-ecosystem respiration relationship, however, has not been fully explored. Here, we test linear and threshold models of ecosystem respiration across 210 globally distributed eddy covariance sites over an extensive temperature range. We find thresholds to the global temperature-ecosystem respiration relationship at high and low air temperatures and mid soil temperatures, which represent transitions in the temperature dependence and sensitivity of ecosystem respiration. Annual ecosystem respiration rates show a markedly reduced temperature dependence and sensitivity compared to half-hourly rates, and a single mid-temperature threshold for both air and soil temperature. Our study indicates a distinction in the influence of environmental factors, including temperature, on ecosystem respiration between latitudinal and climate gradients at short (half-hourly) and long (annual) timescales. Such climatological differences in the temperature sensitivity of ecosystem respiration have important consequences for the terrestrial net carbon sink under ongoing climate change

Climate Change Impact on Neotropical Social Wasps

Establishing a direct link between climate change and fluctuations in animal populations through long-term monitoring is difficult given the paucity of baseline data. We hypothesized that social wasps are sensitive to climatic variations, and thus studied the impact of ENSO events on social wasp populations in French Guiana. We noted that during the 2000 La Niña year there was a 77.1% decrease in their nest abundance along ca. 5 km of forest edges, and that 70.5% of the species were no longer present. Two simultaneous 13-year surveys (1997–2009) confirmed the decrease in social wasps during La Niña years (2000 and 2006), while an increase occurred during the 2009 El Niño year. A 30-year weather survey showed that these phenomena corresponded to particularly high levels of rainfall, and that temperature, humidity and global solar radiation were correlated with rainfall. Using the Self-Organizing Map algorithm, we show that heavy rainfall during an entire rainy season has a negative impact on social wasps. Strong contrasts in rainfall between the dry season and the short rainy season exacerbate this effect. Social wasp populations never recovered to their pre-2000 levels. This is probably because these conditions occurred over four years; heavy rainfall during the major rainy seasons during four other years also had a detrimental effect. On the contrary, low levels of rainfall during the major rainy season in 2009 spurred an increase in social wasp populations. We conclude that recent climatic changes have likely resulted in fewer social wasp colonies because they have lowered the wasps' resistance to parasitoids and pathogens. These results imply that Neotropical social wasps can be regarded as bio-indicators because they highlight the impact of climatic changes not yet perceptible in plants and other animals