Concurrent starch accumulation in stump and high fruit production in coffee (Coffea arabica).

In coffee, fruit production on a given shoot drops after some years of high yield, triggering pruning to induce resprouting. The timing of pruning is a crucial farmer's decision affecting yield and labour. One reason for fruit production drop could be the exhaustion of resources, particularly the non-structural carbohydrates (NSC). To test this hypothesis in a Coffea L. arabica agroforestry system, we measured the concentrations of NSC, carbon (C) and nitrogen (N) in leaves, stems and stumps of the coffee plants, 2 and 5 years after pruning. We also compared shaded vs full sun plants. For that purpose, both analytical reference and visible and near infrared reflectance spectroscopy (VNIRS) methods were used. As expected, concentrations of biochemical variables linked to photosynthesis activity (N, glucose, fructose, sucrose) decreased from leaves to stems, and then to stumps. In contrast, variables linked more closely to plant structure and reserves (total C, C:N ratio, starch concentration) were higher in long lifespan organs like stumps. Shading had little effect on most measured parameters, contrary to expectations. Concentrations of N, glucose and fructose were higher in 2-year-old organs. Conversely, starch concentration in perennial stumps was three times higher 5 years after pruning than 2 years after pruning, despite high fruit production. Therefore, the drop in fruit production occurring after 5-6 years was not due to a lack of NSC on plant scale. Starch accumulation in perennial organs concurrently to other sinks, such as fruit growth, could be considered as a 'survival' strategy, which may be a relic of the behaviour of wild coffee (a tropical shade-tolerant plant). This study confirmed that VNIRS is a promisingly rapid and cost-effective option for starch monitoring (coefficient of determination for validation, R2val = 0.91), whereas predictions were less accurate for soluble sugars, probably due to their too similar spectral signature.

SoilTemp: A global database of near-surface temperature.

Current analyses and predictions of spatially explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long-term average thermal conditions at coarse spatial resolutions only. Hence, many climate-forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing or cold-air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free-air temperatures, microclimatic ground and near-surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near-surface temperature data from all over the world. Currently, this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes.

Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests.

Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate's temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE.

Publisher Correction: Stand age and species richness dampen interannual variation of ecosystem-level photosynthetic capacity.

In the version of this Article originally published, the wrong Supplementary Information pdf was uploaded, in which the figures did not correspond with those mentioned in the main text and the R code was not presented properly. This has now been replaced.

Stand age and species richness dampen interannual variation of ecosystem-level photosynthetic capacity.

The total uptake of carbon dioxide by ecosystems via photosynthesis (gross primary productivity, GPP) is the largest flux in the global carbon cycle. A key ecosystem functional property determining GPP is the photosynthetic capacity at light saturation (GPPsat), and its interannual variability (IAV) is propagated to the net land-atmosphere exchange of CO2. Given the importance of understanding the IAV in CO2 fluxes for improving the predictability of the global carbon cycle, we have tested a range of alternative hypotheses to identify potential drivers of the magnitude of IAV in GPPsat in forest ecosystems. Our results show that while the IAV in GPPsat within sites is closely related to air temperature and soil water availability fluctuations, the magnitude of IAV in GPPsat is related to stand age and biodiversity (R2 = 0.55, P < 0.0001). We find that the IAV of GPPsat is greatly reduced in older and more diverse forests, and is higher in younger forests with few dominant species. Older and more diverse forests seem to dampen the effect of climate variability on the carbon cycle irrespective of forest type. Preserving old forests and their diversity would therefore be beneficial in reducing the effect of climate variability on Earth's forest ecosystems.

Intraspecific Trait Variation and Coordination: Root and Leaf Economics Spectra in Coffee across Environmental Gradients.

Hypotheses on the existence of a universal "Root Economics Spectrum" (RES) have received arguably the least attention of all trait spectra, despite the key role root trait variation plays in resource acquisition potential. There is growing interest in quantifying intraspecific trait variation (ITV) in plants, but there are few studies evaluating (i) the existence of an intraspecific RES within a plant species, or (ii) how a RES may be coordinated with other trait spectra within species, such as a leaf economics spectrum (LES). Using Coffea arabica (Rubiaceae) as a model species, we measured seven morphological and chemical traits of intact lateral roots, which were paired with information on four key LES traits. Field collections were completed across four nested levels of biological organization. The intraspecific trait coefficient of variation (cv) ranged from 25 to 87% with root diameter and specific root tip density showing the lowest and highest cv, respectively. Between 27 and 68% of root ITV was explained by site identity alone for five of the seven traits measured. A single principal component explained 56.2% of root trait covariation, with plants falling along a RES from resource acquiring to conserving traits. Multiple factor analysis revealed significant orthogonal relationships between root and leaf spectra. RES traits were strongly orthogonal with respect to LES traits, suggesting these traits vary independently from one another in response to environmental cues. This study provides among the first evidence that plants from the same species differentiate from one another along an intraspecific RES. We find that in one of the world's most widely cultivated crops, an intraspecific RES is orthogonal to an intraspecific LES, indicating that above and belowground responses of plants to managed (or natural) environmental gradients are likely to occur independently from one another.

Increased light-use efficiency sustains net primary productivity of shaded coffee plants in agroforestry system.

In agroforestry systems, shade trees strongly affect the physiology of the undergrown crop. However, a major paradigm is that the reduction in absorbed photosynthetically active radiation is, to a certain extent, compensated by an increase in light-use efficiency, thereby reducing the difference in net primary productivity between shaded and non-shaded plants. Due to the large spatial heterogeneity in agroforestry systems and the lack of appropriate tools, the combined effects of such variables have seldom been analysed, even though they may help understand physiological processes underlying yield dynamics. In this study, we monitored net primary productivity, during two years, on scales ranging from individual coffee plants to the entire plot. Absorbed radiation was mapped with a 3D model (MAESPA). Light-use efficiency and net assimilation rate were derived for each coffee plant individually. We found that although irradiance was reduced by 60% below crowns of shade trees, coffee light-use efficiency increased by 50%, leaving net primary productivity fairly stable across all shade levels. Variability of aboveground net primary productivity of coffee plants was caused primarily by the age of the plants and by intraspecific competition among them (drivers usually overlooked in the agroforestry literature) rather than by the presence of shade trees.

Stand age and species richness dampen interannual variation of ecosystem-level photosynthetic capacity.

The total uptake of carbon dioxide by ecosystems via photosynthesis (gross primary productivity, GPP) is the largest flux in the global carbon cycle. A key ecosystem functional property determining GPP is the photosynthetic capacity at light saturation (GPPsat), and its interannual variability (IAV) is propagated to the net land-atmosphere exchange of CO2. Given the importance of understanding the IAV in CO2 fluxes for improving the predictability of the global carbon cycle, we have tested a range of alternative hypotheses to identify potential drivers of the magnitude of IAV in GPPsat in forest ecosystems. Our results show that while the IAV in GPPsat within sites is closely related to air temperature and soil water availability fluctuations, the magnitude of IAV in GPPsat is related to stand age and biodiversity (R2 = 0.55, P < 0.0001). We find that the IAV of GPPsat is greatly reduced in older and more diverse forests, and is higher in younger forests with few dominant species. Older and more diverse forests seem to dampen the effect of climate variability on the carbon cycle irrespective of forest type. Preserving old forests and their diversity would therefore be beneficial in reducing the effect of climate variability on Earth's forest ecosystems.

Root biomass, turnover and net primary productivity of a coffee agroforestry system in Costa Rica: effects of soil depth, shade trees, distance to row and coffee age.

Background and Aims In Costa Rica, coffee (Coffea arabica) plants are often grown in agroforests. However, it is not known if shade-inducing trees reduce coffee plant biomass through root competition, and hence alter overall net primary productivity (NPP). We estimated biomass and NPP at the stand level, taking into account deep roots and the position of plants with regard to trees. Methods Stem growth and root biomass, turnover and decomposition were measured in mixed coffee/tree (Erythrina poeppigiana) plantations. Growth ring width and number at the stem base were estimated along with stem basal area on a range of plant sizes. Root biomass and fine root density were measured in trenches to a depth of 4 m. To take into account the below-ground heterogeneity of the agroforestry system, fine root turnover was measured by sequential soil coring (to a depth of 30 cm) over 1 year and at different locations (in full sun or under trees and in rows/inter-rows). Allometric relationships were used to calculate NPP of perennial components, which was then scaled up to the stand level. Key Results Annual ring width at the stem base increased up to 2·5 mm yr-1 with plant age (over a 44-year period). Nearly all (92 %) coffee root biomass was located in the top 1·5 m, and only 8 % from 1·5 m to a depth of 4 m. Perennial woody root biomass was 16 t ha-1 and NPP of perennial roots was 1·3 t ha-1 yr-1. Fine root biomass (0-30 cm) was two-fold higher in the row compared with between rows. Fine root biomass was 2·29 t ha-1 (12 % of total root biomass) and NPP of fine roots was 2·96 t ha-1 yr-1 (69 % of total root NPP). Fine root turnover was 1·3 yr-1 and lifespan was 0·8 years. Conclusions Coffee root systems comprised 49 % of the total plant biomass; such a high ratio is possibly a consequence of shoot pruning. There was no significant effect of trees on coffee fine root biomass, suggesting that coffee root systems are very competitive in the topsoil.

Joint control of terrestrial gross primary productivity by plant phenology and physiology.

Terrestrial gross primary productivity (GPP) varies greatly over time and space. A better understanding of this variability is necessary for more accurate predictions of the future climate-carbon cycle feedback. Recent studies have suggested that variability in GPP is driven by a broad range of biotic and abiotic factors operating mainly through changes in vegetation phenology and physiological processes. However, it is still unclear how plant phenology and physiology can be integrated to explain the spatiotemporal variability of terrestrial GPP. Based on analyses of eddy-covariance and satellite-derived data, we decomposed annual terrestrial GPP into the length of the CO2 uptake period (CUP) and the seasonal maximal capacity of CO2 uptake (GPPmax). The product of CUP and GPPmax explained >90% of the temporal GPP variability in most areas of North America during 2000-2010 and the spatial GPP variation among globally distributed eddy flux tower sites. It also explained GPP response to the European heatwave in 2003 (r(2) = 0.90) and GPP recovery after a fire disturbance in South Dakota (r(2) = 0.88). Additional analysis of the eddy-covariance flux data shows that the interbiome variation in annual GPP is better explained by that in GPPmax than CUP. These findings indicate that terrestrial GPP is jointly controlled by ecosystem-level plant phenology and photosynthetic capacity, and greater understanding of GPPmax and CUP responses to environmental and biological variations will, thus, improve predictions of GPP over time and space.

Recent decline in the global land evapotranspiration trend due to limited moisture supply.

More than half of the solar energy absorbed by land surfaces is currently used to evaporate water. Climate change is expected to intensify the hydrological cycle and to alter evapotranspiration, with implications for ecosystem services and feedback to regional and global climate. Evapotranspiration changes may already be under way, but direct observational constraints are lacking at the global scale. Until such evidence is available, changes in the water cycle on land−a key diagnostic criterion of the effects of climate change and variability−remain uncertain. Here we provide a data-driven estimate of global land evapotranspiration from 1982 to 2008, compiled using a global monitoring network, meteorological and remote-sensing observations, and a machine-learning algorithm. In addition, we have assessed evapotranspiration variations over the same time period using an ensemble of process-based land-surface models. Our results suggest that global annual evapotranspiration increased on average by 7.1 ± 1.0 millimetres per year per decade from 1982 to 1997. After that, coincident with the last major El Niño event in 1998, the global evapotranspiration increase seems to have ceased until 2008. This change was driven primarily by moisture limitation in the Southern Hemisphere, particularly Africa and Australia. In these regions, microwave satellite observations indicate that soil moisture decreased from 1998 to 2008. Hence, increasing soil-moisture limitations on evapotranspiration largely explain the recent decline of the global land-evapotranspiration trend. Whether the changing behaviour of evapotranspiration is representative of natural climate variability or reflects a more permanent reorganization of the land water cycle is a key question for earth system science.

Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate.

Terrestrial gross primary production (GPP) is the largest global CO(2) flux driving several ecosystem functions. We provide an observation-based estimate of this flux at 123 +/- 8 petagrams of carbon per year (Pg C year(-1)) using eddy covariance flux data and various diagnostic models. Tropical forests and savannahs account for 60%. GPP over 40% of the vegetated land is associated with precipitation. State-of-the-art process-oriented biosphere models used for climate predictions exhibit a large between-model variation of GPP's latitudinal patterns and show higher spatial correlations between GPP and precipitation, suggesting the existence of missing processes or feedback mechanisms which attenuate the vegetation response to climate. Our estimates of spatially distributed GPP and its covariation with climate can help improve coupled climate-carbon cycle process models.

Thermogenesis and flowering biology of Colocasia gigantea, Araceae.

The thermogenesis and flowering biology of Colocasia gigantea (Blume) Hook. f. were studied from December 2005 to February 2006 on Espiritu Santo, Vanuatu (South Pacific). Endogenous thermogenesis was measured in two ways: (1) continuously over 5-day periods, and (2) over 3 h during maximum heating. The study showed that heat was generated by the male part of the spadix and probably the lower zone of the sterile region. The temperatures of the male part peaked twice: (1) between 0625 and 0640 (during the female phase) and (2) 24 h later (during the male phase). The average maximum temperature was 42.25 +/- 0.14 degrees C during the female phase (16.63 degrees C above the ambient temperature) and 35.14 +/- 0.22 degrees C during the male phase (10.61 degrees C above the ambient temperature). In the lower zone of the sterile region, thermogenesis was documented only during the female phase. The average maximum temperature was 35.44 +/- 0.41 degrees C (9.82 degrees C above the ambient temperature). Thermogenic heating appeared to be closely associated with the activities of pollinating insects.