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1.
Nature ; 598(7881): 468-472, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34552242

RESUMO

The leaf economics spectrum1,2 and the global spectrum of plant forms and functions3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species2. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities4. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability4,5. Here we derive a set of ecosystem functions6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems7,8.


Assuntos
Ciclo do Carbono , Ecossistema , Plantas/metabolismo , Ciclo Hidrológico , Dióxido de Carbono/metabolismo , Clima , Conjuntos de Dados como Assunto , Umidade , Plantas/classificação , Análise de Componente Principal
2.
J Exp Bot ; 74(3): 769-786, 2023 02 05.
Artigo em Inglês | MEDLINE | ID: mdl-36273326

RESUMO

Automating dynamic fine root data collection in the field is a longstanding challenge with multiple applications for co-interpretation and synthesis for ecosystem understanding. High frequency root data are only achievable with paired automated sampling and processing. However, automatic minirhizotron (root camera) instruments are still rare and data are often not collected in natural soils or analysed at high temporal resolution. Instruments must also be affordable for replication and robust under variable natural conditions. Here, we show a system built with off-the-shelf parts which samples at sub-daily resolution. We paired this with a neural network to analyse all images collected. We performed two mesocosm studies and two field trials alongside ancillary data collection (soil CO2 efflux, temperature, and moisture content, and 'PhenoCam'-derived above-ground dynamics). We produce robust and replicated daily time series of root dynamics under all conditions. Temporal root changes were a stronger driver than absolute biomass on soil CO2 efflux in the mesocosm. Proximal sensed above-ground dynamics and below-ground dynamics from minirhizotron data were not synchronized. Root properties extracted were sensitive to soil moisture and occasionally to time of day (potentially relating to soil moisture). This may only affect high frequency imagery and should be considered in interpreting such data.


Assuntos
Ecossistema , Procedimentos Cirúrgicos Robóticos , Dióxido de Carbono , Raízes de Plantas , Solo
3.
Glob Chang Biol ; 29(16): 4569-4585, 2023 08.
Artigo em Inglês | MEDLINE | ID: mdl-36880889

RESUMO

Biodiversity is essential for maintaining the terrestrial ecosystem multifunctionality (EMF). Recent studies have revealed that the variations in terrestrial ecosystem functions are captured by three key axes: the maximum productivity, water use efficiency, and carbon use efficiency of the ecosystem. However, the role of biodiversity in supporting these three key axes has not yet been explored. In this study, we combined the (i) data collected from more than 840 vegetation plots across a large climatic gradient in China using standard protocols, (ii) data on plant traits and phylogenetic information for more than 2,500 plant species, and (iii) soil nutrient data measured in each plot. These data were used to systematically assess the contribution of environmental factors, species richness, functional and phylogenetic diversity, and community-weighted mean (CWM) and ecosystem traits (i.e., traits intensity normalized per unit land area) to EMF via hierarchical partitioning and Bayesian structural equation modeling. Multiple biodiversity attributes accounted for 70% of the influence of all the variables on EMF, and ecosystems with high functional diversity had high resource use efficiency. Our study is the first to systematically explore the role of different biodiversity attributes, including species richness, phylogenetic and functional diversity, and CWM and ecosystem traits, in the key axes of ecosystem functions. Our findings underscore that biodiversity conservation is critical for sustaining EMF and ultimately ensuring human well-being.


Assuntos
Biodiversidade , Ecossistema , Humanos , Filogenia , Teorema de Bayes , Água , Solo
4.
Glob Chang Biol ; 29(12): 3395-3408, 2023 06.
Artigo em Inglês | MEDLINE | ID: mdl-36929655

RESUMO

Monitoring and estimating drought impact on plant physiological processes over large regions remains a major challenge for remote sensing and land surface modeling, with important implications for understanding plant mortality mechanisms and predicting the climate change impact on terrestrial carbon and water cycles. The Orbiting Carbon Observatory 3 (OCO-3), with its unique diurnal observing capability, offers a new opportunity to track drought stress on plant physiology. Using radiative transfer and machine learning modeling, we derive a metric of afternoon photosynthetic depression from OCO-3 solar-induced chlorophyll fluorescence (SIF) as an indicator of plant physiological drought stress. This unique diurnal signal enables a spatially explicit mapping of plants' physiological response to drought. Using OCO-3 observations, we detect a widespread increasing drought stress during the 2020 southwest US drought. Although the physiological drought stress is largely related to the vapor pressure deficit (VPD), our results suggest that plants' sensitivity to VPD increases as the drought intensifies and VPD sensitivity develops differently for shrublands and grasslands. Our findings highlight the potential of using diurnal satellite SIF observations to advance the mechanistic understanding of drought impact on terrestrial ecosystems and to improve land surface modeling.


Assuntos
Clorofila , Ecossistema , Secas , Fluorescência , Fotossíntese , Carbono , Sudoeste dos Estados Unidos
6.
New Phytol ; 233(6): 2415-2428, 2022 03.
Artigo em Inglês | MEDLINE | ID: mdl-34921419

RESUMO

Sun-induced fluorescence in the far-red region (SIF) is increasingly used as a remote and proximal-sensing tool capable of tracking vegetation gross primary production (GPP). However, the use of SIF to probe changes in GPP is challenged during extreme climatic events, such as heatwaves. Here, we examined how the 2018 European heatwave (HW) affected the GPP-SIF relationship in evergreen broadleaved trees with a relatively invariant canopy structure. To do so, we combined canopy-scale SIF measurements, GPP estimated from an eddy covariance tower, and active pulse amplitude modulation fluorescence. The HW caused an inversion of the photosynthesis-fluorescence relationship at both the canopy and leaf scales. The highly nonlinear relationship was strongly shaped by nonphotochemical quenching (NPQ), that is, a dissipation mechanism to protect from the adverse effects of high light intensity. During the extreme heat stress, plants experienced a saturation of NPQ, causing a change in the allocation of energy dissipation pathways towards SIF. Our results show the complex modulation of the NPQ-SIF-GPP relationship at an extreme level of heat stress, which is not completely represented in state-of-the-art coupled radiative transfer and photosynthesis models.


Assuntos
Clorofila , Monitoramento Ambiental , Clorofila/análise , Ecossistema , Monitoramento Ambiental/métodos , Fluorescência , Fotossíntese , Estações do Ano
7.
Glob Chang Biol ; 28(24): 7313-7326, 2022 12.
Artigo em Inglês | MEDLINE | ID: mdl-36097831

RESUMO

Elevated atmospheric CO2 (eCO2 ) influences the carbon assimilation rate and stomatal conductance of plants, thereby affecting the global cycles of carbon and water. Yet, the detection of these physiological effects of eCO2 in observational data remains challenging, because natural variations and confounding factors (e.g., warming) can overshadow the eCO2 effects in observational data of real-world ecosystems. In this study, we aim at developing a method to detect the emergence of the physiological CO2 effects on various variables related to carbon and water fluxes. We mimic the observational setting in ecosystems using a comprehensive process-based land surface model QUINCY to simulate the leaf-level effects of increasing atmospheric CO2 concentrations and their century-long propagation through the terrestrial carbon and water cycles across different climate regimes and biomes. We then develop a statistical method based on the signal-to-noise ratio to detect the emergence of the eCO2 effects. The eCO2 effect on gross primary productivity (GPP) emerges at relatively low CO2 increase (∆[CO2 ] ~ 20 ppm) where the leaf area index is relatively high. Compared to GPP, the eCO2 effect causing reduced transpiration water flux (normalized to leaf area) emerges only at relatively high CO2 increase (∆[CO2 ] >> 40 ppm), due to the high sensitivity to climate variability and thus lower signal-to-noise ratio. In general, the response to eCO2 is detectable earlier for variables related to the carbon cycle than the water cycle, when plant productivity is not limited by climatic constraints, and stronger in forest-dominated rather than in grass-dominated ecosystems. Our results provide a step toward when and where we expect to detect physiological CO2 effects in in-situ flux measurements, how to detect them and encourage future efforts to improve the understanding and quantification of these effects in observations of terrestrial carbon and water dynamics.


Assuntos
Dióxido de Carbono , Ecossistema , Dióxido de Carbono/farmacologia , Carbono , Água , Mudança Climática , Ciclo do Carbono , Atmosfera , Plantas
8.
Glob Chang Biol ; 28(4): 1493-1515, 2022 02.
Artigo em Inglês | MEDLINE | ID: mdl-34799950

RESUMO

It is well documented that energy balance and other remote sensing-based evapotranspiration (ET) models face greater uncertainty over water-limited tree-grass ecosystems (TGEs), representing nearly 1/6th of the global land surface. Their dual vegetation strata, the grass-dominated understory and tree-dominated overstory, make for distinct structural, physiological and phenological characteristics, which challenge models compared to more homogeneous and energy-limited ecosystems. Along with this, the contribution of grasses and trees to total transpiration (T), along with their different climatic drivers, is still largely unknown nor quantified in TGEs. This study proposes a thermal-based three-source energy balance (3SEB) model, accommodating an additional vegetation source within the well-known two-source energy balance (TSEB) model. The model was implemented at both tower and continental scales using eddy-covariance (EC) TGE sites, with variable tree canopy cover and rainfall (P) regimes and Meteosat Second Generation (MSG) images. 3SEB robustly simulated latent heat (LE) and related energy fluxes in all sites (Tower: LE RMSD ~60 W/m2 ; MSG: LE RMSD ~90 W/m2 ), improving over both TSEB and seasonally changing TSEB (TSEB-2S) models. In addition, 3SEB inherently partitions water fluxes between the tree, grass and soil sources. The modelled T correlated well with EC T estimates (r > .76), derived from a machine learning ET partitioning method. The T/ET was found positively related to both P and leaf area index, especially compared to the decomposed grass understory T/ET. However, trees and grasses had contrasting relations with respect to monthly P. These results demonstrate the importance in decomposing total ET into the different vegetation sources, as they have distinct climatic drivers, and hence, different relations to seasonal water availability. These promising results improved ET and energy flux estimations over complex TGEs, which may contribute to enhance global drought monitoring and understanding, and their responses to climate change feedbacks.


Assuntos
Ecossistema , Árvores , Poaceae/fisiologia , Tecnologia de Sensoriamento Remoto , Solo , Árvores/fisiologia , Água
9.
Glob Chang Biol ; 28(6): 2111-2123, 2022 03.
Artigo em Inglês | MEDLINE | ID: mdl-34927310

RESUMO

Understanding the critical soil moisture (SM) threshold (θcrit ) of plant water stress and land surface energy partitioning is a basis to evaluate drought impacts and improve models for predicting future ecosystem condition and climate. Quantifying the θcrit across biomes and climates is challenging because observations of surface energy fluxes and SM remain sparse. Here, we used the latest database of eddy covariance measurements to estimate θcrit across Europe by evaluating evaporative fraction (EF)-SM relationships and investigating the covariance between vapor pressure deficit (VPD) and gross primary production (GPP) during SM dry-down periods. We found that the θcrit and soil matric potential threshold in Europe are 16.5% and -0.7 MPa, respectively. Surface energy partitioning characteristics varied among different vegetation types; EF in savannas had the highest sensitivities to SM in water-limited stage, and the lowest in forests. The sign of the covariance between daily VPD and GPP consistently changed from positive to negative during dry-down across all sites when EF shifted from relatively high to low values. This sign of the covariance changed after longer period of SM decline in forests than in grasslands and savannas. Estimated θcrit from the VPD-GPP covariance method match well with the EF-SM method, showing this covariance method can be used to detect the θcrit . We further found that soil texture dominates the spatial variability of θcrit while shortwave radiation and VPD are the major drivers in determining the spatial pattern of EF sensitivities. Our results highlight for the first time that the sign change of the covariance between daily VPD and GPP can be used as an indicator of how ecosystems transition from energy to SM limitation. We also characterized the corresponding θcrit and its drivers across diverse ecosystems in Europe, an essential variable to improve the representation of water stress in land surface models.


Assuntos
Ecossistema , Solo , Desidratação , Secas , Florestas , Humanos
10.
New Phytol ; 230(4): 1394-1406, 2021 05.
Artigo em Inglês | MEDLINE | ID: mdl-33525059

RESUMO

The impact of extreme climate episodes such as heatwaves on plants physiological functioning and survival may depend on the event intensity, which requires quantification. We unraveled the distinct impacts of intense (HW) and intermediate (INT) heatwave days on carbon uptake, and the underlying changes in the photosynthetic system, in a Mediterranean citrus orchard using leaf active (pulse amplitude modulation; PAM) and canopy level passive (sun-induced; SIF) fluorescence measurements, together with CO2 , water vapor, and carbonyl sulfide (COS) exchange measurements. Compared to normal (N) days, gross CO2 uptake fluxes (gross primary production, GPP) were significantly reduced during HW days, but only slightly decreased during INT days. By contrast, COS uptake flux and SIFA (at 760 nm) decreased during both HW and INT days, which was reflected in leaf internal CO2 concentrations and in nonphotochemical quenching, respectively. Intense (HW) heatwave conditions also resulted in a substantial decrease in electron transport rates, measured using leaf-scale fluorescence, and an increase in the fractional energy consumption in photorespiration. Using the combined proxy approach, we demonstrate a differential ecosystem response to different heatwave intensities, which allows the trees to preserve carbon assimilation during INT days but not during HW days.


Assuntos
Dióxido de Carbono , Citrus , Ecossistema , Fluorescência , Fotossíntese , Óxidos de Enxofre
11.
Glob Chang Biol ; 26(12): 7067-7078, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-33090630

RESUMO

Global change is affecting terrestrial carbon (C) balances. The effect of climate on ecosystem C balance has been largely explored, but the roles of other concurrently changing factors, such as diversity and nutrient availability, remain elusive. We used eddy-covariance C-flux measurements from 62 ecosystems from which we compiled information on climate, ecosystem type, stand age, species abundance and foliar concentrations of N and P of the main species, to assess their importance in the ecosystem C balance. Climate and productivity were the main determinants of ecosystem C balance and its stability. In P-rich sites, increasing N was related to increased gross primary production and respiration and vice versa, but reduced net C uptake. Our analyses did not provide evidence for a strong relation between ecosystem diversity and their productivity and stability. Nonetheless, these results suggest that nutrient imbalances and, potentially, diversity loss may alter future global C balance.


Assuntos
Carbono , Ecossistema , Clima , Nitrogênio , Fósforo
12.
Glob Chang Biol ; 26(9): 5235-5253, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32497360

RESUMO

The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO2 between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO2 release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO2 fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co-acting factors that modulate GPP and RECO flux dynamics. To overcome this limitation, we developed a hybrid data-driven approach based on combined neural networks (NNC-part ). NNC-part incorporates process knowledge by introducing a photosynthetic response based on the light-use efficiency (LUE) concept, and uses a comprehensive dataset of soil and micrometeorological variables as fluxes drivers. We applied the method to 36 sites from the FLUXNET2015 dataset and found a high consistency in the results with those derived from other standard partitioning methods for both GPP (R2  > .94) and RECO (R2  > .8). High consistency was also found for (a) the diurnal and seasonal patterns of fluxes and (b) the ecosystem functional responses. NNC-part performed more realistic than the traditional methods for predicting additional patterns of gross CO2 fluxes, such as: (a) the GPP response to VPD, (b) direct effects of air temperature on GPP dynamics, (c) hysteresis in the diel cycle of gross CO2 fluxes, (d) the sensitivity of LUE to the diffuse to direct radiation ratio, and (e) the post rain respiration pulse after a long dry period. In conclusion, NNC-part is a valid data-driven approach to provide GPP and RECO estimates and complementary to the existing partitioning methods.


Assuntos
Dióxido de Carbono , Ecossistema , Ciclo do Carbono , Redes Neurais de Computação , Fotossíntese , Respiração , Estações do Ano
13.
Glob Chang Biol ; 26(8): 4379-4400, 2020 08.
Artigo em Inglês | MEDLINE | ID: mdl-32348631

RESUMO

Anthropogenic nitrogen (N) deposition and resulting differences in ecosystem N and phosphorus (P) ratios are expected to impact photosynthetic capacity, that is, maximum gross primary productivity (GPPmax ). However, the interplay between N and P availability with other critical resources on seasonal dynamics of ecosystem productivity remains largely unknown. In a Mediterranean tree-grass ecosystem, we established three landscape-level (24 ha) nutrient addition treatments: N addition (NT), N and P addition (NPT), and a control site (CT). We analyzed the response of ecosystem to altered nutrient stoichiometry using eddy covariance fluxes measurements, satellite observations, and digital repeat photography. A set of metrics, including phenological transition dates (PTDs; timing of green-up and dry-down), slopes during green-up and dry-down period, and seasonal amplitude, were extracted from time series of GPPmax and used to represent the seasonality of vegetation activity. The seasonal amplitude of GPPmax was higher for NT and NPT than CT, which was attributed to changes in structure and physiology induced by fertilization. PTDs were mainly driven by rainfall and exhibited no significant differences among treatments during the green-up period. Yet, both fertilized sites senesced earlier during the dry-down period (17-19 days), which was more pronounced in the NT due to larger evapotranspiration and water usage. Fertilization also resulted in a faster increase in GPPmax during the green-up period and a sharper decline in GPPmax during the dry-down period, with less prominent decline response in NPT. Overall, we demonstrated seasonality of vegetation activity was altered after fertilization and the importance of nutrient-water interaction in such water-limited ecosystems. With the projected warming-drying trend, the positive effects of N fertilization induced by N deposition on GPPmax may be counteracted by an earlier and faster dry-down in particular in areas where the N:P ratio increases, with potential impact on the carbon cycle of water-limited ecosystems.


Assuntos
Ecossistema , Água , Nutrientes , Plantas , Estações do Ano
14.
Glob Chang Biol ; 26(12): 6916-6930, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-33022860

RESUMO

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.


Assuntos
Ecossistema , Transpiração Vegetal , Poaceae , Chuva , Solo , Água
15.
Glob Chang Biol ; 26(12): 7268-7283, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-33026137

RESUMO

Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil-to-atmosphere CO2 flux, commonly though imprecisely termed soil respiration (RS ), is one of the largest carbon fluxes in the Earth system. An increasing number of high-frequency RS measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open-source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long-term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured RS , the database design accommodates other soil-atmosphere measurements (e.g. ecosystem respiration, chamber-measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package.


Assuntos
Gases de Efeito Estufa , Atmosfera , Dióxido de Carbono/análise , Ecossistema , Gases de Efeito Estufa/análise , Metano/análise , Óxido Nitroso/análise , Reprodutibilidade dos Testes , Respiração , Solo
16.
Nature ; 514(7521): 213-7, 2014 Oct 09.
Artigo em Inglês | MEDLINE | ID: mdl-25252980

RESUMO

The response of the terrestrial carbon cycle to climate change is among the largest uncertainties affecting future climate change projections. The feedback between the terrestrial carbon cycle and climate is partly determined by changes in the turnover time of carbon in land ecosystems, which in turn is an ecosystem property that emerges from the interplay between climate, soil and vegetation type. Here we present a global, spatially explicit and observation-based assessment of whole-ecosystem carbon turnover times that combines new estimates of vegetation and soil organic carbon stocks and fluxes. We find that the overall mean global carbon turnover time is 23(+7)(-4) years (95 per cent confidence interval). On average, carbon resides in the vegetation and soil near the Equator for a shorter time than at latitudes north of 75° north (mean turnover times of 15 and 255 years, respectively). We identify a clear dependence of the turnover time on temperature, as expected from our present understanding of temperature controls on ecosystem dynamics. Surprisingly, our analysis also reveals a similarly strong association between turnover time and precipitation. Moreover, we find that the ecosystem carbon turnover times simulated by state-of-the-art coupled climate/carbon-cycle models vary widely and that numerical simulations, on average, tend to underestimate the global carbon turnover time by 36 per cent. The models show stronger spatial relationships with temperature than do observation-based estimates, but generally do not reproduce the strong relationships with precipitation and predict faster carbon turnover in many semi-arid regions. Our findings suggest that future climate/carbon-cycle feedbacks may depend more strongly on changes in the hydrological cycle than is expected at present and is considered in Earth system models.


Assuntos
Ciclo do Carbono , Carbono/metabolismo , Clima , Ecossistema , Biomassa , Retroalimentação , Hidrologia , Modelos Teóricos , Plantas/metabolismo , Chuva , Solo/química , Temperatura , Fatores de Tempo , Ciclo Hidrológico
17.
Glob Chang Biol ; 25(9): 2855-2868, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31237398

RESUMO

Drought, fire, and windstorms can interact to degrade tropical forests and the ecosystem services they provide, but how these forests recover after catastrophic disturbance events remains relatively unknown. Here, we analyze multi-year measurements of vegetation dynamics and function (fluxes of CO2 and H2 O) in forests recovering from 7 years of controlled burns, followed by wind disturbance. Located in southeast Amazonia, the experimental forest consists of three 50-ha plots burned annually, triennially, or not at all from 2004 to 2010. During the subsequent 6-year recovery period, postfire tree survivorship and biomass sharply declined, with aboveground C stocks decreasing by 70%-94% along forest edges (0-200 m into the forest) and 36%-40% in the forest interior. Vegetation regrowth in the forest understory triggered partial canopy closure (70%-80%) from 2010 to 2015. The composition and spatial distribution of grasses invading degraded forest evolved rapidly, likely because of the delayed mortality. Four years after the experimental fires ended (2014), the burned plots assimilated 36% less carbon than the Control, but net CO2 exchange and evapotranspiration (ET) had fully recovered 7 years after the experimental fires ended (2017). Carbon uptake recovery occurred largely in response to increased light-use efficiency and reduced postfire respiration, whereas increased water use associated with postfire growth of new recruits and remaining trees explained the recovery in ET. Although the effects of interacting disturbances (e.g., fires, forest fragmentation, and blowdown events) on mortality and biomass persist over many years, the rapid recovery of carbon and water fluxes can help stabilize local climate.


Assuntos
Dióxido de Carbono , Incêndios , Brasil , Ecossistema , Florestas , Árvores
18.
Remote Sens Environ ; 231: 111272, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-36082142

RESUMO

Terrestrial gross primary productivity (GPP) plays an essential role in the global carbon cycle, but the quantification of the spatial and temporal variations in photosynthesis is still largely uncertain. Our work aimed to investigate the potential of remote sensing to provide new insights into plant photosynthesis at a fine spatial resolution. This goal was achieved by exploiting high-resolution images acquired with the FLuorescence EXplorer (FLEX) airborne demonstrator HyPlant. The sensor was flown over a mixed forest, and the images collected were elaborated to obtain two independent indicators of plant photosynthesis. First, maps of sun-induced chlorophyll fluorescence (F), a novel indicator of plant photosynthetic activity, were successfully obtained at both the red and far-red peaks (r2 = 0.89 and p < 0.01, r2 = 0.77 and p < 0.01, respectively, compared to top-of-canopy ground-based measurements acquired synchronously with the overflight) over the forested study area. Second, maps of GPP and absorbed photosynthetically active radiation (APAR) were derived using a customised version of the coupled biophysical model Breathing Earth System Simulator (BESS). The model was driven with airborne-derived maps of key forest traits (i.e., leaf chlorophyll content (LCC) and leaf area index (LAI)) and meteorological data providing a high-resolution snapshot of the variables of interest across the study site. The LCC and LAI were accurately estimated (RMSE = 5.66 µg cm-2 and RMSE = 0.51 m2m-2, respectively) through an optimised Look-Up-Table-based inversion of the PROSPECT-4-INFORM radiative transfer model, ensuring the accurate representation of the spatial variation of these determinants of the ecosystem's functionality. The spatial relationships between the measured F and modelled BESS outputs were then analysed to interpret the variability of ecosystem functioning at a regional scale. The results showed that far-red F is significantly correlated with the GPP (r2 = 0.46, p < 0.001) and APAR (r2 = 0.43, p < 0.001) in the spatial domain and that this relationship is nonlinear. Conversely, no statistically significant relationships were found between the red F and the GPP or APAR (p > 0.05). The spatial relationships found at high resolution provide valuable insight into the critical role of spatial heterogeneity in controlling the relationship between the far-red F and the GPP, indicating the need to consider this heterogeneity at a coarser resolution.

19.
Ecol Lett ; 21(11): 1629-1638, 2018 11.
Artigo em Inglês | MEDLINE | ID: mdl-30141251

RESUMO

A fundamental challenge in experimental ecology is to capture nonlinearities of ecological responses to interacting environmental drivers. Here, we demonstrate that gradient designs outperform replicated designs for detecting and quantifying nonlinear responses. We report the results of (1) multiple computer simulations and (2) two purpose-designed empirical experiments. The findings consistently revealed that unreplicated sampling at a maximum number of sampling locations maximised prediction success (i.e. the R² to the known truth) irrespective of the amount of stochasticity and the underlying response surfaces, including combinations of two linear, unimodal or saturating drivers. For the two empirical experiments, the same pattern was found, with gradient designs outperforming replicated designs in revealing the response surfaces of underlying drivers. Our findings suggest that a move to gradient designs in ecological experiments could be a major step towards unravelling underlying response patterns to continuous and interacting environmental drivers in a feasible and statistically powerful way.


Assuntos
Simulação por Computador , Ecologia , Ecossistema
20.
Glob Chang Biol ; 24(11): 5017-5020, 2018 11.
Artigo em Inglês | MEDLINE | ID: mdl-30136335

RESUMO

Written Summary: It is important to understand how sun-sensor geometry affects satellite sun-induced fluorescence (SIF) in order to take full advantage of these measurements, particularly given their close relationship with gross primary production (GPP). We displayed the bidirectionality of SIF at different viewing zenith angles in the solar principal plane and observed a clear bowl shape of SIF from the backward to forward scattering directions. Therefore, it is important to consider the bidirectionality of SIF when using OCO-2 SIF data to evaluate the SIF-GPP relationship.


Assuntos
Biomassa , Clorofila/análise , Ecossistema , Fluorescência , Tecnologia de Sensoriamento Remoto/métodos , Sistema Solar , Tecnologia de Sensoriamento Remoto/instrumentação , Astronave
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