Methods and technologies to ecology research
Quantitative estimation of the contribution of predictary variables to community composition is a hotspot in community ecology. However, multicollinearity and joint contributions among predictors make it difficult to estimate the importance of predictor in specific analysis scenarios. To address this issue, the “rdacca.hp” package provides a new quantitative indicator by introducing the concept of hierarchical partitioning (HP) to assign individual effects for individual predictors (or groups of predictors) across all possible model subsets. The package solves the problem of estimating the relative importance of predictors with multicollinearity in canonical analysis. The “rdacca.hp” package has become an important tool for community ecological analysis. To further promote users’ understanding and use of the “rdacca.hp” package, we demonstrate the general steps for using this package in canonical analysis with an example analyzing the important environmental and spatial drivers that shape the oribatid mites (Oribatida) community. Subsequently, we conduct a bibliometric analysis of recent studies using “rdacca.hp” package. The results show that, since its launch, the package has been widely used as a fundamental quantitative framework in ecology, environmental science and related disciplines. Finally, we discuss the further application and extension of the “rdacca.hp” package. In conclusion, this paper aims to advocate the understanding and application of the “rdacca.hp” package for domestic researchers.
Soil respiration is mainly composed of the CO2 released from atmosphere-soil interface and change of CO2 stored in the soil. Understanding the production and migration of CO2 in the soil is essential for measuring the carbon cycle in terrestrial ecosystems. The flux gradient method calculates soil CO2 flux by measuring the diffusion-driven CO2 concentration gradient and diffusion coefficient. The flux of soil CO2 and its stable carbon isotopes composition (δ13C) at different depths can be calculated based on Fickʼs law. The amount of CO2 released from soil and the amount of CO2 stored in different soil layers can thus be measured. The underground soil CO2 (13CO2 and 12CO2) concentration is mainly controlled by pore tortuosity, the depth of root distribution, microbial activity and total soil CO2 production. The underground CO2 transmission process is mainly controlled by the CO2 concentrations, porosity and water content at different depths of the soil. These physical, chemical and biological features of the soil are key factors affecting the application of the soil flux gradient method, and directly determine the precision and accuracy of soil CO2 and its δ13C flux calculation. The gradient method is a useful complement to the chamber method, which can clarify the process of production and migration of soil CO2 at different depths and thus the impacts on the release and storage of soil CO2, elucidating the contribution of soils at different depths to CO2 release and uncovering the underlying environmental and physical mechanisms.
Aims The continuous observation datasets of water, heat, and carbon fluxes measured by the eddy covariance technique are important basis for accurate assessment of regional carbon sequestration and water-holding capacity. However, the rate of gaps in flux datasets is high and common due to various reasons, and different gap-filling methods increase the uncertainties of the related studies. The aim of this study is to introduce and test the applicability of boosted regression trees model (BRT), one of the up-to-date machine learning algorithms, for the gap- filling to flux datasets.
Methods Based on the published valid dataset of water, heat and CO2 flux, and main environmental factors, including air temperature, atmospheric water vapor pressure, wind speed, solar shortwave radiation, topsoil temperature, and topsoil water content of an alpine Potentilla fruticosa scrubland on the northeastern Qingzang Plateau from 2003 to 2005, the BRT were trained to fill flux data gaps and the results were compared to those corresponding data serials provided by Chinese Flux Observation and Research Network (ChinaFLUX).
Important findings The results showed that the BRT performed well for a large amount of samples (N > 10 000) and the regression slopes of observation data against predicted value were between 1.01 and 1.05 with R2 > 0.80. The BRT revealed that the daytime 30-min CO2 flux (net ecosystem CO2 exchange, NEE) in the growing season (i.e., May to October) was mainly controlled by solar shortwave radiation and atmospheric vapor pressure, whose relative contributions to NEE variability were up to 74.7%. The topsoil temperature was the determinant for NEE at night during the growing season and the whole day during the non-growing season, and its relative contribution was 68.5%. The 30-min sensible heat flux (H) and latent heat flux (LE) were both linearly related to solar radiation, and their relative contributions were above 58.6%. 30-min flux data gap amount filled by the BRT was significantly less than those by ChinaFLUX. Except for daily net ecosystem CO2 exchange (p = 0.14), daily gross ecosystem CO2 exchange (GEE), ecosystem respiration (RES), H, and LE of the BRT were significantly less than those of ChinaFLUX by 17.5%, 21.0%, 2.7%, and 2.2%, respectively. However, there was a reasonable consistency between the daily fluxes of 2003-2005 interpolated by the BRT and by ChinaFLUX due to the small magnitude difference (the regression slopes of the two data series were between 0.95 and 1.17). Except for monthly GEE and RES, monthly NEE, H, and LE of the BRT had no significant difference between the BRT and ChinaFLUX (p > 0.09). Compared with the ChinaFLUX gap-filling method, BRT can simulate the nonlinear relationships between fluxes and environmental factors without complicated mathematical expressions and quantify the relative contribution of environmental factors to the flux data gaps, which is a feasible technique for the integrated analysis of flux data.
Aims Aboveground biomass (AGB) is one of the most important factors affecting grassland ecosystem function and is commonly measured in grassland research. AGB is often measured using the harvest method, which can cause great disturbance to plant communities, especially for those long-term monitoring plots. A non-destructive method for AGB estimation is thus needed.
Methods Here, we conducted field measurements at a land-use manipulation experiment in a typical steppe in Nei Mongol, China. We obtained the fractional vegetation cover (FVC) using digital photographs. We also measured leaf area index (LAI), vegetation height, and plant species richness. Three different models were used to estimate AGB: univariate regression model, stepwise regression model, and random forest model.
Important findings We found that FVC, LAI, mean vegetation height, maximum vegetation height and richness were highly correlated with AGB variation. AGB can be accurately predicted by a stepwise regression model developed based on the local plant community. The determination coefficient (R2) and root-mean-square error (RMSE) of the stepwise regression model can reach 0.91 and 35.60 g·m-2, respectively. Overall, our study provides a rapid and non-destructive method for AGB measurement that can be used as an alternative to the traditional harvest method.
Solar radiation is fundamental to the maintenance and development of forest ecosystem functions and services. Therefore, modeling the radiation transfer process in forest is of great significance for understanding forest ecosystem processes. In recent years, the rapid development of three-dimensional radiative transfer models makes it possible to accurately simulate the distribution and dynamics of radiation within forest canopies. In order to better understand three-dimensional radiative transfer models and make them better serve forest ecosystem research, we review the principles, applications and future prospects of these models. Firstly, common principles of three-dimensional radiative transfer models such as radiosity and ray tracing are briefly introduced, and then the applications of three-dimensional radiative transfer models in forest ecosystem research are summarized. Finally, future opportunities of integrating multiple datasets and models to better facilitate forest ecosystem research, such as model coupling and making various models easier to use, are discussed. With the accumulation of ecological big data and improvement of ecosystem progress models, three-dimensional radiative transfer models will play a more important role in theoretical research and practices of forest ecology in the future.
Recent advances in solar-induced chlorophyll fluorescence (SIF), which is a complement to optical remote sensing based on greenness observation, have made it possible to monitor the photosynthesis of plants in terrestrial ecosystems using state-of-the-art technologies. With the rapid development of tower-based, unmanned aerial vehicle (UAV), airborne and space-borne SIF observation technology and improving understanding of SIF mechanism, SIF is providing essential data support and mechanism understanding for the estimation of biological traits and gross primary production of terrestrial ecosystem, early detection of abiotic stress, extraction of photosynthetic phenology and monitoring of transpiration. In this review, we first introduce the fundamental theory, the observation systems and technologies and the retrieval method of SIF. Then, we review the applications of SIF in terrestrial ecosystem monitoring. Finally, we propose a roadmap of activities to facilitate future directions and discuss critical emerging applications of SIF in terrestrial ecosystem monitoring that can benefit from cross-disciplinary expertise.
Plant functional traits are the measurable characteristics that indicates plant adaptation to the environment, and understanding the patterns of certain characteristics, and their drivers is an essential component of plant ecology and earth system modeling research. Traditional field-based approaches for characterizing plant functional traits are time-consuming, labor-intensive and expensive, and usually focus on the traits of peak growing season and dominant species, making the scaling extension and spatiotemporal coverage of plant functional traits a great challenge. In contrast, newly emerging multi-scale hyperspectral remote sensing techniques potentially provide new avenues to easily identify and characterize functional traits. Here we first overview the principles and brief history of hyperspectral remote sensing technology for plant functional traits monitoring. Then, we detailed the principal methods for modelling the spectral-trait relationships, including empirical and semi-empirical statistical methods and inversion methods relying on physical-based modelling, among which the statistical partial least squares regression is widely used. We then used case studies to demonstrate the application while illustrating the remaining problems of plant functional traits monitoring using the hyperspectral remote sensing techniques respectively at leaf, community and landscape scales. Finally, we highlight four important future directions to advance hyperspectral remote sensing of plant functional traits, including: 1) exploring the generalizability and underlying mechanisms of spectral-trait modelling; 2) developing novel, transparent methodology that scales the spectral-trait relationships from leaf, canopy to satellite levels; 3) elucidating the pattern and drivers of remotely sensed plant functional traits and diversity across various spatiotemporal scales; and 4) investigating the linkage among environment, plant functional diversity, biodiversity and ecosystem functioning.
Spectral diversity is a biodiversity dimension based on electromagnetic radiation spectrum reflected by plant, showing the variation of spectral reflective ratio in different bands among interspecific and intraspecific plant individuals. Spectral diversity has become an important technique for plant diversity monitoring and assessment since the differences of spectral reflectance can comprehensively indicate the differences of biochemical components and morphological and structural characteristics among plants. Here we introduce the concept of spectral diversity and its ecological significance, compare the technical advantages and disadvantages among multiple sources and platforms producing spectral data, summarize the monitoring and evaluation methodologies of plant diversity based on the applications of spectral diversity, and discuss the ability of spectral diversity to integrate different biodiversity dimensions and the prospect of the application of spectral diversity in biodiversity research. Spectral diversity will serve the monitoring and assessment of plant diversity at multiple spatial scales, especially combined with near-ground remote sensing based on unmanned aerial vehicle technology, can achieve fine-scale monitoring and assessment of plant diversity, and thus has broad application prospects in biodiversity conservation and management.
Aims The generalized complementary principle of evapotranspiration is one of the important methods to estimate evapotranspiration when the observed data are scarce. In implementing this method, an accurate estimation of parameter αe is critical. The temporal and spatial variation of αe and the applicability of different methods for calculating αe were investigated at eight flux stations under different climatic conditions and ecosystem types in China. Methods Firstly, the annual and monthly values of αe were calibrated based on the measured data. The spatiotemporal variability of αe was investigated and the influence of αe with different temporal scales on the calculation accuracy of the generalized complementarity principle model were compared. Considering that αe can not be calibrated without measured evapotranspiration data, the applicability of two statistical models of annual αe values based on aridity index (AI)(Liu method and Brutsaert method) were evaluated to determine whether αe can be determined using AI. Finally, the error sources of each calculation method were analyzed. Important findings αe value varies with season, and the monthly variations of αe differ among different flux stations. In terms of spatial variation, the annual values of αe at humid sites were larger than those at arid sites. The αe calculated by Liu method and Brutsaert method were close to the calibrated values. In applying the generalized complementary principle model, high simulation accuracy can be achieved by using the calibrated annual αe, and the accuracy can be further improved by using the monthly αe. Two AI-based methods also achieved accurate simulation results, which further confirmed the potential of predicting αe based on AI in the absence of observed data. The generalized complementary principle model can simulate the annual variation trend of evapotranspiration when using annual αe, but the estimated value were biased in some months. The evapotranspiration calculated by Liu method and Brutsaert method were underestimated in summer months of the drought sites, which may be caused by the fact that the AI was overestimated in summer months when rainfall was concentrated. The results further demonstrate the potential of the generalized complementary principle in estimating evapotranspiration in a wide range of natural environments.
Aims Spatial patterns of vessel in xylem are diverse and closely related with water transportation functions in angiosperms. However, the pattern was generally described qualitatively in anatomy, which were unable to reveal their links to xylem functions and to species distribution. We used point pattern analysis to study vessel spatial pattern in xylem cross-sectional images to quantify their features.
Methods Images of 17 types of vessel configurations were selected in terms of wood porosity, vessel arrangement, and vessel grouping. Optimum Strauss-Hardcore models for coordinates in the images were fitted. Correlations among vessel variables and model coefficients were tested.
Important findings We found that (1) Strauss-Hardcore model fitted all the data well and its three parameters, i.e., hardcore distance, local aggregation distance, and point-pair interaction or point aggregation index, and had apparent biological significance. (2) Classifications of wood xylem by traditional anatomical indices could not precisely present the spatial pattern of vessels compared to spatial point analysis, and local aggregation index from Strauss-Hardcore model was mainly influenced by vessel grouping, especially frequency of radial multiples and vessel clusters. (3) Among the 17 vessel patterns analyzed, diffusive or semi-ring species with xylem consisting of solidary vessels showed negative point-pair interaction and aggregation index was less than 0.4, whereas species with obvious vessel arrangement and multiple or clusters of vessel grouping in xylem owned positive point-pair interaction and bigger aggregation index. (4) The former group of species demonstrated inhibition- inhibition-random pattern at three local scales while the latter species showed inhibition-aggregation-random pattern according to the fitted Strauss-Hardcore models. The findings showed that point process modeling could precisely describe vessel distribution features in 2-D xylem sections and provide insights on vessel development. Therefore, this method may support 3-D vessel system simulation and experimental studies on structure-function of angiosperm xylem.
Aims Various colors combined with symbols are usually employed to differentiate vegetation types in vegetation mapping, aiming to convey the vegetation information to readers more visually and clearly. How to differentiate vegetation types by appropriate setting of color and symbol is a key step in vegetation mapping, especially for regions with diverse vegetation types. Usually, vegetation map legends are based on the vegetation classification system. Recently, the vegetation classification system of China has been revised according to the achievements in vegetation surveys and research over the past decade. Therefore, it is necessary to put forward a new color and symbol setting scheme for vegetation mapping. Our objective is to improve the scientificity and artistry of current vegetation mapping. Methods The principles of color and symbol setting of existing vegetation maps and other thematic maps were summarized. Based on the principles of systematicness, scientificity and symbolism, the changes and combinations of the three color attributes (hue, lightness and saturation) and the basic visual variables (shape, size, direction, color, density and brightness) of the symbols were taken in consideration for the national vegetation map legends. In the legends, Vegetation Formation Groups were differentiated by hues, and Vegetation Formations and Vegetation Subformations were represented by different lightnesses and saturations. In addition, different symbols attached to the colors were employed to distinguish vegetation types. The colors and symbols were designed to reflect the physiognomy and habitats of the vegetation types as much as possible. Important findings The principles of color and symbol setting for vegetation mapping and a new scheme of national vegetation map legends were presented. These results would provide guideline for vegetation cartographer to set the legends of the new Chinese vegetation map (1:500 000).
Chlorophyll fluorescence (ChlF) is the key to studying the physiological mechanisms of plant photosynthesis, quantifying the spatiotemporal pattern of vegetation photosynthesis, and accurately understanding the productivity of terrestrial ecosystems under the background of climate change. However, few studies have been conducted on combined observations of actively and passively induced ChlF. Here, we compared the advantages and disadvantages of active and passive observations of ChlF and showed the instrument composition of the combined observations of actively and passively induced ChlF at leaf and canopy scales. The application prospects of joint observations of actively and passively induced ChlF focus on exploring energy distribution among photosynthesis, fluorescence and heat dissipation at the chloroplast-leaf-canopy scale, clarifying the mechanism underlying the relationship between ChlF and gross primary productivity, verifying satellite-based sun-induced chlorophyll fluorescence and interpreting the shape of the ChlF spectrum. Our work suggests that the combined observation of actively and passively induced ChlF is essential to reveal the mechanisms underlying the relationships between fluorescence and photosynthesis at various scales and to improve vegetation productivity models at the global scale.
Exchanges of energy and matter between terrestrial biosphere and atmosphere and hydrosphere create critical feedbacks to Earth’s climates. To quantify how terrestrial ecosystems respond and feedback to global changes, terrestrial biosphere model (TBM) has been developed and applied in global change ecology during the past decades. In TBMs, myriad of biogeophysical, biogeochemical, hydrological cycles and dynamics processes on different spatial and temporal scales are represented. The TBMs have been applied on assessing and attributing past changes in terrestrial biosphere, and on predicting future changes and their feedbacks to climates. Here, we provide an overview of processes included in TBMs and TBMs applications on carbon and hydrological cycles, as well as their application on exploring human impacts on terrestrial ecosystems. Finally, we outline perspectives for future development and application of TBMs.
As the increasing pressure caused by climatic changes and human activities, the structure and function of terrestrial ecosystems are undergoing dramatic changes. Understanding how ecosystem processes change at large spatial-temporal scales is crucial for dealing with the threats and challenges posed by global climate change. Traditional field survey method can obtain accurate plot-level ecosystem observations, but it is difficult to be used to address large-scale ecosystem patterns and processes because of spatial and temporal discontinuities. Compared to traditional field survey methods, remote sensing has the advantages of real-time acquisition, repeated monitoring and multi spatial-temporal scales, which can compensate for the shortcomings of traditional field observation methods. Remote sensing can be used to identify the type and characteristic of ground objects, and extract key ecosystem parameters, energy flow and material circulation through retrieving the information contained by electromagnetic signals. Remote sensing data have become an indispensable data source in ecological studies, especially at the ecosystem, landscape, regional or global scales. With the emergence of new remote sensing sensors (e.g., light detection and ranging, and solar-induced chlorophyll fluorescence) and near-surface remote sensing platforms (e.g., unmanned aerial vehicle and backpack), remote sensing is entering the three-dimensional era and the observation platform become more diverse. These three-dimensional, multi-source and time-series remote sensing data bring new opportunities to fully understand ecosystem processes across different spatial scales. This paper reviews the advances of the application of remote sensing in terrestrial ecosystem studies. Specifically, this study focuses on the derivation of biological factors from remote sensing data, including vegetation types, structures, functions and biodiversity of terrestrial ecosystems. We also summarize the current status of the remote sensing technology in ecosystem studies and suggest the future opportunities of ecosystem monitoring in China.
Wildlife as one major group in ecosystem research and conservation management, play a critical role in regulating the structure and function of ecosystems and maintaining the health and balance of ecosystems. Scientific monitoring data are the basis for wildlife research, protection and management decisions. However, the wildlife diversity, their relationship and related mechanisms with the environment and ecosystem balance have been paid insufficient attention due to the limitations of traditional monitoring technologies. With the development and application of automatical and information technologies, wildlife monitoring technology has achieved great breakthroughs and changes. In this paper, we described four new technologies widely used for wildlife monitoring recently, including camera-trapping technology, Global Positioning System (GPS) tracking technology, DNA-barcode technology and next-generation sequencing technology. We introduced the basic concepts and principles, then summarized the advantages and major application progress of these four key technologies as well as the problems existing in the application. Finally, we discussed the trend of the wildlife monitoring technologies.
Microbiome is the combination of all microorganisms and their genetic information in a specific environment or ecosystem, which contains abundant microbial resources. A comprehensive and systematic analysis of the structure and function of microbiome will provide new ideas in solving the core issues in the fields of energy, ecological environment, industrial and agricultural production and human health. However, the study of microbiome largely depends on the development of relevant technologies and methods. Before to the advent of high-throughput sequencing technology, microbial research was mainly based on techniques such as isolation, pure-culture and fingerprint. However, due to the technical restrictions, scientists could only get limited knowledge of microorganisms. Since the beginning of 21st century, the revolutionary advances in the technology of high-throughput sequencing and mass spectrometry have greatly improved our understanding on the structure and ecological functions of environmental microbiome. However, the application of microbiomics technology in microbial research still faces many challenges. In addition, the descriptive studies focusing on the structure and diversity of microbiome have already matured, and the study of microbiomics is facing a critical transition period from quantity to quality and from structure to function. Hence, this paper will firstly introduce the basic concepts of microbiomics and a brief development history. Secondly, this paper introduces the related technologies and methods of microbiomics with their development process, and further expounds the applications and main problems of microbiomics technologies and methods in ecological study. Finally, this paper expounds the frontier direction of the development of microbiomics technology and methods from the technical, theoretical and application levels, and proposes the priority development areas of microbiome research in the future.
Biomarkers are biogenic organic compounds that carry the chemical structures specific to their biological sources and survive long-term preservation in environmental and geological systems. The abundance of biomarkers may indicate the relative contribution of specific biological sources to the natural organic matter while their chemical and isotopic compositions may also inform on the transformation stage of organic matter and the environmental settings. Compared with conventional bulk analysis, biomarkers offer highly specific and sensitive tools to track the sources, transformation and dynamic changes of natural organic matter components and have therefore been widely used in ecological and biogeochemical studies in the past decades. In particular, combined with ecosystem observations and control experiments, biomarkers have shown great potentials in revealing changes in microbial activity and carbon sources, soil organic matter dynamics, stabilization mechanisms and response to global changes. The recently-developed biomarker-specific isotope analysis also exhibits a great promise in revealing ecosystem carbon and nitrogen turnover and food web structures. This review summarizes several major categories of commonly used biomarkers, their analytical methods, applications in ecosystem studies and existing pitfalls, and discusses future directions of research to provide guidance for biomarker users in ecology and environmental sciences.
In the past several decades, the development of nitrogen (N) stable isotope techniques has improved the understanding of N cycling in terrestrial ecosystems. This review briefly introduced the history of N stable isotope techniques in studying N cycling in terrestrial ecosystems and summarized typical studies focusing on different aspects of ecosystem N cycling in recent years, including using 1) 15N natural abundance to identify plant N sources, indicate N status of ecosystems, and quantify N transformation rates; 2) 15N enriched tracers to study N fates, redistribution and gaseous loss from ecosystems. In the end, this review points out challenges and future applications of N stable isotope techniques on studying N cycling in terrestrial ecosystems.
Recently developed in recent decades, the carbon isotope tracing technology is one of the most reliable methods, which has been widely used in the study of carbon (C) cycling in terrestrial ecosystems due to its high specificity and sensitivity. Here, the principle, analysis method and application process of C isotope tracing technology in C cycling in terrestrial ecosystem have been reviewed. Four different methods are currently being used in laboratory or field conditions, including natural abundance method, Free-Air Concentration Enrichment (FACE) technology coupling with 13C dilution method, pulse and continuous labeling with 13C enriched CO2, and labeling with 13C enriched substrates. Results of field experiments and lab incubation experiments employing carbon isotope tracing technology were combined in order to quantify the transformation and distribution of photosynthetic C in plant-soil system. Furthermore, these techniques also help to understand the contribution of plant photosynthetic C to soil organic matter, the stabilization of soil organic matter and its microbial mechanism, to illustrate the dynamic changes of soil organic carbon (SOC), evaluate the contribution of new and old organic C to soil C storage, and estimate the micromechanism of SOC input, conversion and the stabilization in terrestrial ecosystems. Carbon cycle is affected by climate, vegetation, human activities and other factors, and therefore it is imperative to further develop a sensitive, accurate, multiscale and multidirectional isotope tracing system by combining carbon isotopes with mass spectrometry, spectroscopy and molecular biological technology. We have summarized the coupled application of carbon isotope tracing technology and the insitu detection involving molecular and biological approaches, and discussed the existing issues of carbon isotope tracing technology.
Stable oxygen and hydrogen isotope analysis provides an important tool to trace, integrate or indicate water fluxes from leaf, whole-plant to ecosystem levels. Through measuring and analyzing the natural varitions in the hydrogen and oxygen isotope compositions of water from different components of ecosystem, we can partition evapotranspiration of ecosystem, determine source of plant water uptake, and study mechanism of leaf water isotope enrichment. As such, water isotope analysis has emerged as an indispensable technique to study the mechanism and ecological effects of different water cycle processes in ecosystem. In this paper, we briefly reviewed the history in development and application of water isotope analysis for terrestrial ecosystem studies, which then followed by more detailed introduction of the application principles and technical essentials. Furthermore, we reviewed progresses in diverse water-isotope based research field ranging from evapotranspiration partitioning, plant water uptake apportionment, sourcing of dew flux and precipitation vapor, to exploration leaf water isotope enrichment mechanisms and water-carbon coupling. Finally, we summarized technological and methodological challenges to be solved in the future ecological research, so as to fully realize the potential of water isotope analysis in various field of ecological research.
Massive fossil fuel burning and the rapid urbanization have caused significant increases in atmospheric carbon dioxide (CO2) and ozone (O3) concentrations. The increased gas concentration has significant impacts on the structure and function of terrestrial plants and ecosystems. Rising CO2 concentration increased the plant growth and productivity, while elevated O3 decreased grain yield and carbon sequestration capacity. The Free-Air Concentration Enrichment (FACE) is one kind of facility closest to the natural conditions for simulating effects of rising atmospheric gas concentration on ecosystems. FACE has been widely used in various ecosystems and provides key basis to understand the ecological progress in response to global change and parameters for risk assessment in terrestrial ecosystem models. In this paper, CO2/O3-FACE facility around world and their technology are reviewed. The advantages and disadvantages of the design of each FACE in different terrestrial ecosystems were discussed. The current status of global FACE facility and progress in research achievements are also introduced. Furthermore, the problems in running current FACE and the frontiers of scientific questions are also highlighted.
Due to the sharp increase in carbon emissions from human activities, global surface air temperature has increased significantly by approximately 1 °C since the Industrial Revolution, and it will continue to increase by up to 4 °C by the end of 21st century. This unprecedented climate change will not only affect the adaptation strategies of terrestrial vegetation, but also profoundly affect the structure and function of terrestrial ecosystems. The feedbacks of terrestrial ecosystem carbon cycling to warming is the key factor controlling the speed of future climate change. Therefore, a large number of ecosystem-scale field warming manipulation experiments have been conducted globally to study the carbon budget of terrestrial ecosystems and to improve the prediction accuracy of earth system models. However, due to differences in techniques and methods of these field warming experiments, results among different studies are difficult to compare and synthesize. This paper reviews the common techniques and methods of field warming manipulation experiments, including active warming and passive warming. It also summarizes advantages and disadvantages, applicable objects and related publications for these techniques and methods. Moreover, it briefly introduces future directions of field warming manipulation experiments—the next-generation field warming techniques, namely whole-soil-profile warming and whole-ecosystem warming, and calls for establishing a coordinated distributed network of field warming manipulation experiments using these techniques.
The exchange flux of greenhouse gases, such as carbon (CO2, CH4), nitrogen (N2O) and water vapour (H2O), is the core of material cycle in the ecosystem and the bond of interaction among geosphere, biosphere and atmosphere. The development of stable isotope infrared spectroscopy and mass spectrometry technology and methods makes it possible to measure carbon stable isotopic composition (δ 13C) and oxygen stable isotopic composition (δ 18O)(CO2), δ 13C (CH4), nitrogen stable isotope composition (δ 15N) and δ 18O (N2O), hydrogen stable isotopic composition (δD) and δ 18O (H2O), which realizes the observation of greenhouse gas and its isotope flux at the soil, plant and ecosystem scales in combined with chamber-based technology and methods for flux measurement. Taking the chamber-based technology and methods for CO2 and its δ 13C flux measurement as an example, this review which summarizes the basic principle and classification of the flux measurement system, expounds the theory requirements and assumptions of system design, summarizes the application advance and problems of chamber-based technology and methods for flux measurement in soil, plants (leaf, stem, and root) and ecosystem scales from the field to indoor, and prospects the importance of precision and accuracy of gas analysis and measurement data and the representativeness of measurement data in chamber-based flux measurement.
Flux-gradient method and eddy covariance technique are classical micrometeorological methods, which observe fluxes of mass and energy. Flux-gradient method can effectively measure the greenhouse gas and isotope fluxes between ecosystem (or soil) and atmosphere although gas analyzer with high measuring frequency was not available or the fetch was small. Flux-gradient method can be viewed as an ancillary measurement and useful complement of eddy covariance technique. This paper reviewed from the following aspects: the fundamental theory, concepts and assumptions of flux-gradient method; the methods measuring the gradient of greenhouse gases and the theory on turbulent diffusion coefficients; the applications of this method in measuring greenhouse gas fluxes, especially on isotope fluxes, over various ecosystems including forest, cropland, grassland, wetland and water bodies. Finally, the considerations and suggestions were provided regarding the measurement on concentration gradients of greenhouse gases and isotopes, and the calculation of turbulent diffusion coefficients.
Carbon (C) and water cycles are the most critical processes in terrestrial ecosystems, which links the materials and energy flows through the pedosphere-biosphere-atmosphere integration. Most attention has been paid to the responses of C and water and their feedbacks to global climate change. Flux observation is the basic pathway to quantify the rate of material and energy exchange across soil-plant-atmosphere continuum. As an only technique can directly measure the carbon, water and energy fluxes between vegetation and atmosphere, eddy covariance (EC) technique has been considered as a standard method for flux observation internationally. With broad applications of EC technique on global C and water cycles, long-term flux observations provide scientific data on assessing ecosystem C sequestration capability, water and energy balance, and ecosystem feedback to climate change; optimizing and validating models on regional and global scales; and understanding responses of ecosystem functions to extreme events. Based on long-term flux observation in individual site, scientists have described the seasonal and inter-annual dynamics, and quantified the baseline rates of ecosystem carbon and water fluxes across different climate and vegetation types. With the development of regional and global flux networks, researchers further understood the spatial patterns of ecosystem carbon and water fluxes and their climatic control mechanisms at regional and global scales. This paper briefly introduces the basic principles, hypothesis and instrument system composition, summarizes the major applications of EC observation on C and water fluxes in terrestrial ecosystems, and finally discusses future directions of EC observation network.
With the development of isotope ratio infrared spectroscopy (IRIS) technology, it is now possible for the in situ high temporal resolution and high precision measurement of carbon isotopic composition (δ 13C) and oxygen isotopic composition (δ 18O) of atmospheric CO2, which overcomes the low temporal resolution and labor intensive shortcoming of traditional isotope ratio mass spectrometry (IRMS). The dependence of δ 13C and δ 18O on CO2 concentration (termed as concentration dependence) and the drift due to sensitivity to changing environmental conditions (termed as instrumental drift) are the two main sources of error affecting the IRIS measurements. Therefore, it is important to obtain precise measurements by constructing a proper calibration strategy to solve the concentration dependence and instrumental drift. In this study, we briefly discussed the definition and related theoretical principle of concentration dependence, and elaborated the theoretical and empirical calibration methods of concentration dependence. Moreover, we introduced the calibration methods of instrumental drift, and reviewed the state of the art of calibration methods and its application of IRIS technology. Additionally, we briefly discussed the definition and method of data traceability to the international standard, and reviewed its application of IRIS technology. Finally, we recommend that concentration dependence is corrected by using three standards or above with known CO2 concentration and its δ 13C and δ 18O, bracketing the CO2 concentration of samples. The instrumental drift is corrected by setting appropriate calibration frequency and all dataset are traceable to the international standard. In the future, the comparative study of different IRIS instruments and calibration methods should be enhanced, and the similar methods should be used for measuring CH4, N2O and H2O isotopes by IRIS technique. The IRIS technology combined with other technology will provide a new opportunity for ecological research.
Aims Climate warming strongly influences reproductive phenology of plants in alpine and arctic ecosystems. Here we focus on phenological shifts caused by warming in a typical alpine meadow on the Qinghai-Xizang Plateau. Our objective was to explore phenological responses of alpine plant species to experimental warming. Methods Passive warming was achieved using open-top chambers (OTCs). The treatments included control (C), and four levels of warming (T1, T2, T3, T4). We selected Kobresia pygmaea, Potentilla saundersiana, Potentilla cuneata, Stipa purpurea, Festuca coelestis and Youngia simulatrix as the focal species. Plant phenology was scored every 3-5 days in the growing season. The reproductive phenology phases of each species were estimated through fitting the phenological scores to the Richards function. Important findings Under soil water stress caused by warming, most plants in the alpine meadow advanced or delayed their reproductive events. As a result, warming significantly delayed phenological development of K. pygmaea. Warming significantly advanced reproductive phenology of P. saundersiana, S. purpurea and F. coelestis, but not of P. cuneata and Y. simulatrix. In addition, warming significantly shortened the average flowering duration of alpine plant species. The potentially warmer and drier growing seasons under climate change may shift the reproductive phenology of the alpine systems in similar pattern.
Stable isotope technique has been widely used in ecology research with the increasing concern on global change. Our objectives are to better understand the impacts of nitrogen addition and other environment changes on the nitrogen cycling of terrestrial ecosystem, predict the consequent changes in environmental conditions, and provide a reference for policy making to help ensure the sustainable development of terrestrial ecosystems. Based on the relationship between nitrogen (N) isotope composition (δ 15N) in ecosystem N status and soil N cycle, we summarized the effects and mechanisms of N input and other environment changes on δ 15N of plant and soil. Most studies show significant positive relationships between N input and δ 15N values of plant and soil. Higher N input increases soil N availability, which leads to 15N enrichment in soil because of mass discrimination during soil N cycling processes. Foliar δ 15N also will be higher as plants take up the relatively 15N-enriched soil available N. 15N natural abundance can be a useful tool for assessing nitrogen saturation and N cycling.
Aims There have been many studies of carbon isotope composition (δ13C) of C3 plants in China, and δ13C has been widely used as an index of water use efficiency (WUE); however, most studies focused on single sites or small regions. Therefore, our objective was to study the spatial pattern of δ13C, the relationships between δ 13C and climate factors and whether δ 13C can represent WUE in large regions. Methods We obtained leaf δ 13C for 478 C3 species from 187 sites in China based on the literature. Important findings The range of δ13C was from -33.50‰ to -22.00‰, and the mean was -(27.10‰ ± 1.70)‰. There were significant differences among δ13C of grasses, shrubs and trees, with grasses having the highest value and trees the lowest. The result was different from studies in single sites and small regions. For different phylogenic plants, δ 13C of seed plants was significantly higher than ferns, the difference between gymnosperms and angiosperms was not statistically significant and monocotyledons had significantly higher values than dicotyledons. Leaf δ 13C had irregular variation with increasing longitude, but significantly increased with increasing latitude. Leaf δ 13C significantly increased with mean annual temperature and decreasing mean annual precipitation. The relationship between δ 13C and precipitation was similar to that of WUE and precipitation, so we conclude that δ 13C of C3 plants can be used as an index of WUE in large regions as well as in single sites or small regions.
Enrichment of atmospheric greenhouse gases resulted from human activities such as fossil fuel burning and deforestation has increased global mean temperature by 0.6 ℃ in the 20th century and is predicted to increase it by 1.4-5.8 ℃ in this century. The unprecedented global warming will have profound long-term impacts on terrestrial plants and ecosystems. Responses of terrestrial plants and ecosystems to global warming may feed back to climate change via ecosystem and global carbon cycling. As one of the major methodology in global change research, ecosystem warming studies can facilitate model projections on potential changes in terrestrial biomes in terms of parameterization and validation. The difficulty in comparing and integrating the results from various ecosystems manipulated with different warming facilities exacerbated the uncertainties of model prediction. In this paper, field facilities in simulating climate warming and their potential applications in different terrestrial biomes were discussed. Critical scientific questions that could be addressed by ecosystem warming studies were proposed.