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Tropical leaf phenology—particularly its variability at the tree-crown scale—dominates the seasonality of carbon and water fluxes. However, given enormous species diversity, accurate means of monitoring leaf phenology in tropical forests is still lacking. Time series of the Green Chromatic Coordinate (GCC) metric derived from tower-based red–greenblue (RGB) phenocams have been widely used to monitor leaf phenology in temperate forests, but its application in the tropics remains problematic. To improve monitoring of tropical phenology, we explored the use of a deep learning model (i.e. superpixel-based Residual Networks 50, SP-ResNet50) to automatically differentiate leaves from non-leaves in phenocam images and to derive leaf fraction at the tree-crown scale. To evaluate our model, we used a year of data from six phenocams in two contrasting forests in Panama. We first built a comprehensive library of leaf and non-leaf pixels across various acquisition times, exposure conditions and specific phenocams. We then divided this library into training and testing components. We evaluated the model at three levels: 1) superpixel level with a testing set, 2) crown level by comparing the model-derived leaf fractions with those derived using image-specific supervised classification, and 3) temporally using all daily images to assess the diurnal stability of the model-derived leaf fraction. Finally, we compared the model-derived leaf fraction phenology with leaf phenology derived from GCC. Our results show that: 1) the SP-ResNet50 model accurately differentiates leaves from non-leaves (overall accuracy of 93%) and is robust across all three levels of evaluations; 2) the model accurately quantifies leaf fraction phenology across tree-crowns and forest ecosystems; and 3) the combined use of leaf fraction and GCC helps infer the timing of leaf emergence, maturation and senescence, critical information for modeling photosynthetic seasonality of tropical forests. Collectively, this study offers an improved means for automated tropical phenology monitoring using phenocams.

Tropical forests are responsible for the evaporation and transpiration of large quantities of water into the atmosphere annually. Surface conductance (gs) is a poorly understood phenomenon that plays a central role in regulating this evapotranspiration. We studied the calculations, variations, and environmental factors controlling gs based on eddy flux measurements from 10 tropical forest sites that covered a wide range of water gradients across continents. We found that boundary layer conductance (gb) is comparable with aerodynamic conductance for momentum (gaM) and thus, it should not be ignored in estimations of total aerodynamic conductance for water vapor (gaV). Based on the findings, we have made some recommendations for gaM estimation both with and without measurements of turbulence. The seasonal variation of gs is low in humid sites but large in sites with a dry season. A value of 24.8 ± 13.8 mm s−1 was suggested for maximum surface conductance (gsmax) for tropical forests. Both water vapor deficit (D) and radiation (Q) play an important role in controlling gs. The model driven by both D and Q could capture the diurnal variations of gs well and could be implemented in large-scale models in future. We believe the findings of this study could contribute substantially to our understanding of tropical forest gs.

The phylum Caldiserica was identified from the hot spring 16S rRNA gene lineage 'OP5' and named for the sole isolate Caldisericum exile, a hot spring sulfur-reducing chemoheterotroph. Here we characterize 7 Caldiserica metagenome-assembled genomes (MAGs) from a thawing permafrost site in Stordalen Mire, Arctic Sweden. By 16S rRNA and marker gene phylogenies, and average nucleotide and amino acid identities, these Stordalen Mire Caldiserica (SMC) MAGs form part of a divergent clade from C. exile. Genome and meta-transcriptome and proteome analyses suggest that unlike Caldisericum, the SMCs (i) are carbohydrate- and possibly amino acid fermenters that can use labile plant compounds and peptides, and (ii) encode adaptations to low temperature. The SMC clade rose to community dominance within permafrost, with a peak metagenome-based relative abundance of ∼60%. It was also physiologically active in the upper seasonally-thawed soil. Beyond Stordalen Mire, analysis of 16S rRNA gene surveys indicated a global distribution of this clade, predominantly in anaerobic, carbon-rich and cold environments. These findings establish the SMCs as four novel phenotypically and ecologically distinct species within a single novel genus, distinct from C. exile clade at the phylum level. The SMCs are thus part of a novel cold-habitat phylum for an understudied, globally-distributed superphylum encompassing the Caldiserica. We propose the names Candidatus Cryosericota phylum nov., Ca. Cryosericia class nov., Ca. Cryosericales ord. nov., Ca. Cryosericaceae fam. nov., Ca. Cryosericum gen. nov., Ca. Cryosericum septentrionale sp. nov., Ca. C. hinesii sp. nov., Ca. C. odellii sp. nov., and Ca. C. terrychapinii sp. nov.

Once inorganic electron acceptors are depleted, organic matter in anoxic environments decomposes by hydrolysis, fermentation, and methanogenesis, requiring syntrophic interactions between microorganisms to achieve energetic favorability. In this classic anaerobic food chain, methanogenesis represents the terminal electron accepting (TEA) process, ultimately producing equimolar CO 2 and CH 4 for each molecule of organic matter degraded. However, CO 2 :CH 4 production in Sphagnum- derived, mineral-poor, cellulosic peat often substantially exceeds this 1:1 ratio, even in the absence of measureable inorganic TEAs. Since the oxidation state of C in both cellulose-derived organic matter and acetate is 0, and CO 2 has an oxidation state of +4, if CH 4 (oxidation state −4) is not produced in equal ratio, then some other compound(s) must balance CO 2 production by receiving 4 electrons. Here we present evidence for ubiquitous hydrogenation of diverse unsaturated compounds that appear to serve as organic TEAs in peat, thereby providing the necessary electron balance to sustain CO 2 :CH 4 > 1. While organic electron acceptors have previously been proposed to drive microbial respiration of organic matter through the reversible reduction of quinone moieties, the hydrogenation mechanism that we propose, by contrast, reduces C C double bonds in organic matter thereby serving as (1) a terminal electron sink, (2) a mechanism for degrading complex unsaturated organic molecules, (3) a potential mechanism to regenerate electron-accepting quinones, and, in some cases, (4) a means to alleviate the toxicity of unsaturated aromatic acids. This mechanism for CO 2 generation without concomitant CH 4 production has the potential to regulate the global warming potential of peatlands by elevating CO 2 :CH 4 production ratios.

The association between spectral reflectance and canopy processes remains challenging for quantifying large-scale canopy phenological cycles in tropical forests. In this study, we used a tower-mounted hyperspectral camera in an eastern Amazon forest to assess how canopy spectral signals of three species are linked with phenological processes in the 2012 dry season. We explored different approaches to disentangle the spectral components of canopy phenology processes and analyze their variations over time using 17 images acquired by the camera. The methods included linear spectral mixture analysis (SMA); principal component analysis (PCA); continuum removal (CR); and first-order derivative analysis. In addition, three vegetation indices potentially sensitive to leaf flushing, leaf loss and leaf area index (LAI) were calculated: the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI) and the entitled Green-Red Normalized Difference (GRND) index. We inspected also the consistency of the camera observations using Moderate Resolution Imaging Spectroradiometer (MODIS) and available phenological data on new leaf production and LAI of young, mature and old leaves simulated by a leaf demography-ontogeny model. The results showed a diversity of phenological responses during the 2012 dry season with related changes in canopy structure and greenness values. Because of the differences in timing and intensity of leaf flushing and leaf shedding, Erisma uncinatum, Manilkara huberi and Chamaecrista xinguensis presented different green vegetation (GV) and non-photosynthetic vegetation (NPV) SMA fractions; distinct PCA scores; changes in depth, width and area of the 681-nm chlorophyll absorption band; and variations over time in the EVI, GRND and NDVI. At the end of dry season, GV increased for Erisma uncinatum, while NPV increased for Chamaecrista xinguensis. For Manilkara huberi, the NPV first increased in the beginning of August and then decreased toward September with new foliage. Variations in red-edge position were not statistically significant between the species and across dates at the 95% confidence level. The camera data were affected by view-illumination effects, which reduced the SMA shade fraction over time. When MODIS data were corrected for these effects using the Multi-Angle Implementation of Atmospheric Correction Algorithm (MAIAC), we observed an EVI increase toward September that closely tracked the modeled LAI of mature leaves (3–5 months). Compared to the EVI, the GRND was a better indicator of leaf flushing because the modeled production of new leaves peaked in August and then declined in September following the GRND closely. While the EVI was more related to changes in mature leaf area, the GRND was more associated with new leaf flushing.

As a preparatory study for future hyperspectral missions that can measure canopy chemistry, we introduce a novel approach to investigate whether multi-angle Moderate Resolution Imaging Spectroradiometer (MODIS) data can be used to generate a preliminary database with long-term estimates of chlorophyll. MODIS monthly chlorophyll estimates between 2000 and 2015, derived from a fully coupled canopy reflectance model (ProSAIL), were inspected for consistency with eddy covariance fluxes, tower-based hyperspectral images and chlorophyll measurements. MODIS chlorophyll estimates from the inverse model showed strong seasonal variations across two flux-tower sites in central and eastern Amazon. Marked increases in chlorophyll concentrations were observed during the early dry season. Remotely sensed chlorophyll concentrations were correlated to field measurements (r2 = 0.73 and r2 = 0.98) but the data deviated from the 1:1 line with root mean square errors (RMSE) ranging from 0.355 μg cm−2 (Tapajos tower) to 0.470 μg cm−2 (Manaus tower). The chlorophyll estimates were consistent with flux tower measurements of photosynthetically active radiation (PAR) and net ecosystem productivity (NEP). We also applied ProSAIL to mono-angle hyperspectral observations from a camera installed on a tower to scale modeled chlorophyll pigments to MODIS observations (r2 = 0.73). Chlorophyll pigment concentrations (ChlA+B) were correlated to changes in the amount of young and mature leaf area per month (0.59 ≤ r2 ≤ 0.64). Increases in MODIS observed ChlA+B were preceded by increased PAR during the dry season (0.61 ≤ r2 ≤ 0.62) and followed by changes in net carbon uptake. We conclude that, at these two sites, changes in LAI, coupled with changes in leaf chlorophyll, are comparable with seasonality of plant productivity. Our results allowed the preliminary development of a 15-year time series of chlorophyll estimates over the Amazon to support canopy chemistry studies using future hyperspectral sensors.

Understanding how land surface seasonality emerges from individual tree crown phenology is a key challenge of tropical ecology. We used daily images over a full year from a tower-mounted RGB camera to quantify the leaf phenology of 267 individual tree crowns in an evergreen Central Amazon forest. The Green Chromatic Coordinate, an index of each crown's greenness, showed rapid large-amplitude positive and negative changes, each generally occurring once per year. Rapid increase was attributed to leaf flushing and occurred in 85% of all crowns. Rapid negative change occurred in 42% of individuals, caused mostly by massive pre-flush leaf abscission (31% of all crowns). Flushing was concentrated in the five driest months (55% of crowns) compared to the five wettest months (10%). Inter-crown variance of greenness was lowest in the wet season when fewer crowns were abruptly abscising or flushing leaves. With a one month lead, flushing frequency closely tracked seasonal light availability (R = 0.89) and was inversely correlated with rainfall (R = − 0.88). We linked the post-flush age of each crown's leaf cohort to the Enhanced Vegetation Index (EVI) of crowns at different phenostages on a nadir view QuickBird image. When aggregated to landscape-scale, this camera-based EVI closely followed (R = 0.95) the MODIS MAIAC EVI of the same site, fully corrected for sun-sensor geometry effects. Leaf phenology therefore drives the dry season green-up detected by MODIS in the Central Amazon. It is also consistent with evolutionary strategies to couple photosynthetic efficiency with light availability and to avoid predation and disease on vulnerable young leaves.

The fate of carbon stored in permafrost-zone peatlands represents a significant uncertainty in global climate modeling. Given that the breakdown of dissolved organic matter (DOM) is often a major p ...

Stable carbon isotopes can be used to partition the net ecosystem–atmosphere exchange (NEE) of carbon dioxide (CO2) into its photosynthetic and respiratory components, but the method has not been generally adopted due to instrumental and theoretical limitations. Here, motivated by recently improved instrumentation, we extend the theory of isotopic flux partitioning to include photorespiration, foliar daytime ‘dark’ respiration, and other refinements, arriving at a general yet practical formulation from which all previous formulations can be derived as simplifying approximations. We use a full growing season of isotopic eddy covariance flux data from a temperate deciduous forest to demonstrate the method, quantify its uncertainties, and determine biases associated with previously published formulations. We find that when δ13C of CO2 is acquired with high precision (0.02‰ RMSE for 100 s integration times), the statistical uncertainty in the partitioned fluxes is comparable to that in NEE itself—i.e., as good as practicably possible. Assessable systematic uncertainty is ±17% of gross ecosystem production (GEP), due mostly to uncertainty in the isotopic fractionation by carboxylation. Additional, currently unquantifiable systematic uncertainty is associated with treating the canopy as a single “big leaf”. Both sources of systematic uncertainty could be greatly reduced by feasible supporting leaf-level measurements. Our extended theory corrects systematic biases in previous isotopic approaches, including overestimation (by 13%) of GEP due to the omission of photorespiration. The partitioning determines the isotopic signature of photosynthesis, which we find to vary seasonally between −24 and −28‰ such that the isotopic disequilibrium between ecosystem carbon input and output remains stable at approximately −0.5‰ through most of the growing season. The key advantage of isotopic partitioning over standard, regression-based partitioning is that it enables controls on the ecosystem-scale photosynthetic and respiratory fluxes to emerge from observations, without having to assume functional relations to environmental drivers a priori. As an example, we show how isotopic partitioning reveals certain large variations in daytime NEE to be caused by shifts in the flux tower sampling footprint between regions of high and low respiratory flux—a finding unobtainable by standard partitioning. For this reason, isotopic partitioning can be more precise than standard partitioning for quantifying environmental controls on NEE.

Zero-order drainage basins, and their constituent hillslopes, are the fundamental geomorphic unit comprising much of Earth's uplands. The convergent topography of these landscapes generates spatially variable substrate and moisture content, facilitating biological diversity and influencing how the landscape filters precipitation and sequesters atmospheric carbon dioxide. In light of these significant ecosystem services, refining our understanding of how these functions are affected by landscape evolution, weather variability, and long-term climate change is imperative. In this paper we introduce the Landscape Evolution Observatory (LEO): a large-scale controllable infrastructure consisting of three replicated artificial landscapes (each 330 m 2 surface area) within the climate-controlled Biosphere 2 facility in Arizona, USA. At LEO, experimental manipulation of rainfall, air temperature, relative humidity, and wind speed are possible at unprecedented scale. The Landscape Evolution Observatory was designed as a community resource to advance understanding of how topography, physical and chemical properties of soil, and biological communities coevolve, and how this coevolution affects water, carbon, and energy cycles at multiple spatial scales. With well-defined boundary conditions and an extensive network of sensors and samplers, LEO enables an iterative scientific approach that includes numerical model development and virtual experimentation, physical experimentation, data analysis, and model refinement. We plan to engage the broader scientific community through public dissemination of data from LEO, collaborative experimental design, and community-based model development.

A fundamental question connecting terrestrial ecology and global climate change is the sensitivity of key terrestrial biomes to climatic variability and change. The Amazon region is such a key biome: it contains unparalleled biological diversity, a globally significant store of organic carbon, and it is a potent engine driving global cycles of water and energy. The importance of understanding how land surface dynamics of the Amazon region respond to climatic variability and change is widely appreciated, but despite significant recent advances, large gaps in our understanding remain. Understanding of energy and carbon exchange between terrestrial ecosystems and the atmosphere can be improved through direct observations and experiments, as well as through modeling activities. Land surface/ecosystem models have become important tools for extrapolating local observations and understanding to much larger terrestrial regions. They are also valuable tools to test hypothesis on ecosystem functioning. Funded by NASA under the auspices of the LBA (the Large-Scale Biosphere-Atmosphere Experiment in Amazonia), the LBA Data Model Intercomparison Project (LBA-DMIP) uses a comprehensive data set from an observational network of flux towers across the Amazon, and an ecosystem modeling community engaged in ongoing studies using a suite of different land surface and terrestrial ecosystem models to understand Amazon forest function. Here an overview of this project is presented accompanied by a description of the measurement sites, data, models and protocol. © 2013 Elsevier B.V.

Measurements of the isotopic composition of the net ecosystem–atmosphere exchange of CO2 (NEE) have been desired as a means to probe ecosystem carbon cycling and in particular to partition NEE into gross ecosystem photosynthesis and respiration. Several attempts at such measurements have combined eddy covariance (EC) measurements of the total net CO2 flux with flask measurements of the isotopic composition of CO2 in ambient air – an indirect method that has never been validated. Direct EC measurements of the isotopic composition of NEE (i.e. of the net exchanges of 12C16O2, 13C16O2, and 18O12C16O) have been made only twice, in short-term (2-month and 1-month) campaigns. Here we present a full growing season of direct EC measurements of the isotopic composition of NEE in a temperate deciduous forest, and we use these data: (1) to rigorously assess their limiting sources of error, (2) to test the indirect EC/flask method, and (3) to give an indication of the potential for ecological analyses. We describe the method and instrumentation, including the new cryogen-free, continuous-wave, quantum cascade laser spectrometer, which can determine δ13C and δ18O in ambient CO2 with unprecedented noise levels of ±0.02‰ and ±0.03‰ (1 standard deviation), respectively, for a 100 s integration time and a 1-h calibration interval. We find: (1) that precision is jointly limited by the instrumentation and by horizontal ecosystem or landscape heterogeneity, so that there is little to be gained by further improvements to instrument performance; (2) that the isotopic composition of NEE obtained by the EC/flask method can be biased, on a monthly timescale, by 2‰; and (3) that the present measurements are precise enough to elucidate biological mechanisms controlling the ecosystem-scale carbon balance.

The controls on uptake and release of CO 2 by tropical rainforests, and the responses to a changing climate, are major uncertainties in global climate change models. Eddy-covariance measurements potentially provide detailed data on CO 2 exchange and responses to the environment in these forests, but accurate estimates of the net ecosystem exchange of CO 2 (NEE) and ecosystem respiration ( R eco ) require careful analysis of data representativity, treatment of data gaps, and correction for systematic errors. This study uses the comprehensive data from our study site in an old-growth tropical rainforest near Santarem, Brazil, to examine the biases in NEE and R eco potentially associated with the two most important sources of systematic error in Eddy-covariance data: lost nighttime flux and missing canopy storage measurements. We present multiple estimates for the net carbon balance and R eco at our site, including the conventional “ u * filter”, a detailed bottom-up budget for respiration, estimates by similarity with 222 Rn, and an independent estimate of respiration by extrapolation of daytime Eddy flux data to zero light. Eddy-covariance measurements between 2002 and 2006 showed a mean net ecosystem carbon loss of 0.25 ± 0.04 μmol m −2 s −1 , with a mean respiration rate of 8.60 ± 0.11 μmol m −2 s −1 at our site. We found that lost nocturnal flux can potentially introduce significant bias into these results. We develop robust approaches to correct for these biases, showing that, where appropriate, a site-specific u * threshold can be used to avoid systematic bias in estimates of carbon exchange. Because of the presence of gaps in the data and the day–night asymmetry between storage and turbulence, inclusion of canopy storage is essential to accurate assessments of NEE. We found that short-term measurements of storage may be adequate to accurately model storage for use in obtaining ecosystem carbon balance, at sites where storage is not routinely measured. The analytical framework utilized in this study can be applied to other Eddy-covariance sites to help correct and validate measurements of the carbon cycle and its components.

The spatial and temporal dynamics of tropical forest functioning are poorly understood, partly attributed to a weak seasonality and high tree species diversity at the landscape scale. Recent neotropical rainforest studies with local tower flux measurements have revealed strong seasonal carbon fluxes that follow the availability of sunlight in intact forests, while in areas of forest disturbance, carbon fluxes more closely tracked seasonal water availability. These studies also showed a strong seasonal correspondence of satellite measures of greenness, using the Enhanced Vegetation Index (EVI) with ecosystem carbon fluxes in both intact and disturbed forests, which may enable larger scale extension of tower flux measurements. In this study, we investigated the seasonal patterns and relationships of local site tower flux measures of gross primary productivity (Pg) with independent Moderate Resolution Imaging Spectroradiometer (MODIS) satellite greenness measures across three Monsoon Asia tropical forest types, encompassing drought-deciduous, dry evergreen, and humid evergreen secondary tropical forests. In contrast to neotropical forests, the tropical forests of Monsoon Asia are more extensively degraded and heterogeneous due to intense land use pressures, and therefore, may exhibit unique seasonal patterns of ecosystem fluxes that are more likely water-limited and drought-susceptible. Our results show significant phenologic variability and response to moisture and light controls across the three tropical forest sites and at the regional scale. The drier tropical forests were primarily water-limited, while the wet evergreen secondary forest showed a slight positive trend with light availability. Satellite EVI greenness observations were generally synchronized and linearly related with seasonal and inter-annual tower flux Pg measurements at the multiple sites and provided better opportunities for tower extension of carbon fluxes than other satellite products, such as the MODIS Pg product. Satellite EVI-derived Pg images revealed strong seasonal variations in photosynthetic activity throughout the Monsoon Asia tropical region. © 2008 Elsevier B.V. All rights reserved.

Estimates of daily gross primary production (GPP) and annual net primary production (NPP) at the 1 km spatial resolution are now produced operationally for the global terrestrial surface using imagery from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. Ecosystem-level measurements of GPP at eddy covariance flux towers and plot-level measurements of NPP over the surrounding landscape offer opportunities for validating the MODIS NPP and GPP products, but these flux measurements must be scaled over areas on the order of 25 km 2 to make effective comparisons to the MODIS products. Here, we report results for such comparisons at 9 sites varying widely in biome type and land use. The sites included arctic tundra, boreal forest, temperate hardwood forest, temperate conifer forest, tropical rain forest, tallgrass prairie, desert grassland, and cropland. The ground-based NPP and GPP surfaces were generated by application of the Biome-BGC carbon cycle process model in a spatially-distributed mode. Model inputs of land cover and leaf area index were derived from Landsat data. The MODIS NPP and GPP products showed no overall bias. They tended to be overestimates at low productivity sites — often because of artificially high values of MODIS FPAR (fraction of photosynthetically active radiation absorbed by the canopy), a critical input to the MODIS GPP algorithm. In contrast, the MODIS products tended to be underestimates in high productivity sites — often a function of relatively low values for vegetation light use efficiency in the MODIS GPP algorithm. A global network of sites where both NPP and GPP are measured and scaled over the local landscape is needed to more comprehensively validate the MODIS NPP and GPP products and to potentially calibrate the MODIS NPP/GPP algorithm parameters.

AC O2 eddy flux tower study has recently reported that an old-growth stand of seasonally moist tropical evergreen forest in Santarem, Brazil, maintained high gross primary production (GPP) during the dry seasons (Saleska, S. R., Miller, S. D., Matross, D. M., Goulden, M. L., Wofsy, S. C., da Rocha, H. R., de Camargo, P. B., Crill, P., Daube, B. C., de Freitas, H. C., Hutyra, L., Keller, M., Kirchhoff, V., Menton, M., Munger, J. W., Pyle, E. H., Rice, A. H., & Silva, H. (2003). Carbon in amazon forests: Unexpected seasonal fluxes and disturbance- induced losses. Science, 302, 1554-1557). It was proposed that seasonally moist tropical evergreen forests have evolved two adaptive mechanisms in an environment with strong seasonal variations of light and water: deep roots system for access to water in deep soils and leaf phenology for access to light. Identifying tropical forests with these adaptive mechanisms could substantially improve our capacity of modeling the seasonal dynamics of carbon and water fluxes in the tropical zone. In this paper, we have analyzed multi-year satellite images from the VEGETATION (VGT) sensor onboard the SPOT-4 satellite (4/1998-12/2002) and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite (2000-2003). We reported temporal analyses of vegetation indices and simulations of the satellite-based vegetation photosynthesis model (VPM). The Enhanced Vegetation Index (EVI) identified subtle changes in the seasonal dynamics of leaf phenology (leaf emergence, leaf aging and leaf fall) in the forest, as suggested by the leaf litterfall data. The land surface water index (LSWI) indicated that the forest experienced no water stress in the dry seasons of 1998-2002. The VPM model, which uses EVI, LSWI and site-specific climate data (air temperature and photosynthetically active radiation, PAR) for 2001-2002, predicted high GPP in the late dry seasons, consistent with observed high evapotranspiration and estimated GPP from the CO2 eddy flux tower.

Obstructive Sleep Apnea (OSA) is a common condition that can lead to multiple cardiovascular complications. Minimal work has looked at the relationship between Congenital Heart Disease (CHD) and OSA. We sought to determine the prevalence, risk factors, and impact of OSA in the adult with CHD (ACHD

Scott Saleska explains why he and others think the US Environmental Protection Agency should fulfil its duty and regulate greenhouse gases