Estimates Of Respiration Research Articles

Community gross primary production and respiration in epilithic macroalgae and Posidonia oceanica macrophytodetritus accumulation in the Bay of Revellata (Corsica)

We report estimates of community gross primary production (GPP), community respiration (CR), and net community production (NCP) based on the change of dissolved O2 during incubations over epilithic turf-forming macroalgae (Halopteris scoparia, Padina pavonica, and Dictyota dichotoma) on 7 occasions and in accumulations of Posidonia oceanica macrophytodetritus (i.e. litter) on 8 occasions in the Bay of Revellata (Corsica) from March 2009 to May 2011. In the epilithic macroalgae community, GPP ranged between 7.8 and 82.2 mmol O2 m−2 d−1, CR ranged between −108.5 and −13.6 mmol O2 m−2 d−1, and NCP ranged between −53.2 and −5.7 mmol O2 m−2 d−1. In the P. oceanica macrophytodetritus accumulation, GPP ranged between 5.7 and 91.6 mmol O2 m−2 d−1, CR ranged between −112.8 and −27.2 mmol O2 m−2 d−1, and NCP ranged between −46.8 and −9.9 mmol O2 m−2 d−1. GPP in both the epilithic macroalgae community and the P. oceanica macrophytodetritus accumulation peaked in summer and was lowest in fall, following the seasonal variation of incoming light. GPP correlated to macroalgal biomass but was unrelated to the biomass of living macroscopic plant material in the P. oceanica macrophytodetritus accumulation. The annual average of GPP was equivalent in the epilithic macroalgae and P. oceanica macrophytodetritus accumulation communities (17.6 and 19.4mol O2 m−2 yr−1). Both the epilithic macroalgae community and the P. oceanica macrophytodetritus accumulation were net heterotrophic with an annual average NCP of −6.1 and −8.8mol O2 m−2 yr−1, respectively. The NCP of the adjacent P. oceanica meadow at 10 m depth based on simultaneous measurements based on the open water O2 mass balance from moored O2 probes (optodes) was 28.9mol O2 m−2 yr−1. The potential export of dissolved organic carbon from the P. oceanica meadow could quantitatively meet the carbon demand to sustain the net heterotrophy of the adjacent epilithic macroalgae community in the Bay of Revellata. We also show the limitation and possibly over-estimation of extrapolating decay rates based on litter bag experiments with small quantities of material to “real” macrophytodetritus biomass densities.

Global soil respiration estimation based on ecological big data and machine learning model

Soil respiration (Rs) represents the greatest carbon dioxide flux from terrestrial ecosystems to the atmosphere. However, its environmental drivers are not fully understood, and there are still significant uncertainties in soil respiration model estimates. This study aimed to estimate the spatial distribution pattern and driving mechanism of global soil respiration by constructing a machine learning model method based on ecological big data. First, we constructed ecological big data containing five categories of 27-dimensional environmental factors. We then used four typical machine learning methods to develop the performance of machine learning models under four training strategies and explored the relationship between soil respiration and environmental factors. Finally, we used the RF machine learning algorithm to estimate the global Rs spatial distribution pattern in 2021, driven by multiple dimensions of environmental factors, and derived the annual soil respiration values. The results showed that RF performed better under the four training strategies, with a coefficient of determination R2 = 0.78216, root mean squared error (RMSE) = 285.8964 gCm−2y−1, and mean absolute error (MAE) = 180.4186 gCm−2y−1, which was more suitable for the estimation of large-scale soil respiration. In terms of the importance of environmental factors, unlike previous studies, we found that the influence of geographical location was greater than that of MAP. Another new finding was that enhanced vegetation index 2 (EVI2) had a higher contribution to soil respiration estimates than the enhanced vegetation index (EVI) and normalized vegetation index (NDVI). Our results confirm the potential of utilizing ecological big data for spatially large-scale Rs estimations. Ecological big data and machine learning algorithms can be considered to improve the spatial distribution patterns and driver analysis of Rs.

Video-Based Multiphysiological Disentanglement and Remote Robust Estimation for Respiration.

Remote noncontact respiratory rate estimation by facial visual information has great research significance, providing valuable priors for health monitoring, clinical diagnosis, and anti-fraud. However, existing studies suffer from disturbances in epidermal specular reflections induced by head movements and facial expressions. Furthermore, diffuse reflections of light in the skin-colored subcutaneous tissue caused by multiple time-varying physiological signals independent of breathing are entangled with the intention of the respiratory process, leading to confusion in current research. To address these issues, this article proposes a novel network for natural light video-based remote respiration estimation. Specifically, our model consists of a two-stage architecture that progressively implements vital measurements. The first stage adopts an encoder-decoder structure to recharacterize the facial motion frame differences of the input video based on the gradient binary state of the respiratory signal during inspiration and expiration. Then, the obtained generative mapping, which is disentangled from various time-varying interferences and is only linearly related to the respiratory state, is combined with the facial appearance in the second stage. To further improve the robustness of our algorithm, we design a targeted long-term temporal attention module and embed it between the two stages to enhance the network's ability to model the breathing cycle that occupies ultra many frames and to mine hidden timing change clues. We train and validate the proposed network on a series of publicly available respiration estimation datasets, and the experimental results demonstrate its competitiveness against the state-of-the-art breathing and physiological prediction frameworks.

Estimation of ecosystem respiration and photosynthesis in supersaturated stream water downstream of a hydropower plant

The estimation of whole stream metabolism, as determined by photosynthesis and respiration, is critical to our understanding of carbon cycling and carbon subsidies to aquatic food-webs. The mass development of aquatic plants is a worldwide problem for human activities and often occurs in regulated rivers, altering biodiversity and ecosystem functions. Hydropower plants supersaturate water with gases and prevent the use of common whole stream metabolism models to estimate ecosystem respiration. Here we used the inert noble gas argon to parse out biological from physical processes in stream metabolism calculations. We coupled the O2:Ar ratio determined by gas chromatography in grab samples with in-situ oxygen concentrations measured by an optode to estimate aquatic plant photosynthesis and ecosystem respiration during supersaturation events through a parsimonious approach. The results compared well with a more complicated two-station model based on O2 mass balances in non-supersatured water, and with associated changes in dissolved CO2 (or dissolved inorganic carbon). This new method provides an independent approach to evaluate alternative corrections of dissolved oxygen data (e.g. through the use of total dissolved gases) in long term studies. The use of photosynthesis-irradiance models allows the determination of light parameters such as the onset of light saturation or low light use efficiency, which could be used for inverse modelling. The use of the O2:Ar approach to correct for oversaturation may become more applicable with the emergence of portable mass inlet mass spectrometers (MIMS). Photosynthesis was modest (2.9-5.8 g O2 m2 day−1) compared to other rivers with submerged vegetation, likely indicating nutrient co-limitations (CO2, inorganic N and P). Respiration was very low (-2.1 to -3.9 g O2 m2 day−1) likely due to a lack of allochthonous carbon supply and sandy sediment.

Empirical upscaling of OzFlux eddy covariance for high-resolution monitoring of terrestrial carbon uptake in Australia

Abstract. We develop high-resolution (1 km) estimates of gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE) over the Australian continent for the period January 2003 to June 2022 by empirical upscaling of flux tower measurements. We compare our estimates with nine other products that cover the three broad categories that define current methods for estimating the terrestrial carbon cycle and assess if consiliences between datasets can point to the correct dynamics of Australia's carbon cycle. Our results indicate that regional empirical upscaling greatly improves upon the existing global empirical upscaling efforts, outperforms process-based models, and agrees much better with the dynamics of CO2 flux over Australia as estimated by two regional atmospheric inversions. Our nearly 20-year estimates of terrestrial carbon fluxes revealed that Australia is a strong net carbon sink of −0.44 PgC yr−1 (interquartile range, IQR = 0.42 PgC yr−1) on average, with an inter-annual variability of 0.18 PgC yr−1 and an average seasonal amplitude of 0.85 PgC yr−1. Annual mean carbon uptake estimated from other methods ranged considerably, while carbon flux anomalies showed much better agreement between methods. NEE anomalies were predominately driven by cumulative rainfall deficits and surpluses, resulting in larger anomalous responses from GPP than ER. In contrast, we show that the long-term average seasonal cycle is dictated more by the variability in ER than GPP, resulting in peak carbon uptake typically occurring during the cooler, drier austral autumn and winter months. This new estimate of Australia's terrestrial carbon cycle provides a benchmark for assessment against land surface model simulations and a means for monitoring of Australia's terrestrial carbon cycle at an unprecedented high resolution. We call this new estimate of Australia's terrestrial carbon cycle “AusEFlux” (Australian Empirical Fluxes).

Soil respiration estimation in desertified mining areas based on UAV remote sensing and machine learning

Soil respiration estimation in desertified mining areas based on UAV remote sensing and machine learning

Satellite-based land surface temperature and soil moisture observations accurately predict soil respiration in temperate deciduous and coniferous forests

Soil respiration, defined as the total flux of carbon dioxide (CO2) from the soil to the atmosphere, is a key ecosystem process that affects the regional and global carbon (C) cycles and is highly sensitive to temperature and soil moisture. It is challenging to quantify soil respiration at the ecosystem level from commonly used in-situ soil chamber measurements because of large spatial variability. Methods that provide temporally and spatially continuous estimates of soil respiration at various scales are vital to understand the impact of climate change on soil C stock. In this study, we evaluate three commonly used empirical models and a Random Forest machine learning algorithm applied to satellite derived estimates of land surface temperature (LST) and soil moisture to estimate soil respiration in temperate deciduous and coniferous forests in Canada. The models were calibrated using in-situ soil temperature and moisture and validated against in-situ measurements of soil CO2 fluxes (gCm−2day−1) from automatic soil chambers. We separately evaluate the performance of nighttime and daytime satellite-based LST and soil moisture observations in modeling soil respiration. The soil respiration models were also evaluated at daily and monthly time scales against in-situ measurements. Results indicate that models based on satellite LST, and soil moisture can explain more than 70% of the variability in observed soil respiration. Nighttime LST at a monthly time scale resulted in consistently higher accuracy than daytime LST in estimating soil respiration. Satellite observations resulted in comparable accuracy in estimating soil respiration as in-situ measurements. Satellite LST and soil moisture observations are indispensable data sources to estimate soil respiration at ecosystem level and its upscaling to regional and global scales.

Improved process representation of leaf phenology significantly shifts climate sensitivity of ecosystem carbon balance

Abstract. Terrestrial carbon cycle models are routinely used to determine the response of the land carbon sink under expected future climate change, yet these predictions remain highly uncertain. Increasing the realism of processes in these models may help with predictive skill, but any such addition should be confronted with observations and evaluated in the context of the aggregate behavior of the carbon cycle. Here, two formulations for leaf area index (LAI) phenology are coupled to the same terrestrial biosphere model: one is climate agnostic, and the other incorporates direct environmental controls on both timing and growth. Each model is calibrated simultaneously to observations of LAI, net ecosystem exchange (NEE), and biomass using the CARbon DAta-MOdel fraMework (CARDAMOM) and validated against withheld data, including eddy covariance estimates of gross primary productivity (GPP) and ecosystem respiration (Re) across six ecosystems from the tropics to high latitudes. Both model formulations show similar predictive skill for LAI and NEE. However, with the addition of direct environmental controls on LAI, the integrated model explains 22 % more variability in GPP and Re and reduces biases in these fluxes by 58 % and 77 %, respectively, while also predicting more realistic annual litterfall rates due to changes in carbon allocation and turnover. We extend this analysis to evaluate the inferred climate sensitivity of LAI and NEE with the new model and show that the added complexity shifts the sign, magnitude, and seasonality of NEE sensitivity to precipitation and temperature. This highlights the benefit of process complexity when inferring underlying processes from Earth observations and representing the climate response of the terrestrial carbon cycle.

Spatial patterns of China's carbon sinks estimated from the fusion of remote sensing and field-observed net primary productivity and heterotrophic respiration

Accurately assessing the carbon sink and spatial distribution pattern of China's terrestrial ecosystems is of great significance to the implementation of climate change and carbon neutrality strategy. However, the views of various studies are still very controversial due to the differences in carbon sink estimation methods and data sources. In this study, vegetation net primary productivity (NPP) and ecosystem heterotrophic respiration (Rh) estimation models were constructed based on machine learning methods by fusing multisource data, such as remote sensing and ground observation data. The magnitude and spatial pattern of carbon sink in China from 2000 to 2018 were then revealed, and the carbon sink capacity of various ecosystems was quantitatively assessed. The main conclusions include the following: (1) The use of scale-matched carbon input and output data can help reduce the system error in carbon sink estimation. (2) China's terrestrial ecosystem carbon sink since the twenty-first century is approximately 0.458 Pg C/yr, which is equivalent to 22.72% of China's anthropogenic carbon emissions. (3) Deciduous forest has a higher carbon sink capacity than evergreen forest, while coniferous forest has a more stable carbon sink capacity than broad-leaved forest. The magnitude and spatial distribution of carbon sink in China reported in this study provides a scientific reference for achieving carbon neutrality and sustainable development.

Extensive prokaryotic maintenance respiration in the sea influenced by osmoregulation

Microbial respiration is the major process consuming oxygen in the biosphere. The relative energy demand from growth of biomass or maintenance activities determines the regulation of respiration with impact on how the development of hypoxia and CO2 emissions is controlled. This coupling is crucial for understanding the life history and associated ecological interactions of microorganisms. However, the knowledge of rate and regulating factors of maintenance respiration in the biosphere is limited. In this study, we demonstrated significant relationships in marine field samples where the prokaryotic specific growth rate predicts cell-specific respiration, in accordance with theory from culture models, over a 10-fold salinity range. This enables the first reported direct estimates of maintenance respiration in nature to show a 6-fold variation between 0.12-0.62 fmol O2 cell-1 d-1, comprising 29-72% of prokaryotic specific respiration. The lowest maintenance respiration occurred at salinity close to physiological osmolarity, suggesting osmoregulation as one of the more energy-consuming maintenance activities. A conservative global estimate of maintenance respiration accounted for 66% of the total prokaryotic respiration in the ocean´s mixed layer. This means that maintenance activities dominate the use of the energy generated by prokaryotic respiration in the sea, where osmoregulation is one significant energy consumer. Consequently, maintenance respiration and its regulation must be included in ecological and biogeochemical models to accurately project and manage the development of hypoxia and CO2 emissions from the ocean.

Spatial variation in forest soil respiration: A systematic review of field observations at the global scale

As it is responsible for the second largest CO2 flux in the terrestrial ecosystem, the accurate estimation and prediction of soil respiration (SR) are necessary, especially for forest ecosystems, which are a major contributor to the total terrestrial SR. Spatial variation is one of the challenges affecting the accurate estimation and prediction of forest SR in ecosystems. Although a number of studies have examined spatial variation in SR within individual forests, the magnitude and patterns of spatial variation in SR within forest ecosystems (CV of SR [%]) remain unexplored at the global scale. In this study, we collected 94 field observation studies to demonstrate the range and pattern of the CV of SR, and to clarify the controlling factors. Through our analysis, the CV of SR was found to range from 1.8 % to 89.3 % on the global scale; it was highest in the equatorial zone (39.0 % ± 13.8 %) and followed by the warm temperate zone (32.6 ± 14.5 %) and the snow zone (30.0 % ± 16.3 %). There was a significant negative correlation between the CV of SR and soil water content, bulk density, fine root biomass, and elevation at both the global scale and in each climatic zone (P < 0.01). Other factors such as total nitrogen content, mean of diameter at breast height, slope, etc., were also significantly correlated with the CV of SR, but the correlation was different among climatic zones. This study provides an overall perspective of the CV of SR by clarifying the range, patterns, and controlling factors at both the global scale and in each climatic zone. However, further research is needed, especially regarding the mechanisms between the CV of SR and its controlling factors.

Improving the spatial and temporal estimation of ecosystem respiration using multi-source data and machine learning methods in a rainfed winter wheat cropland

The investigation of ecosystem respiration (RE) and its vital influential factors along with the timely and accurate detection of spatiotemporal variations in RE are essential for guiding agricultural production planning. RE observation in the plot region is primarily based on the laborious chamber method. However, upscaling the spatial-temporal estimates of RE at the canopy scale is still challenging. The present study conducted a field experiment to determine RE using the chamber method. A multi-rotor unmanned aerial vehicle (UAV) equipped with a multispectral camera was employed to acquire the canopy spectral data of wheat during each RE test experiment. Moreover, the agronomic indicators of aboveground plant biomass, leaf area index, leaf dry mass as well as agrometeorological and soil data were measured simultaneously. The study analyzed the potential of multi-information for estimating RE at the field scale and proposed two strategies for RE estimation. In addition, a semiempirical, yet Lloyd and Taylor-based, remote sensing model (LT1-NIRV) was developed for estimating RE observed across different growth stages with a small margin of error (coefficient of determination [R2] = 0.60–0.64, root-mean-square error [RMSE] = 285.98–316.19 mg m−2 h−1). Further, five machine learning (ML) algorithms were utilized to independently estimate RE using two different datasets. The rigorous analyses, which included statistical comparison and cross-validation for estimating RE, confirmed that the XGBoost model, with the highest R2 and lowest RMSE (R2 = 0.88 and RMSE = 172.70 mg m−2 h−1), performed the best among the evaluated ML models. The LT1-NIRV model was less effective in estimating RE compared with the other ML models. Based on this comprehensive comparison analysis, the ML model can successfully estimate variations in wheat field RE using high-resolution UAV multispectral images and environmental factors from the wheat cropland system, thereby providing a valuable reference for monitoring and upscaling RE observations.

The ratio of electron transport to assimilation (ETR/AN): underutilized but essential for assessing both equipment's proper performance and plant status.

ETR/AN ratios should be in the range 7.5-10.5 for non-stressed C3 plants. Ratios extremely out of this range can be reflecting both uncontrolled plant status and technical mistakes during measurements. We urge users to explicitly refer to this ratio in future studies as a proof for internal data quality control. For the last few decades, the use of infra-red gas-exchange analysers (IRGAs) coupled with chlorophyll fluorometers that allow for measurements of net CO2 assimilation rate and estimates of electron transport rate over the same leaf area has been popularized. The evaluation of data from both instruments in an integrative manner can result in additional valuable information, such as the estimation of the light respiration, mesophyll conductance and the partitioning of the flux of electrons into carboxylation, oxygenation and alternative processes, among others. In this review, an additional and more 'straight' use of the combination of chlorophyll fluorescence and gas exchange-derived parameters is presented, namely using the direct ratio between two fully independently estimated parameters, electron transport rate (ETR)-determined by the fluorometer-and net CO2 assimilation rate (AN)-determined by the IRGA, i.e., the ETR/AN ratio, as a tool for fast detection of incongruencies in the data and potential technical problems associated with them, while checking for the study plant's status. To illustrate this application, a compilation of 75 studies that reported both parameters for a total of 178 species under varying physiological status is presented. Values of ETR/AN between 7.5 and 10.5 were most frequently found for non-stressed C3 plants. C4 species showed an average ETR/AN ratio of 4.7. The observed ratios were larger for species with high leaf mass per area and for plants subjected to stressful factors like drought or nutritional deficit. Knowing the expected ETR/AN ratio projects this ratio as a routinary and rapid check point for guaranteeing both the correct performance of equipment and the optimal/stress status of studied plants. All known errors associated with the under- or overestimation of ETR or AN are summarized in a checklist that aims to be routinely used by any IRGA/fluorometer user to strength the validity of their data.

A review of zooplankton and deep carbon fixation contributions to carbon cycling in the dark ocean

Models of the marine carbon cycle assume that virtually all heterotrophic production in the open ocean is derived from near-surface carbon fixation (primary production) by phytoplankton. However, current carbon budget estimates show that respiration throughout the ocean far exceeds surface primary production. This disconnect can be grouped into two categories: Inaccurate estimates of water column respiration and carbon transport from metazoans; and missing primary production sources and. heterotrophic processing in the dark ocean. In this review, we examine the contribution to the ocean carbon cycle of chemoautotrophic production, as well as secondary production and respiration from meso-zooplankton and micro-nekton below 400 m depth. About one-third of epipelagic biomass in the ocean migrates diurnally, distributing dissolved organic carbon (DOC) and total nitrogen (TN), along with about 30–80% of the particulate organic carbon (POC) flux, from the upper ocean. Although mostly this occurs in the upper 400 m, migration depths can extend to 3000 m. In addition, up to 80% of the biomass of secondary consumers in the open ocean live part of their life cycle at depths up to 2000 m, contributing significantly to deep-sea respiration and particle flux, particularly over fall/winter in temperate-subarctic oceans, submarine canyons, and deep seas such as the Mediterranean. This active flux provides fresh organic input to the deep ocean at a time of year when surface primary productivity, and thus organic carbon (OC) flux to the deep ocean, is low. The complex spatial, temporal and depth scales of horizontal and vertical migration make modelling of the global oceanic carbon cycle extremely complex, requiring consideration of biomass movements throughout the entire water column over diurnal, lunar and seasonal cycles over broad geographic regions. An additional 10 to 50% of surface primary production occurs within mid-depth oxygen minimum zones (OMZs), fuelled by ammonia excreted from vertically migrating zooplankton concentrated near OMZ boundaries. Crustal sources such as gas and methane seeps, hydrothermal vents and submarine volcanoes support active deep-sea food webs, as well as contributing to upper ocean productivity. Crustal sources are conservatively estimated to provide >30%, and probably up to 50%, of oceanic OC flux to the dark ocean. These estimates are still poorly constrained but can no longer be ignored in global oceanic carbon cycles.

Eddy-enhanced primary production sustains heterotrophic microbial activities in the Eastern Tropical North Atlantic

Abstract. Mesoscale eddies modulate the ocean's physical, chemical, and biological properties. In cyclonic eddies (CEs), nutrient upwelling can stimulate primary production by phytoplankton. Yet, how this locally enhanced autotrophic production affects heterotrophy and consequently the metabolic balance between the synthesis and the consumption of dissolved organic matter (DOM) remains largely unknown. To fill this gap, we investigated the horizontal and vertical variability in auto- and heterotrophic microbial activity (biomass production and respiration) within a CE that formed off Mauritania and along the ∼ 900 km zonal corridor between Mauritania and the Cape Verde islands in the Eastern Tropical North Atlantic (ETNA). Our results show how the physical disturbances caused by the CE affected the biomass distribution of phyto- and bacterioplankton and their metabolic activities. The injection of nutrients into the sunlit surface resulted in enhanced autotrophic pico- and nanoplankton abundance and generally increased autotrophic activity as indicated by chlorophyll a (Chl a) concentration, primary production (PP), and extracellular release rates. However, the detailed eddy survey also revealed an uneven distribution of these variables with, for example, the highest Chl a concentrations and PP rates occurring near and just beyond the CE's periphery. The heterotrophic bacterial activity was similarly variable. Optode-based community respiration (CR), bacterial respiration (BR) estimates, and bacterial biomass production (BP) largely followed the trends of PP and Chl a. Thus, a submesoscale spatial mosaic of heterotrophic bacterial abundance and activities occurred within the CE that was closely related to variability in autotrophic production. Consistent with this, we found a significant positive correlation between concentrations of semi-labile dissolved organic carbon (SL-DOC; here the sum of dissolved hydrolysable amino acids and dissolved combined carbohydrates) and BR estimates. Extracellular release of carbon as indicated by primary production of dissolved organic carbon (PPDOC) was variable with depth and laterally and not always sufficient to compensate the bacterial carbon demand (BCD: BR + BP), with PPDOC accounting for between 28 % and 110 % of the BCD. Bacterial growth efficiency (BGE: BP / BCD) ranged between 1.7 % and 18.2 %. We estimated the metabolic state to establish whether the CE was a source or a sink of organic carbon. We showed that the CE carried a strong autotrophic signal in the core (PP / CR &gt; 1). Our results suggest that submesoscale (0–10 km) processes lead to highly variable metabolic activities in both photoautotrophic and heterotrophic microorganisms. Overall, we revealed that the CEs not only trap and transport coastal nutrients and organic carbon to the open ocean but also stimulate phytoplankton growth, generating freshly produced organic matter during westward propagation. This drives heterotrophic processes and may contribute to the previously observed net heterotrophy in open Atlantic surface waters.

Variation in seagrass meadow respiration measured by aquatic eddy covariance

AbstractAccurate daily metabolic estimates of respiration, gross primary production, and net ecosystem metabolism are necessary to assess ecosystem health and blue carbon contributions of vegetated coastal ecosystems. Using our database of 2115 hourly benthic oxygen (O2) fluxes measured by aquatic eddy covariance, we examine how respiration for a Zostera marina seagrass meadow varies through night and day, and how this affects commonly performed metabolic estimates. Respiration decreased linearly by 29% through the night and a corresponding linear increase in daytime respiration coupled with production described by a standard photosynthesis–irradiance curve accurately predicted measured daytime O2 fluxes. Many studies have questioned the widely used assumption in metabolic estimates that nighttime and daytime respiration are constant and equal. However, if respiration can be approximated as we found here by linear relationships, standard means for calculating daily metabolic numbers remain valid if estimates are based on full 24‐h records of flux data.

A High Accuracy & Ultra-Low Power ECG-Derived Respiration Estimation Processor for Wearable Respiration Monitoring Sensor.

The respiratory rate is widely used for evaluating a person’s health condition. Compared to other invasive and expensive methods, the ECG-derived respiration estimation is a more comfortable and affordable method to obtain the respiration rate. However, the existing ECG-derived respiration estimation methods suffer from low accuracy or high computational complexity. In this work, a high accuracy and ultra-low power ECG-derived respiration estimation processor has been proposed. Several techniques have been proposed to improve the accuracy and reduce the computational complexity (and thus power consumption), including QRS detection using refractory period refreshing and adaptive threshold EDR estimation. Implemented and fabricated using a 55 nm processing technology, the proposed processor achieves a low EDR estimation error of 0.73 on CEBS database and 1.2 on MIT-BIH Polysomnographic Database while demonstrating a record-low power consumption (354 nW) for the respiration monitoring, outperforming the existing designs. The proposed processor can be integrated in a wearable sensor for ultra-low power and high accuracy respiration monitoring.

Multitask Siamese Network for Remote Photoplethysmography and Respiration Estimation.

Heart and respiration rates represent important vital signs for the assessment of a person’s health condition. To estimate these vital signs accurately, we propose a multitask Siamese network model (MTS) that combines the advantages of the Siamese network and the multitask learning architecture. The MTS model was trained by the images of the cheek including nose and mouth and forehead areas while sharing the same parameters between the Siamese networks, in order to extract the features about the heart and respiratory information. The proposed model was constructed with a small number of parameters and was able to yield a high vital-sign-prediction accuracy, comparable to that obtained from the single-task learning model; furthermore, the proposed model outperformed the conventional multitask learning model. As a result, we can simultaneously predict the heart and respiratory signals with the MTS model, while the number of parameters was reduced by 16 times with the mean average errors of heart and respiration rates being 2.84 and 4.21. Owing to its light weight, it would be advantageous to implement the vital-sign-monitoring model in an edge device such as a mobile phone or small-sized portable devices.

Nitrogen concentration and physical properties are key drivers of woody tissue respiration.

Despite the critical role of woody tissues in determining net carbon exchange of terrestrial ecosystems, relatively little is known regarding the drivers of sapwood and bark respiration. Using one of the most comprehensive wood respiration datasets to date (82 species from Australian rainforest, savanna and temperate forest), we quantified relationships between tissue respiration rates (Rd) measured in vitro (i.e. 'respiration potential') and physical properties of bark and sapwood, and nitrogen concentration (Nmass) of leaves, sapwood and bark. Across all sites, tissue density and thickness explained similar, and in some cases more, variation in bark and sapwood Rd than did Nmass. Higher density bark and sapwood tissues had lower Rd for a given Nmass than lower density tissues. Rd-Nmass slopes were less steep in thicker compared with thinner-barked species and less steep in sapwood than in bark. Including the interactive effects of Nmass, density and thickness significantly increased the explanatory power for bark and sapwood respiration in branches. Among these models, Nmass contributed more to explanatory power in trunks than in branches, and in sapwood than in bark. Our findings were largely consistent across sites, which varied in their climate, soils and dominant vegetation type, suggesting generality in the observed trait relationships. Compared with a global compilation of leaf, stem and root data, Australian species showed generally lower Rd and Nmass, and less steep Rd-Nmass relationships. To the best of our knowledge, this is the first study to report control of respiration-nitrogen relationships by physical properties of tissues, and one of few to report respiration-nitrogen relationships in bark and sapwood. Together, our findings indicate a potential path towards improving current estimates of autotrophic respiration by integrating variation across distinct plant tissues.

Changes in water column oxygen, estimates of productivity and the development of anoxia in a major embayment of the southern Benguela eastern boundary upwelling system

The metabolic balance of autotrophy and heterotrophy in St Helena Bay was investigated through estimates of net community production (NCP) and respiration (R), as determined by changes in water column oxygen. Compared to adjacent coastal waters St Helena Bay was shown to be an area of low-enrichment and high-retention. Productivity during the upwelling season was found to be nutrient limited in the upper few metres (0–3 m) and light limited through self-shading below these surface waters. During the upwelling season the maintenance of thermal stratification in the bay leads to consistently low nutrients (nitrate x¯ = 1.41 mmol m−3) in the surface waters (0–3 m). High biomass maintained through mechanisms of cyclonic retention and entrainment from the Namaqua shelf underlies the rapid attenuation of light through the water column and the consequent limitation of subsurface productivity. As a result productivity maxima were located in surface waters (0 or 3 m) irrespective of the low nutrient concentrations in these waters and the presence of subsurface biomass maxima. Estimates of gross community production (GCP) normalised to Chl a therefore declined sharply below 3 m depth. The mean community compensation depth (Zcc), where R = GCP, was determined to be 9.16 m and was shallower than the 1% light depth for PAR (Z1%PAR) at 15.42 m. The role of light in limiting productivity was clearly evident in that 48% of water column biomass was located below the Zcc. The importance of the light environment in controlling the seasonality of productivity was further evident in that daily rates of productivity in surface waters normalised to Chl a were clearly linked to day length. Phytoplankton community composition and successional status were also shown to influence productivity with the contribution of R to GCP increasing with the succession of phytoplankton communities. Estimates of mean water column Chl a (8.60 mg m−3 as determined from discrete water samples and 8.40 mg m−3 as estimated from profiles of fluorescence) and mean integrated water column productivity (5.38 g C m−2 d−1 as determined by the light-and-dark bottle oxygen method) were ~ 1.5 fold higher than past measurements most of which were made during the late 1970s and 80s. Oxygen depletion within St Helena Bay is locally enhanced as a result of the entrainment and retention of biomass in St Helena Bay and causes it to function as an area of higher carbon sequestration.