scholarly journals Allometric scaling of estuarine ecosystem metabolism

2018 ◽  
Vol 115 (26) ◽  
pp. 6733-6738 ◽  
Author(s):  
Nicholas J. Nidzieko
Keyword(s):  
Primary Production ◽  
Residence Time ◽  
Gross Primary Production ◽  
Coastal Ecosystems ◽  
Ecosystem Metabolism ◽  
Estuarine Ecosystem ◽  
Empirical Observation ◽  
Benthic Primary Production ◽  
Size Dependent ◽  
Wide Range

There are still significant uncertainties in the magnitude and direction of carbon fluxes through coastal ecosystems. An important component of these biogeochemical budgets is ecosystem metabolism, the net result of organismal metabolic processes within an ecosystem. In this paper, I present a synthesis of published ecosystem metabolism studies from coastal ecosystems and describe an empirical observation that size-dependent patterns in aquatic gross primary production and community respiration exist across a wide range of coastal geomorphologies. Ecosystem metabolism scales to the 3/4 power with volume in deeper estuaries dominated by pelagic primary production and nearly linearly with area in shallow estuaries dominated by benthic primary production. These results can be explained by applying scaling arguments for efficient, directed transport networks developed to explain similar size-dependent patterns in organismal metabolism. The main conclusion from this synthesis is that the residence time of new, nutrient-rich water is a fundamental organizing principle for the observed patterns. Residence time changes allometrically with size in pelagic ecosystems because velocities change by only an order of magnitude across systems that span more than ten orders of magnitude in size. This nonisometric change in velocity with size requires lower specific metabolic rates at larger ecosystem sizes. This change in transport may also explain a shift from predominantly net heterotrophy to net autotrophy with increasing size. The scaling results are applied to the total estuarine area in the continental United States to estimate the contribution of estuarine systems to the overall coastal budget of organic carbon.

Testing across vegetation types for common environmental dependencies of Gross Primary Production

2020 ◽  
Author(s):  
Keith Bloomfield ◽  
Benjamin Stocker ◽  
Colin Prentice
Keyword(s):  
Soil Moisture ◽  
Primary Production ◽  
Eddy Covariance ◽  
Empirical Analysis ◽  
Gross Primary Production ◽  
Incident Light ◽  
Vapour Pressure Deficit ◽  
Additive Models ◽  
Theoretical Modelling ◽  
Wide Range

<p>Accurate simulations of gross primary production (GPP) are vital for Earth System Models that must inform public policy decisions.  The instantaneous controls of leaf-level photosynthesis, which can be measured in manipulative experiments, are well established.  At the canopy scale, however, there is no consensus on how GPP depends on (a) light or (b) other aspects of the physical environment such as temperature and CO<sub>2</sub>.  Models of GPP make a variety of different assumptions when ‘scaling-up’ the standard model of photosynthesis.  As a troublesome consequence, they make a variety of different predictions about how GPP responds to contemporary environmental change.</p><p>This problem can be tackled by theoretically based modelling, or by empirical analysis of GPP as reconstructed from eddy-covariance flux measurements.  Theoretical modelling has provided an explanation for why ‘light-use efficiency’ (LUE) models work well at time scales of a week or longer.  The same logic provides a justification for the use of LUE as a key metric in an empirical analysis.  By focusing on LUE, we can isolate the controls of GPP that are distinct from its over-riding control by absorbed light.  We have used open-access eddy covariance data from over 100 sites, collated over 20 years (the number of sites has grown with time).  These sites, located in a wide range of biomes and climate zones, form part of the FLUXNET network.  We have combined the flux data with a satellite product (FPAR from MODIS) that provides spatial estimates of the fraction of incident light absorbed by green vegetation.  Soil moisture at flux sites was estimated using the SPLASH model, with appropriate meteorological inputs, and soil water-holding capacity derived using SoilGrids.  LUE was then calculated as the amount of carbon fixed per unit of absorbed light.  We then considered additive models (incorporating multiple explanatory factors) that support non-linear responses, including a peaked response to temperature.  Recognising that our longitudinal data are not fully independent, we controlled for the hierarchical nature of the dataset through a variance structure that nests measurement year within site location.</p><p>In arriving at a final parsimonious model, we show that daytime air temperature and vapour pressure deficit, and soil moisture content, are all salient predictors of LUE.  The same explanatory terms are retained in iterations of this analysis run at timescales from weeks to months.  Model performance was not significantly improved by inclusion of additional variables such as rainfall, site elevation or vegetation category (e.g. Plant Functional Type, PFT).  This empirical analysis supports the notion that GPP is predictable using a single model structure that is common to different PFTs.</p>

scholarly journals P-model v1.0: An optimality-based light use efficiency model for simulating ecosystem gross primary production

2019 ◽  
Author(s):  
Benjamin D. Stocker ◽  
Han Wang ◽  
Nicholas G. Smith ◽  
Sandy P. Harrison ◽  
Trevor F. Keenan ◽  
...  
Keyword(s):  
Primary Production ◽  
Satellite Data ◽  
Vegetation Type ◽  
Meteorological Data ◽  
Gross Primary Production ◽  
Satellite Monitoring ◽  
Light Use Efficiency ◽  
Spatial And Temporal Scales ◽  
Wide Range ◽  
Use Efficiency

Abstract. Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth System Model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a gross primary production (GPP, photosynthesis per unit ground area) model, the P-model, that combines the Farquhar-von Caemmerer-Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation-transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model is forced here with satellite data for the fraction of absorbed photosynthetically active radiation and site-specific meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs and prescribed parameters, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8-day mean, 131 sites) – better than some state-of-the-art satellite data-driven light use efficiency models. The R2 is reduced to 0.69 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.37 (means by site) and 0.53 (means by vegetation type). The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosythesis across a wide range of conditions. The model is available as an R package (rpmodel).

scholarly journals P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

2020 ◽  
Vol 13 (3) ◽  
pp. 1545-1581 ◽  
Author(s):  
Benjamin D. Stocker ◽  
Han Wang ◽  
Nicholas G. Smith ◽  
Sandy P. Harrison ◽  
Trevor F. Keenan ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Quantum Yield ◽  
Primary Production ◽  
Vegetation Type ◽  
Meteorological Data ◽  
Gross Primary Production ◽  
Satellite Monitoring ◽  
Light Use Efficiency ◽  
Wide Range ◽  
Use Efficiency

Abstract. Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr−1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).

scholarly journals Carbon allocation to biomass production of leaves, fruits and woody organs at seasonal and annual scale in a deciduous- and evergreen temperate forest

2010 ◽  
Vol 7 (5) ◽  
pp. 7575-7606 ◽  
Author(s):  
M. Campioli ◽  
B. Gielen ◽  
A. Granier ◽  
A. Verstraeten ◽  
J. Neirynck ◽  
...  
Keyword(s):  
Primary Production ◽  
Interannual Variability ◽  
Biomass Production ◽  
Residence Time ◽  
Forest Canopy ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Sink Strength ◽  
Early Season ◽  
Beech Stand

Abstract. Carbon taken up by the forest canopy is allocated to tree organs for biomass production and respiration. Because tree organs have different life span and decomposition rate, the tree C allocation determines the residence time of C in the ecosystem and its C cycling rate. The study of the carbon-use efficiency, or ratio between net primary production (NPP) and gross primary production (GPP), represents a convenient way to analyse the C allocation at the stand level. Previous studies mostly focused on comparison of the annual NPP-GPP ratio among forests of different functional types, biomes and age. In this study, we extend the current knowledge by assessing (i) the annual NPP-GPP ratio and its interannual variability (for five years) for five tree organs (leaves, fruits, branches, stem and coarse roots), and (ii) the seasonal dynamic of NPP-GPP ratio of leaves and stems, for two stands dominated by European beech and Scots pine. The average NPP-GPP ratio for the beech stand (38%) was similar to previous estimates for temperate deciduous forests, whereas the NPP-GPP ratio for the pine stand (17%) is the lowest recorded till now in the literature. The proportion of GPP allocated to leaf NPP was similar for both species, whereas beech allocated a remarkable larger proportion of GPP to wood NPP than pine (29% vs. 6%, respectively). The interannual variability of the NPP-GPP ratio for wood was substantially larger than the interannual variability of the NPP-GPP ratio for leaves, fruits and overall stand and it is likely to be controlled by previous year air temperature (both species), previous year drought intensity (beech) and thinning (pine). Seasonal pattern of NPP-GPP ratio greatly differed between beech and pine, with beech presenting the largest ratio in early season, and pine a more uniform ratio along the season. For beech, NPP-GPP ratio of leaves and stems peaked during the same period in the early season, whereas they peaked in opposite periods of the growing season for pine. Seasonal differences in C allocation are likely due to functional differences between deciduous and evergreen species and temporal variability of the sink strength. The similar GPP and autotrophic respiration between stands and the remarkable larger C allocation to wood at the beech stand indicate that at the beech ecosystem C has a longer residence time than at the pine ecosystem. Further research on belowground production and particularly on fine roots and ectomycorrhizal fungi likely represents the most important step to progress our knowledge on C allocation dynamics.

scholarly journals Spatiotemporal variability of light attenuation and net ecosystem metabolism in a back-barrier estuary

2018 ◽  
Author(s):  
Neil K. Ganju ◽  
Jeremy M. Testa ◽  
Steven E. Suttles ◽  
Alfredo L. Aretxabaleta
Keyword(s):  
Dissolved Oxygen ◽  
Primary Production ◽  
Chlorophyll A ◽  
Gross Primary Production ◽  
Light Attenuation ◽  
Ecosystem Metabolism ◽  
Spatiotemporal Variability ◽  
Net Ecosystem Metabolism ◽  
Enhanced Production ◽  
Strong Control

Abstract. The light climate in back-barrier estuaries is a strong control on phytoplankton and submerged aquatic vegetation (SAV) growth, and ultimately net ecosystem metabolism. However, quantifying the spatiotemporal variability of light attenuation and net ecosystem metabolism over seasonal timescales is difficult due to sampling limitations and dynamic physical and biogeochemical processes. Differences in the dominant primary producer at a given location (e.g., phytoplankton versus SAV) can also determine diel variations in dissolved oxygen and associated ecosystem metabolism. Over a one year period we measured hydrodynamic properties, biogeochemical variables (fDOM, turbidity, chlorophyll-a fluorescence, dissolved oxygen), and photosynthetically active radiation (PAR) at multiple locations in Chincoteague Bay, Maryland/Virginia, USA, a shallow back-barrier estuary. We quantified light attenuation, net ecosystem metabolism, and timescales of variability for several water properties at paired channel-shoal sites along the longitudinal axis of the bay. The channelized sites, which were dominated by fine bed sediment, exhibited slightly higher light attenuation due to increased wind-wave sediment resuspension. Light attenuation due to fDOM was slightly higher in the northern portion of the bay, while attenuation due to chlorophyll-a was only relevant at one channelized site, proximal to nutrient and freshwater loading. Gross primary production and respiration were highest at the vegetated shoal sites, though enhanced production and respiration were also observed at one channelized, nutrient-enriched site. Production and respiration were nearly balanced throughout the year at all sites, but there was a tendency for net autotrophy at shoal sites, especially during periods of high SAV biomass. Shoal sites, where SAV was present, demonstrated a reduction in gross primary production (GPP) when light attenuation was highest, but GPP at adjacent shoal sites where phytoplankton were dominant was less sensitive to light attenuation. This study demonstrates how extensive continuous physical and biological measurements can help determine metabolic properties in a shallow estuary, including differences in metabolism and oxygen variability between SAV and phytoplankton-dominated habitats.

Predictions of Gross Primary Production from a suite of Terrestrial Biome Models display divergent relationships with key environmental variables

2021 ◽  
Author(s):  
Keith Bloomfield ◽  
Benjamin Stocker ◽  
Trevor Keenan ◽  
Colin Prentice
Keyword(s):  
Soil Moisture ◽  
Primary Production ◽  
Eddy Covariance ◽  
Empirical Analysis ◽  
Gross Primary Production ◽  
Additive Models ◽  
Theoretical Modelling ◽  
Environmental Drivers ◽  
Model Structure ◽  
Wide Range

<p>Accurate simulations of gross primary production (GPP) are vital for Earth System Models that must inform public policy decisions.  The instantaneous controls of leaf-level photosynthesis, which can be measured in manipulative experiments, are well established.  At the canopy scale, however, there is no consensus on how GPP depends on (a) light or (b) other aspects of the physical environment such as temperature and CO<sub>2</sub>.  Models of GPP make a variety of different assumptions when ‘scaling-up’ the standard model of photosynthesis.  As a troublesome consequence, they make a variety of different predictions about how GPP responds to projected environmental change.</p><p>This problem can be tackled by theoretical modelling and by empirical analysis of GPP as reconstructed from eddy-covariance flux measurements.  Theoretical modelling has provided an explanation for why ‘light-use efficiency’ (LUE) models work well at time scales of a week or longer.  The same logic provides a justification for the use of LUE as a key metric in an empirical analysis.  By focusing on LUE, we can isolate the drivers of GPP independent of its over-riding control by absorbed light.  We have used open-access eddy covariance data from over 100 sites, collated over 20 years (the number of sites has grown with time).  These sites, located in a wide range of biomes and climate zones, form part of the FLUXNET network.  We have combined the flux data with a satellite product (EVI from MODIS) that allows spatial estimates of the fraction of incident light absorbed by green vegetation.  Matching soil moisture data were estimated using the SPLASH model, with appropriate meteorological inputs, and soil water-holding capacity derived using SoilGrids.  LUE was then calculated as the amount of carbon fixed per unit of absorbed light.  We then explored additive models (incorporating multiple explanatory factors) that support non-linear responses.  Recognising that our longitudinal data lack independence, we controlled for the hierarchical nature of the dataset through a variance structure that nests measurement year within site location.</p><p>In arriving at a preferred parsimonious model, we show that daytime air temperature and vapour pressure deficit, and soil moisture content are all salient predictors of LUE.  The same explanatory terms are retained in iterations of this analysis run at timescales from weeks to months.  As a model-comparison exercise, we used that portion of our dataset which overlaps the North American Carbon Program to apply our empirical model structure to site-based estimates of GPP generated by 19 discrete Terrestrial Biosphere Models (TBMs).  The comparative analysis reveals wide variation between the TBMs in the shape, strength and even sign of the environmental effects on modelled GPP.</p><p>This empirical analysis suggests it is feasible to predict GPP using a single model structure, common across vegetation categories.  And the appeal of such universal approaches is highlighted by inconsistent relationships with key environmental drivers within extant terrestrial models.</p>

scholarly journals Effects of Habitat-Specific Primary Production on Fish Size, Biomass, and Production in Northern Oligotrophic Lakes

Ecosystems ◽  
2022 ◽  
Author(s):  
Sven Norman ◽  
Karin A. Nilsson ◽  
Marcus Klaus ◽  
David Seekell ◽  
Jan Karlsson ◽  
...  
Keyword(s):  
Primary Production ◽  
Biomass Production ◽  
Brown Trout ◽  
Gross Primary Production ◽  
Maximum Size ◽  
Arctic Char ◽  
Relative Distribution ◽  
Oligotrophic Lakes ◽  
Benthic Primary Production ◽  
Fish Size

AbstractEcological theory predicts that the relative distribution of primary production across habitats influence fish size structure and biomass production. In this study, we assessed individual, population, and community-level consequences for brown trout (Salmo trutta) and Arctic char (Salvelinus alpinus) of variation in estimated habitat specific (benthic and pelagic) and total whole lake (GPPwhole) gross primary production in 27 northern oligotrophic lakes. We found that higher contribution of benthic primary production to GPPwhole was associated with higher community biomass and larger maximum and mean sizes of fish. At the population level, species-specific responses differed. Increased benthic primary production (GPPBenthic) correlated to higher population biomass of brown trout regardless of being alone or in sympatry, while Arctic char responded positively to pelagic primary production (GPPPelagic) in sympatric populations. In sympatric lakes, the maximum size of both species was positively related to both GPPBenthic and the benthic contribution to GPPWhole. In allopatric lakes, brown trout mean and maximum size and Arctic char mean size were positively related to the benthic proportion of GPPWhole. Our results highlight the importance of light-controlled benthic primary production for fish biomass production in oligotrophic northern lakes. Our results further suggest that consequences of ontogenetic asymmetry and niche shifts may cause the distribution of primary production across habitats to be more important than the total ecosystem primary production for fish size, population biomass, and production. Awareness of the relationships between light availability and asymmetric resource production favoring large fish and fish production may allow for cost-efficient and more informed management actions in northern oligotrophic lakes.

scholarly journals Do increasing respiratory costs explain the decline with age of forest growth rate?

2019 ◽  
Vol 31 (3) ◽  
pp. 693-712 ◽  
Author(s):  
P. W. West
Keyword(s):  
Growth Rate ◽  
Water Stress ◽  
Primary Production ◽  
Fine Roots ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Forest Growth ◽  
Mean Annual Temperature ◽  
Data Set ◽  
Wide Range

Abstract Once forests have achieved a full canopy, their growth rate declines progressively with age. This work used a global data set with estimates from a wide range of forest types, aged 20‒795 years, of their annual photosynthetic production (gross primary production, GPP) and subsequent above- plus below-ground biomass production (net primary production, NPP). Both GPP and NPP increased with increasing mean annual temperature and precipitation. GPP was then unrelated to forest age whilst NPP declined progressively with increasing age. These results implied that autotrophic respiration increases with age. It has been proposed that GPP should decline in response to increasing water stress in leaves as water is raised to greater heights as trees grow taller with age. However, trees may make substantial plastic adjustment in morphology and anatomy of newly developing leaves, xylem and fine roots to compensate for this stress and maintain GPP with age. This work reviews the possibilities that NPP declines with age as respiratory costs increase progressively in, any or all of, the construction and maintenance of more complex tissues, the maintenance of increasing amounts of live tissue within the sapwood of stems and coarse roots, the conversion of sapwood to heartwood, the increasing distance of phloem transport, increased turnover rates of fine roots, cost of supporting very tall trees that are unable to compensate fully for increased water stress in their canopies or maintaining alive competitively unsuccessful small trees.

scholarly journals A simplified gross primary production and evapotranspiration model for boreal coniferous forests – is a generic calibration sufficient?

2015 ◽  
Vol 8 (7) ◽  
pp. 5089-5137 ◽  
Author(s):  
F. Minunno ◽  
M. Peltoniemi ◽  
S. Launiainen ◽  
M. Aurela ◽  
A. Lindroth ◽  
...  
Keyword(s):  
Primary Production ◽  
Process Model ◽  
Gross Primary Production ◽  
Regional Scale ◽  
Model Complexity ◽  
Model Parameters ◽  
Water Fluxes ◽  
Site Specific ◽  
Bayesian Model Comparison ◽  
Wide Range

Abstract. The problem of model complexity has been lively debated in environmental sciences as well as in the forest modelling community. Simple models are less input demanding and their calibration involves a lower number of parameters, but they might be suitable only at local scale. In this work we calibrated a simplified ecosystem process model (PRELES) to data from multiple sites and we tested if PRELES can be used at regional scale to estimate the carbon and water fluxes of Boreal conifer forests. We compared a multi-site (M-S) with site-specific (S-S) calibrations. Model calibrations and evaluations were carried out by the means of the Bayesian method; Bayesian calibration (BC) and Bayesian model comparison (BMC) were used to quantify the uncertainty in model parameters and model structure. To evaluate model performances BMC results were combined with more classical analysis of model-data mismatch (M-DM). Evapotranspiration (ET) and gross primary production (GPP) measurements collected in 10 sites of Finland and Sweden were used in the study. Calibration results showed that similar estimates were obtained for the parameters at which model outputs are most sensitive. No significant differences were encountered in the predictions of the multi-site and site-specific versions of PRELES with exception of a site with agricultural history (Alkkia). Although PRELES predicted GPP better than evapotranspiration, we concluded that the model can be reliably used at regional scale to simulate carbon and water fluxes of Boreal forests. Our analyses underlined also the importance of using long and carefully collected flux datasets in model calibration. In fact, even a single site can provide model calibrations that can be applied at a wider spatial scale, since it covers a wide range of variability in climatic conditions.

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