Ecosystem-scale Models Research Articles

Salinity exposure affects lower-canopy specific leaf area of upland trees in a coastal deciduous forest

Sea level rise and increasing storm surges are likely to affect the canopy physiology, ecology, and structure of coastal forests, even well in advance of tree mortality. Laboratory and greenhouse studies have documented that saltwater exposure can trigger changes in leaf-level physiology and morphology, but few in situ studies have examined how tree-specific leaf area (SLA), the ratio of leaf area to mass and a crucial trait and model parameter, is affected by saline soils. We conducted an observational study of SLA in a mid-Atlantic (USA) coastal deciduous forest, taking advantage of a natural gradient in salinity along a tidal creek. Measured SLA of the 239 trees and seven species sampled ranged from Carya glabra (N = 6 trees, mean SLA = 277.9 ± 36.3 cm2/g) to Fagus grandifolia (N = 60, 321.9 ± 62.9 cm2/g); as expected, trees species and canopy position (sun versus shade) significantly affected SLA. For trees (N = 100) directly exposed to the tidal creek, salinity was highly significant after accounting for species (P < 0.001), with trees in the lower reaches of the creek having lower SLA. Leaf area index (LAI), computed from SLA and litter traps, ranged from 4.8 to 15.8 and was inversely related to salinity exposure; the spatial variability in leaf litter production contributed much more to LAI uncertainty than did SLA variability. These in situ results are correlative but consistent with the hypothesis, based on previous greenhouse studies, that the stress of chronic salinity exposure changes species’ leaf morphology. Our findings are useful for understanding the growing effects of saltwater intrusion into upland forests, as well as parameterizing and testing ecosystem-scale models simulating forest stressors and disturbances at the terrestrial-aquatic interface.

Ecosystem-scale modelling of soil carbon dynamics: Time for a radical shift of perspective?

Over the last few years, several researchers working on the development of “biogeochemical” or “ecosystem-scale” models of soil carbon dynamics have reported struggling with a number of difficult challenges. At the same time, work in this area has focused exclusively on microbial activity described at a macro-ecological level, and has entirely bypassed the abundant literature produced in the last two decades on the study of soil processes at the microscale. Juxtaposition of these different observations suggests that a radical shift of perspective is in order. In this general context, the present article carries out an in-depth analysis of several of the key limitations of current ecosystem-scale models and recommends a number of steps to shift the perspective to one that is argued to have a better chance of success in the relatively short time we have to address several pressing soil-related environmental problems. These steps, in particular, require the development of large-spatial-scale models of soil carbon dynamics to be far more interdisciplinary than it has been till now, and to adopt a “bottom-up” approach, building on what the research at the microscale reveals about soil processes. Nevertheless, because it may assist in upscaling efforts, it is argued that some room should be preserved for work to continue on the search for empirical models applicable at large spatial scales.

A standardized quantitative analysis strategy for stable isotope probing metagenomics.

Stable isotope probing (SIP) facilitates culture-independent identification of active microbial populations within complex ecosystems through isotopic enrichment of nucleic acids. Many DNA-SIP studies rely on 16S rRNA gene sequences to identify active taxa, but connecting these sequences to specific bacterial genomes is often challenging. Here, we describe a standardized laboratory and analysis framework to quantify isotopic enrichment on a per-genome basis using shotgun metagenomics instead of 16S rRNA gene sequencing. To develop this framework, we explored various sample processing and analysis approaches using a designed microbiome where the identity of labeled genomes and their level of isotopic enrichment were experimentally controlled. With this ground truth dataset, we empirically assessed the accuracy of different analytical models for identifying active taxa and examined how sequencing depth impacts the detection of isotopically labeled genomes. We also demonstrate that using synthetic DNA internal standards to measure absolute genome abundances in SIP density fractions improves estimates of isotopic enrichment. In addition, our study illustrates the utility of internal standards to reveal anomalies in sample handling that could negatively impact SIP metagenomic analyses if left undetected. Finally, we present SIPmg, an R package to facilitate the estimation of absolute abundances and perform statistical analyses for identifying labeled genomes within SIP metagenomic data. This experimentally validated analysis framework strengthens the foundation of DNA-SIP metagenomics as a tool for accurately measuring the in situ activity of environmental microbial populations and assessing their genomic potential. IMPORTANCE Answering the questions, "who is eating what?" and "who is active?" within complex microbial communities is paramount for our ability to model, predict, and modulate microbiomes for improved human and planetary health. These questions can be pursued using stable isotope probing to track the incorporation of labeled compounds into cellular DNA during microbial growth. However, with traditional stable isotope methods, it is challenging to establish links between an active microorganism's taxonomic identity and genome composition while providing quantitative estimates of the microorganism's isotope incorporation rate. Here, we report an experimental and analytical workflow that lays the foundation for improved detection of metabolically active microorganisms and better quantitative estimates of genome-resolved isotope incorporation, which can be used to further refine ecosystem-scale models for carbon and nutrient fluxes within microbiomes.

Drought imprints on crops can reduce yield loss: Nature's insights for food security.

The Midwestern “Corn‐Belt” in the United States is the most productive agricultural region on the planet despite being predominantly rainfed. In this region, global climate change is driving precipitation patterns toward wetter springs and drier mid‐ to late‐summers, a trend that is likely to intensify in the future. The lack of precipitation can lead to crop water limitations that ultimately impact growth and yields. Young plants exposed to water stress will often invest more resources into their root systems, possibly priming the crop for any subsequent mid‐ or late‐season drought. The trend toward wetter springs, however, suggests that opportunities for crop priming may lessen in the future. Here, we test the hypothesis that early season dry conditions lead to drought priming in field‐grown crops and this response will protect crops against growth and yield losses from late‐season droughts. This hypothesis was tested for the two major Midwestern crop, maize and soybean, using high‐resolution daily weather data, satellite‐derived phenological metrics, field yield data, and ecosystem‐scale model (Agricultural Production System Simulator) simulations. The results from this study showed that priming mitigated yield losses from a late season drought of up to 4.0% and 7.0% for maize and soybean compared with unprimed crops experiencing a late season drought. These results suggest that if the trend toward wet springs with drier summers continues, the relative impact of droughts on crop productivity is likely to worsen. Alternatively, identifying opportunities to breed or genetically modify pre‐primed crop species may provide improved resilience to future climate change.

Parsimony vs predictive and functional performance of three stomatal optimization principles in a big‐leaf framework

Stomatal optimization models can improve estimates of water and carbon fluxes with relatively low complexity, yet there is no consensus on which formulations are most appropriate for ecosystem-scale applications. We implemented three existing analytical equations for stomatal conductance, based on different water penalty functions, in a big-leaf comparison framework, and determined which optimization principles were most consistent with flux tower observations from different biomes. We used information theory to dissect controls of soil water supply and atmospheric demand on evapotranspiration in wet to dry conditions and to quantify missing or inadequate information in model variants. We ranked stomatal optimization principles based on parameter uncertainty, parsimony, predictive accuracy, and functional accuracy of the interactions between soil moisture, vapor pressure deficit, and evapotranspiration. Performance was high for all model variants. Water penalty functions with explicit representation of plant hydraulics did not substantially improve predictive or functional accuracy of ecosystem-scale evapotranspiration estimates, and parameterizations were more uncertain, despite having physiological underpinnings at the plant level. Stomatal optimization based on water use efficiency thus provided more information about ecosystem-scale evapotranspiration compared to those based on xylem vulnerability and proved more useful in improving ecosystem-scale models with less complexity.

KEYLINK: towards a more integrative soil representation for inclusion in ecosystem scale models. I. review and model concept.

The relatively poor simulation of the below-ground processes is a severe drawback for many ecosystem models, especially when predicting responses to climate change and management. For a meaningful estimation of ecosystem production and the cycling of water, energy, nutrients and carbon, the integration of soil processes and the exchanges at the surface is crucial. It is increasingly recognized that soil biota play an important role in soil organic carbon and nutrient cycling, shaping soil structure and hydrological properties through their activity, and in water and nutrient uptake by plants through mycorrhizal processes. In this article, we review the main soil biological actors (microbiota, fauna and roots) and their effects on soil functioning. We review to what extent they have been included in soil models and propose which of them could be included in ecosystem models. We show that the model representation of the soil food web, the impact of soil ecosystem engineers on soil structure and the related effects on hydrology and soil organic matter (SOM) stabilization are key issues in improving ecosystem-scale soil representation in models. Finally, we describe a new core model concept (KEYLINK) that integrates insights from SOM models, structural models and food web models to simulate the living soil at an ecosystem scale.

Understanding the drivers of mortality in African savannah elephants.

Populations of African savannah elephants (Loxodonta africana) have been declining due to poaching, human-elephant conflict, and habitat loss. Understanding the causes of these declines could aid in stabilizing elephant populations. We used data from the Great Elephant Census, a 19-country aerial survey of savannah elephants conducted in 2014 and 2015, to examine effects of a suite of variables on elephant mortality. Independent variables included spatially explicit measures of natural processes and human presence as well as country-level socioeconomic measures. Our dependent variable was the carcass ratio, the ratio of dead elephants to live plus dead elephants, which is an index of recent elephant mortality. Carcass ratios are inversely proportional to population growth rates of elephants over the 4 yr prior to a survey. At the scale of survey strata (n=275, median area=1,222 km2 ), we found strong negative associations for carcass ratios with vegetation greenness at the time of the survey, overseas development aid to the country, and distance to the nearest international border. At the scale of ecosystems (n=42, median area=12,085 km2 ), carcass ratios increased with drought frequency and decreased with human density and overseas development aid to the country. Both stratum- and ecosystem-scale models explained well under one-half of the variance in carcass ratios. The differences in results between scales suggest that the drivers of mortality may be scale-specific and that the corresponding solutions may vary by scale as well.

Ecosystem-Scale Modeling and Field Observations of Sulfate and Methylmercury Distributions in the Florida Everglades: Responses to Reductions in Sulfate Loading

The Florida Everglades has one of the most severe methylmercury (MeHg) contamination issues in the USA, resulting from factors including high rates of atmospheric mercury (Hg) deposition and sulfate inputs from agricultural lands. Sulfate loading stimulates microbial sulfate reduction and production of toxic and bioaccumulative MeHg. Controls on regional Hg emissions have been successful in reducing Hg deposition and MeHg production in wetlands in other areas, but this has not been the case for the Everglades as the Hg deposited here appears to come from unknown global sources of emissions. We posit that reductions in sulfate loading to the Florida Everglades can be an effective alternative approach used to reduce MeHg production. This study tested this hypothesis (1) by evaluating temporal trends in MeHg concentrations in response to a reduction in sulfate loading at a site in central Water Conservation Area (WCA) 3 and (2) using ecosystem-scale models to predict the effects of reductions in sulfate loading on sulfate concentrations in surface water and MeHg Risk. At the WCA site, we report a decline in sulfate concentrations (from about 9 mg/L in the late 1990s to levels of 33% in sulfate loading will significantly benefit the Everglades by reducing MeHg Risk.

Role of Shellfish Aquaculture in the Reduction of Eutrophication in an Urban Estuary.

Land-based management has reduced nutrient discharges; however, many coastal waterbodies remain impaired. Oyster "bioextraction" of nutrients and how oyster aquaculture might complement existing management measures in urban estuaries was examined in Long Island Sound, Connecticut. Eutrophication status, nutrient removal, and ecosystem service values were estimated using eutrophication, circulation, local- and ecosystem-scale models, and an avoided-costs valuation. System-scale modeling estimated that 1.31% and 2.68% of incoming nutrients could be removed by current and expanded production, respectively. Up-scaled local-scale results were similar to system-scale results, suggesting that this up-scaling method could be useful in bodies of water without circulation models. The value of removed nitrogen was estimated using alternative management costs (e.g., wastewater treatment) as representative, showing ecosystem service values of $8.5 and $470 million per year for current and maximum expanded production, respectively. These estimates are conservative; removal by clams in Connecticut, oysters and clams in New York, and denitrification are not included. Optimistically, the calculation of oyster-associated removal from all leases in both states (5% of bottom area) plus denitrification losses showed increases to 10%-30% of annual inputs, which would be higher if clams were included. Results are specific to Long Island Sound, but the approach is transferable to other urban estuaries.

Beyond microbes: Are fauna the next frontier in soil biogeochemical models?

The explicit representation of microbial communities in soil biogeochemical models is improving their projections, promoting new interdisciplinary research, and stimulating novel theoretical developments. However, microbes are the foundation of complicated soil food webs, with highly intricate and non-linear interactions among trophic groups regulating soil biogeochemical cycles. This food web includes fauna, which influence litter decomposition and the structure and activity of the microbial community. Given the early success of microbial-explicit models, should we also consider explicitly representing faunal activity and physiology in soil biogeochemistry models? Here we explore this question, arguing that the direct effects of fauna on litter decomposition are stronger than on soil organic matter dynamics, and that fauna can have strong indirect effects on soil biogeochemical cycles by influencing microbial population dynamics, but the direction and magnitude of these effects remains too unpredictable for models used to predict global biogeochemical patterns. Given glaring gaps in our understanding of fauna-microbe interactions and how these might play out along climatic and land use gradients, we believe it remains early to explicitly represent fauna in these global-scale models. However, their incorporation into models used for conceptual exploration of food-web interactions or into ecosystem-scale models using site-specific data could provide rich theoretical breakthroughs and provide a starting point for improving model projections across scales.

Model behavior of arbuscular mycorrhizal fungi: predicting soil carbon dynamics under climate change

In this commentary, I advocate for more detailed incorporation of arbuscular mycorrhizal (AM) fungi in Earth system models, to improve our projections of global climate change. Current Earth system models display relatively low predictability of soil C stocks, which limit our ability to estimate future climate conditions. A more explicit incorporation of microbial mechanisms can increase the accuracy of ecosystem-scale models that inform the larger-scale Earth system models. Of the numerous microbial groups that can influence soil C dynamics, AM fungi are particularly tractable for integration in models. Arbuscular mycorrhizal fungi are globally abundant and perform critical roles in C cycling, such as augmentation of net primary productivity and soil C storage. Moreover, AM communities exhibit relatively low diversity within ecosystems, compared with other microbial groups. In addition, global datasets of AM ecology are available for use in model development. Thus, AM communities can be readily simulated in next-generation trait-based models that link microbial diversity to ecosystem function. Altogether, we are well-poised to incorporate the dynamics of individual AM taxa in ecosystem models, which can then be coupled to Earth system models. Hopefully, these efforts would advance our ability to predict and plan for future climate change.

Pteropods on the edge: Cumulative effects of ocean acidification, warming, and deoxygenation

We review the state of knowledge of the individual and community responses of euthecosome (shelled) pteropods in the context of global environmental change. In particular, we focus on their responses to ocean acidification, in combination with ocean warming and ocean deoxygenation, as inferred from a growing body of empirical literature, and their relatively nascent place in ecosystem-scale models. Our objectives are: (1) to summarize the threats that these stressors pose to pteropod populations; (2) to demonstrate that pteropods are strong candidate indicators for cumulative effects of OA, warming, and deoxygenation in marine ecosystems; and (3) to provide insight on incorporating pteropods into population and ecosystem models, which will help inform ecosystem-based management of marine resources under future environmental regimes.

Modeling food web structure and selenium biomagnification in Lake Macquarie, New South Wales, Australia, using stable carbon and nitrogen isotopes.

As a consequence of coal-fired power station operations, elevated selenium concentrations have been reported in the sediments and biota of Lake Macquarie (New South Wales, Australia). In the present study, an ecosystem-scale model has been applied to determine how selenium in a seagrass food web is processed from sediments and water through diet to predators, using stable isotopes (δ(13) C and δ(15) N) to establish the trophic position of organisms. Trophic position, habitat, and feeding zone were examined as possible factors influencing selenium bioaccumulation. Selenium concentrations ranged from 0.2 μg/g dry weight in macroalgae species to 12.9 μg/g in the carnivorous fish Gerres subfasciatus. A mean magnification factor of 1.39 per trophic level showed that selenium is biomagnifying in the seagrass food web. Habitat and feeding zone influenced selenium concentrations in invertebrates, whereas feeding zone was the only significant factor influencing selenium concentrations in fish. The sediment-water partitioning coefficient (Kd ) of 4180 showed that partitioning of selenium entering the lake to particulate organic material (POM) is occurring, and consequently availability to food webs from POM is high. Trophic transfer factors (invertebrate = 1.9; fish = 1.2) were similar to those reported for other water bodies, showing that input source is not the main determinant of the magnitude of selenium bioaccumulation in a food web, but rather the initial partitioning of selenium into bioavailable POM. Environ Toxicol Chem 2015;34:608-617. © 2014 SETAC.

Ozone vegetation damage effects on gross primary productivity in the United States

Abstract. We apply an off-line process-based vegetation model (the Yale Interactive Terrestrial Biosphere model) to assess the impacts of ozone (O3) vegetation damage on gross primary productivity (GPP) in the United States during the past decade (1998–2007). The model's GPP simulation is evaluated at 40 sites of the North American Carbon Program (NACP) synthesis. The ecosystem-scale model version reproduces interannual variability and seasonality of GPP at most sites, especially in croplands. Inclusion of the O3 damage impact decreases biases of simulated GPP at most of the NACP sites. The simulation with the O3 damage effect reproduces 64% of the observed variance in summer GPP and 42% on the annual average. Based on a regional gridded simulation over the US, summertime average O3-free GPP is 6.1 g C m−2 day−1 (9.5 g C m−2 day−1 in the east of 95° W and 3.9 g C m−2 day−1 in the west). O3 damage decreases GPP by 4–8% on average in the eastern US and leads to significant decreases of 11–17% in east coast hot spots. Sensitivity simulations show that a 25% decrease in surface O3 concentration halves the average GPP damage to only 2–4%, suggesting the substantial co-benefits to ecosystem health that may be achieved via O3 air pollution control.

Ecosystem‐level controls on root‐rhizosphere respiration

Recent advances in the partitioning of autotrophic from heterotrophic respiration processes in soils in conjunction with new high temporal resolution soil respiration data sets offer insights into biotic and environmental controls of respiration. Besides temperature, many emerging controlling factors have not yet been incorporated into ecosystem-scale models. We synthesize recent research that has partitioned soil respiration into its process components to evaluate effects of nitrogen, temperature and photosynthesis on autotrophic flux from soils at the ecosystem level. Despite the widely used temperature dependence of root respiration, gross primary productivity (GPP) can explain most patterns of ecosystem root respiration (and to some extent heterotrophic respiration) at within-season time-scales. Specifically, heterotrophi crespiration is influenced by a seasonally variable supply of recent photosynthetic products in the rhizosphere. The contribution of stored root carbon (C) to root respiratory fluxes also varied seasonally, partially decoupling the proportion of photosynthetic C driving root respiration. In order to reflect recent insights, new hierarchical models, which incorporate root respiration as a primary function of GPP and which respond to environmental variables by modifying Callocation belowground, are needed for better prediction of future ecosystem C sequestration.

Anthropogenic N Deposition Increases Soil C Storage by Decreasing the Extent of Litter Decay: Analysis of Field Observations with an Ecosystem Model

Recent meta-analyses of experimental studies simulating increased anthropogenic nitrogen (N) deposition in forests reveal greater soil carbon (C) storage under elevated levels of atmospheric N deposition. However, these effects have not yet been included in ecosystem-scale models of soil C and N cycling and it is unclear whether increased soil C storage results from slower decomposition rates or a reduced extent of decomposition (for example, an increase in the amount of litter entering slowly decaying humus pools). To test these alternatives, we conducted a meta-analysis of litter decomposition data. We then used the results from our meta-analysis to model C and N cycling in four sugar maple forests in Michigan using an ecosystem process model (TRACE). We compared model results testing our alternative hypotheses to field data on soil C storage from a 17-year N deposition experiment. Using data from published litter decomposition studies in forests, we determined that, on average, exogenous N inputs decreased lignin decomposition rates by 30% and increased cellulose decomposition by 9%. In the same set of litter decomposition studies increased exogenous N availability increased the amount of litter entering slowly decaying humus pools in a manner significantly related to the lignocellulose index of decaying litter. Incorporating changes to decomposition rates in TRACE did not accurately reproduce greater soil C storage observed in our field study with experimentally elevated N deposition. However, when changes in the extent of decomposition were incorporated in TRACE, the model produced increased soil C storage by increasing the amount of litter entering the humus pool and accurately represented C storage in plant and soil pools under experimental N deposition. Our modeling results and meta-analysis indicate that the extent of litter decay as humus is formed, rather than slower rates of litter decay, is likely responsible for the accumulation of organic matter, and hence soil C storage, under experimental N deposition. This effect should be incorporated in regional to global-scale models simulating the C balance of forest ecosystems in regions receiving elevated N deposition.

An ecosystem-scale model for the spread of a host-specific forest pathogen in the Greater Yellowstone Ecosystem

The introduction of nonnative pathogens is altering the scale, magnitude, and persistence of forest disturbance regimes in the western United States. In the high-altitude whitebark pine (Pinus albicaulis) forests of the Greater Yellowstone Ecosystem (GYE), white pine blister rust (Cronartium ribicola) is an introduced fungal pathogen that is now the principal cause of tree mortality in many locations. Although blister rust eradication has failed in the past, there is nonetheless substantial interest in monitoring the disease and its rate of progression in order to predict the future impact of forest disturbances within this critical ecosystem. This study integrates data from five different field-monitoring campaigns from 1968 to 2008 to create a blister rust infection model for sites located throughout the GYE. Our model parameterizes the past rates of blister rust spread in order to project its future impact on high-altitude whitebark pine forests. Because the process of blister rust infection and mortality of individuals occurs over the time frame of many years, the model in this paper operates on a yearly time step and defines a series of whitebark pine infection classes: susceptible, slightly infected, moderately infected, and dead. In our analysis, we evaluate four different infection models that compare local vs. global density dependence on the dynamics of blister rust infection. We compare models in which blister rust infection is: (1) independent of the density of infected trees, (2) locally density-dependent, (3) locally density-dependent with a static global infection rate among all sites, and (4) both locally and globally density-dependent. Model evaluation through the predictive loss criterion for Bayesian analysis supports the model that is both locally and globally density-dependent. Using this best-fit model, we predicted the average residence times for the four stages of blister rust infection in our model, and we found that, on average, whitebark pine trees within the GYE remain susceptible for 6.7 years, take 10.9 years to transition from slightly infected to moderately infected, and take 9.4 years to transition from moderately infected to dead. Using our best-fit model, we project the future levels of blister rust infestation in the GYE at critical sites over the next 20 years.

A methodology for ecosystem‐scale modeling of selenium

The main route of exposure for selenium (Se) is dietary, yet regulations lack biologically based protocols for evaluations of risk. We propose here an ecosystem-scale model that conceptualizes and quantifies the variables that determine how Se is processed from water through diet to predators. This approach uses biogeochemical and physiological factors from laboratory and field studies and considers loading, speciation, transformation to particulate material, bioavailability, bioaccumulation in invertebrates, and trophic transfer to predators. Validation of the model is through data sets from 29 historic and recent field case studies of Se-exposed sites. The model links Se concentrations across media (water, particulate, tissue of different food web species). It can be used to forecast toxicity under different management or regulatory proposals or as a methodology for translating a fish-tissue (or other predator tissue) Se concentration guideline to a dissolved Se concentration. The model illustrates some critical aspects of implementing a tissue criterion: 1) the choice of fish species determines the food web through which Se should be modeled, 2) the choice of food web is critical because the particulate material to prey kinetics of bioaccumulation differs widely among invertebrates, 3) the characterization of the type and phase of particulate material is important to quantifying Se exposure to prey through the base of the food web, and 4) the metric describing partitioning between particulate material and dissolved Se concentrations allows determination of a site-specific dissolved Se concentration that would be responsible for that fish body burden in the specific environment. The linked approach illustrates that environmentally safe dissolved Se concentrations will differ among ecosystems depending on the ecological pathways and biogeochemical conditions in that system. Uncertainties and model sensitivities can be directly illustrated by varying exposure scenarios based on site-specific knowledge. The model can also be used to facilitate site-specific regulation and to present generic comparisons to illustrate limitations imposed by ecosystem setting and inhabitants. Used optimally, the model provides a tool for framing a site-specific ecological problem or occurrence of Se exposure, quantify exposure within that ecosystem, and narrow uncertainties about how to protect it by understanding the specifics of the underlying system ecology, biogeochemistry, and hydrology.

Modelling environmental controls on ecosystem photosynthesis and the carbon isotope composition of ecosystem‐respired CO2 in a coastal Douglas‐fir forest

We developed and applied an ecosystem-scale model that calculated leaf CO2 assimilation, stomatal conductance, chloroplast CO2 concentration and the carbon isotope composition of carbohydrate formed during photosynthesis separately for sunlit and shaded leaves within multiple canopy layers. The ecosystem photosynthesis model was validated by comparison to leaf-level gas exchange measurements and estimates of ecosystem-scale photosynthesis from eddy covariance measurements made in a coastal Douglas-fir forest on Vancouver Island. A good agreement was also observed between modelled and measured delta13C values of ecosystem-respired CO2 (deltaR). The modelled deltaR values showed strong responses to variation in photosynthetic photon flux density (PPFD), air temperature, vapour pressure deficit (VPD) and available soil moisture in a manner consistent with leaf-level studies of photosynthetic 13C discrimination. Sensitivity tests were conducted to evaluate the effect of (1) changes in the lag between the time of CO2 fixation and the conversion of organic matter back to CO2; (2) shifts in the proportion of autotrophic and heterotrophic respiration; (3) isotope fractionation during respiration; and (4) environmentally induced changes in mesophyll conductance, on modelled delta(R) values. Our results indicated that deltaR is a good proxy for canopy-level C(c)/C(a) and 13C discrimination during photosynthetic gas exchange, and therefore has several applications in ecosystem physiology.

Carbon-controlled nitrogen cycling in a marine 'macrocosm'- an ecosystem-scale model for managing cultural eutrophication

Carbon-controlled nitrogen cycling in a marine 'macrocosm'- an ecosystem-scale model for managing cultural eutrophication