Mixed grasslands in south Canada serve a variety of economic, environmental and ecological purposes. Numerical modeling has become a major method used to identify potential grassland ecosystem responses to environment changes and human activities. In recent years, the focus has been on process models because of their high accuracy and ability to describe the interactions among different environmental components and the ecological processes. At present, two commonly-used process models (CENTURY and BIOME-BGC) have significantly improved our understanding of the possible consequences and responses of terrestrial ecosystems under different environmental conditions. However, problems with these models include only using site-based parameters and adopting different assumptions on interactions between plant, environmental conditions and human activities in simulating such complex phenomenon. In light of this shortfall, the overall objective of this research is to integrate remote sensing products into ecosystem process model in order to simulate productivity for the mixed grassland ecosystem in the landscape level. Data used includes 4-years of field measurements and diverse satellite data (System Pour lObservation de la Terre (SPOT) 4 and 5, Landsat TM and ETM, Advanced Very High Resolution Radiometer (AVHRR) imagery). <p>Using wavelet analyses, the study first detects that the dominant spatial scale is controlled by topography and thus determines that 20-30 m is the optimum resolution to capture the vegetation spatial variation for the study area. Second, the performance of the RDVI (Renormalized Difference Vegetation Index), ATSAVI (Adjusted Transformed Soil-Adjusted Vegetation Index), and MCARI2 (Modified Chlorophyll Absorption Ratio Index 2) are slightly better than the other VIs in the groups of ratio-based, soil-line-related, and chlorophyll-corrected VIs, respectively. By incorporating CAI (Cellulose Absorption Index) as a litter factor in ATSAVI, a new VI is developed (L-ATSAVI) and it improves LAI estimation capability by about 10%. Third, vegetation maps are derived from a SPOT 4 image based on the significant relationship between LAI and ATSAVI to aid spatial modeling. Fourth, object-oriented classifier is determined as the best approach, providing ecosystem models with an accurate land cover map. Fifth, the phenology parameters are identified for the study area using 22-year AVHRR data, providing the input variables for spatial modeling. Finally, the performance of popular ecosystem models in simulating grassland vegetation productivity is evaluated using site-based field data, AVHRR NDVI data, and climate data. A new model frame, which integrates remote sensing data with site-based BIOME-BGC model, is developed for the mixed grassland prairie. The developed remote sensing-based process model is able to simulate ecosystem processes at the landscape level and can simulate productivity distribution with 71% accuracy for 2005.

Mixed grasslands in south Canada serve a variety of economic, environmental and ecological purposes. Numerical modeling has become a major method used to identify potential grassland ecosystem responses to environment changes and human activities. In recent years, the focus has been on process models because of their high accuracy and ability to describe the interactions among different environmental components and the ecological processes. At present, two commonly-used process models (CENTURY and BIOME-BGC) have significantly improved our understanding of the possible consequences and responses of terrestrial ecosystems under different environmental conditions. However, problems with these models include only using site-based parameters and adopting different assumptions on interactions between plant, environmental conditions and human activities in simulating such complex phenomenon. In light of this shortfall, the overall objective of this research is to integrate remote sensing products into ecosystem process model in order to simulate productivity for the mixed grassland ecosystem in the landscape level. Data used includes 4-years of field measurements and diverse satellite data (System Pour lObservation de la Terre (SPOT) 4 and 5, Landsat TM and ETM, Advanced Very High Resolution Radiometer (AVHRR) imagery). <p>Using wavelet analyses, the study first detects that the dominant spatial scale is controlled by topography and thus determines that 20-30 m is the optimum resolution to capture the vegetation spatial variation for the study area. Second, the performance of the RDVI (Renormalized Difference Vegetation Index), ATSAVI (Adjusted Transformed Soil-Adjusted Vegetation Index), and MCARI2 (Modified Chlorophyll Absorption Ratio Index 2) are slightly better than the other VIs in the groups of ratio-based, soil-line-related, and chlorophyll-corrected VIs, respectively. By incorporating CAI (Cellulose Absorption Index) as a litter factor in ATSAVI, a new VI is developed (L-ATSAVI) and it improves LAI estimation capability by about 10%. Third, vegetation maps are derived from a SPOT 4 image based on the significant relationship between LAI and ATSAVI to aid spatial modeling. Fourth, object-oriented classifier is determined as the best approach, providing ecosystem models with an accurate land cover map. Fifth, the phenology parameters are identified for the study area using 22-year AVHRR data, providing the input variables for spatial modeling. Finally, the performance of popular ecosystem models in simulating grassland vegetation productivity is evaluated using site-based field data, AVHRR NDVI data, and climate data. A new model frame, which integrates remote sensing data with site-based BIOME-BGC model, is developed for the mixed grassland prairie. The developed remote sensing-based process model is able to simulate ecosystem processes at the landscape level and can simulate productivity distribution with 71% accuracy for 2005.

Unicellular, eukaryotic organisms - known as protists - form the base of all aquatic food webs. Frequently, marine protists are divided into either phytoplankton or (proto)zooplankton. Phytoplankton use phototrophy to acquire their energy from light to fix carbon dioxide into organic carbon, while protozooplankton use phagotrophy to directly acquire organic carbon from their prey. Mixoplankton that employ mixotrophy, i.e. the combination of phototrophy and phagotrophy within one cell, are often neglected. However, many marine protists are mixoplankton and they are ubiquitous in the worlds’ oceans. In oligotrophic oceans, mixoplankton are the base of food webs and many harmful algal blooms are formed by mixoplankton. Yet, the concept of mixoplankton is slow to mature within coastal water management. This thesis hypothesizes that the whole protist community, including mixoplankton, needs to be taken into account to understand and predict the effect of anthropogenic pressures on coastal systems. This thesis is a cumulative summary of three papers that employ data analysis, model developments and modelling scenarios to test this hypothesis. As a study area the Southern North Sea was chosen as it is an exceptionally well sampled coastal sea that is forecast to be heavily modified in the future. In a first step, routine monitoring data from the Southern North Sea were analyzed. The data analysis showed that the relative occurrence of mixoplankton was highest in seasonally stratified, clear, dissolved inorganic nutrient depleted environments. In a second step, a mathematical model, called PROTIST, was developed with the aim to reproduce the trophic composition of protist communities across abiotic gradients. Not only was PROTIST capable of reproducing the trophic composition of protist communities in the Southern North Sea, a sensitivity analysis conducted on the model results also showed that the occurrence of mixoplankton in the Southern North Sea is driven mainly by the availability of dissolved inorganic phosphate and silica and not by the availability of light. In a third step, PROTIST was used in a 3D model scenario of the North Sea to research whether the planned intensification of seaweed aquaculture affects the composition of protist communities. Preliminary 3Dmodel results show that seaweed aquaculture in the Southern North Sea could decrease nutrient concentrations in winter and lead to an increase in mixoplankton biomass. Pooling the information gained from the different approaches, this thesis concludes that coastal zone management should take mixoplankton into account to understand and predict the effect of future anthropogenic pressures on coastalecosystems. / Doctorat en Sciences agronomiques et ingénierie biologique / info:eu-repo/semantics/nonPublished

The Amazon is the largest tropical rainforest in the world, and thus plays a major role on global water, energy, and carbon cycles. However, it is still unknown how the Amazon forest will respond to the ongoing changes in climate, especially droughts, which are expected to become more frequent. To help answering this question, in this thesis I developed and improved the representation of biophysical processes and photosynthesis in the Ecosystem Demography model (ED-2.2), an individual-based land ecosystem model. I also evaluated the model biophysics against multiple data sets for multiple forest and savannah sites in tropical South America. Results of this comparison showed that ED-2.2 is able to represent the radiation and water cycles, but exaggerates heterotrophic respiration seasonality. Also, the model generally predicted correct distribution of biomass across different areas, although it overestimated biomass in subtropical savannahs. / Earth and Planetary Sciences

Adam Wei is employing homegrown UBC technology to help manage the sustainability of China’s fir trees.

This work exploits the general equilibrium modeling framework to simulate complex systems, an economy and an ecosystem. In an economic application, this work leverages a novel data revision scheme to integrate technological detail on electricity generation and pollution abatement into national accounts data in a traditional economic computed general equilibrium (CGE) model. This integration provides a rich characterization of generation and abatement for multiple fuel sources and pollutants across 72 different generation-abatement technology configurations. Results reveal that the benefits of reductions in oxides of nitrogen and sulfur from a carbon policy in the US electric sector are on the order of $10 bn., which rival the policy's welfare costs and make 12-13% carbon abatement economically justifiable without considering any climate benefits. For ecosystem applications, this work demonstrates how the structure of economic CGE modeling can be adapted to construct a Biological General Equilibrium (BGE) model grounded in the theoretical biology literature. The BGE model contributes a novel synthesis of micro-behavioral, bioenergetic features with macroscopic ecosystem outcomes and empirical food web data. Species respond to prevailing ecosystem scarcity conditions that impinge on their energy budgets driving population outcomes within and across model periods. This adaptive capacity is a critical advance over the commonly-taken phenomenological or first-order parametric approaches. The distinctive design of the BGE model enables numerical examination of how changes in scarcity drives biomass production and consumption in a complex food web. Moreover, the BGE model design can exploit empirical datasets used by extant ecosystem models to offer this level of insight for a wide cast of ecosystems. Monte carlo simulations demonstrate that the BGE framework can produce stable results for the ecosystem robust to a variety of shocks and parameterizations. The BGE model's validity is supported in tests against real-world phenomena within the Aleutian ecosystem - both an invasive species and a harvesting-induced trophic cascade - by mimicking key features of these phenomena. The BGE model's micro-founded dynamics, the stability and robustness of its results, and its validity against real-world phenomena offer a unique and valuable contribution to ecosystem modeling and a way forward for the integrated assessment of human-ecosystem interactions.

Terrestrial ecosystems are essential to human well-being, but their future remains highly uncertain, as evidenced by the huge disparities in model projections of the land carbon sink. The existence of these disparities despite the recent explosion of novel data streams, including the TRY plant traits database, the Landsat archive, and global eddy covariance tower networks, suggests that these data streams are not being utilized to their full potential by the terrestrial ecosystem modeling community. Therefore, the overarching objective of my dissertation is to identify how these various data streams can be used to improve the precision of model predictions by constraining model parameters. In chapter 1, I use a hierarchical multivariate meta-analysis of the TRY database to assess the dependence of trait correlations on ecological scale and evaluate the utility of these correlations for constraining ecosystem model parameters. I find that global trait correlations are generally consistent within plant functional types, and leveraging the multivariate trait space is an effective way to constrain trait estimates for data-limited traits and plant functional types. My next two chapters assess the ability to measure traits using remote sensing by exploring the links between leaf traits and reflectance spectra. In chapter 2, I introduce a method for estimating traits from spectra via radiative transfer model inversion. I then use this approach to show that although the precise location, width, and quantity of spectral bands significantly affects trait retrieval accuracy, a wide range of sensor configurations are capable of providing trait information. In chapter 3, I apply this approach to a large database of leaf spectra to show that traits vary as much within as across species, and much more across species within a functional type than across functional types. Finally, in chapter 4, I synthesize the findings of the previous chapters to calibrate a vegetation model's representation of canopy radiative transfer against observed remotely-sensed surface reflectance. Although the calibration successfully constrained canopy structural parameters, I identify issues with model representations of wood and soil reflectance that inhibit its ability to accurately reproduce remote sensing observations.

Variation of dissolved of organic carbon concentration in stream water is a consequence of process changes in the surrounding terrestrial environment. This study will focus on 1) Identify significant environmental factors controlling the spatial and temporal variation of DOC in terrestrial ecosystems of a watershed southeast of Boston, Massachusetts; 2) Model the DOC leaching from different land cover and examine the relationship between leaching flux and in-stream DOC. Our hypothesis is variations of in stream DOC is closely related to watershed properties and environmental factors at annual, seasonal, and daily scales, especially land cover type, watershed size and hydrology. To explore the relationship of hydrology and DOC variation at ungauged sub-basin, we examined the effectiveness of using simulated stream flow from Soil Water Assessment Tool (SWAT) to study terrestrial DOC dynamics. Our results demonstrated that streamflow, drainage area, and percent of wetland and forest were particularly strong predictors in watersheds with a large proportion of developed area. The resulting linear model is able to explain about 70.2% (R2=0.702) and 65.1% (R2=0.651) of the variance of in-stream DOC concentrations at seasonal and annual scales respectively. Results also suggest that more frequent DOC sampling is necessary to establish the quantitative relationship between simulated stream flows from the SWAT and in-stream DOC concentrations at daily scale. The physically based ecosystem model developed in this study shows that DOC leaching from various land cover are highly correlated (up to 80%) with in-stream DOC by using ecological process with incorporated different hydrological pathways. It shows that leaching of DOC from soil is a significant contributor to the in-stream DOC. The production of DOC is largely controlled by the vegetation type and soil texture. Considering the hydrologic control on DOC transport with different pathways of water at finer spatial and temporal scale highlights the need to identify the quantitative relationships between water and carbon flux.

Old-growth forests provide important habitat for wildlife, support the maintenance of biodiversity and serve as control areas for scientific research. Expanding current old-growth stand area by utilizing neighboring younger, managed stands allows private landowners to meet management needs and enables government agencies and private conservation organizations to meet old-growth forest objectives. Seven old-growth upland oak stands and seven adjacent younger, managed stands of the same site and stand type were measured in the Ridge and Valley, Blue Ridge, and Piedmont provinces of Virginia and Pennsylvania in an effort to characterize species composition, diameter distribution and canopy structure. A computer-based ecosystem/gap model (JABOWA-3) was modified and used to simulate silvicultural manipulations in the younger stands that would reproduce older forest characteristics. Various silvicultural techniques were used to convert the primarily even-aged younger stands into uneven-aged stands and then into old-growth. These manipulations included single-tree selection, herbicide application, culling larger diameter stems and planting seedlings where required. Individual trees within each of the younger, managed stands were removed at various time intervals and these simulated stands were then projected to a point in time in which the stand approximated the diameter distribution and composition of its paired old-growth stand. Several projections were made in each of the younger stands to meet this objective. Once a satisfactory projection was made for conversion of a younger stand to old-growth, a success rate was determined to gauge how close the simulated stand approximated the diameter distribution and composition of its old-growth counterpart. From this information, biologically feasible and environmentally sound management plans were created to carry out the silvicultural manipulations required by the model for each of the sites. / Master of Science

Predictions of how forest productivity and carbon sequestration will respond to climate change are essential for making forest management decisions and adapting to future climate. However, current predictions can include considerable uncertainty that is not well quantified. To address the need for better quantification of uncertainty, we calculated and compared ecosystem model parameter, ecosystem model process, climate model, and climate scenario uncertainty for predictions of Southeastern U.S. pine forest productivity. We applied a data assimilation using Metropolis-Hastings Markov Chain Monte Carlo to fuse diverse datasets with the Physiological Principles Predicting Growth model. The spatially and temporally diverse data sets allowed for novel constraints on ecosystem model parameters and allowed for the quantification of uncertainty associated with parameterization and model structure (process). Overall, we found that the uncertainty is higher for parameter and process model uncertainty than the climate model uncertainty. We determined that climate change will result in a likely increase in terrestrial carbon storage and that higher emission scenarios increase the uncertainty in our predictions. In addition, we determined regional variations in biomass accumulation due to a response to the change in frost days, temperature, and vapor pressure deficit. Since the uncertainty associated with ecosystem model parameter and process uncertainty was larger than the uncertainty associated with climate predictions, our results indicate that better constraining parameters in ecosystem models and improving the mathematical structure of ecosystem models can improve future predictions of forest productivity and carbon sequestration. / Master of Science