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8 results for “ecosystem process modeling”
Nearshore high-frequency temporal water quality observations and process-based modeling of aquatic ecosystem metabolism in Lake Tahoe completed by members of the Blaszczak Lab at the University of Nevada Reno, 2021-2023
The overarching goal of this project was to develop a process-based understanding of how watershed-to-lake connections drive nearshore productivity dynamics in a large oligotrophic mountain lake (Lake Tahoe). We addressed this goal through a combined approach of high-frequency sensor deployment and maintenance, ecosystem metabolism modeling, laboratory incubations, and routine monitoring of water chemistry and other parameters. The data we collected as part of this project and the ecosystem metabolism estimates we generated demonstrate how variable ecosystem productivity is in time and space in the nearshore of Lake Tahoe. Although maintenance of the sensor arrays during the exceptional winter of 2023 was challenging, we were able to capture the data necessary to estimate a complete time series of metabolic activity across two years with very different hydroclimatic conditions. Throughout this project we accomplished the following: 1. We generated over two years of daily estimates of ecosystem metabolism (gross primary productivity, ecosystem respiration, and net ecosystem productivity) from multiple locations on both the east and west shores of the lake and from areas in close proximity to and far away from stream water inflows. 2. We measured ammonium (NH4+) and nitrate (NO3-) concentrations in surface water samples from both Glenbrook and Blackwood creeks and the nearshore of Lake Tahoe for over two years. 3. We quantified rates of NH4+ and NO3- uptake in benthic samples of the dominant substrate type collected during peak streamflow, the receding limb, and baseflow conditions in 2023 from multiple locations in the nearshore using established laboratory incubation methods. 4. Finally, we used a combination of time series models and structural equation modeling to integrate our results and improve understanding of the direct and indirect effects of hydroclimatic variability on observed patterns in ecosystem metabolism in the nearshore. See this git code repository
Model output, drivers and parameters for Ecosystem Recovery from Disturbance is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance Between Vegetation and Soil-Microbial Processes
Files used to generate the data for figures in: Rastetter, EB, Kling, GW, Shaver, GR, Crump, BC, Gough, L. Ecosystem Recovery from Disturbance Is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance between Vegetation and Soil-Microbial Processes. Ecosystems (2020). https://doi.org/10.1007/s10021-020-00542-3. This paper present a framework for assessing biogeochemical recovery of terrestrial ecosystems from disturbance. We identify three recovery phases. In Phase 1, nitrogen is redistributed from soil organic matter to vegetation, but the ecosystem continues to lose nitrogen because the recovering vegetation cannot take up nitrogen as fast as it is released from soil. In Phase 2, the ecosystem begins re-accumulating nitrogen and converges on a quasi-steady state in which vegetation and soil-microbial processes are in balance. In Phase 3, vegetation and soil-microbial processes remain in balance and the ecosystem slowly re-accumulates the remaining nitrogen.
Supporting data sets for "Estimating Carbon Fixation of Plant Organs for Afforestation Monitoring using a Process-based Ecosystem Model and Ecophysiological Parameter Optimization". (the survey of tree breast diameter and tree height in 11-year old Eucommia ulmoides plantation, values of simulation results used in figures and tables.)
<p>Supporting data sets for Miyauchi et al., Ecology and Evolution, 2019 (accepted).</p> <p>The files store: </p> <p>(1) The survey of tree breast diameter and tree height in <em>Eucommia ulmoides</em> plantation<em>.</em> The ring and stem analysis and dry weight of seven harvested sample trees in the plantation.</p> <p>(2) Values of optimization result used fig.7.</p> <p>(3) Values of prediction result used fig.8. and table 4.</p> <p>(4) Values of optimized parameters by optimization methods, parameter range and constrain.</p>
Process-based Modeling of Ecosystem-Level Monoterpene from a Japanese Larch (Larix Kaempferi) Forest
<p>Title ''Process-based Modeling of Ecosystem-Level Monoterpene from a Japanese Larch (Larix Kaempferi) Forest''<br>Zhanzhuo Chen 1,2, Tomomichi Kato 3, Akihiko Ito 4,5, Tatsuya Miyauchi 3, Yoshiyuki Takahashi 4, and Jing Tang 2</p> <p>1 Graduate School of Global Food Resources, Hokkaido University, Sapporo, Hokkaido, 060-0809, Japan<br>2 Center for Volatile Interactions (VOLT), Department of Biology, University of Copenhagen, DK-2100, Copenhagen, Denmark<br>3 Research Faculty of Agriculture, Hokkaido University, Sapporo, Hokkaido, 060-8589, Japan<br>4 Earth System Division, National Institute for Environmental Studies (NIES), Onogawa, Tsukuba, Ibaraki, 305-8506, Japan<br>5 Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan<br>Correspondence to: Tomomichi Kato (tkato@agr.hokudai.ac.jp)</p>
Simulations from four process-based ecosystem models describing primary productivity in a tallgrass prairie long-term irrigation experiment
<p class="MsoNormal"><span>To demonstrate current capabilities in modeling herbaceous ecosystems, we selected four different process-based models that vary in their representation of community change from no community representation to vegetation demographic models. These models were used to simulate a long-term irrigation experiment at a US tallgrass prairie (Konza Prairie Biological Station) following a standardized simulation protocol. Specifically, we were interested in how model output under a monotonic increase in water availability matched up to experimental findings of (1) herbaceous plant community change and (2) aboveground net primary productivity before and after the plant community change. The results of this simulation are included here.</span></p>
Simulations from four process-based ecosystem models describing primary productivity in a tallgrass prairie long-term irrigation experiment
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Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
Biome-BGC is a computer program that estimates fluxes and storage of energy, water, carbon, and nitrogen for the vegetation and soil components of terrestrial ecosystems. The primary model purpose is to study global and regional interactions between climate, disturbance, and biogeochemical cycles.Biome-BGC represents physical and biological processes that control fluxes of energy and mass. These processes include: New leaf growth and old leaf litterfall Sunlight interception by leaves and penetration to the ground Precipitation routing to leaves and soil Snow accumulation and melting Drainage and runoff of soil water Evaporation of water from soil and wet leaves Transpiration of soil water through leaf stomata Photosynthetic fixation of carbon from CO2 in the air Uptake of nitrogen from the soil Distribution of carbon and nitrogen to growing plant parts Decomposition of fresh plant litter and old soil organic matter Plant mortality Fire The model uses a daily time-step. This means that each flux is estimated for a one-day period. Between days, the program updates its memory of the mass stored in different components of the vegetation, litter, and soil. Weather is the most important control on vegetation processes. Flux estimates in Biome-BGC depend strongly on daily weather conditions. Model behavior over time depends on climate--the history of these weather conditions.A companion file with more information about Biome-BGC and its components is available at ftp://daac.ornl.gov/data/model_archive/BIOME_BGC/biome_bgc_4.1.1/comp/BiomeBGC_v411_release.pdf .Biome-BGC, Version 4.1.1 was developed and is maintained by the Numerical Terradynamic Simulation Group, School of Forestry, The University of Montana, Missoula, Montana, USA. Additional information can be found on there web site at: http://www.ntsg.umt.edu/.
PnET-BGC: Modeling Biogeochemical Processes in a Northern Hardwood Forest Ecosystem
This archived model product contains the directions, executables, and procedures for running PnET-BGC to recreate the results of Gbondo-Tugbawa, S.S., C.T. Driscoll , J.D. Aber and G.E. Likens. 2001. The evaluation of an integrated biogeochemical model (PnET-BGC) at a northern hardwood forest ecosystem. Water Resources Research 37:1057-1070. Gbondo-Tugbawa et al,. 2001 Excerptfrom Abstract: An integrated biogeochemical model (PnET-BGC) was formulated to simulate chemical transformations of vegetation, soil, and drainage water in northern forest ecosystems. The model operates on a monthly time step and depicts the major biogeochemical processes, such as forest canopy element transformations, hydrology, soil organic matter dynamics, nitrogen cycling, geochemical weathering, and chemical equilibrium reactions involving solid and solution phases. The model was evaluated against soil and stream data at the Hubbard Brook Experimental Forest, New Hampshire. Model predictions of concentrations and fluxes of major elements generally agreed reasonably well with measured values, as estimated by normalized mean error and normalized mean absolute error. Model output of soil base saturation and stream acid neutralizing capacity were sensitive to parameter values of soil partial pressure of carbon dioxide, soil mass, soil cation exchange capacity, and soil selectivity coefficients of calcium and aluminum. PnET-BGC can be used as a tool to evaluate the response of soil and water chemistry of forest ecosystems to disturbances such as clear-cutting, climatic events, and atmospheric deposition.PnET-BGC, was used to investigate inputs and dynamics of S in a northern hardwood forest at the Hubbard Brook Experimental Forest (HBEF) (Gbondo-Tugbawa et al., 2002). The changes in soil S pools and stream-water were simulated to assess the response 22 SO4 to both atmospheric S deposition and forest clear-cutting disturbances. Watershed studies across the northeastern United States have shown that stream losses of exceed atmospheric sulfur (S) deposition. Understanding the processes responsible for this additional source of S is critical to quantifying ecosystem response to ongoing and potential future controls on SO2 emission.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.