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Regionalization
It is necessary to understand ecological processes at multiple spatial and temporal scales (Gage et al. 1999; Gage et al. 2002). Many ecological processes operate at regional scales and we have taken an approach that integrates concepts of spatial and temporal scaling to understand climate, land use change, agricultural production, invasive species (Isard and Gage 2001; Isard et al. 2005; Nessledge et al. 2006) and carbon sequestration in the North Central Region (Grace et al. 2006b). This region represents the research being conducted at the Kellogg LTER due to its major contribution to production of corn and soybeans (80%). We have also conducted research into land use change in Michigan and have examined the role that fragment plays in affecting ecological services (Skole et al. 2002; Friedman et al. In Press).
Spatial Databases: Regional analysis and modeling requires the gathering and compilation of regional databases. A database has been developed for the North Central Region comprising physical, biological and socioeconomic variables. Each of the data sets is linked to the 1055 counties in the North Central Region and the temporal record begins in 1970. The climate data are daily; the crop production and socio-economic data are based on annual assessment and the physical data (soils, topography, etc.) are relatively static. A digital atlas derived from these data has been developed (Gage et al. In Prep). These data provide a framework for analysis of regional climate impacts on crop productivity (Gage 2003) and for evaluation of future crop production uses.
Modeling Framework: MASIF, A Modeling Framework Application System Integrative Framework has been developed (Gage et al. 2001) to accommodate the large amounts of spatial-temporal inputs and outputs to and from regional scale analysis and simulation environments. This provides an analyst with the ability to rapidly assess regional scale model performance and attributes. MASIF enables visualization of model input data, model simulation results, and statistical patterns of information associated with model inputs and output. Investigations of GRID technologies to manage and access large datasets have been developed to address future cyberinfrastructure needs by LTER and other investigators involved in regional collaboration (Butler et al. 2006).
Crop Simulation Modeling and Climate Analysis: Analysis of historical maize productivity patterns in the North Central Region and development of the ability to explore future scenarios of crop productivity and climate impacts. We have integrated a physiological corn model into the modeling applications system framework interface to facilitate data management and visualization of model inputs/outputs and use of GIS and statistical analysis. The types of outputs from each crop simulation include daily leaf emergence, leaf elongation, leaf area index, leaf biomass, photosynthetic and transpiration rates, and seed growth. Large digital input and output streams from each model run produce about 4 million records of point-based simulation results.
Carbon Simulation Modeling: A soil carbon model, the Soil Organic Carbon Rates and Transformations in EcoSystems (SOCRATES) model has been developed (Grace et al. 2006a) and used to simulate soil organic carbon (SOC) dynamics across the North Central Region (Grace et al. 2006b). Litter inputs were estimated from net primary production (NPP) determined by the Miami model. We generated pre-settlement (1850), current (1990) and future (2100) soil carbon surfaces, with the latter under both conventional and no-tillage scenarios. Carbon emissions from agricultural machinery and inputs of 149 and 126 kg C/ha/yr for conventional and no-tillage systems respectively, as well as CH4 consumption of 2 kg C/ha/yr7, were taken into account in developing net carbon sequestration maps from 1990-2100. GWP’s of 21 and 310 were used for CH4 and N2O respectively.
Michigan Tipping Point: To better understand the dynamics of landscape change, a state wide landscape fragmentation analysis was conducted (Friedman et al. In Press). Land use land cover was assessed using satellite imagery to assess fragmentation conditions to assist land use developers, planners and decision makers. Fragmentation patterns (number of classes, number of patches, patch density, percent of the dominant class, weighted mean perimeter area ratio, and contagion) for eight land use land cover classes (agriculture, built, barren, forest, urban vegetation, other vegetation, wetlands and open water) were calculated.
Selected Publications:
Butler, R. M. Servilla, S. Gage, J. Basney, V. Welch, B. Baker, T. Fleury, P. Duda, D. Gehrig, M. Bletzinger, J. Tao, D.M. Freemon. 2006. CyberInfrastructure for the analysis of ecological acoustic sensor data: a use case study in grid deployment. Challenges of Large Applications in Distributed Environments, 2006 IEEE 25-33.
Friedman, S.K., M. Colunga and S.H. Gage. Quantitative Landscape Fragmentation Characteristics of Michigan. Land Policy Institute. Report Series xx. 88 pp. In Press.
Gage, S.H., and Members of the NC1018 USDA Regional Committee. Atlas of physical, biological and socioeconomic patterns in the North Central Region. . (In Prep)
Gage, S. H. 2003. Climate variability in the North Central Region: Characterizing drought severity patterns. In Climate Variability and Ecosystem Response at Long Term Ecological Research Sites. D. Greenland, D. Goodin. and R. Smith Eds. Oxford Univ. Press.
Gage, S. H., J. Gosz, and W. Michener. 2002. Site to regional scaling. Pages 37-41 in Scaleable Information Networks for the Environment. Withey, A., Michener, W. and Tooby, P. Eds. San Diego Supercomputer Center, San Diego, CA.
Gage, S. H., M. Colunga-Garcia, J. J. Helly, G. R. Safir and A. Momin. 2001. Structural design for management and visualization of information for simulation models applied to a regional scale. Computers and Electronics in Agriculture 33:77-84
Gage, S. H.., S. A. Isard and M. Colunga. 1999. Ecological scaling of aerobiological process. Agric. For. Meteor. Agricultural and Forest Meteorology 97: 249-261.
Grace, P.R., J.N. Ladd, G.P. Robertson and S.H. Gage. 2006a. SOCRATES-A simple model for predicting long-term changes in soil organic carbon in terrestrial ecosystems. Soil Biology and Biochemistry 38: 1172-1176.
Grace P.R., M. Colunga-Garcia, S.H. Gage, G.R. Safir and G.P. Robertson. 2006b. The potential impact of agriculture management and climate change on soil organic carbon of the North Central Region of the United States. Ecosystems 9: 1-13.
Isard, S. A., Gage, S.H., Comtois, P. and Russo, J. 2005. Principles of the atmospheric pathway for Invasive species applied to soybean rust. BioScience: 851-861.
Isard, S. A. and S. H. Gage. 2001. Flow of Life in the Atmosphere: An Airscape Approach to Understanding Invasive Organisms. Michigan State University Press.
Skole, D.L., S. Batzli, S. H. Gage, B. Pijanowski, W. Chomentowski, W.R. Rustem. 2002. Forecast Michigan: Tracking change for land use policy and decision making. Institute for Public Policy and Social Research. Michigan State University.
Nessledge, G.M., B.A. Maurer and S.H. Gage. 2006. Gypsy moth response to landscape structure differs from neutral model predictions: implications for invasion monitoring. Bio Invasions. DOI 10.1007/s/10530-006-9061-1.