Spatial models of energy flow and nutrient dynamics
- 1. SPATIAL MODELS OF ENERGY FLOW AND NUTRIENT DYNAMICS ROQUE MERRICK A. DACULLO, BSBIO-3; BOTANY VISAYAS STATE UNIVERSITY
- 2. ECOSYSTEM SCIENCE SIMULATION MODELING LANDSCAPE DYNAMICS ECOSYSTEM PROCESSES MODELS OF ENERGY FLOW & NUTRIENT DYNAMICS CENTURY FOREST-BGC FIRE-BGC
- 3. CENTURY • A widely used model developed to simulate grassland ecosystems (Parton et al.,1987, 1988).
- 5. CENTURY COMBINED WITH GIS DATA (BURKE, ET AL. 1990) • Inputs- spatial data for climate and soils • Outputs- spatial patterns of NPP, soil organic carbon, net nitrogen mineralization, and oxidized nitrogen emissions • Their study aimed to simulate spatial variability in storage and flux of carbon and nitrogen for the northeastern quarter of Colorado in the U.S. Central Grasslands
- 6. CENTURY COMBINED WITH GIS DATA (BURKE, ET AL. 1990) • Scale-dependent effects identified in their study • Climate data aggregated at coarse-scale yet still produce reasonable estimates of NPP • Soil texture must be represented at finer scale due to nonlinear relationships between soil texture and soil organic matter (SOM)
- 7. • Costanza et al. (1990) developed another model for the Atchafalaya Basin to evaluate a variety of alternative management strategies to reduce coastal erosion. • 2479 1-km2 grid cells connected to one another by simulated fluxes of water, nutrients, and sediments
- 8. FOREST-BGC MODEL • Started as a single-tree water balance model for a year and developed into an integrated carbon, nitrogen, and water cycle model (Running and Hunt, 1993) • Predicts photosynthesis, respiration, evapotranspiration, decomposition, and nitrogen mineralization over broad landscapes • Used to calibrate simple models for implementation at the global scale (Hunt et al, 1991; Running, 1994)
- 10. • Calibration of simple models offers a powerful approach for scaling (Running and Hunt, 1993). • Overton (1975) suggested to use multiscale models that contain submodels operating at different scales and degrees of complexity. • This promises new insight into simulating ecosystem pattern and processes (DeAngelis et al.)
- 11. FIRE-BGC MODEL • Forest gap model linked with BGC and effects of fire disturbances and succession were incorporated (Keane et al., 1996)
- 14. IMPORTANT POINTS DEMONSTRATED BY MODELING STUDIES• Spatial variations in abiotic variables often produce substantial variation, themselves, in ecosystem processes. • Abiotic template is a powerful constraint on ecosystem function. • Abiotic factors vary over multiple spatial scales; appropriate scales must be determined for developing predictive relationships.
- 15. • Furthest limit of knowledge in landscape ecology is the implications of the dynamic landscape mosaic for ecosystem processes. • Absence of a spatially explicit, well-developed theory of ecosystem function • Lack of empirical studies as sources of general conclusions
- 16. REFERENCES: 1. Picket, S.T.A., Cadenasso, M.L. (2004). Landscape Ecology: Spatial Heterogeneity in Ecological Systems. Science. Retrieved from http://links.jstor.org/sici?sici=0036- 8075%2819950721%293%3A269%3A269%3A5222%3C331%3ALESHIE%3E2.0.CO% 3B2-Z 2. Turner et al. (2001). Landscape Ecology in Theory and Practice. New York, USA: Springer. 3. http://www.ncgia.ucsb.edu/conf/SANTA_FE_CD- ROM/sf_papers/mckeown_rebecca/figure1.gif 4. http://firelab.org/sites/default/files/images/projects/fbgc-clime_fire.jpg
- 17. THANK YOU!