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Chapter 5 Stand Dynamics & Gap Models

Chapter 5 Stand Dynamics & Gap Models. NRSM 532, BIOS 534 Spring 2014. Douglas fir (Montana). Height. Southern Pines. Cottonwood. 50. 100. 25. Age (years). Tree Growth (live fast, die young). The Stages of Succession. Restricted Growth (uneven walls). Site Index. 100.

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Chapter 5 Stand Dynamics & Gap Models

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  1. Chapter 5 Stand Dynamics &Gap Models NRSM 532, BIOS 534 Spring 2014

  2. Douglas fir (Montana) Height Southern Pines Cottonwood 50 100 25 Age (years) Tree Growth(live fast, die young)

  3. The Stages of Succession

  4. Restricted Growth (uneven walls)

  5. Site Index 100 SI100 = 90 SI50 = 60 SI25 = 40 Height 0 0 25 50 100 Age

  6. Stand Density LAI / NPP 100 1000 Stand Density (trees ha-1)

  7. Stand Density (Self-Thinning Rule)

  8. Inter-treeCompetition Light Canopy Water Interception Soil Water

  9. Carbon Allocation

  10. HW HW HW Growth Efficiency (Gst-limited) 5 5

  11. Growth Efficiency (Gst-limited)

  12. Growth Efficiency

  13. Growth Efficiency (Ntr-limited)

  14. Biogeochemistry &Gap Models

  15. Biome-BGC

  16. Forest-BGC

  17. Gap Models Represent the regeneration in a forest based on the ‘gaps’ generated by the mortality of large trees

  18. Individual Based Models

  19. Individual-based models of forest growth and succession Simulate the establishment, growth and mortality of each tree in the plot ‘individually’ Overtopped trees are suppressed by canopy dominant trees as a function of light interception Gap Models

  20. Competition for light • Based on Beer’s Law • “The taller the glass, the darker the brew, the less light gets through” • Simply put, farther down from the top of the canopy means less light for growth i.e. The glass is taller

  21. Difference between transmitted and absorbed

  22. Simple Gap Model Establish Establish Establish Grow Grow Grow Die Die Die

  23. Gap model interactions • Most important factor is shade tolerance • Models have been expanded to incorporate other competition effects that are locally important Nutrients Water availability Soil temperature (Phenology)

  24. Example of a scaled up individual-based model (ZELIG)

  25. What can you get from an individual-based model?

  26. Best Known Models • JABOWA Botkin, D.B., J.F. Janak, and JR. Wallis. (1972).J. Ecol., 60: 849-872 • FORET – the ‘GAP’ model from the JABOWA family of models. Simulates growth and competition for resources, particularly light, on patches of 1/10 Ha Shugart et al. (1977) J. of Eviron. Man. 5:161-179.

  27. JABOWA FOREST III Ngugi et al., Ecol Mod (2011)

  28. Hybrid Models

  29. 3-PG • Acronym • Physiological Processes Predicting Growth • Simple, process-based model to predict growth and development of even-aged stands. • Uses basic mean-monthly climatic data, and simple site factors and soil descriptors. • Runs on monthly time step. • Parameterized using stand-level data. • Deterministic

  30. 3-PG Flow Chart

  31. Main Components of 3-PG Production of biomass – Based on environmental modification of light use efficiency and constant ratio of NPP to GPP. Biomass partitioning – Affected by growing conditions and tree size. Stem morality – Based on self-thinning rule. Soil water balance – A single soil layer model with evapo-transpiration determined from Penman-Monteith equation. Stand properties – Determined from biomass pools and assumptions about specific leaf area, branch+bark fraction, and wood density.

  32. Environmental constraints on photosynthesis for Douglas-fir vary seasonally in the Pacific NW, U.S.A. soil water evaporative demand suboptimal temperature Frost limitations Autumn Spring Summer Winter

  33. 3-PG Uses • Stand dynamics for single age, monoculture stands of mostly evergreen trees • Generates: foliage (Leaf Area Index), woody tissue and root biomass, conventional stand attributes (volume, BA, stocking), soil water content and water usage.

  34. (1976 - 2006)

  35. Predicted stressed (red) and improved areas (green) since 1950-75 period

  36. StandCarb • Gap Model • Multi species • Has neighboring effects • Can mimic different types of Disturbance • Can model succession

  37. StandCarb • Spatially explicit interactive cell structure, multiple species, allows for various disturbances. StandCarb Harmon and Moreno 2009

  38. StandCarb Limitations: Much slower to run than BiomeBGC Impractical over large landscapes. Harder to use real data over large areas or long times. Not a lot of documentation Hard to learn how to use correctly No nutrient cycling

  39. Ecosystem Deomgraphic (ED) Model

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