Soil respiration of three chronosequences in Chequamegon National Forest - PowerPoint PPT Presentation

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Soil respiration of three chronosequences in Chequamegon National Forest
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Soil respiration of three chronosequences in Chequamegon National Forest

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  1. Soil respiration of three chronosequences in Chequamegon National Forest James M. Le Moine 24 November 2004

  2. Overview • Introduction to soil respiration • Soil respiration models • Study objectives • Study site, respiration measurements • Statistics • Results • Summary of key findings • Future work

  3. CO2 • Roots • Fungi • Bacteria • Soil Fauna CO2 Soil Respiration Respiration Food Energy + Waste

  4. Soil Respiration and the Carbon cycle Soil: 4th largest pool 2nd largest source to atmosphere http://earthobservatory.nasa.gov

  5. Empirical Relations with Microclimate SRR (gCO2 m-2hr-1) Soil Moisture (%) Soil Temperature (oC) • Type of curve fitted depends on data range • SRRt=SRR0*e(q*t) • Q10= change in response for 10 Cº increase in temperature

  6. NEE Established Trends GPP Ra Flux relative to GPP Rh Time Since Disturbance Redrawn from Barnes et al 1980 • Autotrophic and heterotrophic respiration change with maturation. • Soil respiration is comprised of Rh and Ra.

  7. Unanswered Questions Does soil respiration have a trend with maturation? Do different ecosystems have different trends in soil respiration with maturation? Are soil respiration—temperature relationships consistent across maturation classes?

  8. Objectives • Model SRR from temperature in clearcuts and three age classes of hardwood, jack pines, and red pines • Compare SRR, and its temperature sensitivity across forest types, age classes, and age class within forest types. • Correlate SRR and its temperature sensitivity to common, easily obtained, vegetation and soil metrics • Compare the above correlations across forest types, age classes, and age classes within forest types

  9. Study site Chequamegon National Forest • Heavily managed forest • 34—200 m of outwash sands and loamy sands • Growing season— • 120 to 140 days • Precipitation— • 66 to 70 cm rain and 106—150cm snow Figure adapted from Steve Mather

  10. Vegetation Types and Age Classes • Recent Clear Cuts: 1, 2, and 4 years since cut • Mixed hardwoods • 12, 14, 17,19 years 21, 22, 26 years 71, 74, 79 years • Jack Pine Plantations and naturally regenerated • 11,11,13 years 19, 19, 21, 29 years 68, 68, 69 years • Red Pine Plantations • 11,12,14,14 years 23, 24, 24, 31, 32 years 71, 72, 74 years

  11. 5 m Soil Respiration Measurements • 8 soil respiration collars • Soil respiration rate • Soil temperature (5cm) • Gravimetric soil moisture • Measurements every 2 wks • mid June—early September ’02 • late April—late October ’03

  12. Statistical Analysis • Shapiro-Wilk (α=0.01)—verification of normality • Nonlinear regression—SRRt=SRR0*e(q*t) • Analyses of Variance (ANOVAs) on SRR0, Q10, SRR15 • Vegetation Type and Age Class(Vegetation) • Vegetation Type • Age Class

  13. Nonlinear Regression

  14. Model fits Predicted SRR (g CO2 m-2 h-1) Predicted =0.9408*Actual R2 =0.6776 Actual SRR (g CO2 m-2 h-1) Average model had n=15,SRR0= 0.14, Q10=2.91, R2=0.94

  15. Temperature and Moisture Correlation Spearman=-0.4930, p=0.0001 SRR (g CO2 m-2 h-1) Gravimetric soil moisture (%) Individual plot correlations ranged from -0.59 to -0.80

  16. SRR = 0.0975*e 0.1224*T5 R2 = 0.8783 SRR (gCO2 m-2hr-1) T5 (oC) Temperature Data Range

  17. SRR (gCO2 m-2hr-1) SRR = 0.2093*e 0.0835*T5 R2 = 0.8316 T5 (oC) Temperature Data Range • No data prior to 15 May or post 23 August

  18. Effects on Q10 • Given SRRt=SRR0*e q*t • Q10=e10*q • Q10 all data= e 10*0.1224 • = 3.40 • Q10 parsed= 2.30

  19. Similarity of SRR0

  20. Vegetation type and age class Q10 by Age within Vegetation Type • Nested Age not significant • Young and intermediate hardwoods greater than other groups

  21. Q10 by Vegetation Type Q10 by Age Class a a a a • Hardwood SRR is more temperature sensitive than others • There is no consistent age effect on Q10

  22. SRR15 by Age within Vegetation Type Vegetation type and age class • Nested Age not significant • Young and intermediate SRR15 greater than other groups

  23. SRR15 by Vegetation Type SRR15 by Age Class • Hardwood SRR15 is more temperature sensitive than others • There is no consistent age effect on SRR15

  24. Summary • Temperature alone explains 94% of variation in SRR. Temperature and moisture strongly negatively correlated. • All SRR0 similar. Reflects severe temperature limitation. • Mean Q10 similar to global average of 2.4. As is the range of values (Raich and Schlesinger, 1992) • Considerable variation between replicates. • Statistically only need 1 model for hardwoods and 1 model for all other ecosystems.

  25. Objectives • Model SRR from temperature in clearcuts and three age classes of hardwood, jack pines, and red pines • Compare SRR, and its temperature sensitivity across forest types, age classes, and age class within forest types. • Correlate SRR and its temperature sensitivity to common, easily obtained, vegetation and soil metrics • Compare the above correlations across forest types, age classes, and age classes within forest types

  26. Vegetation and Soils • Age (Ewel, 1987; Field & Fung, 1999; Law et al 2001; Pypker & Fredeen, 2003; and others) • Basal Area (BA) • Foliage Mass (FL) • Canopy Cover (Cover) • Down Woody Debris (CWD, IWD, FWD) Mallik &Hu 2001 • Litter Depth (LD) Euskirchen 2003 • Depth of Organic layer (OD)

  27. LD Ts 10 cm 30 cm SRR LD LD Ms 5 m LD OD 12.5 m Vegetation and Soils

  28. Foliage Mass • H=a*D^b • FL=(c*D^d)*(H^e) • D=diameter at breast height • H=Height • FL=Foliage mass • a, b, c, d, e= species specific coefficients • Crow and Erdman, 1983; Perala & Alban, 1994; Ter-Mikaelian and Korzukhin, 1997; and Young et al. 1980

  29. Linear Correlation • Three steps: • Overall • By Vegetation Type • By Age Class • Pearson correlation for normally distributed data • Spearman correlation for non-normal • Significance = 0.05 • Marginal Significance= 0.05 to 0.10

  30. SRR0 Correlation • Negatively correlated with Q10at all levels • No correlation with vegetation or soil variables

  31. Q10 Correlation with Vegetation • Overall: BA, FL, Cover • Vegetation Type • Hardwood: Cover • Jack Pine: none • Red Pine: -FWD • Age Class • Young: marginal BA • Intermediate: none • Mature: Cover

  32. Q10 Correlation with Soils • Overall: Positive correlation with OD • Vegetation Type • Hardwood: Positive with LD, OD • Jack Pine: none • Red Pine: none • Age Class • Young: none • Intermediate: OD • Mature: none

  33. SRR15 Correlation with Vegetation • Overall: Age, BA, FL, Cover • Vegetation Type • Hardwood: Cover, marginal CWD • Jack Pine: none • Red Pine: Cover, marginally BA • Age Class • Young: BA, FL, Cover • Intermediate: none • Mature: none

  34. SRR15 Correlation with Soils • Overall: LD, OD • Vegetation Type • Hardwood: OD • Jack Pine: none • Red Pine: none • Age Class • Young: LD, OD • Intermediate: none • Mature: OD

  35. Correlation Summary • SRR15 had more correlations with vegetation and soil variables than did Q10 • Canopy cover has most correllates of vegetation variables. Depth of the organic layer is a better correlate than litter depth. • Hardwoods had more correlations than did pines • The young age class had more correlations than did inermediate or mature

  36. Future work • SRR flux source partitioning • SRR as related to stand GPP and NEE • Improve landscape level estimates of CO2 efflux • SRR as related to soil C:N, root abundance, root turnover, and differences in microbial community.

  37. Questions and Comments