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Soil respiration of three chronosequences in Chequamegon National Forest. James M. Le Moine 24 November 2004. Overview. Introduction to soil respiration Soil respiration models Study objectives Study site, respiration measurements Statistics Results Summary of key findings Future work.

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slide1

Soil respiration of three chronosequences in Chequamegon National Forest

James M. Le Moine

24 November 2004

slide2

Overview

  • Introduction to soil respiration
  • Soil respiration models
  • Study objectives
  • Study site, respiration measurements
  • Statistics
  • Results
  • Summary of key findings
  • Future work
slide3

CO2

  • Roots
  • Fungi
  • Bacteria
  • Soil Fauna

CO2

Soil Respiration

Respiration

Food

Energy + Waste

slide4

Soil Respiration and the Carbon cycle

Soil: 4th largest pool

2nd largest source to atmosphere

http://earthobservatory.nasa.gov

slide5

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
slide6

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.
slide7

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?

slide8

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
slide9

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

slide10

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
slide11

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
slide12

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
slide14

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

slide15

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

slide16

SRR = 0.0975*e 0.1224*T5

R2 = 0.8783

SRR (gCO2 m-2hr-1)

T5 (oC)

Temperature Data Range

slide17

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
slide18

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
slide20

Vegetation type and age class

Q10 by Age within Vegetation Type

  • Nested Age not significant
  • Young and intermediate hardwoods greater than other groups
slide21

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
slide22

SRR15 by Age within Vegetation Type

Vegetation type and age class

  • Nested Age not significant
  • Young and intermediate SRR15 greater than other groups
slide23

SRR15 by Vegetation Type

SRR15 by Age Class

  • Hardwood SRR15 is more temperature sensitive than others
  • There is no consistent age effect on SRR15
slide24

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.
slide25

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
slide26

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)
slide27

LD

Ts

10 cm

30 cm

SRR

LD

LD

Ms

5 m

LD OD

12.5 m

Vegetation and Soils

slide28

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
slide29

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
slide30

SRR0 Correlation

  • Negatively correlated with Q10at all levels
  • No correlation with vegetation or soil variables
slide31

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
slide32

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
slide33

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
slide34

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
slide35

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
slide36

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.