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Spatial and temporal variation in soil respiration

as a basis for ecosystem analysis

Background

Context

The efflux of CO2 from the soil surface (here called soil respiration or Rs) is one of thelargest fluxes of the global C cycle, larger than NPP and second only to GPP (Raich andSchlesinger 1992, Rustad et. al. 2000; fig. 1). For this reason and others, ecologists haveattempted to estimate Rs in many ecosystems (e.g., Raich and Potter 1995, Raich andSchlesinger 1992). Rates of Rs have also interested, among others, ecologists studyingecosystem function, energy flow, and especially belowground ecology.

igure 1. The global C cycle (pool units are 1015 g C; fluxes are 1015 g C y-1) averaged for

ethodological limitations

ethods to measure Rs to date, however, are largely unproven and are difficult to apply

Physical factors:

Biological factors:

Diffusivity

Structure

Moisture

Temperature

Root respiration

Decomposition

Soil animals

Carbon sources

Disturbances:

Windthrow

Harvesting

Feedback:

Soil CO2 concentrations

Rs

Physical factors:

Biological factors:

Diffusivity

Structure

Moisture

Temperature

Root respiration

Decomposition

Soil animals

Carbon sources

Disturbances:

Windthrow

Harvesting

Feedback:

Soil CO2 concentrations

Rs

F

the 1980s. Modified from Schlesinger (1997).

M

Mto field conditions. They are unproven in the sense that methods vary widely and arerarely checked with known control fluxes, samples are poorly distributed across spaceand time, and more rigorous statistical techniques are rarely used. Most previous work has focused on obtaining estimates, usually by sampling along a transect, and sometimesrepeating sampling during the growing season—thus, average rates can be presented with

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confidence intervals. Consider, however, that chambers rarely exceed 20-cm in diameter(0.000004 ha), so even 100 samples covers 4/1000ths of a ha. Measured in about1minute, IRGA-based rates also represent only 1/525,600th of a year. With such a losampling intensities, we wonder if rates and intervals, although they may lookreasonable, are far from accurate or repeatable. Further, sampling at fixed inter

a transect may produce misleading estimates if spatial autocorrelation is present, and fewstudies include enough sampling points to adequately assess this. Spatial autocorrelationhas been found in agricultural fields at scales of less than 0.5 m (Nakayama 1990 andRochette et al. 1991) and is likely in forests as well. We, and others, have demonstratethat easy-to-use chemical absorption methods (for example, soda-lime chambers) havelarge concentration-gradient-driven biases (Nay et al. 1994, Heally et al. 1996, andJensen et al. 1996); gas-flux methods using infra-red gas analyzers (IRGA) do work under laboratory conditions but are limited to short sampling periods and are timeconsuming in the field. For example, Rochette et al. (1991), working in an agricultufield using a dynamic-chamber-IRGA method (much like our methodology), found that between 33 and 190 sample points were needed to be within 10 percent of the mean, with

95 percent confidence. Current IRGA techniques require about 5 min per sample, thus toachieve this precision would take 3 to 16 h.

w

vals along

d

ral

nherent variation

easonal differences have been observed by a number of investigators (Joshi et al. 1991,

ns

ost field data show high temporal and spatial variability (e.g., Rochette et al. 1991 and

how

I

SGarrett et al. 1978, Rochette et al. 1991, Gordon et al. 1987) with highest rates occurringduring the summer months, perhaps coinciding with changes in moisture, temperature or photosynthate production. In the Joshi et al. (1991) study in the Central Himalayas, highrates corresponded with the rainy season. Kursor (1989) found soil profile concentrationsof forests in Panama to nearly double during the two-month rainy season. Jensen et al.(1996) suggests using a model with precipitation events to better account for some of theday-to-day fluctuations in Rs. A small amount of precipitation with a small effect on soilmoisture can have a large effect on gas diffusion. Information on diurnal variation in Rsis less extensive. In a grassland study, Grahammer et al. (1991) generally found higherrates during the day, which were attributed to photosynthate production. On the otherhand, Garrett et al. (1978) found little diurnal change in Rs in an Oak-Hickory forest,despite their observance of significant diurnal differences of ambient CO2 concentratioin the air above the soil surface.

MJensen et al. 1996). We have collected similar data sets in a wide range of conditions inthe Pacific Northwest and southeast and interior Alaska (fig. 2). Even the most idealsituations such as agricultural fields or even well-controlled mesocosm experiments shigh variation. Griffins et al. (1996) found spatial variation to be high in a mesocosmexperiment where soil respiration rates ranged from 4 to 25 umol m-2 s-1 CO2 for 150sample points measured over a two day period on an area of 3.6 m

2. Nay and Bormann

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(2000) found similar variability in a box-lysimeter experiment with homogenized soilsand no plants (fig. 3) where significant differences (α=0.05) were detected at 25-cmintervals within 24 h.

Figure 2. Variation in Rs over a

growing season along a 20-m transect

in a mature Doug-fir forest near Scio,

OR. Multiple points represent diurnal

variation. Note the persistence of s

“hot spots” and changes in diurna

variation. Unpublished data courtesy

of Mark Nay and Kim Mattson.

ome

l

12

34

5 6 Cell Column

1.37m 1.37m

Cell Row

A B

C D

E F

Soil-CO-efflux(gCOmh

) 2

2 -2 -1

5 4 3 2 1

0

S o i l r e s p

i r a t i o n ( g C O 2 m - 2 h - 1 )

0.0

0.5

1.0

1.5

2.0

2.5

3.0

30-May-1900

0.0

0.5

1.0

1.5

2.0

2.5

9-Jul-1900

0.0

0.5

1.0

1.5

2.0

2.5

8-Aug-1900

0.0

0.5

1.0

1.5

2.0

2.5

0 2 4 6 8 10 12 14 16 18 20

18-Sep-1900

Position along transect (m)

S

o i l r e s p i r a t i o n ( g C O 2 m - 2 h - 1 )

0.0

0.5

1.0

1.5

2.0

2.5

3.0

30-May-1900

0.0

0.5

1.0

1.5

2.0

2.5

9-Jul-1900

0.0

0.5

1.0

1.5

2.0

2.5

8-Aug-1900

0.0

0.5

1.0

1.5

2.0

2.5

0 2 4 6 8 10 12 14 16 18 20

18-Sep-1900

Position along transect (m)

S

o i l r e s p i r a t i o n ( g C O 2 m - 2 h - 1 )

Figure 3. Spatial variation in box-lysimeter

experiment with initially homogenized soil

and no plants (Nay 1994).

Understanding sources of variation better

We have learned from more than a decade of Rs measures that the high variation in Rs,typically seen in field studies, likely comes from many sources, some which are noteasily foreseen. Additional sources we know little about now are likely to emerge as wellfrom a more systematic study. Gas-flux methods have been a large source of variationand bias, for example, using soda-lime chambers that introduce large concentration-gradient-driven biases and chambers without soil collars that force CO2 into the chamberwhen pressed on the litter layer. Other known sources include:

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Justification

Improved methods to estimate soil Rs, based on a better understanding of the magnitudeand patterns of known and potential variation, will likely be useful to studies of the globalC budget, ecosystem energy flows, and belowground ecology. The initial justification for

this study is to see if we can feasibly measure Rs in 2-ha field plots as part of the PNWStation Long-Term Ecosystem Productivity (LTEP) program. The LTEP approach tomeasuring net primary productivity modifies the approach of Grier et al. (1989):

Npp = ∆B + D + G,

where ∆B is 5-yr change in living biomass, D is detritus production, and G isconsumption via herbivory. The LTEP approach focuses on long-term measures of D plus G as: change in soil organic matter (∆D) plus heterotrophic respiration (Rh,estimated as average annual Rs minus an estimated belowground autotrophic respiration),minus annual inputs minus outputs of biomass and detritus from the defined ecosystem

(I-O):

Npp = ∆B + ∆D + Rh - (I-O).

This formulation fully depends on estimates of Rs that have a precision reasonablysimilar to ∆B and ∆D, which are fairly well known for our field sites (Homann et al.2001). Even with precise measures of Rs, however, this method will require estimatingthe proportion of Rs that is Rh, a problem no one has satisfactorily solved to date,however work there is a growing body of work on this topic (Hanson et al. 2000). The benefit of this approach is that estimated belowground components of productivity are bounded by actual measures of Rs, unlike the alternative, root-turnover methods (Vogt etal 1986, Lauenroth et al. 1986). Measures of Rs in the field could be applied to manyother problems as well, including a direct assessment of combined biological activity ofheterotrophs and autotrophs.

Study Objectives

The purposes of this study are to consider a wide array of potential sources of spatial andtemporal variation, explore patterns of variation at multiple scales of time and space,identify dominant sources of variation in different forest types through systematicsampling at multiple scales, and then to develop more efficient ways to measure stand-and annual-scale Rs with a precision fitted to the analysis being used. Specific objectivesare:

1. Compare Rs rates and patterns of variation across 4 spatial and temporal scales

in 2 to 3 forested sites;

2. Examine soil under sample points and evaluate stand characteristics that may

relate to Rs variation or that could be used as surrogate measures or amplifying

covariates; and

3. Modify current techniques to maximize precision of field measures.

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Details for objective 1 (rates and patterns)

Spatial scales. Three scales will be evaluated with 2-, 1-, and 0.5-, and 0.25-m nestedhexagonal grids (fig. 5). Methods for measuring Rs are described under objective 3.

Rochette et al. 1991 found spatial patterns at about 0.15 m for agricultural soils, thusworking at spatial scales from 0.25 m to 2 m seems an appropriate starting point, giventhat forest soils are much more variable. Converting from transects to hexagons roughlydoubles the number of comparisons that can be used to determine spatial autocorrelation.We expect only a minor decline in variability moving to the smallest scales, given limitedexperience so far. In general, we expect temporal variation to be far smaller than spatialvariation. Quantifying these differences will have a large effect on design of an optimalsampling strategy. Once initial results are analyzed, even broader scales can be added tothis design, such as 4, 8, and 16 m hexagonal grids, to insure that large-scale patterns aredescribed as well.

Figure 5. Nested hexagonal sampling scheme.

Temporal scales. Temporal scales are yet to be established and will depend oncompleting a remote robotic sampling device (see objective 3 details). An initial idea isto measure all points as quickly as possible, again at 2x the time of one cycle, again at 4and 8x the cycle. Additional programs can be applied later once the first tests arecompleted.

1m

Sites. We will apply the nested hexagon grids to three different sites. We will apply onegrid system to an LTEP plot with a 70-90 year-old forest at Hebo on the Sisulaw orIsolation Block on the Willamette National Forests. Depending on additionalcollaboration and funding, we would like to add an old-growth plot in the Pacific Northwest, and a southeast Alaska forest site, perhaps one of our soil variability plots onthe Juneau road system. We expect variance in Rs rates to increase in this sequence:LTEP, old-growth, and wind-affected Alaska podzols, in concert with perceived soilvariability. Stand-average rates of Rs would likely follow variance, but whether patternswill change is unknown.

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Statistical analysis. Our strategy includes data visualization, analysis of spatial andtemporal autocorrelation, and using traditional parametric statistics (regression andANOVA) as appropriate. As an example, we will likely use a nested analysis of varianceto assess differences in variances between estimates at different scales (table 1.). A

similar design will be used to assess different temporal scales. SPLUS will be used fordata visualization and to analyze patterns and determine autocorrelations. Standardtechniques will be used to calculate the optimal spacing of sampling points in time andspace, and the number of samples required to achieve different degrees of precision.

Table 1. ANOVA table for comparison of spatial scales

Source of variation Degrees of

freedom

2.0-m scale 11

1.0-m scale 17

0.5-m scale 17

0.25-m scale 17Total 62

Details for objective 2 (soil factors)

After sampling has been completed, a quantitative soil pit (fig. 6) will be used tomeasure, a wide array of soil characteristics under each Rs sampling point. Thequantitative-pit method permits calculation of attributes on a per-unit-area and volume basis. We intend to measure all relevant physical and biological factors likely to relate toobserved Rs. This list includes C mass and quality; horizon structure, density, and possibly diffusivity; root, microbial, and fungal biomass (especially presence of mats);

particle-size distribution, total and available nutrients, and water-holding capacity. Thesemeasures are standardized and are detailed in the LTEP standard operating proceduresguidelines (on file). Samples will be prepared and added to the LTEP long-term sample base for other possible analyses as well. Diffusivity can be measured with a lab apparatus we developed for Nayet al. (1994).

Figure 6. Vertical sampling frame, fixed in place withpins, for the quantitative-pit method—allowing sampling

of visible horizons, fixed sample area, depths, and v

olumes.

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Details for objective 3 (modify techniques)

Typically, IRGA Rs samples are collected along a transect at a fixed interval. Measuresare made on collars placed into the soil at all sample points several days prior. Thecollars are of the same diameter (20 cm) as the soil respiration chamber and work as a

conduit to the soil to avoid disturbing the soil or pressing out concentrations of CO2 at thetime of measurement. Once on the collar, the chamber circulates air between thechamber and IRGA, and monitors the change in CO2 concentration to estimate Rs.

The current IRGA technology we use is a LI-Cor http://env.licor.com/ LI-6200 PortablePhotosynthesis System adapted for soil respiration measurements (fig. 7). Thisinstrument has been very reliable and has become a standard for field portable IRGA’s,however the system weighs approximately15 kg and is somewhat cumbersome forfieldwork particularly in forestry work with rough terrain and brush. The LI-6200technology is very adaptable though and can be reconfigured and programmed to workfor a wide range of applications. Although the LI-6200 is our current state of technology

it is technology that is well over ten years old. The LI-6200 is no longer available andhas been replaced with the LI-6400 system, which is slightly more compact and weighsapproximately 12 kg (fig. 8). The cost of a LI-6400 is significant though at around $22.5K. LiCor also sells a soil respiration chamber the LI-6400-09 at around $1.5 K to be usedin conjunction with the LI-6400—we are concerned about the small size of this chamber.

Figure 7. Li-Cor LI-6200 Portable Photosynthesis System and soil CO2 flux chamber.

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Figure 8. Li-Cor LI-6400 Portable Photosynthesis

System and LI6400-09 Soil CO2 Flux Chamber.

In this study we would like to explore several changes in how to make soil respiratimeasurements with IRGA technology to increase the number of samples we can collectin a days time:

• Use hexagonal sampling patterns rather than transects (already discussed);

• Develop a lightweight IRGA soil respiration chamber system; and

• Employ robotics for moving and operating IRGA and soil respiration

on

chamoperator present.

Any of these innovations, if adopted, shouldWe will assemb

airflow pump, or batteries. These additionalchamber would maand soil respiration chamber comergonomic for field use. The systemsimpmethod can measure Rs and soil and air tem possibility of measuring soil mafter analyzing patterns of variation, for examencompass more sm

Figure 9. Li-Cor, LI-800 IRGA.

ber to obtain diurnal and other time dependent measures without an

be able to increase sampling efficiency.le a lightweight IRGA soil respiration system with a new lightweight

(1 kg) IRGA the LI-800 (Li-Cor Inc., Lincoln, NE) now available for approx $2.5 K(fig. 9). The LI-800 is only an IRGA and does not contain data logging capabilities, an

components combined with a soil respirationke for a field apparatus that would weigh from 6 to 7 kg. The IRGA

bined into a single unit would make the instrument morewould be designed with only one protocol

lifying setup and insuring consistency in the field and among multiple users. This perature simultaneously. We will explore the

oisture as well. Other modifications are likely to emerge ple chamber size might be modified to

all-scale variation.

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We will also explore the use of robotics to maoint to sample point on a timed programe IRGA in both space and time, a large numhis type of work can be quite monotonous and tedious, m

or

idu/ro

neuver the IRGA and chamber from sample. Because of the point nature of sampling with

ber of sequential measurements are needed.ore suited for a robot than a

mation and components are readily

ty, government, private companies botics.html, http://www.ri.cmu.edu/. p (Parallax Inc., Rocklin, California,

ble, inexpensive with lots of source ple robot that can position the

ling collars (fig. 9). We have consulteda Robotics, Corvallis, OR;

pthTtechnician. Robotics is a much growing field—inf

available via the internet by any number of Universand robot clubs http://www-robotics.cs.umass.eMicrocontrollers such the asic Stamttp://www.parallaxinc.com/

Bh ) are easily programmaode readily available. We propose building a very sim

GA and soil respiration chamber onto sampith Gene Korienek, (Ph.D., Chief Scientist for 3 Sigmttp://www.3sigmarobotics.com/home.html

cIR wh ), who reviewed the conceptual design of this

Figure 9. A

that can visi

on a specifie

ly 2002valuate potential for use in NPP assessment of LTEP analysis: July 2002

project and offered additional help.

possible robotic system with an actuator arm

t sampling points and collect data robotically

d program.

Timeline

Develop lightweight IRGA soil respiration chamber system: Winter 2001Develop robotic positioning system: Winter 2001Beta test sampling with lightweight IRGA and robotics: March 2002Set up field experiment with multi-scale, lightweight IRGA, robotics and instrumentationfor monitor controlling factors at 2-3 sites: April/May 2002Analyze data: June 2002Develop extensive sampling protocol: JuE

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Estimated Budget

Salary Nay – 0.5 FTE (partly covered by SEP Team funds) $30,000

Bormann – 0.1 FTE (partly covered by SEP Team funds) $10,000Kiester – 0.05 FTE $5,000Technicians, 2 for 2 months @ $10.00/hr $3,500

Travel and suppliesTravel to field sites (depends on sites) $5,000Lightweight soil respiration systems: 3 @ $4000 ea. $12,000Consulting $5,000Robotic Apparatus $5,000

Soil analyses $5,000

Total $80,000

Sources of funding include the SEP Teamcoordinated between the ECOP and Roger Clark and Robyn Darbyshire (new comPNW Program), and possible other PNW

DA

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Competitive Grants.

perature as perate mixed

an, and J.E.M. Carvalho.

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http://www-robotics.cs.umass.edu/robotics.htmlhttp://www-robotics.cs.umass.edu/robotics.htmlhttp://www.ri.cmu.edu/http://www.ri.cmu.edu/http://www.ri.cmu.edu/http://www.ri.cmu.edu/http://www-robotics.cs.umass.edu/robotics.htmlhttp://www-robotics.cs.umass.edu/robotics.htmlhttp://www-robotics.cs.umass.edu/robotics.htmlhttp://www-robotics.cs.umass.edu/robotics.htmlhttp://www-robotics.cs.umass.edu/robotics.html


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Li-Cor LI-800 IRGA Specifications

different micro-habitats of a mixed oak-conifer forest and their control by edaphconditions and subs

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Appendix

http://env.licor.com/Products/GasAnalyzers/li800/li800.htm

Measurement Principle: Non-dispersive Infrared

1 5 cm Optical Path: 0-5,000 or 0-20,000 ppm

AnPo 0.3A @ 12V average after warm-up

Op

W

Measurement Ranges4 cm Optical Path: 0-1,000 or 0-2,000 ppm

Maximum Gas Flow Rate: 1 liter/minute (with user-supplied pump)alog Outputs: 4-20mA, 0-0.5V, 0-1V, 0-2.5V, 0-5Vwer Consumption

Power Consumption 1A @ 12V max during warm-upSupply Operating Range: 12 - 30 VDC

erating Temperature Range -25 °C to +45 °CRelative Humidity Range 0 - 95% RH, non-condensingDimensions 22.2 x 15.2 x 7.6cm; 8.75 x 6 x 3 inches

eight 1.0 kg; 2.2 lbs.

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