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Temperature and moisture dependence of decomposition rates of hardwood and coniferous leaf litter
Vol. 18. No. Printedin Great Britain
Soil Bid. Biochem.
4, pp. 427435,
1986
0038-0717/86 $3.00+ 0.00 Pergamon Journals Ltd
TEMPERATURE AND MOISTURE DEPENDENCE OF DECOMPOSITION RATES OF HARDWOOD AND CONIFEROUS LEAF LITTER ALLEN M. Moons Department of Biology, Western Carolina University, Cullowhee, NC 28723, U.S.A. (Accepted 20 Junuary 1986)
Summary-Samples of fresh (autumn) and of year-old (late summer) deciduous forest-leaf litter and humus, and of Douglas fir fine litter and humus, were wetted to known moisture content, nominally between 200 and 40% water (dry basis), and maintained at constant temperatures between 10”and 40°C. Rates of CO, production were measured by KOH absorption and titration. Decomposition rate was found to he a linear function of log - (water potential), and to approach a maximum near 40°C. The temperature-dependence was consistent with models based on irreversible heat inactivation of a rate-controlling enzyme, also with Eyring’s “absolute reaction rate” theory for reactions controlled by a reversibly inactivated enzyme. Activation energies were 66.867.3 kJ mol-’ for litter, and 61.4675 kJ mol-’ for humus decomposition; for enzyme inactivation energies were 150-154 kJ mol-‘.
INTRODUCTION
The litter and humus layers of the forest floor are important storage components for organic material, nutrients and moisture in temperature forests. They are also a source of food for an energetically significant microflora and fauna. The forest floor appears as a component in most models of mass and energy-flow in forest ecosystems (Coniferous Forest Biome Modeling Group, 1977; Goldstein et al., 1974), but the metabolic processes and the attendant changes in the mass of the forest floor are often not modelled explicitly. This is understandable: forest floor processes are complex, difficult to measure, poorly understood, and of limited relevance to the primary goals of many models. Models of energy and carbon flow must deal with the forest floor, however, if only as a temporary storage for organic material shed from the trees, and even hydrology models may incorporate fluctuations in the mass of the forest floor, because it influences moisture interception, infiltration and storage (Moore and Swank, 1975). Olson (1963) proposed an exponential-decay model of the litter layer which has been widely adopted (Wiegert, 1970; Moore and Swank, 1975; Cromack and Monk, 1975). This model gives good results on an annual basis, but its exponential rate of decomposition depends on a constant which is specific for a given litter type and climatic regime, but does not respond to annual or short-term changes in temperature and moisture, or to other variable factors in the environment. A number of workers have considered how litter decomposition responds to temperature, moisture, or both (Bartholomew and Norman, 1946; Jenny et al., 1949; Witkamp and van der Drift, 1961; Witkamp, 1966; Coniferous Biome Modeling Group, 1977; Bunnell et al., 1977). I have determined experimentally how the decomposition rates of litter and humus ‘(O-l and O-2) from mixed Southern hardwood and Douglas fir forests responded to a range of 427 s B.B.lW-F
temperature and moisture conditions and have attempted to develop mathematical descriptions of the results. In a series of experiments, samples of air-dried leaf-litter were wetted to a known moisture content and maintained at a defined temperature, while their rates of CO2 production were measured by chemical absorption and titration. A non-linear curve-fitting procedure was used to generate optimum coefficient values in equations to predict decomposition rates. Values generated by these relationships were compared with the observations. The relationships presented below are those which seemed interesting on theoretical grounds, conformed with the data, and which seemed to have potential practical value. METHODS Litter samples were collected from Watershed 14 at Coweeta Hydrologic Laboratory, near Franklin, North Carolina (a watershed dominated by mature hardwoods, especially oaks, with some understory dogwoods), and from the Reference Stand at the H. J. Andrews Experimental Forest, near Eugene, Oregon (an old growth Douglas fir forest). Samples were collected from Coweeta in December 1980 and July 1981, and from Oregon in July 1981. Late summer collection from Coweeta represented “old” litter which had been on the ground for most of a year; late fall collection represented recently-fallen “new” litter. Seasonality of leaf-fall was not assumed for the Douglas fir collections. Litter samples were collected by cutting around a 20 x 20 cm form with a knife, and then separating the sample from the soil with a spatula. The samples were inserted with a minimum of disturbance into specially-made nylon litter bags with Velcro closures and air-dried in the laboratory on wire racks for at least 6 weeks. They were selected for use in random sequence and weighed. The thickness of the
428
ALLEN M. MOORE
O-l and O-2 layers and of the total sample was recorded. The litter samples were then divided into O-l and O-2 components, which were placed in 20.3 x 20.3 x 7.5 cm sealable plastic boxes (Tupperware). Three replicates of each type of litter were used for each experiment. A fully-crossed sampling design was used. Decomposition rates were measured at each of four moisture levels (200, 120, 80 and 40% dry basis) and four temperatures (40”, 30”, 20” and 1OC). In each experiment, all leaf samples were brought to 200% moisture with distilled water and left sealed in the controlled environment chamber at the experimental temperature for 24 h before measurements began so as to allow the moisture to become evenly distributed. Temperature and moisture sensors were inserted in most boxes during equilibrium. Moisture was measured by Peltier-effect psychrometers (Wescor Inc., Model PST-55) and by resistive humidity sensors (Phys-Chem Research Corp., PCRC-11). Both types of sensor were connected to a datalogger (Wescor DL-520). The psychrometers used thermocouples which also transmitted temperature. Sample wet and dry weights provided a gravimetric measurement of moisture content. The sensors were removed during the 24-h CO2 absorption period. At the end of this period the samples were partially dried overnight, placed on a pan balance and rewetted to the moisture required for the next run (by sprinkling from an Erlenmeyer flask covered with a perforated aluminum foil top on the thin, undisturbed samples), allowed to equilibrate for 23-30 h with the sensors in place, and measured again for decomposition. The moisture measurements made with psychrometric and resistive humidity sensors, which measured water potential and effective and relative humidity directly, proved to be unreliable, correlating poorly with each other and with gravimetric results. The psychrometers measured moisture by comparing the temperatures of two thermocouples, one dry and relatively massive, the other of very fine (25 pm> wire, with a droplet of water condensed on it by the Peltier effect prior to measurement. The temperature differences they measured were minute, and they were extremely sensitive to ambient temperature fluctuations during measurement. They were able to measure moisture only at relatively high levels; the balance between the Peltier-effect and resistive heating in the fine thermocouple wire apparently would not produce sufficiently low temperatures to condense moisture from an environment at less the -5 MPa, so they provided no data when the litter was drier than this. Their performance deteriorated with use. A built-in validity check of the difference between the massive and fine thermocouples before cooling permitted rejection of readings influenced by temperature fluctuations but not of readings taken beyond the usable range of the sensor or from (other than grossly) defective sensors. The psychrometers were designed for use in soil and worked satisfactorily only if in intimate contact with the litter; they did not work in free air in equilibrium with the litter in a closed container, or in larger litter voids. The resistive humidity sensors were used to obtain moisture information when the litter was drier than the useful range of the psychrometers. Each sensor
consisted of an insulating board, 40 x 22 x 1 mm, with a conductive mask applied to the surface; the resistance of the exposed surface of the insulator across channels in the mask was inversely proportional to the humidity of the air. The datalogger measured this resistance and converted it into a value which could be converted into relative humidity. which in turn could be converted to water potential. The sensors were damaged by liquid water, and had to be protected from contact with the litter. Each had to be calibrated individually before each experiment in air of known humidity. The air was passed through a closed air train containing sulfuric acid at known concentrations. The sulfuric acid may have contributed to the sensors’ deterioration. They deteriorated rapidly and irreversibly under the conditions of the study, and recalibration failed to eliminate an increase in the variability of data as they aged. Gravimetric moisture remained as a reliable measurement, but in its original form correlates poorly with physiological availability of water and with decomposition rate (Swift et al., 1979). Transformations were therefore employed which converted gravimetric moisture to water potential via effective relative humidity and vapor pressure. Briefly, the conversions were as follows: Gravimetric moisture content was converted to effective relative humidity, using an empirical formula from the ASHRAE handbook (ASHRAE, 1977) with constants fitted to data from the resistive relative humidity sensors during an early phase of the experiment and before they began to fail. The formula was: r.h. = 1 - exp( - a .GMC*. T), where r.h. = effective relative humidity, fraction of saturation; GMC = gravimetric moisture content, gg-‘; T = absolute temperature, K; a = 0.013 (constant); b = 0.7 (constant). This relative humidity was converted to water potential by the formula (derived from Nobel, 1974): WP = (RT/V).
In (r.h.),
where WP is the water potential, MPa; R = the gas constant, 8.314 J mol-* K-‘; and V = the specific volume of water, 18 cm3 mol-‘. The repeated-measures design, with decomposition measured at several moisture levels in the same set of samples, was used for several reasons. It avoided the “flush” of rapid decomposition that Birch (1959) reported for the first hours after a sample was wetted, and activated the dormant microbiota under uniform and high moisture levels. It resulted in more nearly uniform moisture distribution than could have been achieved by de novo wetting of dry material, particularly in the low-moisture samples. It eliminated differences between samples as a source of variance between moisture levels. Although substrate quality and microbiota could be expected to change as a result of decomposition during the experiment, these samples had been repeatedly wetted and dried and had already undergone some decomposition in the field. They would contain very little labile or leachable material. The repeated-measures design does not complicate statistical analysis greatly. But most important, these experiments were intended to furnish models which could be used to calculate
Litter decay vs temperature and moisture decomposition in the field. Litter in the field is normally wetted thoroughly by rain and subsequently dried gradually; the experimental procedure was intended to simulate the normal course of the drying process after precipitation in the field. The penalty is that any gradual reduction in decomposition in time will be attributed in this model to moisture, whether or not this is the actual cause. A check of three cases in the experiment in which wetted samples were held longer than 30 h (44-51 h) before decomposition was measured showed no downward deviation from expected decomposition (O-l samples at 2O”C, log - (water potential) = 0.8; O-2 samples at 40” and at 2O”C, both at log - (water potential) = 1.4; see Fig. 1), suggesting that within the time-scale of this experiment, any simple time-dependent decrease in decomposition was small. There has been some question whether chemical absorption of CO? gives results comparable to more modern methods, such as i.r. gas analysis. Edwards and Sollins (1973) reported measurements of CO2 production by chemical absorption that were consistently 30% below measurements made with an i.r. gas analyzer. However, Van Cleve et al. (1979) compared four methods of measuring CO, production from homogenized titter samples and determined that values from KOH absorption were actually slightly higher than those made by i.r. gas analysis, and considerably higher than measurements by gas chromatography or Gilson respirometry. These results suggest that the KOH absorption method used here should not introduce bias. The absorbed CO2 was converted to bicarbonate and measured (Wetzel and Likens, 1979). The experimental procedure was as follows: 20.0ml of 1.0 N KOH solution was placed in a 78 x 78 mm disposable plastic weighing boat. The weighing boat was placed on a wire rack over the litter sample inside the sample box, which was then sealed. After 24 h, the weighing boat was removed, the KOH was rinsed into a 100 ml volumetric flask, 10.0 ml of 1.ON HCl was added (to partially neutralize the uncombined KOH and reduce absorption of atmospheric CO, during handling), and the sample was diluted to 100 ml with distilled water. The tlask was then sealed with paraffin film to prevent absorption of atmospheric CO?. Titration was carried out within 48 h. A 10.0 or 20.0 ml aliquot of each sample was titrated roughly to pH 9-10 with 1 N HCl, then precisely to pH 8.3 with 0.02 N HCl using phenolphthalein and a pH meter. The sample was then titrated to pH4.6 with 0.02 N HCI standardized to four significant figures against a potassium hydrogen phthalate standard. The equivalents of acid used in this titration were a measure of the equivalents of bicarbonate ion in the sample aliquot, which were a measure of the moles of CO, absorbed in the aliquot. Two or more replicates were run on each sampte. The results were converted first to moles h-’ and then to gC0, kg-’ litter h-‘. The CO, content of an empty box was measured in the same way during each run and deducted from the sample measurements. Data recorded included pre-run temperature and moisture-sensor data, titration and sample volumes, chamber temperatures recorded every 4 h during each run, and sample weights, from which sample mois-
429
ture content was calculated. A four-factor analysis of variance with repeated measures on the fourth factor was made using the SPSSX MANOVA procedure, with CO, production and its log as alternate dependent variables, with titter horizon, litter age and type, and temperature as between-subjects factors, and with moisture level as a within-subjects factor. A constant was added to the CO, production rate to eliminate negative readings and undefined logs. The log transformation was used because, contrary to the assumptions of ANOVA, the effects of most factors were thought to be muItiplicat~ve rather than additive. The logs of multiplicative factors would be additive, and would conform to the “linear additive” ANOVA model and avoid spurious interaction effects. Analysis confirmed that additional interaction effects were significant when untransformed data were used. An iterative non-linear regression procedure, NONLINWOOD (Daniel and Wood, 1980), was used to fit coefficients in hypothetical regression equations. The calculation and curve-fitting routines used the measured, rather than the nominal values for temperature and moisture. RESULTS
The analysis of variance results (Table 1) show that horizon, litter type, temperature, and a multivariate test of moisture were all highly significant (P < 0.01). The four-way interaction effect was not significant, but the interactions (type x temperature x moisture) and (horizon x temperature x moisture) were both significant (P < 0.01). This suggests that the interaction of temperature and moisture varied according to the horizon and the type of the sample. An examination of Fig. 1, which shows cell means for the various combinations of factors, suggests the nature of some of these relationships. In particular, temperature and a function of moisture appeared to have a multiplicative relationship, and O-2 litter had a generally lower rate of decomposition than O-I litter and showed some differences in its response to temperature and moisture. The effect of litter type and age was also significant, but contributed less to the variation than other factors. Surprisingly, new hardwood litter did not appear to decompose much faster than old litter. The analysis of variance verified that the main effects were significant and that development of regression models to fit them was justified. The significance of the interaction effects suggested that the models may need to be fairly complex. The decomposition rate increased as expected with increasing moisture but was not linearly related to gravimetric moisture content. Plots of decomposition rate against log - (water potential) were linear, however, and had the same x-intercept for all litter types (the water potential at which decomposition was zero). When the data were corrected for temperature using a relationship developed below, the slopes were also uniform, giving the following relationship: D (WP) = D(0)[0.6754 - 0.4253 log,,] WPl], where: lWP1 = absolute value of water potential, MPa; D (WP) = decomposition rate at measured water potential; and D(0) = decomposition rate at a reference water potential of - 0.178 MPa.
ALLEN M. Mooa~
430
Table 1. ANOVA summary for decomposition Source of variation
F
d.f.
Between subjects effects 1 41.40 2 6.52 3 66.20 2 3.00 3 4.9s 6 0.47 6 0.38
Horiz. Type Temp. Horiz. x Type Horiz. x Temp. Type x Temp. Horiz. x Type x Temp.
data Sienificance’ P < 0.01 P < 0.01 P < 0.01 NS P < 0.01 NS NS
Within-subjects effects Moist Horiz. x Moist Type x Moist Temp. x Moist
Horiz. x Type x Moist Horiz. x Temp. x Moist Type x Temp. x Moist Horiz. x Type x Temp. x Moist
3 3 6 9
593.60 11.30 5.16 12.5
6 9 18
2.18 7.65 2.93
I .58
18
P P P P
< < < <
0.01 0.01
0.01 0.01 NS
P < 0.01 P < 0.01 NS
‘Pillai’smultivariate test of significance. Horiz. = litter horizon, O-I and O-2; Type = litter type (old hardwood, new hardwood, Douglas fir); Temp. = temperature; Moist = moisture content of litter.
Hardwood Old
0.6
llttar
Hardwood
O-l
Old
litter
O-2
Hardwood New
O.6 r,,.
Douglas
litter
Hardwood
O-l
New
fir
r
Douglor O-2
Log - (water
potential,
littar
O-2
fir
Litter
MPa 1
Fig. 1. Decomposition rate of six litter types as a function of moisture. The solid lines were calculated by the “difference” model at the indicated temperatures, using parameters calculated for pooled O-1 on the left, and for individual O-2 samples on the right; the broken lines connecting symbols indicate measured values of CO, production at the temperature of the fitted line with which they are associated.
Litter decay vs temperature and moisture
The constants in this formula are from a leastsquares fit to all the data. The “reference water potential” is an artifact of the fitting process; it corresponds to a moisture content of over 200% at 40°C. The line intercepts the x-axis (a decomposition rate of zero) at log,, IWP ( = 1.58. This corresponds to a water potential of - 38 MPa or a relative humidity of about 75% at 20°C. According to the moisture characteristic curve adopted here, this corresponds to a limiting litter-moisture content for decomposition of 25%. The temperature relationship was found to be more complicated than expected. The literature generally suggests a Q,,, relationship of some sort (Witkamp and Frank, 1969; Reichle, 1967; Bunnel et al., 1977). There was as expected a rapid and accelerating increase in decomposition rate between 10” and 30°C suggesting a conventional Q,,, relationship; but at 40” the rates were little if any higher than at 30”. This suggested a tolerance curve, with an optimum temperature for decomposition somewhere in the vicinity of 40”, and an upper limiting temperature somewhat above this. An Arrhenius plot (log of reaction rate vs reciprocal of absolute temperature) shows the rates of first-order chemical reactions as straight lines. When the data were graphed as Arrhenius plots, they showed a clear deviation from linearity at 40” (Fig. 2). The temperature of the maximum decom-
O-l
Litter
O-2
Litter-
-
Difference
formula
Arrhemus
plot
1 r
‘2
-5
N
s
1
Difference
formula
Arrhenius
plot
.z 0
: -
x x
F -1
“-9,s
-2
;: x \
-3
XX\
-4 I: -51
I
II
60
50
40
11 Tab.
I 30
[scale
I 20
I 10
I 0
* T 3
Fig. 2. Arrhenius plots of temperature dependence of decomposition rates. Solid curves are generated by the “difference” model; data points have been corrected for moisture through division by the moisture function. In an Arrhenius plot, reaction rates which follow a simple Arrhenius function plot as a straight line.
431
position rate could be estimated for each litter type, but the shape of the curve beyond 40” is unknown. The rate could plummet to zero within a few degrees, or it could decline gently over a considerably range of temperature. Analogies with vertebrate physiology suggest the former, but literature on cornposting suggests the latter (Haug, 1980). Two models, corresponding to the two analogies, are developed below. DISCUSSION Moisture relationship
The moisture relationship developed above predicts that decomposition will cease at - 38 MPa. This is considerably drier than the moisture level at which Cook and Papendick (1970) reported hyphal development in fungi to be inhibited; they cite no instances of fungal growth below water potentials of -8 to - 10 MPa. Their emphasis was on pathogens of living plants, however, and litter decomposers have been shown to function at lower moisture levels. Swift et nl. (1979) in a review concluded that bacteria are inactive below - 1.0 to - 1.5 MPa in soils, whereas soil fungi are active down to - 15 MPa, and that some Aspergillus and PeniciIlium are active to -40 MPa and below. Chen and Griffin (1966) found certain species of Aspergillus and Penicillium capable of colonizing hair in contact with soil at a water potential of -39.6 MPa. Griffin (1981b) proposed three categories of xeric environments, each with a characteristic microflora. Aspergillus and Penicillium are listed as characteristic genera in environments in which the major constituent is a porous solid, the matric potential is low, and the solute potential is either low or high. Griffin (198 1b) cited work showing germination of conidia of Asperigillus chetlalieri at -50 MPa, but notes that germination at the lowest experimental water potentials was not always followed by continued mycelial growth. A water potential of -40 MPa is referred to as an extreme value at which a few fungi are capable of growth (Griffin 1981a). The ASHRAE handbook (ASHRAE, 1977; Chap. 10: Physiological Factors in Drying and Storing Farm Crops) considered a relative humidity of 65% (- 58 MPa) to be the maximum for safe storage of grains, and reports minimum moisture for growth of various species of Aspergillus of from 68 to 85% (- 52 to - 22 MPa). Griffin (1981 b) considered the relationship between growth rate and water potential, separating the effects of solute potential and matric potential. He considers growth rate “in general” to decline as a linear function of decreasing matric potential, but his curve for solute potential shows some concavity upwards at the lowest potentials, which hints at a logarithmic relationship. An experimental study (Wilson and Griffin, 1975, Fig. 5) shows consistent upward concavity of curves of respiration vs water potential in results from unamended soil; when these curves were re-plotted against the log of water potential, they were strikingly linear. These curves show cumulative oxygen consumption by decomposers over times from 20 to 120 h, and show that the linear form of the relationship was not affected by the duration of the experiment, nor was it an artifact of repeated-measures design.
ALLENM. MOORE
432 Temperature
Table 2. Summmary
Two models of the effect of temperature are considered here. In both of these models, decomposition depends on temperature as if there were a ratecontrolling single-enzyme reaction. Substrate concentration is considered to be in excess of ratecontrolling values; this simplifying assumption may not be valid even though the ratio of litter to microbiota is large, but there is no obvious way to calculate a value for substrate concentration. Only enzyme activity and concentration are invoked to explain the departure of decomposition rate from simple Ql,, models of reaction rate changes with temperature. The difference between the models lies in how enzyme activity is affected by temperature. Metabolic activity consists of two components: an “activity” component which is superimposed on an essential “basal” component (e.g. Brody, 1945). Odum (1967) refers to the basal component of metabolism as “antithermal maintenance”. The “difference” model is based on the following interpretation of this concept: (1) that random damage is done to cellular chemical structures as a result of bond breakage by thermal kinetic energy at the upper end of the statistical distribution of particle energies at ambient temperature, and that (2) a primary function of metabolic work is to repair this damage. This implies further: (3) that essential substances, including the hypothetical “metabolic rate limiting enzyme”, are constantly being destroyed by random thermal processes, (4) that due to the shape of the Maxwell-Boltzmann distribution, the rate of destruction of these substance increases roughly exponentially with temperature, and that (5) these substances are repaired or replaced at a rate which is limited by the metabolic rate. It can be assumed that both normal metabolic activity and the breakdown of essential enzymes would be first-order chemical reactions, the rates of which would show temperature dependence in the form of the Arrhenius equation: dS/dt = A .exp( - E,/RTabs), where dS/dt = the reaction rate, measured by change in quantity of substrate or product per unit time; A = a constant; E, = the activation energy, kJ mol-‘; R = the gas constant, 0.008314 kJ K-’ mol-‘; Tabs= temperature, K. The temperature dependence of the rate of each process is controlled by the activation energy of the rate-limiting step. The observed metabolic rate could then be approximated by the difference between the “forward” and “enzyme breakdown” reactions, each of which would be represented by Arrhenius equations, but with different activation energies. This formulation leads to curves which can be made to match the observed behavior of decomposition quite well. A non-linear curve-fitting procedure, NONLINWOOD (Daniel and Wood, 1980) was used to calculate values for the parameters of the equation shown in Table 2. Best-fitting values were calculated first for the data from each layer of each litter-type separately. Pooling of the data for the three types of O-l litter is not technically justified due to the significant interaction effects reported above, but has been done for the sake of brevity in reporting
Litter type Hardwood, old O-l Hardwood, new O-l Douglas fir, O-l Pooled O-I Hardwood, old O-2 Hardwood, new O-2 Douglas fir, O-2 Pooled O-2
a = 0.6754 + 0.048. b = dS/dt
of difference
Values of fitted constants In A, E, 26.22 26.23 26.05 26.19 25.62 23.44 25.51 23.58
66.83 67.32 67.28 67.19 67.50 61.44 67.41 62.25
model In A,
E,
58.70 58.65 58.50 58.63 58.20 57.00 58.09 56.95
152.55 153.07 153.35 153.00 153.77 150.49 154.00 150.84
0.4253f 0.030 (95% confidence limits).
= [A,exp(-&/AT) - A, exp( - E,/RT)][a
- b log,,( -
WP)]
where: dS/dr
= decomposition rate, g CO, kg-’ litter hK’; A,, A, are litter-type-specific constants for “forward” and “breakdown” reactions; E(, Eb are litter-type-specific activation energies for these reactions, kJ mol-‘; R is the gas constant. 0.008314 kJ mol-’ Km’; T is the temperature K: WP is water potential, MPa; a and b are constants.
the results. Values for O-2 litter are different from those for O-1 litter and vary somewhat among themselves. The calculated parameters for year-old hardwood and Douglas fir O-2 litter match each other quite closely, but parameters for the “new” hardwood O-2 are intermediate between these and the O-l parameters, possibly indicating that some material which would have been classified as O-l in older samples was included with the O-2 material. Parameters for pooled O-2 litter data were calculated. but represent the data less well than the pooled O-l parameters. At temperatures above the optimum the curves fall very rapidly, and pass through the x-axis between 40” and 50°C. (This cannot be observed directly in Fig. 2 because the vertical axis is logarithmic, so the horizontal axis does not correspond to a value of zero for decomposition.) If this effect is real, it could be interpreted according to the theory of antithermal maintenance to mean that above this temperature, thermal degradation of the rate-limiting enzyme is too rapid to be offset by metabolic replacement. Odum (1983) has developed more explicit models than in his earlier work, including models of thermal optima in metabolic rates. These models differ somewhat from the one presented here. Although Odum represented food intake and respiration with Arrhenius equations, he treated activation energy as a constant wherever it occurred. This led him to conclude that the exponential terms would be identical at any given temperature. He therefore assumed that metabolic inputs and losses would balance each other as temperature changed, and looked for the explanation of thermal optima in substrate limitation at high metabolic rates. But substrate limitation seems unattractive as a general explanation of the thermal optimum in litter decomposition rate, and need not be invokved if the effect of varying activation energies for different reactions is recognized. Quinlan (1981) developed a theory of biological reaction rates in which the Michaelis-Menten expression shows a temperature optimum without recourse to enzyme inactivation. In this model. the product of
Litter decay vs temperature and moisture the rate-limiting reaction may recombine to revert to the original reactants; if this reaction has a higher activation energy than the forward reaction and is sufficiently rapid, the rate of formation of products peaks at some temperature and then declines. This results in a model which, although differing greatly in mechanism, and somewhat in mathematical detail, should behave somewhat like the Eyring model proposed below. Eyring model
An alternative set of hypotheses to explain thermal optima were proposed by Eyring (Johnston et al., 1954). He also proposed that metabolic rates could be considered to be limited as if by a single enzymic reaction, but he demonstrated that some enzymes undergo reversibfe thermal inactivation, and combined this phenomenon with his “absolute rate theory” to explain thermal optima. Metabolism would accelerate with temperature (according to Eyring’s rate equation, which differs in detail from, but gives results similar to the Arrhenius expression), but would be divided by a term (1 + K), where K is the equilibrium ratio of inactive to active forms of the enzyme. This equilibrium ratio rises exponentially with absolute temperature, with the rate of rise depending on the enthalpy of the denaturation reaction in the same way that the increase in an Arrhenius rate depends on the activation energy. Eyring’s basic equation for enzyme reactions was fitted to the pooled 0- 1 and to pooled O-2 data, again with the help of Daniel and Wood’s (1980) NON-
‘r
O-l
Litter
-
Eyring
formula
Arrhenlus
plot
433
LINWOOD nonlinear curve-fitting procedure. The equation, and the values of parameters fitting it to the data, are given in Table 3. The curves produced by this formula are quite different from those generated by the “difference” formula presented above. Even when the enthalpy E, is quite high, the decrease in metabolic rate at temperatures above the optimum is gradual, and the rate approaches zero only asymptotically at high temperatures. Although the fit is just as good as that of the “difference” formulation, (R2 = 0.0870 vs 0.0866 for the “difference” model; residual SS = 0.046, vs 0.051 for the ‘difference” model, pooled O-l litter), the high rates of decomposition predicted for temperatures at and above 60°C seem improbable. Lacking data on the behavior of litter decomposition at the high temperatures at which these models diverge, we have little basis for choosing between this model and the “difference” model. Eyring’s has the advantage of a sound basis in the physics of enzyme reactions, but looks unrealistic at high temperatures. Like the “difference” theory proposed above, it requires that only four constants be fitted. At common forest-floor temperatures the two are indistinguishable; only experiments at higher temperatures can provide a valid basis for choosing between them. The importance of experiments at elevated temperatures did not become apparent until after the experiment was complete, and could not be undertaken afterwards because our controlled-environment equipment is not capable of functioning above 40°C. Eyring did not consider this formulation complete; he recognized that irreversible thermal denaturation of enzymes would begin at some temperature and that metabolism would cease. He proposed a temperature- and time-dependent exponential decay representing enzyme loss as a multiplier on the above expression to account for this. This is thoroughly reasonable for an in vitro system of the sort he was studying (light production in the luciferin-luciferase
Table 3. Summary Litter type
1 J? 0 T -1
O-2
Litter
F
-
Eyring
formula
Arrhenius
X X
-
“,
/q,# -2
dS/df
-
-4
-
-5
60
!!
\
XX\
I
I
I
50
40
30
1 20
= [a - b log,,( - WP)].cT (-Et/RT)/[I
X -3
19.88 19.76 19.03 19.80 19.16 16.88 19.00 17.06
Hardwood, old O-1 Hardwood, new O-I Douglas fir, O-l Pooled O-l Hardwood, old O-2 Hardwood, new O-2 Douglas fir. O-2 Pooled O-2
plot
I
I
10
0
= ‘C 1 ‘/ TabsCscale Fig. 3. Arrhenius plots of Eyring function fitted to pooled O-l and to pooled O-2 litter decomposition results, corrected for moisture as in Fig. 2.
of Eyring model
Values of fitted constants I” c Er 66.83 61.32 61.28 67.19 67.50 61.44 61.42 62.25
E,
AS
92.18 88.87 101.18 83. I9 122.04 118.50 123.14 118.87
0.3065 0.2908 0.3264 0.2738 0.3978 0.3834 0.3993 0.3852
exp +exp(-E,/RT),exp(AS/R)]
where: dS/df is decomposition rate, g CO, kg ’ litter h ‘; c is a combined reaction-rate constant; T is absolute temperature, K; Et is the enthalpy of the activated enzyme-substrate complex. kJ mol-‘. Calculated as Er- RT, where Er is the activation energy for metabolism, defined in Table 2; E, is the enthalpy of the reversible inactivation of the enzyme. kJ mol-‘; AS is the entropy of the enzyme-inactivation reaction, kJ mol-’ R is the gas constant, 0.008314 kJ molP’ Km’; WP is water potential, MPa; 0 and b are constants (see Table 2).
434
ALLENM. MOORE
system) in which enzyme, once denatured, could not be replaced; but it makes no allowance for the ability of intact organisms to replace the denatured enzyme. Where this could reasonabfy be expected to occur, a different formulation would be required. Because irreversible denaturation is probable, but is not modelled by the Eyring function, I consider something similar to the “‘difference” formula to be more likely to be a practical model of the temperature dependence of decomposition, pending experimental evidence at higher temperatures.
Brody S. (1945) Bioenergeties and Growth. Reinhold, New York. (Reprinted 1964, Hafner, New York). Bunnell F. L., Tait D., Flanagan P. and Van Cleve K. (19’77) Microbial respiration and substrate weight 1oss-I~ A general modei of the infhtences of abiotic variables.--II. A model of the influences of chemical camposition. Soil Bfofogp d Biachemistry 9%3347. Chen A. W. and Griffin D. M. (1966) Soil physical factors and the ecology of fungi. V. Further studies in relatively dry soils, Dansacfions of the BrStSsh Mycological Society 49,419-426.
Coniferous Forest Biome Modeling Group (1977) ~~~~~~~: a modei of carbon and xcater AOWthrotLpI?a conifrous forest. Buietin 8 (Contribution 2f9) C%& APPLICATIQNS ferious Forest Biome, Ecosystem Analysis Studies, US International Biological Program, University of Washing These equations were primarily intended to be used ton AR-IO, Seattle. by those modelling forest processes. Considering the Cook R. J. and Papendick R. I. (1970) Effect of soil water small differences in the reaction parameters of O-1 on microbial growth, antagonism, and nutrient availand of U-2 litter of rather different types and ages, it ability in relation to soil-borne fungat diseases OFplants. In Roof Diseases and Soil Borne ~athog~ (T. A. may be considered sufficient to select from the paramToussoun er al., Eds), pp. 81-88. University of California eter values provided here in non-critical applications. Press, Berkeley. It should be noted, however, that decomposition Cromack K. Jr and Monk C. (lQ75) Litter production, values derived from air-dried litter may be considdecomposition, and nutrient cycling in a mixed hardwood erably below field values due to the improverished watershed and’ a white pine watershed. In Mineral Cyciing inoculum of microflora. The model should be in Southe~tern ~~~~sterns (F. KoweIi, f. B. Gentrv and field-tested, and the coefficients should be adjusted to M. H, Smith, Ed& pp~ 609624. US Energy Research and match fiefd measurements. With the form of the Development Aaencv Symposium Vol. CONF-740513. model determined, it should be possible to establish National Tech&al Info&&ion Serivice, Springfield. values for different types of litter without setting up Daniel C. and Wood F. S. (1980) Fitting Equations to Data. Wiley, New York. a full factorial design. These equations also seem promising for use by Edwards N. T. and Sollins P. (1973) Continuous measurement of carbon dioxide evolution from partitioned researchers in the practical areas of composting and forest floor components. Ecu&~ 54,406412. sewage treatment, where maximum de~rn~os~ tion Goldstein R. A., Mankin J. B. and Luxmoare R. J, (1974) rates are desired and where elevated temperatures can Documentation of PROSPER: a model of atmospherebe utilized. There seems to be little theoretical guidsoil-plant water flow. Document No. EDFB-IBP-73-9. ance at present for engineers in these fields to use in Oak Ridge National Laboratory. National Technical determining optimum temperatures. For moderateInformation Service, Springfield. temperature situations it will make little difference Griffin D. M. (1981a) Water potential as a selective factor in the microbial ecology of soils. in Wuter Potential which temperature-depencence equation is used, but ~e~at~oas in Soil ~~~rob~o~ugy(J. F. Parr er af., Ed& Soil for clearcut forest studies, cornposting, or other Science Society of America Speciat Publication 9. situations where high temperatures occur, the choice Griffin D. M. (1981b) Water and microbial stress. Artuances may be critical. In such work, experiments should be in Microbiul Ecology 5, 91-136. planned not only to determined parameter values, but Haug R. T. (1980) Compost Engineering Principles and should be extended to appropriate temperature to Practice. Ann Arbor Science, Michigan. facilitate model selection. Jenny H., Gessel S. and Bingham F. (1949) Comparative studies of decomposition rates of organic matter in temperate and tropical regions. Soil Science & 4I9-432. ~~~n~~~~~~e~c~f~-I thank Dr Phillip Solfins and Dr Johnson F. H., Eyring H. and Polissar M. (1954) The Kinetic Basis of MoDcuiar Biology. Wiley, New York. Kermit Cromack af Oregon State University for consulting services, advice and encouragement; Dr Wayne Swank for Moore A. M. and Swank W. (1975) A model of water content and evaporation for hardwood leaf litter. In valuable discussion and liason with Coweeta Hydrologic Mineral Cycling in Southern Ecosytems (F. Howell et al., Laboratory; Dr Joel Milner for advice on statistical methEds), pp. 58-69. Energy Research and Development ods. Richard L. Wall, Cheryl Giadieux and Kim Snyder Agency Symposium Vd. CONF-740513. National Techassisted ably. The project was funded by the National nical Information Service, Springfield. Science Foundation. Nobei P. S. (1974) rntrodu~t~on to Bio,ohysical Phmt Physiology. Freeman, San Francisco. Odum H. T. (1967) Biological circuits and the marine systems of Texas. In PoNu?ion and Marine Ecology (T. A. REFERENCES Olson and F. J. Burgess, Fxls), pp. 999157. Wiley, New York. ASHRAE (1977) ASHRAE Handbook & Product Directory. ~~7~~~~~~~r~~. American Society of Heating, Refrig- odum H. T. (1983) Systems Ecology. Wiley, New York, Olson J. S. (1963) Energy storage and the balance of erating and Air-Conditioning Engineers, New York. producers and deeomposers in ecological systems. &ofBartholomew W. V. and Norman A. G. (1946) The threshogy 44, 322-330. old moisture content for active decomposition of some mature plant materials. Soil Science Society of America Quinlan A. (1981) The thermal sensitivity of generic Michaelis-Menten processes without catalyst denatuProceedings 7, 270-279. ration or inhibition. .iournal sf Thermal Biology 4 Birch H. F. (19S9) Further observations on humus decom103-l 14. position and nitrification. Plartt and Soii 9, 262-286.
Litter decay vs temperature and moisture Reichle D. E. (1967) Radioisotope turnover and energy flow in a terrestrial isopod populations. EcoIogy 48, 35 l-366. Swift M. J., Heal 0. W. and Anderson J. M. (1979) Decomposition in Terrestrial Ecosystems. University of California Press, Berkeley. Van Cleve K., Coyne P. I.1 Goodwin E., Johnson C. and Kellev M. (1979) A comparison of four methods for measuring respiration in organic material. Soil Biology & Biochemistry 11, 237-246.
Wetzel R. G. and Likens G. E. (1979) Limnological Analyses. Saunders, Philadelphia. Wiegert R. G. (1970) Effects of ionizing radiation on leaf fall, decomposition, and litter microarthropods of a montane rain forest. In A Tropical Rain Foresf (H. T. Odum
435
and R. F. Pigeon, Eds), pp. H89-HIOO. TID-24270 (Puerto Rico Nuclear Center Publ. 138), National Technical Information Service, Springheld. Wilson J. M. and Griffin D. M. (1975) Water potential and the respiration of microorganisms in the soil. Soil Biology & Biochemistry 7, 199-204. Witkamp M. (1966) Decomposition of leaf litter in relation to environment, microflora, and microbial respiration. Ecology 47, 194-201.
Witkamp M. and Frank M. L. (1969) Evolution of CO, from litter, humus and subsoil of a pine stand. Pedobiologia 9, 358-366.
Witkamp M. and van der Drift J. (1961) Breakdown of forest litter in relation to environmental factors. Planr and Soil 15, 295-311.
Soil Bid. Biochem.
4, pp. 427435,
1986
0038-0717/86 $3.00+ 0.00 Pergamon Journals Ltd
TEMPERATURE AND MOISTURE DEPENDENCE OF DECOMPOSITION RATES OF HARDWOOD AND CONIFEROUS LEAF LITTER ALLEN M. Moons Department of Biology, Western Carolina University, Cullowhee, NC 28723, U.S.A. (Accepted 20 Junuary 1986)
Summary-Samples of fresh (autumn) and of year-old (late summer) deciduous forest-leaf litter and humus, and of Douglas fir fine litter and humus, were wetted to known moisture content, nominally between 200 and 40% water (dry basis), and maintained at constant temperatures between 10”and 40°C. Rates of CO, production were measured by KOH absorption and titration. Decomposition rate was found to he a linear function of log - (water potential), and to approach a maximum near 40°C. The temperature-dependence was consistent with models based on irreversible heat inactivation of a rate-controlling enzyme, also with Eyring’s “absolute reaction rate” theory for reactions controlled by a reversibly inactivated enzyme. Activation energies were 66.867.3 kJ mol-’ for litter, and 61.4675 kJ mol-’ for humus decomposition; for enzyme inactivation energies were 150-154 kJ mol-‘.
INTRODUCTION
The litter and humus layers of the forest floor are important storage components for organic material, nutrients and moisture in temperature forests. They are also a source of food for an energetically significant microflora and fauna. The forest floor appears as a component in most models of mass and energy-flow in forest ecosystems (Coniferous Forest Biome Modeling Group, 1977; Goldstein et al., 1974), but the metabolic processes and the attendant changes in the mass of the forest floor are often not modelled explicitly. This is understandable: forest floor processes are complex, difficult to measure, poorly understood, and of limited relevance to the primary goals of many models. Models of energy and carbon flow must deal with the forest floor, however, if only as a temporary storage for organic material shed from the trees, and even hydrology models may incorporate fluctuations in the mass of the forest floor, because it influences moisture interception, infiltration and storage (Moore and Swank, 1975). Olson (1963) proposed an exponential-decay model of the litter layer which has been widely adopted (Wiegert, 1970; Moore and Swank, 1975; Cromack and Monk, 1975). This model gives good results on an annual basis, but its exponential rate of decomposition depends on a constant which is specific for a given litter type and climatic regime, but does not respond to annual or short-term changes in temperature and moisture, or to other variable factors in the environment. A number of workers have considered how litter decomposition responds to temperature, moisture, or both (Bartholomew and Norman, 1946; Jenny et al., 1949; Witkamp and van der Drift, 1961; Witkamp, 1966; Coniferous Biome Modeling Group, 1977; Bunnell et al., 1977). I have determined experimentally how the decomposition rates of litter and humus ‘(O-l and O-2) from mixed Southern hardwood and Douglas fir forests responded to a range of 427 s B.B.lW-F
temperature and moisture conditions and have attempted to develop mathematical descriptions of the results. In a series of experiments, samples of air-dried leaf-litter were wetted to a known moisture content and maintained at a defined temperature, while their rates of CO2 production were measured by chemical absorption and titration. A non-linear curve-fitting procedure was used to generate optimum coefficient values in equations to predict decomposition rates. Values generated by these relationships were compared with the observations. The relationships presented below are those which seemed interesting on theoretical grounds, conformed with the data, and which seemed to have potential practical value. METHODS Litter samples were collected from Watershed 14 at Coweeta Hydrologic Laboratory, near Franklin, North Carolina (a watershed dominated by mature hardwoods, especially oaks, with some understory dogwoods), and from the Reference Stand at the H. J. Andrews Experimental Forest, near Eugene, Oregon (an old growth Douglas fir forest). Samples were collected from Coweeta in December 1980 and July 1981, and from Oregon in July 1981. Late summer collection from Coweeta represented “old” litter which had been on the ground for most of a year; late fall collection represented recently-fallen “new” litter. Seasonality of leaf-fall was not assumed for the Douglas fir collections. Litter samples were collected by cutting around a 20 x 20 cm form with a knife, and then separating the sample from the soil with a spatula. The samples were inserted with a minimum of disturbance into specially-made nylon litter bags with Velcro closures and air-dried in the laboratory on wire racks for at least 6 weeks. They were selected for use in random sequence and weighed. The thickness of the
428
ALLEN M. MOORE
O-l and O-2 layers and of the total sample was recorded. The litter samples were then divided into O-l and O-2 components, which were placed in 20.3 x 20.3 x 7.5 cm sealable plastic boxes (Tupperware). Three replicates of each type of litter were used for each experiment. A fully-crossed sampling design was used. Decomposition rates were measured at each of four moisture levels (200, 120, 80 and 40% dry basis) and four temperatures (40”, 30”, 20” and 1OC). In each experiment, all leaf samples were brought to 200% moisture with distilled water and left sealed in the controlled environment chamber at the experimental temperature for 24 h before measurements began so as to allow the moisture to become evenly distributed. Temperature and moisture sensors were inserted in most boxes during equilibrium. Moisture was measured by Peltier-effect psychrometers (Wescor Inc., Model PST-55) and by resistive humidity sensors (Phys-Chem Research Corp., PCRC-11). Both types of sensor were connected to a datalogger (Wescor DL-520). The psychrometers used thermocouples which also transmitted temperature. Sample wet and dry weights provided a gravimetric measurement of moisture content. The sensors were removed during the 24-h CO2 absorption period. At the end of this period the samples were partially dried overnight, placed on a pan balance and rewetted to the moisture required for the next run (by sprinkling from an Erlenmeyer flask covered with a perforated aluminum foil top on the thin, undisturbed samples), allowed to equilibrate for 23-30 h with the sensors in place, and measured again for decomposition. The moisture measurements made with psychrometric and resistive humidity sensors, which measured water potential and effective and relative humidity directly, proved to be unreliable, correlating poorly with each other and with gravimetric results. The psychrometers measured moisture by comparing the temperatures of two thermocouples, one dry and relatively massive, the other of very fine (25 pm> wire, with a droplet of water condensed on it by the Peltier effect prior to measurement. The temperature differences they measured were minute, and they were extremely sensitive to ambient temperature fluctuations during measurement. They were able to measure moisture only at relatively high levels; the balance between the Peltier-effect and resistive heating in the fine thermocouple wire apparently would not produce sufficiently low temperatures to condense moisture from an environment at less the -5 MPa, so they provided no data when the litter was drier than this. Their performance deteriorated with use. A built-in validity check of the difference between the massive and fine thermocouples before cooling permitted rejection of readings influenced by temperature fluctuations but not of readings taken beyond the usable range of the sensor or from (other than grossly) defective sensors. The psychrometers were designed for use in soil and worked satisfactorily only if in intimate contact with the litter; they did not work in free air in equilibrium with the litter in a closed container, or in larger litter voids. The resistive humidity sensors were used to obtain moisture information when the litter was drier than the useful range of the psychrometers. Each sensor
consisted of an insulating board, 40 x 22 x 1 mm, with a conductive mask applied to the surface; the resistance of the exposed surface of the insulator across channels in the mask was inversely proportional to the humidity of the air. The datalogger measured this resistance and converted it into a value which could be converted into relative humidity. which in turn could be converted to water potential. The sensors were damaged by liquid water, and had to be protected from contact with the litter. Each had to be calibrated individually before each experiment in air of known humidity. The air was passed through a closed air train containing sulfuric acid at known concentrations. The sulfuric acid may have contributed to the sensors’ deterioration. They deteriorated rapidly and irreversibly under the conditions of the study, and recalibration failed to eliminate an increase in the variability of data as they aged. Gravimetric moisture remained as a reliable measurement, but in its original form correlates poorly with physiological availability of water and with decomposition rate (Swift et al., 1979). Transformations were therefore employed which converted gravimetric moisture to water potential via effective relative humidity and vapor pressure. Briefly, the conversions were as follows: Gravimetric moisture content was converted to effective relative humidity, using an empirical formula from the ASHRAE handbook (ASHRAE, 1977) with constants fitted to data from the resistive relative humidity sensors during an early phase of the experiment and before they began to fail. The formula was: r.h. = 1 - exp( - a .GMC*. T), where r.h. = effective relative humidity, fraction of saturation; GMC = gravimetric moisture content, gg-‘; T = absolute temperature, K; a = 0.013 (constant); b = 0.7 (constant). This relative humidity was converted to water potential by the formula (derived from Nobel, 1974): WP = (RT/V).
In (r.h.),
where WP is the water potential, MPa; R = the gas constant, 8.314 J mol-* K-‘; and V = the specific volume of water, 18 cm3 mol-‘. The repeated-measures design, with decomposition measured at several moisture levels in the same set of samples, was used for several reasons. It avoided the “flush” of rapid decomposition that Birch (1959) reported for the first hours after a sample was wetted, and activated the dormant microbiota under uniform and high moisture levels. It resulted in more nearly uniform moisture distribution than could have been achieved by de novo wetting of dry material, particularly in the low-moisture samples. It eliminated differences between samples as a source of variance between moisture levels. Although substrate quality and microbiota could be expected to change as a result of decomposition during the experiment, these samples had been repeatedly wetted and dried and had already undergone some decomposition in the field. They would contain very little labile or leachable material. The repeated-measures design does not complicate statistical analysis greatly. But most important, these experiments were intended to furnish models which could be used to calculate
Litter decay vs temperature and moisture decomposition in the field. Litter in the field is normally wetted thoroughly by rain and subsequently dried gradually; the experimental procedure was intended to simulate the normal course of the drying process after precipitation in the field. The penalty is that any gradual reduction in decomposition in time will be attributed in this model to moisture, whether or not this is the actual cause. A check of three cases in the experiment in which wetted samples were held longer than 30 h (44-51 h) before decomposition was measured showed no downward deviation from expected decomposition (O-l samples at 2O”C, log - (water potential) = 0.8; O-2 samples at 40” and at 2O”C, both at log - (water potential) = 1.4; see Fig. 1), suggesting that within the time-scale of this experiment, any simple time-dependent decrease in decomposition was small. There has been some question whether chemical absorption of CO? gives results comparable to more modern methods, such as i.r. gas analysis. Edwards and Sollins (1973) reported measurements of CO2 production by chemical absorption that were consistently 30% below measurements made with an i.r. gas analyzer. However, Van Cleve et al. (1979) compared four methods of measuring CO, production from homogenized titter samples and determined that values from KOH absorption were actually slightly higher than those made by i.r. gas analysis, and considerably higher than measurements by gas chromatography or Gilson respirometry. These results suggest that the KOH absorption method used here should not introduce bias. The absorbed CO2 was converted to bicarbonate and measured (Wetzel and Likens, 1979). The experimental procedure was as follows: 20.0ml of 1.0 N KOH solution was placed in a 78 x 78 mm disposable plastic weighing boat. The weighing boat was placed on a wire rack over the litter sample inside the sample box, which was then sealed. After 24 h, the weighing boat was removed, the KOH was rinsed into a 100 ml volumetric flask, 10.0 ml of 1.ON HCl was added (to partially neutralize the uncombined KOH and reduce absorption of atmospheric CO, during handling), and the sample was diluted to 100 ml with distilled water. The tlask was then sealed with paraffin film to prevent absorption of atmospheric CO?. Titration was carried out within 48 h. A 10.0 or 20.0 ml aliquot of each sample was titrated roughly to pH 9-10 with 1 N HCl, then precisely to pH 8.3 with 0.02 N HCl using phenolphthalein and a pH meter. The sample was then titrated to pH4.6 with 0.02 N HCI standardized to four significant figures against a potassium hydrogen phthalate standard. The equivalents of acid used in this titration were a measure of the equivalents of bicarbonate ion in the sample aliquot, which were a measure of the moles of CO, absorbed in the aliquot. Two or more replicates were run on each sampte. The results were converted first to moles h-’ and then to gC0, kg-’ litter h-‘. The CO, content of an empty box was measured in the same way during each run and deducted from the sample measurements. Data recorded included pre-run temperature and moisture-sensor data, titration and sample volumes, chamber temperatures recorded every 4 h during each run, and sample weights, from which sample mois-
429
ture content was calculated. A four-factor analysis of variance with repeated measures on the fourth factor was made using the SPSSX MANOVA procedure, with CO, production and its log as alternate dependent variables, with titter horizon, litter age and type, and temperature as between-subjects factors, and with moisture level as a within-subjects factor. A constant was added to the CO, production rate to eliminate negative readings and undefined logs. The log transformation was used because, contrary to the assumptions of ANOVA, the effects of most factors were thought to be muItiplicat~ve rather than additive. The logs of multiplicative factors would be additive, and would conform to the “linear additive” ANOVA model and avoid spurious interaction effects. Analysis confirmed that additional interaction effects were significant when untransformed data were used. An iterative non-linear regression procedure, NONLINWOOD (Daniel and Wood, 1980), was used to fit coefficients in hypothetical regression equations. The calculation and curve-fitting routines used the measured, rather than the nominal values for temperature and moisture. RESULTS
The analysis of variance results (Table 1) show that horizon, litter type, temperature, and a multivariate test of moisture were all highly significant (P < 0.01). The four-way interaction effect was not significant, but the interactions (type x temperature x moisture) and (horizon x temperature x moisture) were both significant (P < 0.01). This suggests that the interaction of temperature and moisture varied according to the horizon and the type of the sample. An examination of Fig. 1, which shows cell means for the various combinations of factors, suggests the nature of some of these relationships. In particular, temperature and a function of moisture appeared to have a multiplicative relationship, and O-2 litter had a generally lower rate of decomposition than O-I litter and showed some differences in its response to temperature and moisture. The effect of litter type and age was also significant, but contributed less to the variation than other factors. Surprisingly, new hardwood litter did not appear to decompose much faster than old litter. The analysis of variance verified that the main effects were significant and that development of regression models to fit them was justified. The significance of the interaction effects suggested that the models may need to be fairly complex. The decomposition rate increased as expected with increasing moisture but was not linearly related to gravimetric moisture content. Plots of decomposition rate against log - (water potential) were linear, however, and had the same x-intercept for all litter types (the water potential at which decomposition was zero). When the data were corrected for temperature using a relationship developed below, the slopes were also uniform, giving the following relationship: D (WP) = D(0)[0.6754 - 0.4253 log,,] WPl], where: lWP1 = absolute value of water potential, MPa; D (WP) = decomposition rate at measured water potential; and D(0) = decomposition rate at a reference water potential of - 0.178 MPa.
ALLEN M. Mooa~
430
Table 1. ANOVA summary for decomposition Source of variation
F
d.f.
Between subjects effects 1 41.40 2 6.52 3 66.20 2 3.00 3 4.9s 6 0.47 6 0.38
Horiz. Type Temp. Horiz. x Type Horiz. x Temp. Type x Temp. Horiz. x Type x Temp.
data Sienificance’ P < 0.01 P < 0.01 P < 0.01 NS P < 0.01 NS NS
Within-subjects effects Moist Horiz. x Moist Type x Moist Temp. x Moist
Horiz. x Type x Moist Horiz. x Temp. x Moist Type x Temp. x Moist Horiz. x Type x Temp. x Moist
3 3 6 9
593.60 11.30 5.16 12.5
6 9 18
2.18 7.65 2.93
I .58
18
P P P P
< < < <
0.01 0.01
0.01 0.01 NS
P < 0.01 P < 0.01 NS
‘Pillai’smultivariate test of significance. Horiz. = litter horizon, O-I and O-2; Type = litter type (old hardwood, new hardwood, Douglas fir); Temp. = temperature; Moist = moisture content of litter.
Hardwood Old
0.6
llttar
Hardwood
O-l
Old
litter
O-2
Hardwood New
O.6 r,,.
Douglas
litter
Hardwood
O-l
New
fir
r
Douglor O-2
Log - (water
potential,
littar
O-2
fir
Litter
MPa 1
Fig. 1. Decomposition rate of six litter types as a function of moisture. The solid lines were calculated by the “difference” model at the indicated temperatures, using parameters calculated for pooled O-1 on the left, and for individual O-2 samples on the right; the broken lines connecting symbols indicate measured values of CO, production at the temperature of the fitted line with which they are associated.
Litter decay vs temperature and moisture
The constants in this formula are from a leastsquares fit to all the data. The “reference water potential” is an artifact of the fitting process; it corresponds to a moisture content of over 200% at 40°C. The line intercepts the x-axis (a decomposition rate of zero) at log,, IWP ( = 1.58. This corresponds to a water potential of - 38 MPa or a relative humidity of about 75% at 20°C. According to the moisture characteristic curve adopted here, this corresponds to a limiting litter-moisture content for decomposition of 25%. The temperature relationship was found to be more complicated than expected. The literature generally suggests a Q,,, relationship of some sort (Witkamp and Frank, 1969; Reichle, 1967; Bunnel et al., 1977). There was as expected a rapid and accelerating increase in decomposition rate between 10” and 30°C suggesting a conventional Q,,, relationship; but at 40” the rates were little if any higher than at 30”. This suggested a tolerance curve, with an optimum temperature for decomposition somewhere in the vicinity of 40”, and an upper limiting temperature somewhat above this. An Arrhenius plot (log of reaction rate vs reciprocal of absolute temperature) shows the rates of first-order chemical reactions as straight lines. When the data were graphed as Arrhenius plots, they showed a clear deviation from linearity at 40” (Fig. 2). The temperature of the maximum decom-
O-l
Litter
O-2
Litter-
-
Difference
formula
Arrhemus
plot
1 r
‘2
-5
N
s
1
Difference
formula
Arrhenius
plot
.z 0
: -
x x
F -1
“-9,s
-2
;: x \
-3
XX\
-4 I: -51
I
II
60
50
40
11 Tab.
I 30
[scale
I 20
I 10
I 0
* T 3
Fig. 2. Arrhenius plots of temperature dependence of decomposition rates. Solid curves are generated by the “difference” model; data points have been corrected for moisture through division by the moisture function. In an Arrhenius plot, reaction rates which follow a simple Arrhenius function plot as a straight line.
431
position rate could be estimated for each litter type, but the shape of the curve beyond 40” is unknown. The rate could plummet to zero within a few degrees, or it could decline gently over a considerably range of temperature. Analogies with vertebrate physiology suggest the former, but literature on cornposting suggests the latter (Haug, 1980). Two models, corresponding to the two analogies, are developed below. DISCUSSION Moisture relationship
The moisture relationship developed above predicts that decomposition will cease at - 38 MPa. This is considerably drier than the moisture level at which Cook and Papendick (1970) reported hyphal development in fungi to be inhibited; they cite no instances of fungal growth below water potentials of -8 to - 10 MPa. Their emphasis was on pathogens of living plants, however, and litter decomposers have been shown to function at lower moisture levels. Swift et nl. (1979) in a review concluded that bacteria are inactive below - 1.0 to - 1.5 MPa in soils, whereas soil fungi are active down to - 15 MPa, and that some Aspergillus and PeniciIlium are active to -40 MPa and below. Chen and Griffin (1966) found certain species of Aspergillus and Penicillium capable of colonizing hair in contact with soil at a water potential of -39.6 MPa. Griffin (1981b) proposed three categories of xeric environments, each with a characteristic microflora. Aspergillus and Penicillium are listed as characteristic genera in environments in which the major constituent is a porous solid, the matric potential is low, and the solute potential is either low or high. Griffin (198 1b) cited work showing germination of conidia of Asperigillus chetlalieri at -50 MPa, but notes that germination at the lowest experimental water potentials was not always followed by continued mycelial growth. A water potential of -40 MPa is referred to as an extreme value at which a few fungi are capable of growth (Griffin 1981a). The ASHRAE handbook (ASHRAE, 1977; Chap. 10: Physiological Factors in Drying and Storing Farm Crops) considered a relative humidity of 65% (- 58 MPa) to be the maximum for safe storage of grains, and reports minimum moisture for growth of various species of Aspergillus of from 68 to 85% (- 52 to - 22 MPa). Griffin (1981 b) considered the relationship between growth rate and water potential, separating the effects of solute potential and matric potential. He considers growth rate “in general” to decline as a linear function of decreasing matric potential, but his curve for solute potential shows some concavity upwards at the lowest potentials, which hints at a logarithmic relationship. An experimental study (Wilson and Griffin, 1975, Fig. 5) shows consistent upward concavity of curves of respiration vs water potential in results from unamended soil; when these curves were re-plotted against the log of water potential, they were strikingly linear. These curves show cumulative oxygen consumption by decomposers over times from 20 to 120 h, and show that the linear form of the relationship was not affected by the duration of the experiment, nor was it an artifact of repeated-measures design.
ALLENM. MOORE
432 Temperature
Table 2. Summmary
Two models of the effect of temperature are considered here. In both of these models, decomposition depends on temperature as if there were a ratecontrolling single-enzyme reaction. Substrate concentration is considered to be in excess of ratecontrolling values; this simplifying assumption may not be valid even though the ratio of litter to microbiota is large, but there is no obvious way to calculate a value for substrate concentration. Only enzyme activity and concentration are invoked to explain the departure of decomposition rate from simple Ql,, models of reaction rate changes with temperature. The difference between the models lies in how enzyme activity is affected by temperature. Metabolic activity consists of two components: an “activity” component which is superimposed on an essential “basal” component (e.g. Brody, 1945). Odum (1967) refers to the basal component of metabolism as “antithermal maintenance”. The “difference” model is based on the following interpretation of this concept: (1) that random damage is done to cellular chemical structures as a result of bond breakage by thermal kinetic energy at the upper end of the statistical distribution of particle energies at ambient temperature, and that (2) a primary function of metabolic work is to repair this damage. This implies further: (3) that essential substances, including the hypothetical “metabolic rate limiting enzyme”, are constantly being destroyed by random thermal processes, (4) that due to the shape of the Maxwell-Boltzmann distribution, the rate of destruction of these substance increases roughly exponentially with temperature, and that (5) these substances are repaired or replaced at a rate which is limited by the metabolic rate. It can be assumed that both normal metabolic activity and the breakdown of essential enzymes would be first-order chemical reactions, the rates of which would show temperature dependence in the form of the Arrhenius equation: dS/dt = A .exp( - E,/RTabs), where dS/dt = the reaction rate, measured by change in quantity of substrate or product per unit time; A = a constant; E, = the activation energy, kJ mol-‘; R = the gas constant, 0.008314 kJ K-’ mol-‘; Tabs= temperature, K. The temperature dependence of the rate of each process is controlled by the activation energy of the rate-limiting step. The observed metabolic rate could then be approximated by the difference between the “forward” and “enzyme breakdown” reactions, each of which would be represented by Arrhenius equations, but with different activation energies. This formulation leads to curves which can be made to match the observed behavior of decomposition quite well. A non-linear curve-fitting procedure, NONLINWOOD (Daniel and Wood, 1980) was used to calculate values for the parameters of the equation shown in Table 2. Best-fitting values were calculated first for the data from each layer of each litter-type separately. Pooling of the data for the three types of O-l litter is not technically justified due to the significant interaction effects reported above, but has been done for the sake of brevity in reporting
Litter type Hardwood, old O-l Hardwood, new O-l Douglas fir, O-l Pooled O-I Hardwood, old O-2 Hardwood, new O-2 Douglas fir, O-2 Pooled O-2
a = 0.6754 + 0.048. b = dS/dt
of difference
Values of fitted constants In A, E, 26.22 26.23 26.05 26.19 25.62 23.44 25.51 23.58
66.83 67.32 67.28 67.19 67.50 61.44 67.41 62.25
model In A,
E,
58.70 58.65 58.50 58.63 58.20 57.00 58.09 56.95
152.55 153.07 153.35 153.00 153.77 150.49 154.00 150.84
0.4253f 0.030 (95% confidence limits).
= [A,exp(-&/AT) - A, exp( - E,/RT)][a
- b log,,( -
WP)]
where: dS/dr
= decomposition rate, g CO, kg-’ litter hK’; A,, A, are litter-type-specific constants for “forward” and “breakdown” reactions; E(, Eb are litter-type-specific activation energies for these reactions, kJ mol-‘; R is the gas constant. 0.008314 kJ mol-’ Km’; T is the temperature K: WP is water potential, MPa; a and b are constants.
the results. Values for O-2 litter are different from those for O-1 litter and vary somewhat among themselves. The calculated parameters for year-old hardwood and Douglas fir O-2 litter match each other quite closely, but parameters for the “new” hardwood O-2 are intermediate between these and the O-l parameters, possibly indicating that some material which would have been classified as O-l in older samples was included with the O-2 material. Parameters for pooled O-2 litter data were calculated. but represent the data less well than the pooled O-l parameters. At temperatures above the optimum the curves fall very rapidly, and pass through the x-axis between 40” and 50°C. (This cannot be observed directly in Fig. 2 because the vertical axis is logarithmic, so the horizontal axis does not correspond to a value of zero for decomposition.) If this effect is real, it could be interpreted according to the theory of antithermal maintenance to mean that above this temperature, thermal degradation of the rate-limiting enzyme is too rapid to be offset by metabolic replacement. Odum (1983) has developed more explicit models than in his earlier work, including models of thermal optima in metabolic rates. These models differ somewhat from the one presented here. Although Odum represented food intake and respiration with Arrhenius equations, he treated activation energy as a constant wherever it occurred. This led him to conclude that the exponential terms would be identical at any given temperature. He therefore assumed that metabolic inputs and losses would balance each other as temperature changed, and looked for the explanation of thermal optima in substrate limitation at high metabolic rates. But substrate limitation seems unattractive as a general explanation of the thermal optimum in litter decomposition rate, and need not be invokved if the effect of varying activation energies for different reactions is recognized. Quinlan (1981) developed a theory of biological reaction rates in which the Michaelis-Menten expression shows a temperature optimum without recourse to enzyme inactivation. In this model. the product of
Litter decay vs temperature and moisture the rate-limiting reaction may recombine to revert to the original reactants; if this reaction has a higher activation energy than the forward reaction and is sufficiently rapid, the rate of formation of products peaks at some temperature and then declines. This results in a model which, although differing greatly in mechanism, and somewhat in mathematical detail, should behave somewhat like the Eyring model proposed below. Eyring model
An alternative set of hypotheses to explain thermal optima were proposed by Eyring (Johnston et al., 1954). He also proposed that metabolic rates could be considered to be limited as if by a single enzymic reaction, but he demonstrated that some enzymes undergo reversibfe thermal inactivation, and combined this phenomenon with his “absolute rate theory” to explain thermal optima. Metabolism would accelerate with temperature (according to Eyring’s rate equation, which differs in detail from, but gives results similar to the Arrhenius expression), but would be divided by a term (1 + K), where K is the equilibrium ratio of inactive to active forms of the enzyme. This equilibrium ratio rises exponentially with absolute temperature, with the rate of rise depending on the enthalpy of the denaturation reaction in the same way that the increase in an Arrhenius rate depends on the activation energy. Eyring’s basic equation for enzyme reactions was fitted to the pooled 0- 1 and to pooled O-2 data, again with the help of Daniel and Wood’s (1980) NON-
‘r
O-l
Litter
-
Eyring
formula
Arrhenlus
plot
433
LINWOOD nonlinear curve-fitting procedure. The equation, and the values of parameters fitting it to the data, are given in Table 3. The curves produced by this formula are quite different from those generated by the “difference” formula presented above. Even when the enthalpy E, is quite high, the decrease in metabolic rate at temperatures above the optimum is gradual, and the rate approaches zero only asymptotically at high temperatures. Although the fit is just as good as that of the “difference” formulation, (R2 = 0.0870 vs 0.0866 for the “difference” model; residual SS = 0.046, vs 0.051 for the ‘difference” model, pooled O-l litter), the high rates of decomposition predicted for temperatures at and above 60°C seem improbable. Lacking data on the behavior of litter decomposition at the high temperatures at which these models diverge, we have little basis for choosing between this model and the “difference” model. Eyring’s has the advantage of a sound basis in the physics of enzyme reactions, but looks unrealistic at high temperatures. Like the “difference” theory proposed above, it requires that only four constants be fitted. At common forest-floor temperatures the two are indistinguishable; only experiments at higher temperatures can provide a valid basis for choosing between them. The importance of experiments at elevated temperatures did not become apparent until after the experiment was complete, and could not be undertaken afterwards because our controlled-environment equipment is not capable of functioning above 40°C. Eyring did not consider this formulation complete; he recognized that irreversible thermal denaturation of enzymes would begin at some temperature and that metabolism would cease. He proposed a temperature- and time-dependent exponential decay representing enzyme loss as a multiplier on the above expression to account for this. This is thoroughly reasonable for an in vitro system of the sort he was studying (light production in the luciferin-luciferase
Table 3. Summary Litter type
1 J? 0 T -1
O-2
Litter
F
-
Eyring
formula
Arrhenius
X X
-
“,
/q,# -2
dS/df
-
-4
-
-5
60
!!
\
XX\
I
I
I
50
40
30
1 20
= [a - b log,,( - WP)].cT (-Et/RT)/[I
X -3
19.88 19.76 19.03 19.80 19.16 16.88 19.00 17.06
Hardwood, old O-1 Hardwood, new O-I Douglas fir, O-l Pooled O-l Hardwood, old O-2 Hardwood, new O-2 Douglas fir. O-2 Pooled O-2
plot
I
I
10
0
= ‘C 1 ‘/ TabsCscale Fig. 3. Arrhenius plots of Eyring function fitted to pooled O-l and to pooled O-2 litter decomposition results, corrected for moisture as in Fig. 2.
of Eyring model
Values of fitted constants I” c Er 66.83 61.32 61.28 67.19 67.50 61.44 61.42 62.25
E,
AS
92.18 88.87 101.18 83. I9 122.04 118.50 123.14 118.87
0.3065 0.2908 0.3264 0.2738 0.3978 0.3834 0.3993 0.3852
exp +exp(-E,/RT),exp(AS/R)]
where: dS/df is decomposition rate, g CO, kg ’ litter h ‘; c is a combined reaction-rate constant; T is absolute temperature, K; Et is the enthalpy of the activated enzyme-substrate complex. kJ mol-‘. Calculated as Er- RT, where Er is the activation energy for metabolism, defined in Table 2; E, is the enthalpy of the reversible inactivation of the enzyme. kJ mol-‘; AS is the entropy of the enzyme-inactivation reaction, kJ mol-’ R is the gas constant, 0.008314 kJ molP’ Km’; WP is water potential, MPa; 0 and b are constants (see Table 2).
434
ALLENM. MOORE
system) in which enzyme, once denatured, could not be replaced; but it makes no allowance for the ability of intact organisms to replace the denatured enzyme. Where this could reasonabfy be expected to occur, a different formulation would be required. Because irreversible denaturation is probable, but is not modelled by the Eyring function, I consider something similar to the “‘difference” formula to be more likely to be a practical model of the temperature dependence of decomposition, pending experimental evidence at higher temperatures.
Brody S. (1945) Bioenergeties and Growth. Reinhold, New York. (Reprinted 1964, Hafner, New York). Bunnell F. L., Tait D., Flanagan P. and Van Cleve K. (19’77) Microbial respiration and substrate weight 1oss-I~ A general modei of the infhtences of abiotic variables.--II. A model of the influences of chemical camposition. Soil Bfofogp d Biachemistry 9%3347. Chen A. W. and Griffin D. M. (1966) Soil physical factors and the ecology of fungi. V. Further studies in relatively dry soils, Dansacfions of the BrStSsh Mycological Society 49,419-426.
Coniferous Forest Biome Modeling Group (1977) ~~~~~~~: a modei of carbon and xcater AOWthrotLpI?a conifrous forest. Buietin 8 (Contribution 2f9) C%& APPLICATIQNS ferious Forest Biome, Ecosystem Analysis Studies, US International Biological Program, University of Washing These equations were primarily intended to be used ton AR-IO, Seattle. by those modelling forest processes. Considering the Cook R. J. and Papendick R. I. (1970) Effect of soil water small differences in the reaction parameters of O-1 on microbial growth, antagonism, and nutrient availand of U-2 litter of rather different types and ages, it ability in relation to soil-borne fungat diseases OFplants. In Roof Diseases and Soil Borne ~athog~ (T. A. may be considered sufficient to select from the paramToussoun er al., Eds), pp. 81-88. University of California eter values provided here in non-critical applications. Press, Berkeley. It should be noted, however, that decomposition Cromack K. Jr and Monk C. 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(1973) Continuous measurement of carbon dioxide evolution from partitioned researchers in the practical areas of composting and forest floor components. Ecu&~ 54,406412. sewage treatment, where maximum de~rn~os~ tion Goldstein R. A., Mankin J. B. and Luxmoare R. J, (1974) rates are desired and where elevated temperatures can Documentation of PROSPER: a model of atmospherebe utilized. There seems to be little theoretical guidsoil-plant water flow. Document No. EDFB-IBP-73-9. ance at present for engineers in these fields to use in Oak Ridge National Laboratory. National Technical determining optimum temperatures. For moderateInformation Service, Springfield. temperature situations it will make little difference Griffin D. M. (1981a) Water potential as a selective factor in the microbial ecology of soils. in Wuter Potential which temperature-depencence equation is used, but ~e~at~oas in Soil ~~~rob~o~ugy(J. F. 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