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Decomposition Processes:Modelling Approaches and Applications the Science of the Total Environment A”rnlrmmrionsl ,avms,To.SC,rn,i(i. Rrlrvrh ELSEVIER The Scienceof the Total Environment 183(1996) 137-149 Decomposition processes:modelling approaches and applications Daryl L. Moorhead*a, Robert L. Sinsabaughb, A.E. Linkins”, James F. Reynoldsd ‘Ecology Program, Department of Biological Sciences, Texas Tech University, Lubbock, Texas 79409-3131, USA ‘Biology Department, University of Toledo, Toledo, Ohio 43606. USA ‘Office of the Vice President, Clarkson University, Pots&m, New York 13676, USA ‘Botany Department - The Phytotron, Duke University, Durham, North Carolina 27708-0340, USA Abstract Decomposition is a fundamental ecosystem process, strongly influencing ecosystem dynamics through the release of organically bound nutrients. Decomposition is also a complex phenomenon that can be modified by changes in the characteristics of the decaying materials or prevailing environmental conditions. For these reasons, the impacts of local, regional or global environmental changes on the quality and turnover of dead organic matter are of considerable interest. However, realistic limits to the complexity, as well as temporal and spatial scales,of experimental studies re- strict their usefulnessin extrapolating long-term or large-scaleresults of simultaneous environmental changes. Alter- natively, many simulation models have been constructed to gain insight to potential impacts of anthropogenic activities. Becausestructure and approach determine the strengths and limitations of a model, they must be considered when applying one to a problem or otherwise interpreting model behaviour. There are two basically different types of models: (1) empirical models generally ignore underlying processeswhen describing system behaviour, while (2) mechanistic models reproduce system behaviour by simulating underlying processes.The former models are usually accurate within the range of conditions for which they are constructed but tend to be unreliable when extended beyond these limits. In contrast, application of a mechanistic model to novel conditions assumes only that the underlying mechanismsbehave in a consistent manner. In this paper, we examine models developed at different levelsof resolution to simulate various aspects of decomposition and nutrient cycling and how they have been used to assesspotential impacts of environmental changes on terrestrial ecosystems. Keywords: Decomposition; Ecosystem modelling; Enzyme models 1. Introduction e.g. the Antarctic Dry Valleys (Cathey et al., 1981; Parker and Simmons, 1985). Nonetheless, decom- Decomposition is comparable in importance to posers typically receive less attention from scien- primary production as a fundamental ecosystem tific investigators than plants or other hetero- process. In fact, an ecosystem needs only pro- trophic groups. ducers and decomposers (as biological com- Decomposition is a composite phenomenon in ponents) to exist indefinitely, and some extreme which many different processes contribute to the environments support few other trophic entities, degradation of complex organic compounds (Fig. 1; see also Swift et al., 1979). Theoretically, these * Correspondingauthor, processes ultimately reduce organic materials to QO48-9697/96/$15.000 1996Elsevier Science B.V. All rights reserved SSDI 0048-9697(95)04974-6 138 D.L. Moorhead et al. /The Science of the Total Environment 183 (1996) 137-149 levels of resolution is used to examine various Organic: K7 b Inorganic: .carbohydrates CO2 aspects of decomposition and nutrient cycling. *proteins Decomposkxr -NH4 *fats *photochemical -PO4 -thermal 2. Types of models -enzymic Many mathematical models have been con- Fig. 1. Conceptual model of processes responsible for litter structed to simulate litter decay, and a brief criti- decay. que of common mathematical techniques is provided by Weider and Lang (1982). A more gen- eral scheme (Reynolds and Leadley, 1992) their inorganic constituents. Of particular impor- classified models according to the underlying con- tance to ecosystem dynamics is the release of ceptual approach. This classification system organically bound nutrients, e.g. nitrogen and clarifies the strengths and limitations inherent in phosphorus, which then can be utilized by plants. different modelling strategies and, thus, provides Organic matter decay can be viewed from many valuable insights for potential management ap- levels of resolution, e.g. an ecosystem process, a plications. property of saprophytic community dynamics, an Reynolds and Leadley recognize two fundamen- extension of decomposer microorganism physi- tally different categories, viz, empirical and ology and nutrition, or as a composite biochemical mechanistic models. Empirical models describe the process based on enzyme kinetics. In this paper behaviour of a system at a particular hierarchical reference is made to a hierarchical framework, level of interest, without examining underlying emphasizing relationships between levels of resolu- processes. For this reason, such models often are tion, insights provided within levels, and applica- very accurate predictive tools within the range of tions to specific questions (Table 1). Biomass system behaviour for which they are developed, dynamics of microbial communities, net respira- but their utility in exploring processes on other tory output, etc., are included in the knowledge sites, for other systems, or in response to changing that taxonomic, methodological, and interpretive environmental conditions is highly problematical. problems limit understanding of soil microbiota at In contrast, mechanistic models reproduce system the community, population, species and individual behaviour by simulating underlying processes. The levels (Klopatek et al., 1993). In spite of such dish- application of mechanistic models to conditions culties, decomposition and concomitant nutrient other than those for which they were developed cycling processes are central to many environmen- assumes that the underlying processes behave in a tal management scenarios, e.g. maintenance of site consistent manner. fertility, reclamation following disturbance, deter- Many models incorporate elements of empirical mining sustainable harvest regimes, and assessing and mechanistic approaches. For example, semi- long-term system responses to global changes. In mechanistic models include a mix of mechanistic this paper, the development of models at different and empirical approaches at a given hierarchical level of interest. These models offer potential for extrapolation because they explicitly incorporate Table 1 Hierarchical perspectives of decomposition processes; level of some of the biological underpinnings of the interest and associated context process. Because structure and approach determine the Level of interest Context limitations of a model, they must be considered Biosphere Changing climate, CO,, UV-B when applying one to a problem or interpreting Ecosystem Energy and nutrient cycles model behaviour. Community Plant-microbial interactions Population Microbial growth 2. I. Empirical approaches Physiological Microbial physiology Sub-cellular Extracellular enzymes In general, decomposing materials lose mass as they decay and negative exponential models often D.L. Moorhead et al. / The Science of the Total Environment 183 (1996) 137-149 139 are used to describe this pattern (Weider and where AET is actual evapotranspiration, L is lig- Lang, 1982): nin content of the litter and a, b and c are model parameters. Another empirical model was Mass t = Mass, . emkt (1) developed by Melillo et al. (1982), in which mass loss is an exponential function of time (Eq. 1) and where the litter mass remaining after some time in- the decay rate coefftcient varies with initial lignin terval (t) is calculated as a function of the original and nitrogen content of the litter: mass and a decay rate coefficient (k). Without knowledge of the underlying mechanisms, the im- -k = de (lignin/nitrogen~ (3) portance of environmental factors can be used em- pirically to modify this pattern. For example, the where d and fare model parameters. The primary rate of litter mass loss is affected by temperature strength of such empirical models is their ability to and moisture conditions. Temperature effects predict system behaviour accurately within the often are described by an exponential equation or range of data for which they were developed. The Qlo function. On the other hand, moisture effects models developed by Meentemeyer and Melillo are more complicated, maximum decay rate occurs and colleagues were based on data collected in at some optimum moisture condition and declines closed-canopy, mesic, temperate forests; they have as moisture conditions diverge from this optimum been used very successfully to describe litter decay (Bunnell and Scoullar, 1975). Usually, the effects in such ecosystems. of moisture and temperature are included in Although the Meentemeyer model describes lit- models by scaling the estimated decay rate (Eq. 1) ter decay well in many ecosystems, it does not hold according to temperature and moisture responses for others (Whitford et al., 1981; Schaefer et al., (And&n and Paustian, 1987). 1985). Surface litter loses mass far too rapidly in Not all types of materials decay at the same rate the northern Chihuahuan Desert, USA, to be con- even under identical climatic regimes, but knowl- sistent with Meentemeyer’s equation (Fig. 2); edge of underlying chemistry is needed to predict Schaefer et al. (1985) found
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