Climate fails to predict wood decomposition at regional scales

Journal name:
Nature Climate Change
Volume:
4,
Pages:
625–630
Year published:
DOI:
doi:10.1038/nclimate2251
Received
Accepted
Published online

Decomposition of organic matter strongly influences ecosystem carbon storage1. In Earth-system models, climate is a predominant control on the decomposition rates of organic matter2, 3, 4, 5. This assumption is based on the mean response of decomposition to climate, yet there is a growing appreciation in other areas of global change science that projections based on mean responses can be irrelevant and misleading6, 7. We test whether climate controls on the decomposition rate of dead wood—a carbon stock estimated to represent 73 ± 6 Pg carbon globally8—are sensitive to the spatial scale from which they are inferred. We show that the common assumption that climate is a predominant control on decomposition is supported only when local-scale variation is aggregated into mean values. Disaggregated data instead reveal that local-scale factors explain 73% of the variation in wood decomposition, and climate only 28%. Further, the temperature sensitivity of decomposition estimated from local versus mean analyses is 1.3-times greater. Fundamental issues with mean correlations were highlighted decades ago9, 10, yet mean climate–decomposition relationships are used to generate simulations that inform management and adaptation under environmental change. Our results suggest that to predict accurately how decomposition will respond to climate change, models must account for local-scale factors that control regional dynamics.

At a glance

Figures

  1. Competing conceptual models of relationships between decomposition and climate across regional to global gradients.
    Figure 1: Competing conceptual models of relationships between decomposition and climate across regional to global gradients.

    a, The classical conceptual model where climate is the predominant control. b, A conceptualization where local-scale factors that affect decomposer activity are instead the predominant control on decomposition rates. Decomposition is represented as mass loss of plant litter, and climate as mean annual temperature. Decomposition rates and climate variables are, however, represented using various expressions, including rate constants (k) and functions that integrate mean monthly temperature and precipitation data. The representation of these variables affects the form of the relationship (for example, linear versus curvilinear) but the relationships are always positive, as depicted above. The classical decomposition paradigm, shown in a, posits that climate explains (and controls) variation in decomposition rates at regional to global scales because climate functions as the primary control on the activity of decomposer organisms. In contrast, an emerging idea in projecting ecological responses to global change, shown in b, suggests instead that local-scale controls on biotic activity generate local-level variation in process rates equal to or greater than broad-scale controls such as climate, highlighting the need to understand local context-dependency to project decomposition rates under changing environmental conditions.

  2. Relationships between wood decomposition, climate and fungi when local-scale variation is collapsed into a mean value for each of the five locations across the regional gradient.
    Figure 2: Relationships between wood decomposition, climate and fungi when local-scale variation is collapsed into a mean value for each of the five locations across the regional gradient.

    a,b, Relationships between decomposition and the significant explanatory variables. c, Relative influence on decomposition of the explanatory variables retained in the best-fit model. Location-scale variation is generally collapsed into a mean value for global- and regional-scale decomposition studies; we do it here to evaluate changes in the interpretation of the dominant controls on mass loss compared with when local-scale variation is retained (Fig. 1). Decomposition is expressed as the proportion of wood carbon lost from the initial carbon mass of a common wood substrate. Data points (a,b) represent mean observations from 32 wood blocks placed at each of five locations (n = 5), and are plotted against the top x axis for fungal colonization (a; %) and temperature (b; °C) for comparison with Supplementary Table 1. Regression lines (a,b) are shown for significant main effects and are standardized (bottom x axis; unit-less) for the influence of the other variables in the full linear model, which retained soil temperature, fungi and their interaction (c; all significant at p < 0.05). Plotting the standardized variables (a,b) and coefficients (c), permits us to make a relative comparison of the influence of each variable and model term on mass loss despite the different scales on which the variables are measured (for example, temperature as degrees Celsius versus fungi as percentage colonization). The standardized coefficients reveal that temperature had the strongest influence on mass loss, and that fungi and the temperature × fungi interaction had similar and much smaller effects (c).

  3. Decomposition of wood blocks is greater with higher temperatures, fungal colonization and termite biomass across a regional gradient in eastern US temperate forest.
    Figure 3: Decomposition of wood blocks is greater with higher temperatures, fungal colonization and termite biomass across a regional gradient in eastern US temperate forest.

    ac, Relationships between decomposition and the significant explanatory variables. d, Relative influence on decomposition of the explanatory variables retained in the best-fit model. Decomposition is expressed as the proportion of wood carbon lost from the initial carbon mass of a common wood substrate. Data points (ac) represent observations for individual wood blocks (n = 158), and are plotted against the top x axis for fungal colonization (a; %), temperature (b; °C), and termites (c; g wood-block−1) for comparison with Supplementary Table 1. Regression lines (ac) are shown for significant explanatory variables and are standardized (bottom x axis, unit-less) for the influence of the other variables in the full linear mixed model, which retained soil temperature, moisture, fungi, termites and ants (d). The errors for significant (p < 0.05) model variables do not cross zero (d). Plotting the standardized variables (ac) and coefficients (d) permits us to make a relative comparison of the influence of each variable and model term on mass loss despite the different scales on which the variables are measured. The standardized coefficients reveal that fungi had the most influence on mass loss, and termites the least (d).

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Author information

Affiliations

  1. School of Forestry and Environmental Studies, Yale University, 370 Prospect Street New Haven, Connecticut 06511, USA

    • Mark A. Bradford,
    • Thomas W. Crowther,
    • Daniel S. Maynard &
    • Emily E. Oldfield
  2. SUNY Buffalo State, Biology Department, 1300 Elmwood Avenue Buffalo, New York 14222, USA

    • Robert J. Warren II
  3. Institute of Microbiology of the ASCR, Vídeňská 1083, 14220 Praha 4, Czech Republic

    • Petr Baldrian
  4. National Center for Atmospheric Research, Boulder, Colorado 80307, USA

    • William R. Wieder
  5. Department of Ecology, Evolution, and Environmental Biology, Columbia University, 1200 Amsterdam Avenue New York, New York 10027, USA

    • Stephen A. Wood
  6. Biology Department, University of Central Florida, 4000 Central Florida Boulevard Orlando, Florida 32816, USA

    • Joshua R. King

Contributions

M.A.B. and R.J.W. contributed equally to this work. Together with J.R.K., they conceived and established the study. M.A.B., R.J.W., P.B., T.W.C., E.E.O. and J.R.K. performed field and laboratory work. M.A.B., R.J.W. and S.A.W. analysed data. W.R.W. modelled the decomposition data. M.A.B. wrote the first draft of the manuscript. All authors contributed to data interpretation and paper writing.

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The authors declare no competing financial interests.

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