Climate fails to predict wood decomposition at regional scales

Abstract

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 ago 9,10 , 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. Climate is traditionally thought to be the predominant control on decomposition rates at global and regional scales, with biotic factors controlling only local rates 2,4 . Biotic factors are divided into decomposer organisms, such as soil microbes, and the quality (for example, chemical composition) of the plant litter they decompose. Recent work suggests that litter quality may be more important than climate in controlling decomposition rates across biomes worldwide 3,11 , but the influence of decomposer organisms is still assumed limited across broad climate gradients 12 . A core reason for this assumption is that climate is considered a primary control on the activity of decomposers. As such, across climate gradients, mean temperature and moisture availability are assumed to explain much of the variation in the activity of decomposer organisms and hence decomposition rates of organic matter. These climatedecomposition relationships are used to parameterize and evaluate Earth-system models 13 . It is therefore important to test the assumption that climate drives decomposer activities because proper understanding of these activities is needed to inform model projections such as carbon cycleclimate