Worldwide decomposition rates depend both on climate and the legacy of plant functional traits as litter quality. To quantify the degree to which functional differentiation among species affects their litter decomposition rates, we brought together leaf trait and litter mass loss data for 818 species from 66 decomposition experiments on six continents. We show that: (i) the magnitude of species-driven differences is much larger than previously thought and greater than climate-driven variation; (ii) the decomposability of a species' litter is consistently correlated with that species' ecological strategy within different ecosystems globally, representing a new connection between whole plant carbon strategy and biogeochemical cycling. This connection between plant strategies and decomposability is crucial for both understanding vegetation-soil feedbacks, and for improving forecasts of the global carbon cycle.

Although it is generally accepted that plant community composition is key for predicting rates of ecosystem processes in the face of global change, microbial community composition is often ignored in ecosystem modeling. To address this issue, we review recent experiments and assess whether microbial community composition is resistant, resilient, or functionally redundant in response to four different disturbances. We find that the composition of most microbial groups is sensitive and not immediately resilient to disturbance, regardless of taxonomic breadth of the group or the type of disturbance. Other studies demonstrate that changes in composition are often associated with changes in ecosystem process rates. Thus, changes in microbial communities due to disturbance may directly affect ecosystem processes. Based on these relationships, we propose a simple framework to incorporate microbial community composition into ecosystem process models. We conclude that this effort would benefit from more empirical data on the links among microbial phylogeny, physiological traits, and disturbance responses. These relationships will determine how readily microbial community composition can be used to predict the responses of ecosystem processes to global change.functional redundancy ͉ microbial diversity ͉ modeling

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Extracellular enzymes are the proximate agents of organic matter decomposition and measures of these activities can be used as indicators of microbial nutrient demand. We conducted a global-scale meta-analysis of the seven-most widely measured soil enzyme activities, using data from 40 ecosystems. The activities of b-1,4-glucosidase, cellobiohydrolase, b-1,4-N-acetylglucosaminidase and phosphatase g)1 soil increased with organic matter concentration; leucine aminopeptidase, phenol oxidase and peroxidase activities showed no relationship. All activities were significantly related to soil pH. Specific activities, i.e. activity g )1 soil organic matter, also varied in relation to soil pH for all enzymes. Relationships with mean annual temperature (MAT) and precipitation (MAP) were generally weak. For hydrolases, ratios of specific C, N and P acquisition activities converged on 1 : 1 : 1 but across ecosystems, the ratio of C : P acquisition was inversely related to MAP and MAT while the ratio of C : N acquisition increased with MAP. Oxidative activities were more variable than hydrolytic activities and increased with soil pH. Our analyses indicate that the enzymatic potential for hydrolyzing the labile components of soil organic matter is tied to substrate availability, soil pH and the stoichiometry of microbial nutrient demand. The enzymatic potential for oxidizing the recalcitrant fractions of soil organic material, which is a proximate control on soil organic matter accumulation, is most strongly related to soil pH. These trends provide insight into the biogeochemical processes that create global patterns in ecological stoichiometry and organic matter storage.

Stocks of soil organic carbon represent a large component of the carbon cycle that may participate in climate change feedbacks, particularly on decadal and centennial timescales. For Earth system models (ESMs), the ability to accurately represent the global distribution of existing soil carbon stocks is a prerequisite for accurately predicting future carbon–climate feedbacks. We compared soil carbon simulations from 11 model centers to empirical data from the Harmonized World Soil Database (HWSD) and the Northern Circumpolar Soil Carbon Database (NCSCD). Model estimates of global soil carbon stocks ranged from 510 to 3040 Pg C, compared to an estimate of 1260 Pg C (with a 95% confidence interval of 890–1660 Pg C) from the HWSD. Model simulations for the high northern latitudes fell between 60 and 820 Pg C, compared to 500 Pg C (with a 95% confidence interval of 380–620 Pg C) for the NCSCD and 290 Pg C for the HWSD. Global soil carbon varied 5.9 fold across models in response to a 2.6-fold variation in global net primary productivity (NPP) and a 3.6-fold variation in global soil carbon turnover times. Model–data agreement was moderate at the biome level (R² values ranged from 0.38 to 0.97 with a mean of 0.75); however, the spatial distribution of soil carbon simulated by the ESMs at the 1° scale was not well correlated with the HWSD (Pearson correlation coefficients less than 0.4 and root mean square errors from 9.4 to 20.8 kg C m⁻²). In northern latitudes where the two data sets overlapped, agreement between the HWSD and the NCSCD was poor (Pearson correlation coefficient 0.33), indicating uncertainty in empirical estimates of soil carbon. We found that a reduced complexity model dependent on NPP and soil temperature explained much of the 1° spatial variation in soil carbon within most ESMs (R² values between 0.62 and 0.93 for 9 of 11 model centers). However, the same reduced complexity model only explained 10% of the spatial variation in HWSD soil carbon when driven by observations of NPP and temperature, implying that other drivers or processes may be more important in explaining observed soil carbon distributions. The reduced complexity model also showed that differences in simulated soil carbon across ESMs were driven by differences in simulated NPP and the parameterization of soil heterotrophic respiration (inter-model R² = 0.93), not by structural differences between the models. Overall, our results suggest that despite fair global-scale agreement with observational data and moderate agreement at the biome scale, most ESMs cannot reproduce grid-scale variation in soil carbon and may be missing key processes. Future work should focus on improving the simulation of driving variables for soil carbon stocks and modifying model structures to include additional processes

The factors driving β-diversity (variation in community composition) yield insights into the maintenance of biodiversity on the planet. Here we tested whether the mechanisms that underlie bacterial β-diversity vary over centimeters to continental spatial scales by comparing the composition of ammonia-oxidizing bacteria communities in salt marsh sediments. As observed in studies of macroorganisms, the drivers of salt marsh bacterial β-diversity depend on spatial scale. In contrast to macroorganism studies, however, we found no evidence of evolutionary diversification of ammonia-oxidizing bacteria taxa at the continental scale, despite an overall relationship between geographic distance and community similarity. Our data are consistent with the idea that dispersal limitation at local scales can contribute to β-diversity, even though the 16S rRNA genes of the relatively common taxa are globally distributed. These results highlight the importance of considering multiple spatial scales for understanding microbial biogeography.iodiversity supports the ecosystem processes upon which society depends (1). Understanding the mechanisms that generate and maintain biodiversity is thus key to predicting ecosystem responses to future environmental changes. The decrease in community similarity with geographic distance is a universal biogeographic pattern observed in communities from all domains of life (as in refs. 2-4). Pinpointing the underlying causes of this "distance-decay" pattern continues to be an area of intense research (5-9), as such studies of β-diversity (variation in community composition) yield insights into the maintenance of biodiversity. These studies are still relatively rare for microorganisms, however, and thus our understanding of the mechanisms underlying microbial diversity-most of the tree of liferemains limited.β-Diversity, and therefore distance-decay patterns, could be driven solely by differences in environmental conditions across space, a hypothesis summed up by microbiologists as, "everything is everywhere-the environmental selects" (10). Under this model, a distance-decay curve is observed because environmental variables tend to be spatially autocorrelated, and organisms with differing niche preferences are selected from the available pool of taxa as the environment changes with distance.Dispersal limitation can also give rise to β-diversity, as it permits historical contingencies to influence present-day biogeographic patterns. For example, neutral niche models, in which an organism's abundance is not influenced by its environmental preferences, predict a distance-decay curve (8, 11). On relatively short time scales, stochastic births and deaths contribute to a heterogeneous distribution of taxa (ecological drift). On longer time scales, stochastic genetic processes allow for taxon diversification across the landscape (evolutionary drift). If dispersal is limiting, then current environmental or biotic conditions will not fully explain the distance-decay curve, and thus geographic distance will b...

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