Global ecological predictors of the soil priming effect.

Identifying the global drivers of soil priming is essential to understanding C cycling in terrestrial ecosystems. We conducted a survey of soils across 86 globally-distributed locations, spanning a wide range of climates, biotic communities, and soil conditions, and evaluated the apparent soil priming effect using 13C-glucose labeling. Here we show that the magnitude of the positive apparent priming effect (increase in CO2 release through accelerated microbial biomass turnover) was negatively associated with SOC content and microbial respiration rates. Our statistical modeling suggests that apparent priming effects tend to be negative in more mesic sites associated with higher SOC contents. In contrast, a single-input of labile C causes positive apparent priming effects in more arid locations with low SOC contents. Our results provide solid evidence that SOC content plays a critical role in regulating apparent priming effects, with important implications for the improvement of C cycling models under global change scenarios.

Changes in belowground biodiversity during ecosystem development.

Belowground organisms play critical roles in maintaining multiple ecosystem processes, including plant productivity, decomposition, and nutrient cycling. Despite their importance, however, we have a limited understanding of how and why belowground biodiversity (bacteria, fungi, protists, and invertebrates) may change as soils develop over centuries to millennia (pedogenesis). Moreover, it is unclear whether belowground biodiversity changes during pedogenesis are similar to the patterns observed for aboveground plant diversity. Here we evaluated the roles of resource availability, nutrient stoichiometry, and soil abiotic factors in driving belowground biodiversity across 16 soil chronosequences (from centuries to millennia) spanning a wide range of globally distributed ecosystem types. Changes in belowground biodiversity during pedogenesis followed two main patterns. In lower-productivity ecosystems (i.e., drier and colder), increases in belowground biodiversity tracked increases in plant cover. In more productive ecosystems (i.e., wetter and warmer), increased acidification during pedogenesis was associated with declines in belowground biodiversity. Changes in the diversity of bacteria, fungi, protists, and invertebrates with pedogenesis were strongly and positively correlated worldwide, highlighting that belowground biodiversity shares similar ecological drivers as soils and ecosystems develop. In general, temporal changes in aboveground plant diversity and belowground biodiversity were not correlated, challenging the common perception that belowground biodiversity should follow similar patterns to those of plant diversity during ecosystem development. Taken together, our findings provide evidence that ecological patterns in belowground biodiversity are predictable across major globally distributed ecosystem types and suggest that shifts in plant cover and soil acidification during ecosystem development are associated with changes in belowground biodiversity over centuries to millennia.

A new method to compare statistical tree growth curves: the PL-GMANOVA model and its application with dendrochronological data.

Growth curves are monotonically increasing functions that measure repeatedly the same subjects over time. The classical growth curve model in the statistical literature is the Generalized Multivariate Analysis of Variance (GMANOVA) model. In order to model the tree trunk radius (r) over time (t) of trees on different sites, GMANOVA is combined here with the adapted PL regression model Q = A · T+E, where for b ≠ 0 : Q = Ei[-b · r]-Ei[-b · r1] and for b = 0 : Q  = Ln[r/r1], A =  initial relative growth to be estimated, T = t-t1, and E is an error term for each tree and time point. Furthermore, Ei[-b · r]  = ∫(Exp[-b · r]/r)dr, b = -1/TPR, with TPR being the turning point radius in a sigmoid curve, and r1 at t1 is an estimated calibrating time-radius point. Advantages of the approach are that growth rates can be compared among growth curves with different turning point radiuses and different starting points, hidden outliers are easily detectable, the method is statistically robust, and heteroscedasticity of the residuals among time points is allowed. The model was implemented with dendrochronological data of 235 Pinus montezumae trees on ten Mexican volcano sites to calculate comparison intervals for the estimated initial relative growth A. One site (at the Popocatépetl volcano) stood out, with A being 3.9 times the value of the site with the slowest-growing trees. Calculating variance components for the initial relative growth, 34% of the growth variation was found among sites, 31% among trees, and 35% over time. Without the Popocatépetl site, the numbers changed to 7%, 42%, and 51%. Further explanation of differences in growth would need to focus on factors that vary within sites and over time.