Aboveground overyielding in a mixed temperate forest is not explained by belowground processes.

The relationship between forest productivity and tree species diversity has been described in detail, but the underlying processes have yet to be identified. One important issue is to understand which processes are at the origin of observed aboveground overyielding in some mixed forests. We used a beech-maple plantation exhibiting aboveground overyielding to test whether belowground processes could explain this pattern. Soil cores were collected to determine fine root (FR) biomass and vertical distribution. Correlograms were used to detect spatial arrangement. Near-infrared reflectance spectroscopy was used to identify the tree species proportion in the FR samples and spatial root segregation. An isotopic approach was used to identify water acquisition patterns. The structure and the composition of the ectomycorrhizal fungal community were determined by high-throughput sequencing of DNA in the soil samples. We found no spatial pattern for FR biomass or for its vertical distribution along the gradients. No vertical root segregation was found, as FR density for both species decreased with depth in a similar way. The two species displayed similar vertical water acquisition profiles as well, mainly absorbing water from shallow soil layers; hence, niche differentiation for water acquisition was not highlighted here. Significant alterations in the fungal community compositions were detected in function of the percentage of maple in the vicinity of beech. Our findings do not support the commonly suggested drivers of aboveground overyielding in species-diverse forests and suggest that competition reduction or between-species facilitation of belowground resource acquisition may not explain the observed aboveground overyielding.

Douglas fir stimulates nitrification in French forest soils.

Douglas fir trees presumable stimulate nitrification in the soil. We studied in 21 French Douglas fir forests if and how nitrification is modulated by soil properties, past land use and current forest management. Soil (0-10 cm depth) was collected and initial concentrations of N-NH4+ and N-NO3-, potential net nitrogen mineralization (PNM) and net nitrification (PNN) rates and microbial biomass were measured. At 11 of the 21 sites, annual nitrate fluxes in the soil were measured using anion exchange resin bags. Soils contained between 2.3 to 29.4 mg N-NO3- kg soil-1. About 86% (±14%) of mineral N was nitrate. The proportion of nitrate increased to almost 100% during incubation. PNN varied from 0.10 mg N kg soil-1 day-1 to 1.05 mg N kg soil-1 day-1 (21 sites). Neither the initial nitrate concentration nor PNN was related to soil chemistry (pH, % C, %N, P, CEC), microbial biomass, texture, past land use or thinning. In situ net nitrate accumulation (NNA) estimated with resins beds varied from 4 to 100 kg N-NO3- ha-1 yr-1 (11 sites). It was positively correlated with base saturation, clay content, ELLENBERG N, temperature and negatively with soil organic N, C/N ratio and precipitation.

CAVIAR: an R package for checking, displaying and processing wood-formation-monitoring data.

In the last decade, the pervasive question of climate change impacts on forests has revived investigations on intra-annual dynamics of wood formation, involving disciplines such as plant ecology, tree physiology and dendrochronology. This resulted in the creation of many research groups working on this topic worldwide and a rapid increase in the number of studies and publications. Wood-formation-monitoring studies are generally based on a common conceptual model describing xylem cell formation as the succession of four differentiation phases (cell division, cell enlargement, cell wall thickening and mature cells). They generally use the same sampling techniques, sample preparation methods and anatomical criteria to separate between differentiation zones and discriminate and count forming xylem cells, resulting in very similar raw data. However, the way these raw data are then processed, producing the elaborated data on which statistical analyses are performed, still remains quite specific to each individual study. Thereby, despite very similar raw data, wood-formation-monitoring studies yield results that are still quite difficult to compare. CAVIAR-an R package specifically dedicated to the verification, visualization and manipulation of wood-formation-monitoring data-can help to improve this situation. Initially, CAVIAR was built to provide efficient algorithms to compute critical dates of wood formation phenology for conifers growing in temperate and cold environments. Recently, we developed it further to check, display and process wood-formation-monitoring data. Thanks to new and upgraded functions, raw data can now be consistently verified, standardized and modelled (using logistic regressions and Gompertz functions), in order to describe wood phenology and intra-annual dynamics of tree-ring formation. We believe that CAVIAR will help strengthening the science of wood formation dynamics by effectively contributing to the standardization of its concepts and methods, making thereby possible the comparison between data and results from different studies.