Parallel functional and stoichiometric trait shifts in South American and African forest communities with elevation

The Amazon and Congo basins are the two largest continuous blocks of tropical forest with a central role for global biogeochemical cycles and ecology. However, both biomes differ in structure and species richness and composition. Understanding future directions of the response of both biomes to environmental change is paramount. We used one elevational gradient on both continents to investigate functional and stoichiometric trait shifts of tropical forest in South America and Africa. We measured community-weighted functional canopy traits and canopy and topsoil delta N-15 signatures. We found that the functional forest composition response along both transects was parallel, with a shift towards more nitrogen-conservative species at higher elevations. Moreover, canopy and topsoil delta N-15 signals decreased with increasing altitude, suggesting a more conservative N cycle at higher elevations. This cross-continental study provides empirical indications that both South American and African tropical forest show a parallel response with altitude, driven by nitrogen availability along the elevational gradients, which in turn induces a shift in the functional forest composition. More standardized research, and more research on other elevational gradients is needed to confirm our observations

Leaky nitrogen cycle in pristine African montane rainforest soil

Many pristine humid tropical forests show simultaneously high nitrogen (N) richness and sustained loss of bioavailable N forms. To better understand this apparent upregulation of the N cycle in tropical forests, process-based understanding of soil N transformations, in geographically diverse locations, remains paramount. Field-based evidence is limited and entirely lacking for humid tropical forests on the African continent. This study aimed at filling both knowledge gaps by monitoring N losses and by conducting an in situ 15N labeling experiment in the Nyungwe tropical montane forest in Rwanda. Here we show that this tropical forest shows high nitrate (NO3−) leaching losses, confirming findings from other parts of the world. Gross N transformation rates point to an open soil N cycle with mineralized N nitrified rather than retained via immobilization; gross immobilization of NH4+ and NO3− combined accounted for 37% of gross mineralization, and plant N uptake is dominated by ammonium (NH4+). This study provided new process understanding of soil N cycling in humid tropical forests and added geographically independent evidence that humid tropical forests are characterized by soil N dynamics and N inputs sustaining bioavailable N loss

Spatial and seasonal patterns of rainfall erosivity in the Lake Kivu region: insights from a meteorological observatory network

In the Lake Kivu region, water erosion is the main driver for soil degradation, but observational data to quantify the extent and to assess the spatial-temporal dynamics of the controlling factors are hardly available. In particular, high spatial and temporal resolution rainfall data are essential as precipitation is the driving force of soil erosion. In this study, we evaluated to what extent high temporal resolution data from the TAHMO network (with poor spatial and long-term coverage) can be combined with low temporal resolution data (with a high spatial density covering long periods of time) to improve rainfall erosivity assessments. To this end, 5 minute rainfall data from TAHMO stations in the Lake Kivu region, representing ca. 37 observation-years, were analyzed. The analysis of the TAHMO data showed that rainfall erosivity was mainly controlled by rainfall amount and elevation and that this relation was different for the dry and wet season. By combining high and low temporal resolution databases and a set of spatial covariates, an environmental regression approach (GAM) was used to assess the spatiotemporal patterns of rainfall erosivity for the whole region. A validation procedure showed relatively good predictions for most months (R2 between 0.50 and 0.80), while the model was less performant for the wettest (April) and two driest months (July and August) (R2 between 0.24 and 0.38). The predicted annual erosivity was highly variable with a range between 2000 and 9000 MJ mm ha−1 h−1 yr−1 and showed a pronounced east–west gradient which is strongly influenced by local topography. This study showed that the combination of high and low temporal resolution rainfall data and spatial prediction models can be used to improve the assessments of monthly and annual rainfall erosivity patterns that are grounded in locally calibrated and validated data