Search

Biodiversity positively affects vegetation productivity and the ability of ecosystems to withstand disturbance events and invasions of alien species (ecosystem stability). However, the relationship between biodiversity and ecosystem functioning remains understudied at the landscape or whole ecosystem scales.In particular, biodiversity can not be easily monitored at large spatial scales or frequent intervals. Therefore, confronting measurements of biodiversity and ecosystem functioning at the same spatial and temporal scales remains challenging. In this work, we present new methods to systematically bridge these scale gaps. We focus on collecting ecosystem data and developing metrics able to decypher the effect of plant biodiversity on ecosystem functioning. Based on eddy covariance fluxes from 78 NEON and ICOS sites, we compute key ecosystem functional properties related to ecosystem productivity and stability. Moreover, we calculate biodiversity indices from field surveys of species abundances, functional traits, and structural properties at these sites. Finally, we compute remote sensing metrics of biodiversity based on Sentinel 2 measurements. These metrics exploit the fine scale multispectral information from different and complementary perspectives, and are adapted to match the footprint of typical eddy covariance sites.We investigate the relationship between ground- and satellite-based biodiversity metrics to understand the capability of remote sensing to contribute to biodiveristy-ecosystem function analyses that may one day be scaled globally. Despite dominant environmental and climatic constraints, we hypothesize that ecosystem functional properties covary with biodiversity metrics. To elucidate this point, we analyze the multivariate relationship between the different biodiversity estimates, ecosystem functional properties related to water, carbon, and energy fluxes, structural variables of the vegetation, and climate.Assessing whether biodiversity effects apply to the functioning and stability of ecosystems is pivotal to understanding ecosystem processes and developing appropriate forecast models and climate change mitigation strategies.

The frequency, intensity, and duration of drought are expected to increase in many regions under climate change. A large number of studies have shown that droughts influence terrestrial ecosystems. Yet, assessments of drought impacts on ecosystem carbon cycling usually focus on instantaneous effects during drought, while legacy effects following drought can be important as well. Here, we provide the first synthesis about drought legacy effects on gross primary productivity (GPP) based on 90 long-term (>=7 years) eddy covariance sites across the globe. We predict the ‘potential’ GPP in the 2 years following drought (considered legacy years) based on a random forest model trained by data in non-legacy time periods. Legacy effects are inferred based on the difference between actual and ‘potential’ GPP in legacy periods. Results show widespread drought legacy effects on GPP across the globe. The change in GPP due to legacy effects is of the same order of magnitude as instantaneous effects. Furthermore, using the unconditional dependence test on many different potential factors, we find legacy effects unconditionally depend on aridity, instantaneous impact intensity, and species richness in forests. The conditional dependence test further reveals aridity primarily modulates legacy effects in forests. These findings highlight the significance of drought legacy effects on ecosystem carbon cycling across the globe. We find a dominant role of climatic controls on drought legacy effects, while species diversity effects did not explain variability in drought legacy effects.

Accurate estimation of terrestrial gross primary productivity is essential for the development of credible future carbon cycle and climate simulations. Current remote sensing techniques allow retrieval of sun-induced chlorophyll fluorescence (SIF) as a valid proxy for GPP, but low resolution, sparse coverage, or resolution mismatches between the different satellite sensors hinder our ability to effectively link SIF to many environmental variables at fine scales. In order to better characterize heterogeneous landscapes, several attempts to downscale SIF products to higher resolutions have been made. We investigate the ability of the downscaled GOME-2 product developed by Duveiller et al. (2019), to capture the differences in spatiotemporal dynamics over the Greater Alpine Space. We analyse SIF in connection to land cover and elevation, and calculate land phenology metrics based on seasonal SIF time series. Ground-based GPP validation suggests biome-specific SIF-GPP relationships, but the comparison was hindered by the resolution mismatch of the data. Moreover, missing data are present at high elevations, diminishing the suitability of current SIF products to analyse cloud-prone mountainous areas. Important insights into spatial patterns and seasonal trends could be inferred at forest and other large-area land cover types, typical of mid elevations in the Alps, but many anthropogenic habitats at low elevations, as well as high elevation grasslands and other small-scale heterogeneous environments could not be thoroughly investigated and are likely to be underrepresented or prone to biases. Similar downscaling procedures might be applied at finer scales to e.g. TROPOMI products, or alternative advanced remote sensing SIF techniques and instruments might be needed in order to enable detailed and systematic evaluations of the Alpine region or similar highly heterogenous landscapes, before a process-oriented monitoring and unbiased implementation into climate models may be performed.