Multiscale approaches to the vulnerability and adaptive capacity of ecosystems to global change and resource scarcity

Coordinators :    Lisa Wingate ; Frédéric Frappart

Understanding the potential effects of global change on ever larger scales is often hampered by the logistical constraints of taking measurements over large, sometimes remote and inaccessible areas, and by extrapolating physiological mechanisms from known species to plants with very different temporalities. New technologies now make it possible to acquire a vast amount of essential data, and on a wide range of spatio-temporal scales. Being able to measure changes in physiological processes at the scale of a tree, or even on a smaller scale, or in the immediate vicinity of a flux tower, can serve to enrich a mechanistic model and, at the same time, alert us to a change in biological processes, which could lead to significant decreases in productivity or high forest mortality. Combining this type of in-situ observation with remote sensing observations can boost ecosystem monitoring by multiplying the spatio-temporal scales of observation, and provide a better understanding of ecosystem functions and the threats they face.Over the next 5 years, we will develop a research program aimed at harnessing observations of important ecosystem processes and traits at small scales and linking them to larger-scale 'big data' measurements and trends using mechanistic understanding, models and trait databases. In particular, we propose to investigate the following 5 scientific questions.

Better understand the underlying causes of short-term trends in terrestrial biosphere browning.

Over the past 30 years, satellites have recorded changes in the timing and intensity of seasonal and annual greening of the Earth's surface. Although, overall, the planet is becoming progressively greener (possibly due to the fertilizing effect of CO2), some regions - and there is currently debate as to whether some are actually browning - are tending to brown, sometimes attributed to increased forest dieback due to drought and fire. What are the real determinants of these browning trends, and should we expect them to increase with rising CO2? This is a question that will be explored within the unit using a suite of satellite products and large-scale infrastructures, in particular ICOS-RI and the international phenocam network. ISPA has the expertise to better estimate the various contributions of drought mortality, fire, deforestation and plant metabolism and productivity (nutrients and secondary metabolites) to recent changes in CO2 and climate.

Do leaf traits (chemical composition and spectral properties) tell us anything about the type of mycorrhizal partner?

Studies suggest that temperate ecosystems dominated by plants associated with ectomycorrhizae (EM) are better able to store C in their biomass in response to an increase in CO2 than ecosystems dominated by plants associated with arbuscular mycorrhizae (AM). Mapping mycorrhizal associations on a large scale remains a major challenge, although it has been shown that spectral reflectance indices of vegetation acquired by remote sensing are strongly correlated with mycorrhizal associations and can be used to distinguish AM vs. EM-dominated forest patches. It is also becoming increasingly clear that mycorrhizal associations are 'integrating traits' of below- and above-ground community members and their functions. The main aim of this study will be (a) to examine the links between leaf chemical composition, phenologies and spectral properties among temperate species, and (b) to understand how these leaf traits relate to specific relationships between a plant species and its mycorrhizal partner at multi-scale.

Monitoring the biomass and water status of ecosystems on a large scale (country, continent) is essential to detect the carbon sinks and sources of forests, key terms in the global carbon balance, and to better predict and model their sensitivity to natural and anthropogenic stresses: droughts, forest fires, deforestation, biotic attacks, ... This large-scale monitoring of carbon stocks requires decoupling the influence of seasonal effects linked to water status (surface moisture - SM, VOD) and vegetation growth (VOD) on the signal measured by low spatial resolution (~25 km) satellite sensors operating in the microwave domain (AMSR-2, SMOS, SMAP, ASCAT and soon CIMR). The synergy of a large number of in-situ and space-based observations, available over a wide range of nested spatial scales and at different time steps, will enable us to gain a better understanding of processes based on in-situ observations (e.g. flux measurements from ICOS sites, mini radar sensors of tree water status) and to relate processes and remote sensing information available from landscape to regional scale, using indices derived from different products (NDVI, LAI, land cover, thermal infrared observations of canopy water stress, tree heights derived from lidar measurements, radar observables). This will also enable us to validate SM and VOD products deduced from large-scale spatial observations, and to develop methodologies for estimating SM and VOD at higher spatial resolution using statistical disaggregation methods and high spatial resolution data.

Limiting agricultural production by nutrients on a global scale

The aim here is to gain a better understanding of the contribution of the various mechanisms involved in limiting agricultural production on a global scale by nutrients (soil nutrient dynamics, plant adjustments, interaction between nutrients, etc.). Global yield losses will be estimated for the current period and under different scenarios (e.g. relocation of food production, reduction of livestock farming, expansion of organic farming). For this, we will rely on simple models developed within the unit at plot scale (e.g. phosphorus uptake by plants, soil phosphorus dynamics model), applied at global scale (half-degree lati x lon resolution) under certain assumptions and fed by global databases on agricultural practices in particular. Spatialized databases used as model inputs or to compare outputs come from collaborations (satellite data disaggregated by ecosystem type, extrapolated local data or downscaled national inventories).

"Downscaling" climate to landscape scale

In order to anticipate the future of socio-ecosystems, we are using increasingly reliable climate projections, but with limited spatial resolution, of the order of at least ten kilometers. This spatial resolution is not fine enough to study the climatic risks of crops or the impact of land-use planning and agro-forestry practices on micro-habitats and biodiversity. Top-down scaling approaches are therefore used to account for the influence of topo-geographical properties (latitude, continentality, altitude, slope, aspect, etc.) on local climate. At present, these approaches do not take into account the influence of the type of vegetation cover on the climatic variables at the top of the canopy. However, a number of studies in temperate and tropical zones show that, compared with adjacent grassland or crops, the 'radiative' temperature of a forest, measured by satellite or in situ, has a smaller daily amplitude and is cooler on average. Modelling studies also show that a reduction in wooded area has a lesser effect on air temperature than on radiative temperature, but this effect is nonetheless marked, particularly in the tropics. The impact of vegetation cover type on local and regional climate is also a function of the climatic episode (extreme event, etc.). At ISPA, we aim to develop new approaches to climate downscaling on the scale of heterogeneous landscapes that can account for biophysical processes linked to the morpho-physiology of plant canopies. By combining digital terrain models and decametric land-use maps (Theia CES Sol) with micro-meteorological sensor networks in agricultural and forest landscapes, we will test several downscaling approaches and study the influence of vegetation cover type, on site and in the surrounding area (within a radius of 100 to 500 m), on micro-meteorological variables above the canopy.

Collaborations: INRAE: BIOGECO, TETIS, Nationales: ESPACE-DEV, IMS, LEB, LERMA, OMP, CNRM. Internationales: ICOS-RI, Colorado State University, MIT, PBL Netherlands Environment Assessment Agency, Uni. Leuven, UCSB