Terrestrial ecosystems strongly determine the exchange of carbon, water and energy between thebiosphere and atmosphere. These exchanges are influenced by environmental conditions (e.g.,... Show moreTerrestrial ecosystems strongly determine the exchange of carbon, water and energy between thebiosphere and atmosphere. These exchanges are influenced by environmental conditions (e.g., localmeteorology, soils), but generally mediated by organisms. Often, mathematical descriptions of theseprocesses are implemented in terrestrial biosphere models. Model implementations of this kind shouldbe evaluated by empirical analyses of relationships between observed patterns of ecosystem function-ing, vegetation structure, plant traits, and environmental conditions. However, the question of how todescribe the imprint of plants on ecosystem functioning based on observations has not yet been systemat-ically investigated. One approach might be to identify and quantify functional attributes or responsivenessof ecosystems (often very short-term in nature) that contribute to the long-term (i.e., annual but alsoseasonal or daily) metrics commonly in use. Here we define these patterns as “ecosystem functional prop-erties”, or EFPs. Such as the ecosystem capacity of carbon assimilation or the maximum light use efficiencyof an ecosystem. While EFPs should be directly derivable from flux measurements at the ecosystem level,we posit that these inherently include the influence of specific plant traits and their local heterogeneity.We present different options of upscaling in situ measured plant traits to the ecosystem level (ecosystemvegetation properties – EVPs) and provide examples of empirical analyses on plants’ imprint on ecosys-tem functioning by combining in situ measured plant traits and ecosystem flux measurements. Finally,we discuss how recent advances in remote sensing contribute to this framework. Show less
tEcological assessments such as species distribution modelling and benchmarking site quality towardsregulations often rely on full spatial coverage information of site factors such as soil acidity,... Show moretEcological assessments such as species distribution modelling and benchmarking site quality towardsregulations often rely on full spatial coverage information of site factors such as soil acidity, moistureregime or nutrient availability. To determine if remote sensing (RS) is a viable alternative to traditionaldata sources of site factor estimates, we analysed the accuracy (using ground truth validation measure-ments) of traditional and RS sources of pH and mean spring groundwater level (MSL, in m) estimates.Traditional sources were a soil map and hydrological model. RS estimates were obtained using vegetationindicator values (IVs) from a Dutch national system as an intermediate between site factors and spectralresponse. IVs relate to those site factors that dictate vegetation occurrence, whilst also providing a robustlink to canopy spectra. For pH, the soil map and the RS estimate were nearly as accurate. For MSL, theRS estimates were much closer to the observed groundwater levels than the hydrological model, but theerror margin of the estimates still exceeded the tolerance range of moisture sensitive vegetation. Therelatively high accuracy of the RS estimates was made possible by the availability of local calibrationpoints and large environmental gradients in the study site. In addition, the error composition of the RSestimates could be analysed step-by-step, whereas the traditional sources had to be accepted ‘as-is’. Alsoconsidering that RS offers high spatial and temporal resolution at low costs, RS offered advantages overtraditional sources. This will likely hold true for any other situation where prerequisites of accurate RSestimates have been met. Show less