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
Aim The influence of soil properties on photosynthetic traits in higher plants is poorly quantified in comparison with that of climate.We address this situation by quantifying the unique and joint... Show moreAim The influence of soil properties on photosynthetic traits in higher plants is poorly quantified in comparison with that of climate.We address this situation by quantifying the unique and joint contributions to global leaf-trait variation from soils and climate. Location Terrestrial ecosystems world-wide. Methods Using a trait dataset comprising 1509 species from 288 sites, with climate and soil data derived from global datasets, we quantified the effects of 20 soil and 26 climate variables on light-saturated photosynthetic rate (Aarea), stomatal conductance (gs), leaf nitrogen and phosphorus (Narea and Parea) and specific leaf area (SLA) using mixed regression models and multivariate analyses. Results Soil variables were stronger predictors of leaf traits than climatic variables, except for SLA. On average, Narea, Parea and Aarea increased and SLA decreased with increasing soil pH and with increasing site aridity. gs declined and Parea increased with soil available P (Pavail). Narea was unrelated to total soil N. Joint effects of soil and climate dominated over their unique effects on Narea and Parea, while unique effects of soils dominated for Aarea and gs. Path analysis indicated that variation in Aarea reflected the combined independent influences of Narea and gs, the former promoted by high pH and aridity and the latter by low Pavail. Main conclusions Three environmental variables were key for explaining variation in leaf traits: soil pH and Pavail, and the climatic moisture index (the ratio ofprecipitation to potential evapotranspiration). Although the reliability of global soil datasets lags behind that of climate datasets, our results nonetheless provide compelling evidence that both can be jointly used in broad-scale analyses, and that effects uniquely attributable to soil properties are important determinants of leaf photosynthetic traits and rates. A significant future challenge is to better disentangle the covarying physiological, ecological Show less
