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Agricultural biodiversity and associated ecosystem functions are declining at alarming rates due to widespread land use intensification. They can only be maintained through targeted landscape management that supports species with different habitat preferences, dispersal capacities and other functional traits that determine their survival. However, we need better understanding whether short-term measures can already improve functional diversity in European agroecosystems.
We investigated spatio-temporal responses of bees (solitary bees, bumblebees and honey bees), hoverflies, carabid beetles and spiders to newly established grassland strips in Lower Austria over 3 years, and along a distance gradient to old grasslands. Specifically, we asked if new grasslands, compared to old grasslands and cereal fields, serve as temporal dispersal habitat or corridor, and how species-specific traits affect dispersal patterns. Using a trait-based functional diversity approach, we investigated year and distance effects for nine selected key traits per taxon (e.g. body size, feeding guild and habitat preferences).
Our results show that the functional diversity of predators and pollinators (i.e. functional richness and evenness), as well as community-weighted means of selected key traits in new grasslands significantly differed from adjacent cereal fields, but only slowly adjusted to adjacent old grasslands. These effects significantly decreased with increasing distance to old grasslands for carabids and spiders, but not for mobile bees and hoverflies.
Synthesis and applications. Over 3 years, newly established grassland strips supported larger sized and actively foraging/hunting species in the agricultural landscape. Adjacent crops likely benefit from such measures through enhanced functional diversity and related ecosystem services. However, our results also suggest that 3-year period is too short to enhance the occurrence of pollinators and epigeic predators in new grasslands. Agri-environment measures need to be complemented by the conservation of permanent habitats to effectively maintain species and functional diversity. Our findings should be acknowledged by European policy and agricultural decision makers for the design of more effective agri-environment schemes, taking into account trait-dependent species responses to land use change.
Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.
Land Surface Temperature (LST) is an important parameter for tracing the impact of changing climatic conditions on our environment. Describing the interface between long- and shortwave radiation fluxes, as well as between turbulent heat fluxes and the ground heat flux, LST plays a crucial role in the global heat balance. Satellite-derived LST is an indispensable tool for monitoring these changes consistently over large areas and for long time periods. Data from the AVHRR (Advanced Very High-Resolution Radiometer) sensors have been available since the early 1980s. In the TIMELINE project, LST is derived for the entire operating period of AVHRR sensors over Europe at a 1 km spatial resolution. In this study, we present the validation results for the TIMELINE AVHRR daytime LST. The validation approach consists of an assessment of the temporal consistency of the AVHRR LST time series, an inter-comparison between AVHRR LST and in situ LST, and a comparison of the AVHRR LST product with concurrent MODIS (Moderate Resolution Imaging Spectroradiometer) LST. The results indicate the successful derivation of stable LST time series from multi-decadal AVHRR data. The validation results were investigated regarding different LST, TCWV and VA, as well as land cover classes. The comparisons between the TIMELINE LST product and the reference datasets show seasonal and land cover-related patterns. The LST level was found to be the most determinative factor of the error. On average, an absolute deviation of the AVHRR LST by 1.83 K from in situ LST, as well as a difference of 2.34 K from the MODIS product, was observed.