The aim of the study was to explore the impact of climate and land use type on species richness and abundance of arable weeds found as casual aliens in Finland. We used an empirical data set collected along a European climate gradient, from which 163 plants were selected for the analyses. Using generalised linear modelling (GLMM) and redundancy analysis (RDA), we compared species richness and abundance of all species and three functional groups (dicotyledons and grasses, C 3 and C 4 species, alien and native species to Europe), as well as occurrence of the 13 most common individual species between three land use types (fallow, low-input and conventionally cultivated) and six regions. In general, fallowing and low-input cultivation, as well as warmer climate, supported greater weed species richness and abundance. Temperature was found to be a more important factor for weed abundance than land use. The only exception was C 4 species richness, which did not respond to either factor. The impact of land use was independent of climate for all variables, except C 4 species abundance. Non-cultivated fallows had more weeds than cultivated fields, in most cases. No difference was found in the species richness and abundance of grasses or in the occurrence of individual species between low-input and conventionally cultivated fields. It was evident that climate and land use affect the distribution of arable weed species currently found as casual aliens in Finland. This suggests that climate warming will increase the risk of the population establishment of new weed species in northern regions.
Analyses of extreme weather events and their impacts often requires big data processing of ensembles of climate model simulations. Researchers generally proceed by downloading the data from the providers and processing the data files "at home" with their own analysis processes. However, the growing amount of available climate model and observation data makes this procedure quite awkward. In addition, data processing knowledge is kept local, instead of being consolidated into a common resource of reusable code. These drawbacks can be mitigated by using a web processing service (WPS). A WPS hosts services such as data analysis processes that are accessible over the web, and can be installed close to the data archives. We developed a WPS named 'flyingpigeon' that communicates over an HTTP network protocol based on standards defined by the Open Geospatial Consortium (OGC) [23], to be used by climatologists and impact modelers as a tool for analyzing large datasets remotely.
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