The yellow‐legged hornet (Vespa velutina nigrithorax) has been identified as a major threat to European entomofauna. Species distribution models have been used to assess its invasiveness risk. No studies were, however, developed specifically for peripheral regions, where unique biodiversity can be threatened by this species.
This study aims to address that particular issue by incorporating non‐commonly used drivers and by forecasting regions in Iberian Peninsula where the species has high risk of expansion. Climatic, anthropogenic and land‐use variables were considered. The species potential distribution was assessed using a generalised linear model.
Overall, the model predicted the northern regions of the Iberian Peninsula suitable for the species expansion. Only the driest regions at the south are conservatively predicted to not be occupied by the yellow‐legged hornet. Precipitation and temperature have the highest influence in Vespa velutina nigrithorax distribution, with land‐use also playing an important role in its expansion at regional scale.
These results highlighted the threat of this species to beekeeping activities. Due to high species richness and endemicity levels, peripheral regions integrated in Biodiversity Hotspots need special attention and control measures.
Partition ratios of 8 free l-amino acids (Gln, Glu, His, Lys, Met, Ser, Thr, and Tyr) were measured in 10 different polymer/polymer aqueous two-phase systems containing 0.15 M NaCl in 0.01 M phosphate buffer, pH 7.4. The solute-specific coefficients representing the solute dipole/dipole, hydrogen-bonding and electrostatic interactions with the aqueous environment of the amino acids were determined by multiple linear regression analysis using a modified linear solvation energy relationship. The solute-specific coefficients determined in this study together with the solute-specific coefficients reported previously for amino acids with non-polar side-chains where used in a Quantitative Structure/Property Relationship analysis. It is shown that linear combinations of these solute-specific coefficients are correlated well with various physicochemical, structural, and biological properties of amino acids.
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