Estimation of Efficiency of Processing Soil Samples for Pesticide Residues Analysis

The efficiency of four sample processing methods was tested with eight different types of soils representing the major proportion of cultivated soils. The principle of sampling constant was applied for characterizing the efficiency of the procedures and testing the well-mixed status of the prepared soil. The test material was 14C-labeled atrazine that enabled keeping the random error of analyses ≤ about 1%. Adding water to the soil proved to be the most efficient and generally applicable procedure resulting in about 6% relative sample processing uncertainty for 20 g test portions. The expected error is inversely proportional to the mass of test portion. Smashing and manual mixing of soil resulted in about four times higher uncertainty than mixing with water. Grinding of soil is applicable for dry soils only, but the test procedure applied was not suitable for estimating a typical uncertainty of processing dry soil samples. Adding dry ice did not improve the efficiency of sample processing.     

The triple helix of Plantago lanceolata: genetics and the environment interact to determine population dynamics

The theory of evolution via natural selection predicts that the genetic composition of wild populations changes over time in response to the environment. Different genotypes should exhibit different demographic patterns, but genetic variation in demography is often impossible to separate from environmental variation. Here, we asked if genetic variation is important in determining demographic patterns. We answer this question using a long-term field experiment combined with general linear modeling of deterministic population growth rates (lambda), deterministic life table response experiment (LTRE) analysis, and stochastic simulation of demography by paternal lineage in a short-lived perennial plant, Plantago lanceolata, in which we replicated genotypes across four cohorts using a standard breeding design. General linear modeling showed that growth rate varied significantly with year, spatial block, and sire. In LTRE analysis of all cohorts, the strongest influences on growth rate were from year x spatial block, and cohort x year x spatial block interactions. In analysis of genetics vs. temporal environmental variation, the strongest impacts on growth rate were from year and year x sire. Finally, stochastic simulation suggested different genetic composition among cohorts after 100 years, and different population growth rates when genetic differences were accounted for than when they were not. We argue that genetic variation, genotype x environment interactions, natural selection, and cohort effects should be better integrated into population ecological studies, as these processes should result in deviations from projected deterministic and stochastic population parameters.