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Lorne M Wolfe

Publications and source records attributed to Lorne M Wolfe.

3 recordsLinked to original sources
PubMed2005

Qualitative and quantitative analyses of flower scent in Silene latifolia.

The quantitative and qualitative variability in floral scent of 98 specimens of the dioecious species Silene latifolia belonging to 15 European and 19 North American populations was determined. Floral scent was collected from single flowers using dynamic headspace methods, and analysed by Micro-SPE and GC-MS methods. The flowers showed a nocturnal rhythm, and scent was emitted only at night. The amount of emitted volatiles varied greatly during the season, from 400 ng/flower/2 min in June to 50 ng/flower/2 min in August and September. The qualitative variability in the floral scent was high and different chemotypes, characterised by specific scent compounds, were found. Female and male flowers emitted the same type and amount of volatiles. The differences in floral scent composition between European and North American populations were small. Typical compounds were isoprenoids like lilac aldehyde isomers, or trans-beta-ocimene, and benzenoids like benzaldehyde, phenyl acetaldehyde, or veratrole. Some of these compounds are known to attract nocturnal Lepidoptera species. The high qualitative variability is discussed in relation to the pollination biology of S. latifolia, and the results are compared with other studies investigating intraspecific variability of flower scent.

Canada↗
PubMed2002

Reducing environmental bias when measuring natural selection.

Crucial to understanding the process of natural selection is characterizing phenotypic selection. Measures of phenotypic selection can be biased by environmental variation among individuals that causes a spurious correlation between a trait and fitness. One solution is analyzing genotypic data, rather than phenotypic data. Genotypic data, however, are difficult to gather, can be gathered from few species, and typically have low statistical power. Environmental correlations may act through traits other than through fitness itself. A path analytic framework, which includes measures of such traits, may reduce environmental bias in estimates of selection coefficients. We tested the efficacy of path analysis to reduce bias by re-analyzing three experiments where both phenotypic and genotypic data were available. All three consisted of plant species (Impatiens capensis, Arabidopsis thaliana, and Raphanus sativus) grown in experimental plots or the greenhouse. We found that selection coefficients estimated by path analysis using phenotypic data were highly correlated with those based on genotypic data with little systematic bias in estimating the strength of selection. Although not a panacea, using path analysis can substantially reduce environmental biases in estimates of selection coefficients. Such confidence in phenotypic selection estimates is critical for progress in the study of natural selection.

Arabidopsis↗