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Samuel M Scheiner

Publications and source records attributed to Samuel M Scheiner.

2 recordsLinked to original sources

Developing unified theories in ecology as exemplified with diversity gradients.

A scientific field matures as its theoretical underpinnings consolidate around unified theories: conceptual structures consisting of a few general propositions that encompass a wide domain of phenomena and from which can be derived an array of models. We demonstrate this process with a synthetic theory of ecological gradients and species richness. Our unified theory rests on four propositions. First, variation in some environmental factor effects variation in the number of individuals creating a gradient. Second, in a uniform environment of fixed area, more individuals lead to more species. Third, the variance of an environmental factor increases with its mean for sites of equal area. Fourth, all nonmonotonic relationships (i.e., hump shaped or U shaped) require a trade-off in organismal performance or in population characteristics with respect to the environmental gradient. We identify 17 models that link environmental gradients with diversity, show their relationship to our framework, and describe issues surrounding their empirical testing. We illustrate how a general theory can be used to build new models such as that for the U-shaped productivity-diversity relationship. Finally, we discuss how our theory could be unified further with other theories of diversity and indicate other areas of ecology that are ripe for unification. By providing an example of the process of theory unification, we hope to encourage such efforts throughout ecology.

Analysis of Variance↗

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↗