Shrinking windows of opportunity for oak seedling establishment in southern California mountains

The arcsine square root transformation has long been standard procedure when analyzing proportional data in ecology, with applications in data sets containing binomial and non-binomial response variables. Here, we argue that the arcsine transform should not be used in either circumstance. For binomial data, logistic regression has greater interpretability and higher power than analyses of transformed data. However, it is important to check the data for additional unexplained variation, i.e., overdispersion, and to account for it via the inclusion of random effects in the model if found. For non-binomial data, the arcsine transform is undesirable on the grounds of interpretability, and because it can produce nonsensical predictions. The logit transformation is proposed as an alternative approach to address these issues. Examples are presented in both cases to illustrate these advantages, comparing various methods of analyzing proportions including untransformed, arcsine- and logit-transformed linear models and logistic regression (with or without random effects). Simulations demonstrate that logistic regression usually provides a gain in power over other methods.

A change in climate would be expected to shift plant distribution as species expand in newly favorable areas and decline in increasingly hostile locations. We compared surveys of plant cover that were made in 1977 and 2006-2007 along a 2,314-m elevation gradient in Southern California's Santa Rosa Mountains. Southern California's climate warmed at the surface, the precipitation variability increased, and the amount of snow decreased during the 30-year period preceding the second survey. We found that the average elevation of the dominant plant species rose by approximately 65 m between the surveys. This shift cannot be attributed to changes in air pollution or fire frequency and appears to be a consequence of changes in regional climate.