Precise estimates of past temperatures are critical for understanding the evolution of organisms and the physical biosphere, and data from continental areas are an indispensable complement to the marine record of stable isotopes. Climate is considered to be a primary selective force on leaf morphology, and two widely used methods exist for estimating past mean annual temperatures from assemblages of fossil leaves. The first approach, Leaf Margin Analysis, is univariate, based on the positive correlation in modern forests between mean annual temperature and the proportion of species in a flora with untoothed leaf margins. The second approach, known as the Climate-Leaf Analysis Multivariate Program, is based on a modern data set that is multivariate. I argue here that the simpler, univariate approach will give paleotemperature estimates at least as precise as the multivariate method because (1) the temperature signal in the multivariate data set is dominated by the leaf-margin character; (2) the additional characters add minimal statistical precision and in practical use do not appear to improve the quality of the estimate; (3) the predictor samples in the univariate data set contain at least twice as many species as those in the multivariate data set; and (4) the presence of numerous sites in the multivariate data set that are both dry and extremely cold depresses temperature estimates for moist and nonfrigid paleofloras by about 2°C, unless the dry and cold sites are excluded from the predictor set. New data from Western Hemisphere forests are used to test the univariate and multivariate methods and to compare observed vs. predicted error distributions for temperature estimates as a function of species richness. Leaf Margin Analysis provides excellent estimates of mean annual temperature for nine floral samples. Estimated temperatures given by 16 floral subsamples are very close both to actual temperatures and to the estimates from the samples. Temperature estimates based on the multivariate data set for four of the subsamples were generally less accurate than the estimates from Leaf Margin Analysis. Leaf-margin data from 45 transect collections demonstrate that sampling of low-diversity floras at extremely local scales can result in biased leaf-margin percentages because species abundance patterns are uneven. For climate analysis, both modern and fossil floras should be sampled over an area sufficient to minimize this bias and to maximize recovered species richness within a given climate.
All Science Journal Classification (ASJC) codes
- Ecology, Evolution, Behavior and Systematics
- Agricultural and Biological Sciences(all)