Estimation of temperature and precipitation from morphological characters of dicotyledonous leaves

The utility of regression and correspondence models for deducing climate fr om leaf physiognomy was evaluated by the comparative application of differe nt predictive models to the same three leaf assemblages. Mean annual temper ature (MAT), mean annual precipitation (MAP), and growing season precipitat ion (GSP) were estimated from the morphological characteristics of samples of living leaves from two extant forests and an assemblage of fossil leaves . The extant forests are located near Gainesville, Florida, and in the Flor ida Keys; the fossils were collected from the Eocene Clarno Nut Beds, Orego n. Simple linear regression (SLR), multiple linear regression (MLR), and ca nonical correspondence analysis (CCA) were used to estimate temperature and precipitation. The SLR models used only the percentage of species having e ntire leaf margins as a predictor for MAT and leaf size as a predictor for MAP. The MLR models used from two to six leaf characters as predictors, and the CCA used 31 characters. In comparisons between actual and predicted va lues for the extant forests, errors in prediction of MAT were 0.6 degrees-5 .7 degrees C, and errors in prediction of precipitation were 6-89 cm (=6-66 %). At the Gainesville site, seven models underestimated MAT and only one o verestimated it, whereas at the Keys site, all eight models overestimated M AT. Precipitation was overestimated by all four models at Gainesville, and by three of them at the Keys. The MAT estimates from the Clarno leaf assemb lage ranged from 14.3 degrees to 18.8 degrees C, and the precipitation esti mates from 227 to 363 cm for MAP and from 195 to 295 cm for GSP.