Testing for adaptation to climate in Arabidopsis thaliana: a calibrated common garden approach.
OBJECTIVE
A recent method used to test for local adaptation is a common garden experiment where analyses are calibrated to the environmental conditions of the garden. In this study the calibrated common garden approach is used to test for patterns of adaptation to climate in accessions of Arabidopsis thaliana.


METHODS
Seedlings from 21 accessions of A. thaliana were planted outdoors in College Park, MD, USA, and development was monitored during the course of a growing season. ANOVA and multiple regression analysis were used to determine if development traits were significant predictors of plant success. Previously published data relating to accessional differences in genetic and physiological characters were also examined. Historical records of climate were used to evaluate whether properties of the site of origin of an accession affected the fitness of plants in a novel environment.


RESULTS
By calibrating the analysis to the climatic conditions of the common garden site, performance differences were detected among the accessions consistent with a pattern of adaptation to latitude and climatic conditions. Relatively higher accession fitness was predicted by a latitude and climatic history similar to that of College Park in April and May during the main growth period of this experiment. The climatic histories of the accessions were better predictors of performance than many of the life-history and growth measures taken during the experiment.


CONCLUSIONS
It is concluded that the calibrated common garden experiment can detect local adaptation and guide subsequent reciprocal transplant experiments.
Rutter
Matthew T
MT
Department of Biology, University of Maryland, College Park, MD 20742, USA. rutter@wam.umd.edu
Fenster
Charles B
CB
D000064:Acclimatization; D017360:Arabidopsis / Q000235:genetics / Q000254:growth & development / Q000502:physiology*; D002138:Calibration; D002980:Climate; D005838:Genotype; D008396:Maryland; D012044:Regression Analysis