Data from: Deciphering the adjustment between environment and life history in annuals: lessons from a geographically-explicit approach in Arabidopsis thaliana

The role that different life-history traits may have in the process of
adaptation caused by divergent selection can be assessed by using
extensive collections of geographically-explicit populations. This is
because adaptive phenotypic variation shifts gradually across space as a
result of the geographic patterns of variation in environmental selective
pressures. Hence, large-scale experiments are needed to identify relevant
adaptive life-history traits as well as their relationships with putative
selective agents. We conducted a field experiment with 279 geo-referenced
accessions of the annual plant Arabidopsis thaliana collected across a
native region of its distribution range, the Iberian Peninsula. We
quantified variation in life-history traits throughout the entire life
cycle. We built a geographic information system to generate an
environmental data set encompassing climate, vegetation and soil data. We
analysed the spatial autocorrelation patterns of environmental variables
and life-history traits, as well as the relationship between environmental
and phenotypic data. Almost all environmental variables were significantly
spatially autocorrelated. By contrast, only two life-history traits, seed
weight and flowering time, exhibited significant spatial autocorrelation.
Flowering time, and to a lower extent seed weight, were the life-history
traits with the highest significant correlation coefficients with
environmental factors, in particular with annual mean temperature. In
general, individual fitness was higher for accessions with more vigorous
seed germination, higher recruitment and later flowering times. Variation
in flowering time mediated by temperature appears to be the main
life-history trait by which A. thaliana adjusts its life history to the
varying Iberian environmental conditions. The use of extensive
geographically-explicit data sets obtained from field experiments
represents a powerful approach to unravel adaptive patterns of variation.
In a context of current global warming, geographically-explicit
approaches, evaluating the match between organisms and the environments
where they live, may contribute to better assess and predict the
consequences of global warming.