Data from: Integrating viability and fecundity selection to illuminate the adaptive nature of genetic clines

Genetically-based trait variation across environmental gradients can
reflect adaptation to local environments. However, natural populations
that appear well-adapted often exhibit directional, not stabilizing,
selection on ecologically-relevant traits. Temporal variation in the
direction of selection could lead to stabilizing selection across multiple
episodes of selection, which might be overlooked in short-term studies
that evaluate relationships of traits and fitness under only one set of
conditions. Furthermore, non-random mortality prior to trait expression
can bias inferences about trait evolution if viability selection opposes
fecundity selection. Here, we leveraged fitness and trait data to test
whether phenotypic clines are genetically based and adaptive, whether
temporal variation in climate imposes stabilizing selection, and whether
viability selection acts on adult phenotypes. We monitored transplants of
the subalpine perennial forb, Boechera stricta (Brassicaceae), in common
gardens at two elevations over 2-3 years that differed in drought
intensity, quantifying viability and fecundity fitness components for four
heritable traits: specific leaf area, integrated water-use efficiency,
height at first flower, and flowering phenology. Our results indicate that
genetic clines are maintained by selection, but their expression is
context dependent, as they do not emerge in all environments. Moreover,
selection varied spatially and temporally. Stabilizing selection was most
pronounced when we integrated data across years. Finally, viability
selection prior to trait expression targeted adult phenotypes (age and
size at flowering). Indeed, viability selection for delayed flowering
opposed fecundity selection for accelerated flowering, demonstrating that
neglecting to account for viability selection could lead to inaccurate
conclusions that populations are maladapted. Our results suggest that
reconciling clinal trait variation with selection requires data collected
across multiple spatial scales, time frames, and life history stages.