Climate analysis
In each garden, most traits were significantly correlated with
population provenance climate (Table 3), supporting our third hypothesis
of strong associations between phenotype and climate. A single axis
(PC1) explained 95.8% of the variation in provenance climate and was
influenced primarily by four temperature-related climate variables
(degree-days above 5 °C, degree-days below 18 °C, degree-days above 18
°C, and summer heat-to-moisture index (Wang et al. 2012)), plus
elevation. Populations sourced from areas with higher temperatures,
lower precipitation, lower elevation, and longer growing seasons had
higher PC1 scores. Positive correlations with PC1 indicate that trait
values are higher in those hotter provenance populations, while negative
correlations with PC1 mean that trait values are higher in the colder
provenance populations (Fig. 3). Phenology traits showed the strongest
correlations with provenance climate (r = -0.75 to -0.77 for bud
flush in Yuma and Agua Fria, and r = 0.40 to 0.47 for bud set in
all gardens, Table 3). Here, the negative correlation for bud flush
indicates that the populations from hotter source climates (with higher
PC1 scores) had earlier spring flush dates, at least when growing in the
two hotter gardens. The positive correlation for bud set in all gardens
indicates that populations sourced from colder areas (lower PC1 scores)
had later bud flush dates in the spring. Although lower in magnitude,
the correlation between climate and SLA was also consistent in
direction. In both the hot and the cold garden, SLA was higher (meaning
leaves were thinner) in populations from hot climate origins.
In contrast with the consistent direction of correlations between home
climate and trait value for phenology and SLA, growth traits were more
likely to show garden-dependent relationships between population origin
and population performance (Table 3, Fig. 3). In general, tree height
and basal diameter acted as indicators that overall tree performance is
consistent with local adaptation, with hot, southern populations growing
larger in the hottest Arizona garden, and northern, cold populations
growing larger in the coldest Utah garden.
We also found that traits more strongly correlated with climate had
stronger evidence for selection driving their divergence. The full model
including the three gardens and all traits showed a significant
relationship between the strength of the trait-climate correlation and
QST (Fig. 5, P = 0.0012,R2 = 0.57, F(1,13) =
16.88). Neither garden nor trait identity had significant effects on the
mean QST value. When gardens were modeled separately,
the two warmer gardens showed significant relationships between
trait-climate correlations and QST (Yuma: P =
0.059, R2adj = 0.66; Agua Fria:P = 0.028, R2adj =
0.79), while the coldest garden in Canyonlands did not (P =
0.571, R2adj = 0.18). This
result shows that traits with the highest differentiation among
populations are those where that trait variation is most strongly
correlated with the climate, indicating a strong role for selection by
climate in driving differentiation. Interestingly, this relationship was
masked in the cold garden, where southern populations did not flush out
their leaves early in the spring as they do in their home climate,
thereby reducing the amount of population differentiation in that trait
compared to what we could detect in the warmer gardens (Figs 3-5).