Hybrid zone populations exhibit adaptive potential to novel
climatic conditions
Current patterns of genetic diversity in populations of forest trees are
outcomes of various evolutionary processes structured by past events
(Hampe & Petit, 2005; Mayol et al ., 2015), which often results
in extant populations exhibiting adaptational lags to their current
climate conditions. Although this has been documented for several tree
taxa (Seliger et al ., 2021), it does not necessarily mean these
populations are devoid of adaptive potential. Theory suggests that GEI
should be prevalent in populations exhibiting adaptational lags and
those occurring in heterogenous environments (Via & Lande, 1985;
Ghalambor et al ., 2007). Sequential founder events, high levels
of landscape fragmentation and the associated loss of genetic diversity,
however, can restrict the evolution of GEI (Schmid et al ., 2019).
The populations sampled in this study naturally occur across the
fragmented landscapes of the southwestern United States, where they
experience markedly different seasonal and annual means and fluctuations
in environmental conditions, as well as gene flow from a northern
congener, P. flexilis (Menon et al ., 2018). By treating
gene expression patterns and resulting co-expression modules as
quantitative traits, we revealed strong signals of garden-specific
adaptive trait differentiation (i.e., GEI) thus supporting our first and
second hypotheses. In general, our estimates ofQ ST agree with previously published estimates
from studies of forest trees (Lind et al ., 2018), and more
specifically with the estimates obtained using expression datasets
(Roberge et al ., 2007; Leder et al ., 2015). Although the
presence of neutral population structure in the transcriptomic dataset
used to construct the co-expression modules could generate false
positives, eight of the strongly differentiated modules were also
enriched for various QST categories (Fig. 4). We
suggest these are likely true candidates for garden-specific signatures
of adaptive evolution, given the overall weak population structure in
this hybrid zone (Menon et al ., 2018).
The signals of local adaptation noted under the novel environmental
conditions of our study suggest that populations of long-lived tree
species, such as conifers, might not be limited in their ability to
adapt to rapidly changing climatic conditions. Our results contrast with
predictions of extensive future maladaptation suggested for other
long-lived tree species such as oak (Browne et al ., 2019), poplar
(Fitzpatrick et al ., 2020; Gougherty et al ., 2021) and
spruce (Frank et al ., 2017). This contrast may relate to
methodological differences among past studies and ours. Specifically, by
using a space-for-time substitution study we allow for the architecture
of adaptive evolution to reflect response to novel conditions. However,
similarities between the space-for-time substitution design used in the
present study and by Fitzpatrick et al . (2020) and Browneet al . (2019) suggest that our contrasting results might follow
from the inclusion of hybrid populations, which matters because hybrid
populations frequently show increased additive genetic variance
(Reif et al ., 2007;
Kulmuni, Wiley & Otto, 2023) (Table 2). Nevertheless, we advise caution
in the interpretation that populations of long-lived tree species may be
more adaptable to novel climates than expected because the presence of
additive genetic variation underlying climatically relevant traits is
only one of the important conditions needed for an adaptive response.
Correlations between traits can be modulated by environmental conditions
(Wood & Brodie, 2015) and correlations antagonistic to the direction of
selection can impede adaptive responses (Walsh & Blows, 2009). While
this does not seem to be the case in our study, as is evident throughQST enrichment at the core of the networks and
strong co-expression module differentiation, we cannot conclude that
evolution is occurring in the direction of the novel selective pressure.
Such a conclusion would require a larger sample size and more thorough
quantitative evaluation of the multivariate trait space.