Observational datasets and their utility for predicting
genetic differentiation
Our global observational dataset revealed that different combinations of
biotic and abiotic factors drove variation on each trait. This
trait-specificity would have remained hidden had the environmental and
geographical scale of the study been smaller, since we could have not
analysed together such a variety of environmental conditions and
drivers. In addition, the combination of large-scale field and
experimental studies, rarely implemented in evolutionary ecology (but
see, e.g., Winn & Gross 1993, Woods et al . 2012), allowed us to
assess the potential uses and misuses of observational datasets. In
particular, trait-environment relationships inferred from in situpopulations correctly predicted genetic differentiation for reproductive
but not vegetative traits. For vegetative traits, the predictability
diminished as the presence of plasticity led to interacting or opposing
effects of source and exposure environments, as initially forecasted
(Fig. 1). The predictability of genetic differentiation was also low for
reproductive traits when analysed without accounting for their
size-dependence. Therefore, observational data may reliably inform about
the current drivers of selection and the adaptive capacity of species
only for the traits most closely related with fitness. This might be
important for species- and community-level predictive models that rely
on trait-environment relationships, and for conservation programs
focusing on intraspecific genetic diversity.
Evaluating trait-environment relationships can also be useful for
predicting plant performance in populations introduced outside native
ranges (Alexander et al . 2012, Hulme & Barrett 2013). InP. lanceolata , traits showed broadly similar correlations with
environmental factors in both native and non-native ranges, in agreement
with previous work in other taxa (Maron et al . 2004, Montagueet al . 2008, Rosche et al . 2019; but see Keller et
al . 2009). Notably, the similarities in trait patterns between ranges
held despite the location of non-native populations in warmer and more
arid conditions. This suggests that the trait-environment correlations
largely persist for some species even if they occupy more extreme areas
of environmental space, facilitating ecological predictions in a context
of global change. Yet some trait-environment correlations observed inP. lanceolata were weaker in the non-native range (see also
Alexander et al . 2012). This finding highlights that genetic
differentiation may be less predictable for non-native populations and
that a total equivalence in trait patterns between ranges cannot be
taken for granted due to potential evolutionary divergence. The presence
of weaker trait-environment relationships in non-native populations may
be due to a higher role of plasticity (although the latter is not
clearly supported by a recent meta-analyses across species; see
Palacio-López & Gianoli 2011), or may instead result from repeated
introductions in the non-native range (Smith et al . 2020).
Further sudies on widespread species might help to clarify the processes
and patterns resulting from ecological and evolutionary divergence at
large spatial scales. In particular, our observational network can form
the basis for future experimental work.