Long-term changes
In addition to the short-term evolution, we also observed a long-term
increase in dispersal in the range front treatment over the entire time
span of the three years of the experiment (cycle x treatment
interaction: χ22 = 88.8; p <
0.001; Fig. 1B). This trend is significant, even when omitting the first
50 cycles (χ22 = 51.7; p <
0.001). We found little evidence for a dispersal difference between
range core and control lines, neither overall (contrast core vs control:
p > 0.68) nor when considering individual cycles (11
cycle-by-cycle contrasts with 0.0078 < p < 0.09,
none significant after correction for multiple testing). While no
significant treatment effects were detected in the first growth assay
(cycle 21, see above), range front lines had nearly 2-fold lower values
of r0 than range core lines in assays conducted in year
2 and 3 (year x treatment: F4 = 6.66; p <
0.001; SI Appendix, Fig. S1A). Furthermore, while beginning to grow more
slowly, range front lines continued to produce up to 2-fold higher\(\overline{N}\) than range core and control lines (treatment:
F2 = 34.21; p < 0.001; SI Appendix, Fig. S1B).
Fig. 4A-C illustrates short- and long-term trends in pairwise trait
associations, in relation to the model predictions. For dispersal and
r0 (Fig. 4A), there was no clear relationship between
the two traits after short-term selection (year 1). However, in year 2
and 3, observed data points tend to fall outside the main predicted
ranges, and a negative relationship between dispersal and
r0 emerged (Fig. 4A). This negative association is
highly significant over all lines and years combined (r = -0.627, 95%
CI [-0.771; -0.434]), but also holds for year 2 and 3 separately (SI
Appendix, Fig. S3). The positive relationship between dispersal and\(\overline{N}\), already observed as a short-term trend, further
consolidated in year 2 and 3 (Fig. 4B), again with values mostly falling
outside the main predicted short-term ranges. The correlation is
significant overall (r = 0.599, 95% CI [0.347; 0.725]), as well as
for each year separately (SI Appendix, Fig. S3). Furthermore, diverging
trends in core and front lines lead to a negative association between
r0 and \(\overline{N}\) (Fig 4C). The negative
correlation is of intermediate effect size overall (r = -0.325, 95% CI
[-0.575; -0.031]), and is significant in all three years separately
(SI Appendix, Fig. S3). It should be noted that all of these main trends
of divergence hold, when we correct for year effects, by expressing
front and core line data relative to the control treatment in each year
(SI Appendix, Fig. S5).
Principal Component Analysis (PCA, Fig. 4D) summarises the patterns of
phenotypic divergence. Demography-related traits and dispersal are
pulling in approximately equal strength on PC axis 1, but in opposite
directions (PC 1 loadings: r0 = -0.53; \(\overline{N}\)= +0.57; dispersal = +0.62). Thus, range front lines are characterised
by a combination of higher equilibrium density and dispersal, but lower
intrinsic population growth rate relative to range-core and control
lines (MANOVA: F2,37 = 10.85, p < 0.001). The
separation of clouds indicates the progressive divergence through time,
with a maximum in year 3. There is little differentiation between range
core and control treatments.