Fig. 4: Optimising dispersal strategy for changes in
fecundity. The optimal dispersal rate from sub-habitats 1 and 2 is
dependent on the relationship between the average fecundity of
sub-habitats 1 and 2. At low average fecundities of sub-habitat 2, the
optimal sub-habitat is sub-habitat 1 and so dispersal will favour
remaining in sub-habitat 1. The converse is true at high average
fecundities of sub-habitat 2. When the average fecundities of
sub-habitats 1 and 2 are about equal, mixed dispersal ratios are seen.
Under these conditions, there is additional fitness in sensing the
environment in which the individual has grown and producing
site-specific dispersal ratios in response. Variables used: v = 21, S1 =
18, S2 = 0 – 23, f = 5, \(\mu\) = 0, c = 0.
When the severity (v) of the fluctuations in sub-habitat 1 is low, the
region in which sensing is adaptive is small, however, as v increases,
the region widens (Fig. 5a). By plotting the width of region 2 from
Figure 4 with an increase of v, the effect of the severity of
fluctuations in sub-habitat 1 can be seen. With an increase in v comes
an increase in the width of region 2. The more severe the fluctuations,
the bigger the scope for site-specific dispersal rates. In this way, as
the fluctuations become more severe, there is a greater potential for
site-specific dispersal rates to evolve. When the fluctuations are
small, the effect is negligible. This is more apparent with an increase
in dispersal mortality (Fig. 5b). At low severity, there is no scope for
site-specific dispersal. With high severity, site-specific dispersal
creates additional fitness and in order to produce site-specific rates,
a mechanism for sensing will evolve. This demonstrates the importance of
severity as a driver for the evolution of sensing and site-specific
dispersal.