Dissertation - HQ

150 Oceanography vs. behaviour
controls increase significantly: from 140 · 170 · 3 · 6 = 176,400 to
300 · 160 · 5 = 240,000 for the state and from seven to hundreds for
controls. On the other hand, stochasticity sources are removed: there
is no explicit energy budget anymore, so no feeding, and survival is
not represented (i.e. is considered homogenous spatially). So the use of
transition matrices is inappropriate because they would be very large
but only one final state would be reachable from any given initial state
Loops instead of and decision. Therefore, gain is computed at each state inside a loop,
matrix computation and transition matrices are never built. The loop is coded in Fortran 90
and compile time vectorisation (using Intel Fortran Compiler) as well
as local parallelisation (through OpenMP) accelerate the process. The
extraction/interpolation of current speeds and computation of optimal
decisions takes between one and two hours for the set of parameters
specified above, on a cluster node with four 2.33 GHz double core CPUs.
Both optimal decision and end points after advection are stored in a
NetCDF file, so the advection does not have to be done again for the
forward computation of trajectories. Computing trajectories is therefore
virtually instantaneous and can be done in an interpreted language (R
in this case).
6.4.4 Large impact of swimming
Swimming
greatly enhances
self-recruitment
First, the impact of swimming is assessed by comparing trajectories of
passive and active larvae in the same situation (identical release sites and
date, same PLD). Figures 6.15 and 6.16 highlight the tremendous impact
of swimming, even for slow swimming larvae, in both configurations
used here. When larvae are treated as passive particles, most of them
are advected away from their release location. In the island case, passive
retention in regions of weak flow is almost never sufficient to retain
particles for the whole larval phase. In the promontory case, where
backward flow is more stable behind the cape, a small percentage of
larvae can be passively retained and self-recruit. These are just five
or ten trajectories, in one flow situation, but they are representative
of the overall magnitude of the impact of swimming. The effect is
estimated quantitatively by computing the percentage of recruiting (i.e.
self-recruiting) larvae starting from the promontory or the island at
three release dates (Table 6.2). In all cases, self-recruitment is quasiimpossible
for passive particles but swimming shifts the regime to a
situation where most larvae can self-recruit.
Table 6.2 Mean percentage of successful trajectories for the coral reef fish P.
amboinensis and a temperate fish, in the island and promontory configurations.
Coral-Reef Temperate
passive active passive active
Island 0 95 0 45
Promontory 2 95 1 72