Dissertation - HQ

General discussion 161
such integration of behavioural ecology and physical oceanography in
models of larval fish dispersal 80 , or of fish in general 234 , through the
use of optimal control in particular. Indeed, this approach allows to
model larval behaviour as it should be: a dynamic response to the environment.
Behaviour emerges from the interaction of individuals with
their environment, rather than being determined a priori. Its fineness
is therefore only bounded by the complexity of the description of said
environment. This ensures broad application and great flexibility of
such models, as knowledge of the pelagic ecosystem progresses. We
shall, however, discuss the hypotheses of these models in more details.
The first justification for the use of optimisation models forms the
basis of the theory of optimal behaviour. As stated in the Introduction
(section I.4.2, page 17) and earlier in this chapter (page 117): as soon
as a behaviour is heritable, occurs in an environment stable at the
generation level, is variable, and its variations result in differential
fitness, it is under selection. And natural selection will favour those
forms that provide greater fitness 52,54 . All four conditions are satisfied
during the early life history of fishes. During this pre-reproductive
phase, fitness can be reduced to survival, both during and immediately
after the larval phase. This is the justification for optimisation criteria
such as maximising survival along successful trajectories (section 6.3), or
minimising energy expenditure (because energy participates to growth
and growth in turn affects survival 48,50,170,237,238 – section 6.4). While
most larvae do not always respond optimally to their environment, the
optimal behaviour theory states that they will tend to. Furthermore,
this model should not be viewed as a description of the behaviour
of each and every larva during its pelagic stage. Instead it aims at
evaluating an upper bound to the influence of larval behaviour, in a
context in which modellers have been looking at the lower bound most
of the time (either passive particles, vertical migration only, or very
simplified rules of behaviour 84,86,87,143 ). Moreover, this upper bound
is made quite conservative regarding the behavioural abilities of fish
larvae (swimming, energy reserves) by the choice of mean parameters for
swimming speed and high energetic requirements. Due to the scarcity
of information available throughout the larval phase, this safe approach
is necessary but behaviour by fish larvae may have an even greater
impact.
Even if natural selection tends to optimise behaviours, it selects within
the limits of what is energetically, ontogenetically, and mechanically
possible. In this modelling framework, our theoretical larvae “know”
their environment and all its future states, in a probabilistic sense
(e.g. they “know” the distribution of the predation probability, not its
realisation). Do larvae have the sensory abilities to detect predators,
plankton, and direction of currents on a large spatial scale, and to
predict their evolution in time? Probably not. But this is not what our
modelling hypothesis implies. Consider the downward movement at the
tip of the cape in Figure 6.17 for example. Larvae in the field probably
Natural selection tends
to optimise behaviours
Optimal control
reasons on the distal
causes of behaviour