Benefits and Costs of learning
All mobile organisms face a wide variety of spatial challenges that
influence individual fitness and present opportunities for decision
making shaped by learning. Foraging opportunities and energetic
constraints are patchy in space and time, in large part because the
underlying physical and biotic processes are also patchy. Optimal
foraging theory (Stephens and Krebs 1986; McNamara and Houston 1985;
Mangel and Clark 1988) provides a framework for understanding how
benefits accrue from foraging in patches that offer the highest returns
of energy or nutrient intake per unit time relative to time or energetic
costs. Lost opportunities for social interaction, breeding
opportunities, reproductive care, or shelter, and the risks of mortality
due to predation, parasitism, or disease can then be taken into account.
When the rate of environmental change varies across time and space, as
is common along elevation or rainfall gradients, theory suggests an
animal may be able to improve its fitness through appropriate patterns
of nomadic or migratory movement
(e.g.,
Fryxell & Sinclair 1988). Field studies support this theory. For
example, migratory ungulates can choose patches at a landscape scale
that yield appreciable improvement in rates of energy gain, even when
such gains are transitory and require continual nomadic repositioning
(Fryxell et
al. 2004; Holdo et al.2009).
Memory can also influence the choice of movement patterns. For example,
when undergoing seasonal transitions between ranges, migratory ungulates
can obtain fitness benefits by remembering previous trajectories
(Bracis & Mueller
2017; Jesmer et al. 2018, Merkle et al .
2019).
Researchers have investigated how learning can influence and confer
advantages to moving organisms. Agent-based models of foragers with and
without spatial memory have shown how fitness accrues from moving to
acquire reliable information, even when that movement process requires
sampling sub-optimal patches
(Braciset al. 2015). This is particularly clear when naïve animals are
presented with an unfamiliar environment and movement is exploratory.
However, even experienced individuals can benefit by spatially sampling
a dynamic environment, in particular when resources can be depleted
(Boyer & Walsh 2010). In this case, movement keeps current the
information needed for appropriate decision making.
Given that foraging often results in resource depletion, fitness may
also be improved through informed departure criteria based on marginal
value leaving rules
(Charnov
1976; Arditi & Dacorogna 1988; Brown 1988). The field of “sampling
behavior”
(Stephens
1987) extends ideas originally developed within the optimal foraging
theory framework, which traditionally assumed that animals are
omniscient
(Krebs
& Inman 1992; Stephens et al. 2007b). One sampling framework
considers when animals should visit a patch to assess whether it has
changed in value
(Green
1980), whereas another framework focuses on the value to tracking a
changing environment (Shettleworth et al . 1988). Foragers that
sample patches or track changing conditions are learning about the
current state of the environment (Stephens 1987). Informed decision
making about which patches to feed in and how long to do so requires
reliable expectations regarding resource availability, predation risk,
and energetic costs across an individual’s home range, as well as the
capacity to estimate these same variables at a given spatial location.
For example, primates foraging on fruit track the productivity of
different trees and possibly fruit ripeness
(Janson
& Byrne 2007).
Learning can also help improve fitness even when spatial movement
processes are not directly tied to foraging (e.g., territorial defense,
migration, reproduction) (Box 2). For example, learning can provide
advantages in dominance interactions (Kokko et al. 2006),
efficiency of movement (Stamps 1995), and effective escape from
predators (Brown 2001), all of which can translate into fitness benefits
(Brown et al . 2008; Patrick & Weimerskirch 2017). For
territorial species, learning can influence how conflicts drive pattern
formation (Stamps
& Krishnan 1999, 2001; Sih & Mateo
2001)
and alter strategies for territorial defense
(Potts & Lewis
2014; Schlägel & Lewis 2014; Schlägel et al.2017)
For migratory species, this includes determining least-cost migration
corridors between seasonal ranges
(Bischof et
al. 2012; Poor et al .
2012).
While learning may have benefits, acquiring information based on
experiences encountered does not come without costs. For example,
information gathering can require substantial investment in time and/or
energy, and may heighten risk
(Eliassenet al. 2007) or come at the expense of lost opportunities for
foraging, social interaction, or search for suitable breeding sites
(Dall et al . 2005). The machinery for learning can also exact an
ongoing energetic cost (Niven 2016).