E v olu tion of A ltru ism : V a m p ire B a ts

E v olu tion of A ltru ism : V a m p ire B a ts

with Gennaro Di Tosto and Antonietta Di Salvatore
Evolution of Altruism:
Vampire Bats
• Relatively independent of relatedness (0.11 on average)
• Often cited (Dawkins, 1976) as an example of reciprocal
altruism (Trivers, 1981), based on inclusive fitness.
– If altruism spreads, donors’ (offspring) are
reciprocated.
– Altruistic acts increase donors' fitness.
• But, is it so?
– About 7% hunters find no prey,
– Survive thanks to luckier fellows regurgitating a portion of food
ingested
An example of altruism in nature.
• Each night, vampires go hunting
The Case of Vampires
 Food-sharing works "independently on degree of
relatedness and an index of opportunity for
reciprocation” (Wilkinson, 1984).
• Each night vampires go out hunting (finding
herbivores to suck blood from)
– animals reproduce and
– perform social activities (nursing, grooming and
sharing food)
• The species of vampires studied by Wilkinson
lives in Central America, in small groups
sharing cavity of trees (roosts), where
Ethological Data
• No accumulation: short-term consumption
• Infrequent lethal food scarcity (1.65 double
unsuccessful hunt p. animal p. year) .
• Average lifetime around 10 years
• as in nature (Wilkinson 1990), 93% agents find food
• remaining 7% starve, unless helped from fellows
• Bats are modeled as objects.
• Roost is a social space containing any number of bats.
• In-roosts are allowed to perform sharing food and
grooming (no other social activity has been modeled).
• In one simulation tick (= 24 hours), two stages:
– daily: grooming and food sharing
– nightly: hunting
The Simulation Model (1)
• No direct retaliation: victims of cheating die on
the spot.
– gives away blood for 6 hs of its autonomy, giving 18
hs to recipient.
• Each day, agents choose grooming partners from
roost
• If starving, grooming partner is asked for help
• If full (have had good hunt), this
The Simulation Model (2)
Process Description
Real Data
indicators
Model
Indicators
Simulation
Simulation data
Hints
Simulation and Vampires Data
Graph
1000 agents in 25 roosts for 360 ticks. Red:
population; Blue: n. of roosts x10.
Above: with food sharing
Below: without food sharing
• Ethological observations: with
help yearly rate of death is
24%
• Simulation results (Wilkinson,
1990): with no help mortality
is 82%.
• But in the long run,
population extinguishes
anyway...
Findings (1a)
Graph
1000 agents in 25 roosts for 3600 ticks. Red:
population; Blue: n. of roosts x10.
Above: with food sharing
Below: without food sharing
• In 10 years, population
extinguishes in both
conditions
• but the one with helps
has been around much
longer.
Findings (1b). Benefit of Altruism
– … Or “in-roost” recognition?
– Why agents reciprocate?
• Unlikely calculation of probability of
reciprocation
• No punishment of cheaters (victims
die on the spot)
– Individual recognition (Wilkinson,
1986) ...
• Reciprocal altruism
How Did Food-Sharing Evolve?
Altruism evolves (Maynard Smith, 1964; Cohen
and Eshel, 1976) with nonrandom matching
(altruists are matched with altruists)
• Harsch controversy (Palmer, 2002), due to
collectivist reading.
– asexual reproduction with inheritance.
– new groups are formed either randomly or
nonrandomly
• Haystack models: between-group advantage of
cooperation Vs within-group advantage of
defection:
– groups with adaptive habits produce new groups;
– groups missing adaptive habits decline to extinction.
• Groups = units of selection and reproduction
(Williams, 1971; Sober and Wilson, 1999),
competing on same evolutionary stage:
Group Selection Theory
• The same number of agents
distributed over a variable
number of roosts
• All previous conditions apply
• Population growth by
altruistic roost formation
• Lineage is followed:
• Food-sharing always allowed
• Reproduction is possible
• Variable percentage of
cheaters (never giving help
when asked, even if full;
unlike altruists, they sustain
no costs)
• No retaliation
• Cheaters are expected to
prosper, reducing the
efficiency of the system as a
whole
– If fitness of donors’ is higher
than average, then RAT
proves more valid.
– Otherwise, GST is preferable.
Multi-Roost World
One-Roost World
Experimental Conditions
Graph
Single roost with 300 agents for 20000 ticks.
Red: population; Dark Blue: n. of roosts
(x10); Light Blue: n. of cheaters (x10)
• Cheaters cause demographic
catastrophes.
• No reciprocity emerges:
cheaters exploit others,
incurring neither retaliation
nor isolation.
• But cheating is self-defeating
in the long run:
– after exploiting altruistic
in-roosts to death,
– cheaters are bound to die.
Findings (2a). One-Roost World
Graph
300 agents in 10 roosts for 8000 ticks. Red: population;
Dark Blue: n. of roosts (x10); Light Blue: n. of
cheaters (x10)
Above: 10% cheaters
Below: 20% cheaters
– Roosts with cheaters disappear,
– If roost without cheaters, it repopulates
world.
• Roosting matters!
– Altruists take off
– Number of roosts grows.
• If altruists survive cheaters, they
repopulate roost and produce new
ones.
• If no altruist survives cheaters, roost
extinguishes.
• After a critical period in which global
fitness declines:
Findings (2b). Multi-Roost World
Our findings support Wilkinson’s:
• Altruism reduces mortality: even one single cheater may
lead the roost to extinction.
• Cheaters survive longer than altruists but are selfdefeating in the long run
• Groups (roosts) plays a crucial role in the evolution of
altruism, acting as units of selection and reproduction.
• Simple loop:
– From micro-to-macro: altruists prevent extinction…
– and from macro-to-micro: ...via roost reproduction
Discussion
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Roost size and
mutation rate:
parameter space
exploration
is there an ideal
roost size?
what happens if we
let the roost size
evolve?
Ongoing Experiments