Termites in the Nation's
Service (part 2):
More details than you wanted
Prof. Nina H. Fefferman
Visiting DIMACS from :
Tufts Univ. School of Medicine,
Dept. Public Health and Family
Medicine
Remember from last time:
Temporary
Immunity
Zootermopsis angusticollis
Spores
land on
termite
Metarhizium
anisopliae
Allogroomed
off
Burrow
through
cuticle
Death
Not a termite
What I didn‘t tell you last time:
We’ll go through the details for the first round of models
– simple, two dimensional, circular nest
• Termites have 7 age stages (6 instars, nymph), and they age
over time (how long they were in each stage was empirically
determined)
 You can just look at them to determine stage in the real world
These aren’t
the right
termites, but
close enough
to show you
what it looks
like
http://creatures.ifas.ufl.edu/urban/termites/dampwood_life_cycle.htm
• Each 3rd instar and older termite was allowed, but not
required, to move one cell per iteration at random in
any direction (unless they were at the outer border, naturally)
Again, I’m
lying with
this picture
- we were
modeling
natural nest
cavities, but
I thought
you’d like to
see what
the lab setup can look
like
• Health was set at random (0h100) for each individual before
the first iteration
• Immune status during an iteration was naïve, inoculated,
immune, or diseased
• Pathogen infection status was defined as either diseased or
healthy
• Termites determined as dead or alive according to health
Termites died either because their health decreased to 0 or to less
than a stage specific threshold used to represent a health threshold
below which sick individuals were removed/cannibalized
Details of disease transfer: these rules applied to
each cell (and the termites occupying it) in order
• (In the baseline model, all termites are initially defined to be naïve)
• Naïve or inoculated (can happen by step 2) termites in cells with
primary exposure became diseased
• A naïve termite became diseased if the % of diseased termites
the cell with it was greater than a defined stage-specific
threshold
• For each diseased termite in a cell, each naïve termite had an
independent stage-specific probability of receiving a dose of
fungal spores
• Termites that received this inoculum became inoculated
• Termites receiving an inoculum were restricted from
moving for two iterations (empirically determined)
• Termite health values decreased by 10
(representing the cost of mounting the immune response)
The sorts of experiments that can be run to get these
parameters:
Remember that
diseased individuals
die, so the
difference in
survival has to do
with rates of
infection
Traniello JF, Rosengaus RB, Savoie K. Proc Natl Acad Sci U S A. 2002 May 14;99(10):6838-42.
Survival distributions of Tween 80 controls (□), naïve/challenged nymphs
(■), and socially immunized/challenged nymphs (•). (Inset) Histogram
illustrating the relative hazard ratios of death of naïve/challenged
Using experiments like that when we could
to determine threshold values:
• Naïve termites had a stage-specific probability of becoming
inoculated if the number of immune termites in the same
cell with them was > a different stage-specific threshold
• Inoculated termites in the same cell with a diseased termite
became diseased with a 90% probability
• Inoculated termites occupying a cell with no disease present
become immune with a 70% probability
• The duration of immunity was specified as 300 iterations (30
days), after which the termite would again become naïve
Little Details
•
•
•
•
•
•
1 “day” = 10 iterations
Only disease affects health value
Initially, each termite has an equal probability of being any instar
Models run with a starting population of 1000
Model allowed to run for 3600 iterations (one year)
Dead termites assumed to be removed, walled off, or
cannibalized  incapable of infecting other termites
• Clutches of 25 eggs were added every 300 iterations (equivalent to
30 days)
• New eggs and first-instar larvae were placed in a small circle at
nest center. As they developed into second instars from
first, they were allowed to move ‘outward’ by one cell
(in order to prevent an artificially dense center)
• The model placed older instar larvae randomly throughout the
nest, though older individuals had a higher probability of
being located farther from the center
Model Modifications
Difference from Baseline Model
Adult-biased Demography
70% of ‘worker’ at the outset of the first iteration were adults
Early Instar Biased
Demography
70% of ‘workers’ at the outset of the first iteration were in
instars 1 and 2
Random Spatial Assignment
of Individuals
Each worker is assigned to a random position in the nest,
regardless of developmental stage
No Nest Hygiene
The threshold for ‘artificial death’ = 0 for all stages
No Social Hygienic Behavior
Stage dependent thresholds for inoculation from either disease
exposure or socially triggered immunity are set to 0
No Nest Hygiene or Social
Hygienic Behavior
The threshold for ‘artificial death’ = 0 for all stages and Stage
dependent thresholds for inoculation from either disease
exposure or socially triggered immunity
No Immunity
Inoculated workers who did not become diseased reverted to
naïve status
60% of population immune prior to presence of disease
35% of population immune prior to presence of disease
Maintenance of Immunity
20% of population immune prior to presence of disease
15% of population immune prior to presence of disease
10% of population immune prior to presence of disease
Under two disease presence scenarios:
Low level constant
fungal presence
-
-
Fungus was present
in 20 cells chosen at
random for each
iteration
Lasted, in each cell,
a random number of
days ranging from
1-10
Periodic high level
fungal presence
Fungus was present
in 70 cells chosen at
random every 90
days
- Lasted, in each cell,
for 10 days
-
Some results from the models:
Notice that these studies looked at how
well the colony did “overall”
This is different from traditional studies of disease
defense mechanism efficacy
Traditional examinations of disease defense efficacy
come mainly from studies of vaccine efficacy
These models define the benefits of immune protection
only in terms of the reduced probability of an
individual getting a disease
But in the models we’ve just discussed, we were
looking at a total ‘societal immunocompetence’
Direct benefits (as with traditional models)
and
Indirect benefits associated with the
prevention of cascading effects
(e.g. deaths caused by breakdown of social infrastructure)
An example :
Pathogen
Direct
Individual
Survival
Indirect
Sanitation
Maintenance
Mechanisms:
• Few deaths  Maybe we don’t upregulate sanitation
• Lots of deaths  Insufficient funds or manpower 
down regulation
There is already a foundation for this
population-wide concept
There is the concept of indirect protection provided to
susceptible individuals by the mere presence of
many immune individuals
Immune individuals don’t contract or transmit disease,
so, if there are a lot of them in a population, the
disease fails to propagate
This effectively shields the susceptible individuals from
ever coming in contact with the pathogen
This is called ‘herd-immunity’, and is well
studied
But why stop there?
Traditional approach :
Benefit = ((Mortality rate of individual without immune response)
– (Mortality of individual with immune response))
* (Average probability of exposure)
Approach we just took:
Benefit = ((% Surviving in population capable of an immune response)
– (% Surviving in population incapable of an immune response))
/ (Size of initial population)
The benefit to the entire population in both cases =
The sum of individual benefits taken over all members of the
population
Once we define societal immunocompetence, we can talk
about the balance between physiological costs of an
immune response and the protection it affords on a
population-wide scale
unlikely
evolutionarily stable
Benefits
some
vaccinations
grooming
selected
against
herd
immunity
crowding
Costs
Schematic of costs and benefits of disease
resistant physiology and behaviors
If we can find this equilibrium, we can
understand how things like
•
Vaccination practices
and/or
•
Periodicity of recurring epidemics (with
associated induction of short term
immunity)
•
Etc.
shape the evolutionary interactions of hosts
and pathogens on both an individual and
group/societal level
Where do these ideas lead?
Hopefully many places, but initially:
• Study of task allocation based on disease risks to
maximize societal immunocompetence
Social insect colonies need different tasks
completed to make the society function – just like
people, only simpler
In the framework of costs and benefits, we can look
at the influence of disease on the efficiency of the
colony in completing these tasks
Part of the problem is figuring out how tasks
need to get done:
We know not all tasks are constant (you don’t always have to clean up
after a flood), but there are tasks that need to be
done no matter what else has to happen (foraging for
food)
A lot of models have looked at optimal “recruitment”,
but nothing has looked at optimal efficiency for
colony task completion
Also, each of these tasks are associated with their own
risks, pathogen related and otherwise
To start with, let’s look at the simplest trade-off
system
4 Basic elements of concern:
Amount of
work in each
task
completed in
each unit of
time
Age of
worker
Risk
associated
with task
completion
Is the task
currently a
limiting
factor for
the colony?
How do they all relate?
In social insects, there are three basic possibilities
for task allocation decisions:
1) Determined by age
2) Repertoire increases with age
3) Completely random
So which does better under what assumptions of
pathogen risk?
Just a few scenarios:
1) As you age, you learn more complicated tasks
(i.e. produce more “work” in less time), but these
more complicated tasks are riskier
2) The youngest individuals are put on the
riskiest tasks so that the lost investment
is minimized
3) Everything is completely random, risks,
amount of work for each task, everything
Additionally, we need to add into the mix – sometimes we
need specific tasks more than usual, or more than any
other… how do we hedge our bets to make sure that we
can always have enough workers to devote to those when
we need them?
We’d also want to include the benefit in each
task to “societal immunocompetence”
And we want to check all of this under different
probability distributions of “disasters” or “dire
need” interfering with a status quo
This research is in its infancy, so if anyone is
interested…
Thanks for listening to me once again
I hope you’ve had fun
Some of what I’ve talked about is work in collaboration with
James Traniello, Rebeca Rosengaus and Sam Beshers