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

Spores land on termite

Allogroomed off

Temporary

Immunity

Burrow throug

h cuticle

Death

Not a termite

Zootermopsis angusticollis

Metarhizium anisopliae

Remember from last time:

•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

http://creatures.ifas.ufl.edu/urban/termites/dampwood_life_cycle.htm

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

These aren’t the right

termites, but close

enough to show you

what it looks like

• 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 set-up can look

like

• Health was set at random (0h100) 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 healthTermites 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

• (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)

Details of disease transfer: Details of disease transfer: these rules applied to each cell (and the termites occupying it) in

order

The sorts of experiments that can be run to get these parameters:

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

Remember that diseased

individuals die, so the difference in

survival has to do with rates of

infection

• 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

Using experiments like that when we Using experiments like that when we could to determine threshold values:could to determine threshold values:

• 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

Little DetailsLittle Details

Model Modifications

Difference from Baseline ModelDifference from Baseline Model

Adult-biased DemographyAdult-biased Demography 70% of ‘worker’ at the outset of the first iteration were adults70% of ‘worker’ at the outset of the first iteration were adults

Early Instar Biased Early Instar Biased DemographyDemography

70% of ‘workers’ at the outset of the first iteration were in 70% of ‘workers’ at the outset of the first iteration were in instars 1 and 2instars 1 and 2

Random Spatial Random Spatial Assignment of IndividualsAssignment of Individuals

Each worker is assigned to a random position in the nest, Each worker is assigned to a random position in the nest, regardless of developmental stageregardless of developmental stage

No Nest HygieneNo Nest Hygiene The threshold for ‘artificial death’ = 0 for all stagesThe threshold for ‘artificial death’ = 0 for all stages

No Social Hygienic No Social Hygienic BehaviorBehavior

Stage dependent thresholds for inoculation from either Stage dependent thresholds for inoculation from either disease exposure or socially triggered immunity are set to 0disease exposure or socially triggered immunity are set to 0

No Nest Hygiene or Social No Nest Hygiene or Social Hygienic BehaviorHygienic Behavior

The threshold for ‘artificial death’ = 0 for all stages and The threshold for ‘artificial death’ = 0 for all stages and Stage dependent thresholds for inoculation from either Stage dependent thresholds for inoculation from either

disease exposure or socially triggered immunitydisease exposure or socially triggered immunity

No ImmunityNo Immunity Inoculated workers who did not become diseased reverted to Inoculated workers who did not become diseased reverted to naïve statusnaïve status

Maintenance of ImmunityMaintenance of Immunity

60% of population immune prior to presence of disease60% of population immune prior to presence of disease

35% of population immune prior to presence of disease

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 disease10% of population immune prior to presence of disease

Under two disease presence scenarios:Under two disease presence scenarios:

- Fungus was Fungus was present in 20 cells present in 20 cells chosen at random chosen at random for each iteration for each iteration

- Lasted, in each Lasted, in each cell, a random cell, a random number of days number of days ranging from 1-10ranging from 1-10

- Fungus was Fungus was present in 70 cells present in 70 cells chosen at random chosen at random every 90 days every 90 days

- Lasted, in each Lasted, in each cell, for 10 dayscell, for 10 days

Low level constant fungal

presence

Periodic high level fungal

presence

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)

Direct

Sanitation Maintenanc

e

Individual Survival

Pathogen

Indirect

An example :

Mechanisms:

• Few deaths Maybe we don’t upregulate sanitation• Lots of deaths Insufficient funds or manpower

down regulation

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?

There is already a foundation for this population-wide concept

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

Costs

Ben

efit

s

unlikely

selected against

evolutionarily stable

herd immunity

grooming

some vaccinations

Schematic of costs and benefits of disease resistant physiology and behaviors

crowding

Evolutionarily Stable

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

Age of worker

Amount of work in

each task completed

in each unit of time

Is the task currently a

limiting factor for

the colony?Risk

associated with task

completion

4 Basic elements of concern:

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,

everythingAdditionally, 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 funSome of what I’ve talked about is work in collaboration

with James Traniello, Rebeca Rosengaus and Sam Beshers