Defended Hosts are Frassheads

Last week, I told you guys that parasitoid wasps respond to H. defensa, which is a bacterial endosymbiont that protects aphids from wasps.  Next week, I’m going to talk about how H. defensa affects aphid fitness.  But first, what is the magnitude of the protective effect?  Well, it varies with the strain of H. defensa and the aphid species and probably lots of other factors, too.  But in the example that I’m going to discuss next week, aphids without H. defensa have a 38% probability of becoming mummies if they get attacked by wasps, while aphids with H. defensa only have a 4% chance of becoming mummies if they get attacked by wasps (Vorburger et al. 2013).  So, H. defensa reduces the probability of mortality after attack by 89.5%!


Stay tuned to see what happens to Sal and Lisa.  Are they up Frass Creek without a paddle?


Vorburger, C., P. Ganesanandamoorthy, and M. Kwiatkowski. 2013. Comparing constitutive and induced costs of symbiont conferred resistance to parasitoids in aphids. Ecology and Evolution 3(3):706-13.


How do parasitoids respond to defended hosts?

Last week, I talked about the new Godzilla movie and how I thought that the MUTOs should have been parasitoids.  This week, let’s talk about some awesome, real life parasitoids: parasitoid wasps (Aphidius ervi).

Quickly, the life cycle works like this: the female wasp finds an aphid nymph, she stabs the aphid with her ovipositor, and then she typically lays one egg inside the aphid.  After one day, the egg hatches into a larval parasitoid, and the larva hangs out inside the aphid while eating the aphid’s innards.  After about one week of this, the aphid dies.  Actually, the aphid’s corpse becomes a “mummy,” and the larva pupates inside the mummy before eventually emerging as an adult parasitoid.  Mating happens, and then the female wasps go off to infect more aphids.

But here’s an interesting complication: some aphids are protected by bacterial symbionts (Hamiltonella defensa).  The degree to which aphids are protected varies with the strain of H. defensa, but the take-home message is that when a wasp lays an egg inside an aphid, the egg is much less likely to survive to adulthood if the aphid has H. defensa symbionts (Oliver et al. 2012).  However, if a wasp lays two eggs inside an aphid with H. defensa symbionts – which is not what wasps usually do – then one larva is more likely to survive than it would have been if it had been a single egg.  In other words, only one larva is going to make it out of there alive, even when two eggs are laid, but one of the larvae is better off than it would have been if it were a single egg.

You might be thinking, “Why, what an interesting tidbit.  Who cares?”  NATURAL SELECTION CARES.  Just kidding, natural selection isn’t sentient, but natural selection should favor any wasp strategies that increase wasp fitness.  And wasp fitness is higher when more wasp eggies turn into wasp larvae and then adult wasps.  And wasp larvae are less likely to die in aphids with H. defensa if two eggs are laid in the aphid, instead of the typical single egg.  See where I’m going with this?

Yes, wasps can differentiate between aphids with and without H. defensa.  And when aphids have H. defensa, wasps are much more likely to lay two eggs in those defended aphids than they are to lay two eggs in undefended aphids.  And that, my friends, is amazing.  While wasps still probably have reduced fitness when infecting defended aphids, the superinfecting tactic (=laying two eggs) likely compensates for some of the reduced fitness.


(Yes, sometimes aphids have conversations in my head, and I write them down. You’re welcome.)

Check out the open access paper to learn about the mechanism behind the success of superinfection:


Oliver, K.M., K. Noge, E.M. Huang, J.M. Campos, J.X. Becerra, and M.S. Hunter. 2012. Parasitic wasp responses to symbiont-based defense in aphids. BMC Biology 10:11.

Godzilla Parasites


A few weeks ago, I went to see Godzilla.  I hadn’t looked up the plot summary or anything beforehand, so imagine my surprise when out of the giant pulsing “spore” (ahem, egg) emerged something that looked a lot like a cross between a giant water bug and Alien…not Godzilla.  And then imagine my UTTER GLEE when they said that the thing that was not Godzilla was a parasiteSwoon.  I immediately conjured up all kinds of plot possibilities, and I couldn’t wait to see how the parasites attacked Godzilla!

But then I quickly realized that the “parasites” were not parasites at all.  The parasites acquired energy from radioactive material.  For instance, they ate nuclear warheads.  And that alone doesn’t make them parasites.*  It makes them autotrophs.  I thought I might have missed the parasite explanation, so after the movie, I did some googling.  But all I could find was some people saying that the parasites (or their young) might try to feed on Godzilla’s radioactive energy.  I would totally buy that, if the parasites had searched for Godzilla in the movie.  But instead, Godzilla searched for the parasites.  In fact, he was their “predator.”  WHAT?!  Yo, Hollywood.  You need a parasite ecology consultant?  HMU.

So, I wrote you guys a different plot, with actual Godzilla parasites in it.  Except that they aren’t parasites, per se.  They’re parasitoids.*  Enjoy!


The female parasitoid hatches from an egg in a mine in the Philippines.  The female parasitoid goes to the Janjira nuclear plant to feed and causes a giant explosion.  A lady dies, and it’s sad.  The female parasitoid forms a chrysalis in the wreckage.

Sometime in the next 15 years, the other egg from the mine in the Philippines is taken to the USA to be studied and whatnot.  Then the radioactive body of the male parasitoid – which is thought to be dead – is stored in Yucca Mountain.

After 15 years, the female parasitoid emerges from her chrysalis.  She has wings!  (Yes, it is the male who has wings in the movie, but I don’t like it that way.)  She destroys a bunch of stuff and kills a dude and it’s sad.

The male (he’s alive!) and female parasitoids start communicating via echolocation (ok, whatever, I’ll go with it).  They start trying to find each other, stopping only to ransack ships and whatnot so that they can eat radioactive material.  When they find each other, the male fertilizes the female.  The male also gives her a nuptial gift of a nuclear warhead, because that was really cute.  Then he dies because he’s a male and he no longer has a purpose in life.  ONE MONSTER DEAD.  Huzzah!

Now the female needs a host for her eggs.  So, while armed forces are trying to shoot her to bits, she uses her highly adapted sensory apparatus to seek out Godzilla.  When she finds Godzilla, she stabs her ovipositor (yes, she has one of those now) into Godzilla’s body cavity and deposits a single egg.

Godzilla2(And you guys thought my artwork was limited to snails!)

Then the female parasitoid tries to fly off to find another Godzilla so that she can lay another egg, because that’s what parasitoids do.  But Godzilla grabs her head and breathes plasma down her throat, and she dies. SECOND MONSTER DEAD.  Huzzah!

The world starts to rejoice because all the parasitoids are dead, but suddenly San Francisco is being trampled by Godzilla!  Someone left some giant war heads in San Francisco, and Godzilla is being manipulated by the parasitoid larvae into finding and eating more radioactive material!  Oh no!  But wait, one of the nuclear warheads has an analog detonator thingy, so the parasitoid’s EMP abilities can’t stop it from detonating now that it has been activated!  Godzilla eats it!  1 hour and 29 minutes later, Godzilla and the parasitoid within explode.  ALL THE MONSTERS ARE DEAD!

Some soldier and his lady kiss and stuff.  The end!

*If you don’t remember the difference between a parasite, a predator, and a parasitoid, check this out.

How do we model aggregately distributed macroparasites?

Note:  This is the last post in a series of posts about aggregation of macroparasites.  You can see the first three posts here, here, and here.

So far, we’ve established that macroparasites are typically aggregately distributed among hosts, and this aggregation is important.  For instance, overdispersed parasite distributions can stabilize host population dynamics.  Therefore, when we model macroparasite transmission, we should probably include parasite aggregation in the models.  But how can we do that?  And more importantly, how can we do it in a biologically relevant way?

As we’ve discussed before, we usually model microparasite transmission with compartmental models, where we divide the host population into Susceptible hosts (S), Infected hosts (I), and resistant hosts (R).  To model macroparasite transmission, we can further divide up the infected hosts (I).  For instance, we can have hosts with one parasite (p1), hosts with two parasites (p2), host with three parasites (p3), … host with 500 parasites (p500),…and hosts with pn parasites.  As you might imagine, you end up with a LOT of equations to keep track of when you do this.  Also, if the parameters (e.g., mortality rate) differ among hosts with different numbers of parasites, you might need to estimate and keep track of a lot of parameters.

What you might want instead are just two equations: one for the total number of hosts, N, and one for the total number of adult parasites, P.  And that’s actually pretty easy to do!  But, it isn’t a closed system of equations.  That is, N and P depend on the proportion or number of hosts with each number of parasites, so you still need to deal with all that pn stuff.  Anderson and May (1978) very cleverly got around that problem, though.  If you know the mean number of parasites per host (P/N) and k (the aggregation parameter of the negative binomial distribution), you can easily figure out the theoretical proportion of hosts with n parasites.  If you plug in that pn(t) equation, you end up with a closed system.  Yay!

That Anderson and May (1978) model did great things.  It is very, very useful.  But it isn’t good for everything.  Specifically, when people use the model to predict what will happen under various disease management strategies, they usually assume that management reduces the mean number of parasites per host (P/N), but does not change k, the aggregation parameter.  But as we talked about before, biological processes that might be changed by management strategies affect the degree of parasite aggregation.  So, assuming a constant k may not be very realistic.

Part of this issue gets into realm of phenomenological vs. mechanistic models.  We know that the negative binomial distribution is a very good way to describe the pattern of macroparasite distributions.  It’s a good phenomenological model.  But the concern is that it might not be good to model macroparasite distributions in a phenomenological way if the goal of the model is to see what happens when we alter the underlying ecological processes.  In that case, we probably want to allow aggregation of macroparasites to emerge from mechanistic processes in the model.

If you’re trying to model parasite transmission and you don’t want to use a constant k, there are ways to let k vary with time or other variables.  There are also ways to let aggregation emerge from mechanistic processes, especially if you’re using agent based models.  But I think we’re going to see a lot more about this in the future, because people are still concerned that macroparasite models aren’t accurately depicting real systems.  For instance, see the recent Yakob et al. (2014) paper in the International Journal of Parasitology.

Ok!  That’s it for macroparasite aggregation for now!  Next week, I’ll switch topics and talk about Godzilla’s parasites.  Stay tuned!


Anderson, R.M., May, R.M., 1978. Regulation and stability of host–parasite population interactions I. Regulatory processes.. J. Animal Ecol. 47, 219–247.

Yakoba, L., R.J. Soares Magalhães, D.J. Graya, G. Milinovicha, N. Wardropb, R. Dunningc, J. Barendregt, F. Bieri, G.M. Williams, A.C.A. Clements. 2014. Modelling parasite aggregation: disentangling statistical and ecological approaches. International Journal for Parasitology 44: 339-342.

Why does aggregation of macroparasites matter?

Note:  This is the third post in a series about aggregation of macroparasites.  You can see the first post here and the second post here.

Second note:  You may have noticed that this post is several days (more than a week) late.  Sorry!  I’m setting up a mesocosm experiment right now, but the normal post schedule with resume soon.

In the last two posts, we established that macroparasites are pretty much always aggregately distributed among hosts, and this aggregation can result from several ecological processes (e.g., variation in infection rates among hosts).  This week, we will answer the next obvious question: who cares?!  Why is macroparasite aggregation important enough to study?  There are many reasons, but we’ll focus on these three things:

  1. Individual host fitness
  2. Parasite transmission – superspreaders!
  3. Regulation of host populations

Individual Fitness:

As we’ve already discussed, the more parasites that a host is infected with, the more likely that host is to suffer negative fitness consequences.  Highly infected hosts might have lower fecundity, slower growth rates, or higher mortality rates, for instance.  So, when we’re considering individual-level fitness, we need to consider individual-level parasite loads.  That is, assuming that all hosts harbor some mean number of parasites is probably not going to cut it.

They say that repeating something over and over is a good teaching technique.  How about using the same graph in three different posts?  :P  Remember, “awfulness” – a proxy for mortality rates, for instance – doesn’t necessarily increase linearly with parasite abundance.

They say that repeating something over and over is a good teaching technique. How about using the same graph in three different posts? :P Remember, “awfulness” – a proxy for mortality rates, for instance – doesn’t necessarily increase linearly with parasite abundance.

Having a bunch of parasites might also make a host more susceptible to future infection – the “vicious circle” of disease where having parasites leads to lower body condition which leads to higher susceptibility to parasites which leads to MORE parasites, etc.  Once upon a time, I made a cartoon about that. viciouscycle Parasite transmission – superspreaders!

Say you’re about to go eat some delicious sushi, and the chef lets you decide between a fish with just a few parasites and a fish with tons of parasites.  Which one do you pick?  (Yes, I know, I’m evil.)  Obviously, you’re more likely to get infected by the parasites – assuming that they’re trophically transmitted and you can serve as the next host – if you eat a huge dose of parasites. This brings us to the topic of superspreaders, which we have discussed once or twice on this blog already.  If we’re talking about just one host species, superspreader hosts are responsible for a disproportionate amount of the parasite/pathogen transmission.  For instance, superspreaders may be individuals that 1) are heavily infected, 2) are shedding more ‘infectious particles’ (i.e., parasites) than other hosts, or 3) both.  (Other options exist – like individuals with normal infection levels who have many social contacts and thus transmit more infectious particles than others).  These individuals are the ones that exist in the tail of the negative binomial distribution: AggregationGraph Superspreaders are important because if we can identify which individuals are the superspreaders, we can target them for disease management.  For instance, if we can easily recognize the wormiest hosts – by doing fecal egg counts, etc. – then we can target those individuals with anthelmintic drugs.

Regulation of Host Populations I could talk about regulation of host populations by parasites all day.  Like here.  But let’s just focus on how aggregation of parasites affects host populations.  And for that, let’s visit the very important Anderson and May (1978) paper.  I was originally going to make this post very mathy, but instead, I’m just going to summarize one main point of Anderson and May (1978), and you guys can check out the PDF here for the beautiful details.

If you start with a host population that has exponential growth in the absence of parasites, no parasite aggregation, no affects of parasites on host mortality or fecundity, etc., you can get two basic kinds of population behavior – damped oscillations to a constant population sizes or population cycles.  The potential problem with this basic, simplified model is that it is neutrally stable – meaning that if you perturb the system from an equilibrium, it shifts to a different equilibrium.  And that means that the model is structurally unstable: a small change in parameters (e.g., infection rate) can cause the model to shift from one qualitative behavior to another, and that may not be biologically realistic.

Even though the basic model has some less than ideal characteristics, we can use it as a baseline model to see what happens when we add complications to the model.  In a series of two papers, Anderson and May (1978) added a bunch of complications: overdispersion of parasites, underdispersion of parasites, parasite-induced host mortality, density-dependent parasite population growth, and other things.  Some of these things made the model dynamics more stable, and some of them make the model dynamics more unstable.  And the relevant point for this post is that aggregation of parasites is a stabilizing force in host population dynamics.

So, aggregation of parasites among hosts is important because individual-level parasite loads determine individual host fitness and transmission potential, and the individual-level impacts scale up to affect transmission in host populations and also the stability of host population dynamics.  Next week, we’ll talk about how to model macroparasite aggregation.  Stay tuned!!

References: Anderson, R. M., and R. M. May. 1978. Regulation and stability of host-parasite population interactions. I. Regulatory processes. Journal of Animal Ecology 47:219-247.

Excuses, excuses, excuses!

So, I’m a week late on posting.  Sorry, Guys!  I was setting up a mesocosm experiment, and I didn’t have time to finish editing the aggregation posts.  I’ll have them up shortly!  To make it all up to you, I’m going to show you something really pretty.  We (s)nail polished a bunch of snails – like, 640 of them – last week, and you can bask in the adorableness that is painted snails while you wait for the next post.


Why are macroparasites aggregately distributed among hosts?

Note: This is the second post in a series about macroparasite aggregation.  You may need to read the first post to understand this one.

Last week, I told you guys that macroparasites are (almost) always aggregately distributed among hosts.  This week, I want to talk about why macroparasites are aggregately distributed.  That is, we want to know which ecological processes cause parasite aggregation.

First, let’s think about what determines the number of macroparasites on any given host.  To increase the number of macroparasites on a host, new parasites either need to immigrate to the host or new parasites need to be born on the host.  To decrease the number of macroparasites on a host, some of the current parasites either need to emigrate or die.  Therefore, to understand why some parasites have few or many parasites, we need to know more about parasite births/deaths and immigration/emigration.

When we talk about parasite “immigration,” we’re talking about the rate at which hosts get infected.  Let’s imagine for a minute that every host has the same probability of becoming infected.  For instance, think back to our discussion of density-dependent transmission (you might need to do a little review here).  In a regular old SIR model, the rate that a given susceptible individual becomes infected is B*I, where B is the transmission rate and I is the number or density of infected individuals.  If every host has the same probability of becoming infected, then B is the same for all hosts.  For instance, let’s say that it doesn’t matter if you’re Bob, Sally, or Thor, you have a 10% chance of getting infected by this parasite on any given day of your life (B = 0.1).  In a scenario like that, we expect parasites to have Poisson distributions among hosts, with mean/variance = 1.  Explaining why exactly that is the case is a bit hard – it’s just how Poisson processes work – but the key point is that by using some basic statistics, we know that when infection is a random process with a fixed probability of infection, parasites will be Poisson-distributed.

Think back again to that previous post and remember that transmission rate B was a “conglomerate” term, where B = c*v, c was the average per capita contact rate between individuals, and v was average probability of successful transmission of the parasite, given that a contact occurred.  And let’s say that each time step, a host has a 50% chance of contacting another host (c=0.5), and there is a 20% chance that a contact will lead to successful transmission of the parasite (v=0.2).  To generate a Poisson distribution of parasites using those values, you need to know that the mean in a Poisson distribution (lambda) is also called the rate parameter, and it is equal to some rate, r, multiplied by the length of time that the process has been occurring, t.  In our case, r = B = c*v = 0.5*0.2 = 0.1.  And then we can just pick a t – say, 20 time steps.  So, let’s generate a Poisson distribution for this infection process that has been running for 20 time steps:

parasites<-rpois(n=1000, lambda=0.5*0.2*20)

var(parasites)/mean(parasites) ##I get 1.02

hist(parasites, right=FALSE, ylab=”Number of Hosts w/ X Parasites”)



Before, all hosts (Sally, Bob, and Thor) had the same 0.5*0.2 = 0.1 = 10% chance of getting infected each day.  But what if the hosts don’t have the same probability of getting infected?  That is, what if B varies among hosts?  In that case, a regular Poisson distribution is no longer appropriate.  What we need instead is a compound distribution, where infection is still a random Poisson process, but now the rate parameter (r*t = lambda) isn’t a constant.  Instead, the rate parameter itself is a random variable.  This may seem like it’s getting unbearably complicated, but we can illustrate this idea with a very simple example.  Let’s say that the rate parameter, r*t, is no longer a constant 0.1, but now it is a random variable that follows a gamma distribution.

rate<-rgamma(10000, shape=1, scale=10)

parasites<-rpois(n=10000, lambda=rate)

##parasites<-rpois(n=10000, lambda=rgamma(10000, shape=1, scale=10)) ##same

var(parasites)/mean(parasites) ##I get 11.07

hist(parasites, right=FALSE, ylab=”Number of Hosts w/ X Parasites”)


What just happened?  We let the rate of infection vary among hosts in a gamma-distributed way, and then the distribution of parasites among hosts went from being something with a variance/mean ratio of 1 to a variance/mean ratio > 1.  The distribution became overdispersed/aggregated!!! And as it turns out, a Poisson-Gamma compound distribution is the same thing as a negative binomial distribution.  You can learn more about parasites and Poisson-Gamma distributions in May (1978).

When infection varies among hosts, does the distribution of infection rates need to be gamma-distributed?  Nope!  For instance, a Poisson-Poisson compound distribution (also called a Neyman Type A distribution) will also be overdispersed.  You guys can try that one in R.  Also, you can learn more about parasites and the Neyman Type A distribution in Anderson and Gordon (1982).

That was a lot of stats.  Now let’s get back to the biology.  What we’ve just shown is that when parasite immigration rates (i.e., infection rates) are constant among hosts, parasites will have a Poisson distribution among hosts.  They will not be aggregately distributed.  But when infection rates vary among hosts – say, infection rates have gamma distributions – parasites will be aggregately distributed among hosts.  Basically, any kind of variation in infection rates acts as an aggregating process.  There are also processes that act to un-aggregate parasites, like density-dependent parasite mortality, but you’ll have to go check out Anderson and Gordon (1982) if you want to know more about that.

Finally, we want to know why infection rates might vary among hosts.  Clearly, such variation is important, but what causes it?  If parasites/infective stages are not uniformly distributed in the environment – if they have a gamma distribution among patches, for instance – and then host contact with parasites within a patch is random (Poisson), you get a Poisson-Gamma distribution, which is the same as a negative binomial distribution (May 1978).  In other words, heterogeneity in parasite distributions in the environment can case variation in infection rates.

There has also been a lot of interest in variation in infection rates due to variation in host susceptibility.  For instance, when the host’s immune response is related to normally-distributed host body condition, you can theoretically get aggregation of parasites among hosts (Morrill and Forbes 2012).  Infection rates might also vary according to host sex, host age, host genotype, host behavior, and a bunch of other things that you can read more about in this book chapter.

Everything we just talked about was related to parasite immigration.  I should point out that whenever you have parasite births on hosts, you also end up with aggregated distributions (Anderson and Gordon 1982).  Many macroparasites don’t multiply on their hosts, though – they reproduce and then broadcast their infective stages into the environment – so births on hosts can’t explain why most macroparasites have aggregated distributions.

Ok, to recap: any time infection rates vary among hosts or parasites reproduce and multiply on hosts, you can get aggregation of parasites among hosts.  And variation in infection rates can be caused by a variety of abiotic and biotic processes, like variation in host immunity.

Now you know why macroparasites have aggregated distributions!!  Next week, we’ll talk about why aggregated distributions are important.  Stay tuned!


Anderson, R. M., and D. M. Gordon. 1982. Processes influencing the distribution of parasite numbers within host populations with special emphasis on parasite-induced host mortalities. Parasitology 85:373-398.

May, R.M. 1978. Host-parasitoid systems in patchy environments: A phenomenological model. Journal of Animal Ecology 47: 833-844.

Morrill A, Forbes MR. 2012. Random parasite encounters coupled with condition-linked immunity of hosts generate parasite aggregation. International Journal for Parasitology 42(7):701-6..