Emerging Infectious Diseases

Next week, I’m going to talk about the role of livestock, wildlife, and the environment in emerging infectious diseases (EIDs) of humans. This week, I want to talk more generally about emerging infectious diseases.

Let’s start with the most straightforward part: “infectious.” EIDs are caused by some kind of transmissible pathogen. Therefore, heart disease and obesity are not EIDs, even though there are major epidemics of these diseases in some countries. (As a side note, there are some cool papers that relate the spread of non-infectious diseases, like obesity, through social networks to the spread of memes.) And “disease” means that there is pathology or fitness decreases experienced by the hosts as the result of a pathogen.

There are two ways that infectious diseases can be “emergent.” First, an emerging pathogen can be novel to a naïve or highly susceptible host population, meaning that it never existed in that population or species before. For instance, the newest emerging fungal pathogen of salamanders in Europe (Batrachochytrium salamandrivorans) exists in populations of relatively resistant salamanders in Asia, but has not previously existed in European salamanders (Martel et al. 2014). B. salamandrivorans was likely introduced into Europe via the pet trade, and European salamanders are highly susceptible to the pathogen.

Pathogens can also be considered emergent when they have existed in a population previously (i.e., endemic pathogens), but the pathogens weren’t noticed by humans until recently and/or infection rates or mortality rates recently increased due to some change in ecological or environmental conditions (e.g., changing amounts of forest fragmentation and the re-emergence of Lyme disease).  Next week, I’ll go into much more detail about how disease emergence depends on ecological and environmental conditions.

Finally, why should we care if a pathogen causing an EID is novel to the focal host population or endemic to the population? Because the control measures that we use will depend on whether the pathogen is novel or endemic. For instance, targeting the trade of salamanders originating in Asia appears to be the best option to stop the spread of B. salamandrivorans, and that would not be the case if B. salamandrivorans were endemic to salamanders all over the world.

WrongWorm References:

Martel, A., et al. 2014. Recent introduction of a chytrid fungus endangers Western Palearctic salamanders. Science 346(6209): 630-631.

Spartants on Epiphytes: Species Assembly Rules

On Monday night, I read an amazing paper. It was so amazing that I knew I’d need to save it for my post on Parasite Ecology’s second birthday…which I then realized was less than two days away. So, in the past 24 hours, I have fallen hopelessly in love with ants (again!), reorganized two months worth of queued posts, and made a symbiont cartoon incorporating the phrase, “This is Sparta!” – something that I’ve been waiting for the right moment to do for TWO YEARS. The moment has arrived. Happy Birthday, Parasite Ecology! 

As we’ve talked about previously, some ants live on plants in special hollow chambers that the plants provide called domatia. But ants can also live on plants that don’t provide domatia, such as the epiphytic birds’ nest ferns that trap leaf litter in the canopies of tropical forests. Amazingly, up to 12 ant species can be found living together on just one of these epiphytes! But what determines exactly which species can be found living together?

Ant competition might be a major structuring force that determines which ant species live together in a given epiphyte community, and ant species that are most similar in size may be more likely to compete. And in fact, in a survey of 86 epiphytes, ant species that were similar in size were less likely to co-occur than one would predict based on a null model (Fayle et al. 2015). But that doesn’t prove that competition is driving the observed pattern, so Fayle et al. (2015) conducted an experiment where they inoculated epiphytes with single or multi-host communities of ants, and then two days later, they inoculated the same epiphytes with an “invader” species. There was strong competition between similarly sized ants, but not between ants of disparate sizes. Furthermore, the threshold size ratio between the invading and resident ants that determined whether there was strong competition or not was roughly the same in both the observational study and the invasion experiments.  And get this:

“In each replicate, an invading colony was introduced into a fern, which was supported on a fluon-coated cylinder above a fluon- coated container, and left for 24 h with ants ejected from the fern falling into the container. Competition manifested as direct attacks between workers of different colonies, with ants sometimes being thrown from the edge of the fern (Appendix S3).”  


Ok, but we’re not done! Fayle et al. (2015) took things a step further and ran simulations with different sets of species assembly rules to see which set of rules, if any, could re-create the community diversity patterns that they observed in the field. Their null model was that size-based competition didn’t matter, so that every species had the same probability of invading a community, regardless of the sizes of the resident species. They compared this to four other sets of rules (1a, 1b, 2a, 2b), where the relationship between the invader-resident size difference and the strength of competition was described as a (1) threshold assembly rule or a (2) saturating assembly rule and competition between ants was assumed to be (a) between the invader and the resident of the most similar size (nearest neighbor competition) or (b) between the invader and all resident species (diffuse competition).  The rules that resulted in the best fit to the observed diversity patterns were the combination of the saturating relationship between the invader-resident size difference and the strength of competition and nearest neighbor competition (2a). SO. COOL.

Go check out the paper! It’s open access.


Fayle, TM, P Eggleton, A Manica, KM Yusah, and WA Foster. 2015. Experimentally testing and assessing the predictive power of species assembly rules for tropical canopy ants. Ecology Letters 18: 254–262.

Koala Chlamydia

Happy Valentine’s Day!!

Koalas are adorable. (Seriously, go google image search koalas.) Koalas are also a threatened species, where population sizes in Australia are declining rapidly. Part of this decline is due to human encroachment into koala habitat: we cut down their trees, run them over with our cars, and let our dogs attack them. Another major cause of koala population declines is disease.

What pathogen is wiping out koalas? Well, it’s one that you probably wouldn’t guess: chlamydia. The bacteria is transmitted between males and females during sex and between mothers and their joeys, and the disease can be very, very unpleasant for infected koalas. The range of symptoms includes lesions, urinary incontinence, secondary yeast infections, sterilization due to infection in the reproductive tract, conjunctivitis and even blindness when infection occurs in the eyes, and death in the worst cases.

For koala populations as a whole, chlamydia-induced infertility is a huge problem. The prevalence of infection can be very high in koala populations, which leaves very few reproductively functional individuals. The good news is that there’s also a huge effort underway to treat and rehabilitate sick and injured koalas. Additionally, a vaccine has been developed to prevent koala chlamydia, and early trials with the vaccine have been successful.  Let’s hope that in the near future, a typical koala conversation will look like this:    

Koalifications Koalas aren’t the only animals that have STDs, of course. For instance, you might rekoal a post (I’m so sorry, really) that I wrote last Valentine’s Day about insect STDs. Go check it out!

Avoiding cadavers is good for you

There is a caveat to the title of this post. Avoiding cadavers is good for you if you’re very close to the cadaver. But that’s getting ahead of the game, so let’s back up:

There is growing evidence that uninfected animals can often sense and avoid infected individuals or infectious agents in the environment.  Or, conversely, uninfected animals may be attracted to infected individuals (see my old post about how disease is sexy).  Gypsy moths fall under the first category. Specifically, gypsy moths can become infected by a lethal (to them) bacilovirus when they ingest leaves contaminated by infectious gypsy moth cadavers, and previous work with these gypsy moths showed that they will preferentially eat uncontaminated leaves over contaminated leaves.

Obviously, these studies on the avoidance behavior of individuals were conducted because the authors thought that avoidance should affect an individual’s probability of becoming infected. These behaviors may affect population-level infection dynamics, too. So, does avoidance behavior affect pathogen transmission?

Eakin et al. (2015) used a really cool combination of laboratory experiments, field data, and modeling to answer this question. First, they parameterized two submodels using data from a laboratory experiment where they allowed individual caterpillars to feed on leaves that had one infectious cadaver on the surface. In the first submodel, they used model selection to figure out which mechanistic model best predicted a caterpillar’s probability of becoming infected. They found that the best model incorporated both how close to the infectious cadaver the uninfected caterpillar fed and how much the uninfected caterpillar ate. Just one bite of an infectious cadaver (which are ~78 bites total) increased the risk of infection by 0.4-4.7%. In the second submodel, they used model selection to figure out which stochastic simulation model best explained a caterpillar’s feeding decisions. They found that the best model included avoiding infectious cadavers. But here’s an interesting thing: the caterpillars don’t really detect and avoid cadavers until they are 0.7 mm away. Because eating a bite or two of infectious material doesn’t increase infection risk dramatically (just 0.4-4.7%), Eakin et al. (2015) suggest that caterpillars shouldn’t really go out of their ways to avoid cadavers; they should keep munching away until there is a cadaver right in front of them. Neat!

Finally, using the two parameterized submodels, Eakin et al. (2014) showed that the model predictions fit field data quite well, and the model that includes cadaver avoidance slightly outperformed the model without cadaver avoidance. At the highest densities of infectious cadavers, avoidance can reduce infection rates by 7% in a single transmission bout in the field. Cool!

I glossed over some cool math – like stochastic simulation models – so you should go check out the paper. Also, here is a relevant cartoon that, if nothing else, demonstrates my peculiar unique brand of humor:


(I don’t think Eakin et al. (2014) are selling caterpillar art on the Interwebs. But if they suddenly start to sell caterpillar art, I want 10% of the profit. Just saying. PS – this is a thing.)


Eakin, L., M. Wang, and G. Dwyer. 2015. The Effects of the Avoidance of Infectious Hosts on Infection. The American Naturalist 185:1.

Hungry Bears Part 3

If you haven’t seen it yet, there’s a really cool paper in Ecology Letters about the indirect effects of bears on ecological communities (Grinath et al. 2015). Did you know that bears will eat ants? Well, they will! Especially during periods when they are food limited. And when bears disturb ant nests, the ants stop tending the leaf and tree hoppers (=herbivorous insects) that they farm for honeydew. Without ants patrolling nearby, tree hoppers experience higher predation pressures from other arthropod predators, like lady beetles and spiders. And when the densities of herbivorous insects decline, plant fitness increases. So, by eating ants, bears can increase plant fitness! Nuts!


I glossed over some of the details of this story, such as variation in the effects of bears across years. To get all of the details and to see some cool structural equation modeling, go check out the paper!


Grinath, J.B., B.D. Inouye, and N. Underwood. 2015. Bears benefit plants via a cascade with both antagonistic and mutualistic interactions. Ecology Letters 18(2): 164-173.