One Model to Rule Them All?

A common topic on this blog has been how to classify the different types of natural enemies. Where do we draw the line between predators, parasites, micropredators, parasitoids, etc.? What characteristics do we look for to determine which type of enemy we’re looking at? In a previous post, I showed you a classification scheme that used four criteria to divide up the predators, parasites, and parasitoids: (1) “Does the enemy attack more than one victim?” (2) “Does the enemy eliminate victim fitness?” (3) “Does the enemy require the death of the victim?” and (4) “Does the enemy cause intensity-dependent pathology?” In a later post, I talked about a classification scheme that extended this concept from considering only natural enemies to including other types of symbionts, such as mutualists. In that classification scheme, there were only two criteria: (1) the relative duration of the association and (2) the effects of the symbiont (predator, parasite, mutualist, etc.) on the fitness of other partner.

So, how many criteria do we need? To answer this question, we really need a precise way to categorize each type of natural enemy. And nothing gives a precise definition like an elegant mathematical expression! But if you’ve done any mathematical modeling of enemy-victim interactions, you know that there are tons of models out there: multiple predator-prey models, multiple parasite-host models, multiple parasitoid-host models, etc. And it can be tricky to figure out how these models relate to each other.  At least, it used to be tricky! Last week, Lafferty et al. (2015) published a general consumer-resource model that can be simplified to produce any specific enemy-victim model that you want. And they have a neat little program that can do the algebra for you! (At least, Kevin showed that program at ESA 2015. I didn’t see a link to it in the article.)

Now that we can define all of the natural enemy types using a common mathematical model, what criteria do we use to differentiate between the types? The first criterion is whether the enemy can try again if it has a failed attack (“predators”: autotrophs, detrivores, scavengers, predators, social predators, and micropredators) or whether one failed attack results in natural enemy death (“parasites”: parasitoids, parasitic castrators, macroparasites, microparasites, and decomposers. The decomposer thing is blowing my mind.) The second criterion is how many victims each enemy attacks at a given life stage (predators attack many victims, but parasites attack one victim), which is similar to the relative duration of association, and the third criterion is how the enemy affects the victim’s fitness (predators kill their victims, micropredators do not kill their victims). Those second and third criteria are similar to the previous classification scheme used by Lafferty and Kuris (2002), but the first criterion is a new one. And I’m not sure if Lafferty et al. (2015) would argue that we don’t need the intensity-dependent fitness cost criteria, or not. So it looks like we need three or four criteria. Cool stuff!


Lafferty, K.D., G. DeLeo, C.J. Briggs, A.P. Dobson, T. Gross, and A.M. Kuris. 2015. A general consumer-resource population model. Science 349 (6250): 854-857.

Parasite Ecology at ESA 2015

I didn’t go to all of the parasite ecology talks at ESA 2015, and I can’t even fit all the ones that I went to into one blog post.  But for those of you who weren’t there – and for those who were but just want to revel in your memories of awesome ESA 2015 parasite ecology – this post contains some of my favorites. Go check out the abstracts and keep an eye out for the upcoming papers!

I really enjoyed the session on models and mechanisms of fungal disease. The highlights for me were:

  1. Alex Strauss’ talk on the ways that habitat, predators, and host diversity affect fungal epidemics in Daphnia (SEM happened!) Also, Alex was the Runner Up in the parasite cartoon contest!
  2. Cherie Briggs’ talk on whether host density affects chytrid transmission in mountain yellow-legged frogs, and whether a typical density dependent transmission model can give rise to data that don’t show density effects
  3. Jessica Hite’s talk about how fungal parasites mediate competition between juvenile and adult Daphnia, leading to a change from out-of-phase to in-phase juvenile and adult population cycles and thus destabilizing host population dynamics
  4. Joseph Hoyt’s talk about bat contacts, bat sociality, and white-nose syndrome, where he used fluorescent powder to keep track of bat contacts

I also saw a bunch of great talks in other sessions. Some of my favorites were:

  1. Sara Weinstein’s talk about rats and raccoon roundworm transmission. Also, Sara was the Honorable Mention in the parasite cartoon contest!
  2. T’ai Roulston’s talk about whether parasites cause bumblebees to “camp” out on flowers at night, instead of staying in the nest. (I’m going to try to blog more about this soon!)
  3. Ben Van Allen’s talk about how cannibalism can reduce virus transmission in the fall army worm, and maybe in many other systems, too?
  4. And of course Kevin Lafferty’s talk about how a new general consumer resource model can be used for all predators, parasites, and micropredators. (I’ll try to blog this one soon, too!)
  5. Max Joseph’s talk covered a few topics, including this intriguing question: how should we model symbiont transmission, and how does our choice of model affect the relationship between diversity and disease (i.e., dilution effect)?
  6. Fletcher Halliday’s talk about the role of resource availability (and thus host community structure) on parasite richness and abundance – for PLANT parasites. That’s right. You can’t call me a taxa-ist if I include at least one plant talk. ;)

There have you have it! Click through to read those abstracts!

Winner of the Unofficial ESA 2015 Parasite Ecology Cartoon Contest

As promised, myself and a team of judges were hunting parasite cartoons at ESA 2015. And we found them! I’m just going to give you guys the Honorable Mention, the Runner Up, and the Supreme Grandmaster (=Winner) of ESA 2015 parasite cartoons.

Honorable Mention:

Congratulations to Sara Weinstein – who talked about the role of invasive species (=rats) in the transmission of native parasites (=raccoon roundworm) – for being our Honorable Mention! Sara used several cartoons in her talk, but the crowning jewel was a line drawing of rats and raccoons foraging together in trashcans.

 Runner Up:

Our Runner Up is Alex Strauss, who won the contest last year! Alex clearly has a knack for cartoons (and great talks). During his talk about the relative roles of the healthy herds effect, the dilution effect, the “predator spreader” effect in a Daphnia-fungus system, he used a cartoon of a Chaoborus biting off a Daphnia‘s head! Obviously, that was awesome. (Update: Meg said that Eric Dagley is the artist of the Chaoborus cartoon – nice job, Eric! This doesn’t change our contest outcome – using other peoples’ cartoons was allowed.)


Nicholas Skaff gave a talk about the role of fine-scale wetland characteristics in the risk of West Nile Virus infection in humans. He had to explain several hypotheses regarding how wetland size, wetland connectivity, and wet/dry cycles might affect human infection risk. And to do that, he used some really helpful diagrams, which included cartoons of ponds, birds, frogs, fish, mosquitoes, sunshine, rain clouds…and probably other things, but I can’t read my own handwriting in my notes. So, congrats, Nicholas! You are the Supreme Grandmaster, and you’ve won a year of bragging rights!!

Stay tuned next week to hear about my favorite talks from the conference!

The Future of Disease Ecology

To celebrate ESA’s Centennial, ecologists are looking forward and identifying the future challenges and goals of ecology. In that spirit, I want to direct you to this open access volume of the journal Epidemics, where a lot of really smart people published their thoughts about the future challenges and goals of modelling infectious disease dynamics. From the preface:

Our aim is to provide a forward-looking summary of research and practice in infectious disease dynamics. Given the current state of the art, what are the most important challenges facing our field, and where are the most exciting breakthroughs likely to occur in the coming decade?

Even if you’re an empiricist with little interest in modelling, I think there are many great ideas for future work in this volume. Go check it out!

Also, if you’re at ESA 2015 this week, remember that myself and a team of my minions will be looking for the Best Parasite Ecology Cartoon at ESA!!! May the odds be ever in your favor.

Effects of Symbionts on Host Population Dynamics

Sometimes it can be really difficult to determine whether a symbiont is a mutualist, a commensal, or a parasite of its host. The context-dependent nature of these relationships is partially to blame for our inability to stick a label on any given symbiosis, because the net outcome of the relationship might vary every time we try to measure it! But even if we limit our focus to just one set of conditions, it can be difficult to say for sure what net impact the symbiont has on host fitness, because we may not be able to simultaneously quantify all of the ways that the symbiont might affect the host. For instance, we might find that the symbiont does not affect host survival, and then we might be tempted to call the symbiont a commensal. But if we didn’t measure host reproductive output as well as survival, how do we know that the symbiont didn’t affect that particular host vital rate? And to make things more confusing, how long-term must our measurements be? Do we need to record every detail from the second the host is born until the second it dies? And then do we need to do the same for each of the host’s offspring?

In this post, I’m not going to answer any of those questions. (GOTCHA!) Instead, let’s talk about a beautifully written and relevant paper about ants on cacti (Ford et al. 2015). If you need a refresher on the incredibly cool ecological relationships between ants and myrmecophytic plants (and/or you want to see some sweet photography by Alex Wild), check out this post. Otherwise, I’ll assume you’re on board with the terminology that I’m using.

The fishhook barrel cactus sports a bunch of extrafloral nectaries that are visited by ants. If insects visit the plant’s fruits or flowers when there are ants on guard, those insects might get attacked by the ants. If the ants are deterring herbivorous insects, they might be positively affecting host fitness. To determine whether that was the case, Ford et al. (2015) quantified the effects that ants had on three host vital rates: number of fruits, plant growth rate, and plant survival. Ants increased the number of fruits produced by the plants, but ants did not affect plant growth rates or survival.

If ants are increasing fruit output, that should have important implications for cactus population dynamics, right? Actually…maybe not! Ford et al. (2015) used some integral projection models to show that cactus population dynamics were really sensitive to cactus growth rates and survival probabilities, but cactus population dynamics weren’t affected by the number of fruits produced per plant – the one vital rate that ants affected.

So, Ford et al. (2015) used three years of very detailed survey data to show that ants don’t seem to be having any affect on cactus population dynamics. But what if they were to follow these populations for a longer period? What if ants don’t really affect the cactus population most years, but they have a big effect during the rare years where there are “catastrophes” (i.e., outbreaks of herbivorous insects) or “bonanzas” (i.e., relatively wet years)? Ford et al. (2015) used some simulations to show that ants could potentially have positive effects on host population dynamics in the long-tem, especially when there were high frequencies or intensities of certain catastrophes or bonanzas. Neat!



Ford, K.R., J.H. Ness, J.L. Bronstein, and W.F. Morris. 2015. The demographic consequences of mutualism: ants increase host‑plant fruit production but not population growth. Oecologia.

An Ode to Quantifying Infection Risk in Addition to Prevalence

When you’re studying parasites (or symbionts or pathogens), the prevalence of the parasite in the host population is one of the easiest response variables to measure. That’s not to say that it is easy; there are certainly a variety of methodological difficulties that crop up, and it can be expensive to run lots of blood tests if you’re looking at seroprevalence. But getting a prevalence estimate is certainly a lot easier than pinpointing when each host becomes infected (e.g., via mark-recapture methods) and/or calculating the actual risk of infection (i.e., the rate that susceptible hosts become infected = force of infection). For that reason, we often use prevalence as a response variable, and hope that we can infer things about parasite transmission based on those data. Sometimes, it works out great! For instance, in 1854, John Snow (the physician, not the Brother on the Wall) mapped the locations of Cholera cases in London. By pinpointing an area of high incidence on the map, he found a water pump that was probably an important source of infection in the epidemic. But do areas of high disease incidence or prevalence always occur in areas of high disease exposure?

Littorina littorea, the common periwinkle, is an abundant and widespread marine snail that hangs out in the intertidal zone (various levels of exposure to the air with the tides) and the subtidal zone (almost never exposed to air). Periwinkles are hosts for a few different trematode species, but for today, we’ll just focus on Cryptocotyle lingua, which infects snails, then fish, then shorebirds. Snails get infected when they consume trematode eggs from shorebird feces. ‘Loitering’ shorebirds are 6-20 times more likely to hang out in the high intertidal zone than the low intertidal zone, and as a result, the density of shorebird feces in the high intertidal zone is 70 times higher than in the low intertidal zone (Byers et al. 2005). Therefore, it is not surprising that when uninfected ‘sentinel’ snails were placed in field cages in the high and low intertidal zones, snails were four times more likely to become infected in the high intertidal zone (Byers et al. 2005). In fact, the probability that an uninfected snail would become infected in the low intertidal zone was effectively zero. That makes sense, because bird guano was almost never found in that zone.

So, when Byers et al. (2005) went out and sampled periwinkles in the high and low intertidal zones, they found way higher prevalences of infection in the high intertidal zone, where infection risk was high, right? WRONG! The prevalence of infection was much higher in the low intertidal zone, even though snails do not become infected there! How could that be?

First, let’s back up and talk about an important selection pressure in the low intertidal zone: predation. There are extreme size-dependent predation pressures in that zone that pretty much prevent small/young snails from living there. So, the only snails in the low intertidal zone are bigger/older snails. Big/old snails are much more likely to be infected by trematodes than small/young snails, because they have had longer to be exposed and become infected. But we know that the big snails aren’t becoming infected in the low intertidal zone, so where are they coming from? It may be that young snails hang out in the high intertidal zone, escaping predation but experiencing high infection risk, until they are big enough to safely live in the low intertidal zone. Once big enough, the snails migrate to that low zone, which provides better foraging opportunities, and the high density of big, infected snails results in high prevalences of infection (76% infection!) in an area that has effectively zero risk of infection. Isn’t that neat?!

So, as Byers et al. (2005) point out, “disease risk and prevalence patterns need not be tightly coupled in space.” I think that’s important to remember when we’re deciding what response variables we want to consider in ecological and epidemiological studies.



Byers, J.E., A.J. Malek, L.E. Quevillon, I. Altman, and C.L. Keogh. Opposing selective pressures decouple pattern and process of parasitic infection over small spatial scale. Oikos.

World’s coolest vector of infectious pathogens

If you weren’t born before the 1980s, you probably don’t know what an entire street lined with elm trees looks like, because Dutch Elm Disease spread through both Europe and North America in the early and middle 1900s and decimated elm populations. Pockets of elms still persist in places like Amsterdam and Winnipeg, but it is a never-ending battle to keep those trees disease free.

So, which highly virulent pathogen is responsible for totally reshaping temperate tree communities? The culprit is a fungus (well, a few fungal species, actually) that is vectored by a tiny and totally adorable beetle (well, a few beetle species, actually). Ladies and gentlemen, I present to you the horrible, terrifying, death-spreading elm bark beetle:


Joking aside, these beetles are completely amazing. They have symbiotic relationships with fungi, where the fungi range from weak parasites to commensalists to mutualists depending on the beetle species, the fungus species, and perhaps environmental conditions. In the simplest cases, bark beetles act as transport vessels for the fungi, without getting anything in return for their dispersal services. In contrast, ambrosia beetles cultivate fungus gardens in the galleries that they excavate in dead trees, and the beetles consume the fungus as their sole source of nutrition. When the young beetles emerge from their natal galleries to disperse, they take fungal spores with them to their new galleries. Isn’t that cool?! Whereas fungus farming has evolved just once each in ants and termites, it has evolved many times in the ambrosia beetles (Hulcr and Dunn 2011). And get this: at least one species of ambrosia beetle is eusocial!

If bark beetles and ambrosia beetles typically only excavate in dead trees, why did the elm bark beetle go rogue and start attacking live elms? Well, it turns out that it wasn’t an isolated event. There are more than a dozen examples of the beetles shifting from dead to live trees in recent history, with catastrophic results for some of the tree species that are being attacked (Hulcr and Dunn 2011). Humans are unintentionally shipping these beetles and their fungal associates to novel regions around the globe. And in novel regions, bark beetles searching for dead trees by following volatile cues might mistake living trees for dead trees. Or, as Hulcr and Dunn (2011) put it, some living trees might smell dead. Even if the beetles realize their mistake and don’t completely excavate a gallery in a living tree – choosing instead to go search for a dead tree – their initial boring activities might inoculate the tree with the fungal symbionts.

But here’s a conundrum: if a fungal symbiont transported by bark beetles doesn’t really affect trees in the beetle’s native range, why should the fungus be highly virulent in the introduced range? After all, the beetles can only introduce a little fungal innoculum into a giant living tree. Well, it may be that the majority of the tree pathology is caused by the tree’s response to the pathogen, rather than the direct actions of the fungus, just like the animal immune response to pathogens is often worse for the host than the actual damage caused by the pathogen. It may be that trees wildly overreact to the novel fungal pathogen by expanding the walls of the xylem so much that the tree ends up dying.


Anyways, bark beetles are really cool vectors, and I think disease ecologists should pay more attention to them.


Hulcr, J., and R.R. Dunn. 2011. The sudden emergence of pathogenicity in insect–fungus symbioses threatens naive forest ecosystems. Proceedings of the Royal Society B 278: 2866–2873.