The Dilution Effect – Numbers, Densities, and Prevalences

This post is the first in a series of posts that I’m going to be writing about a current hot topic hypothesis in the field of disease ecology.  This week is an introduction to the dilution effect hypothesis, and next week I’ll talk about the recent debates as to whether the dilution effect really happens – and how often it happens – in disease systems.  Stay tuned! 

The dilution effect hypothesis suggests that there is a negative relationship between (human) disease risk and host diversity.  That is, high host diversity “dilutes” disease risk.  This idea is best explained with an example, so let me introduce the host-parasite system that has been studied the most with regards to the dilution effect:

Lyme disease is caused by the spirochete bacterium Borrelia burgdorferi that is vectored by the black-legged tick.  Larval ticks are born uninfected, but they can become infected by taking a blood meal from an infected host.  The first blood meal often comes from a white-footed mouse, and mice tend to be very competent hosts – they’re good at transmitting B. burgdorferi to ticks.  After the blood meal, the larva leaves the mouse, and will eventually molt into a nymph.  The nymph then needs to take a blood meal, and it may feed on a variety of mammalian hosts, including humans.  (In fact, because nymphs are so hard to see, humans are much more likely to be infected by B. burgdorferi by being bitten by a nymphal tick than an adult tick.)  So, the nymph takes a blood meal, possibly transmitting B. burgorferi to the host if the tick is infected, and possibly becoming infected with B. burgdorferi if the host is infected but the tick isn’t.  As before, after the blood meal, the nymph drops off the host and then molts into an adult tick.  Adult ticks also need a blood meal, and so they, too, must find a host, which is very commonly the white-tailed deer.  Adults mate, and then the females lay eggs in the leaf litter that will later hatch into uninfected larval ticks.

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The tick life cycle and Lyme disease transmission. Infected animals are lime, for Lyme disease. Figure adapted (significantly) from here. Pretend my larval ticks only have six legs.  There are more than 4 host species for ticks; these are just examples.  That orange thing is a human.

As I said before, when we talk about the dilution effect, we’re trying to determine human disease risk under various biodiversity scenarios.  In the Lyme disease system, human disease risk is high when there are many infected ticks in a given area, and there are two ways to get high numbers of infected ticks in an area: high total densities of ticks and/or high prevalences of infected ticks.  The following two scenarios illustrate how total density and infection prevalence in ticks affect the number of infected ticks and human disease risk:

Scenario One:  You have two identical areas.  In each area, 50% of the ticks are infected (=constant prevalence).  In Area 1, there are 10 ticks, and in Area 2, there are 20 ticks.  Based on a 50% prevalence, in Area 1, there are 5 infected ticks, and in Area 2, there are 10 infected ticks.  So, human disease risk is higher in Area 2.  In this example, varying total tick density varied human disease risk.

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Scenario Two:  Again, you have two identical areas.  This time, each area has 10 ticks (=constant density).  In Area 1, 50% of ticks are infected, and in Area 2, 80% of ticks are infected.  So there are 5 and 8 infected ticks, and human disease risk is higher in Area 2.  But in this example, tick density was constant, and varying the prevalence in tick infection varied human disease risk.

prevalence3

Hopefully, it is now clear that human disease risk is related to the number of infected ticks in an area, and we can change the number of infected ticks in an area by changing the total tick density and/or the prevalence of infected ticks.  So, how do we change those two variables?  Well, as it turns out, there are a wide variety of host species for ticks, and they vary in their ability to 1) provide blood meals to ticks and thus affect tick density and 2) infect ticks with B. burgdorferi (=host competency) and thus affect the prevalence of tick infection. So, the density of ticks and the prevalence of tick infection should be related to which hosts there are in a given area.

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Deer provide many blood meals to hungry adult ticks, so having high deer densities may increase total tick densities. Conversely, opossums tend to groom off and eat their ticks, so opossums might decrease total tick densities. Furthermore, host species vary in reservoir competence – their ability to transmit B. burgdorferi to ticks. White-footed mice are very competent hosts, and deer and opossums are poor reservoir hosts.  (50% transmission success from an opossum is high, but I can’t be bothered to color in only part of a tick.  You get the graphics you pay for on this blog!)

We now have all of the information that we need to discuss the dilution effect!  Imagine again that you have two identical areas, but one has high host diversity and one has low host diversity.  We’ll assume that In the high biodiversity area (Area 1), you have a mix of mice and lower competency hosts, like opossums and deer.  In the low biodiversity area (Area 2), the hosts tend to be mostly mice, which are highly competent hosts.  If the two areas have the same tick density, but Area 1 has high host biodiversity and Area 2 has low host biodiversity, Area 2 should have higher prevalence of infected ticks and thus higher disease risk for humans.  The disease risk in Area 1 is diluted by biodiversity, because ticks are feeding on hosts that are less likely to infect them.  That’s the dilution effect!

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Human disease risk is higher in the low biodiversity area (Area 2, right).

You might be wondering what happens if Area 1 has higher biodiversity and lower prevalence of infected ticks, but ALSO higher tick density because of the presence of deer?  Uhm.  Well.  Good question.  In that case, if increased tick density cancels out decreased tick infection prevalence, you might not see a change in human disease risk – a “neutral effect” instead of a “dilution effect.”  Or you might even see an increase in human disease risk if the increase in tick density outweighs the reduced prevalence of infected ticks – an “amplification effect” instead of a “dilution effect.”

If this doesn’t seem complicated enough for you, try thinking about different forests with different levels of biodiversity and different host densities and different host infection prevalences and different tick densities and different prevalences of tick infection.  Where is Lyme disease risk highest for humans?

Under what conditions should we see a dilution effect?

Hopefully, this introduction has emphasized that you won’t necessarily see a dilution effect in every host-parasite system, and if you do, you might not see a dilution effect all the time.  The dilution effect is context-dependent, and there are some very specific conditions that need to be met in order for a dilution effect to occur:

  1. The vector (in this case, the tick) needs to be a host generalist.  In the Lyme disease system, ticks feed on a range of host species, and not just humans.
  2. The vectors must usually become infected by biting infected hosts, rather than through vertical transmission of infection from parent to offspring.  In the Lyme disease system, larval ticks are born uninfected and become infected via taking blood meals from infected hosts.
  3. Hosts must vary in reservoir competence.  In the Lyme disease system, mice are very competent hosts and opossums and deer are not.
  4. The most resilient host species – the ones that are left in low biodiversity communities – must also be the highly competent host species.  In the Lyme disease system, mice are the most abundant hosts in low biodiversity systems, and they are highly competent reservoirs.
  5. Increased host biodiversity doesn’t also cause an increase in vector density.  Or if it does, the increase in vector density is outweighed by the decrease in the prevalence of infected vectors.  Not sure if this happens in the Lyme disease system or not.  (So, I’ve only ever seen this stated as an assumption in Wood and Lafferty 2013, and they don’t cite it, so I guess they were the first to include it as an assumption.  It’s very important!)

So, that’s the dilution effect!  Come back next week to witness some sassy language as scientists argue about whether the dilution effect really happens in disease systems!

Reference:

Wood, C. L., and K. D. Lafferty. 2013. Biodiversity and disease: a synthesis of ecological perspectives on Lyme disease transmission. Trends in Ecology & Evolution 28: 239–47.

5 thoughts on “The Dilution Effect – Numbers, Densities, and Prevalences

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