Forest conservation and restoration to reduce human diarrheal disease

In the NCEAS SNAPP Ecological Levers for Health working group, we’re collecting examples of local or regional interventions that can have direct, measurable benefits for human health (via reduced infectious disease) AND the environment – win–win solutions. The case studies that we’ve collected thus far are so cool that we just can’t wait to share them! In September, I shared a story about vulture conservation and rabies. This week, I’ll tell you about plants and diarrheal disease.

Two years ago, I adopted a tiny blue Aussie puppy: a wild, brilliant beast with a thirst for adventure… and water. I mean he really likes water – jumping in it, biting it, blowing bubbles in it, fishing sticks out of it, etc. And the dirtier that water is, the better. So I set my puppy loose at the duck pond at our local gem of a dog park, where I had seen my previous dog and dozens of other dogs safely drink the water. Days later, my precious puppy developed severe diarrhea, and just hours after the onset of his symptoms, he became terrifyingly lethargic. The enteric pathogens that he had guzzled in the pond water might have killed him if we had not immediately sought out veterinary care. But fortunately, antibiotics and rehydration allowed Carrot to make a full recovery, and he grew up to be the healthy mud monster pictured below. From that experience, I re-learned an important lesson from disease ecology: pathogens often have minimal effects on adult animals, which have developed resistance/immunity during prior exposure, but the same pathogens can be deadly for juveniles during their first exposure.

MudMonster

This principle doesn’t just apply to puppies: diarrheal disease is the second leading cause of global childhood mortality for children under five years old. That’s hundreds of thousands of children dying every year because they did normal childhood things – like eating, drinking, and playing outside – and became infected by waterborne or foodborne pathogens like rotaviruses, Vibrio cholerae, and Salmonella. Many more children become infected by these pathogens and survive their illnesses, only to experience lasting physiological impacts. For instance, diarrhea leads to malnourishment, and malnourishment increases a child’s risk of future infection and diarrhea, creating a vicious cycle of ill health that can retard physical and mental development.

How can we remedy this huge global burden of childhood morbidity and mortality? The good news is that we already have substantially reduced the global impacts of childhood diarrheal disease by (1) improving hygiene and sanitation to reduce peoples’ exposure to the pathogens and (2) using oral rehydration therapy to treat children who are suffering from diarrhea, so that they do not die from dehydration. However, millions of people still lack access to clean water resources and quality healthcare, and an unthinkable number of children are still dying each year, and thus there is still much to do. Today, I want to broaden the scope of potential solutions: are there ecological solutions that can help reduce human exposure to enteric pathogens as a complement to current public health efforts?

But before we discuss specific ecological solutions, it’s worth discussing how these pathogens enter and persist in water sources in the first place. In some cases, the pathogens are pumped into public water sources directly from sewage pipes or human bodies (e.g., people swimming and defecating at water access points). In other cases, the pathogens reach public water sources via runoff from the environment after they’ve been excreted by humans and/or animals. When these pathogens reach a waterbody, they do not necessarily find and infect a human. For instance, if the pathogen is buried under the sediment in a stream, degraded by sunlight, consumed by microorganisms, or destroyed by plant biocides, it will never reach a human host. So ideally, ecological solutions will reduce the number of pathogens reaching waterbodies and/or increase pathogen death rates in those waterbodies. With this in mind, let’s talk about one class of ecological solutions for waterborne enteric pathogens: can plants be a win–win solution for conservation and human health?

PlantsFightPathogens

In my freshmen year as an undergrad, my favorite professor made us “draw and describe, in excruciating detail, the difference between an urban and rural hydrograph.” I received full marks, so let’s assume I’m an expert: in urbanized areas with lots of buildings and paved, impervious surfaces, stormwater reaches streams and rivers quickly, whereas in rural areas with lots of trees and permeable soil, stormwater reaches streams and rivers relatively slowly (see below). And of course, it isn’t just water that reaches those streams and rivers. In urban environments, pollutants and pathogens within the stormwater also make it to downstream waterbodies faster, meaning that fewer pathogens die before reaching water sources where they can encounter and infect people. Therefore, human-caused hydrological changes should affect human disease burdens.

hydrograph_urban-flood

And we’re seeing that. For instance, in a massive study of 300,000 children in 35 nations, deforestation upstream from a child’s house was found to be strong predictor of whether the child had high risk of diarrheal disease, presumably because many pathogens were entering the waterbodies upstream (Herrera et al. 2017). (But this was only true for the poor children – the wealthier children living in cities probably had better access to sanitation infrastructure.) Similarly, in Brazil, children living near protected forests were less likely to experience diarrheal disease (Bauch et al. 2015). These large-scale correlational studies suggest that protecting forests might be a win–win solution for conserving biodiversity and reducing childhood diarrhea!

Of course, many forests have already been cut down, so it’s too late to preserve them for human health. In those cases, reforestation/restoration might be a win–win solution. For instance, Herrera et al. (2017) predicted that increasing upstream forest cover by 30% would reduce childhood diarrheal risk as much as improved sanitation and hygiene!

5 RickettsFig

But re-forestation is a big undertaking, and as far as I know, no one has experimentally evaluated the effects of re-forestation on human disease yet. An easier/faster intervention to slow the rate that pathogens and other pollutants reach streams and rivers might be replanting vegetation just within riparian buffers. It’s still unclear whether replanting riparian vegetation can reduce human infection, but in some studies, the number of enteric pathogens and/or fecal indicator bacteria within streams has decreased after riparian buffers were restored, which suggests that human infectious risk would be reduced by stream-side vegetation. This remains an important avenue for future research.

So, preserving or restoring forests and/or riparian buffers can reduce the number of pathogens reaching waterbodies and potentially reduce human infection, but can plants also reduce the number of pathogens that reach human hosts after reaching waterbodies? Potentially! For instance, at Indonesian islands without wastewater treatment systems, there are fewer human bacterial pathogens in seagrass meadows than in nearshore waters that lack seagrass meadows (Lamb et al. 2017). Furthermore, disease burdens in corals are lower near seagrass meadows, too, suggesting that preserving or restoring seagrass meadows could be a win–win for human health and conservation. This is a great correlational study, but is there any experimental evidence that aquatic/marine plants reduce environmental pathogen loads or human disease burdens?

Yep! You may have seen something similar to the photograph below in a town near you. It’s a constructed wetland. Specifically, it’s the Dominguez Gap Wetland, which was created to treat stormwater before it reached the LA River and then the Pacific Ocean. Constructed wetlands like this one are typically designed to filter heavy metals, excess nitrogen and phosphorous, and other chemical pollutants from stormwater. But they can also remove viruses, bacteria, protozoans, and other pathogens from runoff waters. For instance, by forcing viruses to hang out in the slow-flowing water for a while, the wetlands ensure that many viruses die from UV exposure long before they reach downstream waterbodies. Several studies have shown that constructed wetlands successfully reduce environmental pathogen loads, and now we need studies that link constructed wetlands and human disease risk.

constructedwetlands

However, there are many varieties of constructed wetlands – they vary in retention time, turbidity, whether they contain plants or not, whether there is subsurface or surface water flow, etc. And some designs are better at removing pathogens from stormwater than others. Furthermore, even really efficient constructed wetlands might fail to reduce pathogen loads to levels that are safe for human use, depending on how many pathogens are entering the environment. Therefore, if we want to use constructed wetlands to reduce human exposure to enteric pathogens, we need to design them carefully.

So there you have it! “Plants” – or environmental characteristics associated with plants – can reduce the number of human pathogens that reach waterbodies and pathogen survival time within waterbodies. And lower pathogen loads in waterbodies presumably reduce human disease, especially childhood diarrheal risk. As far as I can tell, no one is currently using forest protection/restoration or constructed wetlands on a large scale to try to prevent childhood diarrhea, but “plants” could be “ecological levers for health” that advance both conservation and human health goals.

If you know of any existing, planned, or in-progress forest protection, reforestation, or constructed wetland interventions aimed at reducing human diarrheal diseases, please let me know! And if you can think of any other win–win solutions for conservation and human health, we’d love to hear about them.

References:

Bauch, Simone C., Anna M. Birkenbach, Subhrendu K. Pattanayak, and Erin O. Sills. “Public Health Impacts of Ecosystem Change in the Brazilian Amazon.” Proceedings of the National Academy of Sciences 112, no. 24 (June 16, 2015): 7414–19. https://doi.org/10.1073/pnas.1406495111.

Collins, Rob, Malcolm Mcleod, Mike Hedley, Andrea Donnison, Murray Close, James Hanly, Dave Horne, et al. “Best Management Practices to Mitigate Faecal Contamination by Livestock of New Zealand Waters.” New Zealand Journal of Agricultural Research 50, no. 2 (June 1, 2007): 267–78. https://doi.org/10.1080/00288230709510294.

Daigneault, Adam J., Florian V. Eppink, and William G. Lee. “A National Riparian Restoration Programme in New Zealand: Is It Value for Money?” Journal of Environmental Management 187 (February 1, 2017): 166–77. https://doi.org/10.1016/j.jenvman.2016.11.013.

Falabi, J. A., C. P. Gerba, and M. M. Karpiscak. “Giardia and Cryptosporidium Removal from Waste-Water by a Duckweed (Lemna Gibba L.) Covered Pond.” Letters in Applied Microbiology 34, no. 5 (2002): 384–87.

Graczyk, Thaddeus K., Frances E. Lucy, Leena Tamang, Yessika Mashinski, Michael A. Broaders, Michelle Connolly, and Hui-Wen A. Cheng. “Propagation of Human Enteropathogens in Constructed Horizontal Wetlands Used for Tertiary Wastewater Treatment.” Applied and Environmental Microbiology 75, no. 13 (July 1, 2009): 4531–38. https://doi.org/10.1128/AEM.02873-08.

Hench, Keith R., Gary K. Bissonnette, Alan J. Sexstone, Jerry G. Coleman, Keith Garbutt, and Jeffrey G. Skousen. “Fate of Physical, Chemical, and Microbial Contaminants in Domestic Wastewater Following Treatment by Small Constructed Wetlands.” Water Research 37, no. 4 (February 1, 2003): 921–27. https://doi.org/10.1016/S0043-1354(02)00377-9.

Herrera, Diego, Alicia Ellis, Brendan Fisher, Christopher D. Golden, Kiersten Johnson, Mark Mulligan, Alexander Pfaff, Timothy Treuer, and Taylor H. Ricketts. “Upstream Watershed Condition Predicts Rural Children’s Health across 35 Developing Countries.” Nature Communications 8, no. 1 (October 9, 2017): 811. https://doi.org/10.1038/s41467-017-00775-2.

Johnson, Kiersten B., Anila Jacob, and Molly E. Brown. “Forest Cover Associated with Improved Child Health and Nutrition: Evidence from the Malawi Demographic and Health Survey and Satellite Data.” Global Health, Science and Practice 1, no. 2 (August 2013): 237–48. https://doi.org/10.9745/GHSP-D-13-00055.

Lamb, Joleah B., Jeroen A. J. M. van de Water, David G. Bourne, Craig Altier, Margaux Y. Hein, Evan A. Fiorenza, Nur Abu, Jamaluddin Jompa, and C. Drew Harvell. “Seagrass Ecosystems Reduce Exposure to Bacterial Pathogens of Humans, Fishes, and Invertebrates.” Science 355, no. 6326 (February 17, 2017): 731–33. https://doi.org/10.1126/science.aal1956.

Maseyk, Fleur J. F., Estelle J. Dominati, Toni White, and Alec D. Mackay. “Farmer Perspectives of the On-Farm and off-Farm Pros and Cons of Planted Multifunctional Riparian Margins.” Land Use Policy 61 (February 1, 2017): 160–70. https://doi.org/10.1016/j.landusepol.2016.10.053.

Pattanayak, Subhrendu K., and Kelly J. Wendland. “Nature’s Care: Diarrhea, Watershed Protection, and Biodiversity Conservation in Flores, Indonesia.” Biodiversity and Conservation 16, no. 10 (September 1, 2007): 2801–19. https://doi.org/10.1007/s10531-007-9215-1.

Quiñónez-Díaz, M. J., M. M. Karpiscak, E. D. Ellman, and C. P. Gerba. “Removal of Pathogenic and Indicator Microorganisms by a Constructed Wetland Receiving Untreated Domestic Wastewater.” Journal of Environmental Science and Health. Part A, Toxic/Hazardous Substances & Environmental Engineering 36, no. 7 (2001): 1311–20.

Russell, Richard C. “Constructed Wetlands and Mosquitoes: Health Hazards and Management Options—An Australian Perspective.” Ecological Engineering 12, no. 1 (January 1, 1999): 107–24. https://doi.org/10.1016/S0925-8574(98)00057-3.

Vymazal, Jan. “Removal of Enteric Bacteria in Constructed Treatment Wetlands with Emergent Macrophytes: A Review.” Journal of Environmental Science and Health. Part A, Toxic/Hazardous Substances & Environmental Engineering 40, no. 6–7 (2005): 1355–67.

Wu, Shubiao, Pedro N. Carvalho, Jochen A. Müller, Valsa Remony Manoj, and Renjie Dong. “Sanitation in Constructed Wetlands: A Review on the Removal of Human Pathogens and Fecal Indicators.” The Science of the Total Environment 541 (January 15, 2016): 8–22. https://doi.org/10.1016/j.scitotenv.2015.09.047.

Other photo credits from our Tweets:

  1. Universal Children’s Day
  2. Water use art

What is a vector?

Precise definitions are important in science, because I said so (and other better reasons). In parasite ecology, the tricky definitions that students often mix up are things like parasite versus parasitoid and microparasite versus macroparasite. In fact, at least 20 visitors per week happen upon this blog because they want look up one of those terms.

But it isn’t just students and the general public who struggle with tricky definitions in parasite ecology. Within the field, we can’t even agree on what to call our discipline! (Disease ecology? Parasitology? Epidemiology?) And it has recently come to my attention that a term that I thought had bullet-proof definition is somewhat controversial among parasite ecologists.

In an awesome special issue of the Philosophical Transactions of the Royal Society B that just came out this month, there was a thought-provoking article entitled, “What is a vector?” The idea for the article came from a working group of the British Ecological Society’s ‘Parasites & Pathogens’ Special Interest Group, where participants unexpectedly found that they did not all use the same definition of “vector.” The article contained a whole list of possible definitions that the authors found in the literature, including this subset, which I have re-numbered for my own purposes:

Definition 1A: “Any organism (vertebrate or invertebrate) that functions as a carrier of an infectious agent between organisms of a different species.”

Definition 1B: “Any organism (vertebrate or invertebrate) or inanimate object (i.e., fomite) that functions as a carrier of an infectious agent between organisms.”

Definition 2: “Any organism that can transmit infectious diseases between humans or from animals to humans.”

Definition 3A: “Hosts that transmit a pathogen while feeding non-lethally upon the internal fluids of another host.”

Definition 3B: “Blood-feeding arthropods such as mosquitoes, ticks, sandflies, tsetse flies and biting midges that transmit a pathogen while feeding non-lethally upon the internal fluids of another host.”

I couldn’t believe that parasite ecologists differed so much with their working definitions, so I put them into a poll, and then I asked you guys to tell me which definitions you use. (Thanks for your participation!) To my great surprise, your answers were all over the place! No one really used Definition 2 (the “anthropocentric” definition), but all of the other definitions received some support. As of 21 March, the most popular definition was 1A, and 1A+B were more popular than 3A+B.

Wilson et al. (2017) do a great job of discussing the pros and cons of each definition, and they also take a stab at a possible mathematical definition (the “sequential” definition), so I’d recommend giving their paper a read for a lot more coverage than you’re going to get here. I’m just going to cover two points that were surprising to me.

First surprise: I did not expect that so many people would prefer 1B over 1A, because I don’t like including fomites in the definition of a vector. My primary reason for preferring IA is that we already have a term for inanimate objects that transmit infectious agents (i.e., fomites). Wilson et al. (2017) provide a good discussion on the utility of thinking about some fomites (e.g., drug needles) in pseudo-biological terms that would normally apply to vectors. But I think that I’d still prefer to call things like needles “fomites,” even if it’s helpful to think of their parallels with living vectors.

Second surprise: I did not expect that so many people would prefer 1A+B to 3A+B. As Wilson et al. (2017) discuss, the 1A+B definitions are broad; for instance, the wording suggests that we include intermediate hosts (i.e., snails infected by trematodes) as vectors! It also suggests that any host capable of interspecific transmission could be a vector. 

In the end,Wilson et al. (2017) suggested that parasite ecologists think carefully about their definitions of the term “vector,” and then they scored a closing home run with, “all vector definitions are wrong, but some are (we hope) useful.” WOMP WOMP.

Give the paper a read, and share your thoughts in the comments!

Reference:

Wilson, A. J., E. R. Morgan, M. Booth, R. Norman, S. E. Perkins, H. C. Hauffe, N. Mideo, J. Antonovics, H. McCallum, and A. Fenton. 2017. What is a vector? Phil. Trans. R. Soc. B 372:20160085.

Food provisioning and wildlife disease dynamics

Humans change environments in many different ways, including accidentally or purposefully provisioning wildlife with novel food resources. For example, bird feeders, salt licks, ecotourism feeding stations, and dumpsters all provide concentrated food resources for wildlife. Does this food provisioning influence disease dynamics?

First, let’s consider how food provisioning might influence disease dynamics for parasites with any given transmission mode. The big rates that we care about here are the transmission rate (a function of contact rate and transmission success), birth and death rates, and immigration and emigration rates. If resource provisioning increases host population density via increased aggregation of individuals, increased birth rates or decreased death rates, and/or increased immigration or decreased emigration rates, then transmission rates for pathogens with direct contact density-dependent transmission should increase. In the same scenarios, pathogens with frequency-dependent transmission may not be affected by provisioning, or transmission rates may even decline if high birth rates dilute the prevalence of infection in the population. For pathogens that are transmitted via environmental stages, environmental stages may build up at resource provisioning sites when high densities of animals hang out there for long periods, increasing transmission. For pathogens that are transmitted via intermediate hosts, transmission may be reduced if hosts switch from foraging on intermediate hosts to foraging on human-provided resources.

Factors besides transmission mode might also be important. For instance, regardless of transmission mode, if resource provisioning increases host resistance – for instance, by increasing body condition – then transmission rates should decrease. Conversely, if resource provisioning decreases host resistance – for instance, if the provisioned food is nutritionally poor or there is high competition at sites with provisioning and body condition is reduced – then transmission rates should increase. Resource provisioning might also alter host tolerance to infection, so that hosts aren’t as sick but continue shedding infectious agents longer than they would if they didn’t have supplemental resources.

Ok, I think that covers most of the possibilities. Back to our question: does food provisioning influence disease dynamics? Yes, sometimes. Most of the mechanisms listed above were supported by at least one study in a recent review by Becker et al. (2015). Based on the long, messy list above, you can probably guess that sometimes food provisioning increases transmission, sometimes it decreases transmission, and sometimes nothing notable happens. The big take-home message is that there isn’t just one universal outcome when we provision wildlife with supplemental resources, and it’s important that we conduct more and better studies aimed at elucidating the epidemiological mechanisms underlying the observed relationships. Cool stuff!

Have you read this post about house finch conjunctivitis yet?

curseofmagicalseeddispenser

Reference:

Becker, D.J., D.G. Streicker, and S. Altizer. 2015. Linking anthropogenic resources to wildlife–pathogen dynamics: a review and meta-analysis. Ecology Letters 18: 483–495.

Refugia, connectivity, and transmission

When populations become small, their probability of extinction typically goes up, because demographic and environmental stochasticity are more likely to set the population on an irreversible decline. However, when one population goes locally extinct, a species is not necessarily lost; the area might be re-colonized by migrants from a different population later, if other populations exist. Metapopulation theory tells us that a balance between population extinctions and re-colonizations in heterogeneous patches that are linked by dispersal can allow a species to persist regionally, even when it goes extinct locally.

What happens when we add infectious diseases into our host metapopulation model? Connectivity might be detrimental to regional persistence when infectious diseases are introduced, because pathogens in one population can invade the other populations via host dispersal, whereas pathogens are limited to a single population when populations aren’t linked via dispersal. Or…not?

Heard et al. (2015) recently published a quite complicated and fancy metapopulation model that suggests that connectivity actually increases the probability of metapopulation persistence in an Australian frog species endangered by the fungal pathogen (Bd), which causes the disease chytridiomycosis. From survey work, they knew that the prevalence of Bd in growling glass frogs was lower in warm and/or salty wetlands. They could also show that the probability of a local extinction in any given frog population increased with the prevalence of Bd in the frog population. By linking local extinction risk to Bd prevalence and microclimate, they could create metapopulation models using known dispersal distances for the growling glass frog, and they could run simulations regarding metapopulation persistence under scenarios where they eliminated frog dispersal among populations or not. They had two important findings. First, if you ignore the fact that Bd prevalence varies with microclimate, the probability of metapopulation persistance is predicted to be much lower than it actually is. The warm and/or salty wetlands act as important low Bd prevalence frog population refugia that can seed the other populations in a metapopulation when they go locally extinct, such that microclimate variability increases persistence. Second, “re-seeding” can only happen if dispersal occurs among populations, so connectivity increases metapopulation persistence in this system.

One lingering question is whether this system is a good example of the role of connectivity in all host metapopulations plagued by infectious diseases. Heard et al. (2015) argue that Bd is basically everywhere already – and there to stay – because it can be maintained in both environmental reservoirs and reservoir hosts. Therefore, they suggest that for growling glass frogs, dispersal of hosts among populations doesn’t really play a role in disease dynamics. In systems where the pathogen is not yet widespread (i.e., regions currently being invaded), where reservoirs are less likely to provide long-term maintenance of the pathogen, or where dispersal of reservoir hosts is particularly important to pathogen spread, host dispersal could start to have detrimental impacts on long-term metapopulation persistence. This is cool stuff that deserves more attention!

Reference:

Heard, G.W., C.D. Thomas, J.A. Hodgson, M.P. Scroggie, D.S. Ramsey, and N. Clemann. 2015. Refugia and connectivity sustain amphibian metapopulations afflicted by disease. Ecology Letters, 18: 853–863.

frogald.png

Dilution Effect Debate Continues!

Admit it, the title of this post made you cringe! Or it set off a little fuse in your brain and you’re just seconds away from your head exploding. Take a zen moment, and then continue reading for some interesting science.

If you don’t know what the dilution effect hypothesis is or why disease ecologists are debating the hypothesis, you might want to check out some of my previous posts before reading this one. But briefly: scientists have found a negative relationship between host biodiversity and the risk of infection to particular host species in some disease systems in some areas. That negative relationship is called the “dilution effect,” because host biodiversity is “diluting” parasite transmission. The debates have arisen because disease ecologists can’t agree on is how often the dilution effect occurs in natural systems: always, sometimes, or never? I’ve described the core arguments on both sides of the debate in this post.

Meta-analyses are one way to figure out how commonly the dilution effect occurs in natural systems. By collecting all of the available empirical and/or observation  studies that consider biodiversity-disease relationships and lumping them together  into one analysis, we can figure out whether the dilution effect always, sometimes, or never occurs in natural systems. Before my last post on the dilution effect debates was published, two such meta-analyses argued that the dilution effect only sometimes occurs (it’s “idiosyncratic”) in systems where the focal hosts are humans and non-human primates. Additionally, in the meta-analysis regarding how biodiversity influences human risk of infection with zoonotic pathogens, Salkeld et al. (2013) found evidence of a publication bias for studies that find a dilution effect, suggesting that studies finding neutral or amplification effects are less likely to be published.

Fast forward to Civetello et al. (2015), who did a larger meta-analysis that included more studies in more host-parasite systems. As I posted about a few weeks ago, Civetello et al. (2015) found broad support for the dilution effect, which I suppose we can say means that the dilution effect “often” or “usually” occurs, which puts us somewhere between “always” and “sometimes.”

Now back to the “debate” part of this post: Salkeld et al. (in press) responded to the Civetello et al. (2015) paper with some concerns regarding the larger meta-analysis. In particular, they worried that including laboratory studies might muddle the analysis, because the way that we manipulate systems in the lab doesn’t necessarily correspond to what really happens in nature. Also, they pointed out that if there is a publication bias – as they previously found – then it might not be particularly meaningful that the dilution effect is commonly reported in the ecological literature. However, Civetello et al. (2015) didn’t evaluate whether there was a publication bias in their analysis. (Note that McCallum et al. 2015 also pointed out some of these possible concerns.)

Civetello et al. (in press) responded to these worries by doing another meta-analysis. They used only a subset of the studies from their previous paper, so that they were including only field studies of human pathogens, like in Salkeld et al. (2013). Civetello et al. (in press) still found an overall dilution effect, and they suggested that adding in the studies published since Salkeld et al. (2013) provided more statistical power to see the dilution effect than Salkeld et al. (2013) had. Civetello et al. (in press) also looked for publication bias in their subsetted dataset and didn’t find any evidence for bias, but they point out that the analysis to look for a publication bias had to violate some assumptions of independence, so it might not be particularly meaningful.

It’s unlikely that things are totally resolved here, but I think everyone is on the same page regarding the future directions for diversity-disease relationships: we’ve spent time looking for general trends and determining how common the dilution effect is in natural systems, and now it’s time to switch our focus to the mechanisms underlying the dilution effect.

References:

Civitello DJ, et al. (2015) Biodiversity inhibits parasites: Broad evidence for the dilution effect. Proc Natl Acad Sci USA 112:8667–8671.

McCallum H. (2015) Lose biodiversity, gain disease. Proc Natl Acad Sci USA 112: 8523–8524.

Salkeld DJ, Padgett KA, Jones JH (2013) A meta-analysis suggesting that the relationship between biodiversity and risk of zoonotic pathogen transmission is idiosyncratic. Ecol Lett 16(5):679–686.

Salkeld DJ, Padgett KA, Jones JH, Antolin MF (2015) Public health perspective on patterns of biodiversity and zoonotic disease. Proc Natl Acad Sci USA, 10.1073/pnas.1517640112.

Why infectious disease research needs community ecology

If you haven’t seen it yet, there’s a recent Science paper by Johnson et al. (2015) entitled, “Why infectious disease research needs community ecology.” If you’re a disease ecologist, it probably won’t come as a surprise to you that infectious disease research needs community ecology. If you’re not a disease ecologist, check out this paper for a quick, informative read!

I was happy to see that Johnson et al. (2015) emphasized the importance of looking at symbiont communities as well as host communities when considering the spread of parasites and pathogens. The importance of symbiont communities is still not as widely recognized as I think it should be, and this paper does a great job of giving concrete examples of systems where coinfection by parasites/pathogens or the presence of non-pathogenic symbionts influence the spread of a single parasite within a host population or community.

If you’re looking for more examples where understanding the spread of parasites and pathogens required a detailed understanding of community ecology, check out some of these previous blog posts:

Considering symbiont communities is important:

Symbionts protect hosts from parasitoids

Diverse symbiont communities protect hosts from several natural enemies

Symbionts protect hosts from parasites

Considering host communities is important:

Host community diversity may reduce focal host infection risk

Multiple host and vector species for prairie dog plague

Spillover of pathogens from wildlife to livestock to humans

Fecal transplants and other kinds of microbial community restoration

Competition among host species influences transmission

Considering heterogeneity among individuals and species is important:

House finches and Mg

Tasmanian devils and facial tumor disease

The superhosts!

Mouse raves and contact heterogeneity

Coinfection and superspreaders

Reference:

Johnson, P.T.J., J.C. de Roode, and A. Fenton. 2015. Why infectious disease research needs community ecology. Science 349(6252): 1259504.

Are superspreaders also superreceivers?

For simplicity, we often assume that all hosts have an equal probability of becoming infected by and transmitting parasites and pathogens. But of course, we know that it isn’t how real systems work. For instance, in real systems, hosts vary in their propensity to become infected by pathogens, and that variation is one probable cause of the parasite ecology “law” that macroparasites are aggregately distributed among hosts. We call hosts that are highly susceptible to a given pathogen “superreceivers,” and hosts that are highly likely to transmit a pathogen are “superspreaders.”

Here’s a question for you to ponder: are superspreaders usually superreceivers and/or are superreceivers usually superspreaders? For instance, sex workers are at high risk for contracting HIV (=superreceivers) because they frequently change sex partners, and they’re also highly likely to spread HIV (=superspreader), if they have it, in comparison to the average person. In that case, the superreceivers are also superspreaders. When that happens, we might predict really explosive epidemics whenever “patient zero” is a superreceiver+superspreader, because R0 will be very, very high.

But consider the Tasmanian devil example that I posted about recently. Tasmanian devils that bite lots of individuals are highly likely to contract Tasmanian devil facial tumor disease; they’re superreceivers. But being bitten by an infected individual doesn’t seem to transmit the infectious cancer to the receiving host, so devils that bite frequently don’t transmit any more frequently than devils that don’t bite frequently. Therefore, the superreceivers in that system aren’t superspreaders.

Now let’s talk about a really cool system that I somehow haven’t blogged about yet. House finches are hosts for an emerging bacterial pathogen (Mycoplasma gallisepticum – Mg) that jumped from poultry into house finches in the 1990s. This pathogen causes conjunctivitis in the house finches – a symptom you don’t often think about in wildlife! In a really neat recent paper, Adelman et al. (2015) showed that birds that spent more time on bird feeders were more likely to become infected by (superreceivers) and transmit (superspreaders) Mg. This is a really cool example of a pathogen that appears to be transmitted by “fomites”: inanimate objects that the pathogen can survive on when off the host.

We probably don’t have enough examples in the literature to determine whether superspreaders are usually superreceivers or to look for generalities in systems where this occurs. But we’re accumulating more examples all the time! Stay tuned.

…if academics were at higher risk of developing conjunctivitis when they sought out free food, I’d have some very squinty-eyed colleagues.

Finch

Reference:

Adelman, J.S., S.C. Moyers, D.R. Farine, and D.M. Hawley. 2015. Feeder use predicts both acquisition and transmission of a contagious pathogen in a North American songbird. Proc Biol Sci. 282(1815): 20151429.

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:

caterpillarsonitsy3

(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.)

Reference:

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

Parasite Ecology at ESA 2014

I didn’t go to all of the parasite ecology talks at ESA 2014, 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 2014 parasite ecology – here are some of my favorites.  Also, just for fun (and because I may or may not have an addiction that I could totally give up at any time, I swear), I gave everyone a relative parasite cartoon score.

Monday was the ESA Early Career Fellows Symposium.  Meg Duffy and Pieter Johnson both gave brilliant parasite ecology talks.  Meg Duffy showed that parasite infection can reduce host feeding rates in Daphnia, which paradoxically ends up increasing host density via interactions with the algal resource (i.e., a hydra effect).  She had some sweet Daphnia + fungus cartoons, so I give her an 8 on my cartoon scoring scale (second place!).  Pieter Johnson showed that amphibian host diversity can increase parasite richness (“diversity begets diversity”) and decrease Ribeiroia infection risk (=dilution effect), and he emphasized that parasite richness and infection risk aren’t the same thing.  (Speaking of Ribeiroia, have you guys seen this tshirt?  You’re welcome.)  I may have missed some cartoons while I was scribbling in my notebook, but I think he had some silhouettes of snails and vertebrates and some fluorescent cercariae cartoons:  4.  

Christopher Johnson talked about competition between two mutualistic species (i.e., two butterfly species) for a shared resource (i.e., nectar from the host plant) – in other words, a symbiont competition model, where the resource is the host or services/resources from the host.  And instead of R*s, there were M*s.  And then there were phase diagrams and talk of symbiont species coexistence.  Yeah.  Amazing. 

On Tuesday, I saw a talk by Eric Schauber, who used agent based modeling to consider how the “need to be social” affects among group pathogen transmission.  Awwwesome.  He had some cartoon vertebrates (goats?), so that’s a 2. 

On Wednesday, Max Joseph gave a really cool talk about how a negative relationship between “disease risk” and host richness can emerge from a model that treats hosts as habitat patches, where symbionts have different niche requirements.  Oh, and so can a positive relationship between host and parasite richness!  I’d just been lamenting the loose way that people refer to “disease risk,” so I was really glad when he stressed the importance of quantifying what we mean by disease risk when talking about the dilution effect.  Get ready, World.  Big things are happening with dilution effect theory.

Angela Brennan asked:  what is the right scale to look at the impacts of host density on disease transmission?  That’s a tricky one…

Cat Searle gave a neat talk about invasive species, their competence as hosts, and their role in pathogen transmission, using a Daphnia model system.  She had some cartoons of space aliens as her invasive species, so that’s a 6.  Oh, and for all of the undergraduates reading this blog, she’s looking for grad students!

Continuing on the Daphnia vein, Alex Strauss looked at the outcomes of introducing diluting host species that both reduce parasite transmission to the focal host species and compete with the focal host species.  And DING DING DING DING!  For his cartoons of Daphnia, algae, fungi, and other tiny organisms (score = 10), Alex wins the Best Parasite Cartoons of ESA 2014 Award!! 

Finally, on Thursday, I really liked Dan Preston’s talk about tadpole behavioral responses to predators and parasites.  But I’m going to try to blog that one next week, so you’ll have to stay tuned!

The Dilution Effect Debates

Last week, I wrote an introductory post about the dilution effect.  Topics covered included 1) What is the dilution effect? 2) How does the dilution effect work in the Lyme disease system? 3) How do tick density and the prevalence of infected ticks affect human disease risk?  4) What conditions must be met in order for the dilution effect to occur in any given system?  You may need to read that post to understand this post.

Introduction to the Debates:

There has recently been a series of papers debating the generality of the dilution effect in host-pathogen systems.  The big questions in these debates are:

  1. How often does the dilution effect occur in disease systems?  Does high biodiversity always dilute disease risk?  Does high biodiversity sometimes dilute disease risk?  Does high biodiversity never dilute disease risk?
  2. Is the literature biased towards studies that find dilution effects?
  3. Does the dilution effect really happen in the Lyme disease system?
  4. Are the five conditions/assumptions that must be met for the dilution effect to occur reasonable?  How often are those five conditions met?
  5. At what spatial scales do we see a dilution effect?

Before I start talking about the debates, I need to explain why Lyme disease is such a big focus.  In 2000, Drs. Richard Ostfeld and Felicia Keesing introduced the dilution effect hypothesis to the ecological literature using Lyme disease as their example system.  Much of the subsequent work regarding the dilution effect has involved the Lyme disease system, and much of that work has been carried out by Ostfeld, Keesing, and their collaborators.  So, when someone wants to cite something about the dilution effect, they usually cite something about Lyme disease and usually something published by Ostfeld and/or Keesing.  And when someone wants to argue that the dilution effect is far more uncommon than the literature suggests, they criticize the Lyme disease work. So, my first disclaimer is that I have tried to be unbiased in summarizing these debates, but that was hard!  The two review papers are much longer than the responses by Ostfeld and Ostfeld and Keesing, so we don’t get to hear as much of their side of the debate. Therefore, we need to cut them some slack throughout. Ostfeld and Keesing are phenomenal scientists who repeatedly promote further research and discussion regarding the generality of the dilution effect, and I hope this post facilitates that goal in its own small way. My second disclaimer is that I am not Randolph, Dobson, Ostfeld, Keesing, Lafferty, or Wood.  So, I can only summarize their opinions based on what they published.  I have done that to the best of my ability. AND NOW:  MORTAL KOMBAT!!!!

I remind the audience that this cartoon is just for fun. No scientists were injured in the making of this blog post.

Randolph and Dobson vs. Ostfeld

The first review paper was written by Randolph and Dobson (hereafter RD).  Ostfeld (hereafter O) then responded to the review paper in a short, 2-page commentary.  Then R responded to O with a short rebuttal.

  1. Randolph, S.E., and A.D.M. Dobson. 2012. Pangloss revisited: a critique of the dilution effect and the biodiversity-buffers-disease paradigm. Parasitology 139: 847–63.
  2. Ostfeld, R. S. 2013. A Candide response to Panglossian accusations by Randolph and Dobson: biodiversity buffers disease. Parasitology 140:1196–1198.
  3. Randolph, S. 2013. Commentary on ‘A Candide response to Panglossian accusations by Randolph and Dobson: biodiversity buffers disease’ by Dr. R. Ostfeld. Parasitology 140: 1199–1200.

The General Argument: RD think that the idea that “biodiversity protects against disease”1 is a suspiciously “convenient”1 claim; it’s a “panglossian view”1 that suggests that we can both preserve biodiversity and reduce human disease risk at the same time.  They call this idea a “mantra”1 – RD think that researchers talk about the dilution effect like it’s a universal law, whereas RD think that the dilution effect is “an extension ad absurdum that simplifies and obscures reality.”1 Ostfeld strongly disagrees!  Ostfeld thinks that the dilution effect hypothesis isn’t “naively optimistic”2, as RD suggest; instead, it’s “a pragmatic philosophy based on empirical observations.”2  O doesn’t like the use of the word “mantra,” because he argues that no one has ever claimed that the dilution effect always occurs in natural systems.  However, he does argue that the dilution effect is more common than amplification or neutral effects – and he cites this paper to back up his claim. The Nitty Gritty: Clearly, RD find the dilution effect literature lacking.  Their paper is quite long, so I can’t cover all the details in this post.  To the best of my understanding, their main arguments are:

  1. By focusing mostly on the prevalence of infected ticks, the dilution effect literature often ignores the importance of tick density.  [Condition 5 in my previous post.]
  2. Researchers are biased towards finding a dilution effect.  That bias leads people to 1) design experiments and models destined to find a dilution effect and 2) misinterpret results so that data supporting neutral or amplification effects are said to support dilution effects.  (I can’t say whether RD think the bias happens intentionally or not.)
  3. Continuing on that vein, the literature is biased towards studies that find a dilution effect.
  4. Trying to make generalizations about biodiversity metrics and disease risk is silly.  We should really be focusing on the role of community composition in disease risk.
  5. The dilution effect doesn’t always happen.  It only happens under very specific conditions, and those specific conditions may rarely arise.

1. By focusing mostly on the prevalence of infected ticks, the dilution effect literature often ignores the importance of tick density.  [Condition 5 in my previous post.] For the dilution effect to happen, increasing host biodiversity must reduce the prevalence of infected ticks without also increasing the density of ticks.  Or, if tick density increases, it must not overwhelm the effect of reduced infected tick prevalence.  RD find it unlikely that this is the case.  They argue that increased biodiversity will tend to lead to increased host density, which should lead to increased tick density.  Ostfeld responds that there isn’t any evidence to support that increased host density leads to increased tick density.  Furthermore, Ostfeld notes that opossums might actually decrease tick density by grooming off and eating ticks.  R returns that there is a “range of papers”3 supporting increased tick density with increased host density.

2. Researchers are biased towards finding a dilution effect.  That bias leads people to 1) design experiments and models destined to find a dilution effect and 2) misinterpret results or mis-analyze results so that data supporting neutral or amplification effects are said to support dilution effects.  This is quite a claim!  Lest you should think I’m blowing this out of proportion, here is a direct quote from R, who thinks that there has been “an unwarranted stream of studies that either misinterpreted results in favour of the dilution effect, or sought out those idiosyncratic ecological conditions under which it might actually occur.”3 The example that RD use to back up this argument is a Plos One paper by Ostfeld et al. (2006).  Ostfeld et al. (2006) looked at 13 years of data from several field plots where measures of Lyme disease risk, climatic variables, and the abundances of mice, deer, chipmunks, and acorns had been recorded.  They found that the abundance of deer didn’t matter to Lyme disease risk at all, and climatic variables were only slightly important.  Instead, Lyme disease risk tended to be high three years after a year with high acorn abundance.  That is, acorn abundance is high one year, mouse abundance is high the next year, and Lyme disease risk is high the third year when there are lots of hungry nymphal ticks looking for hosts. RD don’t like the statistical techniques used by Ostfeld et al. (2006).  First, they argue that Ostfeld et al. (2006) had a pseudoreplicated study design that didn’t allow them to properly incorporate deer abundance in their models.  Second, they argue that the relationship between mouse abundance and infected tick density was entirely driven by a single outlier, which leaves too much riding on a single year.  You can see the full criticism of the statistics here.  Ostfeld argues that because the statistics weren’t done in a frequentist framework, the pseudoreplication argument is irrelevant.  And because the outlier was a rare mast year, it should not be removed.  You can see the full response by Ostfeld et al. here.

3. The literature is biased towards studies that find a dilution effect RD call the dilution effect a “mantra” and argue that researchers “preach” that biodiversity always reduces disease risk.  Ostfeld argues that no one ever said that biodiversity always reduces disease risk – the conditions necessary for the dilution effect to occur are carefully outlined, and yes, sometimes researchers find amplification or neutral effects.  But Ostfeld insists that recent reviews, like one by Cardinale et al. (2012), show that the dilution effect is more common than neutral or amplification effects. RD argue that the dilution effect is not more common than neutral or amplification effects.  In fact, RD find the use of the Cardinale et al. (2012) review as a citation “selective,”3 because Cardinale et al. (2012) “specifically warn against making sweeping statements that biodiversity always brings benefits to society.”3  RD find the recent meta-analysis by Salkeld et al. (2013), which demonstrates a publication bias towards studies that find a dilution effect, to be more relevant.  (I’ll come back to this in the Wood and Lafferty vs. Ostfeld and Keesing debate, below.)

4. Trying to make generalizations about biodiversity and disease risk is silly.  We should really be focusing on the role of community composition in disease risk. RD spend a lot of time arguing that trying to make generalizations about how species richness or particular diversity metrics affect disease risk is silly, because community composition is likely much more important to disease transmission.  O agrees, and points out that the literature clearly designates community composition as “the most relevant metric of host diversity.”2  In other words – yeah, duh, composition is part of diversity, and everyone knows that it is important.  I think RD’s point is that if preserving pristine habitats with high biodiversity isn’t enough – if it depends on exactly what species occur in those highly biodiverse systems – then we should make that very clear when we suggest that ‘preserving biodiversity’ is a good way to manage disease risk.  For instance, it might not be as important that there are five vertebrate host species, but that one of those species is the opossum, which is not a good reservoir host and tends to groom off and eat ticks.

5. The dilution effect doesn’t always happen.  It only happens under very specific conditions, and those specific conditions may rarely arise. The dilution effect literature very carefully outlines the conditions under which a dilution effect is predicted to occur.  RD question whether those conditions actually happen in real systems.  I already discussed the density/prevalence condition above.  RD also “question whether increased richness will usually involve a greater proportion of transmission non-competent host species.”3  This is in reference to Condition 4: the most resilient host species – the ones that are left in low biodiversity communities – must also be the highly competent host species (and vice versa). Ostfeld points out that this is an ongoing area of research.  Are highly resilient species usually highly competent?  You can see some recent high-profile studies here and here.

RD vs. O – Who won the debate? Just for fun, let’s pick a debate winner!  A panel of parasite ecologists (including myself) voted:  RD = 2 votes, O = 1 vote, Tie = 2 votes.   RD won by a hair!

But hey, who is to say that my panel of judges rules supreme?  I open the voting up to YOU, my wonderful audience.  Who won?!

AND NOW:  MORTAL KOMBAT II !!!!

WL vs OK Fight

Wood and Lafferty vs. Ostfeld and Keesing In this debate, the review paper was written by Wood and Lafferty (hereafter WL).  Ostfeld and Keesing (hereafter OK) then responded to the review paper in a short, 1-page commentary.  Then Lafferty and Wood responded to OK with a short rebuttal.

  1. 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: 239-247.
  2. Ostfeld, R. S., and F. Keesing. 2013. Straw men don’t get Lyme disease: response to Wood and Lafferty. Trends in Ecology Evolution 28:502–503.
  3. Lafferty, K. D., and C. L. Wood. 2013. It’s a myth that protection against disease is a strong and general service of biodiversity conservation: Response to Ostfeld and Keesing. Trends in Ecology Evolution 28:503–4.

WL’s Synthesis: WL present a synthesis of two ideas regarding how biodiversity affects Lyme disease risk: the “traditional perspective” and the “dilution effect perspective.”  To understand their synthesis, we need to discuss four things:

  1. How can we use forestation as a proxy for biodiversity?
  2. What is the “traditional” perspective?
  3. What is the dilution effect perspective?
  4. How can we synthesize the two perspectives?

How can we use forestation as a proxy for biodiversity?

Measuring host diversity can be pretty tricky.  For instance, if you go out to a forest plot and you don’t see any deer, is that because there aren’t any, or is it because you just aren’t seeing them?  To get around the difficulty of measuring host diversity, much of the work in the Lyme disease system uses forest fragmentation as a proxy for biodiversity.  When forest fragmentation is high, vertebrate host diversity tends to be low – the “just mice” end of the spectrum.  When forest fragmentation is low (conversely, when forestation is high), vertebrate host diversity tends to be high.  Because much of the Lyme disease work is based on forest fragmentation, WL use forestation and biodiversity somewhat interchangeably throughout their synthesis.

What is the “traditional” perspective?

Lyme disease (re)-emerged as an important infectious disease of humans in the United States in the late 1970’s.  Though Lyme disease had existed in the US long before the 1970’s, there was a period when ticks and Lyme disease were not widespread.  Where did the ticks go?  Well, ticks are ectoparasites than live on forest animals.  So, if we remove forests – say, by cutting down forests to make way for agriculture  – there should be no ticks and thus no Lyme disease risk (see the origin on the figure below). Similarly, you’re much more likely to contract Lyme disease in a rural area than an urban area.  This is the “traditional perspective.”  No forests = no ticks = no Lyme disease risk, so forestation and/or biodiversity must amplify Lyme disease risk.

What is the dilution effect perspective?

By now, you should know what the dilution effect perspective is.  But to recap, the dilution effect hypothesis states that increased host diversity decreases human disease risk.  That is, biodiversity dilutes disease risk.

How can we synthesize the two perspectives?

Heyyyy, wait a second!  How can host diversity both amplify and dilute disease risk?  Well, that’s WL’s point, exactly.  They argue that it is mostly an issue of scale.  At broad spatial scales (i.e., rural vs. urban), biodiversity amplifies disease risk – that’s the “traditional perspective.”  At fine spatial scales (i.e., forest patches), biodiversity dilutes disease risk.  They argue that this should lead to a linear, saturating, or hump-shaped relationship between biodiversity and Lyme disease risk, depending on how much of a role the dilution effect plays.  So, as you go from a completely unforested area to a somewhat forested area, you’ll see an increase in disease risk.  But as you go from a fragmented forested area to an unfragmented forested area (continuing right on the X axis), you might see a decrease in disease risk, if the dilution effect happens.

Infection is lime, for Lyme disease.  The tick cartoons represent how I think about this working – tick density increases as you go from no forest to some forest, and tick infection prevalence decreases as you go from fragmented to unfragmented forest.   Feel free to argue with me about that.

Ostfeld and Keesing’s Arguments:

OK didn’t like WL’s synthesis of the two Lyme disease perspectives.  I would recommend reading their whole response, because it’s only one page, and you can access the PDF here for free.  But for those of you who want the less-detailed tl;dr version: OK argue that the logic for the “traditional perspective” is “fallacious”; they think that concluding that Lyme disease risk increases with forestation/biodiversity just because having no forests would result in no Lyme disease is ridiculous.  But WL argue that it isn’t ridiculous at all; locations with literally no forests are real places, and those places don’t have Lyme disease risk.  OK also argue that there isn’t any evidence that Lyme disease risk increases with biodiversity, but I think WL would argue that all of the studies regarding the “traditional perspective” are evidence for a positive relationship.  WL also argue that at global scales (i.e., tropical vs. temperate regions), you see the same positive relationship between biodiversity and disease risk.

Is the dilution effect more common than amplification and neutral effects?

In their introduction, WL summarize the review paper by RD, where RD suggest that Lyme disease proponents ‘preach’ that biodiversity always dilute disease risk.  Again, OK point out that no researchers ever argue that the dilution effect is universal.  And again, OK argue that “current evidence that high diversity dilutes far more often than it amplifies, at scales from local to global, is strong [5–7].”2 Lafferty and Wood strongly disagree!  In fact, most of of the WL response to OK is about that one sentence.  First, like RD, WL argue that the three papers that OK cite to support their claim don’t support the claim at all.  Second, WL argue that OK also didn’t cite some recent meta-analyses that found that 1) the dilution effect is idiosyncratic, and 2) there is a publication bias towards dilution effect studies*.

WL vs. OK:  Who Won? Voting time again!  We voted, and WL won by a landslide!  WL = 5 votes and OK = 0 votes.

WL vs OK Winner

But what do YOU think?

The Future of the Dilution Effect:

So, have these debates resolved anything?  Is the dilution effect hypothesis smashed to smithereens?  Not exactly, but it does seem like the evidence for a strong, common dilution effect is iffy.  Hopefully the debates will lead to major progress in this area, and less of a publication bias in the future.  My reading group also talked about data/studies that we’d like to see in the future.  This is what we came up with:

  1. More meta-analyses!  The two discussed by Lafferty and Wood are recent and great.  We’d also like to see meta-analyses that consider more disease systems – not just primates, and not just zoonotic diseases. (Here’s one!)
  2. Better experiments!  Can somebody do this with zooplankton and microcosms, please?
  3. MORE MATH!  (Or is that just me?)  Sing it to me in the language of mathematics, and then I’ll really believe you.  And let’s get our Ns, As, N/As, and I/Ns straight.

——————————————————————————————————————– *There’s a really good blog post about the Salkeld et al. 2013 meta-analysis.