Landscape ecology and infectious disease: macro meets micro

Posted by Tara C. Smith on April 8, 2008
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I spent all day yesterday in Madison, Wisconsin, at a conference on Landscape Ecology and infectious disease. I’ll discuss a few of the talks and issues below, but I wanted to start out with a bit of an introduction and explain just what landscape ecology (LE) is.

The introductory talk, which covered this ground, was presented by Dr. Michael Wimberly of South Dakota State University. He noted that defining LE wasn’t an easy task. At its most basic, of course, it’s a field looking at ecology from a landscape perspective–taking a big picture view, if you will. However, what one means by a “landscape” can vary widely. The landscape could be a few square feet of soil in a rainforest; it could be a village; or even an entire country or beyond. Whichever landscape one is looking at, it all boils down to scale, and LE methods can be used to move from one scale to another (e.g., from a village to a county to a state). These different levels of analysis are important not only for the traditional areas LE is used to study–examining ecological impacts of things like fire or climate change on forest ecology, for example–but are also becoming increasingly accepted in infectious disease modeling. More on that after the jump…

Of course, using maps and ecologic data to investigate infectious diseases is nothing new. John Snow’s map detailing the 1854 London cholera outbreak is perhaps one of the most famous tales in all of epidemiology, and geographic data is commonly used in conjunction with infectious disease data in order to form a more robust picture of disease. LE of infectious disease seeks to integrate the various scales that can affect disease development–from the top (land use and climate, for example) down to the very micro level (genetics of the pathogen or host). This is an emerging area of collaboration. Wimberly noted that there have been a multitude of studies looking at how humans influence the landscape–deforestation, dam-building, roads, etc.–but far fewer studies had been carried out examining how the landscape influences humans. He and others hope to change that, bringing more landscape ecologists to the study of human health.

Following this brief introduction, Wimberly and other speakers discussed their own research employing LE for the study of infectious disease dynamics. Dr. Uriel Kitron described his work on Chagas’ disease in Argentina, a chronic arthropod-borne disease caused by the parasite Trypanosoma cruzi. The area in Argentina where he worked has been largely deforested in the past decade. Additionally, large landowners have purchased swaths of land to use for agriculture (mainly growing soybeans and raising cattle), which has led to further destruction of the landscape. People in the region typically live in thatched-roof houses, built as “compounds” with living spaces for the people and animal corrals in close proximity. The bugs that transmit T. cruzi frequently are found in these roofs. Insecticide treatments can be employed, but these take quite a bit of time, and if just one area isn’t sprayed thoroughly, a re-infestation can occur. Kitron wanted to examine how these ecological changes affected prevalence of both the arthropod and the parasite responsible for Chagas’ disease.

Therefore, Kitron and colleagues examined the overall landscape, creating a database of maps of the area; carried out vector and reservoir studies in and between villages; and also examined the potential role of wildlife in spread and maintenance of the disease. They also took into account the socio-economic status of individuals in the village in order to get a fuller picture of disease development and pathogen transmission.

A similar type of study was carried out in Ecuador with Dr. Joseph Eisenberg, investigating how roads and other factors influence the transmission of antibiotic-resistant E. coli and other organisms. Again, this study examines the antibiotic resistance problem on multiple scales: in the individual, looking at antibiotics they may have used; at the village level, where drinking water may have low levels of antibiotics in the water due to their use in agriculture or due to contamination from area hospitals; at the population level, examining movement of people along the river basins, or changes in social structure that may result in an increase in microbial traffic into a community, for example. While these all are areas that have been examined in other studies, LE was used as the framework to tie them all together and to integrate all the data into one coherent, overall picture, rather than have them as discrete data pieces that, while valuable individually, together serve to produce a better understanding of disease development and transmission than when analyzed standing alone.

However, there remain many challenges: some data-based, some culture-based. For one, it’s difficult to get this type of work funded. The NIH and NSF offer a joint program to investigate the ecology of infectious diseases, but it’s a small program and becoming increasingly competitive as more investigators enter this arena. For the agencies separately, the NIH often views these type of studies as too “basic” for their funding, while the NSF views them too “health-associated” to receive their funding. So while “interdisciplinary research” is a hot buzzword, securing funding for this type of research is more difficult than one might think.

A second issue deals with the actual data: the problem of scaling, especially scaling up from “coarse-grained” data (in other words, not having enough resolution or “fine scale” data for some phenomena). There also are an incredible number of factors to look at, when you think about variables on a multitude of different levels: genetics of the host and the microbe (and a vector species, when present); behavior and interactions between them; variation in the landscape with regard to structure, temperature, water, climate, land use, culture…the potential permutations are immense, and trying to fit them all into one LE model can be challenging. Nevertheless, it represents another way to get microbiologists thinking about the bigger ecological picture when it comes to their organism of interest, and encourages those already thinking big to zoom into the tiniest members of their ecosystem of choice.

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Comments

  1. #1 Hank Roberts
    April 8, 2008

    Wonderful post, thank you.

    Has anyone looked for villages in surroundings that have been deforested for much longer, that have similar living conditions, to see if there are other bugs living in the thatch that don’t carry the disease organism or that outcompete those that do?

  2. #2 agnostic
    April 8, 2008

    Something that just popped into my mind — so I don’t know if anyone’s already thought of / published on this — is that you could model some diseases as an activator-inhibitor system.

    Say a disease is spread through rats (like Black Plague, yeah yeah, technically the fleas on the rats). The population of rats will tend to cause disease to flare up.

    But say there’s a natural predator, like cats, which tends to inhibit the flaring up — by killing the vectors.

    However, the predators roam over a wider range — there’s usually much fewer of them than of the prey — so they are “spread too thin.” This is just like the inhibitor having a greater diffusion rate than the activator.

    So, instead of a spatially uniform level of disease, you get “hot spots” of disease, surrounded by no disease. It’s like the theory of how animal coat patterns form — spots of color surrounded by lack of color.

    On a practical level, we would want to introduce predators whose “patrol area” was similar to that of the vectors, so they wouldn’t be spread too thin, and could thus kill more of the vectors.

  3. #3 doug l
    April 9, 2008

    I find it most interesting that the beaurocracies set up to direct funding to research most relevant to you, the NIH and NSF, are not being very effective in what would seem the best way to any rational individual with a basic grasp of the problem. Speaking of scale, I wonder if this ever happens with other large scale beaurocracies designed to address problems, like maybe the IPCC? It’s as if they’ve taken-on a life of their own and perhaps their landscape itself might prove an interesting research area, epidemiologically speaking.

  4. #4 Tom Lackner
    April 11, 2008

    Speaking as an ecologist, all I can say, “Yeah, we’ve been whining about this for years.” Funding bodies, both public and private, like discrete studies with tidy results. You can’t blame them; it’s a lot easier to get those studies published, because there are very few journals like AmNat or Ecological Monographs which will let you ramble for 30 pages before you get to the punchline. But the fact is, the larger the ecological scale the more difficult it is to pin down relationships.

    Getting more cats may or may not have reduced recurrent plague irruptions. You have to examine things like the suitability of the environment for cats; prey switching as rats become rare; what is the parasite load on the cats; what refugia are the rats using when their numbers become cyclically low … This is a large study, involving insect ecologists, mammalian ecologists, epidemiologists, animal nutritionists, and so on.

    Tracking down the natural Ebola host and determining why it becomes irruptive is a great example of how difficult it is to study anything involving several species over large areas. With many competitors for the research dollar, of whom several may offer either more promising cures or more “relevant” research opportunities, it is scarcely surprising that landscape ecology doesn’t get more time and money. What we need is an Office of Meaningful but Long-Term Research on the Interralationships of Many Species Over Large Landscapes. But can you imagine what the popular but not scientifically literate press would say about an OMLTRIMSOLL?