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.