Background

The relationship between microbiome composition and host health has recently generated a great deal of attention and research. The importance of host-associated microbiomes is still poorly understood, although significant relationships between gut micobiome composition and host health have been described. Although the gut microbiome has received the most attention, each body site is home to its own distinct microbial community. The nasal microbiome has received relatively little attention, although a few studies suggest that there is a relationship between nasal microbiome composition and incidence of infections. Using a unique system of closely studied semi-wild African Buffalo, I propose to study the drivers of nasal microbiome composition in a social species.

Data Collection

Our study herd of semi-wild African Buffalo (Syncerus caffer) is kept in a 900 hectare predator-free enclosure located in Kruger National Park, South Africa. Every 2-3 months, all 60-70 individuals are captured, and biological samples are collected for diagnosis as part of a larger study that the Jolles lab is conducting on Foot-and-Mouth Disease. Age, sex, and body condition are recorded, in addition to a number of other physiological parameters. Degree of relatedness is known for each pair of individuals in the herd. Each animal is fitted with a GPS collar that is programmed to record location every 30 minutes, and with a contact collar that records identity and duration of contacts with other buffalo. The GPS data used in this exploratory analysis was collected between October-December 2015.

Overarching Hypotheses

My research is guided by the following two hypotheses:

1. Conspecific contacts drive nasal microbiome similarity and disease transmission.
2. Habitat overlap drives nasal microbiome similarit and disease transmission.

I also propose to examine the relationship between the other parameters (age, sex, body condition, relatedness, etc) on nasal microbiome composition, but for this class I focused on the spatial parameters.

Approaches

The first step toward addressing hypothesis 1 is to quantify conspecific contacts. I tested several approaches during the course of this class. The first approach, presented during my first presentation was to generate buffers in ArcGIS around each animal at a given time point and generate a measure of “crowdedness.” The second method was to generate a contact matrix in R (see figure 1) to show distances between each individual at a given time point. Detailed methods and R code are given in my first blog post.

Figure 1: Sample distance matrix, generated for a subset of 5 animals at a single time point. Distances range between 3-18 meters.

As an intermediate step towards addressing my second hypothesis, it will be necessary to measure the percent habitat overlap between every pair in the herd. I used a suite of tools to carry out a test analysis using GME, ArcMap, and excel. Detailed methods are outlined in my second blog post. In summary, I used GME to generate kernel densities and 95% isopleths for two individuals, then used the identity feature in Arc to calculate area of overlap. Figure 2 shows a sample of the output from the identity tool.

Figure 2: Habitat overlap between two individuals in the study herd. Individuals’ home ranges are colored yellow and purple, respectively. Area of overlap between the two animals is outlined in red. Overlap between the two animals is 80% and 90%, respectively.

Eventually, once I have determined contact network and habitat overlap matrices, I will look at correlation between microbiome similarity and habitat overlap and average distance with conspecifics.

Significance

1. Distance matrix: this analysis showed potential usefulness for researching herd behavior and structure, and I will likely return to it in future analysis. However, since my question of interest relates to microbe transmission, which is likely to be associated only with close contacts, I plan to focus my current efforts on utilizing the contact collars I described in the data section. Although I will lose some spatial information, it will simplify my analysis and increase temporal resolution.
2. Habitat overlap: The method described here for measuring habitat overlap shows great promise for use in my research, especially if the process can be automated. I will explore iteration functions in GME and ArcGIS ModelBuilder to find the best way to expedite this analysis across multiple pairs of individuals at multiple time points.

Potential Issues

A few problems that I will likely need to deal with as my analysis progresses:

1. The possibility of high correlation between conspecific contacts and habitat overlap. I will try to control for this by looking for animals that have high spatial overlap but low contact rate, and vice versa.
2. Non-matching pairwise percent overlap. For example, individual 13 showed 90% overlap with individual 1, whereas 1 showed only 80% overlap with 13. I can deal with this by looking at pairwise averages, unless the percent overlap is too different, in which case I will need to explore other options.

Lessons Learned

Software Packages:

Thanks to help from my classmates, I became much more comfortable and familiar with geospatial functions in R. I also used GME for the first time and discovered that it has great potential usefulness for my future analyses. I became familiar with ModelBuiler in ArcGIS while attempting to iterate the buffering analysis. I still have a lot to learn with all these tools, but I feel much more confident than I did prior to this class.

Statistical methods:

Although I did not utilize any of the statistical methods outlined in the syllabus due to the unique nature of my dataset, I learned a great deal from watching my classmates present. I expect that hotspot analysis, multivariate statistics, and different types of regression models will be part of my future. In particular, I plan to use regression models and PCA to help analyze my data in the future.