BIOL 208 Lab 5: Disease Ecology PDF

# Lab 5: Disease Ecology ## Overview This lab introduces you to the concept of data mining from publicly available data repositories. To examine how abiotic factors affect vector-borne disease prevalence, you will collect data from online data repositories and conduct a regression analysis to dete...

# Lab 5: Disease Ecology ## Overview This lab introduces you to the concept of data mining from publicly available data repositories. To examine how abiotic factors affect vector-borne disease prevalence, you will collect data from online data repositories and conduct a regression analysis to determine if your factors exist in a cause-and-effect relationship. ## Objectives At the conclusion of this lab, participants will be able to: - Identify examples of vector-borne diseases and abiotic factors that affect their ecology. - Design an experiment based on a cause-and-effect hypothesis. - Compile data from publicly available data repositories. - Analyze and interpret data through regression analysis. - Assess how disease vector ecology can affect human health. ## Connections to the lecture material - Are diseases an example of an exploitative species interaction? - How are populations regulated by biotic and abiotic factors? - What factors influence the population sizes of pathogens, hosts, and vectors? ## Understanding disease using ecology and evolution Although studying diseases is largely the domain of epidemiology and medicine, the relationship between host and disease prevalence is shaped by the ecology and evolution of both organisms. The disease triangle (Figure 5-1) shows the three requirements needed in order for infection to occur: - A pathogen - A susceptible host - A conducive environment **Figure 5-1. Disease triangle showing the interaction of host, pathogen and environment in causing disease.** **Susceptible Host** **Disease** **Conducive Environment** **Pathogen** Applying the basic principles of population biology and species interactions to the study of diseases can help us understand how diseases arise, how they move between hosts (within and between populations), and helps us predict how the disease will spread at local and global scales. ## Population biology of diseases Disease population biology models differ from standard population biology models because the pathogen population size will be affected by factors within hosts as well as factors that affect how they move between hosts. Within a host, the size of the disease population will depend on: - The availability of resources required for growth - Competition for those resources from other diseases - The host's own immune response Because hosts have a finite life span and this life span is often shortened by infection, diseases must continue to seek out new hosts in order to proliferate. How quickly a disease spreads within a population will be determined, in part, by the rate of transmission, which in turn is determined by the life-history of the pathogen. However, another major factor affecting the spread of disease is the ecology and population biology of its host. Therefore, to study the population biology of diseases we must consider the factors that shape their host populations as well as the factors that influence the interaction between host and pathogen. ## Animal and insect vectors of human disease When diseases are transmitted from human to human, their distribution and rate of infection can be described by their: - Life history - Pathogenicity - Transmission rate - Host density However, some diseases do not pass directly between humans but are instead "vectored" by animal or insect hosts. The ecology of these animal vectors can have a profound effect on how these diseases are acquired and spread in human populations. **Example: Hantavirus** Hantavirus is an RNA virus that results in a pulmonary disease, Hantavirus pulmonary syndrome, which is often fatal to humans. Hantavirus infects rodents, yet rodent individuals remain asymptomatic. As a result, rodents can act as a reservoir for the disease without suffering illness themselves. The disease spreads to humans when humans are exposed directly to rodent saliva through rodent bites or to rodent excrement. In North America, Hantavirus is carried by deer mice, white-footed mice, rice rats and cotton rats; fortunately, house mice do not harbour the disease. ## Factors affecting ecology of a Hantavirus vector (deer mice) The disease ecology of Hantavirus in humans is directly linked to the ecology of these rodents. For instance: - Since these species tend to live in rural areas, people who spend time on farms or in the woods are more likely to contract the disease. - Hantavirus infections in deer mice are positively correlated to mouse (vector) population size such that larger deer mice populations have higher infection rates. - Rodent population size is linked to food availability. - Since rodents have a largely herbivorous diet, food availability is linked to environmental conditions that promote plant growth such as precipitation. In other words, there is an indirect relationship between the amount of rain in an area and the likelihood that you will contract Hantavirus! In this lab activity you will use the internet to ask important ecological questions about an animal-vectored human disease. In the case of disease ecology, online resources can provide detailed information about the number and location of reported infections in humans every year. This data can be analyzed to reveal patterns in the prevalence, virulence and transmission of a disease. The internet can also provide detailed climate data as well as information on the population biology of human and animal hosts, all of which can help explain the frequency and distribution of a disease. In this lab activity, you will collect data from online sources and then perform a regression analysis to assess the relationship between your chosen factor and prevalence of the disease. ## Correlation vs. Linear Regression Ecologists are often interested in determining whether two factors are related to each other. A "relationship" is defined by two possible scenarios: - Both variables are responding to a common underlying cause (but not directly causative) or - One variable causes direct changes in the other variable (direct cause-and-effect). When we suspect variables may be responding to a common cause what we wish to estimate is the degree to which these two variables vary together (i.e., are correlated). Once established, a significant correlation between two variables may lead to hypotheses about a cause-effect relationship, but it is important not to confuse correlation with causation. Only use the term "correlation" to describe your results if you conducted a correlation statistical analysis. Use the word "relationship" to describe your results when using a linear regression. Do not use "correlation" to describe the results of a linear regression. We use a regression analysis, instead of a correlation analysis, when we already suspect a cause-and-effect relationship between the two variables. ## Linear Regression In many cases, ecologists have already formulated a hypothesis regarding a direct cause-and-effect relationship between two variables or have even established such a relation. For example, in this lab you have chosen an abiotic factor that you already suspect has a direct causative effect on the prevalence of your chosen disease, so you will be performing a linear regression to analyze your data. ## Regression Line Equation and r² Value Linear regression works by trying to draw a straight line that minimizes the distance of each data point from that line. This is called the line of "best fit". The line of best fit can be represented by a mathematical equation describing the relationship of variable "x" (e.g. Abiotic factor) on variable "y" (e.g. disease prevalence) (Figure 5-2). **Figure 5-2.** Example of a linear regression graph. Notice that the trendline, line equation and r² value are all shown on the graph. **Variable 2 (Effect)** **Variable 1 (Cause)** The general equation for a straight line is y = a + bx, where: - y is the dependent variable (on the y axis) - x is the independent variable (on the x-axis) - a is the y-intercept - b is the slope of the line By using the equation for the regression line, the y values for any given x value (or vice versa) may be interpolated (estimated) within the observed data range. Using statistical analysis you can evaluate both the: - Strength of this linear relationship - Reliability of the equation A measure of the strength of the relationship is the coefficient of determination (r²), which indicates whether the data points fit closely to the line or vary widely on each side. The value of r² ranges from 0 to +1. A large r² (closer to +1) indicates a very strong linear relationship. The reliability of the equation is expressed by the probability (p-value) of obtaining this linear relationship if the null hypothesis (no relationship of x on y) were true. Therefore, when you report the statistical results from a linear regression analysis you will always report the coefficient of determination (r²), df, and the p-value. In this lab you will use Excel to perform a linear regression on your data (see step-by-step guide in your lab manual appendix). Please see eClass for a video tutorial on using Excel to calculate a regression analysis and the Stats Appendix. For BIOL208 you can use the Excel Data Analysis ToolPak to calculate all the required values for your regression analysis: - Click the "Data" tab in Excel - Click on "Data Analysis" Toolpak (see Stats Appendix for how to install the ToolPak). - Select "Regression" - Input the ranges for "Input Y Range" and "Input X Range" - If you have data labels included in your variable range selections, click the checkbox "Labels". - Leave the "New Worksheet Ply" selected - click "OK" - Retrieve the following data from the output table – "R Square", "Residual df", and "variable P-value" (the word "variable" will be replaced with your X range data label). - Format your statistical statement as shown below: - (r², df, p-value) - Eg. (r2= 0.125, df =6, p=0.39)