Cemetery Demography Lab_F23

Population Ecology: Tales from the Crypt Introduction Life tables are an analytical tool that population ecologists use to study survival and mortality in a population. These data can be critical in conservation efforts (such as reintroductions or pest reductions) where ecologists would like to know how well a transplanted population is doing. A life table can be constructed in two ways: a cohort life table follows one group of individuals, born at the same time, until the death of all individuals; a time-specific life table is based upon age structure in one time period. Today, you will construct a time-specific life table by collecting data on human deaths during a specific time period. We will need to make two key assumptions in this process: 1) We will assume we are sampling each age class in proportion to its numbers in the population. 2) We will assume that the age-specific mortality rates are constant during our time period. Population structure and growth are a result of the influence of birth rate, death rate, immigration, and emigration. Life tables present the data by calculating the age-specific mortality and survivorship of a population in order to compare populations under different conditions. However, assessing the differences between columns of data can be difficult; a preferred approach is to create survivorship curves (( l x ) versus time). During the last two centuries, humans have undergone marked advances in medicine & technology. How have these changes affected mortality rates? What implications do they have for human population growth? Population biologists look for three types of patterns in survivorship curves: Type I Type II Type III Survivorship Age Class Age Class Age Class Type I curves are observed in populations with low mortality in young age classes but very high mortality as an individual ages. Type II curves represent populations where the mortality rate is constant, regardless of age. Type III curves occur in populations with high mortality in early age classes and very low mortality in older individuals. Populations displaying a Type III survivorship curve generally need to have high birth rates in order for the population size to remain constant. High birth rates ensure that enough offspring survive to reproduce, ensuring the population sustains itself. Populations characterized by a Type I survivorship curve often have low birth rates because most offspring survive to reproduce, and very high birth rates result in exponential population growth.

75-100 people per group. DO NOT use the same person twice within your sample and make sure that you end with an equal number of people in each group (e.g., 78 males and 78 females). 4. Summarize your data by tallying the number of individuals within each 10-yr lifespan period. In other words, of your 100 people in each group, how many lived only 0-9 years, 10-19, 20-29, 30-39, etc. Do this for each group you are comparing. For example, 5 males and 10 females lived 0-9 years, etc. Record these numbers in the tables below. 5. INDIVIDUALLY use the log(Lx) data to create survivorship curves for comparing your two groups. You should generate your graph of the survivorship curve in R, using the example code provided in swirl with modifications based on your particular experiment. Record these numbers and your graph below. 6. Consider what sort of stats you should use to analyze your data. The sample R code given lets you know what type of test should be run and how to do it on your data. Focus on the biological meaning of your results, e.g., group1 displays a different life history curve from group 2. Record your results below. Please fill in the following tables based on your hypotheses and randomly-generated subset of data. Use the example provided to calculate your numbers. You may also choose to code these in R. Variables of Note: x: This is the time-specific interval. We refer to each interval by the first value listed, e.g. "0-5" is the x=0 time interval. Obs #: This is the raw data for d x , the number of deaths that occur in time x. Count these in your data. n x : This is the number of individuals alive at the beginning of each time x. We derive this by summing all individuals observed in our dataset (we make an assumption here that the frequency of individuals in each time interval approximates the lifespan). l x : This is the cumulative survival probability, n x /n 0 , after scaling to a per 1000 basis (standard for life table analysis to facilitate comparisons between datasets) d x : This is the number of deaths which occur in time x, after scaling to a per 1000 basis. S x : This is the proportion of individuals who survive to the next time interval, n x+1 /n x q x : The is the probability of death in age class x, d x /n x log (l x ): This is the log 10 of the l x values. Ex. Human Life Table Time (yr) Obs # deaths in the time interval Obs # Alive at Start Survivorship # Surviving Surv. Rate For Graph x (counts) n x l x L x S x log(L x ) 0-9 1 115 1.00 1000 0.99 3.00 10-19 0 114 0.99 991 1.00 3.00 20-29 10 114 0.99 991 0.91 3.00 30-39 5 104 0.90 904 0.95 2.96 40-49 2 99 0.86 860 0.98 2.93 50-59 14 97 0.84 843 0.86 2.93 60-69 35 83 0.72 721 0.58 2.86 70-79 25 48 0.42 417 0.48 2.62 80-89 20 23 0.20 200 0.13 2.30 90-99 2 3 0.03 26 0.33 1.41

100-109 1 1 0.01 8 0.00 0.90 Group 1: _________________________ Human Life Table 1 Time (yr) Obs # deaths in the time interval Obs # Alive at Start Survivorship # Surviving Surv. Rate For Graph x (counts) n x l x L x S x log(L x ) 0-9 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90-99 100-109 Group 2: _________________________ Human Life Table 2 Time (yr) Obs # deaths in the time interval Obs # Alive at Start Survivorship # Surviving Surv. Rate For Graph x (counts) n x l x L x S x log(L x ) 0-9 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90-99 100-109 Graph:

You should prepare a lab report RESULTS subsection using your cemetery data analysis. References Vodopich, D.S. 2010. Ecology Laboratory Manual . McGraw-Hill Companies, Inc. NY, NY