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Overview
Overview
Demography is the study of populations. In a sociological context, demography focuses mainly on human populations, but the same concepts can be readily used in ecology. Many aspects of a population, such as growth, survivorship, and longevity, can be mapped and predicted using demographic data with a diverse range of applications. This can range from tracking the stability of a species facing conservation concerns to assessing social disparities in survivorship between groups of people. In this lab, you will use online public cemetery records to compare the survivorship of two different groups of people of your choice (such as between genders, geographic locations, cultural backgrounds, socioeconomic groups, or historic periods).
Objectives
Objectives
Students will be able to conduct their own research using existing data sources to address a demographic question of their choosing.
Once data is obtained, students should be able to create and interpret survivorship curves to answer that question.
Background
Background
Introduction
Introduction
Many aspects are considered when doing demographic studies, such as lifespan, reproductive output, and rates of mortality. There are a number of ways that the data related to these factors can be synthesized, analyzed, and graphed. Two of these, called life tables and survivorship curves, are useful in helping us understand the interaction between an organism and its environment. Species vary in schedules, or timing, of mortality and reproduction. This may impact which strategies related to offspring production are favored through evolution. For example, oysters experience high mortality early in life but produce huge numbers of offspring annually, whereas elephants have a high probability of survival after birth but females produce only one calf at a time.
Life tables are ways to compile information related to mortality and reproduction for each stage, or age, an organism encounters. They contain many different columns of information about a population, including information related to mortality (e.g., number of individuals dying at a given age, lx; expected remaining years of life, ex) and reproduction (e.g., number of female offspring produced at each age, mx).
Survivorship curves are graphical representations of the numbers or fractions of individuals that survive to a given age. There are species that have (a) low mortality at a young age, (b) constant mortality throughout life, or (c) high mortality at a young age (Types I, II, and III, respectively). These curves can help us determine possible causes of population limitation—periods of heaviest mortality may have the greatest impact on population growth. These graphs may be plotted on a log-scale to help consider relative survivorship for each age or stage. For example, birds generally display a Type II survivorship curve. This means the chance of surviving to the next stage is constant throughout their life. Plotting survival on a log scale allows this to be displayed as a straight-line since a log-scale plots proportional (not absolute) changes.
CNX OpenStax / CC BY (https://creativecommons.org/licenses/by/4.0)
Knowing patterns of death and reproduction can be important in managing plant and animal populations. For example, fisheries managers might adjust the allowed catch after good and poor years of reproduction; managers trying to eliminate invasive plants might be able to target specific ages of plants for removal; and managers seeking to protect rare species might know what ages of individuals most need protection.
There are a variety of ways to construct life tables and survivorship curves. The best way, as represented by the studies of Darwin’s finches by Peter and Rosemary Grant and their colleagues, is to follow a number of cohorts over time. In this way, Gibbs and Grant (1987) showed that survival of Geospiza fortis varies among years. For example, the 1978 cohort had a higher overall survival rate than did the 1981 cohort. Although cohort-derived life tables (also called dynamic life tables) and survivorship curves provide the best quality data, they are difficult and time consuming to construct.
Sometimes scientists develop static life tables and use those to estimate survival rates. Two major ways to develop a static life table and survivorship curve are to (1) discover the age at death of members of a population or (2) count the number of individuals in each age class alive at one time. Deevey (1947) constructed a life table from Adolph Murie’s data on Dall sheep. Murie had collected and aged horns that he found lying on the ground, thus providing an estimate of the age at death of each sheep (static method 1). Alternatively, ecologists often collect and age samples of animal or plant populations and use these data to construct a static life table and related survivorship curve (static method 2). A classic data set that was used in this way is the number of red deer in different age classes (Lowe, 1969).
There are assumptions involved in both of the static methods. Both methods assume a stable age distribution—a situation in which the percentage of individuals in each age class does not change from one time period to the next. They also assume the population size is constant (no change in birth rates and no net emigration/immigration). Using ages of death averages any changes in survivorship rate over many cohorts. Using the age structure of a population gives less reliable information because it simply provides a snapshot of the population at one instant in time. However, the snapshot may be better than nothing.
In this exercise, you can use either static method to construct a life table and survivorship curve for humans. You can either
collect data from two groups (using one of the methods outline below) and compare their survivorship
collect data from one group using both methods outlined below to assess if both methods give you the same results
Methods
Methods
In this exercise, you will use a static method to construct a life table.
First, make sure you understand how to develop a life table and survivorship curve using each approach. To use the age at death of members of a population, explore the "Dall Sheep Example” tab in the attached file “StudentLifeTableTemplate” to be sure you understand how to fill out a life table and graph a survivorship curve.
Note the data you collect you how many organisms died during each interval. You then can find the number of deaths per 1000 (dx, just a way to standardize among populations of different sizes!) by dividing the number of deaths in each stage by the total number of deaths (organisms) you observed and multiplying that by 1000 (look at the formulas!). Next, calculate the number of organisms per 1000 that survived to the next stage(nx). The first stage always get a 1000 here (since all had to be born to be counted in death records!). If you subtract the number of deaths per 1000 in each stage from the number per 1000 that survived to each stage, you get the number surviving to the next stage! You can turn this into a proportion (lx) by dividing nx by 1000. Look at the Sheep example, then complete the table. Finally, graph a survivorship curve by plotting the age at the beginning of each interval against lx. The example shows how you can also plot survivorship on a log-scale (see Data Summaries in Google Sheets for more help).
Next consider how you could use census data to plot a survivorship curve. For our example (under the Census data example), we have a page from the 1930 United States census records. We can use it to produce survival curves for African American women in Arkansas.
Note the data is already put into bins (and is often in percentages), which makes our job easier. The major change is we have to assume the number surviving to each stage per 1000 (nx) is equal to the sum of individuals in a given class and all classes past it (since those in later stages had to survive to the point). If we divide that sum by the sum of individuals (percentages in this case!) and multiply it by 1000 we get nx (again, look at the formula; note the $ sign tells the spreadsheet software to use the same cell range even if we move the formula to another cell!). From there we can again calculate lx and plot survivorship curves. Make sure you can complete the example.
After you feel confident about how to construct and interpret survivorship curves, brainstorm about the kinds of questions that interest you and choose what groups you will compare (or what group you will use to compare methods).
What populations? That’s up to you. You can compare males vs. females, people living at different times, people living in different places, or, perhaps, people of different ethnicities or socio-economic backgrounds. You may even think of other interesting comparisons. As you plan your project, make sure you identify a specific, interesting question, and hypothesize an answer to your question.
Supplement this brainstorming with an examination of cemeteries that is available from the following sources:
http://www.interment.net/us/index.htm (note records are free to browse by state, but the options under searching death records require payment; searching state records is all that is needed for the class)
https://academics.hamilton.edu/biology/ewilliam/cemetery/default.html#datasets (contains multipe datasets that display aggregated data)
Census data (starting in 1850 the census data includes ages of people, and these data may be used to develop survivorship curves like Lowe did for the red deer)
The data available for different censuses may differ. In the attached sample (Fig. 3), the data are aggregated according to age, sex, and race, for Mississippi and Arkansas. But Congress wanted different information at different times and so the same questions cannot be answered from each census. For example, some censuses report population in urban/rural-farm/rural-non-farm categories. To discover what questions were asked, look at samples of the forms. Examples of blank census forms can be viewed in a booklet entitled “200 Years of U.S. Census Taking: Population and Housing Questions, 1790-1990” (1989. U.S., Department of Commerce). This document is available on-line at http://www.census.gov/history/pdf/200years.pdf.
CDC data (https://www.cdc.gov/nchs/products/nvsr.htm, search for life tables for a start!)
State Vital Records data (e.g., https://www.health.ny.gov/statistics/vital_statistics/ )
Feel free to explore a few before you choose. Note data may be presented in slightly different ways in different sources, so you need to choose the right approach for developing your life tables.
Select two groups that you wish to compare. Make sure to keep your two groups similar except for the factor you are investigating. For example, if you wish to compare males vs. females, make sure they lived during similar times. If you are working in a group, trade off collecting and entering the data so everyone in your group gets a chance to do both. Be prepared to explain your question and the rationale that led to your hypothesis.
Information is given below on additional templates that may assist.
If you are using data on the age of death of individuals (like the Dall Sheep data, common for cemetery data)
If you are using data on the age of death of individuals (like the Dall Sheep data, common for cemetery data)
Visiting a cemetery (in person or virtually) and calculating the age at death of people will allow you to construct a life table and survivorship curve in the same way that Murie did with Dall sheep.
Record the birth and death years of at least 50 people from each of the two groups in your “Cemetery-type Data” sheet in the Excel file “StudentLifeTableTemplate. You do not need the same number of people in each group. ENTER YOUR DATA INTO THE GREEN CELLS ONLY. Yellow cells contain formulas that will calculate the age at death for you.
Using the data on from the Age at Death column, Count the number of people who died in each age range on the “Life Table from Cemetery-type Data” sheet in the Excel file “StudentLifeTableTemplate”. Column A will have the age groups and column B will have the number of people dying in each age group. Only enter data into the green columns for each group.
The number of deaths per 1000 individuals in Column 3 will be calculated for you (this is done by dividing the number of deaths during that age interval by the total number of deaths and multiplying that number by 1000).
Similarly, the number of survivors per 1000 individuals in column 4 will be calculated for you (by subtracting the number of deaths per 1000 during that age interval from the number of survivors at the beginning of that age interval).
Construct a survivorship curve for each group (like the one shown in the Sample Census Data sheet you can download here!). You will use the column titled "nx: Number survivors per 1000 at beginning of age interval". Then answer the review questions based on what you found.
If you are using data on the distribution of individual ages at a certain (like the Red deer data, common for census data)
If you are using data on the distribution of individual ages at a certain (like the Red deer data, common for census data)
Examining U.S. census data will allow you to take a snapshot of age structure at a specific point in time and develop a survivorship curve in the same way that Lowe developed a survivorship curve for red deer. You can use the example sheet developed above. Make sure you pay attention to if the data was stored in raw (real numbers) or % format!
Review Questions
Review Questions
1. How did your two groups compare in terms of survivorship?
2. What factors do you think might have contributed to the trends you observed (e.g., social, historic, environmental)?
3. Why do you think it might be difficult to disentangle some of these factors from one another, such as socioeconomic/environmental?
References
References
Adapted from:
Larson, E. (2020). Demography from "virtual cemeteries". ESA Data Access - Inclusive Pedagogy, (Version 1.1). QUBES Educational Resources. doi:10.25334/65XT-CM66
Janet Lanza. 2 March 2012, posting date. Demography from physical cemeteries, "virtual cemeteries," and census dataTeaching Issues and Experiments in Ecology, Vol. 8: Experiment #1 [online]. http://tiee.esa.org/vol/v8/experiments/lanza/abstract.html
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