Ecological Society of America

I used this module in a small (12 students) introductory Ecology course for undergraduate Biology majors. We met once a week for three hours. I used this module because it was possible to adapt using local (Puerto Rico) data, making it more relevant. It also involved several major learning objectives like formulating hypotheses, designing experiments, collecting and analyzing data, and interpreting data. I have included Instructor Notes that describe how I modified and supplemented the module with additional assignments to introduce the terminology as well as to extend and relate the discussion to broader concepts previously covered (like climate change). 

Included here are:

1. Instructor notes on what I modified, followed, and what I will change in future implementations of this module.
2. Pre-exercise assignment
3. Short introductory presentation reviewing terminology
4. Modified data entry excel file and modified data analysis file. 
5. Post-exercise assignment

Institution/institution type: Augustana College (residential liberal arts college)

Course/Course format: General Ecology (Junior and senior biology and environmental studies majors, does not count towards general education requirements so it is a majors only course). The course had 36 enrolled students. Lab met once a week for three hours.

What I hoped to accomplish with this module was to give students an opportunity to test their own hypotheses about human survivorship, and strengthen their ability to understand survivorship curves.

I introduced the module during a broader unit on population ecology. By the time students were working on this module, we had already covered survivorship curves and basic life table parameters in lecture (Molles 6^th edition, chapter 10). Students had already worked simple, deterministic, problems in class, for example, determining which survivorship curve a population has given l_x values, and given s_x, m_x, and n_x, values predict n_t+1.  I used this module along with the plant population viability analysis module to form a lab unit on populations.  This module is also good for reinforcing the distinction between static and cohort life table approaches.

I have used this module many times, and before that I used this module’s predecessor from the 1993 Experiments to Teach Ecology (Nancy Flood’s Cemetery Demography)

I used example problems, as stated above, and assessed these by using clickers in lecture. I use clickers to provide a low stakes, immediate feedback atmosphere for quantitative work in class (questions are worth participation credit, and all responses receive credit), and we continued working example problems until a significant majority of the class was consistently getting correct answers.  These skills were also assessed on an exam.

I use selected parts of this module, because I want the experience to fit into a single, three-hour, lab session.

1) We do virtual, rather than physical cemeteries.  This is a trade-off, as the physical cemetery opens up nice liberal arts connections to local history and changing trends in funerary art. Further, students always enjoyed the field trip. However, with the physical cemetery, students were constrained to exploring a hypothesis I knew would work with the data they would find there (male vs female and/or 1860s vs 1890s cohorts). We tended to spend the whole three hours collecting data. Switching to virtual cemeteries cuts out travel time and permits students come up with their own hypotheses, and this makes this much more of an open inquiry experience. Since one of my primary objectives for this experience is that students explore hypotheses of their own choosing, the virtual cemetery simply works better.

2) We eliminate the oral presentation.  The Lanza module calls for an oral presentation of the data. This is another trade-off, of course. Oral presentations are important, but at the end of a three-hour session, students would not be ready to do that. Instead, I ask students to submit a more traditional lab report.

Here is the way I run the session.

1) Students break into pairs and come up with a hypothesis. Students clear their hypothesis with me before they begin looking for data, so I can steer them away from hypotheses that are too specific and would therefore take too long to find enough data to address. I also need to steer students away from hypotheses that can’t be addressed using survivorship curves inferred from census or cemetery data.  For example, some students will want to address something very contemporary, like zika virus, and I have to remind them that most of the affected individuals are still alive (so will not be represented in any cemetery) and that the event can’t possibly be represented in the US census. It takes some groups a few tries to come up with a hypothesis that is a good match for their interest and for the technique we want to practice.

2) Students collect data and generate survivorship curves as per the module. Students doing method 1 take longer to collect the data, but usually need less support on the calculations and graphing (steps 7-10). Students doing method 2 tend to find the data they need more quickly, but need lots of support getting through steps 5 and 6 (the calculation of l_x).This is exactly what Lanza predicts in the module.  Most of the middle hour of my class is spent circulating around the computer lab helping students with the analyses.

By the end of the lab session, most groups will have generated survivorship curves and some groups will actually have the lab report ready to submit. Groups that do not leave with their curves finished tend to be groups that did method 2 (the census method) and who really struggled with the calculations.  If I really needed every student to finish the session with graphs in hand, I might be tempted to limit them to method 1 (ages at death). 

For the lab report itself, I ask the following (lab reports in my class are always excel spreadsheets):

1) The names of all group members.

2) (5 points) a brief statement of the hypothesis you are testing and the methods you chose to test it. Why did you choose this hypothesis?

3) (5 points) Tables of the raw data and calculations used to generate graphs. Please put these tables one or more tabs and don’t put them on the main report tab. Name all tabs in the spreadsheet for easy reference.

4) (45 points) Clearly labeled survivorship curves formatted to make it easy to see if your hypothesis is supported by the data. Format your curves as survivorship per 1000, just as Lanza indicates.  You will probably find it useful to plot your curves on the same set of axes so you can see differences in the curves.

5) (40 points) Do the data support your hypothesis?  We are not using a statistical test to answer this question, but make careful reference to the figures you generated in item 4 to justify your answer.  What historical trends or events may explain any observed difference or lack of difference? You do NOT need to answer questions 1-9 in the Lanza article. However, these questions (especially 1-8) will give you some good ideas to consider as you try to explain the trends in your graph or graphs).

6) (5 points) A bibliography of any data sources consulted or references used.

This module works very well for me. While I am still not totally at peace about abandoning the physical cemetery work, I do feel that the trade-off in favor of more open inquiry for students is worth it.   I anticipate continuing to use this module. Students are often surprised by the outcomes. For example, one group this year compared survivorship in urban Chicago vs a more rural area in downstate Illinois for individuals born in the 1890s. They assumed that urban would beat rural due to access to healthcare, but were astounded to find a large difference in the other direction. This prompted a good discussion on public health, the industrial revolution, and student’s assumptions about the costs and benefits of different lifestyles.  Another group compared survivorship from a Mexican cemetery to a Californian one from the same era. The students correctly predicted higher Californian survivorship, but were surprised how large the observed difference was. Again, this was a springboard for a good discussion about public health trends and social justice.  Of all the labs I do, this one connects most easily and fully to the larger liberal arts context of the school.    

After many essential conversations with members of my QUBES Faculty Mentoring Network, I implemented the Demography from Cemeteries module in my course MAT 1314 Modeling for the Life Sciences, a new course offered for the first time in the spring 2016 semester. This new course was designed at the request of the Department of Biology and the Program in Cognitive and Behavioral Neurosciences, to complement redesigned courses in Biocalculus and Statistics for the Life Sciences. The class was in lecture-discussion format, meeting twice per week for 75 minutes each. We did not have a long lab session time available to us. Since this was a quantitative course designed for life sciences students rather than a quantitative module in what is otherwise a life sciences course, the focus was on the quantitative skills. The primary objectives were to acquire some understanding of and facility with using and interpreting life tables. Other objectives included familiarity with Excel in a scientific context, experience in selecting a data set, formulating and addressing questions that are both on topic for the classroom discussion and appropriate for the data set available. Finally, the broader objective was to place the questions and tools of the module within the context of the remainder of the course, since this module came at the end of the semester.

Since the students arrived with some experience with Excel and data, I was able to present the module as follows. I introduced the concepts for the module in the final portion of one class period and handed out a subset of the Lanza paper for the students to read for the next class, along with a couple of brief handouts on cohort vs. static life tables. In the next class, I spent part of the class period talking through the planned exercise and addressed any student questions from the reading. I also talked briefly about several online references to life tables (links included above) as we looked through them together in class, projected on the screen. I asked the students to search for an appropriate data set online, download it, and bring it to the next class in Excel, ready to work. In the next class, I took questions and went on to other material, leaving them several days to complete their assignment and submit it to me online.  We did not take the time for oral presentations to the class as suggested in the Lanza paper.

Because this was a Math course rather than a Science course, the quantitative focus pervaded the entire semester, so there was inherent scaffolding in some sense. On the other hand, I tried throughout the semester when dealing with seemingly more theoretical structures such as differential equations and difference equations to talk about how data could inform, validate, and verify the models, so a module specifically on data was a natural follow-on by the end of the semester.​

In my next version of the course, I would place the module earlier in the mix of the course topics, spend more time on other uses of life tables, and be a little more explicit in constructing examples rather than simply talking through finished examples.

​Douglas Norton

Villanova University

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I implemented the module as written, with very little modification.  I attached the files I used and shared with students, as well as examples of student posters that were completed as part of the project.

Institution/institution type:  University of Texas- Rio Grande Valley;  Large Primarily Undergraduate Institution; Hispanic Serving Institution

Course/Course format:  Ecology (BIOL 3409)-Upper division elective, Lecture/Lab

TIEE Module:  Cemetery Module

Quantitative skill focus: Data collection, presentation, and interpretation

I implemented the Demography from Cemeteries module in an upper level ecology course this past spring.  Although this is an upper level required course within the Biology Department  only prerequisites are basic biology and anatomy.   Preliminary assessment of the enrolled students suggest that they are not quantitatively grounded, many (66%) having yet not taken basic statistics or advanced math.  A majority (>50%) of the students were pre-med students.  As such, the course was a general course that focused on specific knowledge, skills, and abilities that help students with a better understanding and appreciation of how specific ecological concepts can be applied towards a better understanding of their world. 

The class had a total of 100 students enrolled--maxed out with four lab sections with 25 students each.  The class was in lecture-discussion format, meeting twice per week for 75 minutes each, and included a weekly lab of 2.5 hours. 

In general, the course was arranged in 10 discrete modules over the course of the 16 week semester.  The modules ranged from one to two class weeks, and ranged in topics from evolutionary ecology, biogeography, and in the example of this module, life history.  The primary objectives of this module was to develop skills in data collection, using the data to draw survivorship curves and build life tables to better understand life histories and survivorship in local human populations . Other objectives included familiarity with Excel in a scientific context, experience in selecting a data set, formulating and addressing questions .

The learning objectives for this module were as follows:

Knowledge

Life Cycles and Life History

Life Tables and Survivorship Curves

Skills

Collect and graph field data (excel)

Presentation skills (oral)

Teamwork skills

Attributes

General understanding of the differences of life history of different populations

Ability to apply this understanding to other organisms and in different contexts

The subject of life history was addressed across 2-1.25 hr lectures and 2-2.5 hr labs.  Students were expected to read the corresponding section in their digital textbook (Simutext).   Although it is difficult to lead an off-campus field trip (with 100 students across 4 labs), the graduate student TAs agreed to help pilot this exercise given that the cemetery was relatively close to campus.  The lab was conducted in two separate sessions:  Week 1:  a field trip  to a local cemetery located less than 2 miles from campus , and Week 2: short presentations from each pair or three student team of students.  The lab complemented content addressed in lecture, but was facilitated by GTA's.

I adapted the materials minimally (see attached), only to include more local relevance for the students.  For example, I included details about the specific cemetery that we were to visit, and modified the instructions so that they were specific to the course format (times, etc).

This module was offered early in the course (in the first month of the semester), and introduced the skill of developing and interpreting graphs based on quantitative data.  The students practiced this skill in the first mid-term (offered soon after this module) and again in the final exam.   

Students were asked to present their graph (product) to their peers in a 5 minute oral presentation during the second week of the lab portion of this module.  Students were asked to describe their comparison of life histories of two different populations Student presentations were evaluated by TA using a rubric (attached here).  

Later in the course, students were given a midterm that included demographic data of primate populations from which they were to draw survivorship curves (graded).  There were 4 questions that followed up on the interpretation of these curves comparing populations and implications of different factors that influence survivorship and life histories.

If possible, I would explore examining the implications of larger data sets by having students pick from a set of comparisons (male vs. female, Hispanics vs. non Hispanics, etc).  In this module, I had students do more of an inquiry based approached which allowed them to make comparisons between any populiations they wanted. Limiting the choice of comparisons may better take advantage of the large class size (~100 students) to see if more data can help improve understanding of the concept of life histories and introduce the idea of sample size in data collection.

I would also modify the module to include a discussion of what might of factored in to the survivorship of folks in the past, and how would that differ from factors that influence survivorship today.  The region is infamous for the highest rates of obesity and diabetes in the country, and such a discussion may add cultural relevance to this exercise, and might add particular appeal to the majority of students who aspire to be medical professionals

I think that this module worked well for me in a large class setting, but only because I was able to take the students out to the field.  I am not sure if it would have been less impactful by using archived data, because many students really enjoyed walking around and learning about their own histories.  Some students saw graves of people of local historical importance, or even graves of possible relatives.