Population size, or the abundance of organisms in a study site, is the most fundamental of the primary demographic statistics. In this lab you will apply a simple mark- recapture technique to estimate population size in cultures of a seed beetle, Callosobruchus maculatus and use statistics to quantify your confidence in the estimate.
Chapter 7.1 Lockwood and Schneider
Students should be able to
Explain the mathematical methodology for estimating population size in nature by means of mark-recapture techniques.
Perform an experiment to estimate the number of adults in a series of bean beetle cultures and evaluate the reliability of estimates.
Estimating the abundance of organisms, especially in naturally occurring populations, is a fundamentally important activity in ecological research. An accurate census informs us of changes in population size due to recruitment (births), mortality (deaths), and migration. Reliable estimates of population abundance are also at the core of managing natural resources, protecting endangered species, and mitigating environmental impacts of habitat loss to residential, commercial, or industrial uses.
There are many ways to perform a population census, each method having benefits for particular scenarios as well as underlying assumptions about the study organism. Smaller areas can be carefully sampled using the quadrat method, which works well for sessile or slowly moving organisms. Surveys or transects, meanwhile, can be employed to measure larger organisms and larger areas. In this study, we will use a third type of census method known as “mark-recapture” that’s used to study highly mobile organisms like fish, birds, and mammals.
Mark-recapture techniques allow ecologists to track movement of individuals in space and in time through a population. In its simplest form, this type of study requires a pair of surveys. On the first survey, ecologists capture, mark, and release individuals back into the population. On the second survey, which could be the next day or the next year(s) depending on the study, ecologists capture another set of individuals from the population to determine the proportion that are marked. The proportion of recaptured marked individuals is then used to estimate population size. Ecologists can also use statistics to measure their confidence in the estimated population size.
The type of “mark” in these studies can vary depending on the species. It might a bracelet (bird banding), a collar (wolves), an ear clip (antelope, deer), a fin clip (fish), a paint dot (insects), or a biodegradable dermal injection (salamanders). For some species, a radio tag can be used to continuously track location (white sharks, mountain lions). Still for other species, digital photographs and automated image analysis of fin scars (whales) or pigment patterns (cheetahs) can be used like fingerprints to identify and track individuals. The most important aspect of any mark is that it doesn’t affect the organism’s chances of growth, survival, or reproduction. For example, a large bright tag might make an individual more conspicuous to its predators, less likely to attract a mate, or more likely to be recaptured by scientists.
Data from mark-recapture experiments are so important that researchers continue refining analytical techniques to maximize the information yield from mark-recapture data (Lebreton et al., 1992; Schwarz and Seber, 1999; Schtickzelle et al., 2003). The Petersen method, also known as the Lincoln index (Haag & Tonn, 1998), is what we’ll use in this activity. It’s the easiest of the mark-recapture census methods to perform because it is based on a single episode of marking and recapturing individuals. The important assumptions of the Petersen method are:
The population being sampled is closed (no births/deaths/migration) so that population size remains constant throughout the sampling period.
Every individual has the same chance of being caught; in other words, sampling is random.
Marks are not lost in the interval between mark and recapture.
Population estimation with the Petersen method is focused on solving for N in the following equation:
M/N = R/S
where
M = number collected and marked in first sample
N = total population size
S = size of second sample
R = number of marked organisms in second sample (recaptured)
For example, let’s say 50 juvenile frogs are captured, marked (M), and released back into a pond. Several days later, 30 are captured (S, second capture), 10 of which are marked (R for recaptured). To estimate total population size (N),
M/N = R/S
N = (M x S) / R
N = (50 x 30)/10 = 150
In this example, the estimate of frog population size is 150. How reliable is this estimate though? An estimate, by definition, carries with it a level of uncertainty. Ecologists use statistics to construct a range of estimates known as a “confidence interval”. We covered this topic in the population statistics lab and will use it again today.
Remember that a confidence interval of our estimated population size (NEST ) is a numerical range within which the actual, or true, population size (NTRUE ) will fall with a certain level of probability (Sokal and Rohlf, 1981). For example, in our frog example, we understand that if we did this experiment multiple times that 95% of the time the confidence interval would contain the true mean.
The more confident we become in our estimate, the narrower this range becomes. Still looking at the frog example where N=150, let's say we improved our sampling methods and ended up calculating a narrower 95% confidence interval of 145-155. In this case, the 95% confidence interval (145-155) is narrower than the previous example (125-175), therefore we have stronger confidence in our estimate of population size in this second example. Make sure you understand this relationship: the more confident we are in our estimate, the narrower the 95% confidence interval.
Similarly, if decrease our confidence and create a 90% confidence interval, we end up with smaller interval. Think about it this way - you are 100% sure the size of the frog population is between 0 and infinity, but that's not a very useful guess. We have less confidence the population size is between any two numbers, and as those numbers get closer our confidence level continues to decrease.
In ecology, the de facto standard level for confidence intervals is 95 per cent, i.e. a 95% confidence interval fitted around our point estimate of population size. Calculating these requires large sample sizes and intensive calculations on proportions, so in this lab activity we will employ a simplified method to calculate 95% confidence intervals using a binomial approximation (we’ll look up values in a table).
In this study, you will apply the Petersen method to obtain point estimates of population size in a stock of bean beetle, Callosobruchus maculatus (Coleoptera: Bruchidae). You will then evaluate their reliability by fitting 95% confidence intervals around those point estimates, and later comparing your point estimates against the actual number of beetles in your study population.
Each group of students will receive a petri dish filled with a single layer of mung beans and some beetles. Mark the culture with your groups name so you can identify it. Each group should mark 20 live beetles by applying a small drop (or dot) of nail polish to the back of the beetle’s thorax. Avoid painting the wing covers since C. maculatus beetles are agile enough to wipe off paint from this area. Divide the counting and marking workload equally among group members. Note the exact number of beetles that you’ve marked, and then allow marked beetles to hide (“disperse”) among the mung beans and unmarked individuals in the colony dish before the next lab session.
You and your lab partner(s) are now ready to estimate population size in your colony dish. Using soft brushes, each person in your group should be allowed exactly two minutes to withdraw randomly as many beetles (both marked and unmarked) as possible from your group’s petri dish. At the end of each person’s sampling, count the number of marked and unmarked beetles that were withdrawn, and then return the sampled beetles to a new petri dish. Each person in your group should repeat the sampling from this same petri dish. You can check the accuracy of your counting by noting that the number of marked and unmarked beetles should sum to the total number of beetles captured.
Finally, you need to collect data to assess the accuracy of your population-size estimates. To determine accuracy of your population-size estimates, collect and count any remaining living beetles (both marked and unmarked) in your colony dish. Adding this number to any beetles you collected during your recapture should provide a total population size for comparison.
Estimate the total number of beetles in your study population using the equation provided above. Also calculate the 95% confidence limits for your point estimate of total population size. To do this, find the number of beetles you recaptured (R) in the chart below. Re-calculate estimates for population size using the numbers from Lower and Upper columns instead of the R value you actually observed; these estimates will give you lower and upper bounds for a 95% confidence interval. Perform these calculation for each individuals data and for the data aggregated for the group. See the spreadsheet below for help.
To use the example data sheet, only changed cells that are marked green; yellow cells have equations in them and should update automatically. In the above example, 20 beetles were marked on day one. 3 samples were taken on the second day; these yielded 3 recaptured (marked) individuals and 9 unmarked (sample 1); 1 recaptured (marked) individuals and 8 unmarked (sample 2); and 3 recaptured (marked) individuals and 9 unmarked (sample 3). After taking 3 samples, they counted an additional 140 living beetles in the culture.
These samples are used to produced estimates of population size. 95% confidence intervals are found using the chart to identify the lower and upper values that should replace R in estimate equation. Things to note include:
The lower and upper values you replace R with for the confidence interval lead to the upper and lower bounds (respectively) due to the formula
Smaller recapture rates (marked and unmarked alike) lead to wider confidence (notice how high the range is when only one marked beetle is is recaptured); thus combining the three samples leads to the smallest confidence interval.
Due to the type of data (counts) and approximation method we are using, the confidence interval may not be symmetric around your estimate of R (this is different than other confidence intervals we explored earlier in class).
Despite the large range, the true population size of 173 falls within all calculated confidence intervals.
After you have finished counting, consider the following questions:
Among the counters in your group, which person’s point estimate was closest to the actual population size? Which person’s estimate was farthest? What are some possible reasons for these differences?
Among the counters in your group, which person’s confidence interval was the narrowest? Which person generated the widest 95% confidence interval?
Identify any specific factors in your group's counting method that may have compromised the validity of your estimate of population size.
Of the three variables—that is, C, M, and R—required to obtain a Petersen estimate of population size (NEST), which variable ought to be maximized? Please explain your conclusion.
Modified and reprinted with permission from
Olvido, A. E., and L. S. Blumer. 2005. Introduction to mark-recapture census methods using the seed beetle, Callosobruchus maculatus. Pages 197-211, in Tested Studies for Laboratory Teaching, Volume 26 (M.A. O'Donnell, Editor). Proceedings of the 26th Workshop/Conference of the Association for Biology Laboratory Education (ABLE), 452 pages.