Getting quantitative in conservation science

The theme of this weeks class was the quantitative side of conservation science with a lecture on population ecology, the theory of island biogeography and metapopulations.  We talked maths, stats and computers covering the topics of matrix algebra, demography, stochasticity, hierarchical models, programming in R and more. I hope we learned that being quantitative doesn’t have to be scary, as maths aren’t really that hard, and they can even be fun!

island_biogeog1

Coordinating the data collection from our archipelago of tupperware containers in our island biogeography experiment.

We also got hands on with being quantitative and tested the theory of island biogeography by setting up our own archipelago of tupperware containers in the grass outside of the Crew Annex and pitching handfuls of star-shaped chocolate breakfast cereal our species immigration from a point representing our mainland.  We even used a laser range finder to measure the distances between the “mainland” and our “islands” to calculate our species area and species isolation relationships!  We learned that the theory of island biogeography holds pretty well for our model tupperware-breakfast cereal system with significant relationships for both the species-area and species-isolation curves in our data.  Check out our data, R script (see bottom of post) and super cool figures (see below) that we coded in about 5 minutes or less, the first intro to programming for some members of our class.

Then we broke off into tutorial groups to try our hands at mark recapture to estimate the number of grizzly bears (animal-shaped cereal) in populations in Banff National Park (five large tupperware containers) over time.  I asked the students to derive their own equations for the mark recapture experiment and that was somewhat of a challenge given the short amount of time that we had for the activity.  My group had some moments of frustration exclaiming that maths were indeed “hard, too hard” – but they worked through that to get some solid population estimates together. And, by the end of class, all five groups had produced estimates with error of the populations over time (see figure below from the whiteboard).  We learned that population censuses using mark-recapture techniques are a trade off between the number and sizes of the samples you collect and the precision of your results, and that there can be a lot of error when you don’t standardize your methods in advance!

mark_recapture1

The hand-drawn figure of our mark recapture data – check out those error bars and that outlier measurement in 2005!

So all and all, I hope we learned that conservation science is a quantitative science and that maths and being quantitative shouldn’t be something to be afraid of, but something that we all can embrace as ecologists/conservation scientists in training!

Our very first class R script:


## Island Biogeography
## Conservation Science 20-10-2015

# Set the working directory to the folder on your computer where you saved the data
setwd(“path to the folder here”)

# Load the data – convert from .xlsx to .csv before uploading
data <- read.csv(file=”Island_Biogeo.csv”,head=TRUE,sep=”,”)

# Make sure the dataset has loaded properly. “Island” is the name of your island, “Size” is the area of the bottom of your container (in cm2), “Distance” is the distance from your standing point to your island (in m) and “Immigration” is the number of species that were found in your container
head(data)

# Plot the species-area relationship – pch = 19 makes the points filled circles, col = sets the colour of the points
plot(data$Immigration ~ data$Size, pch = 19, col = “red”, ylab = “Immigration”, xlab = “Size”)

# Is the relationship significant?
lm1 <- lm(Immigration ~ Size, data=data)
summary(lm1)

# Plot the regression line on the graph
abline(lm1)

# Plot the species-isolation relationship – pch = 19 makes the points filled circles, col = sets the colour of the points
plot(data$Immigration ~ data$Distance, pch = 19, col = “blue”, ylab = “Immigration”, xlab = “Distance”)

# Is the relationship significant?
lm2 <- lm(Immigration ~ Distance, data=data)
summary(lm2)

# Plot the regression line on the graph
abline(lm2)

# Further beautification of these figures could occur such as relabling the axes, adding error around the linear model, etc.

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3 thoughts on “Getting quantitative in conservation science

  1. Pingback: Good teaching – student and teacher perspectives from the Conservation Science course – Teaching Matters blog

  2. Pingback: Qikiqtaruk Book Club Part IV: Theory and high-level processes in the Arctic | Tundra Ecology Lab – Team Shrub

  3. Pingback: Getting quantitative & testing Island Biogeography Theory | Cons. Sci.

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