Ecological theories and breakfast cereal naturally go hand in hand, at least for us Conservation Science folks. We have a tradition of using cereal for scientific experiments (check out our results from 2015 and 2016), testing MacArthur and Wilson’s Island Biogeography Theory, and some of us among the teaching staff have been known to play “Guess the theory” during breakfast – a stimulating start of day putting your ecological knowledge to the test!
This Tuesday morning, though, there was no question as to which theory we were referring to – Island Biogeography. In particular, we tested the ideas that the bigger an island is, the more species it can support, and the more isolated an island is, the fewer species that will have the opportunity to colonise.
The premise of our experiment is simple. While in our usual day to day lives it may be true that no man (or woman) is an island, for the purpose of this experiment, everyone was indeed an island, or at least they were responsible for one. Each student grabbed a container of a particular size and placed it at a random distance from the “mainland”. We then temporally abandoned our islands, returned to the mainland, which happened to be supporting a great abundance of breakfast cereal species. With hands full of cereal and lined up along the mainland, we turned our backs to our islands, and threw the cereal in their direction.
We set out our hypotheses, measured, counted, and then went through a quick coding exercise to unwrap the data presents!
Fig. 1. Species-area and species-isolation relationships presented using different analytical approaches.
Our data were quite zero-inflated as you can see from the first set of plots above. Overall, there was a trend for more species on bigger islands, and fewer species on more isolated islands. After seeing the plots, we discussed how we can improve our cereal experiment in the future. Perhaps we should have used more cereal and repeated our colonisation processes a few times to increase our sample size. It probably didn’t help that we were colonising our islands as hurricane Ophelia was passing through the UK – the winds could have carried away our cereal species in unpredictable directions. Most of the students chose small containers, so we had few data points for islands with large areas. On the third plot above, which we made by fitting a smooth curve, you can spot an interesting hump-shaped species-isolation relationship. Well, we think we know why! Our islands may have been a bit too close to the mainland, so when we threw the cereal in the air, it would fly over the closest islands and be more likely to land in the islands at an intermediate distance.
Fig. 2. Species-area (A) and species-isolation (B) relationships for brown and white species.
Our breakfast cereal species came in different colours – brown (chocolate) and white (honey), but colour didn’t seem to affect the species-area and species-isolation relationships.
We wrapped up our morning of island hopping with another visit to the mainland, with our metaphorical mainland being Kluane National Park this time. We then took the roles of conservation scientists, tasked with estimating the size of the local brown bear population. Our resources were limited – a box of cereal, a tupperware container, a marker and our minds. Working in small groups, we designed a mark-recapture experiment. Here are the results:
Fig. 3. Population estimates of the brown bear population in Kluane National Park in Canada. Numbers derived using a mark-recapture technique and cereal as a proxy for bears. Black dots indicate actual population numbers. Error bars show standard deviations for Isla and Gergana’s groups, and standard errors for Mariana’s group.
We discussed experimental design, as well as its implications – for example Pedro and Zac’s groups didn’t calculate a measure of uncertainty around their estimates, and Gergana’s group ate their cereal, so they couldn’t count the actual population. Mariana’s group got the most accurate answer, and the first method Gergana’s group used led to the most precise estimate. The first method Gergana’s group used was to mark only 20 individuals, then for their second method, they marked 40, thinking that as more individuals are marked, the estimates become more precise, not the case this time though!
Keen to learn more about coding, models and data visualisation? Here are a few relevant Coding Club tutorials:
- Efficient data formatting & manipulation
- Making beautiful & informative graphs
- Intro to linear models