We, the Biodiversity Critical Thinking tutorial group (a.k.a. Team Isla), decided to undertake a mini data synthesis project at the end of our Critical Thinking Tutorials to test the research question: “Are rare species more likely to be declining in abundance than common species?”
This project is a learning experience in how science really works and a chance to test a really cool question, that we don’t think has ever been explicitly tested in this way before.
STEP 1: Coming up with our research question
We began by each suggesting a question in ecological or environmental science we were interested in investigating, these included:
- Rewilding: where has it been done? Is it realistic?
- Are native species better at providing ecosystem services than non-native? Do native species have higher productivity?
- Antarctic – is it the world’s largest biodiversity cold spot? Is biodiversity changing faster there and could it be a new hotspot one day?
- Home made electricity – what is the potential? Is it cost effective? What are the options? Could we look at an already self-sustaining village (e.g., Findhorn) and figure out the best way to make a difference in Scotland?
- How do current protected area maps overlay with future species ranges and climate models?
- Are rare species really common and common rare? What does this mean for conservation?
By a voting elimination we decided on idea number 6: Are rare species really common and common rare?
STEP 2: Defining our methods and collecting our data
First we discussed what rare versus comment meant and decided that rareness versus commonness relates to some combination of the following things:
- The geographic range extent
- The local abundance
- Habitat specificity
We agreed that there were ways we could explore the first two components of rarity/commonality using available data for UK species.
We also discussed how we wanted to link rarity to conservation status and that population data over time, would allow us to calculate whether a species has a declining, stable or increasing population – an indicator of whether a population is threatened.
We started out by downloading the Living Planet Index (LPI, http://livingplanetindex.org/data_portal) data of populations for species all around the world. We then subsetted out only the species that have population records from the UK. These 211 populations will be the basis for our data analysis. Then we decided we could use the Global Biodiversity Information Facility (GBIF, http://www.gbif.org/) to download occurrence data for all of these species and then use those occurrence data to calculate the geographical range extent for these species. We can also use their population numbers and the number of occurrence records to estimate their local abundance.
We encountered some difficulties with downloading data from these data portals including formatting issues with the data, multiple data sets for the same species or for subspecies within the same species, inconsistent taxonomy, and more! But, with a bit of R code and some patience, we got everything sorted!
At moments we felt a bit like we were in the scientific cloud – but I think we will make it out the other side.
STEP 3: Testing our hypotheses and writing up our results
With much excitement and two pieces of R code and many many .csv files of data from the Global Biodiversity Information Facility and the Living Planet Index we came together for our last Critical Thinking Session to analyse our data and test our hypotheses.
Here are our research question and hypotheses that we clarified in our last session.
Are rare species (those with smaller geographic ranges and lower local abundance) more likely to have declining population trends than common species?
Range extent negatively correlates with rate of population change
H1: As geographic range increases populations will have a lower rate of population change.
H2: As geographic range increases populations will have a higher rate of population change.
H0: The rate of population change will not vary with geographic range size.
Now that the data were in hand, our methods were finalized and our hypotheses clarified, we were ready to begin to “unwrap our data present”, the idea of finding out the exciting result from a previously unanalysed dataset – a term coined on TeamShrub, Isla’s research group.
But wait, there was one problem! We hadn’t actually merged our LPI slope and GBIF range size data, this should be a quick fix we thought, but that is where we were wrong. We had downloaded the LPI data using the common names for the species and the GBIF data using the Latin names. All we needed was a key to link the two, but in the process of setting up our key, we did something wrong and the two datasets just wouldn’t merge. Sadly, we learned an important lesson about using a common taxonomy and we used up the remaining minutes of our session trying to finish the analyses and not getting to the final data present unwrapping, but I guess this is a very realistic real-life experience of how science goes sometimes!
After the session and over e-mail we got the merging problem fixed and the data present is now unwrapped and the results are very interesting!!! Stay tuned for the write up of our mini science project to discover what we found out about populations of common versus rare species in the UK…
by Isla and the Biodiversity Critical Thinking Group