Global warming poses a threat to biodiversity, as warmer climates force species to move, and butterflies are among the first to respond to this.
As our analysis showed, they’re already on their way. Using methods developed in close collaboration with researchers in the field, we analysed 1.5 million observations, reported to the database Artdataportalen, and were able to identify 40 species that have moved north.
To our knowledge, this is the first such analysis using observational data to see how species’ ranges are changing in Sweden and a novel way to illustrate some of the concrete effects of climate change.
Climate change is the issue of our times, but it can be difficult to report on it in a way that engages audiences. Biodiversity loss especially can feel abstract.
That’s why we wanted to focus on the very tangible effects these are having on people’s everyday lives. We wanted to find a data-driven, quantifiable way of telling the story of changes that are already occurring around us. Using observational data we were able to show our audience how things look in their area, revealing and putting numbers to a change that many of them can already sense.
Everything was done using publicly available data on species observations reported to the Artportalen database, maintained by the Swedish University of Agricultural Sciences (SLU).
This story was published by Sveriges Natur, Sweden’s largest environmental magazine, and picked up by other media including Sweden’s national broadcaster SVT. We are also proud that researchers at SLU have asked to use our results and the method we developed in order to do further analysis.
We feel this project achieved our goal, of turning climate change reporting into something tangible, innovative and data-rich.
The bulk of the data analysis and visualisation was done using R. The actual observations we used came from Artportalen, where we downloaded all observations of butterfly species between 2005 and 2019.
We decided, in close collaboration with researchers, to focus on the northern border of each species’ range, comparing the 90th percentile of observations’ latitude in 2005-2009 with 2015-2019, to identify species which had moved north during a ten-year period.
We also depended on QGIS for some additional geospatial analysis. In order to avoid capturing changes in observers rather than butterfly ranges, we filtered out areas where no observations at all had been made during the first time period.
What was the hardest part of this project?
One of the hardest parts was that we were using observational data for the first time, and that, as no similar analysis has been done in Sweden, there was no blueprint for how to proceed correctly.
We had to spend some time developing and refining a method for how best to analyse observational data, and butterfly movements, two things we were not experts on. The way we made this work was by turning to experts (always crucial in data journalism!). From the very start, we worked very closely with researchers, using their guidance both to develop the method, and interpret the results.
What can others learn from this project?
The project serves as a use case of how a small team of data journalism experts can reach and empower local reporters. All the data used for the story was public, but not necessarily accessible for ordinary reporters or members of the public.
It also serves as a starting point for a broader conversation about how we can produce data journalism on climate change which actually engages people.
From a more technical standpoint, we could also use this project to introduce topics like analysis and mapping in R, or doing geospatial analysis in QGIS, including things like matrices and points-in-polygon analysis.a