Progress on the VC55 Springtail Atlas

I have to admit that my springtail work has been on the back burner over the last month for a number of reasons. Summer is a good time to have a break and recharge the batteries, although the difference between the drought summer of 2018 and the wet August of 2019 is dramatic. What I have done recently is to take a look at the VC55 springtail records and do some data visualization using the R platform. Firstly, I mapped all previous VC55 springtail records (n = 560) to the end of 2018 (black squares) (all data copyright Leicestershire and Rutland Environmental Records Centre), then overlaid the records added in 2019 (blue triangles):


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This is encouraging because although there are a few other people sporadically recording springtails in VC55, I have specifically targeted most of my efforts this year at extending the geographical coverage into new areas. This is particularly important because so much biological recording defaults to the regular honeypots. It is easy to see this by merging the dataset into a heatmap of observations:


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I can make this a little more informative by breaking down the records into families and overlaying this on the heatmap:


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The problem with this is that the data set (~600 records) just isn't large enough to say very much about the whole County, or to put it another way, the density of records is too low for good geographical coverage. In addition, there's another problem. I'm not aware of any systematic springtail recording which includes negative results, i.e. the absence of species as well as the presence. We know (anecdotally and from the heatmap) that recording effort is not equally distributed, but the limited dataset means that we simply don't have accurate geographical coverage. Resource limitations mean that there will probably never be widespread systematic recording of springtails, so my approach to this problem has been to adopt a benchmark species to infer recording effort. Orchesella cincta is said to be the most widely distributed species in lowland Britain, so I have adopted this to infer geographical coverage:


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Thankfully (for my theory), the O. cincta data fits the heatmap pretty well, so validating this proxy for our atlas. What comes next is clearly getting off my backside and getting back out in the field :-)


Acknowledgements:

• All data Copyright Leicestershire and Rutland Environmental Records Centre.
• Data visualization performed using the R platform, v. 3.6.1 (R Core Team (2014) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org).
• J. Cann for assistance with data visualization.