Benchmarking Spider Recording

Recently, I've been thinking a lot about occupancy models for invertebrates (see: Filling the White Holes). Other taxa, notably birds and butterflies (through the BTO Wetland Bird Survey and Butterfly Conservation's UK Butterfly Monitoring Scheme (UKBMS), respectively) have good negative data, i.e. an indication of where species are absent as well as where they are present. For most invertebrate taxa, partly because of lack of resource (recording effort) but mostly because of the inefficiency of recording, all we have are "White Holes" - gaps in the data which are difficult to interpret. The BAS Spider Recording Scheme does not include "negative data" because it's virtually impossible (without DNA approaches) to be certain a spider is truly absent from a particular area. This makes occupancy models difficult if not impossible to derive. The alternative is to fall back to benchmark species as indicators of recording coverage.

Following Filling the White Holes I had an interesting online chat with Geoffrey Hall who introduced me to the idea of axiophytes (it's a botany thing). BSBI gives the following criteria for axiophytes:

• 90% restricted to these conservation habitats
• Recorded in fewer than 25% of tetrads in the county

It seems that Pliny made it up (as so many other things) in his "Natural History". As far as I can tell, neither axioentomos nor axioarachnos exist, so I've just made up two new words (take that Pliny). The point is that axiowhatevers focus attention on presence (even if it is rare presence), rather than absence, so I still tend towards the idea of using a "universal" benchmark species rather than an axiomatic one. Clearly, the choice of a benchmark species is crucial. For springtails, I am happy to use Orchesella cincta, widely acknowledged to be the commonest species of springtail in lowland Britain. I would be amazed if this species was not present in every quadrat in VC55, it's identifiable in the field with no need to put it under a microscope. For spiders it's a little more complex. The obvious choice is Araneus diadematus. While this might seems to be the most obvious choice, it's only the 4th most commonly recorded species in VC55 (n=724) with only 39% of the count of the most frequently recorded species (Tenuiphantes tenuis, n=1,849). I'm pretty sure that this does not reflect the actual situation, but rather recording bias/snobbery (of which I am probably guilty). While the ubiquity of this species is a good reason to think that this is a valid choice, I needed to test the hypothesis. As a starting point I used quadrat mapping - arbitrarily dividing VC55 into a grid and looking at the number of records within each section. A 25x25 grid worked but the the intervals were a bit small and a 10x10 grid is more informative (all VC55 Spider records to end 2018):


The grid for Araneus diadematus looks like this:


To make sense of this, I converted the distributions into histograms:


The distributions look look similar, but to be sure, I ran some further analysis:


While there is a correlation between the Araneus diadematus distribution and the overall VC55 spider records dataset and this is statistically significant (p = 4.49e-11), it's not a great fit (R² = 0.48). In contrast, when I run the same exercise on the benchmark species I use for normalizing springtail recording effort (Orchesella cincta), I get an R² value of 0.87 (p = 2.2e-16), so that's a much better fit from a dataset which is nearly ten times smaller than the VC55 spider data. So I conclude that Araneus diadematus is a valid benchmark for VC55 spider recording - but it's not a great one. If you can think of a better candidate benchmark species, please let me know.


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.