Spider Explosion!

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Heavy rain (an understatement) during the last week has washed any remaining spiders off the foliage and the action is now all down in the leaf litter. It's been sift-and-sort all the way this week. I've examined leaf litter from four different sites and the number of Microneta viaria has exploded. I have found them as the predominant species at all the sites I have visited, and at one site, they are all I found! This has solved one mystery for me. For the past few months I have been finding immature specimens of a spider with a grey prosoma and orange opisthosoma I have not been able to identify:



It's now clear that these were immature Microneta viaria and now I can see why there were so many of them! Fortunately, they are easy to identify when mature. Both the male palps and female epigyne are characteristic and the prolateral spine on the inside of tibia1 is also helpful.




Epigyne

I think it's no accident that this has coincided with large numbers of springtails in the leaf litter (particularly Dicyrtomina saundersi) - food availability has spurred mass maturation.

How to do biological recording

An interesting new preprint addresses an issue I've been concerned about for a while, how to control for recording effort in assessing how species are doing (Rapid assessment of the suitability of multi-species citizen science datasets for occupancy trend analysis: https://www.biorxiv.org/content/10.1101/813626v1).

The biggest volume of biological data is recorded by unstructured citizen science schemes. Because the data is collected in an essentially random way, many taxon experts are sceptical about the value of these schemes in accurately reflecting populations in the field. Although the statistics are complicated, the method of the new paper seeks to turn unstructured data into occurrence data, i.e. data where we can be sure (to any specified degree) of the presence or absence of a species in a given time period, or the absence of sufficient data to make a determination. The method to do this is to call each 1km grid square a recording site and to count the number of visits each year, one visit constituting one record by any person in a 24 hour period. From this it is possible to calculate the degree of confidence in the occurrence or absence of a species at the site. Ideally (for high confidence) there would be four or more visits from experienced recorders per site per year, but even in the absence of this, the method provides a way of turning the massive amount of unstructured biological recording data available into findings which are easier to interpret and to place confidence limits on.


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Unsurprisingly, it turns out that butterflies and moths are the runaway winners, the East Midlands performs creditably, and species which get a lot of publicity do better than those for which there are only a handful of experts who can identify them. Nevertheless, if tools could be developed to enable easy utilisation of the method, this would present a valuable way forwards.


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Note: R package "unmarked" is of relevance: https://cran.r-project.org/web/packages/unmarked/index.html