How are hoverflies doing in 2018?

Initial impressions suggest that 2018 has been a very odd year for hoverflies. The season started very late, and there was precious little activity during the winter. Numerous observers have remarked upon the low numbers of species and the apparent absence of some of the most reliable species such as Eristalis pertinax and E. tenax. At this stage of the season it is quite difficult to be sure what is going on but I hope I have come up with a way of presenting the data in a way that tells its own story.

The following graphs are based on the following processes:

• Records extracted from Facebook and other media such as Flickr have been used. The reason for this choice is that it is one block of data that I know has been assembled consistently. Also, it is the only block of data that I have ready access to (other data go on the HRS database that Stuart manages).
• I created a baseline by generating a set of tables for each species in the years 2013 to 2017 inclusive and then used these data to create an average for each week for each species. This is the background data used for comparative analysis and shows up as a blue line on the charts.
• I then overlaid weekly data for each species up to the end of week 30 in 2018; i.e. up to Saturday 28^th July.

There are charts for nine species, all of which are relatively straightforward to identify from photographs and are regularly recorded by contributors. This choice is ideal because it comprises species for which there are good numbers of records and which therefore make the outputs as statistically robust as possible.

Interpreting graphs is always tricky and in this case we have to think about the shapes of the charts and not the absolute numbers, although there will be places where numbers of records are strikingly different.

The reason for concentrating on patterns is that the graph of average numbers is based on a five-year period in which we have seen recording activity grow dramatically but in that time there has also been a shift towards members maintaining their own spreadsheets (a big thank you to all who have). It means that no two years are made up of the same group of recorders, so, in addition to seasonal and yearly variation, there are also demographic changes too!

The critical consistencies are that the data have been checked/ extracted by a tight nucleus of specialists and extracted by just three of us (Geoff Wilkinson, Ian Andrews and me). In addition, we see a growing throughput of novices who gradually turn into more experienced recorders whose ability to recognise hoverflies increases over the years. Some may drop out, but there will also be new recruits.

Results

These nine species tell a significant story.

Firstly, the numbers of hoverflies recorded were low during the winter lower than average) and the spring burst of activity was delayed for about two weeks. This is amply demonstrated by both Episyrphus balteatus (Figure 1) and Eristalis pertinax (Figure 4).

The impact of the drought in July is clearly demonstrated in Volucella pellucens (Figure 8) and Eupeodes corollae (Figure 2). There is possibly also a bigger impact on Rhingia campestris (Figure  6),. I expect the summer brood to fail in south-eastern England if experience in other droughts holds true; but the impact will probably only really become clear when the summer generation emerges or fails to emerge.

One of the more perplexing issues is whether we can detect the impact of the long wet winter? I think this may be apparent in Volucella zonaria (Figure 9). I think there is probably enough evidence to suggest that numbers are substantially down this year. We cannot discount the impact of drought because there was an uncharacteristic dip in the emergence at a point when it might have been expected that numbers would be rising rapidly; nevertheless, the overall numbers of this highly noticeable species appear to be heavily down on the average. I suspect there has been a winter impact and we might be able to detect this using occupancy models at the end of the year

I am also somewhat surprised by the degree to which the chart for Volucella inanis (Figure 7) seems to be mirroring previous years. In broad terms, the first generations of Eristalis tenax (Figure 5) and Rhingia campestris (Figure 6) are also  pretty close matches against the long-term average.

Finally, there always have to be winners in this sort of situation and I think this is amply demonstrated by the graph for Eupeodes latifasciatus (Figure 3), which has been far more abundant than in the last 5 years.

Figure 1. Episyrphus balteatus

Figure 2. Eupeodes corollae

Figure 3. Eupeodes latifasciatus

Figure 4. Eristalis pertinax

Figure 5. Eristalis tenax

Figure 6. Rhingia campestris

Figure 7. Volucella inanis

Figure 8. Vlucella pellucens

Figure 9. Volucella zonaria

Recorder activity - a possible proxy for looking at the impact of weather on datasets?

I have had the feeling that we had lost a lot of recorders from the UK Hoverflies Facebook Group and that activity on this page was down on 2017. However, upon closer inspection I think activity on this page rather more accurately reflects the prevailing conditions and we might even be able to use past years' activity as an indication of the conditions that prevailed then.


I attach two graphs. The first comprises the numbers of contributors in each week in 2017 and 2018. The second is the numbers of records generated. The results come with a health warning because in 2017 we started to encourage members to keep their own spreadsheets. This trend has continued into 2018 and we now have about 95 people maintaining their own spreadsheet or putting records onto iRecord. So, the graphs are not entirely comparable. Nevertheless, I think there are sufficient consistencies to make something of the results.

Figure 1. Numbers of contributors in each week

Numbers of records in each week

What is immediately apparent is that we had a much later winter this year, and that is clearly shown in both graphs. A clear drop in the numbers of records since the start of June (week 23) is not matched with recorder activity, whose decline is far more marked about 3 weeks later. So it looks like the numbers of records is not completely related to the numbers of recorders. It is, of course, possible that a small number of  very active recorders have switched to spreadsheets, but I am unconvinced that this is the case (I can think of one). To test this theory I split the recorder data (500 recorders) into a series of classes. The most active recorders this year range from one who cas contributed nearly 500 records to a longer tail of between 200 and 50 records. These I have placed in a single class because they form a suitably sized group (37). The gradated scale is then shown in Figure 3 with increasing numbers in each class.

Figure 3. Recorder activity in 2018 split into 5 groups depending upon the volume of records submitted.

We can see that the same broad pattern emerges in all of the groupings of recorder activity, which suggests that the fall in the numbers submitted is not simply a change in recording method but that it actually reflects a change in recorder activity. In reality, it looks as though recorder activity was probably greater in 2018 until the start of the drought! We will have a far clearer picture at the end of the year, but I think the trends are sufficiently clear from this sample of data.

What is also clear, is that the numbers of contributors has dropped during the drought. In other words, recorder behaviour may be a useful proxy for hoverfly abundance. We should, of course, also recognise that it may be that the hovers are about but the weather is discouraging recording! My standard ground-truth of graphs is to ask whether I have detected any changes during the course of my own field work? Have I changed my recording behaviour and why? Answer, yes I have and this is because it is darned hard work and unproductive on many days! I suspect a lot of datasets will show similar trends, but it will be interesting to see whether this is the case for all invertebrates? If press reports are true, then the trends for butterflies may be different.

Phenology histograms - are they an over-simplification?

I had quite a lot of correspondence yesterday on interpreting phenology histograms with two separate research teams. It got me thinking about the HRS data and how we present such data. In the developing HRS website, it is possible to change the date range and lattitude range to interrogate the phenology of particular species. This should make life a lot easier but, even then, I have spotted problems.

Cheilosia albipila is a classic example. The phenology histogram (Figure 1) suggests that it is double brooded, but we know that this is not the case. The adults fly relatively early in the spring and by June the larvae are big enough to record by splitting thistle stems. So, a representation of all records will give the impression that it is multi-brooded. This is clearly wrong, so what should we make of the histogram?

Figure 1. Cheilosia albipila - all records

I think a number of interpretations are needed.

Firstly, we need to interrogate the database and work out which records are of larvae and which are of adults. Where no stage is given and the record falls outside the main flight time I think we need to be somewhat sceptical about it unless we know the recorder and the sort of fieldwork they do. We might well have to go back to the original recorders too.

This is a species that is relatively infrequently recorded, so the histogram is composed of a comparatively small number of data points. The coarser the data, the more care must be taken in interpreting the outputs, but it seems that flight times have not changed markedly. Most records will be of adults at spring flowers but there may be occasional ones of adult females sitting on marsh thistle rosettes.

We get data from a lot of sources, including Mapmate synchs, so it is not possible to scan every record before it is uploaded. That means we have got to do some retrospective analysis and adjustment to the data. It looks like it could be a very big job!

Secondly, there are species where we know comparatively little about the larvae and can be fairly sure that larval records don't make up much of the data. The phenology histogram for Cheilosia pubera seems to confirm this (Figure 2). There are a couple of questionable outliers that might be larvae, but they might equally be misidentifications. In my experience C. pubera has a pretty short emergence period and is gone by the end of June.

Figure 2. Cheilosia pubera - all records

The above are two quite simple cases because the larvae are plant miners and coincide their development with optimum plant growth and nutrient mobilisation. It becomes a lot more complicated when investigating species whose larvae live in nutrient-poor rot holes or within decaying timber. In these cases it is quite likely that larvae may take two or more years to develop. What can we make of the histograms in these cases?

Callicera aurata provides a nice example because we know quite a lot about it from people who investigate rot holes. It seems that larvae in Callicera pass at least one winter as a larva and possibly more than one winter as such. So, we can say that it is almost certainly not multi-brooded or even with a partial second brood. Yet, its phenology (Figure 3) certainly implies that there are two emergence peaks. I don't think this involves full or partial second generations, but simply means that emergence is staggered. The dip in late July might simply reflect regular hot periods that reduce hoverfly activity? The lack of winter records of larvae might also beg the question whether we have got all of the data - clearly we don't!

Figure 3. Callicera aurata - all records

Callicera rufa is a species that has been studied in far more detail and we know quite a lot about larval colonisation of freshly created artificial rot holes. So what can we make of the phenology histogram (Figure 4)?

Figure 4. Callicera rufa - all records

This histogram is very misleading because most of the records are of larvae found in Scotland. It is often possible to find several different age clasess in the same rot hole, and clearly they can be found at most times of the year. In the last ten years, however, new populations have been found in England, with lots of data coming from several sites in Shrophsire and Norfolk.

The histogram of data from England (Figure 4) is largely free of larval records (not entirely) and the histogram reflects this. It also tells me that there must be a data glitch because I know there are records from the Saddleworth area later in the year but these don't show up because I have set the lattitude too far south!

Figure 4. Callicera rufa English Midlands only

These few examples highlight some of the challenges that exist in presenting and interpreting data. As the dataset increases in volume and complexity, we will need to provide detailed interpretations because the majority of readers will not have access to raw data and many will not know much about the biology of the animals in question.

Interpreting phenology histograms

Since the great changes brought about by the UK Hoverflies Facebook page I have had growing concerns about a small part of the HRS dataset. Most of it is probably fine, but as time goes by it is becoming increasingly clear that many more species than we thought hitherto have relatively short emergence periods. There can be regional variation which means that for species that occur widely, but not at high densities, the aggregated phenology histograms are misleading. In many cases they show a very elongate tail into the Autumn. I don’t think this interpretation is correct, although there is always the possibility of occasional aberrations.

I’ll use four species as examples. Two are widespread and regularly recorded both by traditional net and microscope recorders and by photographers; the third is a recent arrival but is almost entirely recorded by photographers and the final one is a very early spring species. The important separation is that since we have had the Facebook group I have done the bulk of the determinations that have found their way onto the dataset (until this year). In that time, all three species have been found to have very tight emergence patterns.

Epistrophe nitidicollis generally occurs in May and June, perhaps into July. It is quite infrequently seen and I suspect is regularly reported as a misidentification of a Syrphus. I am pretty sure the tail on the graph (Figure 1) arises from such misidentifications.

Figure 1. Phenology of Epistrophe nitidicollis

Epistrophe melanostoma arrived in the UK in 1986 or thereabouts and has since become established in south-east England. It is very similar to E. nitidicollis and this is one reason why it may not have been recognised prior to 1990 when a Dutch specialist (Paul Beuk) was living in the UK and found it on my local patch. Remarkably, we get most records from photographs and, as can be seen from Figure 2, there is no extended tail of records. More importantly, whilst I think there is growing evidence that E. nitidicollis is declining in abundance, E. melanostoma is becoming far commoner.

Figure 2. Phenology of Epistrophe melanostoma

Platycheirus tarsalis is another spring species. It is quite difficult to separate from P. manicatus and I suspect that there have been numerous mis-identifications in the past. Unlike P. tarsalis, P. manicatus has at least two broods and continues well into the Autumn. I think this probably explains the tail in the graph for P. tarsalis (Figure 3), although there may also be confusion with female P. peltatus amongst recorders who don’t use keys.

Figure 3. Phenology of Platycheirus tarsalis

Melangyna quadrimaculata flies from late February to April, perhaps into May in Scotland but I have never seen it there despite many trips in late May and early June. Nevertheless, there is a tail in the graph (Figure 4), which I think can be explained by misidentification of very dark male Leucozona laternaria; I have seen this mistake made on several occasions and suspect that in my novice days I too might have made this mistake!

Figure 4. Phenology of Melangyna quadrimaculata

How do we resolve this issue? I am at a loss, because there will probably be some genuine records in the tails. However, there are almost certainly a lot of misidentifications. I suspect the best we can do is to provide a written interpretation to accompany graphs but there remains the danger that the graphs will be mis-interpreted, and the data glitches will be reinforced by further misidentifications. Alternatively, should we clean up the graphs and adopt a cut-off date after which we don’t accept records? That runs another set of risks because we might then be accused of massaging the data to fit our interpretation.

We are faced with a conundrum! I think we need to develop a far more comprehensive analysis of the phenology of those species that are regularly recorded. I suspect it will prove to be illuminating!