The first part of this post took us from fieldwork to pinned specimens. After insect specimens are mounted and labelled, the real taxonomic work starts.
The Linnean hierarchy (class, order, family, genus, etc.) isn’t just a list of categories to be memorized, it’s a powerful organizational tool for biodiversity data. That’s because we can sort specimens from level to level in that hierarchy and move them along the assembly line to progressively finer resolution. We’ve already sorted our specimens to order back in step 3 of the previous post. The next step – sorting to family – takes us to the microscopes (flies are small).
9. There are more than 100 families of Diptera in North America, but only a couple of dozen make up the bulk of specimens in our projects. The rare families are exciting little breaks from routine. There is a steep learning curve as new students find their way around the characters used to distinguish families, and the process can be frustrating early on, especially when specimens run to the wrong side of “usually” or “rarely” statements in the key. But with patience and experience most people are soon sorting most specimens to family without having to rely on keying out any but the more confusing specimens.
10. It’s time for some triage and redistribution. In a large-scale ecological study we often have target families that we plan to analyse, but other families that are either intractable, or not of immediate interest. At this stage, those latter two groups will be set aside “for later” and the target families are divided up among unit trays and drawers and cabinets.
11. Insect cabinets aren’t just for storage; they’re a filing system for biodiversity. Drawers within cabinets, and unit trays within drawers, separate and organize the families and lower taxa to facilitate the subsequent steps in the sorting process. Organization and careful curation become important; as new specimens are added and target groups taken out for identification, we need to keep tabs on what is where. It would not be a nice feeling to finish your final graphs and tables and then find a thousand specimens you kind of forgot about.
12. The drawer with Family A is now on the desk of Student X. It’s time to pull out the Manual of Nearctic Diptera. The “MND” revolutionized the study of North American Diptera when it was published in the 1980s. The beautifully illustrated keys to families and genera allowed us, finally, to identify our flies to the generic level. Granted, some of the keys are difficult without an excellent reference collection to rely on, some are less than perfect, and many are now out of date, but it was still a huge leap forward. Sorting our family-level material to genus gives us one more degree of taxonomic resolution. By now, ecological patterns are usually emerging, with particular genera starting to cluster in particular sites or habitats or trap types.
13. As we key specimens to genus, the key in the MND will indicate whether or not there is a published key to the species, and how many species there are. If we’re lucky there is a key and it’s recent, if not (and we are often not lucky) this is where things can get complicated. It comes as a surprise to some people that we don’t know, and cannot identify, all the Diptera species in North America. We simply do not have the taxonomic resolution to put a species name on every specimen: some are undescribed; some have not previously been recorded in the region; sometimes nobody has written a key to species; and sometimes we have only female flies and we need a male in order to identify the species. In these cases, we can at least look for differences between species and give them a “morphospecies” code. We know they are distinct; we just don’t know what to call them. This is not always a problem in ecological studies because we are more interested in, for example, whether or not Species X is found in all our sites and in what numbers. We don’t need a name to answer that question; we just need to know what those specimens are “the same as”.
14. Verification and confirmation! It’s an eye-opener to students who are new to insect identification: just because a specimen keys to Species X, that does not necessarily mean it is Species X. Keys can be incomplete or confusing or just simply badly constructed (all of us who have used keys over the years have horror stories, I’m sure). A key is the quickest and usually the easiest way to identify a specimen, but is not the only way. Checking original descriptions, comparing with reference collections, talking to an expert at another institution who has described many of the known species – these are all valuable tools in getting the identity of specimens correct.
15. Once the specimens are identified, things get a little tedious for a while. The label data from all those specimens must be entered into spreadsheets for data analysis. Insect labels are small and packed with information. Data entry is a long, finicky and absolutely essential part of the research process for two reasons: it’s the only (realistic) way to get the specimen data into a form suitable for quantitative analysis; and it provides a mechanism to make the data available to other researchers for verifying our results and for future analyses. Data entry is not the time for watching videos on your laptop, or chatting with friends who drop by to help ease the boredom. This is concentration time. Errors in transcribing the data to the computer, or errors in data entry within the spreadsheet can confuse or obliterate any patterns in the data and effectively sink the project. I’ve seen it happen in our lab. It’s not pretty. This is the sort of job you only want to do once.
16. Time for quantitative analyses! For most ecological questions, a pie chart just isn’t going to cut it (pun intended). There is a vast array of analytical techniques available now for biodiversity data. Similarly, hand-drawn phylogenetic trees are becoming a rarity now that we tend to deal with large data sets. As with ecological data, there is a wide range of options and programs for phylogenetic analysis. It’s important to know which analyses are appropriate to your data and your research questions. Sometimes there are freely available software packages that will give you the answers you need relatively easily; sometimes you need to delve into the thrilling world of programming in R. Either way, it’s exciting to see the patterns start to take shape.
17. Next step – write the paper! Hmm . . . maybe that’s a separate post all on its own.