January 27 - Species Changes
Slaty Skimmer
The January 5 Journal elicited some observations and a question regarding Slaty Skimmer from jheiser. This got me thinking a bit (I do read all the comments!) The first Ohio Slaty Skimmer was observed in 1896 (Osburn, Erie Co), so it been around for a while, albeit in low numbers few and far between.
This began to change to change in the 1990's when we started to see a few every year. The big increase began in 2017 with the start of the statewide survey.
Maps below are changes through time. The dark shading is for the defined time period, light would be the previous period. White means no records.
Here's where we start, looking at data prior to 1950. This would be about 60 years of data. These early years probably represent vagrant individuals.
Next up is the period from 1950 to 1989, this would be the years when voucher numbers increased. Data from about 40 years.
Next is data from the first Statewide Survey, the 10 years of the 1990s.
Next, the early 2000's. Note the southern counties - artifact or the arrival of resident populations?
Next, the new Statewide Survey gets started and look at the breakout. 42 new County Records. Big increases on the numbers being reported.
Now the most recent 3 years, continuing the increase in range and numbers. We now have Slaty Skimmers in 86 Ohio Counties (just missing Seneca and Jefferson). Nearly half the total records have arrived in the last 3 years.
The final map highlighting Slaty counties with larger survey numbers - each diamond represent 50+ observations (1 diamond means something in the 50-99 range, 6 diamond means over 300).
Comentarios
Super interesting!
I'd envisioned them expanding from the south to the north, but this appears to be the opposite. There must be some confirmation bias, here, but I wonder if there are other possible explanations.
I'm amazed at the time frame for which you have data.
Very interesting!
I think interesting trends might be easier to track if the slaty skimmer data displayed by counties were normalized by total ode observations for each county for the period studied. For instance, the top 8 counties for 2022 ode observations include the following: Lucas, Stark, Ashtabula, Franklin, Coshocton, Champaign, Montgomery, Summit. Each of these counties has diamonds which is not too surprising since they each have so many total observations (they represent 9% of the counties, but account for about 44% of the observations). On the other hand, counties with diamonds like Warren, Williams, Fairfield, Jackson, Scioto, and Vinton account for only 2.4% of the data. Finding so many slaty skimmers in these counties is really impressive. I used 2022 data because it was handy, but I expect similar results for at least the last 5 years or so when most Ohio counties were sampled. Of course, normalization may be a lot easier said than done.
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As Mr. Spock would say, "fascinating."
Hi Mike, good points. In looking at the last 6 years by county, on annual Slaty Skimmer numbers as percent of total observations: Scioto. Fairfield, Vinton, Ashland, Adams, Jackson, and Lawrence stand out (averaging over 6% Slaty). Williams would just miss the mark. So your observations are close.
Similar normalization treatment to almost all years/county prior to 2015 sees the index values go to 0.
I don't know that the data is consistent enough to dive much deeper - other thoughts?
Thank you for taking the time to visually communicate these numbers. -Marty C.
Just curious what the percentage of Slaty obs were in Coshocton and Summit counties? Did they exceed the 6% of total ode value?
Might be interesting to take a few species and plot the normalized values (% of total obs) by county to see if there are any north to south or east to west trends. I don't think this would work well for species like Slaty with small number observations in many counties, but the more common species may show some interesting spatial trends. Of course, this assumes observations are comparable/similar from county to county in terms of technique, months observed coverage, etc. but I think that is a general assumption for all the analysis that has been done.
To observe a fairly unusual species like Slaty with confidence the total number of observations in a county would have to be fairly high in my opinion. I'm not good enough with statistics to give a firm quantitative number at say the 90% confidence level, but say it is more than 100 observations. It would be interesting to have the counties with a low number of total observations identified in your first 4 or 5 maps. Perhaps the increase in counties observing Slaty is just a function of the increased number of counties with total observations over 100 (or some other larger number).
Mike, the only counties over 6% were the ones mentioned. Cosh was 5, Summ was 4. There are other species to look at, ones with the most County Records would include Carolina Saddlebags, Slender Bluet, Comet Darner, Dusky Dancer.
Prior to 2000, there are only 23 instances of a county having more than 100 observations in a year, mostly Williams Co. So, at least in the first 3 maps, everything is low - maybe the analysis would only need a couple maps.
The data set is expanding, maybe a couple additional years of data will be helpful.
Jim...thanks for the reply. The normalized data on Slaty for Cosh and Summit was very interesting. Maybe a slaty value of around 4-5% is "average" now and because of their low numbers, variations of 1-2% don't mean much. Plotting more common species' county normalized values would probably provide results that would be more meaningful in a statistical sense.
If the increase in counties observing Slaty as indicated in the maps is just a function of the increased number of counties with more total ode observations, then there is not much more that can be said. Using normalized numbers on the maps might help a little, but in general many total observations are needed for the less common species and as you imply, we will probably need a number of additional good years of observations to detect meaningful statistical trends for slaty and other less-common odes.
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