I Am Biased & An Aberration

Statistical problems with my observations

Until the government imposed lock-downs this year I travelled north during winter to escape the cold weather. As I make a lot of observations compared to others, some interesting things start to become apparent. One such artifact is seasonality records for the Scarlet Percher dragonfly Diplacodes haematodes show a bulge during August and September. This is a fairly common species in New South Wales and Queensland and, with mature males being bright red and perching beside water habitat, also readily photographed. Until this past summer nearly half of the records were from me and the chart pictured here was even more distorted. Anybody looking at that would think there is a strange peak emergence at the end of winter but actually its just a freak emerging from the south. As more users come on board this curve will gradually be flattened but highlights the problem of me. 馃槉

I am somewhat of a pariah - I'm not doing what everybody else is doing and I'm doing a lot of it. This includes local observations too. For example, a common mushroom here is the attractive, bright blue Pixie's Parasol Mycena interrupta but over 90% of the records on iNaturalist are mine.

But other biases are less apparent. I am more inclined to photograph something I know and ignore stuff I expect won't be identified. Historically I've been ignoring small herbs and bryophytes as unidentifiable and therefore not worth recording. For example, so far this year I have recorded the common moss Cyathophorum bulbosum 20 times, which represents over half the observations here. Its not like it suddenly appeared, I've just not been able to recognize it previously and thought I wouldn't be able to have it identified anyway. Another example is Indian Weed or Eastern St Paul's-Wort Sigesbeckia orientalis: half the Australian records are from me and half of those are from this year.

So by being one of the more active observers I am introducing statistical biases. I hope you don't mind. 馃槑

Posted on 09 讘诪讗讬, 2020 23:41 by reiner reiner


Hi Reiner
Very interesting observation but I , at least, forgive your 'biased' entries. We just need a lot more people making observations, as you say, to smooth out the curves a bit. But I don't think I will ever keep up with you.
Linda R

驻讜专住诐 注诇-讬讚讬 lroganentsocvic 诇驻谞讬 讘注专讱 3 砖谞讬诐 (住诪谉)

Yes, I've long known this about you, and weight your observations accordingly ... ;)
All good, all threads in a huge tapestry that will slowly resolve somewhat. Hopefully it takes a long time, as a defacto measure of resilience and distribution. We will barely dent Coleopteran diversity.

驻讜专住诐 注诇-讬讚讬 dustaway 诇驻谞讬 讘注专讱 3 砖谞讬诐 (住诪谉)

Thanks @reiner for your very pertinent comments.Self assessment of your prolific submission rate is entirely appropriate, for the reasons you so clearly explain. I trust you are not being critical of your approach to iNat usage because if I'm certain about anything, it's that EVERY iNat user who contributes submissions on an ongoing basis, regardless of how many or how few,has (knowingly or unknowingly) selection biases that, one way or another, skew the net statistical significance for each and every species.
I will use butterflies and moths as another example:-Monarch butterflies are so well known, easy to notice,and so widely distributed globally that almost every primary school kid (e.g. in North America and Australasia ) knows what they are and has seen them,in life or in pictures , so iNat gathers huge amounts of Monarch data.Many observations of this taxon are from citizens whose overall inat submission numbers are small, and a disproportionate number of such users are what I will term transients, people who sign up, have a brief fling (nothing wrong with that) then 'disappear ' (for any number of reasons...loss of interest, illness, time constraints etc etc).I could add many more confounding variables or potential biases , but my point is clear enough already .
Inherent in any biodata gathering website ,particularly those embracing citizen science in such a user friendly format as iNat, is a long list of biases, mostly observer based. Keep up your fantastic work, please and thank you 馃檪

驻讜专住诐 注诇-讬讚讬 davemmdave 诇驻谞讬 讘注专讱 3 砖谞讬诐 (住诪谉)

Oh, and if you are an aberration,I don't even want to think about what word would best describe me!(Lol)

驻讜专住诐 注诇-讬讚讬 davemmdave 诇驻谞讬 讘注专讱 3 砖谞讬诐 (住诪谉)

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