Correlation software

Update: I have made much progress with the software and it seems to work well and is reasonably robust. I look forward to releasing something to a public repository before too long (currently it is October 2022). In the meantime, if you want to request a correlation list for some favorite taxon let me know and I will do it for you, but only plants please.

Apparently this roughly corresponds to "phytosociology" which honestly sounds like a stupid name.

Teaser: in the PNW, which fern is geographically most associated with Polystichum munitum?
A) Struthiopteris spicant (deer fern)
B) Athyrium filix-femina (lady fern)
C) Polypodium glycyrrhiza (licorice fern)

After much incompetence, I finally have a preliminary Python program that can take iNaturalist observations (as provided to GBIF) and produce Pearson correlation coefficients for observation counts over a grid of latitude/longitude bins. Just in case anyone happens to read this and is interested. :-)

e.g. this output roughly makes sense:

--taxonA "Polystichum munitum" --taxonB "Athyrium filix-femina"
Latitude 32.0 to 52.0, increment 0.10, bins 200
Longitude -130.0 to -116.0, increment 0.10, bins 140
17926 taxon A
6026 taxon B
Correlation coefficient 0.673


compared to

--taxonA "Polystichum munitum" --taxonB "Myriopteris gracillima"
Latitude 32.0 to 52.0, increment 0.10, bins 200
Longitude -130.0 to -116.0, increment 0.10, bins 140
17926 taxon A
704 taxon B
Correlation coefficient 0.089


There are some issues I am not sure how to deal with, in particular how to handle grid blocks where both species have no observations (for now I just leave them in).

Oh, I also have a program that measures physical distances (each observation for taxonA gets a distance to the closest observation of taxonB).

General bias issues:
1) ease of recognition/identification
2) charisma/size
3) geographical accessibility, most problematic in rugged mountain and severe desert areas

(thinking about these)

Publicado el octubre 4, 2022 05:30 TARDE por jhorthos