Charting plant distribution and ranges

I’ve had some interesting observations this Spring. On the Central Coast of CA, I have seen some plants in abundance in places where they are usually quite rare, for example: CA Peony (Paeonia californica), and Manroot (Marah fabaceus). Is there a way to use iNatrualist to chart a plant’s range from year-to-year?

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assuming the observations aren’t all obscured…

maybe something like this: Map showing spread of spotted lanternfly since 2016 - Nature Talk - iNaturalist Community Forum, which is one application of the “experimental compare tool” (see Easy way to compare lists - Feature Requests - iNaturalist Community Forum).

or more of a timeseries animation: Map animation of observations over time - Feature Requests - iNaturalist Community Forum

there are other ways to achieve similar results, but those would require some other coding or manipulation of data on your own probably. if you want more details, just provide more information about exactly what you’re trying to achieve.

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Just don’t expose the locations or distributions of native plants with a high horticultural or medicinal value, lest they fall to the ever-growing plant poaching market

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you could download data of the observations of these “expanding” taxa as a csv file and then you can analyze it

In general, iNat’s location obscuration settings should take care of that concern, although if someone shared a map of their own observations it’s possible that they could inadvertently reveal precise locations of plants vulnerable to collection.

Fortunately, a plant like Marah fabacea is plentiful and poaching a mature specimen would be quite a challenge as the tuber can grow to several meters long and weigh more than 100 kg. The first photo on this page gives you a good indication of how the plant got the common name California Manroot.

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Only if you figure out a way to standardize or account for the exponential growth of the site…

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I tried this kind of analysis in this thread on the Disappearance of Barbary Ground Squirrels. Given the sparse data, it wasn’t really possible to draw many conclusions.

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