Making checklists, using batch

when I am using batch mode for making checklist entries, I get a few problems, like wrong assignments (because of faulty synonyms), unassigned taxa (genus grafted to “life”) and in addition, I might make mistakes when creating new species. Therefore the question: is there a possibility to compile lists of these faulty items? I tried, but cannot find ways to do it …

(1) all species where the species name (headline) is NOT identical to species name (synonyms list)
(2) all species where genus in name is not the grafted genus
(3) all species grafted to “life” (sometimes, the genus is but the species isn’t…)
(4) all genera where synonym is a species (not a genus)
(5) all genera where synonym is genus name + a single character

if I cannot do this directly as a curator, I would appreciate if someone could do that for me and send me CSV files of the above lists. Note: for my own use, it could be confined to “Lepidoptera” and to Africa-Asia-Europe. Thanks for any hints and help!


i think your #3 can be addressed by an existing page that finds ungrafted taxa:

i think you can figure out your #1 and #2 by looking at the taxa.csv file in the periodic taxonomy export file ( a few notes:

  • viruses won’t follow the convention of the species name consisting of genus + specific epithet
  • it looks like the genus and specific epithet in the taxa.csv file are extracted directly from the scientific name in the case of species. so to get the actual name of the genus in this case, you’d have to get a species taxon’s parent ID from the parent column, follow that chain until you hit genus, and then get the genus name from the genus taxon.
  • i originally thought you could figure out #1 using this file, but the issue noted in the above point means that you’d have to do something more complex. staff could probably get this kind of thing easily since they can directly query the database, but i think normal users are left with much less elegant options to get this kind of information.

i’m not sure what you mean by #4 and #5. examples?

I recently noted some of these issues here. You’re asking to know the list of taxa with these difficulties, although I see the issue more as one that will improve when checklists and Places are updated/revamped by iNat in the future. These problems are more common for newly added species not yet in taxonomy too, so any “list of problems” would have to be done after they’re already (partially) added to checklists. That’s why I see this issue as mostly beyond manual correction by individual users.

Also do you mean homonyms by saying synonyms?

example for (4): on the taxon page of Xyleutes there was a name “Paralophonotus auroguttata” listed as “scientific name” and “wrong”. That’s what I call a synonym on iNaturalist. It seems obvious that a species name cannot be a synonym to a genus name. Must be a mistake. I have now removed that synonym of course!

But it caused some bad errors. At first, it slipped the genus name Xyleutes automatically into several country checklists. I noticed it a bit too late. However, the genus Xyleutes doesn’t appear on those lists (correctly) because a checklist contains only species + ssp. But in addition, the taxon page of the genus Xyleutes now shows these countries shaded on the map, which is completely incorrect. It’s particularly misleading in this case, because Xyeutes is a SE Asian genus and the species which was synonymized is an African species. And now I cannot even remove those shaded countries anymore, because that only works with species, not with a genus!

From what I’ve seen checklists can include genera too, like when users make a new genus ID for an autopopulating Place checklist.

Resolved with the help of @bouteloua . Thanks!

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i think i understand what you’re talking about, but do you have an actual example of something that hasn’t been corrected yet, or a screenshot from when this example existed? this might be a case like #1 that would be easy for iNat staff to find but would take some hoop jumping for regular folks like us to find, but i’m not sure without seeing exactly what the bad data looks like.

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