subsppLabelR: a wrapper package in R to automatically label and filter subspecies occurrence data

preprint OA: closed CC-BY-4.0
📄 Open PDF Full text JSON View at publisher
Full text 1,535 characters · extracted from oa-doi-fallback · click to expand
Abstract Occurrence data is the basis for many fundamental ecoevolutionary analyses, and many ways of filtering them to be robust enough for analysis have been developed. One issue that still remains is separating out the boundaries of taxa, especially taxa below the species level which often have vague definitions. subsppLabelR is an R package that uses labeled data on taxa to automatically define boundaries between them, with various levels of uncertainty. I tested the features of the package on three species of bird that vary in the number and location of subspecies, as well as one sister-species pair with a known overlap in their distribution. I then used existing ecological niche modeling software to compare and contrast their niche space. subsppLabelR performs well in scenarios where subspecies are well-sampled and cover large geographic areas, but rare or highly endemic subspecies are difficult to resolve without further input. This package serves to automatically define geographic boundaries of taxa, identifying any sympatric overlaps, and provides an alternate way to clean occurrence data for ecological and evolutionary analyses. Data/Code for peer review statement Code and data are available with peer review. Raw Data and the package are available as .ZIP files as well as an R script. Competing Interest Statement The authors have declared no competing interest. Footnotes The authors declare no conflicts of interest. Data availability statement Data will be available on Dryad (DOI pending acceptance).

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2026) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00
unpaywall
last seen: 2026-05-23T02:00:01.238055+00:00
License: CC-BY-4.0