Taxonomic identification errors generate misleading ecological niche model predictions of an invasive hawkweed
Bibliographic record
Abstract
Ecological niche models (ENMs) have been proposed and applied as tools for predicting the extent of exotic species invasion risk and for identifying areas at risk of invasion. Despite the acknowledged concern of relying on occurrence records of variable and (or) unknown quality, the effect of taxonomically uncertain occurrence records on ENMs has not been investigated. We first present a schematic model describing how taxonomic uncertainty could yield varying predictions of invasion potential depending on the spatial characteristics of all versus “reliable” occurrence records. We then explore the issue in more detail by way of a case study on the morphologically and taxonomically difficult yellowdevil hawkweed (Pilosella glomerata (Froel.) Fr.), which is invasive in North America. We compared the climate niche properties and ENM predictions of invasion risk by P. glomerata in North America among models based on (i) all available occurrence records and (ii) records that are taxonomically “reliable”. “Total” records yielded niche properties that were significantly more heterogeneous than reliable records, and consequently, the potential invasion range of P. glomerata based on total records was predicted to be substantially larger. Our results provide rare empirical evidence that vetting occurrence records for taxonomic reliability is of critical importance for niche modeling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".