Deriving inventories of non-native plant species from iNaturalist: Insights from urban centres of the Western Cape, South Africa
Bibliographic record
Abstract
Accurate, up-to-date inventories of non-native species are important to document and improve our understanding of biological invasions globally and inform management decisions. Traditional methods for the collation of inventories are time- and resource intensive, and lists become outdated if not regularly updated. The community science platform, iNaturalist, can contribute to the collation of regularly updatable (“living”) inventories of non-native species. However, robust and transparent workflows are needed to optimise data quality to take full advantage of iNaturalist. We present a semi-automated workflow for the collation and completeness assessment of non-native vascular plant inventories from iNaturalist. The workflow is informed by the World Checklist of Vascular Plants (WCVP) to compare native ranges to a reference area. The utility of the workflow is demonstrated by compiling non-native species inventories of 100 urban centres in the Western Cape province, South Africa. A total of 947 taxa of wild-growing, i.e. casual, naturalised and invasive plants were observed in these urban centres which showed varying levels of sample completeness. Most small towns had too few records for a completeness assessment. Larger urban centres and those near the coast were typically better sampled. This work highlights the potential for iNaturalist to construct non-native species inventories given sufficient coverage and thorough curation.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".