Community science can inform invasive species management: <i>Melaleuca</i> (Myrtaceae) in South Africa
Bibliographic record
Abstract
Abstract Community science initiatives are revolutionising our ability to detect and respond to biological invasions. Non‐native trees and shrubs are among the world's most damaging invasive species and community science data can be used to inform protocols for managing these invasions. This study explores the utility of iNaturalist in informing management practices for the widely cultivated and naturalised genus Melaleuca L. (Myrtaceae; here including the genus Callistemon ) in South Africa. We applied data from iNaturalist to assess the distribution and invasion ecology of Melaleuca species in South Africa. Melaleucas, first recorded in South Africa in 1882, have been widely used as garden ornamentals and street trees in the country for over 50 years. Naturalisation of melaleucas in South Africa was first reported in 1998 and the first records of naturalisation/invasiveness for other species are accumulating rapidly. Data on all Melaleuca species in South Africa were downloaded from iNaturalist and analysed using Geographic Information System software. In September 2023, iNaturalist had 3221 records of melaleucas across the country. After checking and filtering, and applying criteria to increase reliability, 2815 records remained, with confirmed identifications of 26 species. These species were recorded in a total of 138 quarter‐degree cells (QDCs) in South Africa (7% of the country); Research Grade (RG, wild‐growing) records occurred in 21 QDCs, records of cultivated plants in 75 QDCs, and 42 QDCs had records of both cultivated and wild‐growing plants. An Invasiveness Index was calculated for each species in the country, provinces, and municipalities, to show which species are already invasive or have substantial invasion debt. Thirty‐two percent of the filtered records were RG (naturalised). The municipality with the highest number of records is the City of Cape Town, with 43% RG records. iNaturalist provided useful information on the occurrence of five Melaleuca species for which no information was available before this study was undertaken. Case studies of invaded habitats highlight that melaleucas have the potential to alter ecosystems incurring substantial control costs. Practical implications : Our study highlights the value of community science data in the detection, monitoring, and management of invasive plant species.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".