A living inventory of planted trees in South Africa derived from iNaturalist
Bibliographic record
Abstract
Trees have a multifaceted influence on ecosystems globally. Treescapes have been manipulated by humans over millennia for ecological, economic, and cultural reasons that have changed over time and continue to change. In South Africa tree planting over three centuries has radically affected the composition of treescapes, contributing important ecosystem services, but also disservices. Rapid global change calls for diverse interventions to create more resilient ecosystems. Many nature-based solutions involve manipulating tree cover in rural and urban landscapes. There is a need for a spatially-explicit database of planted trees in South Africa to serve as the foundation for policy and management decisions. We used the community science platform iNaturalist to create a comprehensive database of planted trees in South Africa. Records were carefully checked to verify the accuracy of taxon identifications, locality data, and categorization as planted rather than wild-growing trees. The cleaned database contained 35,303 records of 805 planted tree taxa; over 90 % of records were identified to species level. Almost a third of taxa (32.2 %) belong to three families: Fabaceae (97 taxa), Myrtaceae (80 taxa) and Arecaceae (78 taxa). Rarefaction and extrapolation curves suggest fairly comprehensive sampling, but several regions are under-sampled. Non-native taxa dominate, with Melaleuca viminalis having the most records. The Western Cape has the highest number of records and taxa, particularly in urban areas. Both native and non-native trees provide key ecosystem services, but non-natives dominate. The database provides the first spatially-explicit open-access resource for guiding decisions on tree planting and the management of planted trees in South Africa. It offers a snapshot of tree planting trends, predominantly from recent years, leading to some underrepresentation of historically planted species. The database has numerous potential uses, including guiding management of trees pests and diseases, urban greening initiatives, monitoring for new invasions, and planning nature-based solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".