Species richness of alien plants in South Africa: Environmental correlates and the relationship with indigenous plant species richness
Bibliographic record
Abstract
:This study explores the correlates of alien plant species richness in South Africa at the scale of quarter-degree squares (QDS; ª 25 ¥ 27 km; 675 km2). We considered all alien plant species for which we had records and a subset of these – those that invade natural and semi-natural vegetation. The main source of data for species richness of indigenous and alien plant species was a national database based on herbarium specimens. For invasive alien species, data were from a national atlassing project. First, we explored the importance of energy availability and habitat heterogeneity as correlates of indigenous, alien, and invasive alien plant species richness. Linear regression models showed that species richness in the three groups of plants was explained by the same variables: a principal component of climatic factors and topographic roughness were the top-ranking variables for all groups. Next, we examined the role of indigenous species richness together with a range of environmental and human-activity variables in explaining species richness of alien and invasive alien plants. Results reveal an interplay of natural features and variables that quantify the dimension of human activities. If indigenous species richness is ignored, human-activity variables are more strongly correlated with alien species richness than with invasive alien species richness. Numbers of alien and invasive species in QDSs are significantly correlated with indigenous plant species richness in the 1,597 QDSs selected for analysis, a pattern consistent with findings from other parts of the world. Analysis of residuals between observed and predicted values showed that patterns differed between biomes. The results are useful for planning long-term intervention policy at the national scale; they suggest that areas with rich native biodiversity will face a sustained onslaught from invasive alien species and that ongoing management actions will be required to reduce and mitigate impacts from biological invasions in these areas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".