Alien flora of Nigeria: taxonomy, biogeography, habitats, and ecological impacts
Bibliographic record
Abstract
Abstract Biological invasions remain one of the greatest threats to biodiversity and livelihoods, and are predicted to increase due to climate change and globalization. In this study, we produced a comprehensive checklist of alien plants in Nigeria from online flora databases, herbarium records, published field surveys, and questionnaires administered to botanical gardens. The resulting alien flora was classified into naturalized, invasive, and cultivated plants. We then fitted a random forest model to identify the attributes which facilitate the naturalization of alien plants in Nigeria. We also used separate chi-squared tests to investigate if the frequency of these attributes is significantly different between the naturalized and invasive plants. The results include 1,381 alien plant taxa, comprising 238 naturalized, 190 invasive, and 953 cultivated species. The naturalized and invasive plants (428 species) are from 91 families, with Fabaceae and Poaceae having the highest representations. The random forest model showed that life forms and local economic uses were the most important drivers of alien plant naturalization in Nigeria. Chi-squared tests revealed a non-random distribution of life forms, higher frequencies of naturalized plants from the Indomalaya and the Neotropics, greater introductions during the British colonial rule, and that naturalized species are mostly used for medicinal, ornamental, food, or animal fodder purposes. Naturalized and invasive plants were recorded in all regions of Nigeria and are mostly found in urban and agricultural landscapes. This baseline information can support further ecological studies and conservation actions in Nigeria.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".