An assessment of the distribution and potential ecological impacts of invasive alien plant species in eastern Africa
Bibliographic record
Abstract
With a few exceptions, comprehensive lists of alien plants that invade natural ecosystems are lacking in sub-Saharan Africa. Some available lists are either preliminary or localised, or focus on agricultural weeds. This study set out to compile a list of alien plant species that are invading natural ecosystems and rangelands in five countries in eastern Africa, and to map the distribution of the species that threaten ecosystem integrity and productivity. The location of all alien plant species seen during surveys between 2008 and 2016 was recorded using a hand-held GPS device, as well as their status in terms of either being present and/or naturalised, or invasive and spreading. Individual occurrence records were summarised at the scale of half degree grid cells (∼55 km × 55 km). The survey covered almost half (522) of the 1063 grid cells in Ethiopia, Kenya, Tanzania, Uganda and Rwanda. We recorded 164 invasive alien species in 110 genera and 47 families. We provide further information on the distribution and impacts of 30 species considered to have the greatest impacts in terms of transforming natural ecosystems, as well as on a further 21 species with limited distributions that could potentially become ecosystem transformers. Invasive alien plants are clearly a widespread and growing problem in eastern Africa, and capacity to manage them effectively remains a problem. A great deal of work needs to be done to raise awareness of the problem, and to identify appropriate responses that will be effective in resource-poor countries.
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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 | 0.000 | 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) | 0.001 | 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".