Contrasting impacts of an alien invasive shrub on mammalian savanna herbivores revealed on a landscape scale
Bibliographic record
Abstract
Abstract Aim Habitat loss and fragmentation is one of the main drivers of defaunation, that is the loss of large mammals. Biological invasions could be drivers of such phenomenon. However, their impact on large herbivore communities has not been studied to our knowledge. We made use of a landscape‐scale control programme of one of the world's worst invaders, the shrub Chromolaena odorata, as a natural experiment to assess how this alien invader affects habitat use by 14 species of ungulates in an African savanna. Location Hluhluwe‐iMfolozi Park, KwaZulu‐Natal, South Africa. Methods At the height of the invasion in 2004, a large‐scale control programme was initiated that successfully reduced densities of C. odorata across the park. We estimated mammalian herbivore habitat and patch use by dung counts and the presence and density of C. odorata along 24 line transects with a total length of 190 km during the peak of the invasion (2004) and a decade after the initiation of a successful control programme (2014). To account for differences in herbivore assemblies between habitats and the preferential invasion of closed savanna woodlands, we analysed the recolonization of previously invaded patches by herbivores based on the change in dung abundance. Results Herbivore species differed in how they responded to invaded patches of this non‐native shrub. Grazers were the most negatively affected, especially those that avoid predators by running. Browsers were negatively impacted only at the highest invasive shrub densities. Some species, especially bushpig, positively selected invaded patches. Main conclusions Large herbivores varied in their response to invasion with differences in impact depending on feeding strategy and predator avoidance strategy, but the majority of ungulates responded positively to the removal of this invasive shrub.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".