Native herbivores and plant facilitation mediate the performance and distribution of an invasive exotic grass
Bibliographic record
Abstract
Summary 1. Exotic plant species have become increasingly prominent features of ecological landscapes throughout the world, and their interactions with native and exotic taxa in these novel environments may play critical roles in mediating the dynamics of such invasions. 2. Here, we summarize results from comparative and experimental studies that explore the effects of two factors – herbivory and facilitation – on the performance and distribution of an invasive South African grass, Ehrharta calycina , in a coastal foredune system in northern California, USA. 3. Using a 2‐year exclosure experiment, we show that a native herbivore, black‐tailed jackrabbits ( Lepus californicus ), significantly reduced the height, shoot production, fecundity and above‐ground biomass of this exotic grass. 4. Data from two comparative studies and a neighbour‐removal experiment revealed that Ehrharta frequently escaped herbivores by associating with three neighbouring plant species – an exotic perennial grass, Ammophila arenaria , an exotic perennial succulent, Carpobrotus edulis , and a native perennial shrub, Baccharis pilularis . Ehrharta growing in association with neighbours was taller, had fewer grazed shoots, produced greater numbers of spikelets and had greater above‐ground biomass than unassociated individuals. Furthermore, removing neighbours generally eliminated these benefits in 7 months, although effects differed among neighbour species. 5. An additional neighbour‐removal experiment conducted in the absence of jackrabbits indicated that neighbour removals did not have significant impacts on Ehrharta height, shoot production, spikelet production or above‐ground dry biomass. These results suggest that the primary means by which Ehrharta benefits from neighbouring plants is protection from herbivores – either because they are less apparent to herbivores or less accessible – and that Ehrharta likely incurred minimal costs from associating with neighbours. 6. Ehrharta was more frequently associated with neighbours than expected due to chance, and less frequently found in open dune habitat. These results are consistent with the hypothesis that the effects of herbivory and facilitation have been sufficiently strong to shape the local distribution of this invader in the landscape. 7. Synthesis . Our research has demonstrated that herbivory and facilitation have jointly influenced the dynamics of a biological invasion, and highlights the importance of evaluating the effects of multiple interactions on invasions in a single system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".