Negative competitive effects of invasive plants change with time since invasion
Bibliographic record
Abstract
Competitive impacts of invasive species may vary across invaded ranges, owing to spatio‐temporal gradients in adapted traits and abundance levels. Higher levels of interspecific competition in recently invaded areas may lead invaders to be more competitive. Here, using meta‐analysis and home range estimation techniques, we examine how negative competitive effects of invasive species vary across different spatio‐temporal invasion contexts. We conducted a meta‐analysis of 26 studies that used greenhouse microcosm and common garden pairwise experiments to measure the growth response of native plants in the presence of terrestrial plant invaders (totaling 36 species), and compared this to the time since invasion at the collection site (number of years between the estimated year of initial invasion, by spread of the invader, and the time of collection for the study). We show that negative competitive effects decline across sites that had been invaded for longer periods of time, with effects of invasive grasses declining more rapidly over time than forbs, herbs and shrubs. To our knowledge, only two studies have directly measured competitive or consumptive effects of invaders across a gradient of time since invasion; our study is the first to identify a general pattern of temporal variation of competitive effects that may be attributed to intraspecific trait differences. Management efforts may be guided by such spatio‐temporal patterns of invader impact, particularly for grasses.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".