Propagule Pressure: A Null Model for Biological Invasions
Bibliographic record
Abstract
Invasion ecology has been criticised for its lack of general principles. To explore this criticism, we conducted a meta-analysis that examined characteristics of invasiveness (i.e. the ability of species to establish in, spread to, or become abundant in novel communities) and invasibility (i.e. the susceptibility of habitats to the establishment or proliferation of invaders). There were few consistencies among invasiveness characteristics (3 of 13): established and abundant invaders generally occupy similar habitats as native species, while abundant species tend to be less affected by enemies; germination success and reproductive output were significantly positively associated with invasiveness when results from both stages (establishment/spread and abundance/impact) were combined. Two of six invasibility characteristics were also significant: communities experiencing more disturbance and with higher resource availability sustained greater establishment and proliferation of invaders. We also found that even though ‘propagule pressure’ was considered in only ~29% of studies, it was a significant predictor of both invasiveness and invasibility (55 of 64 total cases). Given that nonindigenous species are likely introduced non-randomly, we contend that ‘propagule biases’ may confound current paradigms in invasion ecology. Examples of patterns that could be confounded by propagule biases include characteristics of good invaders and susceptible habitats, release from enemies, evolution of ‘invasiveness’, and invasional meltdown. We conclude that propagule pressure should serve as the basis of a null model for studies of biological invasions when inferring process from patterns of invasion.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".