When does ecosystem engineering cause invasion and species replacement?
Bibliographic record
Abstract
Introduced exotic species can dominate communities and replace native species that should be better adapted to their local environment, a paradox that is usually explained by the absence of natural enemies and by habitat alteration resulting from anthropogenic disturbance. Additionally, introduced species can enhance their invasion success and impact on native species by modifying selection pressures in their new environment through ecosystem engineering. We analyse a simple dynamic model of indirect competition for habitat between a non‐engineering resident species and an engineering exotic species. The conditions for invasion and competitive exclusion of the resident by the exotic species and the range of dynamic outcomes suggested by the model are determined by the form of density dependence. We give simple criteria for the success of the invading species on dimensionless quantities involving rates of ecosystem engineering and of habitat degradation. The model's predictions offer an additional explanation for a range of invasion dynamics reported in the literature, including lag times between introduction and establishment. One intriguing result is that a series of failed invasions may successively reduce environmental resistance to subsequent invasion, through a cumulative effect of habitat transformation. More work is needed to determine the frequency and conditions in which engineering is required for successful establishment, and whether highly‐successful (or high‐impact) invaders are more likely to possess ecosystem engineering traits.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".