IS SPREAD OF INVASIVE SPECIES REGULATED? USING ECOLOGICAL THEORY TO INTERPRET STATISTICAL ANALYSIS
Bibliographic record
Abstract
We investigate a recent proposal that invasive species display patterns of spatial "spread regulation" analogous to density-dependent regulation of population abundances. While invasive species do offer valuable tests of ecological theories about spatial spread, we argue that the statistical approach used in the study is not useful, and that the proposed definition of "spread regulation" is likely to be confusing. While concepts of negative feedbacks in spatial spread may be reasonable, the proposed definition of "spread regulation" encompasses accelerating, constant, or decelerating spread. There is no compelling biological or practical reason to adopt such a definition. Moreover, we show that the statistical patterns (from time series of ratios of newly to recently invaded sites) proposed as evidence of spread regulation are predictable from basic diffusion models or other common models of constant spread with some stochasticity in dynamics and/or observations. Because such a wide range of processes would generate the observed patterns, no clear biological conclusions emerge from the proposed approach to spread analysis. When regarded in the context of the impacts and management of invasive species, the proposed regulation concept has the potential to create costly misunderstandings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".