Testing the assumption of environmental equilibrium in an invasive plant species over a 130 year history
Bibliographic record
Abstract
Invasive plants are an increasing threat to global biodiversity. Effective management depends on accurate predictions of their spread. However, modelling the geographic distribution of invasive species, particularly with correlative species distribution models (SDMs), is challenging. SDMs assume that species are in equilibrium with their environment (i.e., they occur in all suitable environments); this assumption is likely to be violated for a species that is actively invading new environments. This assumption is rarely assessed, and when violated can have consequences for model reliability. Using the invasive vine Vincetoxicum rossicum, we tested the hypotheses that: 1) invasive species’ distribution in environmental and geographic space increase to a plateau over time; 2) this plateau is a useful proxy for equilibrium distribution, a key assumption underlying SDMs. We compare V. rossicum’s expansion in environmental and geographic space between historical and current time periods and infer equilibrium when its distribution has remained stable for an extended period. We also compare the performance of SDMs from historical time periods in predicting the current geographic distribution of V. rossicum. We found that V. rossicum has reached equilibrium in environmental space, but is still expanding its geographic distribution. SDM performance was poor in the first 30 years following introduction, but improved as V. rossicum approached environmental equilibrium. Our findings demonstrate the power of including temporal dynamics and the need to consider environmental and geographic equilibrium separately when modelling the distribution of invasive species. In light of our findings, we address shortcomings of the current approach to defining an equilibrium distribution and present a new perspective for reconciling the potentially confounding influence of dispersal limitation when assessing equilibrium distribution.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".