Preadaptation and Naturalization of Nonnative Species: Darwin's Two Fundamental Insights into Species Invasion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Predicting which nonnative species become invasive is critical for their successful management, and Charles Darwin provided predictions based on species' relatedness. However, Darwin provided two opposing predictions about the relatedness of introduced nonnatives to indigenous species. First, environmental fit is the dominant factor determining invader success; thus, we should expect that invasive species are closely related to local native residents. Alternatively, if competition is important, we should expect successful invaders are distantly related to the native residents. These opposing expectations are referred to as Darwin's naturalization conundrum. The results of studies that examine nonnative species relatedness to natives are largely inconsistent. This inconsistency arises from the fact that studies occur at different spatial and temporal scales, and at different stages of invasion, and so implicitly examine different mechanisms. Further, while species have evolved ecological differences, the mode and tempo of evolution can affect species' differences, complicating the predictions from simple hypotheses. We outline unanswered questions and provide guidelines for collecting the data required to test competing hypotheses.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it