Comparison of native and exotic distribution and richness models across scales reveals essential conservation lessons
Bibliographic record
Abstract
Comparing native and exotic plant species distribution and richness models can help to reveal the causes of invasive exotic species proliferation and provide recommendations for preserving native‐dominated ecosystems. However, models may have limited applicability if potentially divergent patterns across scales, spatial autocorrelation and correspondence with community‐wide patterns such as species richness are not considered. I modeled the distributions of 20 dominant native and 20 dominant exotic species among and within patches in a heavily‐invaded and threatened ecosystem in western North America, examining the roles of scale and species origin on variable selection, spatial autocorrelation and model accuracy to determine conditions that favour native over exotic dominants, and derive recommendations for effective management. I also compared distribution models with native and exotic species richness models, to determine the extent to which dominant native and exotic species were representative of synoptic community patterns. Predictability was lower for exotic dominants, possibly because they are environmental generalists, and was lower within than among patches. Predictors were generally shared between distribution and richness models; however, species‐specific differences were common within both native and exotic species groups. Predictors for individual species across scales were frequently different and sometimes opposing. Distribution and richness models suggest that management assuming environmental affiliation at one scale may be ineffective at another; that site prioritization to maximize native versus exotic richness may not preserve the habitat of some common native species; and that intensive management to reduce exotics may be difficult due to low predictability and shared affiliations with natives. Comparing native and exotic distribution and richness models at two scales enabled scale‐specific conservation recommendations and elucidated trade‐offs between management for richness and representation that distribution models at an individual scale would not have allowed.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".