Exploring the role of life history traits and introduction effort in understanding invasion success in mammals: A case study of Barbary ground squirrels
Bibliographic record
Abstract
Abstract Invasive species –species that have successfully overcome the barriers of transport, introduction, establishment, and spread– are a risk to biodiversity and ecosystem function. Introduction effort is one of the main factors underlying invasion success, but life history traits are also important as they influence population growth. In this contribution, we first investigated life history traits of the Barbary ground squirrel, Atlantoxerus getulus , a species with a very low introduction effort. We then studied if their invasion success was due to a very fast life history profile by comparing their life history traits to those of other successful invasive mammals. Next, we examined whether the number of founders and/or a fast life history influences the invasion success of squirrels. Barbary ground squirrels were on the fast end of the “fast-slow continuum”, but their life history was not the only contributing factor to their invasion success, as the life history profile is comparable to other invasive species that do not have such a low introduction effort. We also found that neither life history traits nor the number of founders explained the invasion success of introduced squirrels in general. These results contradict the concept that introduction effort is the main factor explaining invasion success, especially in squirrels. Instead, we argue that invasion success can be influenced by multiple aspects of the new habitat or the biology of the introduced species.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".