Have old species reached most environmentally suitable areas? A case study with<scp>S</scp>outh<scp>A</scp>merican phyllostomid bats
Bibliographic record
Abstract
Abstract Aim We used ecological niche modelling to test different models explaining the lineage age–area relationship. We hypothesized that lineage age should influence the proportion of potential range unfilled by phyllostomid bat species. We made explicit predictions about possible relationships between the proportion of unfilled potential range and lineage age. Our goal was to analyse empirical data and fit the model that best describes our data. Location SouthAmerica. Methods We modelled the ecological niche of 49 phyllostomid bat species usingMaxentandSupportVectorMachine (SVM). We calculated the proportion of unfilled potential range as the amount of area outside the current distribution divided by the current distribution (realized range size). Using a dated phylogeny, we regressed the proportion of unfilled potential range on lineage age. To compare our predictions we also regressed realized range size on lineage age. Results Unfilled potential range was weakly associated with lineage age. This relationship was an inverse function of lineage age, explaining between 0 and 17% of the proportion of unfilled potential range. Furthermore, the relationship between realized range size and lineage age exhibited a logarithmic function, with lineage age explaining between 13 and 20% of the variation in realized range size. Main conclusions Different regression models indicated that old phyllostomid species have smaller unfilled ranges than young species. That is, old species have filled most of the areas that are suitable for them. Furthermore, old species have larger realized ranges than young species. We thus refuted both the lineage age–area and taxon cycle models and lent support to the stasis post‐expansion model. This suggests that bat species can reach most of their potential range rapidly after cladogenesis and such occupation remains more or less constant through time.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".