Using environmental niche models to test the ‘everything is everywhere’ hypothesis for <i>Badhamia</i>
Bibliographic record
Abstract
It is often discussed whether the biogeography of free-living protists is better explained by the 'everything is everywhere'(EiE) hypothesis, which postulates that only ecology drives their distribution, or by the alternative hypothesis of 'moderate endemicity' in which geographic barriers can limit their dispersal. To formally test this, it would be necessary not only to find organisms restricted to a geographical area but also to check for their presence in any other place with a similar ecology. We propose the use of environmental niche models to generate and test null EiE distributions. Here we have analysed the distribution of 18S rDNA variants (ribotypes) of the myxomycete Badhamia melanospora (belonging to the protozoan phylum Amoebozoa) using 125 specimens from 91 localities. Two geographically structured groups of ribotypes congruent with slight morphological differences in the spores can be distinguished. One group comprises all populations from Argentina and Chile, and the other is formed by populations from North America together with human-introduced populations from other parts of the world. Environmental climatic niche models constructed separately for the two groups have significant differences, but show several overlapping areas. However, only specimens from one group were found in an intensively surveyed area in South America where both niche models overlap. It can be concluded that everything is not everywhere for B. melanospora. This taxon constitutes a complex formed by at least two cryptic species that probably diverged allopatrically in North and South America.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".