Determinants of floristic diversity and vegetation composition on the islands of Lake Burollos, Egypt
Bibliographic record
Abstract
Abstract. A floristic and environmental survey was carried out on 22 uninhabited islands (0.1‐8.4 ha) in Lake Burollos, Egypt. A total of 58 vascular plant species was recorded. The number of habitats on each island was counted. There was a positive correlation between island area and number of habitat types. Island area was significantly positively correlated with various measures of floristic diversity, including the total number of species present, and the numbers of annual, herbaceous, and shrublet species. Perennial and shrub species numbers did not differ significantly with island area. In addition to island area, elevation and soil salinity, as well as distance to the Mediterranean Sea, all contributed significantly to variation in species composition in the terrestrial habitats. Water salinity and transparency accounted for 69% of the variation in aquatic species numbers. There was a weak effect of isolation on similarity of species composition on islands. Eight vegetation types, represented by 13 indicator species identified after TWINSPAN analysis, were distinguished by soil characteristics. Species richness was inversely correlated with clay, organic carbon and total nitrogen in the soil, but positively correlated with calcium carbonate content. From a management perspective, long‐term monitoring of threatened habitats in the lake is urgently required as a starting point to preserve biodiversity. Finally, we conclude that the present study supports the hypothesis indicating that larger areas feature higher species richness due to increased numbers of habitats.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".