Influence of Arctic Conditions on the Diatom Diversity of Islands Within the Conservation of Arctic Flora and Fauna Region
Bibliographic record
Abstract
The distribution of algal biodiversity is important for better understanding the factors controlling ecosystem functioning in harsh Arctic and sub-Arctic habitats. In this region, diatoms are the most representative and best indicators of environmental parameters. Here, we analyzed the distribution of 1268 diatom species across 18 islands within the Conservation of Arctic Flora and Fauna (CAFF) region based on our own and published data. To assess the representativeness of these floras, we examine genus–species associations, a method applicable to future algal flora studies. Diatom diversity is sufficiently high across the islands, with species richness decreasing toward higher latitudes. Species–area relationship models indicate that latitude—and even more so factors describing climatic extremes, like BIO19 (19 standard bioclimatic variables)—affects species richness stronger than islands area, and combined environmental and geographic predictors explain up to 81% of the observed variation. Bioindicator analysis reveals an increase in water pH, content in organic matter, and the number of eutrophic and mixotrophic species southward, which correlates with rising temperatures, as shown by the BIO19 and DHI (Dynamic Habitat Index) environmental indices. The latitudinal distribution of bioindicators demonstrates the ecological response of diatom communities to climatic factors and allows the identification of key indicator groups experiencing optimal conditions. So, island diatom species prefer benthic and planktonic–benthic habitats, well-oxygenated waters with low salinity, and moderate temperatures, which prevail on all islands except Vancouver Island, located outside the CAFF (Conservation of Arctic Flora and Fauna) region. A comparative analysis of bioindicators as a response to Arctic conditions of island diatoms revealed circumpolar similarities, connections with the corresponding parts of the Eurasian and North American continents, as well as similarities in the conditions of the islands closest to the pole. Almost a third of the island diatom list (324 species) was assigned IUCN (International Union for Conservation of Nature) vulnerability categories, 112 of which were in the three most threatened categories (Ex, Cr, and En) with their number increasing southwards, but 944 species have not been evaluated yet.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".