Ecological correlates of riverine diatom and macroinvertebrate alpha and beta diversity across Arctic Fennoscandia
Bibliographic record
Abstract
Abstract Arctic freshwaters support biota adapted to the harsh conditions at these latitudes, but the climate is changing rapidly and so are the underlying environmental filters. Currently, we have limited understanding of broad‐scale patterns of Arctic riverine biodiversity and the correlates of α‐ and β‐diversity. Using information from a database set up within the scope of the Arctic Council's Conservation of Arctic Flora and Fauna Circumpolar Biodiversity Monitoring Plan, we analysed patterns and correlates of α‐ and β‐diversity in benthic diatom and macroinvertebrate communities across northern Norway, Sweden, and Finland. We analysed variation in total β‐diversity and its replacement and richness difference components in relation to location of the river reach and its drainage basin (Baltic Sea in the south, the Barents Sea in the east and the north, and the Norwegian Sea in the west), in addition to climate and environmental variables. In both macroinvertebrates and diatoms, the replacement and richness difference components showed wide variation. For macroinvertebrates, the richness difference component was the more important, whereas for diatoms, the replacement component was the more important in contributing to variation in β‐diversity. There was no significant difference in β‐diversity between the three main drainage basins, but species composition differed among the drainage basins. Based on the richness difference component of β‐diversity, climate variables were most strongly associated with community variation in macroinvertebrates. In diatoms, both environmental and climate variables were strongly correlated with community compositional variation. In both groups, there were also significant differences in α‐diversity among the three main drainage basins, and several taxa were significant indicators of one of these drainage basins. Alpha diversity was greater in areas with a continental climate, while the oceanic areas in the west harboured greatly reduced flora and fauna. The correlates of biodiversity were relatively similar in macroinvertebrates and diatoms. Climate variables, in particular temperature, were the most strongly associated with biodiversity patterns in the Arctic rivers of Fennoscandia. Sedimentary geology may be associated with increased productivity and, to a lesser extent, with sensitivity to acidification. There was considerable variation in community composition across Arctic Fennoscandia, indicating the necessity of protecting several stream reaches or even whole catchments within each region to conserve total riverine biodiversity. Furthermore, it is likely that the predicted changes in temperature in Arctic areas will influence riverine diversity patterns across Fennoscandia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".