First circumpolar assessment of Arctic freshwater phytoplankton and zooplankton diversity: Spatial patterns and environmental factors
Bibliographic record
Abstract
Abstract Arctic freshwaters are facing multiple environmental pressures, including rapid climate change and increasing land___use activities. Freshwater plankton assemblages are expected to reflect the effects of these stressors through shifts in species distributions and changes to biodiversity. These changes may occur rapidly due to the short generation times and high dispersal capabilities of both phyto___ and zooplankton. Spatial patterns and contemporary trends in plankton diversity throughout the circumpolar region were assessed using data from more than 300 lakes in the U.S.A. (Alaska), Canada, Greenland, Iceland, the Faroe Islands, Norway, Sweden, Finland, and Russia. The main objectives of this study were: (1) to assess spatial patterns of plankton diversity focusing on pelagic communities; (2) to assess dominant component of __ diversity (turnover or nestedness); (3) to identify which environmental factors best explain diversity; and (4) to provide recommendations for future monitoring and assessment of freshwater plankton communities across the Arctic region. Phytoplankton and crustacean zooplankton diversity varied substantially across the Arctic and was positively related to summer air temperature. However, for zooplankton, the positive correlation between summer temperature and species numbers decreased with increasing latitude. Taxonomic richness was lower in the high Arctic compared to the sub___ and low Arctic for zooplankton but this pattern was less clear for phytoplankton. Fennoscandia and inland regions of Russia represented hotspots for, respectively, phytoplankton and zooplankton diversity, whereas isolated regions had lower taxonomic richness. Ecoregions with high __ diversity generally also had high __ diversity, and turnover was the most important component of __ diversity in all ecoregions. For both phytoplankton and zooplankton, climatic variables were the most important environmental factors influencing diversity patterns, consistent with previous studies that examined shorter temperature gradients. However, barriers to dispersal may have also played a role in limiting diversity on islands. A better understanding of how diversity patterns are determined by colonisation history, environmental variables, and biotic interactions requires more monitoring data with locations dispersed evenly across the circumpolar Arctic. Furthermore, the importance of turnover in regional diversity patterns indicates that more extensive sampling is required to fully characterise the species pool of Arctic lakes.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".