Environmental variability influences the structure of benthic algal communities in an oligotrophic lake
Bibliographic record
Abstract
Theoretical models suggest that environmental variability can promote biodiversity, both in terms of richness and evenness of species. These findings have been supported experimentally in simplified communities with simplified regimes of environmental variability. Here we test the importance of environmental variability in a more natural setting by comparing the composition of benthic algal communities along a natural gradient of environmental variability created by internal waves in an elongated lake basin. Algal communities were grown on tiles at five upwind and five downwind sites in the lower littoral zone of South Arm, in Lake Opeongo, Ontario, Canada. Upwind sites were 1°C colder, on average, than downwind sites and had more variable water temperatures. The ranges of mean water temperatures and of water temperature variability were greater among upwind sites than among downwind sites. We used water temperature variability as a proxy for environmental variability. Total algal biovolume was similar at all sites, but the composition of these communities varied systematically with temperature variability. An observed shift in the relative dominance between Achnanthidium minutissimum , Surirella sp. and Pinnularia spp. is consistent with what we know about these species. Diatom richness increased from 12 to 15 genera with increasing variability at upwind (p=0.10, r 2 =0.65) and downwind sites (p=0.11, r 2 =0.63). Community evenness also increased with increasing variability (p=0.03, r 2 =0.82 at upwind sites; p=0.0002, r 2 =0.99 at downwind sites). These relationships were observed despite low nutrient and light availability, as well as the presence of grazers. Furthermore, environmental variability affected diatom communities in the presence of several competing factors (temperature, light, nutrients and disturbance). Our results suggest that increased environmental variability can lead to higher biodiversity in a complex natural system.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".