Quantifying rotifer species richness in temperate lakes
Bibliographic record
Abstract
Summary 1. Biodiversity assessments of lakes depend on the ability to identify the complement of species present, although the degree of sampling required is often uncertain. We utilise long‐term data to predict rotifer species richness in three habitats in three Polish lakes using rarefaction sampling methods. 2. Richness in littoral and psammon habitats did not saturate, even with up to 130 samples. Highest richness was observed in psammon habitat (119 species) in Lake Mikolajskie, followed by littoral habitat in Lakes Łuknajno (114 species) and Kuc (110 species). Littoral habitats in Lakes Łuknajno (56%) and Kuc (51%) had the most species not shared with other habitats in the same lake. 3. Species richness (Chao2) estimates ranged between 44 for pelagic and 135 for psammon habitat in Lake Mikolajskie, to 100 for psammon and 137 for littoral habitat in Lake Kuc, and 65 for pelagic and 162 for littoral habitat in Lake Łuknajno. Whole lake estimates were 167, 205 and 171 species, respectively, for these lakes, higher than the 150 to 160 species predicted by Dumont and Segers (Hydrobiologia, 1996, 341 , 125). 4. Using standardised sampling, richness was significantly higher in littoral than either pelagic or psammon habitats. Contrasts of standardised rarefaction curves revealed that richness in Lakes Kuc and Mikolajskie was described as well by littoral‐only or psammon‐only samples, respectively, as by those randomly drawn from across all habitats in the lake. 5. Species richness estimates for Lake Mikolajskie were highest in summer, followed by autumn and spring. Interannual estimates differed by up to 427%, nearly an order of magnitude greater than maximal seasonal variation of 70%. 6. Results indicate that much higher sampling intensity is required to establish species richness than is presently carried out in most lakes. Because many species can be detected only with very intensive sampling, conservation programmes must consider sampling intensity when designing studies.
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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) | 0.019 | 0.004 |
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; both teacher heads agree on what is shown here.
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".