Depth distribution of the native freshwater mussel (<i>Echyridella menziesii</i>) in warm monomictic lakes: Towards a general model for mussels in lakes
Bibliographic record
Abstract
Abstract Freshwater mussels are large, long‐lived and can be important contributors to benthic biomass and processes. They are currently one of the most endangered groups of organisms and it is urgent to develop tools to predict their distribution and the potential effect of their decline or disappearance on these ecosystems. Cyr ( ) showed that the distribution of Elliptio complanata (Unionidae) in Canadian Shield lakes is constrained by physical forces. Here we test Cyr's model in a very different group of mussels (Hyriidae), in different types of lakes (warm monomictic lakes) that cover a wider range of sizes and morphologies. We use data on the depth distribution of Echyridella menziesii along 38 depth transects in 11 warm monomictic lakes located in New Zealand to test three hypotheses: (1) wave‐mixed depth and bottom slope are good predictors of the depth of maximum mussel density, (2) thermocline depth does not limit the distribution of mussels in warm monomictic lakes, and (3) the lower boundary of mussel distribution is determined by the mud deposition boundary. Mussels in New Zealand lakes reach their maximum density within the epilimnion, at increasing depths with increasing lake size and increasing site exposure. The only exceptions were found in large lakes or parts of large lakes with shallow bathymetric slopes, where high mussel densities were found in relatively shallow waters. Mussel density peaks are found much deeper in the volcanic New Zealand lakes than in (glacial) Canadian Shield lakes, a discrepancy we hypothesise could be due to lower sediment stability along the steep slopes of volcanic lakes. Mussels appear to have a very broad range of depth distribution in these warm monomictic lakes. The deepest samples collected in deep oligotrophic lakes (down to 12–30 m) all contained mussels, often in substantial numbers (up to 2–186 mussels/m 2 ), and can only offer a minimal estimate of the lower boundary of their distribution. However, the distribution of mussels in highly productive lakes is limited by hypolimnetic anoxia, and therefore by position of the thermocline. Echyridella menziesii are found in a wide range of substrates. Mussels were found below the mud deposition boundary in many lakes, suggesting that the presence of fine flocculent organic sediments does not prevent them from living in deep areas. The distribution of E. menziesii in warm monomictic lakes appears to be governed by relatively simple physical processes. This supports findings for a very different group of mussels in Canadian Shield lakes and suggests that general models could be developed to predict the distribution of mussels in 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".