Are the landscape‐level drivers of water column and surface sediment diatoms different?
Bibliographic record
Abstract
Summary Threats to biodiversity are fostering new collaboration between aquatic ecologists and palaeolimnologists, who have traditionally asked ecological questions on different time scales. While the differences between surface sediment and water column or snapshot sampling are well understood, less so are the consequences of comparing the predominant drivers of aquatic assemblages resulting from these two types of sampling. Using diatom data from the 2007 USEPA National Lakes Assessment (NLA) program (468 lakes), we compared the main environmental and spatial drivers of diatom community composition between samples derived from the water column and surface sediments. We hypothesised that, in explaining community variation across the conterminous United States, the effect of environment would be stronger in diatom assemblages preserved in surface sediments because of the inclusion of benthic members and temporal integration. We used a combination of ordination overlays and variation partitioning to examine differences in community drivers between palaeolimnological (surface sediment) and water column sampling. We found that these two types of sampling were significantly correlated with respect to the drivers of community composition in addition to having congruent patterns of ordination. Congruency between sampling methods further increased when the water column data were temporally integrated and may be explained by variation in seasonally dynamic taxa. To our knowledge, this is the first study that has tested for differences in environmental structuring patterns between palaeolimnological and water column samples using such a highly replicated and landscape‐level approach. On the basis of our results, we encourage ecologists to consider the joint analysis of these two types of data sets where data are available.
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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) | 0.001 | 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".