Patchiness in American lobster benthic recruitment at a hierarchy of spatial scales
Bibliographic record
Abstract
The overarching goal of ecology is to uncover natural patterns and the processes that underlie them. Importantly, both patterns and processes are dependent on scale. In this study, we assessed spatial patterns in benthic recruitment (density of young-of-year within a few months post-settlement) of American lobster in the Canadian Gulf of Maine by deploying, over 3 consecutive years, between 413–505 cobble-filled collectors on structurally complex cobble bottom in a spatially nested design: regions (127 and 674 km 2 ), areas within regions (0.4–4 km 2 ), sites within areas (0.003–0.23 km 2 ), and “sub-sites” within sites (0.00004–0.06 km 2 ). We quantified spatial patterns of benthic recruitment using a repeated-measures nested ANOVA, variance component analysis and a randomization approach developed for this study. These analyses indicated that the area scale (0.4–4 km 2 ) was most important to patchiness in benthic recruitment, with a significant but smaller amount of variation in recruitment at the region scale (127 and 674 km 2 ), and virtually no significant variation at the smaller spatial scales. Despite interannual variability in benthic recruitment, these spatial patterns and scales of patchiness were largely consistent across years. Of the 11 study areas surveyed, 3 were identified as recruitment “hotspots” and 4 as recruitment “coldspots”, based on density frequency distributions. The location of these different recruitment “hotspots” and “coldspots” suggests that patchiness at the area scale may be related to the effect of local currents and topographical features on larval retention. The lack of significant patchiness at the smallest scale of the collector is at first surprising, given previous work on substrate selection by competent post-larvae, but likely arose because our sampling tool offered a standard and high-quality substrate, indirectly confirming the importance of substrate to small-scale patterns of benthic recruitment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".