Advancing acoustic studies of pelagic fish and zooplankton at the western Atlantic-Arctic gateway
Bibliographic record
Abstract
The patterns of distribution among fish and zooplankton in the Arctic and their deep ocean habitats remain poorly described. Climatic disruptions to natural variability in temperature, light, and nutrient supply can modify species distributions, impacting the ecosystem at various levels. Across spatial and vertical domains, these environmental factors form complex relationships with species assemblages, which are challenging to measure with conventional sampling methods. Hydroacoustics is a promising approach to studying the distribution of organisms in hard-to-reach ecosystems. This thesis aims to advance new hydroacoustic methodologies for the study of pelagic fish and zooplankton and identify the structural drivers of their communities and distribution along the boundaries of Arctic-Atlantic modulated ecosystems. In my thesis, I used several emerging technologies, including lowered acoustic probes and broadband acoustic measurements, to assess the distributional patterns of pelagic fish and zooplankton at meter-level to ocean basin scales. I found that measurements of lanternfish inhabiting deep-scattering layers require detailed analysis that includes the identification and removal of anomalous signals, which can arise from a combination of physical and biological processes. To deal with this, I introduced a machine learning approach that helps identify anomalous signals and improves the precision of acoustic density measurements. When examining similar lanternfish dominated communities at the basin scale, I report that temperature-driven water mass boundaries act as a barrier to the dispersal of mesopelagic communities at high-latitudes, in both the northern and southern hemispheres. Furthermore, this same boundary appears to impact the vertical distribution of pelagic life, with likely impacts the vertical transport of nutrients and carbon. In the high Arctic, I report mixing processes in glacial fjord ecosystems can impact the vertical distribution of copepods and the morphological patterns of marine snow. This work highlights contrasting conditions outside of two adjacent marine terminating glaciers, which each offer a glimpse of what future Arctic conditions may bring. Overall, my work contributes to a greater understanding of the structural drivers of Arctic pelagic communities and can be used to understand their sensitivities to future change.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".