Upriver migrating sea lamprey exhibit similar responses to hydrodynamic features as other up and downriver-moving species
Bibliographic record
Abstract
Identifying commonalities in how fish navigate rivers near infrastructure will enhance water operations and design by improving our ability to predict engineering outcomes (e.g., barrier construction/removal, fish passage installation) in novel settings before the cost of real-world implementation. Evidence from intermediate-scale computer models (time scales of minutes to days and spatial scales <2 km) suggests that fish movement behavior in rivers is frequently governed by responses to one or more of the following hydrodynamic features: (1) flow direction (i.e., rheotaxis), (2) flow velocity magnitude, (3) turbulence, and (4) depth, plus (5) the integration of information over recent time periods (i.e., memory/experience). However, the lack of consistent modeling approaches, infrequent assessment of each response in isolation and combination, and a focus on a limited number of species means the generality of these responses is uncertain. We use a computer model, specifically a pattern-oriented modeling approach incorporating individual based models (IBMs), to apply responses to the four hydrodynamic features plus memory/experience in different combinations to study their value for reproducing the movement of an infrequently modeled species and lifestage, upriver migrating adult sea lamprey, Petromyzon marinus . The study site was the region downstream of the Sault Ste. Marie lock and dam complex located between Canada and the U.S.A on the St. Marys River joining Lake Superior and Lake Huron. Our analysis indicates that rheotaxis and a response to velocity magnitude as well as recent past experience improve sea lamprey spatio-temporal movement prediction compared to other, simpler forms of modeled behavior. Sea lamprey movement is also biased toward lower levels of turbulence (e.g., turbulent kinetic energy) or its precursor (i.e., the spatial gradient in water speed). A response to water depth was not found to be important, but the modeled domain was two-dimensional which limited our assessment. As similar responses to hydrodynamic features are found in very different fish, commonalities appear to underlie river navigation across a range of species and life stages that share the goal-oriented behavior of upriver and downriver movement. The systematic approach of our analysis highlights the accuracy trade-offs of each response, individually and in combination, that often accompany alternative behavioral formulations in a computer model of fish movement. The model structure provides a framework to which future findings from the analyses of additional species in different contexts can be added.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".