Marine Fish Passage—Underappreciated Threats to Connectivity Within the Marine Environment
Bibliographic record
Abstract
ABSTRACT Habitat fragmentation is a major threat to aquatic biodiversity loss. However, much of the focus is on the connectivity of freshwaters, with much less attention given to marine ecosystems. We contend that coastal infrastructure including bridges, causeways, tidal turbines, land infilling and harbours, wharfs, quays, piers and docks have resulted in underappreciated impacts on the connectivity of fish movements resulting in passage challenges at sea. For each type of marine infrastructure, we synthesised the present status of knowledge to characterise the problems and future challenges and also identify mitigation options and passage solutions to restore connectivity for fishes. Bridges can disrupt currents, generate light and noise/vibration, and emit electromagnetic signals, so more work is needed to modify in‐water designs to minimise the negative impacts on fishes. Causeways involve infilling, resulting in full in‐water barriers, requiring fishes to circumnavigate these structures and there is limited research on mitigation (e.g., fishways). Tidal turbines are placed in areas with high currents, which can hinder movements and result in entrainment; however, monitoring fish movements is challenging in these unique areas. Offshore energy has grown in recent years and can impact fish connectivity via altered sediment dynamics and water currents, as well as through the generation of noise pollution and electromagnetic fields. Land filling results not only in habitat loss but also in fragmentation, and it will be imperative to identify important habitats and corridors to minimise impacts there. Finally, infrastructure associated with boats (e.g., harbours, docks) negatively impacts nearshore habitat, which can alter movement trajectories. In the collective, we found evidence that diverse types of marine infrastructure can impact connectivity and, ultimately, fish movement and migrations. Interestingly,bespoke fish passage solutions in marine environments seem rare. As coastal development will increase in the future, it is imperative that we assess the potential connectivity issues resulting from marine infrastructure and that we generate solutions to mitigate these issues for marine organisms.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".