Cobble reef restoration in the Baltic Sea: Implications for life below water
Bibliographic record
Abstract
Abstract Many coastal ecosystems are impacted by human pressures. Rocky reefs are structurally complex habitats that often support elevated fish abundance and marine biodiversity. In the Baltic Sea, rocky reefs have suffered from extraction for decades, leading to a decrease in hard substrata and complex habitat availability. This study is the first to restore cobble reefs and examine the biological effects. Baited and unbaited underwater video systems (BRUVS and UBRUVS, respectively) were employed across five years to monitor fish communities before reef deployment in 2017 and after reef deployment in 2018 and 2021. Using a before–after control‐impact (BACI) study design with replicates, relative abundances of Atlantic cod (Gadus morhua), herring (Clupeidae sp.), goldsinny wrasse (Ctenolabrus rupestris), two‐spotted goby (Pomatoschistus flavescens), shore crab (Carcinus maenas), and flatfish (Pleuronectiformes spp.) were compared across time and test sites. Comparisons were conducted across 1) restored cobble reefs, 2) natural cobble reefs, and 3) sand‐bottom test sites. This study found positive reef restoration effects revealed consistently by BRUVS and UBRUVS in three species: Atlantic cod, goldsinny wrasse and two‐spotted goby. These findings indicate that A) it is possible to restore cobble reefs and the associated mobile fauna, but also that B) continued marine extraction of cobble degrades complex habitats to the detriment of various marine species. To preserve Atlantic cod, and other sensitive species, we emphasize ecosystem restoration and warn against marine cobble reef extraction in vulnerable areas. Restoration of marine habitats may contribute to achieving the UN sustainable development goal covering life below water.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".