Kelp forests as nursery and foundational habitat for reef fishes
Bibliographic record
Abstract
Conservation of marine biodiversity requires an understanding of the habitats needed to support and replenish species of interest. It also requires knowledge about the abundance and diversity of multispecies assemblages. Variation in the distribution and composition of kelp forests, one of the most productive marine coastal habitats globally, can have major influences on reef fishes-a group of ecologically and socioeconomically important species. In the face of widespread and escalating loss of kelp forests, quantification of these effects is urgently needed to assess and project cascading impacts on biodiversity. Here, we evaluate relationships between kelp forests and associated reef fish populations using a global meta-analysis of experimental kelp removals and comparative surveys of kelp and adjacent non-kelp habitats. These analyses show that kelp forests increase the abundance of reef fishes, though the significance of this effect varied depending on the structural complexity of kelp forests. In experimental studies, kelp forests have a significant positive effect on fish species richness, revealing that kelp act as true foundation species by supporting the diversity of associated multispecies assemblages. Importantly, regardless of kelp forest morphology and type of study (observational or experimental studies), kelp forests enhance the recruitment of early life history stages suggesting they are nursery habitats for many reef fish taxa. Lastly, kelp forests differentially affected species with different functional traits; small body size fishes from low trophic levels (e.g., herbivore and detritivores, micropredators, and mesopredators) and large body size fish from higher trophic level (e.g., piscivores, general carnivores) were both facilitated by kelp forests. Taken together, these results indicate that the loss of kelp forest, particularly those with more complex morphology, can reduce total abundance and diversity of fish, with possible cascading consequences for coastal ecosystem function.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".