Biomarkers of recovery: Characterizing trophic flow following ecological restoration
Bibliographic record
Abstract
Abstract Coastal kelp forests are important sources of primary productivity and provide essential habitat and ecosystem services. In many areas around the world, the formation and persistence of urchin barrens threatens kelp forest ecosystems. Over the past several decades, restoration efforts have emerged aiming to increase the abundance of foundation species like kelp in such systems. However, we lack a comprehensive understanding of how successful kelp restoration affects the nutritional landscape and the fitness of kelp forest herbivores. We bridge this knowledge gap with a Before-After-Control-Impact Paired Series (BACIPS) focused on kelp forest restoration where reductions of herbivorous sea urchins in Haida Gwaii resulted in substantial increases in kelp abundance in habitat previously characterized as barrens. Specifically, we document body size specific shifts in the fatty acid (FA) profiles of red sea urchins ( Mesocentrotus franciscanus ) and northern abalone ( Haliotis kamtschatkana ). FAs associated with bacteria and diatoms were elevated in tissues of urchins and abalone in barrens habitat while kelp biomarkers were elevated in restored kelp forest habitat. For urchins, these shifts tracked the increase in gonad mass following kelp forest recovery. For abalone, these results varied depending on animal body size. Specifically, abalone exhibited a continuous size-specific shift from biofilm-associated markers at small sizes to kelp-associated markers as animals increased in size. For both species, a marked increase in essential fatty acids was observed following kelp restoration. Our results demonstrate kelp restoration via sea urchin reduction enhances not only the quantity but also the quality and diversity of food in previously degraded habitats, and subsequently enhances the amount and nutritional quality of roe (i.e., gonads) in sea urchins therein.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".