An ecological niche mapping tool for kelp forest conservation
Bibliographic record
Abstract
Restoration and protection of kelp forest ecosystems is critical to maintain marine biodiversity, support coastal communities, and meet global conservation targets such as the Kunming-Montreal Global Biodiversity Framework's 30x30 and Kelp Forest Challenge. Much of the success of kelp forest restoration and protection depends heavily on selecting ecologically suitable sites that align with species-specific environmental requirements. This paper introduces a novel kelp forest restoration site selection tool that synthesizes the realized environmental niche of 105 kelp species across 25 biophysical factors. Using over 426,000 global observations of kelp and high-resolution oceanographic datasets, the tool provides quantitative niche data summarized by species and ecoregion. It incorporates key variables such as temperature, salinity, light, and nutrient availability, offering users practical guidance to identify optimal restoration sites. Accessible via an interactive web application, the tool supports conservation practitioners, policymakers, and researchers by enabling evidence-based site selection, maximizing restoration success, and informing broader marine ecosystem management. This tool represents a significant advancement in kelp forest conservation, facilitating global restoration efforts and contributing to the ambitious goal of restoring one million hectares of kelp forest by 2040. Future developments will address qualitative ecological factors and socio-cultural considerations to enhance its utility.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".