Seascape connectivity modeling predicts hotspots of fish-derived nutrient provisioning to restored coral reefs
Bibliographic record
Abstract
Coastal habitat quality and quantity have been significantly eroded by stressors operating and interacting across the land-sea interface, prompting a recent proliferation in coastal restoration programs worldwide. These initiatives often recognize connectivity as a critical driver of ecosystem functioning, yet most do not include connectivity as a spatially explicit, quantitative criterion during the planning process. Here, we demonstrate the use of spatial graph models to quantify potential functional connectivity for 2 multi-habitat-utilizing reef fish species known to transport nutrients from nearshore mangrove and seagrass nurseries to oligotrophic offshore reefs. Applying the method across sites considered by a multi-million dollar coral restoration program in the Florida Keys, USA, revealed locations where out-planted corals are likely to benefit most from enhanced functional connectivity in the form of nutrient provisioning and other consumer-driven processes. Opportunities for positive fish-coral interactions varied between fish species, owing to selective patterns of habitat use, highlighting the need for species-specific connectivity assessments, even within a trophic guild. Connectivity estimates for candidate restoration sites were influenced more strongly by habitat composition (which influences fish foraging and shelter resources) than by proximity to potential mangrove and seagrass nurseries, emphasizing the importance of considering both seascape composition and configuration in restoration design. Ecologically and economically effective restoration strategies are urgently required to curb rapid declines in coral reef architectural complexity, ecological function, and resilience. Our study illustrates the utility of spatial graphs as a data- and resource-efficient technique for quantifying and communicating complex ecological connectivity information in service of such efforts.
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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 | 0.002 |
| 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 | 0.000 |
| 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".