Food Web Connectivity in a Mangrove–Seagrass–Patch Reef (MSP) Seascape: Lessons from a Tropical Back-Reef in Puerto Rico
Bibliographic record
Abstract
Alterations to the functions and connectivity of mangrove–seagrass–patch reef (MSP) seascapes have the potential to impact the survival, foraging activities, and movement of reef-dependent invertebrates (e.g., crabs and shrimp) and fishes. In the current study, we examined carbon flow in the Guánica Biosphere Reserve in southwestern Puerto Rico using pigment analysis of particulate organic matter and stable isotope analysis of carbon (δ13C) and nitrogen (δ15N) in flora and fauna. Several lines of evidence pointed to N2 fixers (cyanobacteria) being important for fueling primary productivity in this oligotrophic ecosystem including low (<0.7 µg L−1) chlorophyll, prevalence of cyanobacteria based on pigment signatures, and the isotope signatures of seagrass and red mangrove leaf tissue (enriched δ15N values) and consumers (depleted δ15N values). Food web mixing models based on stable isotopes (δ13C and δ15N) revealed that multiple producers (phytoplankton, benthic microalgae, seagrasses, etc.) contributed organic matter to the consumers (zooplankton, invertebrates, and fishes) in the MSP seascape at the center of the reserve. Contribution estimates for common benthic invertebrates (crabs and shrimp) were taxon-specific, and the highest input was generally linked to particulate organic matter (POM) and benthic microalgae (BMA)/seagrass producer categories, although meaningful mangrove contribution was observed for some taxa. Similarly, contribution estimates for fishes were highest for POM and BMA/seagrass, with the latter producer category being more important for species known to migrate from mangroves or patch reefs to seagrass beds at night (bluestriped grunt, French grunt, and white grunt). Although all fish investigated were observed in mangrove prop-root habitats, input of organic matter from mangroves to these consumers was typically limited for most of the species examined. Understanding these complex seascapes contributes to our understanding of the ecology of these vital ecosystems.
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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 | 0.000 | 0.000 |
| 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".