Epibiont community composition of red mangroves Rhizophora mangle are contingent on root characteristics
Bibliographic record
Abstract
Foundation species traits that structure communities are rarely experimentally examined; thus, a predictive understanding of their functions lags behind patterns of observed species associations. Red mangrove Rhizophora mangle roots form complex living habitats that support diverse epibiont communities, making them a model system for testing links between variation in foundation species traits and associated biodiversity. Here, we compared epibiont community composition between living and non-living mangrove roots, as well as root mimics, to test how foundation species traits affect community structure. We also quantified the community structure of associated mobile invertebrates to examine their relationship with secondary foundation species (e.g. sponges, bivalves) that grow on the roots. After 14 mo of colonization and succession, substrate composition (i.e. mangrove, wood, PVC) had significant effects on community composition, richness, and abundance of sessile epibionts and mobile invertebrates. Non-living mangrove roots were 5 times more likely to deteriorate, and consequently had the lowest epibiont richness and abundance. We found strong positive relationships between mobile invertebrate richness and the abundance, measured as biomass, and richness of sponges and bivalves, suggesting that variation among roots in secondary foundation species play an important role in mediating mobile invertebrate community composition. This study highlights the functional role of habitat structure and how rapidly that function can be lost without biogenic maintenance. Our results indicate the importance of facilitation cascades in fostering diverse mobile invertebrate communities and highlight both advantages and limitations in using artificial structures in restoration programs.
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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".