Nearshore seascape connectivity enhances seagrass meadow nursery function
Bibliographic record
Abstract
Abstract Diverse habitats composing coastal seascapes occur in close proximity, connected by the flux of materials and fauna across habitat boundaries. Understanding how seascape connectivity alters important ecosystem functions for fish, however, is not well established. For a seagrass‐dominant seascape, we predicted that configuration and composition of adjacent habitats would alter habitat access for fauna and trophic subsidies, enhancing nursery function for juvenile fish. In an extensive Zostera marina seagrass meadow, we established sites adjacent to (1) highly complex and productive kelp forests ( Nereocystis luetkeana ), (2) unvegetated sand habitats, and (3) in the seagrass meadow interior. Using SCUBA , we conducted underwater observations of young‐of the‐year ( YOY ) rockfish ( Sebastes spp.) recruitment across sites. Using generalized linear mixed effects models, we assessed the role of seascape adjacency relative to seagrass provisions (habitat complexity and prey) on YOY recruitment. YOY rockfish collections were used to trace sources of allochthonous vs. autochthonous primary production in the seagrass food web, via a δ 13 C and δ 15 N isotopic mixing model, and prey consumption using stomach contents. Overall, seagrass nursery function was strongly influenced by adjacent habitats and associated subsidies. Allochthonous N . luetkeana was the greatest source of energy assimilated by YOY rockfish within seagrass sites. In seagrass sites adjacent to N. luetkeana kelp forests, YOY s consumed higher quality prey, which corresponded with better body condition relative to sites adjacent to sand. Moreover, kelp forest adjacency enhanced YOY rockfish recruitment within the seagrass meadow, suggesting that habitat complexity is a key seascape feature influencing the nursery function of nearshore habitats. In general, to promote seascape connectivity, the conservation and restoration of nursery habitats should prioritize the inclusion of habitat mosaics of high structural complexity and productivity. We illustrate and emphasize the importance of using a seascape‐level approach that considers linkages among habitats for the management of important nearshore ecosystem functions.
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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) | 0.002 | 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".