Identification and scenario-based optimization of ecological corridor networks for waterbirds in typical coastal wetlands
Bibliographic record
Abstract
The limited space for coastal wetland conservation may conflict with achieving the 30 % protected area target under the Kunming-Montreal Global Biodiversity Framework. Identifying ecological corridors and enhancing connectivity in coastal wetlands is crucial for prioritizing critical areas. Our study presents a methodology for identifying ecological corridors for migratory waterbirds, using Yancheng coastal wetlands, a crucial stopover in the East Asian-Australasian Flyway, as a case study. We developed a novel framework for identifying and optimizing bird ecological corridors. Our approach integrates multiple key methodologies, including a comprehensive scoring system for selecting indicator species, an ecological source identification method combining MaxEnt and Morphological Spatial Pattern Analysis (MSPA), and a scenario-based simulation approach for optimizing corridor networks. We combined distribution data of 40 coastal waterbird species across four guilds with MaxEnt-simulated suitable habitat and MSPA-identified ecological source patches to improve source selection accuracy. Employing Circuit Theory, we identified critical ecological bottlenecks and facilitators. To optimize the ecological corridor network, we simulated three scenarios: (I) adding ecological stepping stones, (II) enhancing barrier points, and (III) simulating pinch point degradation. Scenario III proved most effective due to its lower optimization costs, robust planning, and high applicability, making it particularly effective in maintaining ecological network connectivity. Our scenario-based approach successfully enhanced connectivity and stability within coastal wetlands by targeting specific protection and habitat quality needs for diverse waterbird guilds. This methodology represents a significant step forward in migratory waterbird conservation in Yancheng and provides a valuable model for other globally significant biodiversity hotspots.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".