Coastal wetlands worldwide: climatic zonation, ecosystems and biogeography
Bibliographic record
Abstract
Key points All wetland ecosystems provide valuable services to local people and influence global carbon budgets; climate delimits four major ecosystems: arctic and subpolar marshes, temperate marshes, subtropical tidal wetlands and tropical mangrove swamps; wetlands worldwide have specialized plant forms adapted to physical extremes of soil salinity, instability and low oxygen; global biogeographic studies are the tools of wetlands biodiversity studies; primary regional differences in species composition reflect ancient paleogeographic changes; plate tectonics and icesheets isolated tropical ‘Old World’ from ‘New World’ biological provinces; coastal wetlands are azonal vegetation types highly influenced by soil conditions; differences in temperature and precipitation regimes shift the ecosystem boundaries north or south on different coasts of continents; seasonal variations in temperature–precipitation conditions drive subregional soil water salinity differences. In this chapter, general distinctions (both physical and biological features) of major latitudinal and climatic zones are outlined, so the reader can appreciate the broad similarities and dramatic differences in coastal wetlands across the globe (Chapters 7 to 11). We also look at the phytogeographical variations found on different coasts of the same continent and explain why the tropical floras of Atlantic and Pacific regions often are different, even within the same latitudinal range. Climate zones and coastal wetland ecosystems On a worldwide basis, there are four broad latitudinal zones of tidal wetlands (Figure 1.1). These world coastal wetland zones are: arctic and subpolar marshes; temperate zone marshes; subtropical marshes and mangroves; and tropical mangrove swamps. Each of these major ecosystems comprises a set of biological components interacting with their physical environment and providing characteristic wetland services (Figure 6.1) These services are defined by the Ramsar Convention as ‘the benefits people obtain from ecosystems, including provisions (food and water), regulating functions (buffering from floods and storms), nutrient cycling, and cultural needs (recreation, spiritual, and esthetic qualities)’. The vital services for human wellbeing include water purification and nitrate detoxification, climate regulation through sequestering and releasing of carbon in the biosphere, and mitigation of climate-driven sea level rise and storminess, which can result in erosion and devastating coastal floods (Millennium Ecosystem Assessment, 2005). Barbier et al . (2011) provide further details of the quality of estuarine and coastal ecosystem services and they estimate the financial losses to be expected from elimination of these services.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".