Chapter 3 Ecosystem Services and Disservices of Mangrove Forests and Salt Marshes
Bibliographic record
Abstract
Coastal wetlands such as mangrove forests and saltmarshes provide a range of important benefits to people, broadly defined as ecosystem services. These include provisioning services such as fuelwood and food, regulating services such as carbon sequestration and wave attenuation, and various tangible and intangible cultural services. However, strong negative perceptions of coastal wetlands also exist, often driven by the perceived or actual ecosystem disservices that they also produce. These can include odour, a sense of danger, and their real or perceived role in vector and disease transmission (e.g., malaria). This review provides an introduction to the ecosystem services and disservices concepts, and highlights the broad range of services and disservices provided by mangrove forests and saltmarshes. Importantly, we discuss the key implications of ecosystem services and disservices for the management of these important coastal ecosystems. Ultimately, a clear binary does not exist between ecosystem services and disservices; an ecosystem service to one stakeholder can be viewed as a disservice to another, or a service can change seasonally into a disservice, and vice versa. It is not enough to only consider the beneficial ecosystem services that coastal wetlands provide: instead, we need to provide a balanced view of coastal wetlands that incorporates the complexities that exist in how humans relate to and interact with these important coastal 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".