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Ecological Intelligence / Subject

seagrass

Peer-reviewed Asia

Most measured carbon in one Indonesian seagrass study lay beneath the plants

Evidence
Peer-reviewed
Geography
Derawan Islands, East Kalimantan, Indonesia
System
Seagrass meadows; coastal blue carbon; marine protected areas

WHAT THE EVIDENCE SAYS

At six monitoring stations, measured seagrass carbon-stock estimates ranged roughly 154–244 tonnes C per hectare, with approximately 99% attributed to sediment. Comparison with an earlier 2014 dataset suggested a lower mean stock by 2025; authors translated the difference into a scenario-derived carbon-dioxide-equivalent estimate.

WHY IT MATTERS / OUR READ

Blue-carbon accounting is largely a soil and sediment problem. Repeated cores and comparable methods, not simply maps of green seagrass cover, are necessary to judge long-term storage and whether a management intervention changed it.

CAVEATS / UNCERTAINTY

The roughly 36% historical difference is not an experimentally estimated cause of loss; surveys differed in context and methods. The reported annual emissions figure is calculated from stock change, not a directly measured atmosphere–ocean flux.

SOURCE

Discover Environment · Derawan seagrass carbon field comparison
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Derawan surveys highlight sediment storage and an apparent stock decline, while estimated emissions depend on assumptions.

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Government data Europe

Jersey has moved 21.7% of its territorial waters out of dredging and trawling

Evidence
Government data
Geography
Jersey, Channel Islands
System
Shallow marine reefs, maerl, seagrass and kelp habitats

WHAT THE EVIDENCE SAYS

Effective September 1, the Government of Jersey expanded the share of territorial waters protected from dredging and trawling from 6.5% to 21.7%. The network is designed around sensitive shallow reefs and OSPAR-listed habitats including maerl beds, seagrass meadows and kelp, with government monitoring planned to track the effects of the new measures.

WHY IT MATTERS / OUR READ

The useful signal is that protected-area quality depends partly on which pressures are actually restricted, not only on how much area is mapped. Jersey's expansion links mapped sensitive habitats to specific mobile-gear exclusions. The next test is implementation: whether compliance, monitoring and fisheries adaptation translate those rules into measurable ecological outcomes.

CAVEATS / UNCERTAINTY

This is an official implementation announcement, not a before-after ecological outcome study. The 21.7% figure describes waters protected from dredging and trawling rather than a universal no-take designation, and some additional or sensitive areas remain phased or contested in the 2030 protection pathway.

SOURCE

Government of Jersey
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States Assembly scrutiny comments
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New regulations expand the island's marine protected-area network from 6.5% to 21.7%, targeting maerl, seagrass, kelp and shallow reef habitats while leaving ecological outcomes to future monitoring.

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Peer-reviewed Europe

A 50-m eelgrass model turns national monitoring into restoration-scale maps

Evidence
Peer-reviewed
Geography
Danish coastal waters
System
Eelgrass meadows and temperate coastal waters

WHAT THE EVIDENCE SAYS

Using 37,497 national monitoring observations from 2011–2022, researchers trained a 50-m random-forest model for eelgrass across Denmark's roughly 7,000-km coastline. Independent test accuracy was 80.3% with AUC 0.878; modeled depth limits agreed with field observations across 85 water bodies (r=0.779), and light availability was the strongest predictor.

WHY IT MATTERS / OUR READ

Spatial models become more decision-useful when they predict an ecological threshold and show where confidence is high enough to guide field verification or restoration investment. The important product is not just a suitability map but an uncertainty-aware decision surface.

CAVEATS / UNCERTAINTY

The Danish-trained model is not directly transferable to other coasts without local observations and environmental predictors. It estimates distribution and restoration feasibility rather than guaranteeing establishment or persistence after planting.

SOURCE

Estuarine, Coastal and Shelf Science · Original evidence
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Denmark's machine-learning framework predicts eelgrass extent and depth limits with independent validation, while making uncertainty visible enough to support site-level planning.

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