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Drainage and deforestation of tropical peat swamp forests (PSF) in Southeast Asia cause carbon emissions and biodiversity loss of global concern. Restoration efforts to mitigate these impacts usually involve peatland rewetting by blocking canals. However, there have been no studies to date of the optimal rewetting approach that will reduce carbon emission whilst also promoting PSF regeneration. Here we present results of a large-scale restoration trial in Sumatra (Indonesia), monitored for 7.5 years. Water levels in a former plantation were raised over an area of 4800 ha by constructing 257 compacted peat dams in canals. We find peat surface subsidence rates in the rewetted restoration area and adjoining PSF to be halved where water tables were raised from ~ - 0.6 m to ~ - 0.3 m, demonstrating the success of rewetting in reducing carbon emission. A total of 57 native PSF tree species were found to spontaneously grow in the most rewetted conditions and in high densities, indicating that forest regrowth is underway. Based on our findings we propose that an effective PSF restoration strategy should follow stepwise rewetting to achieve substantial carbon emission reduction alongside unassisted regrowth of PSF, thereby enabling the peat, forest and canal vegetation to establish a new nature-based ecosystem balance., (© 2024. The Author(s).)

Coastal elevation data are essential for a wide variety of applications, such as coastal management, flood modelling, and adaptation planning. Low-lying coastal areas (found below 10 m +Mean Sea Level (MSL)) are at risk of future extreme water levels, subsidence and changing extreme weather patterns. However, current freely available elevation datasets are not sufficiently accurate to model these risks. We present DeltaDTM, a global coastal Digital Terrain Model (DTM) available in the public domain, with a horizontal spatial resolution of 1 arcsecond (∼30 m) and a vertical mean absolute error (MAE) of 0.45 m overall. DeltaDTM corrects CopernicusDEM with spaceborne lidar from the ICESat-2 and GEDI missions. Specifically, we correct the elevation bias in CopernicusDEM, apply filters to remove non-terrain cells, and fill the gaps using interpolation. Notably, our classification approach produces more accurate results than regression methods recently used by others to correct DEMs, that achieve an overall MAE of 0.72 m at best. We conclude that DeltaDTM will be a valuable resource for coastal flood impact modelling and other applications., (© 2024. The Author(s).)

Coastal flood risk assessments require accurate land elevation data. Those to date existed only for limited parts of the world, which has resulted in high uncertainty in projections of land area at risk of sea-level rise (SLR). Here we have applied the first global elevation model derived from satellite LiDAR data. We find that of the worldwide land area less than 2 m above mean sea level, that is most vulnerable to SLR, 649,000 km 2 or 62% is in the tropics. Even assuming a low-end relative SLR of 1 m by 2100 and a stable lowland population number and distribution, the 2020 population of 267 million on such land would increase to at least 410 million of which 72% in the tropics and 59% in tropical Asia alone. We conclude that the burden of current coastal flood risk and future SLR falls disproportionally on tropical regions, especially in Asia.

The sensitivity of tropical forest carbon to climate is a key uncertainty in predicting global climate change. Although short-term drying and warming are known to affect forests, it is unknown if such effects translate into long-term responses. Here, we analyze 590 permanent plots measured across the tropics to derive the equilibrium climate controls on forest carbon. Maximum temperature is the most important predictor of aboveground biomass (-9.1 megagrams of carbon per hectare per degree Celsius), primarily by reducing woody productivity, and has a greater impact per °C in the hottest forests (>32.2°C). Our results nevertheless reveal greater thermal resilience than observations of short-term variation imply. To realize the long-term climate adaptation potential of tropical forests requires both protecting them and stabilizing Earth's climate., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Background: Reduction of carbon emissions from peatlands is recognized as an important factor in global climate change mitigation. Within the SE Asia region, areas of deeper peat present the greatest carbon stocks, and therefore the greatest potential for future carbon emissions from degradation and fire. They also support most of the remaining lowland swamp forest and its associated biodiversity. Accurate maps of deep peat are central to providing correct estimates of peat carbon stocks and to facilitating appropriate management interventions. We present a rapid and cost-effective approach to peat thickness mapping in raised peat bogs that applies a model of peat bottom elevation based on field measurements subtracted from a surface elevation model created from airborne LiDAR data., Results: In two raised peat bog test areas in Indonesia, we find that field peat thickness measurements correlate well with surface elevation derived from airborne LiDAR based DTMs (R 2 0.83-0.88), confirming that the peat bottom is often relatively flat. On this basis, we created a map of extent and depth of deep peat (> 3 m) from a new DTM that covers two-thirds of Sumatran peatlands, applying a flat peat bottom of 0.61 m +MSL determined from the average of 2446 field measurements. A deep peat area coverage of 2.6 Mha or 60.1% of the total peat area in eastern Sumatra is mapped, suggesting that deep peat in this region is more common than shallow peat and its extent was underestimated in earlier maps. The associated deep peat carbon stock range is 9.0-11.5 Pg C in eastern Sumatra alone., Conclusion: We discuss how the deep peat map may be used to identify priority areas for peat and forest conservation and thereby help prevent major potential future carbon emissions and support the safeguarding of the remaining forest and biodiversity. We propose rapid application of this method to other coastal raised bog peatland areas in SE Asia in support of improved peatland zoning and management. We demonstrate that the upcoming global ICESat-2 and GEDI satellite LiDAR coverage will likely result in a global DTM that, within a few years, will be sufficiently accurate for this application.

Tropical peatland fires play a significant role in the context of global warming through emissions of substantial amounts of greenhouse gases. However, the state of knowledge on carbon loss from these fires is still poorly developed with few studies reporting the associated mass of peat consumed. Furthermore, spatial and temporal variations in burn depth have not been previously quantified. This study presents the first spatially explicit investigation of fire-driven tropical peat loss and its variability. An extensive airborne Light Detection and Ranging data set was used to develop a prefire peat surface modelling methodology, enabling the spatially differentiated quantification of burned area depth over the entire burned area. We observe a strong interdependence between burned area depth, fire frequency and distance to drainage canals. For the first time, we show that relative burned area depth decreases over the first four fire events and is constant thereafter. Based on our results, we revise existing peat and carbon loss estimates for recurrent fires in drained tropical peatlands. We suggest values for the dry mass of peat fuel consumed that are 206 t ha(-1) for initial fires, reducing to 115 t ha(-1) for second, 69 t ha(-1) for third and 23 t ha(-1) for successive fires, which are 58-7% of the current IPCC Tier 1 default value for all fires. In our study area, this results in carbon losses of 114, 64, 38 and 13 t C ha(-1) for first to fourth fires, respectively. Furthermore, we show that with increasing proximity to drainage canals both burned area depth and the probability of recurrent fires increase and present equations explaining burned area depth as a function of distance to drainage canal. This improved knowledge enables a more accurate approach to emissions accounting and will support IPCC Tier 2 reporting of fire emissions., (© 2015 John Wiley & Sons Ltd.)

Purpose: To evaluate the clinical outcome of eyes which underwent primary trabeculectomy with adjunctive mitomycin C (MMC) for primary glaucoma., Patients and Methods: A prospective analysis of 25 eyes in 23 patients who underwent primary trabeculectomy with MMC for primary glaucoma was performed. Clinical outcome measures including postoperative intraocular pressure, change in logarithm of the minimum angle of resolution (LogMAR) visual acuity, and incidence of complications were measured up to 1 year postoperatively., Results: Mean intraocular pressure decreased from 26.0 +/- 4.4 mmHg preoperatively to 12.5 +/- 3.9 mmHg (p < 0.0001) 1 year postoperatively. The mean LogMAR visual acuity changed from 0.23 +/- 0.19 preoperatively to 0.23 +/- 0.20 1 year postoperatively (p = 1.0). One eye developed a temporary hypotonous maculopathy and 4 eyes progressed in cataract formation., Conclusion: Primary trabeculectomy with MMC in eyes with primary glaucoma showed excellent pressure reduction. There were no cases of persistent hypotonous maculopathy or bleb endophthalmitis.

Graves' ophthalmopathy is thought to be an autoimmune disease. Whether this disease is organ specific or one of the manifestations of the closely linked Graves' thyroid disease is still a matter of speculation. Many papers describe autoimmunity to orbital antigens, in particular to extraocular muscles, in patients with Graves' ophthalmopathy. In this paper we have tried to give an overview of the evidence for circulating antibodies against extraocular muscle antigens and to discuss their significance in relation to the immunopathogenesis of the disease.