School of Life, Sport and Social Sciences, Edinburgh Napier University, Sighthill Campus, Edinburgh, EH11 4BN, UK.
Kenya Marine and Fisheries Research Institute, P.O. Box 81651, Mombasa, Kenya.
Glob Chang Biol. 2017 Jan;23(1):224-234. doi: 10.1111/gcb.13438. Epub 2016 Aug 23.
Despite covering only approximately 138 000 km , mangroves are globally important carbon sinks with carbon density values three to four times that of terrestrial forests. A key challenge in evaluating the carbon benefits from mangrove forest conservation is the lack of rigorous spatially resolved estimates of mangrove sediment carbon stocks; most mangrove carbon is stored belowground. Previous work has focused on detailed estimations of carbon stores over relatively small areas, which has obvious limitations in terms of generality and scope of application. Most studies have focused only on quantifying the top 1 m of belowground carbon (BGC). Carbon stored at depths beyond 1 m, and the effects of mangrove species, location and environmental context on these stores, are poorly studied. This study investigated these variables at two sites (Gazi and Vanga in the south of Kenya) and used the data to produce a country-specific BGC predictive model for Kenya and map BGC store estimates throughout Kenya at spatial scales relevant for climate change research, forest management and REDD+ (reduced emissions from deforestation and degradation). The results revealed that mangrove species was the most reliable predictor of BGC; Rhizophora muronata had the highest mean BGC with 1485.5 t C ha . Applying the species-based predictive model to a base map of species distribution in Kenya for the year 2010 with a 2.5 m resolution produced an estimate of 69.41 Mt C [±9.15 95% confidence interval (C.I.)] for BGC in Kenyan mangroves. When applied to a 1992 mangrove distribution map, the BGC estimate was 75.65 Mt C (±12.21 95% C.I.), an 8.3% loss in BGC stores between 1992 and 2010 in Kenya. The country-level mangrove map provides a valuable tool for assessing carbon stocks and visualizing the distribution of BGC. Estimates at the 2.5 m resolution provide sufficient details for highlighting and prioritizing areas for mangrove conservation and restoration.
尽管红树林的覆盖面积仅约为 138000 平方公里，但它们却是全球重要的碳汇，其碳密度值是陆地森林的三到四倍。评估红树林森林保护的碳惠益面临的一个关键挑战是缺乏对红树林沉积物碳储量的严格空间解析估计；大多数红树林碳储存在地下。以前的工作主要集中在对相对较小区域的碳储量进行详细估计，这在一般性和应用范围方面存在明显的局限性。大多数研究仅侧重于量化地下碳（BGC）的最上层 1 米。对超出 1 米深处的碳存储以及红树林物种、位置和环境背景对这些存储的影响的研究则很少。本研究在肯尼亚南部的两个地点（Gazi 和 Vanga）调查了这些变量，并利用这些数据为肯尼亚生成了一个特定于国家的 BGC 预测模型，并在与气候变化研究、森林管理和 REDD+（减少毁林和森林退化所致排放）相关的空间尺度上绘制了肯尼亚整个国家的 BGC 储量估计图。结果表明，红树林物种是 BGC 最可靠的预测因子；Rhizophora muronata 的平均 BGC 最高，为 1485.5 t C ha 。将基于物种的预测模型应用于肯尼亚 2010 年物种分布的基础地图（分辨率为 2.5 米），得出肯尼亚红树林 BGC 的估计值为 69.41 Mt C [±9.15 95%置信区间（C.I.）]。当应用于 1992 年的红树林分布地图时，BGC 的估计值为 75.65 Mt C（±12.21 95% C.I.），肯尼亚的 BGC 储量在 1992 年至 2010 年间减少了 8.3%。国家一级的红树林地图为评估碳储量和可视化 BGC 分布提供了宝贵的工具。2.5 米分辨率的估计值为突出和优先考虑红树林保护和恢复的区域提供了足够的详细信息。
