Evaluating, predicting and mapping belowground carbon stores in Kenyan mangroves
Year Published:
2017
Study Number:

105

Country:
Kenya
Author:

Selena K. Gress, Mark Huxham, James G. Kairo, Lilian M. Mugi, Robert A. Briers

Abstract:

Despite covering only approximately 138 000 km2, 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 mucronata had the highest mean BGC with 1485.5 t C ha-1. Applying the species-based predictive model to a base map of species distribution in Kenya for the year 2010 with a 2.5 m2 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 m2 resolution provide sufficient details for highlighting and prioritizing areas for mangrove
conservation and restoration.

 

Main Results and Conclusions:
  • In Kenyan mangroves the average sediment depth of belowground carbon storage (BGC storage) was 2.53 m across species and sites.
    • “Mean sediment depth across both sites was 2.53 m…” (227)
    • “...there were no significant differences in sediment depth between sites or species.” (227)
  • There was no significant change of carbon density across different sediment depths.
    • “Depth as a covariate had no effect on carbon density.” (227)
    • “No strong effect of species on sediment depth was evident.” (229)
    • “The absence of depth effect here may suggest that a decrease in carbon concentration and an increase in bulk density with depth cancel out any depth effect in carbon density” (231)
  • Both site and species did have an effect on the total BGC storage.
    • “Belowground carbon stores to 1 m depth did not differ significantly between sites but species had a significant effect on BGC…” (228)
    • “For data combined across sites, Rhizophora had the highest mean BGC... which was significantly higher than Avicennia Mix... Avicennia had the second highest BGC....(significantly greater than Avicennia Mix…). Ceriops had the lowest BGC to mean depth….” (232)
  • BGC storage increased with distance from the shoreline.
    • “There was a trend for BGC to increase with distance from the seaward fringe (DFC) at both sites…” (229)
    • “For BGC to 1 m and mean depth, there was a significant interaction between DFC and site. This may be confounded by species differences in BGC, as the variance inflation factor suggests.” (232)
  • Through this study of BGC storage in two sites, they were able to create a “predictive model” to determine BGC storage for mangroves throughout Kenya.
    • “We used the data from two sites to produce a model that could provide a first estimate of BGC in mangroves across Kenya, assuming that any variables that showed large differences in their effects between these two sites could not be included in a country-wide model.” (229)
    • “The country-level mangrove map provides a valuable tool for assessing carbon stocks and visualizing the distribution of BGC.” (232)
  • Because researchers were able to find mangrove sites within Kenya that had a deeper depth than they were able to measure (2.97 m), they predict that the majority of belowground carbon storage estimates in the world (which stop at 1 m) are underestimated, including their own data.
    • “Because sediment depth could only be measured to a maximum of 2.97 m, due to the length of the rod, there was an underestimation of sediment depth in plots at both sites.” (227)
    • “The percentages of underestimated plots (a mean of both sites) were as follows: Avicennia 43%, Avicennia Mix 50%, Rhizophora 58%, Rhizophora Mix 29%, Ceriops 38%.” (227)

 

Works Cited: