A global map of mangrove forest soil carbon at 30 m spatial resolution

Transcription

1 Supplemental Information A global map of mangrove forest soil carbon at 30 m spatial resolution By Sanderman, Hengl, Fiske et al.

2 SI1. Mangrove soil carbon database. Methods. A database was compiled from peer-reviewed literature, grey literature (reports, published theses and national soil surveys) and from contributions of unpublished data from a number of researchers and organizations. In total, 201 sources of data were identified and 160 were included in the database. The 41 studies that were excluded either did not contain enough information to estimate geographic location or only contained averaged data across a wide geography. For each soil profile, geographic coordinates (as well as an estimation of locational accuracy), horizon depths, and an estimate of organic carbon concentration (OCC) and method of determination were required. Whenever available, bulk density (BD) measurements were also recorded for each horizon. In some cases, only organic carbon stocks (OCS) were reported to a certain depth. For each site, if possible, year of sampling, mangrove height, dominant species, landscape position and hydrogeomorphic setting were also recorded. In total, we identified 1812 useful soil profiles with 8082 data points (i.e., unique soil horizons). Of these data, 1470 profiles and 7041 data points do not have data use restrictions and are included in the public dataset (doi: /dvn/ocyuit). Several data harmonization steps were taken to ensure data was as consistent as possible across all studies: 1. Organic carbon concentration (OCC) was measured by a number of techniques including losson-ignition (LOI = proxy for organic matter content), total carbon (TC) on an elemental analyzer without mention of any consideration for inorganic carbon, or as total organic carbon

3 (TOC) either by elemental analyzer after acidification or by a chemical oxidation technique. Without any site specific information on whether carbonates were present in studies that stated they used a TC measurement, we have assumed that TC = TOC. For studies that reported LOI, we used a conversion factor of 0.50 instead of the Van Bemmelen factor (Allen et al 1974) of 0.58 which has been widely used and recommended in the mangrove research community (Mcleod et al 2011). A recent critical review of LOI to TOC found that 0.50 was much more appropriate across a range of soils (Pribyl 2010). An analysis of 146 samples with both LOI and TOC data reported in our database, suggested that a value of 0.50 was more appropriate (linear regression, slope = 0.47, R 2 = 0.92). 2. Bulk density (BD) was often not measured or was suspected of being erroneously measured. We developed a pedotransfer function based on OCC to predict BD for every sample (Fig. S1). A double negative exponential model was chosen because it best preserves a reasonable BD for purely organic soils. The model was developed in an iterative fashion. First, all 5983 points with both BD and OCC data were used to generate an initial model and then the points falling outside of the 95% prediction bands (n = 1782) were excluded and a final model was then generated. 3. The predicted BD value was then used whenever there was no measured value reported. For all measured values, we compared the reported and predicted value and if the reported value fell outside of the 95% prediction intervals of the BD model then the predicted value was used instead of the reported value. There are numerous chances for introducing significant error into BD measurements especially in peat soils (Köchy et al 2015) and this final data screening step was meant as a way of eliminating the potential for highly erroneous carbon density values.

4 4. After applying the corrections and harmonization steps in 1 & 2, we calculated organic carbon density (OCD, kg C m -3 ) for each measured horizon (assuming that the values apply at the center of the horizon): OCD [kg C m -3 ] = 1000 OCC [g g -1 ] BD [g cm -3 ] (100 - CF [%]) where OCC is the carbon content, BD is bulk density, and CF are the coarse fragments volume fraction. In cases where only a stock was reported (OCS, kg C m -2 ), the stock was divided by the depth increment (m) to obtain an OCD estimate: OCD [kg C m -3 ] = OCS / (D_lower - D_upper) 5. Many of the data points that were extracted from the literature have coordinates outside of the Giri et al (2011) global mangrove forest distribution (GMFD). In fact, 505 of the 1,812 sites fell outside this defined spatial domain. For studies conducted prior to the year 2000, it is possible that the site was cleared. The coordinates supplied in studies sometimes fell into water or townships. There is about a 50 meter (1 2 pixel) offset between GMFD and satellite imagery (Fig. S2a). Additionally, the GMFD is not perfect and it clearly misses some real mangrove forests in some regions of the world (Fig. S2b). To resolve cases such as Fig. S2a, we adjusted the GMFD by growing all vectors by one pixel on the outer edges (in an attempt to minimize the water-land gap) and then filtering out any pixel that falls over water by using Landsat NIR band. The resulting area of this adjusted GMFD domain is 16.6 Mha, slightly larger than recognized by Giri et al (2011) and Spalding (2010). For points such as shown in Fig. S2b, they are still included in the database but could not be used in the spatial model

5 development. In total, 199 soil profiles still outside the adjusted GMFD domain but 1,613 were used in the spatial model. For soil profiles with at least 3 horizons of data, a trend analysis was performed to determine if the change in OCD with depth was significantly different than a flat model (i.e., no change in OCD with depth). Briefly, linear, log-linear, log-log and quadratic models were fit to OCD versus depth data for each site in R (R core team 2009) and the p-value and direction of slope (for all models except quadratic) were recorded. The model with the lowest p-value, if below a critical threshold (0.20, 0.10 and 0.05), was then noted. If no model was significant below the critical p-value, then the null model of a flat depth distribution was accepted. Database description and results. The 1812 soil profiles originated from 47 nations (Fig. S3a) with a latitudinal range of 29.1 along the Gulf Coast of the United States to on the Victorian coast of Australia. Of the 15 most mangrove-rich countries (Giri et al 2011), only Myanmar, Papua New Guinea and Cuba are not represented. Principal components analysis indicated that these training points captured a large fraction of the total spatial variability found across the global mangrove domain (Fig. S4). Over 73% of the data were from samples collected since 2010 (Fig. S3b). The mean maximum sampling depth was 1.1 m with a median of 1.0 m (Fig. S3c). There were 210 sites with data to only 0.05 m and 163 sites stopped sampling at 3.0 m. A total of 359 out of the 1812 sites reported that they sampled the entire soil profile to a basal layer (indicated by dark portion of bars in Fig. S3c) sites included data from at least three soil horizons and 148 sites only reported OCS for the entire sampled profile.

6 A simple estimate of organic carbon stock (OCS) can be made to one meter depth by assuming there is no down-profile variation in OCD: OCS (Mg C ha -1 ) = 100 (profile mean OCD) 100 cm. Using this simple scaling, we find a mean (± 1 standard deviation) OCS of 278 ± 157 Mg C ha -1 with a range of Mg C ha -1 for the upper meter of soil (Fig. S5a). There was a clear latitudinal trend decreasing from the equator towards the temperate zones in both hemispheres when data were binned into 1 degree increments (Fig. S5b). Importantly, OCD was not invariant down the soil profile at many locations (Fig. S6). For the 1039 profiles with at least 3 horizons of data, 21 34% had significant negative trends, 8 15% had significant positive trends, 9 14% had a quadratic trend and 63 36% had a flat distribution (range in percentages for critical P-value of , Fig. S6d-e). For sites with significant decreases in OCD with depth, a linear decline was found to be the most appropriate model approximately half the time.

7 Figure S1. Bulk density pedotransfer function. Bulk density was estimated as a function of organic carbon (OC) concentration using a double inverse exponential function. A two-step process was applied: First, an initial model was created using all data (R-square = 0.62, n = 5983); next, all points falling outside the 95% prediction bands of the initial model (red crosses) were removed and a second model was fit to the remaining data (R-square = 0.85, n = 4201).

8 Figure S2. Two examples of points that fell outside of the original Global Mangrove Forest Distribution (GMFD, Giri et al. 2011) domain. In (A), our adjustments (pixel growing and NIRbased water mask) to the GMFD resulted in this point falling within the adjusted domain. In (B), the GMFD has clearly misidentified the landward extent of this extensive mangrove forest and our adjustment of one pixel does not rectify this issue, therefore these sites were excluded from the spatial modeling analysis.

9 Figure S3. Mangrove soil carbon database description: (A) number of soil profiles from each nation, ranked by mangrove area defined by Giri et al (2011), with the top 15 nations indicated next to bars; (B) sample frequency by year of sample collection; and (C) sample frequency by maximum sampling depth. In (C), total number of profiles is divided into those where a full profile was collected (to an identified basal layer - full profile ) and those where the study did not indicate if a full profile was sampled ( max depth ).

10 Figure S4. Principal components analysis of training points (n = 1613) and randomly selected points across the global mangrove distribution (n = 15,000). Results from the first 3 principal components are shown along with % of variance explained by each component.

11 Figure S5. (A) Soil organic carbon stocks (SOCS) calculated to 1 m depth using mean organic carbon density (OCD) of each profile and assuming no down-profile variation in OCD. Above histogram, box and whisker plot indicates mean (dotted line), median (solid line), 25th and 75th percentile (box), 5th and 95th percentiles (whiskers) with dots indicating outliers. (B) Distribution of SOCS by latitude. Data were binned to the nearest degree of latitude. Error bars represent 1 standard deviation. Best natural log fit to binned data shown in red (R-square = 0.50) along with 95% confidence intervals.

12 Figure S6. Example soil organic carbon density (OCD) profiles for (A) sites with no significant depth trend, (B) sites with significant decreases, and (C) sites with significant increases in OCD with depth. Results of depth trend analysis showing count of which model performed the best with decreasing critical p-values (D) and direction of trend (negative or positive) when different from a flat model (E).

13 Figure S7. Spatial prediction errors calculated using Random Forest Quantile Regression. Error is presented as the mean relative standard deviation (s.d. divided by 1 m SOC stock) and aggregated by the same hex bins as in Fig. 2.

14 Figure S8. Example of the deforestation analysis from East Kalimantan, Indonesia, focused on the Mahakam River Delta. Colored pixels are where deforestation has occurred between 2000 and 2015 based on Hansen et al (2013) global forest change data version 1.3 (source: Hansen/UMD/Google/USGS/NASA). Where deforestation has occurred on land that was identified as mangrove habitat in the year 2000, the colors represent the cm SOC stock using the same scale as in Fig 5. Non-mangrove deforestation shown in red.

15 Additional References. Allen S E, Grimshaw H M, Parkinson J A, Quarmby C and others 1974 Chemical analysis of ecological materials. (Blackwell Scientific Publications.) Köchy M, Don A, van der Molen M K and Freibauer A 2015 Global distribution of soil organic carbon Part 2: Certainty of changes related to land use and climate Soil Online: Mcleod E, Chmura G L, Bouillon S, Salm R, Björk M, Duarte C M, Lovelock C E, Schlesinger W H and Silliman B R 2011 A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2 Front. Ecol. Environ Online: Pribyl D W 2010 A critical review of the conventional SOC to SOM conversion factor Geoderma Online: