carbon stock corridors to mitigate climate change and promote … · 2014-09-09 · tropical...

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCLIMATE2105

NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange 1

Carbon stock corridors to mitigate climate change and promote biodiversity in the tropics

1) VCS_Corridors_Supplementary_Methods.pdf This file contains supplemental methods on our protected area selection approach, resistance surface calculation, corridor network construction, TOPSIS scoring, corridor dimension analysis, and depiction of geographic distribution using equal area hexagons. 2) VCS_Corridors_Supplementary_Figures.pdf This file contains nine supplementary figures with legends and references. These figures provide information on the corridor network construction methodology, differences in corridor VCS derived from different biomass datasets, differences in corridor VCS compared to a business as usual carbon preservation approach, differences in Human Footprint score between corridor VCS and a business as usual approach, comparison of TOPSIS results for two different biodiversity scenarios, geographic distribution of VCS density in corridors, and summaries of VCS and VCS density by country. 3) VCS_Corridors_Supplementary_Tables.pdf This file contains four supplementary tables and references. These tables provide information on corridor dimensions, land ownership in the Legal Amazon, and country level Human Footprint summaries for corridor VCS and high VCS areas identified using a business as usual approach.

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Tropical Vegetation Carbon Stock Corridors to Mitigate Climate Change and Promote Biodiversity 1 Supplementary Methods 1.1 Protected Areas We acquired protected area boundaries from the 2010 release of the World Database on Protected Areas (WDPA)1. Features not entirely within the domain of the biomass data were excluded, as were features where “design_type” was ‘International’2. These include areas such as World Heritage Sites and Man and Biosphere Reserves. We also removed areas where "status" was 'Adopted', 'Informally designated', 'Inscribed', 'Proposed', 'Recommended', 'Voluntary - recognised', or 'Voluntary - unrecognised'. This left only features where “status” was ‘Designated’ or “status” was empty. We included protected areas from all IUCN categories as well as those without a categorical designation. 1.2 Resistance Surface We calculated the resistance surface by taking the inverse of VCS values, creating a resistance surface ranging from 1 (high resistance), to close to zero (low resistance). We assigned a value of 1 to terrestrial cells with no VCS. Wide water bodies are considered significant barriers to movement for many terrestrial organisms. To account for this, we identified all water bodies at least two pixels wide (~900m) using the water class in the MODIS MOD12Q1 plant functional type land cover classification. We recoded these water pixels with a resistance value of 1000. This procedure prevented corridors from being drawn across large water bodies while allowing corridors to cross narrow water features. 1.3 Corridor Network After identifying first order neighbors for each protected area, we used the conditional minimum transit cost algorithm (CMTC)3 to map corridors between each pair of protected areas. The CMTC algorithm calculates the lowest cost path between protected areas, conditional on moving through a given cell. It does this for each cell in the area of interest, generating a cost-distance surface where cell values show the cumulative weighting of geographic distance by the cost of traversing intervening cells. We thresholded the cost-distance surface by selecting grid cells within 1% of the least cost path cost-distance value. The threshold was selected to achieve average corridor widths of at least 1 km. Corridors one pixel in length were excluded, as were those between protected areas separated by greater than 500 km. In some cases, corridors were still able to cross the narrowest portions of large rivers. We used the ESRI World Rivers database to filter out these corridors. We used the ESRI World Hydro Database 2012 to identify ocean pixels and filter out corridors that traversed them. In a few cases, VCS was very unevenly distributed between protected areas, resulting in corridors with disproportionately wide sections. We dealt with this by removing corridors with a standard deviation of CMTC values greater than 50. This is an arbitrary threshold but was effective in removing disproportionately wide corridors. 1.4 TOPSIS Scoring The TOPSIS approach was developed in the field of decision theory to help decision makers choose among a set of alternatives with differing values for a set of criteria. It ranks alternatives relative to ideal (“best”) and negative-ideal (“worst”) alternatives. It is a compensatory method, where low scores in one criterion can be compensated for by high scores in another. Specifically, a set of alternatives, A1, A2, ... , Am, are to be ranked according to a set of criteria, C1, C2, ..., Cn, measured for each alternative. In this case, the criteria are VCS, biodiversity, and economic opportunity cost measured in corridors. Thus, each corridor can be considered an alternative to be ranked. We calculated biodiversity, VCS, and deforestation threat as positive criteria. We took the following steps to calculate corridor TOPSIS scores based on procedures described in reference 4.


1) Calculate the Euclidean norm of each variable

where indexes alternatives, indexes criteria, and corresponds to an individual entry in the matrix of alternatives and criteria. Criteria were equally weighted. 2) Calculate the “best” and “worst” solutions from the matrix of normalized variables

where is the set of positive criteria and is the set of negative criteria. 3) For each alternative, calculate the Euclidean statistical distance, along n dimensions, from the “best” and “worst” solutions.

4) Calculate the score of each alternative relative to the “best” solution.

5) Divide by economic opportunity cost (EOC).

Where is the TOPSIS score per hectare, is EOC in dollars per hectare, and is TOPSIS score per dollar. The TOPSIS score can be thought of as a multicriteria benefit per hectare. Dividing by EOC in units of dollars per hectare then yields a per dollar cost estimate of multicriteria benefit. We multiplied this cost estimate by 10,000 to ease interpretation yielding values in units of $10,000 per multicriteria benefit.


Because species richness and endemism richness measure different aspects of biodiversity, we generated separate scenarios for each. In both biodiversity scenarios, some of the highest scoring corridors were identified near major highways and rivers providing market access for timber and cattle ranching (Figs. 3a and 3b). In general, high EOC associated with potential for soybean production resulted in low scores for corridors in the arc of deforestation. A set of high scoring corridors for the endemism richness scenario (Fig. 3a) were identified along the Madeira River, connecting indigenous reserves to the Juma sustainable development reserve (Fig. 3d). These contained higher than average scores for endemism richness and VCS and lower than average scores for EOC. High richness and high threat offset high EOC related to soybean cultivation in a corridor connecting the Japuira and Escondido indigenous reserves (Figs. 3c and 3e). Similar overall patterns were seen for the species richness scenario (Fig. 3b). We identified several corridors at the edge of highly suitable soybean areas (Fig. 3f) and some of the highest scoring corridors were in the Marajó várzea in Pará where relatively low EOC coincided with high threat and high species richness (Fig. 3g). There was good correspondence between scenarios with close to 80% agreement in the top 100 corridors (Supplementary Fig. S6). 1.5 Corridor Dimensions We estimated the average width of corridors by dividing corridor area by the Euclidean distance along the least cost path. A log-log regression of width on length shows that a 1% increase in corridor length results in a 0.9% increase in corridor width. The estimated regression equation, is y = 10-1.14 + x0.89 + 10(0.1)

. Corridor lengths and widths were comparable across regions, averaging between 41-55 km and 2-3 km, respectively. Variability in length and width was high, with standard deviations greater than 60 km and 3 km, respectively, in all regions (Supplementary Table S1). Corridor lengths were primarily a function of protected area spacing (Pearson correlation coefficient (r) between Euclidean distance and distance along the corridor center line = 0.98). Corridor widths were strongly correlated with length, increasing 0.67% for every 1% increase in length (Supplementary Table S2). 1.6 Geographic Distribution To depict geographic variation in corridor VCS, we averaged VCS within corridors within 7800 km2 equal area hexagonal grid cells5. This shows heterogeneity of corridor VCS within forests of the Amazon and Congo Basins, Indonesia, and Papua New Guinea, while also showing isolated areas of high VCS in places like Central America, western Africa, Madagascar and Laos (Supplementary Fig. S7). Gaps in coverage correspond to large protected areas, major rivers, and, in some cases, absence of protected areas. Of the twenty highest corridor biomass countries (Supplementary Fig. S8), several have large absolute carbon stocks in corridors (Supplementary Fig. S9) and eight are considered “mega-diverse”6, harboring a large fraction of the planet’s biodiversity: Colombia, DRC, Ecuador, Indonesia, Malaysia, Papua New Guinea, Peru and the Philippines. References

1. IUCN and UNEP. The World Database on Protected Areas (WDPA). (2010). at <www.protectedplanet.net>

2. Jenkins, C. N. & Joppa, L. Expansion of the global terrestrial protected area system. Biol. Conserv. 142, 2166–2174 (2009).


3. Pinto, N. & Keitt, T. H. Beyond the least-cost path: evaluating corridor redundancy using a graph-theoretic approach. Landsc. Ecol. 24, 253–266 (2009).

4. Jahanshahloo, G. R., Lotfi, F. H. & Izadikhah, M. Extension of the TOPSIS method for decision-making problems with fuzzy data. Appl. Math. Comput. 181, 1544–1551 (2006).

5. Ellis, E. C., Antill, E. C. & Kreft, H. All is not loss: plant biodiversity in the anthropocene. PLoS One 7, e30535 (2012).

6. Mittermeier, R. A., Mittermeier, C. G. & Gil, P. R. Megadiversity: Earth’s biologically wealthiest nations. México, Distrito Federal: Cemex. (CEMEX, 1997).

Data Availability All corridors between protected areas (i.e. results presented in Fig. 1) are available as GIS files through the Woods Hole Research Center (http://www.whrc.org/corridors).


Tropical Vegetation Carbon Stock Corridors to Mitigate Climate Change and Promote Biodiversity Supplementary Figures

Figure S1. Example of network and corridor mapping steps. a) Protected areas in crosshatch with the biomass layer in the background running from low biomass reds to high biomass greens. b) thiessen polygons created using cost distance as a measure of separation, borders between thiessen polygons were used to identify which protected areas to connect c) minimum conditional transit cost surface between two protected areas, lighter values indicate lower transit costs d) least cost corridor created by thresholding the minimum conditional transit cost surface to the lowest 1% of cells e) least cost corridors in gray mapped between all neighboring protected areas. Code for computing corridors was adapted from reference 1.


Figure S2. Percent difference between corridor vegetation carbon stock (VCS) derived from reference 2 and reference 3 biomass datasets. Difference is relative to reference 2. Purple areas show where corridor VCS from reference 3 is higher; green areas show where corridor VCS from reference 2 is higher.

Figure S3. Scatterplot of national level VCS and corridor VCS differences. We calculated the absolute difference in corridor VCS density derived from the Baccini, reference 2, and from the Saatchi, reference 3, biomass datasets. We did the same for national level VCS density and plotted the relationship between the differences. Countries with large corridor VCS density differences tend to have large national level differences. Units are tons of carbon per hectare.

Figure S4. Percent difference between corridor and business as usual (BAU) scenarios in area needed to preserve the same amount of VCS. The BAU scenario was generated by selecting the minimum set of pixels in a given country, that, when summed, contain VCS equal to that in corridors. All values are positive indicating that the BAU scenario is more efficient in terms of area needed. For example, the darkest green indicates that the BAU scenario requires 61 to 80 percent less area than the corridor scenario to preserve the same amount of VCS.


Figure S5. Difference in mean Human Footprint4 scores between corridor and business as usual scenarios. Positive values indicate that corridor scenarios have higher human footprint scores. See Supplementary Table 4 for country names corresponding to abbreviations.


Figure S6. Comparison of economic opportunity cost (EOC) normalized TOPSIS scores for biodiversity scenarios. The black line depicts the fraction of agreement between the first N corridors ordered from high to low for the endemism richness and species richness scenarios. The dashed gray line depicts level of agreement expected at random.

Figure S7. Geographic distribution of VCS density in corridors. VCS density is depicted in 7800 km2 hexagonal grid cell. Units are tons of carbon per hectare.


Figure S8. VCS density in corridors. Entries are reported by country and ordered by VCS density. VCS density is in units of tons of carbon per hectare (tC ha-1).


Figure S9. VCS in corridors. Countries are ordered by VCS density as in figure S8. VCS is in gigatons of carbon (GtC).


References 1. McRae, B., & Kavanagh, D. (2011). Linkage Mapper Connectivity Analysis Software. The Nature

Conservancy. 2. Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by

carbon-density maps. Nat Clim Chang 2, 182–185 (2012). 3. Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three

continents. Proc. Natl. Acad. Sci. U.S.A. 108, 9899–904 (2011). 4. Wildlife Conservation Society (WCS), and C. for I. E. S. I. N. (CIESIN)/Columbia U. Last of the Wild

Project, Version 2, 2005 (LWP-2): Global Human Footprint Dataset (Geographic). (2005). at <http://sedac.ciesin.columbia.edu/data/set/wildareas-v2-human-footprint-geographic>


Tropical Vegetation Carbon Stock Corridors to Mitigate Climate Change and Promote Biodiversity Supplementary Tables Length (sd) Width (sd) Africa 46.0 (64.9) 2.17 (3.10) Asia 55.1 (65.5) 2.57 (2.97) South America 58.6 (64.1) 2.72 (3.16) Pan-Tropics 52.1 (65.0) 2.44 (3.11) Table S1. Corridor length and width summaries by region. Units are in kilometers and standard deviations are shown in parentheses. Intercept -0.26 (0.0024)*** log(Euclidean distance along the least cost path) 0.88 (0.0035)*** Adjusted R-Squared 0.83 N 13,317 Standard errors are reported in parentheses. *** indicates significance at the 99% level Table S2. Linear regression results of least cost path distance on Euclidean distance between protected areas.

WDPA Area (km2) Percent of Amazon Federal 652,847 12.78 State 451,293 8.84 Indigenous 1,066,877 20.89 Total 2,171,017 42.51

From ref 1. Area (km2) Percent of Amazon Federal 378,173 12.40 State 515,463 9.00 Indigenous 987,226 23.6 Total 1,880,862 45 Table S3. Area and percent of the Legal Amazon by ownership status. Values in the top panel were derived from a GIS analysis of the World Database of Protected Areas. Values in the bottom panel were derived from supplemental information provided in reference 1.


Table S4.Mean Human Footprint2 values by country. Summarized for corridor and business as usual (BAU) scenarios, with standard deviations in parentheses. The Human Footprint index ranges from zero to one hundred where zero corresponds to areas with relatively little influence from human activities and one hundred corresponds to areas dominated by human activity.

Country ISO Corridor BAU

Corridor Minus BAU Country ISO Corridor BAU

Corridor Minus BAU

Angola AGO 17 (9) 21 (7) -4 (2) Jamaica JAM 35 (8) 31 (6) 4 (1) Argentina ARG 18 (8) 15 (6) 3 (2) Kenya KEN 20 (12) 28 (8) -7 (3) Bangladesh BGD 23 (8) 20 (7) 2 (1) Laos LAO 20 (6) 18 (6) 2 (0) Belize BLZ 27 (10) 20 (5) 7 (5) Liberia LBR 25 (8) 22 (7) 4 (1) Benin BEN 25 (6) 23 (6) 2 (0) Madagascar MDG 24 (10) 19 (6) 4 (4) Bolivia BOL 13 (9) 7 (7) 6 (3) Malawi MWI 26 (8) 21 (6) 5 (2) Botswana BWA 15 (8) 12 (7) 3 (1) Malaysia MYS 22 (12) 13 (10) 9 (3) Brazil BRA 19 (12) 4 (4) 15 (7) Mali MLI 20 (6) 20 (6) 0 (1) Brunei BRN 42 (13) 27 (9) 15 (4) Mauritania MRT 28 (8) 28 (9) -1 (-1) Burkina Faso BFA 26 (5) 23 (5) 3 (0) Mexico MEX 27 (10) 21 (9) 5 (1) Burundi BDI 26 (6) 24 (6) 2 (0) Mozambique MOZ 21 (8) 19 (8) 2 (0) Cambodia KHM 24 (7) 20 (5) 3 (2) Myanmar MMR 18 (7) 17 (7) 1 (1) Cameroon CMR 23 (9) 15 (7) 8 (1) Namibia NAM 14 (7) 18 (7) -4 (0) Cayman Islands CYM 41 (6) 26 (6) 15 (0) Nicaragua NIC 28 (13) 14 (8) 13 (4) Central African Republic CAF 16 (10) 18 (8) -2 (2) Niger NER 27 (5) 9 (11) 18 (-6) Chad TCD 26 (5) 24 (5) 2 (-1) Nigeria NGA 27 (7) 28 (7) -1 (0) Chile CHL 13 (7) 12 (8) 1 (-1) Panama PAN 26 (13) 14 (9) 11 (5) China CHN 34 (10) 26 (9) 8 (1) Papua New Guinea PNG 18 (10) 16 (10) 2 (0) Colombia COL 17 (12) 4 (6) 13 (6) Paraguay PRY 10 (8) 11 (9) -1 (-1) Costa Rica CRI 33 (10) 29 (7) 4 (3) Peru PER 12 (9) 6 (5) 6 (3) Côte d'Ivoire CIV 26 (6) 27 (6) -1 (0) Philippines PHL 33 (8) 29 (8) 4 (1) Cuba CUB 37 (10) 28 (6) 9 (4) Puerto Rico PRI 41 (11) 33 (6) 8 (4) Dem. Rep. of the Congo COD 18 (8) 17 (7) 1 (1) Republic of Congo COG 11 (8) 6 (5) 5 (3) Dominican Republic DOM 30 (7) 27 (6) 3 (2) Rwanda RWA 26 (5) 27 (5) -1 (0) East Timor TLS 25 (6) 27 (7) -2 (-2) Saudi Arabia SAU 33 (12) 32 (13) 1 (-1) Ecuador ECU 18 (12) 10 (9) 8 (3) Senegal SEN 26 (10) 29 (7) -3 (3) El Salvador SLV 36 (10) 34 (10) 1 (0) Sierra Leone SLE 31 (7) 30 (6) 1 (1) Equatorial Guinea GNQ 24 (5) 25 (5) 0 (0) Singapore SGP 54 (18) 43 (21) 11 (-2) Eritrea ERI 26 (6) 24 (8) 1 (-2) Somalia SOM 26 (5) 18 (6) 8 (-1) Ethiopia ETH 26 (8) 22 (9) 4 (-1) South Africa ZAF 21 (6) 20 (6) 1 (1) French Guiana GUF 10 (7) 6 (5) 5 (3) Sri Lanka LKA 34 (9) 37 (7) -2 (1) Gabon GAB 8 (7) 7 (6) 1 (1) Sudan SDN 19 (9) 15 (7) 3 (2) Gambia GMB 33 (7) 37 (8) -4 (-1) Suriname SUR 10 (7) 5 (5) 5 (2) Ghana GHA 27 (6) 27 (7) 0 (0) Tanzania TZA 24 (8) 18 (7) 6 (1) Guatemala GTM 30 (8) 28 (6) 3 (2) Thailand THA 26 (9) 21 (6) 5 (2) Guinea GIN 26 (6) 27 (5) -1 (0) Togo TGO 26 (6) 26 (6) 0 (0) Guinea-Bissau GNB 28 (6) 31 (9) -4 (-3) Trinidad and Tobago TTO 33 (10) 22 (15) 12 (-4) Guyana GUY 6 (4) 6 (5) 0 (-1) Uganda UGA 30 (8) 27 (9) 3 (-1) Haiti HTI 32 (7) 32 (7) 1 (0) Venezuela VEN 16 (10) 5 (4) 11 (6) Honduras HND 32 (8) 13 (11) 19 (-3) Vietnam VNM 27 (7) 23 (5) 3 (2) Hong Kong HKG 51 (16) 48 (18) 3 (-2) Yemen YEM 24 (5) 25 (6) -1 (-1) India IND 34 (10) 26 (8) 7 (2) Zambia ZMB 21 (9) 14 (7) 6 (2)

Indonesia IDN 24 (10) 18 (8) 6 (2) Zimbabwe ZWE 31 (8) 30 (7) 2 (1)


References 1. Soares-Filho, B. et al. Role of Brazilian Amazon protected areas in climate change

mitigation. Proc. Natl. Acad. Sci. U.S.A. 107, 10821–6 (2010). 2. Wildlife Conservation Society (WCS), and C. for I. E. S. I. N. (CIESIN)/Columbia U. Last of

the Wild Project, Version 2, 2005 (LWP-2): Global Human Footprint Dataset (Geographic). (2005). at <http://sedac.ciesin.columbia.edu/data/set/wildareas-v2-human-footprint-geographic>


carbon stock corridors to mitigate climate change and promote … · 2014-09-09 · tropical...

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