Supplementary Information (Online only): Identification of areas in Brazil that optimize conservation of forest carbon, jaguars, and biodiversity

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Appendix S6: Comparisons of carbon stored per hectare (in forest remnants 2008) within municipalities for two different datasets: Ruesch Gibbs (2008) and Saatchi et al (2011).
Descriptive stats and T tests
Table S6: Differences and correlation between Ruesch and Gibbs (2008) and Saatchi et al (2011) carbon datasets.
Appendix S7. Support References GIS support datasets, Carbon and REDD projects references
Jaguar and Biodiversity references

Appendix S5. Figure S5: “REDDspots” and (JCUs & corridors, Protected areas and Indigenous lands)
All “REDDspots” occurred in municipalities that contained JCUs and corridors: “urgent protection” JCUs (n=10, 6%), “conservation and research” JCUs (n=47, 29%), “conservation or exploratory research” JCUs (n=17, 10%), “barrier corridors” (n=44, 27%) and, “no barrier corridors” (n=41, 26%). REDDspots were significantly associated with Protected Areas (PA) or Indigenous Territories (IT) (Χ²= 566.47, df =1, P<0.0001), with 63% of the municipalities (n=100) containing REDDspots also containing some PA or IT (Fig S5b).

Figure S5. a) “REDDspots” and municipalities in which they overlap (or not) with current or proposed REDD projects. b) “REDDspots” and Protected areas and Indigenous land.

Appendix S6: Comparisons of carbon stored per hectare (in forest remnants 2008) within municipalities for two different datasets: Ruesch & Gibbs (2008) and Saatchi et al (2011).
Practical implications caused by the difference between datasets

In order to validate our use of the Ruesch & Gibbs (2008) carbon dataset, we compared it with Saatchi et al. (2011). Comparing tonnes of carbon per hectare we confirmed that there was a significant correlation between the two datasets for all municipalities (Pearson’s r = 0.75, P << 0.0001, t = 84.9, df = 5563, with 95% confidence interval CI= 0.74 – 0.76) (Fig S6, Fig S7 and Table S6) (Crawley, 2007). In comparisons by biome we found that Amazonia, Atlantic Forest, Cerrado, Caatinga and the Pantanal all showed significant correlations between the two datasets (Table S6). The only biome where there was not a significant correlation between the two datasets was the Pampas (Table S6), however since the Pampas does not contain any JCUs this biome was not included in any of our analyses.

As we discuss below, these correlations demonstrate a strong degree of consistency between the datasets. The variability that exists between the two datasets when compared within biomes is possibly related to the varying number of municipalities in each biome, as well as with the variable degree of uncertainty in Saatchi et al (2011), itself probably a result of the varying numbers of field control samples taken for each biome (see Saatchi et al 2011 supplementary material).
Descriptive stats and T tests
The descriptive statistics showed that estimated mean and median carbon levels for Brazilian municipalities were lower in the Saatchi et al (2011) data (mean 91.7 tC/ha, median 64.1 tC/ha) than in the Ruesch & Gibbs (2008) data (mean 154.3 tC/ha, median 138.9 tC/ha) (Fig S6, Fig S7 and Table S6). The difference between the means (and medians) of the two datasets varied for each biome (Table 6).
We used a paired T test (and a T wilcoxon test Cohen & Cohen (2008)) to compare pairwise estimates of carbon (of both datasets) within municipalities for Brazil. The estimates of carbon were significantly different for both tests (P << 0.0001).
One explanation why the Saatchi et al (2011) data exhibited lower mean and median values for total carbon is that their methodology summed above and below ground biomass (derived from empirical data and specified functions) and then divided the total biomass by two to estimate carbon biomass. Saatchi et al (2011) considered the mean of above and below ground total biomass value while Ruesch and Gibbs (2008) combined estimates of above and below ground carbon with established “Tier-1 carbon fractions” from the IPCC (2006). This means that variations in carbon values in Ruesch & Gibbs (2008) may stem from the different “carbon fractions” associated with different vegetation types, while variation in Saatchi et al 2011 stems from the sampling methodologies used to estimate biomass.
Applying the same threshold values to the Saatchi’s et al (2011) data naturally reduced the number of municipalities that were classified as having high carbon (≥ 230 tC/ha) from 159 to 103 municipalities.
Table S6: Differences and correlation between Ruesch and Gibbs (2008) and Saatchi et al (2011) carbon datasets.

Biome	Brazil	Amazonia	Atlantic Forrest	Cerrado	Caatinga	Pantanal	Pampa
Ruesch & Gibbs, (2008)	M = 154.3 Med=138.9	M = 237.3 Med=252.4	M = 162.3 Med=147.5	M = 118.1 Med=112.6	M = 131.7 Med=130.8	M = 178.4 Med=183.6	M = 131.3 Med=129.8
Saatchi et al., (2011)	M= 91.7 Med=64.1	M=219.1 Med=227.9	M= 99.6 Med= 78.1	M= 60.8 Med= 54.4	M= 44.2 Med= 37.1	M= 67.5 Med=64.9	M=83.5 Med=78.8
Difference of means (tC/ha)	62.6	18.2	62.7	57.3	87.5	110.9	47.8
Difference of medians (tC/ha)	74.8	24.5	69.4	58.2	93.7	118.7	51
Pearson’s Correlation and 95% CI	r = 0.75, P<<0.0001 0.74– 0.76 t = 84.9, df = 5563	r = 0.74, P << 0.0001 0.73– 0.76 t = 75.3, df = 4564	r = 0.70, P <<0.0001 0.69 – 0.72 t = 53.0, df = 2837	r = 0.64, P <<0.0001 0.60 – 0.68 t = 26.3, df = 990	r = 0.31 , P <<0.0001 0.26 – 0.37 t = 11.07, df = 1116	r = 0.89, P < 0.01 0.4 – 0.98 t =4.3, df = 5	r = -0.12, P = 0.21 - 0.29 – 0.06 t = -1.27, df = 116

All statistics were performed in R (R Development Core Team. 2009).
Our comparison of the two datasets confirms that there is a significant rank correlation between Reusch and Gibbs (2008) and Saatchi et al. (2011) across all municipalities, however Saatchi et al. (2011) yield a consistently lower average carbon value (both mean and median). Using the lower mean carbon values in Saatchi would affect which municipalities met our threshold carbon value in each scenario and would ultimately result in 56 of the 159 municipalities that we identified as REDDspots being excluded.

The high rank correlation between the two datasets indicates that our REDDspot methodology was likely to identify a very similar set of municipalities as being optimal areas for maximising both carbon and Jaguar conservation, irrespective of which dataset was used. The primary difference between the two datasets is the lower number of municipalities that crossed our threshold for being “high carbon areas” using the Saatchi et al. (2011) dataset, compared to Reusch & Gibbs (2008). However we do not feel that this represents a fundamental difference because while we tried to base our thresholds on values in the scientific literature (Harris et al. 2008) these thresholds were also somewhat arbitrary and could easily be manipulated upwards or downwards depending on the policy context.

This study illustrates one novel way in which policy-makers could combine different sources of information to select the areas that simultaneously deliver the best benefits for biodiversity, forest and carbon conservation. This framework can continue to be adapted and modified as policy objectives shift and data become available, however in this instance we have also demonstrated that our results are relatively stable irrespective of the dataset used.

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Figure S6. Scatter plots of Ruesch and Gibbs (2008) and Saatchi et al (2011) datasets showing the tonnes of carbon per hectare for each municipality. (a) and (b) show the data for all Brazilian municipalities, in (a) arranged in order of descending carbon level based on Ruesch and Gibbs (2008), and in (b) based on Saatchi et al (2011). Graphs (c) – (h) show the data for each biome arranged in order of descending carbon level based on Ruesch and Gibbs (2008). The estimated carbon levels from Saatchi et al (2011) are generally lower than the estimated levels of carbon by Ruesch and Gibbs (2008).

Figure S7. Scatter plots (a) and (b) compare the estimated carbon levels for all “REDDspot” municipalities in the Amazon and Atlantic Forest Biomes respectively. In both cases data are arranged in order of descending carbon level based on Ruesch and Gibbs (2008)*.

Figure S8. Plots showing the correlation between the Ruesch and Gibbs (2008) dataset and the Saatchi et al (2011) dataset. (a) shows the data for all Brazilian municipalities, (b) – (g) show the data for each biome. Pampas was the only biome in which the correlation between the two datasets was not significant.

Appendix S7. Support References

GIS support datasets, Carbon and REDD projects references

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Cenamo, M. C., et al. editor. 2009. Casebook of REDD projects in Latin America, IDESAM, Manaus, Brazil.

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