Supplementary Information (Online only):

Identification of areas in Brazil that optimize conservation of forest carbon, jaguars, and

biodiversity

De Barros, Alan E.; Macdonald, Ewan A.; Matsumoto, Marcelo H.; Paula, Rogério C.;

Nijhawan, Sahil; Malhi,Y.; Macdonald, David W.

*Correspondence: E-mail: alanbiology@gmail.com

Appendix S1.Table S1: Datasets used in GIS analysis.

Appendix S2. Table S2: REDD related strategies in Brazil.

Appendix S3. Supplementary results and parameters and values adopted in the Promethee

method

Fig. S3: Occurrence (within municipalities) of JCU and biodiversity categories in relation to

forest carbon storage and annual deforestation rate.

Table S3a: Fundamental criteria used to compare all scenarios with distinct levels of carbon and

deforestation

Table S3b: All criteria used to compare the top four scenarios

Appendix S4. Table S4: List of municipalities highlighted as “REDDspots”.

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Appendix S5. Fig S5: “REDDspots” and (JCUs & corridors, Protected areas and Indigenous

lands).

Appendix S6. Comparisons of carbon stored (in forest remnants 2008) within Brazilian

municipalities for two different datasets: Ruesch & Gibbs (2008) and Saatchi et al (2011).

Table S6: Differences and correlation between Ruesch and Gibbs (2008) and Saatchi et

al (2011) carbon datasets.

Fig. S6: Scatter plots comparing two datasets of carbon: Ruesch and Gibbs (2008) and

Saatchi et al (2011) datasets

Fig. S7: Scatter plots for REDDspots and correlation plots for biomes comparing

Ruesch and Gibbs (2008) and Saatchi et al (2011) carbon datasets.

Appendix S7. Support references

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Appendix S1. Datasets used:

Table S1: Datasets used in the GIS analysis. In some cases the datasets that we used in our analysis were themselves compiled from a number of pre-existing datasets; to make this clear we have listed the dataset that we used in regular font, but also added the original datasets below in italics.Dataset Data description SourceCarbon dataset Global biomass and soil carbon map

a) global carbon biomassb) soil carbon map

Kapos et al (2008) a) Ruesch & Gibbs (2000) b) G.S.D.T (2000)

Biodiversity 1 Global map of priority areas for biodiversity with six prioritization schemes: a) Conservation International’s Hotspots b) WWF Global 200 ecoregions - terrestrialc) WWF Global 200 ecoregions - freshwaterd) Birdlife International Endemic Bird Areas, EBA5 e) WWF/IUCN Centres of Plant Diversity, CPD5f) Amphibian Diversity Areas

Kapos et al (2008)

a) Mittermeier et al., (2004)b) Olson & Dinnerstein, (2002)c) Olson & Dinerstein, (2002)d) Birdlife International, (2008)e) WWF and IUCN (1994)f) Duellman, (1999)

Biodiversity 2 Biological Importance in Brazil, Ministry of Environment MMA (2007)Biomes Brazilian main biomes MMA & IBGE (2004)Deforestation and remnant forest 1

Available deforestation and forest remnant landcover data for Amazon Biome from year 2002 to 2008.PRODES/ National Institute for Space Research (INPE).

INPE (2009)

Deforestation and remnant forest 2

Available deforestation and forest remnant landcover data for Biomes of Atlantic Forest, Cerrado, Pantanal, Caatinga and Pampas from year 2002 to 2008. Ministry of Environment/ Brazilian Environment and Natural Resources Institute (MMA - CSR/IBAMA)

MMA/IBAMA (2009)

Municipalities Municipalities layer referent to 2007 from Brazilian Institute of Geography and Statistics (IBGE)

IBGE (2008)

Jaguar Conservation Units

Priority areas for jaguar conservation. Updated data from CENAP/ ICMbio and Panthera Foundation. 20 JCUs inside the Brazilian territory (4 of urgent action priority, 9 of interest for research and conservation, 5 of interest for conservation and 2 of research interest).

Paula et al. (2010), Nijhawan et al. 2010, Nijhawan (2012), Sanderson et al. (2002), Desbiez &Paula 2012

Jaguar Corridors Jaguar network corridors, CENAP/ ICMbio and Panthera Foundation. 18 corridors considering barriers, 13 corridors without consider barriers. * Corridor polygons merged and adapted from original.

Nijhawan et al. 2010, Rabinowitz & Zeller (2010)

REDD projects We identified municipalities where REDD projects occur because many projects did not have available GIS layers.

Cifor (2010), SFB/MMA (2009), Idesam (2010) and others, see STable 2.

Protected areas,Indigeous Lands

Brazilian Federal, State and municipal PA of integral protection and sustainable use and Indigenous Territories

MMA & FUNAI (2010)

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Appendix S2. Table S2: REDD related strategies in Brazil.

Table S2: This table summarises all of the REDD related initiatives that we used in our analysis. These data refer to all activities in Brazil that relate to

Reducing emissions from deforestation and degradation. (Data referent to January 2011).

State  REDD Area (Ha) No. of municipalities

Source Status Type* Land Tenure / rights

Amazonas Southern Amazon Project Not informed 5 Cifor, IDESAM, SFB

early stage of planning

AD, ADG, AGF Mixed/ Government

Amazonas Apui mais verde Project 10000 1 Cifor, IDESAM, SFB

early stage of planning

AD, AGF Private

Mato Grosso

Alto Teles Pires Carbon Project REDD+

Not informed 13 Cifor early stage of planning

AD, ADG, RS,AGF

Private

Mato Grosso

ONF/Peugeot Carbon Sink 7000 1 Cifor early stage of planning

AGF,RS, AD Private

Mato Grosso

Juruena Carbon Sink 7000 1 Cifor early stage of planning

AGF, AD Private

Mato Grosso

Prolifico Foundation avoided deforestation

5000 1 Cifor early stage of planning

AD Private

Goias, Para, Roraima

Registry for Socio-Environmental Responsibility (RSR) GO,PA, RR

NA 9 IPAM, Aliança da Terra

early stage of planning

AD, ADG, AGF Private

Mato Grosso

Registry for Socio-Environmental Responsibility (RSR) and reforestation in Xingu

946300 11 Cifor, IPAM early stage of planning

AD, ADG, AGF Private

Mato Grosso, Para

EcoSecurities Not informed 17 SFB early stage of planning

AD Private

Para REDD Project North of PA 7400000 6 SFB, Cifor, Idesam

early stage of planning

AD, ADG Government

Para Sao Felix do Xingu pilot and neighbor municipalities registry

8400000 7 SFB, Cifor early stage of planning

AD, ADG Mixed/ Government

Para RainTrust REDD 578000 3 Cifor, Winrock International

early stage of planning

AD Private

Para Tembé/ Rio Guamá Project 150000 3 Cifor early stage of planning

AD Indigenous Groups

Para COOPED Project/ Marajó 100000 1 C-trade early stage of planning

AGF, AD Mixed

Para, Amazonas, Roraima

Mapuera Project 5020418 5 Cifor, C-trade early stage of planning

AD Indigenous Groups

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Mato Grosso, Para

Securing Protection of Kayapo (and others Indigenous) territories in SE Amazon and Xingu Socio-Environmental Carbon Project

14000000 18 Cifor, CI early stage of planning

AD Indigenous Groups

Rondonia, Mato Grosso

Cinta Larga REDD project 2700000 3 Cifor early stage of planning

AD Indigenous Groups

Not informed

Willowriver - Amazon Reserve rainforest protection

Not informed Not informed Cifor, Willowriver website

early stage of planning

AD, ADG Private

Goias, Mato Grosso

Brasil Mata Viva Not informed 15 BMV website early stage of planning

AD, ADG, RS Private

Acre Acre State Carbon Project 5800000 8 Cifor, GCF, SFB, Idesam

in preparation

AD, ADG, RS,AGF

Mixed/ Government

Amazonas Itacoatiara Project 102000 1 SFB in preparation

AD, ADG Private

Mato Grosso

NW Mato grosso Pilot 758600 7 SFB, Idesam, Cifor

in preparation

AD, ADG, AGF Mixed/ Government

Para Ecomapuá Amazon REDD Project 94171 1 SFB, Cifor, Idesam

in preparation

AD, ADG, RS,AGF

Private

Rondonia, Mato Grosso

Surui Project 248000 3 SFB, Cifor, IDESAM

in preparation

AD, RS Indigenous Groups

Tocantins Genesis REDD Project 1076.49 1 SFB, Cifor, Idesam

in preparation

AD, RS, AGF Private

Amazonas Forest Allowance Program 9928678 25 SFB, Cifor in progress AD, ADG,AGF Mixed/ Government

Amazonas Juma Reserve REDD Project 589611 1 SFB, Cifor, Idesam

in progress AD, ADG Government

Para Avoided Deforestation on small rural properties in the region of the Transamazon Highway

31745 3 SFB, Cifor, Idesam

in progress AD, ADG Private

Parana Pilot Project for Reforestation and Conservation of Atlantic Rainforest

11900 1 SFB, IDESAM in progress AD, ADG, RS Private

Parana Action Plan against Global Warming

6700 1 SFB, Cifor in progress AD, ADG, RS Private

Goias, Mato Grosso do Sul

Emas Taquari Biodiversity Corridor Carbon Project

681 3 CCBA, forest carbon portal

in progress RS Private

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Bahia The Monte Pascoal - Pau Brasil Ecological Corridor: Carbon, Community

11 to 1000 1 CCBA, forest carbon portal

in progress RS Private

Acre WWF/ SKY Forest Carbon Network Initiative

Not informed 2 Cifor support program

AD Private

Brazil Amazonian Fund Not informed Brazil SFB support program

AD, ADG, RS,AGF

Government

Mato Grosso

Project historical series of deforestation

Not applicable

Not informed SFB support program

baseline Mixed

* AD =Avoided Deforestation, ADG = Avoided degradation, RS = restoration, AGF = Agroforestry. (Modified from Cifor 2010).

Status of projects (in January 2011): “in progress” refers to projects that have already been implemented. “in preparation” corresponds to projects in an

advanced stage of planning, “support program” corresponds to projects giving parallel support to REDD projects, and “early stage of planning” corresponds

to areas that host planned, future REDD projects.

Any information regarding the locations of private projects not given online or in reports was obtained through direct contact with the projects (email, phone).

Most of initiatives were mentioned in the Cifor (May and Millikan, 2010), IDESAM (Cenamo, M. C., et al. 2009) and SFB ( SFB - MMA 2009) publications. We

also cite other references below (All accessed by January 2011).

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Appendix S3. Supplementary results and parameters and values adopted in the Promethee

method.

Figure S3. Occurrence (within municipalities) of JCU and biodiversity categories in relation to

forest carbon storage and annual deforestation rate. Carbon storage relative to (a) Jaguar

Conservation Units (JCU) priority categories, (b) IBP scores, and (c) MMA biodiversity priority

scores. Annual deforestation rate (2002-2008) relative to (d) JCU priority categories, (e) IBP

scores, and (f) MMA biodiversity priority scores. See main paper (Methods, Table 1 and Fig.1

and 3) for further details.

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Table S3a. Fundamental criteria used to compare all scenarios with distinct levels of carbon and deforestation

Criteria Scenarios All JCUs Total PF

  A B C D E F G H I J K L M N O P Brazil

Carbon stocks (tC/ha) 266.6 267.6 275.2 281.5 250.4 246.8 241.5 237.4 244.4 240.4 228.7 221.3 171.6 156.7 151.8 165.4 138.9 0.25

Annual deforestation

rate %(2002-2008) 2.82 2.55 1.59 0.20 3.25 2.79 1.26 0.17 3.11 2.63 1.11 0.17 2.53 2.08 1.06 0.24 0.28 1.00

Probability of JCU

selection 0.41 0.42 0.44 0.45 0.37 0.36 0.36 0.36 0.36 0.35 0.33 0.33 0.21 0.20 0.21 0.24 0.20 0.30

Proportion of Forest

Remnant 0.42 0.43 0.44 0.54 0.38 0.41 0.43 0.46 0.38 0.41 0.44 0.44 0.38 0.43 0.45 0.36 0.23 0.20

Probability of UNEP

Biodiversity selection 0.80 0.82 0.88 0.94 0.82 0.85 0.91 0.93 0.83 0.86 0.91 0.93 0.89 0.91 0.92 0.92 0.92 0.10

Probability of MMA

Biodiversity selection 0.92 0.91 0.90 0.89 0.90 0.90 0.88 0.84 0.90 0.89 0.87 0.83 0.81 0.76 0.74 0.76 0.72 0.10

Using the PROMETHEE-GAIA program, we compared 16 different scenarios for carbon and deforestation. This table show the median values

for all the municipalities in each scenario, for each criterion. All JCUs: Scenario P was the most inclusive scenario and included all

municipalities that contained JCUs and corridors, therefore the values correspond to the medians of all municipalities within JCUs & corridors.

Total in Brazil: Medians of all municipalities in Brazil are provided here for comparison only. PF: is the preference factor adopted to establish

strict differences in the comparisons between scenarios for each fundamental criterion; here it is expressed as relative proportion. Preference

factors were calculated with the aid of the program and based on data ranges.

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Table S3b. All criteria used to compare the top 4 scenarios.

Criteria

Scenarios Total in Preference

A B C D Brazil factor

Carbon stocks (tC/ha) * 266.6 267.6 275.2 281.5 138.9 0.25

Annual deforestation rate (2002-2008) * 2.82 2.55 1.59 0.20 0.28 1.00

Probability of JCU ocurrence * 0.41 0.42 0.44 0.45 0.20 0.30

Proportion of Forest Cover * 0.42 0.43 0.44 0.54 0.23 0.20

Probability of UNEP Biodiversity ocurrence * 0.80 0.82 0.88 0.94 0.92 0.10

Probability of MMA Biodiversity ocurrence * 0.92 0.91 0.90 0.89 0.72 0.10

Jaguar Index * 0.88 0.96 1.07 1.38 0.00 0.30

UNEP index * 1.00 1.00 1.83 2.00 2.00 0.25

MMA index * 1.03 1.06 1.11 1.13 0.38 0.30

PAs within JCUs (Km2) * 236.75 240.52 185.38 160.92 0.00 0.50

% of Total Annual Deforestation rate (02-08) in Brazil 26.00 27.89 33.52 36.62 100.00 2.00

% of Total Forest Cover in Brazil 7.60 8.83 19.06 55.59 100.00 2.00

% of Total Carbon in Brazil 9.12 10.61 23.31 70.09 100.00 2.00

% of Total JCU & corridors area in Brazil 10.09 11.82 24.29 75.10 100.00 2.00

% of Total UNEP biodiversity in Brazil 8.50 9.43 16.57 41.58 100.00 2.00

% of Total MMA biodiversity area in Brazil 8.73 9.95 20.31 55.40 100.00 2.00

Number of Municipalities 86 95 159 371 5565 2.00

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From the initial PROMETHEE-GAIA analysis, we selected the 4 best performing scenarios and compared them against a number of “fundamental criteria”

(shown in bold) and “representativeness criteria”. Criteria marked with an asterisk (*) show the median value for all municipalities in that scenario,

otherwise the values correspond to total sums. The column “Totals in Brazil” gives the median or sums of all municipalities in Brazil and is provided for

comparison only. Preference factor: relative factors used in the pairwise comparisons.

Appendix S4.

Table S4: List of municipalities highlighted as “REDDspots” by scenario C (n = 159)*.Municipality State Tonnes

C/ha in remnant

forest (Ruesch & Gibbs

2008)

Tonnes C/ha in remnant

forest (Saatchi

et al. 2011)

Annual deforestatio

n rate (% 2003-2008)

Main legal biome

Remaining vegetation

cover within municipality

(ha)

Threshold categories applied to remaining vegetation

cover within municipalit

y

Current or

proposed REDD

JCU Priority

Main Jaguar Status

category

Main UNEP score

in the municipalit

y

Main MMA score in the municipality

Bocaiúva do Sul Paraná 340.56 240.55 0.42

Atlantic Forest 57363 > 60% Absent JP1 CR 4

Extremely high

Rio NegrinhoSanta

Catarina 331.95 172.85 0.84Atlantic Forest 31666 30 - 60% Absent JP5CN CR 3 Very High

Urucurituba Amazonas 324.44 229.83 0.91 Amazonia 13082< 20%,

>10000 ha Absent JP4CB NT 4Extremely

highBoa Vista do Ramos Amazonas 322.41 273.46 0.31 Amazonia 111672 30 - 60% Absent JP4CB NT 4

Extremely high

ItaiópolisSanta

Catarina 321.29 174.5 0.95Atlantic Forest 66905 30 - 60% Absent JP5CN CR 3

Extremely high

Santa Terezinha

Santa Catarina 318.95 169.3 0.48

Atlantic Forest 44734 > 60% Absent JP5CN CR 3

Extremely high

Parintins Amazonas 311.39 266.13 0.73 Amazonia 142271 20 - 30% Absent JP2 NT 3Extremely

high

Porto Vitória Paraná 311.38 170.57 0.34Atlantic Forest 8433 30 - 60% Absent JP5CN CR 4 Very High

Rio Negro Paraná 309.94 121.11 0.78Atlantic Forest 17378 20 - 30% Absent JP5CN CR 4 High

Barreirinha Amazonas 307.1 275.64 0.35 Amazonia 343580 30 - 60% Absent JP2 NT 3Extremely

high

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CaçadorSanta

Catarina 305.96 229.06 0.32Atlantic Forest 29950 30 - 60% Absent JP5CN CR 3

Extremely high

União da Vitória Paraná 305.41 177.63 0.3

Atlantic Forest 41164 30 - 60% Absent JP5CN CR 4 Very High

Agudos do Sul Paraná 305.24 98.21 0.84Atlantic Forest 5218 20 - 30% Absent JP5CN CR 3 High

Timbó GrandeSanta

Catarina 302.57 191.86 0.36Atlantic Forest 28707 30 - 60% Absent JP5CN CR 3 High

PapanduvaSanta

Catarina 302.29 160.41 0.51Atlantic Forest 31547 30 - 60% Absent JP5CN CR 3 Very High

Bituruna Paraná 300.82 196.04 0.53Atlantic Forest 48044 30 - 60% Absent JP5CN CR 4 Very High

Tabatinga Amazonas 300.78 296.01 0.56 Amazonia 81042 20 - 30% Absent JP3 NT 3Extremely

high

Guajará Amazonas 300.41 324.22 0.3 Amazonia 691144 > 60% Absent JP3 NT 2Extremely

high

Rurópolis Pará 298.91 347.34 0.91 Amazonia 523325 > 60% Absent JP2 NT 1Extremely

high

Cruzeiro do Sul Acre 298.71 329.49 0.38 Amazonia 685179 > 60% Present JP3 NT 2Extremely

high

Paula Freitas Paraná 298.01 147.23 0.6Atlantic Forest 13393 30 - 60% Absent JP5CN CR 4 Very High

Candeias do Jamari Rondônia 295.93 260.17 2.09 Amazonia 474854 > 60% Absent JP4CB NT 2

Extremely high

Medicilândia Pará 295.76 324.48 2.4 Amazonia 640960 > 60% Absent JP4CB NT 2Extremely

high

Portel Pará 295.49 302.75 0.3 Amazonia 2067003 > 60% Absent JP4CB NT 0 Very High

Major VieiraSanta

Catarina 295.33 150.35 0.38Atlantic Forest 16649 30 - 60% Absent JP5CN CR 3 Very High

Senador José Porfírio Pará 295.11 359.64 0.6 Amazonia 1072596 > 60% Present JP2 NT 1

Extremely high

Coronel Domingos Soares Paraná 293.28 217.99 1.02

Atlantic Forest 54597 30 - 60% Absent JP5CN CR 4

Extremely high

Prainha Pará 292.79 290.1 0.6 Amazonia 680036 30 - 60% Absent JP3 NT 2Extremely

high

Itapuã do Oeste Rondônia 292.6 274.56 1.08 Amazonia 303090 > 60% Absent JP4CB NT 2 Very HighSão Francisco do Guaporé Rondônia 292.17 249.28 2.83 Amazonia 505060 30 - 60% Absent JP2 NT 2

Extremely high

Porto Velho Rondônia 292 271.45 2.01 Amazonia 2455884 > 60% Absent JP2 NT 2Extremely

high

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Tijucas do Sul Paraná 291.01 169.88 1.07Atlantic Forest 30902 30 - 60% Absent JP1 CR 3

Extremely high

Boca do Acre Amazonas 290.83 311.14 0.56 Amazonia 1984949 > 60% Present JP3 NT 3Extremely

high

Costa Marques Rondônia 290.8 269.23 4.02 Amazonia 281745 30 - 60% Absent JP2 NT 2Extremely

high

Anapu Pará 290.48 345.91 1.49 Amazonia 808781 > 60% Present JP2 NT 0Extremely

high

CanoinhasSanta

Catarina 290.41 139.97 0.32Atlantic Forest 32652 20 - 30% Absent JP5CN CR 4 Very High

Porto de Moz Pará 290.12 293.79 1.28 Amazonia 922820 30 - 60% Absent JP4CB NT 2Extremely

highSão João do Carú Maranhão 289.85 291.42 5.91 Amazonia 4526 < 5000 ha Absent JP1 NT 0 Very High

Manacapuru Amazonas 289.79 235.75 0.43 Amazonia 360168 30 - 60% Present JP3 NT 3 Very High

Santa CecíliaSanta

Catarina 289.69 208.9 0.47Atlantic Forest 31656 20 - 30% Absent JP5CN CR 3 Very High

Centro Novo do Maranhão Maranhão 289.2 303.49 2.47 Amazonia 161218

< 20%, >10000 ha Absent JP1 NT 0 Very High

Igarapé-Miri Pará 289.06 263.75 0.36 Amazonia 102275 30 - 60% Absent JP4CB NT 0Extremely

high

Porto Grande Amapá 287.86 349.42 0.59 Amazonia 323167 > 60% Absent JP3 NT 0 HighPimenteiras do Oeste Rondônia 287.4 217.29 1.69 Amazonia 231784 30 - 60% Absent JP4CB NT 2

Extremely high

Lábrea Amazonas 287.05 281.04 0.42 Amazonia 6222016 > 60% Present JP3 NT 2Extremely

high

Porto UniãoSanta

Catarina 287.02 189.13 0.37Atlantic Forest 36291 30 - 60% Absent JP5CN CR 4 High

Itaituba Pará 285.7 345.18 0.33 Amazonia 5622830 > 60% Absent JP2 NT 1Extremely

high

IrineópolisSanta

Catarina 285.42 147.89 0.39Atlantic Forest 17247 20 - 30% Absent JP5CN CR 4 High

MafraSanta

Catarina 285.09 146.37 1.05Atlantic Forest 43921 30 - 60% Absent JP5CN CR 4 High

Terra Santa Pará 285.04 234.41 0.73 Amazonia 74313 30 - 60% Absent JP3 NT 4Extremely

high

ColnizaMato

Grosso 284.53 309.07 1.59 Amazonia 2414992 > 60% Present JP2 NT 2Extremely

high

Lebon RégisSanta

Catarina 284.26 182.66 0.31Atlantic Forest 42315 30 - 60% Absent JP5CN CR 3

Extremely high

Sena Madureira Acre 283.97 349.84 0.32 Amazonia 2221906 > 60% Present JP3 NT 3 High

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Monte Alegre Pará 283.96 339.66 0.47 Amazonia 1206309 30 - 60% Present JP3 NT 2 Very High

Alto Paraíso Rondônia 283.9 262.17 6.52 Amazonia 100185 30 - 60% Absent JP4CB NT 2 Not Priority

Paragominas Pará 283.89 295.69 1.71 Amazonia 869494 30 - 60% Present JP1 NT 0Extremely

highNovo Repartimento Pará 283.61 317.75 4.36 Amazonia 768784 30 - 60% Absent JP4CB NT 1

Extremely high

Amapá Amapá 283.33 259.56 0.69 Amazonia 168676< 20%,

>10000 ha Absent JP3 NT 2Extremely

high

Moju Pará 283.23 301.79 2.04 Amazonia 487991 30 - 60% Absent JP4CB NT 0 Very High

Pacajá Pará 283.04 321.5 3.19 Amazonia 571026 30 - 60% Present JP2 NT 0 Very High

Trairão Pará 282.97 349.5 0.53 Amazonia 1087073 > 60% Absent JP2 NT 1Extremely

highSão João da Baliza Roraima 282.94 308.83 0.49 Amazonia 376796 > 60% Present JP2 NT 2

Extremely high

Buritis Rondônia 282.27 281.87 10.07 Amazonia 114126 30 - 60% Absent JP2 NT 2 Very High

Cujubim Rondônia 281.64 291.38 3.75 Amazonia 250982 > 60% Absent JP4CB NT 2 High

São Luiz Roraima 281.02 258.2 1.9 Amazonia 98201 > 60% Absent JP2 NT 2Extremely

high

RondolândiaMato

Grosso 280.75 322.35 0.53 Amazonia 1073652 > 60% Present JP4CB NT 2Extremely

high

Ipixuna do Pará Pará 280.55 289.45 3.42 Amazonia 257175 30 - 60% Absent JP4CB NT 0Extremely

high

Tartarugalzinho Amapá 280.43 318.32 0.92 Amazonia 239735 30 - 60% Absent JP3 NT 1Extremely

highMachadinho D'Oeste Rondônia 280.42 288.49 3.06 Amazonia 533137 > 60% Absent JP2 NT 2

Extremely high

Altamira Pará 279.76 346.1 0.37 Amazonia 14505422 > 60% Present JP2 NT 1Extremely

high

Cerejeiras Rondônia 279.18 154.51 1.59 Amazonia 75909 20 - 30% Absent JP4CB NT 2Extremely

high

Viseu Pará 278.98 229.36 2.32 Amazonia 107138 20 - 30% Absent JP1 NT 0 Very HighItinga do Maranhão Maranhão 278.36 264.31 4.63 Amazonia 105517 20 - 30% Absent JP4CB NT 0 Very HighBom Jesus do Tocantins Pará 278.28 273.36 2.63 Amazonia 98510 30 - 60% Absent JP5CN NT 1

Extremely high

Brasil Novo Pará 278.02 281.44 6.07 Amazonia 321044 30 - 60% Absent JP4CB NT 2Extremely

high

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Goianésia do Pará Pará 277.49 277.37 3.25 Amazonia 291439 30 - 60% Absent JP5CN NT 0 HighGovernador Jorge Teixeira Rondônia 277.23 331.05 0.94 Amazonia 355104 > 60% Absent JP2 NT 2

Extremely high

Paulo Frontin Paraná 276.35 127.11 0.58Atlantic Forest 12257 30 - 60% Absent JP5CN CR 4

Extremely high

Iranduba Amazonas 276.04 218.48 0.69 Amazonia 102438 30 - 60% Present JP3 NT 4Extremely

high

Tailândia Pará 275.18 310.79 3.78 Amazonia 233614 30 - 60% Absent JP4CB NT 0Extremely

high

Clevelândia Paraná 274.59 163.13 0.35Atlantic Forest 10440

< 20%, >10000 ha Absent JP5CN CR 3

Extremely high

Baião Pará 273.77 220.93 1.42 Amazonia 143613 30 - 60% Absent JP4CB NT 1 Very HighVila Bela da Santíssima Trindade

Mato Grosso 273.36 148.87 3.69 Amazonia 225124

< 20%, >10000 ha Absent JP4CB NT 2 Very High

Caroebe Roraima 272.87 311.54 0.36 Amazonia 1061682 > 60% Absent JP2 NT 2Extremely

highNovo Progresso Pará 272.41 338.44 1.45 Amazonia 2964502 > 60% Present JP2 NT 1 Very High

Cabixi Rondônia 272.07 141.06 5.07 Amazonia 33680 20 - 30% Absent JP4CB NT 2Extremely

highSão Félix do Xingu Pará 272.05 327.24 1.75 Amazonia 6317498 > 60% Present JP2 NT 1

Extremely high

Ji-Paraná Rondônia 271.78 304.68 0.43 Amazonia 421993 > 60% Absent JP4CB NT 2Extremely

highRondon do Pará Pará 271.56 280.99 5.47 Amazonia 294482 30 - 60% Absent JP5CN NT 0 High

Buriticupu Maranhão 270.76 198.18 6.16 Amazonia 67442 20 - 30% Absent JP5CN NT 0 Very HighSanta Cruz do Xingu

Mato Grosso 270.53 244.51 1.9 Amazonia 259768 30 - 60% Present JP2 NT 1 High

Vale do Anari Rondônia 269.79 296.59 2.88 Amazonia 193577 > 60% Absent JP4CB NT 2Extremely

highAlta Floresta D'Oeste Rondônia 269.33 273.26 2.17 Amazonia 297148 30 - 60% Absent JP4CB NT 2 Very High

Nova Mamoré Rondônia 268.91 301.52 3.11 Amazonia 599234 30 - 60% Absent JP2 NT 2 Very High

Mocajuba Pará 268.78 143.6 0.31 Amazonia 13659< 20%,

>10000 ha Absent JP4CB NT 0 Very High

Bom Jardim Maranhão 268.59 296.36 1.99 Amazonia 247791 30 - 60% Absent JP1 NT 0 Very High

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Marabá Pará 268.49 315.72 3.53 Amazonia 697686 30 - 60% Present JP5CN NT 1 High

AripuanãMato

Grosso 268.2 325.45 1.27 Amazonia 2075289 > 60% Present JP4CB NT 2Extremely

high

Itupiranga Pará 267.87 314.54 3.81 Amazonia 340099 30 - 60% Present JP5CN NT 1 High

ApiacásMato

Grosso 267.56 306.4 1.07 Amazonia 1701541 > 60% Absent JP2 NT 2Extremely

high

Ulianópolis Pará 267.42 257.49 5.99 Amazonia 173818 30 - 60% Absent JP4CB NT 0 Very HighBom Jesus das Selvas Maranhão 266.81 216.5 6.52 Amazonia 57481 20 - 30% Absent JP5CN NT 0 Very HighNova Esperança do Piriá Pará 266.38 293.81 2.55 Amazonia 72458 20 - 30% Present JP1 NT 0

Extremely high

ParanaítaMato

Grosso 266.19 281.73 4.52 Amazonia 253185 30 - 60% Absent JP2 NT 2Extremely

high

MarcelândiaMato

Grosso 265.47 267.47 1.85 Amazonia 864688 > 60% Present JP2 NT 1 Very High

Seringueiras Rondônia 265.19 280.23 2.81 Amazonia 187955 30 - 60% Absent JP2 NT 2Extremely

high

CotriguaçuMato

Grosso 264.92 317.24 2.27 Amazonia 741586 > 60% Present JP2 NT 2Extremely

high

Feliz NatalMato

Grosso 264.45 266.41 1.32 Amazonia 875459 > 60% Present JP2 NT 1 Very HighNova Bandeirantes

Mato Grosso 263.83 308.76 3.33 Amazonia 636230 > 60% Absent JP2 NT 2 Very High

Mangueirinha Paraná 263.81 173.23 0.77Atlantic Forest 13677

< 20%, >10000 ha Absent JP5CN CR 4

Extremely high

Tucuruí Pará 263.05 260.62 1.59 Amazonia 89258 30 - 60% Present JP4CB NT 1Extremely

high

Tomé-Açu Pará 262.66 303.47 2.36 Amazonia 227449 30 - 60% Absent JP4CB NT 0Extremely

high

Acará Pará 262 307.04 2.38 Amazonia 196688 30 - 60% Absent JP4CB NT 0 Very HighSão Miguel do Guaporé Rondônia 258.57 280.96 1.73 Amazonia 413310 30 - 60% Absent JP2 NT 2

Extremely high

Breu Branco Pará 258.35 232.69 5.67 Amazonia 102363 20 - 30% Absent JP4CB NT 0 High

Dom Eliseu Pará 258.28 279.93 5.4 Amazonia 196955 30 - 60% Absent JP4CB NT 0Extremely

high

União do SulMato

Grosso 257.48 265.17 2.01 Amazonia 354188 > 60% Present JP2 NT 1Extremely

high

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Alto Alegre dos Parecis Rondônia 257.43 288.74 2.8 Amazonia 175595 30 - 60% Absent JP4CB NT 2

Extremely high

MatupáMato

Grosso 256.81 309.22 2.79 Amazonia 306314 30 - 60% Present JP2 NT 1 Very High

Maranhãozinho Maranhão 256.45 305.96 3.94 Amazonia 2025 < 5000 ha Absent JP1 NT 0 HighOurilândia do Norte Pará 255.91 309.54 0.4 Amazonia 948561 > 60% Present JP2 NT 1

Extremely high

Campo Novo de Rondônia Rondônia 254.96 311.27 6.5 Amazonia 136936 30 - 60% Absent JP2 NT 2

Extremely high

Cidelândia Maranhão 253.95 184.72 8.91 Amazonia 15698< 20%,

>10000 ha Absent JP5CN NT 0 Very HighPeixoto de Azevedo

Mato Grosso 253.83 298.54 1.67 Amazonia 989174 > 60% Present JP2 NT 1 Very High

Jacundá Pará 253.68 151.41 6.5 Amazonia 29945< 20%,

>10000 ha Absent JP5CN NT 0 High

Cantá Roraima 252.95 250.49 0.92 Amazonia 525015 > 60% Absent JP2 NT 2Extremely

high

QuerênciaMato

Grosso 252.39 226 2.44 Amazonia 1048299 30 - 60% Present JP2 NT 1 Very HighFrancisco Beltrão Paraná 251.68 122.46 2.05

Atlantic Forest 1431 < 5000 ha Absent JP5CN CR 4 Not Priority

Espigão D'Oeste Rondônia 251.48 289.32 1.83 Amazonia 216005 30 - 60% Present JP4CB NT 2

Extremely high

São José do Xingu

Mato Grosso 250.6 215.83 1.76 Amazonia 281776 30 - 60% Present JP2 NT 1 Very High

Mirante da Serra Rondônia 250.54 263.3 3.25 Amazonia 35997 30 - 60% Absent JP2 NT 2

Extremely high

Santa Tereza do Oeste Paraná 250.29 115.41 2.04

Atlantic Forest 1259 < 5000 ha Absent JP3 CR 3

Extremely high

Nova Ipixuna Pará 249.31 114.2 7.36 Amazonia 23689< 20%,

>10000 ha Absent JP5CN NT 1 Very High

Cacaulândia Rondônia 248.85 242.59 7.98 Amazonia 48597 20 - 30% Absent JP2 NT 2Extremely

high

Monte Negro Rondônia 247.31 259.19 7.85 Amazonia 55808 20 - 30% Absent JP2 NT 2Extremely

high

Cacoal Rondônia 246.81 274.06 2.03 Amazonia 129660 30 - 60% Present JP4CB NT 2Extremely

high

VargeãoSanta

Catarina 246.14 194.22 0.55Atlantic Forest 1189 < 5000 ha Absent JP5CN CR 3 Very High

Ariquemes Rondônia 244.4 227.38 5.9 Amazonia 121450 20 - 30% Absent JP4CB NT 2 Not Priority

Nova UbiratãMato

Grosso 243.77 220.21 3.98 Amazonia 683755 30 - 60% Present JP2 NT 1 Very High

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ConfresaMato

Grosso 242.38 208.3 7.31 Amazonia 162876 20 - 30% Absent JP4CB NT 1 High

  Maranhão 241.97 281.54 1.44 Amazonia 11354< 20%,

>10000 ha Absent JP1 NT 0 Very High

Honório Serpa Paraná 241.03 120.81 1.09Atlantic Forest 3552 < 5000 ha Absent JP5CN CR 4 Not Priority

JuínaMato

Grosso 240.91 309.07 0.66 Cerrado 2192173 > 60% Present JP4CB EN 2Extremely

highGuarantã do Norte

Mato Grosso 240.36 259.12 4.83 Amazonia 161417 30 - 60% Present JP2 NT 1 Very High

Manfrinópolis Paraná 240.31 111.63 1.45Atlantic Forest 242 < 5000 ha Absent JP5CN CR 4 Not Priority

Gaúcha do Norte

Mato Grosso 239.78 203.78 2.03 Amazonia 987778 30 - 60% Present JP2 NT 1 Very High

Ouro VerdeSanta

Catarina 239.68 96.02 0.29Atlantic Forest 228 < 5000 ha Absent JP5CN CR 3 Very High

São José das Palmeiras Paraná 238.4 70.58 0.64

Atlantic Forest 2823 < 5000 ha Absent JP4CB CR 4 High

Mucajaí Roraima 237.87 314.99 0.83 Amazonia 769106 > 60% Absent JP3 NT 2Extremely

highSão José do Cedro

Santa Catarina 237.38 99.02 0.55

Atlantic Forest 1282 < 5000 ha Absent JP3 CR 3 Very High

Mariópolis Paraná 236.17 102.73 0.64Atlantic Forest 669 < 5000 ha Absent JP5CN CR 3 Not Priority

Vila RicaMato

Grosso 236.03 250.43 6.5 Amazonia 272166 30 - 60% Absent JP4CB NT 1 High

São BernardinoSanta

Catarina 235.59 111.6 0.68Atlantic Forest 1028 < 5000 ha Absent JP5CN CR 3 Not Priority

Alvorada D'Oeste Rondônia 234.13 264.9 2.37 Amazonia 86194 20 - 30% Absent JP2 NT 2

Extremely high

Bannach Pará 232.41 245.83 7.56 Amazonia 78795 20 - 30% Present JP2 NT 1 High

Arame Maranhão 232.23 200.6 2.21 Amazonia 102328 30 - 60% Absent JP5CN NT 0 Very High

Pimenta Bueno Rondônia 231.8 263.68 2.54 Amazonia 285206 30 - 60% Present JP4CB NT 2 Very HighVitória do Xingu Pará 230.7 175.17 6.27 Amazonia 64266 20 - 30% Absent JP4CB NT 1

Extremely high

Vilhena Rondônia 230.68 301.03 0.67 Amazonia 835356 > 60% Absent JP4CB NT 2Extremely

high

* The 159 municipalities shown here are the result of our primary analysis which focussed on the use of the Reusch & Gibbs (2008)

carbon dataset. However in Supplementary Material (Appendix S6), we investigated how our results would be affected by the use of a

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different dataset (Saatchi et al 2011). In some municipalities, Saatchi et al (2011) provide a lower estimate for tonnes of carbon per

hectare in remnant forest; consequently, fewer municipalities met our threshold criteria being “High carbon” (>230 Tonnes C/ha) and

thus fewer municipalities were selected as REDDspots (n=103).

See Table1 in the main paper for further explanation about categories.

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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.

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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).

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).

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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

ForrestCerrado Caatinga Pantanal Pampa

Ruesch & Gibbs, (2008)

M = 154.3Med=138.9

M = 237.3Med=252.4

M = 162.3Med=147.5

M = 118.1Med=112.6

M = 131.7Med=130.8

M = 178.4Med=183.6

M = 131.3Med=129.8

Saatchi et al., (2011)

M= 91.7Med=64.1

M=219.1Med=227.9

M= 99.6Med= 78.1

M= 60.8Med= 54.4

M= 44.2Med= 37.1

M= 67.5Med=64.9

M=83.5Med=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<<0.00010.74– 0.76t = 84.9,df = 5563

r = 0.74, P << 0.00010.73– 0.76t = 75.3,df = 4564

r = 0.70, P <<0.00010.69 – 0.72t = 53.0,df = 2837

r = 0.64, P <<0.00010.60 – 0.68t = 26.3, df = 990

r = 0.31 , P <<0.00010.26 – 0.37t = 11.07, df = 1116

r = 0.89, P < 0.010.4 – 0.98t =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

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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).

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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)*.

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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.

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Appendix S7. Support References

GIS support datasets, Carbon and REDD projects references

Amazon Environmental Research Institute (IPAM). Avoided Deforestation on small rural properties in the region of

the Transamazon Highway. Available from http://www.ipam.org.br/biblioteca/livro/id/250# and

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transamazon-highway (accessed January 2011).

Brasil Mata Viva Standard (BMV Standard) Payment for Environmental Services and sustainable production.

Available from http://www.brasilmataviva.com.br/index.php (accessed January 2011).

C-TRADE Comercializadora de Carbono Ltda. Projetos de REDD e Biomassa. Available from http://c-

tradeweb.com/wp/projetos/redd/ (accessed January 2011).

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

Ecologica Institute. Genesis REDD project. Available from

http://climate-standards.org/projects/files/tocantins/ccba_pdd_tocantins_redd_v_1.pdf (accessed January

2011)

Ecomapua. Carbon projects. Ecomapua, Para, Brazil. Available from www.ecomapua.com.br (accessed January

2011).

Forest Carbon Portal. Forest Carbon Project Inventory: search for (REDD) & (Brazil). Available from

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Fundação Amazônia Sustentável (FAS). Foresta Allowance project and JUMA REDD project http://www.fas-

amazonas.org/en/ (accessed January 2011).

Global Soil Data Task Group - GSDT (2000). Global Gridded Surfaces of Selected Soil Characteristics -

International Geosphere-Biosphere Programme - Data and Information System (IGBP-DIS.. Data set.

Available on-line from the Oak Ridge National Laboratory Distributed Active Archive Center

[http://www.daac.ornl.gov], Oak Ridge, Tennessee, U.S.A. doi: 1 O.3334/ORNLDAAC/569.

Governor's Climate and Forest Taskforce (GCFtaskforce). REDD Plan for Acre State, Amazon, Brazil. Available

from http://www.gcftaskforce.org/docs (accessed January 2011).

Harvey C. A., O. Zerbock, S. Papageorgiou, and A. Parra. 2010. What is needed to make REDD+ work on the

ground? Lessons learned from pilot forest carbon initiatives. Conservation International, Arlington,

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Virginia, USA. 121 pp. Available from

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Socioambiental do Xingu – CCSX. Available from: http://www.aliancadaterra.org.br/novo/artigos.php?

id=7 http://www.ipam.org.br/biblioteca/livro/A-Nova-Estrutura-Setorial-Brasileira-para Reducao-de-

Emissoes-por-Desmatamento-e-Degradacao-REDD-e-a-Potencial-Reducao-de-Emissoes-por-

Desmatamento-Evitado-na-Regiao-do-Xingu/550# http://www.ipam.org.br/programas/projeto/Cadastro-de-

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May, P. H., and B. Millikan, editors. 2010. The context of REDD+ in Brazil: Drivers, agents and institutions.

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