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ASSESSING CARBON STOCKS USING INDIGENOUS PEOPLES’ FIELD MEASUREMENTS IN 1

AMAZONIAN GUYANA 2

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Nathalie Butt1, Kimberly Epps2, Han Overman3, Takuya Iwamura2,4, Jose M.V. Fragoso4. 4

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1ARC Centre of Excellence for Environmental Decisions, School of Biological Sciences, The 6

University of Queensland, St. Lucia, 4072, Australia. n.butt@uq.edu.au 2Department of 7

Environmental Earth System Science, Stanford University, USA. 3College of Environmental 8

Science and Forestry, State University of New York, USA. 4Department of Biology, Stanford 9

University, USA. 10

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ABSTRACT 14

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Accurate estimations of carbon stocks across large tracts of tropical forests are key for 16

participation in programs promoting avoided deforestation and carbon sequestration, such as 17

the UN REDD+ framework. Trained local technicians can provide such data, and this, 18

combined with satellite imagery, allows robust carbon stock estimation across vegetation 19

classes and large areas. In the first comprehensive survey in Guyana conducted by indigenous 20

people, ground data from 21 study sites in the Rupununi region were used to estimate above 21

ground tree carbon stocks across a diversity of ecosystems and land use types. Carbon stocks 22

varied between village sites from 1 Tg to 22.7 Tg, and these amounts were related to stem 23

counts as well as tree size. This variation was linked to vegetation type across the region, 24

with trees in savannas holding on average 14.5 MgC ha-1 and forest 141.9 MgC ha-1. The 25

results indicated that previous estimates based on remotely sensed data for this area may be 26

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inaccurate. There were also differences in carbon stocks between the north and south 27

Rupununi regions, as well as between village sites and uninhabited control areas. Differences 28

between north and south probably reflect vegetation type, regional hydrology, geology and 29

topography, while differences between inhabited and uninhabited areas are presumably 30

driven by community use. Recruiting local technicians for field work allowed a) large 31

amounts of ground data to be collected for a wide region otherwise hard to access, and b) 32

ensured that local people were directly involved in Guyana’s Low Carbon Development 33

Strategy. This is the first such comprehensive survey of carbon stocks and vegetation types 34

over a large area in Guyana, one of the first countries to develop such a program. The 35

potential inclusion of forests held by indigenous peoples in REDD+ programs is a global 36

issue: we clearly show that indigenous people are capable of assessing and monitoring carbon 37

on their lands, and should therefore be partners in such programs. 38

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Keywords 40

Tropical forest; tree carbon stocks; indigenous land management; REDD+; Guyana; land 41

cover satellite imagery. 42

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INTRODUCTION 53

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The importance of trees and forests, especially tropical forests, as carbon sinks and stocks is 55

well established, with forests globally sequestering 2.4 ± 0.4 PgC yr-1 by one estimate (Pan et 56

al. 2011). Forests are under multiple development pressures, including logging, fragmentation 57

for mining, and clearing for agriculture. The latter is particularly critical in tropical regions, 58

where land conversion accounted for carbon emissions of 1.3 ± 0.7 PgC yr-1 between 1990 59

and 2007 (Pan et al. 2011). In addition to development stressors, climate change itself is a 60

key threat to forests in the Amazon basin (Malhi et al. 2008), and in order to mitigate rapid 61

climate change, it is essential that forests are kept as intact as possible so they can continue as 62

carbon sinks (Gibbs et al. 2007). 63

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Recognition and understanding of the global importance of forest carbon stocks and forest 65

ecosystem functioning has led to the development of several schemes whereby the 66

maintenance of forest cover and carbon sequestration is remunerated, such as the UN 67

REDD/REDD+ program (Reducing Emissions from Deforestation and forest Degradation; 68

http://www.un-redd.org). These schemes, and others, such as national and international 69

carbon trading programs, and voluntary payments for carbon sequestration services, require 70

the measurement of carbon stock baselines, and subsequent monitoring and reporting of 71

carbon pools (Cedgren 2009), through a combination of remote sensing and ground truthing 72

methods. To achieve support for REDD+ schemes and ensure that they fairly compensate 73

forest stewards, it is essential that local stakeholders understand the carbon measurement 74

process. This has been achieved through participatory approaches whereby local, trained, 75

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citizen scientists provide useful data across large areas, as has been demonstrated (Butt et al. 76

2013, Danielsen et al. 2013, Torres 2014). 77

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Guyana was one of the first countries to submit a REDD Readiness Plan for testing national 79

payments for carbon storage to the Forest Carbon Partnership Facility, a global partnership 80

between national governments and other entities focussed on REDD+ 81

(www.forestcarbonpartnership.org/sites/forestcarbonpartnership.org/files/Documents/PDF/Pa82

nama_PC_meeting_summary_for_website_clean.pdf), and also one of the first to establish a 83

national REDD program. The Guyana REDD+ Investment Fund (GRIF) was set up as part of 84

Guyana’s Low Carbon Development Strategy (LCDS), as a climate finance mechanism by 85

which avoided deforestation could be compensated until an international REDD+ mechanism 86

became operational (http://www.guyanareddfund.org/index.php#). The fund was capitalized 87

by the government of Norway. 88

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In order to become part of a scheme such as REDD/REDD+, Measurement, Reporting and 90

Verification (MRV) activities need to be coordinated and carried out by capable people on 91

the ground, to complement remotely sensed monitoring data. This partnership approach 92

reflects the importance placed on both the rights of indigenous people as stakeholders, and 93

their involvement in the REDD process, by the Guyana-Norway Agreement (Cedergren 94

2009, Gutman and Aguilar-Amuchastegui 2012). Citizen science and other participatory 95

approaches to monitoring provide an effective method of forest monitoring, and in the 96

Guyana context Amerindian communities manage their resources, inform other community 97

members of carbon stocks on their land, and gain insight and training in forest monitoring, 98

which should enable an informed decision-making process with regard to opting in or out of 99

the REDD program within the LCDS. Transparent and effective multi-stakeholder 100

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consultations are ongoing and evolving in Guyana: an audit in July 2012 indicated that only 101

three of the ten verification indicators reviewed by Rainforest Alliance (Donovan et al. 2012) 102

had been fully met, four partially met and three not met. Two of those that were not met 103

(“Protection of the rights of indigenous peoples” and “Transparent and effective multi-104

stakeholder consultations continue and evolve”), referred specifically to the involvement of 105

indigenous peoples in the process. 106

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Project Fauna was a Guyana-based initiative developed by a team of researchers from various 108

research institutions and local indigenous leaders to examine the connected nature of 109

indigenous people and their environment (Fragoso et al. 2005, Luzar et al. 2011). The main 110

goal of the project was an assessment of how biodiversity influences and is influenced by 111

changes in indigenous human culture, and of how land use change influences these elements 112

of coupled human natural systems (Luzar and Fragoso 2013, Read et al. 2010, Luzar et al. 113

2011). Socioeconomic and biodiversity variables were measured on titled lands using a 114

participatory approach. The project also measured above ground vegetation carbon to 1) 115

address the issue of links between carbon and biodiversity and contribute to the discussion of 116

bundled ecosystem services, and 2) advance indigenous community understanding of carbon, 117

carbon politics and REDD+ programs, thus enabling their participation in the national 118

discussion on carbon value and payments. 119

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From 2007 to 2010, Project Fauna trained 335 indigenous technicians across 30 Amerindian 121

communities in the Rupununi region (Figure 1) to monitor wildlife populations and hunting 122

patterns, and to describe vegetation structure. The success of this program (Luzar and 123

Fragoso 2013, Luzar et al. 2011, Read et al. 2010) led Project Fauna to initiate a vegetation 124

and carbon assessment pilot study (Epps 2010; http://www.stanford.edu/group/fragoso/), 125

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which aimed to: 1) build the scientific capacity of local communities in understanding the 126

sources and stocks of carbon in the environment, and how to measure carbon in above ground 127

vegetation (AGV); 2) estimate the carbon stocks in distinct vegetation types and on titled 128

lands, and; 3) compare tree carbon around villages to that in areas unused by people. Here we 129

describe the results of the tree measurement/carbon assessment program carried out in the 130

Rupununi region of south western Guyana, and outline the implications for the inclusion of 131

indigenous people in monitoring their own carbon stocks in REDD+ schemes globally. 132

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METHODS 135

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Study area 137

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The Rupununi region is classed as ‘moist tropical forest’ by the IPCC (2003), with 2000 to 139

4000 mm yr-1 rainfall, and is dominated by savannas and forests (Read et al. 2010, Hammond 140

2005). Ten types of vegetation were described in the study area (Cummings 2013; Levi et al. 141

2013), and these were grouped into eight categories to maintain adequate sample sizes: High 142

Forest Flooded, High Forest Upland, Ite Swamp, Low Forest Flooded, Low Forest Upland, 143

Muri Shrub Upland, Savanna Flooded and Savanna Upland (Table 1). The 2006 Amerindian 144

Act establishes land rights for Guyana’s indigenous people (Fig. 1), who may claim title of 145

their community lands. Indigenous communities that have received ‘titled lands’ have rights 146

to forest and above ground resources within their boundaries (Cummings 2013). Although 147

rights to carbon stocks have not been defined, the government has acknowledged this right by 148

giving Amerindian communities the choice of opt in or out of enrolling their lands in 149

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Guyana’s national REDD program and to receive compensation from government under a 150

REDD+ agreement (http://www.lcds.gov.gy March 2013 report). 151

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Of the 23 villages in the larger study (Luzar et al. 2011), members of 17 communities carried 153

out the tree measuring work in the 20 sites: 15 ‘village’ sites and 5 ‘control’ sites Records 154

from one village were omitted, due to inexplicable tree size discrepancies between this site 155

and both the literature and data from our study for the local forest types (see Discussion for 156

more detail). (Table 2; Figure 1). Transects 4 km long were randomly placed around the 157

villages and in five control areas identified as regions where no hunting, logging or gathering 158

occurs (see Luzar et al. 2011). There were up to eight 10 m x 10 m (0.01 ha) plots per 159

transect, with 111 transects and 604 plots of 0.01 ha sampled overall (Table 2). This provided 160

6.04 hectares of AGV (tree) data. The frequency of vegetation types varied widely by site and 161

by region, with High Forest Upland and Low Forest Upland the most common types. Ite 162

Swamp and Savanna Flooded were the least common vegetation types (only two plots for 163

each of these types) (Table 2). 164

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Training & data collection 166

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Three-day training sessions were held in three locations across the region over a two-week 168

period (villages 6, 14, 19; Fig. 1), and comprised both classroom instruction and field 169

demonstrations and practise. Common sampling protocols for major carbon pools were used, 170

in line with other forest assessment projects, such as IPCC (2003), and RAINFOR (Metcalfe 171

et al. 2009, Marthews et al. 2012): tree diameters were standardly measured in cm at breast 172

height (1.3 m). On average, 14 volunteers were trained at each of the three training sessions. 173

The first part of the workshop focussed on carbon definitions, the carbon cycle and the 174

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measurement of carbon in the field. Two field workers per site sampled trees > 10 cm DBH 175

in the plots in their transects, and met with Project Fauna staff monthly to hand over data 176

sheets and resolve any technical problems which might have arisen. 177

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Data analysis 179

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To reflect the fact that the Rupununi region covers two distinct geographic and political 181

regions, separated by the Kanuku Mountains (Fig. 1), plot data were divided into ‘north’ and 182

‘south’, based on differences in coarse vegetation types (Huber et al. 1995) and geology. The 183

north Rupununi is dominated by continental sands and silts, the south Rupununi by younger 184

granites and volcanic formations (Government of Guyana 2001). Thus, in addition to the 185

eight categories of vegetation types, and a comparison between village and control sites, we 186

also consider difference between the north and south region (Table 2). The distinction 187

between north and south is also important politically, as the north is inhabited by the Makushi 188

people and the south by the Wapishana, whose language, cultural practices and occupation 189

patterns differ (Luzar and Fragoso 2013). Based on the regional rainfall regime, the 190

allometric equation for ‘moist tropical forest’ was used to calculate biomass (IPCC 2003), 191

and multiplied by 0.5 to derive above ground tree carbon: 192

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Y = 0.5 • exp[–2.289 + 2.649 • ln (DBH) – 0.021 • (ln(DBH))2] 194

Where Y = kgC. 195

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Statistical analyses (two-sample t-tests assuming unequal variance) were carried out for 197

different vegetation types, control vs village tree carbon, on the north/south data overall and 198

for each vegetation type. 199

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The data were error-checked and ‘cleaned’ before analysis by a scientist in the field, 201

including cross-checking with tree size data available from another project for the same 202

transects (Cummings 2013). Data from this work were available for trees larger than 25 cm 203

DBH for 12 control and village sites in common with our dataset. The DBH values did not 204

differ significantly between the two datasets (0.2<P<1). 205

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Estimation of carbon biomass within village titled lands 207

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An important part of the project’s engagement with local indigenous communities was the 209

provision of estimates, for their own use, of carbon stocks within their titled lands. As the 210

spatial distribution of vegetation types within titled land boundaries varied from that of the 211

sample plots, the tree carbon stock measurements for each vegetation type were applied to an 212

area-wide vegetation map in order to calculate titled land carbon stocks. A land cover 213

classification map was constructed based on the Landsat TM imageries (Path 231, Row 57 214

and 58) and the ground truth data (Figure S1; Levi et al. 2013, Cummings 2013). Titled land 215

estimations of carbon stocks were calculated by applying the carbon value per hectare for 216

each vegetation type (from the plot-based calculations) to the land cover Landsat vegetation 217

classes (Table S1). These values were then summed for the area within the border of a 218

village’s titled land. 219

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RESULTS 222

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Above ground tree carbon 224

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The mean DBH of the trees across all the sample plots ranged from 10 cm to 153.4 cm, and 226

varied by vegetation type (Figure 2). High and Low Forest Upland and Ite swamp had the 227

largest trees, and Muri Shrub Upland and Savanna Flooded the smallest. Mean carbon per 228

hectare ranged from 20.3 MgC ha-1 to 220.1 MgC ha-1, a function of vegetation type and type 229

of site (‘village’ or ‘control’). The mean value was 123.7 MgC ha-1. Grouped by broad 230

vegetation type, mean ‘forest’ carbon per hectare was 145.5 MgC ha-1 and ‘savanna’ carbon 231

per hectare was 28.2 MgC ha-1. 232

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The Upland High and Low Forest (175.7 MgC ha-1; 130.4 MgC ha-1) and Flooded High and 234

Low Forest (118.4 and 109.7 MgC ha-1) had the highest carbon per unit area, and both 235

Savanna types had the least tree carbon (4.5 MgC ha-1; 28.3 MgC ha-1) (Figure 3). 236

Comparison of carbon stocks by vegetation type revealed significant differences between all 237

types apart from High Forest Flooded and Low Forest Flooded/Upland, Low Forest Flooded 238

and Low Forest Upland, and Muri Shrub Upland and Savanna Upland (Table 3a). 239

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The north-south analysis showed that the northern forests had significantly (P < 0.05) greater 241

carbon than the southern forests (150.1 MgC ha-1 and 118.4 MgC ha-1), and this was driven 242

primarily by the Low Forest Upland vegetation group (Table 3b). Stem numbers per 243

vegetation type differed significantly between north and south regions for Low Forest Upland 244

(495 ha-1 and 323 ha-1, respectively), and this was reflected in the differences in carbon 245

between the two regions (Table 3b). High Forest Upland carbon was also markedly different 246

between regions. The ‘control’ sites had larger carbon stocks than the village sites (172.4 247

MgC ha-1 and 127.4 MgC ha-1) (P=0.05) and a greater mean number of stems per site (182 248

stems and 122 stems). 249

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Above ground carbon stocks of titled lands 251

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Estimation of carbon stocks for each village’s titled land based on technicians’ ground data 253

and satellite images reveals that the total carbon stock per titled land varies significantly 254

(Table 4). The village titled lands with the greatest mean tree carbon MgC ha-1 were 7N and 255

9N, and the sites with the smallest mean tree carbon per hectare were 12N and 14S (Figure 256

4). The variance clearly reflects the extent of the different vegetation types in each titled land 257

and the differences in size of titled land area. The lowest titled land carbon estimate, for site 258

12N - 37 MgC/ha, derives from an area of mainly grassland (Fig. 1). 259

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DISCUSSION 262

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This first comprehensive assessment of carbon stocks and vegetation types across a large 264

region in Guyana showed the value and efficiency of using Amerindian stakeholders in 265

REDD+ work. The implications for the LCDS and REDD+ for Guyana and indigenous 266

people have not previously elucidated with an underpinning of observed forest data. 267

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Vegetation type & regional variation 269

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The indigenous field technicians working with Project Fauna collected and provided 271

sufficiently accurate data to enable the estimation and assessment of their carbon stocks, as 272

reported in other studies employing participatory methods (Butt et al. 2013, Danielsen et al. 273

2013). The data collected by one of the 21 communities were unfortunately too problematic 274

to be used in our analyses – the reasons remain unclear and the data were unable to be 275

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salvaged due to a break in the research chain. Importantly, it was easy to detect when a 276

problem had occurred with data quality as the diameter measurements were so different to 277

those of other sites. Overall, local technicians were motivated to be as accurate as possible as 278

they had a vested interest in knowing how much carbon is held on their titled lands, now that 279

it has value through the climate finance mechanism. While this is a positive step, it will be 280

important to ensure that human bias in terms of potential conflicts of interest (i.e., reporting 281

larger carbon stocks than actually exist through provision of erroneously large diameter 282

measurements) should be avoided. There are several possible ways of achieving this, 283

including cross-checking measurements for the same site using different teams; re-measuring 284

plots where diameter values are systematically higher than the overall mean, and; informing 285

of potential penalties from deliberate over-estimates, such as disqualification from payment 286

schemes. 287

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Although Guyana does not allow independent trading of carbon by individual land title 289

holders, it would provide pro-rated payments to villages that ‘opt in’ to the LCDS mechanism 290

and sign a REDD+ agreement with government (http://www.lcds.gov.gy, March 2013 291

report). Knowledge of amounts and patterns of carbon content on the land would facilitate 292

negotiation and decision making by Amerindian communities choosing to opt in or out of the 293

national REDD program. It would also increase national and international understanding of 294

the contribution of Amerindian titled lands to carbon stocks and carbon loss relative to other 295

land use types in the country. 296

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Carbon stocks vary across the eight different vegetation types found in the Rupununi forest-298

savanna region. Differences also occurred amongst forest categories, for example upland high 299

forest supported more carbon than flooded high forest (175.7 MgC/ha; 118.4 MgC/ha). This 300

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suggests that carbon stock baselines, such as for REDD and REDD+ programs, originating 301

from generalized forest data from remote sensing, and correlated with national and 302

international data bases, may not reflect local and regional level carbon stocks (Mitchard et 303

al. 2014). This will have implications for measuring carbon emission changes from the 304

baseline under carbon payment programs where inaccurate generalizations may result in 305

incorrect values. By vegetation type, Upland High Forest had > 25% more carbon than 306

Upland Low Forest, and > 35% more carbon than both types of Flooded Forest: this amount 307

of difference in carbon stock between forest is policy relevant, as it can inform both the UN/ 308

program bodies, and developing countries, on the value of investing in expensive ‘Tier III’ 309

assessments (satellite imagery and ground measurements). 310

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While the village sites differed as to the extent of dominance of each vegetation type, overall 312

the most frequent vegetation types across the whole area were High Forest Upland - which 313

generally includes taller trees and denser forest - and Low Forest Upland. Muri shrub was 314

only found in the northern sites, while there were more upland savanna sites in the south. 315

This variation in dominance of vegetation types from village site to village site means that 316

carbon stocks also vary between villages, as this is a function of stem density and tree size. 317

The size of carbon stock for each village therefore depends on the local vegetation type, 318

which has implications for the potential contribution of the titled land to the national REDD 319

Program, and the pro-rated compensation a village might receive once it opts in to the LCDS 320

program. 321

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The north-south analysis showed that the northern forests had significantly larger carbon 323

stocks than the southern forests, driven by Upland Low Forest. In addition to rainfall 324

variation between the two areas, they differ in coarse vegetation type and extent (Huber et al 325

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1995, ter Steege 2001), and their geology (Government of Guyana 2001), which drives 326

variation in geomorphology, hydrology and soils. These differences will affect the amount of 327

above ground vegetation that can be supported, and thus the size of the carbon stock 328

(Baraloto et al. 2011). 329

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Human resource use impacts on carbon stocks 331

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The ‘control’ sites had larger carbon stocks per hectare than the village sites (for the four 333

vegetation types that occurred in the control sites: High Forest Flooded and Upland and Low 334

Forest Flooded and Upland), and greater stem density (~50%) in these sites. Although there 335

was no significant difference in tree diameter size between the control and village sites, the 336

large variation in stem density may reflect the impact of local forest resource use: sites near 337

villages have probably been subject to greater extraction intensity than those farther away. 338

This indicates that the carbon values for undisturbed forests should not be simply applied to 339

forest areas of titled community lands, but rather that this difference in land use impact 340

should be explicitly acknowledged in carbon stock evaluations. 341

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Volunteer-collected data applied to carbon stock estimations have been shown to be accurate 343

within a range of ±10% (Butt et al. 2013, and see Danielsen et al. 2013): we can state with 344

reasonable confidence that trees > 10 cm DBH in the forests of the Rupununi region hold 345

between 111 MgC ha-1 and 136 MgC ha-1 on average. The northern part of the region had 346

between 135 MgC ha-1 and 165 MgC ha-1, and the southern part between 106 MgC ha-1 and 347

130 MgC ha-1. The estimates derived from this project are in line with AGV carbon in other 348

areas and other tropical forests globally and regionally, as derived from a combination of on-349

the-ground and remotely-sensed data (Saatchi et al. 2011). ter Steege (2001) gave an 350

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estimation of 150 MgC ha-1, which included (standing) dead trees, while Conservation 351

International (CI) estimated around 180 MgC ha-1 (Cedergren 2009), and the FRA gave a 352

South American average of 110 MgC ha-1 for Guyana forests (FAO 2006). The Guyana UN 353

REDD+ project uses Alder and Kuijk (2009) Forestry Commission study values for their 354

baseline estimates of forest carbon biomass (Cedergren 2009). These were reported as tCO2, 355

including roots, equating to 167 MgC ha-1. The large variation amongst carbon stock 356

estimates for similar forest types and regions could be the result of a number of factors, and 357

strongly suggests the need for a standardized approach to carbon assessments. 358

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Ours is the first forest inventory of the Rupununi region of Amazonian Guyana beyond the 360

nine sample units surveyed from 1968-73 for trees >30 cm DBH (Alder and Kuijk 2009), and 361

the first to sample in savannah and forest, where tropical carbon stocks in general are not 362

well established (Houghton 2005). A comprehensive carbon stock assessment from Colombia 363

gave 112 MgC ha-1 for primary forest in the region, and 21 MgC ha-1 for secondary forest 364

(Sierra 2007), and cite lack of clear distinction between these forest types as one of the 365

problems related to carbon stock estimation in tropical forests. 366

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By being aware of what carbon stocks are, and how to measure them in their local areas, 368

indigenous groups in Guyana can better participate in national and international carbon 369

market discussions and programs, and more efficiently monitor any compensation to which 370

they are entitled through results-based carbon payments, such as those being implemented by 371

Norway in Guyana, and in the REDD+ programs in general. Indigenous people in Guyana 372

believe that their participation in the national REDD program and LCDS must be informed 373

by self-assessment of carbon stocks (North Rupununi District Development Board and the 374

Deep South Toshao’s Council, Fragoso pers. comm.), and this work provides an example of 375

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communities who have demonstrated they can effectively measure and monitor their regional 376

carbon stocks, and thus play a key role in the ongoing LCDS and MRV activities necessary 377

for REDD+. 378

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Carbon stock estimate per titled land 380

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Applying the ground-measured carbon data to the satellite land cover classes (Table A1.1) 382

enabled the estimation of carbon stocks for each of the titled land areas in the region. This 383

provided the participating villages and groups with detailed carbon estimates of their lands. It 384

is crucial to engage local indigenous communities in the ‘ground-truthing’ of forest carbon 385

data as they otherwise often miss the opportunity to receive their share of carbon payment 386

due to the lack of information (Vitel et al. 2013, Jildal et al. 2008, Corbera et al. 2008). Our 387

results revealed that there is a large variance in the average carbon stock among village titled 388

lands (Table 4), which, apart from titled land area, probably reflects the non-homogenous 389

distribution of vegetation type. For each village area, the extent of land cover classes within 390

the titled land was calculated from the satellite imagery (Table A1.2), and this gave 391

landscape-scale information, and provided an understanding of the differences in carbon 392

stocks between different vegetation types in their areas, and how satellite data can contribute 393

to carbon assessments at large scales. This emphasizes the need for freely available higher-394

resolution remotely sensed imagery in the tropics. 395

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Future work 397

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Forest types need clear identification and characterization in all regions where local 399

measurements are to be used to estimate carbon stocks. Lack of clarity can not only result in 400

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large uncertainty in carbon estimates, but may also confound comparisons with satellite 401

imagery forest data, which are important for coherent mapping of aboveground carbon 402

(Goetz 2009). 403

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We suggest a standard protocol for undertaking large-scale carbon stock estimates, 405

combining satellite imagery and ground measurements, as follows: 1) use the highest-406

resolution satellite imagery available and establish which vegetation types can be definitively 407

identified; 2) select multiple (GPS) locations in each vegetation type and assess its carbon 408

with tree measurements. This would provide a carbon value and range for each forest type 409

identifiable on highest resolution satellite imagery, which can then be applied to any area of 410

forest or titled land. This method, by establishing whether forest types differ significantly in 411

terms of carbon stock, would determine whether or not this level of satellite imagery would 412

need to be used in all assessments. The level of accuracy of lower-resolution (cheaper) 413

satellite imagery for vegetation type identification can be tested using the results from 2, and 414

thus establish the level of detail the lower-resolution imagery can provide (it may not be able 415

to distinguish between all vegetation types). Where different vegetation types have the same 416

per hectare carbon, it will not be necessary to distinguish between them and thus lower-417

resolution imagery could be used to assess carbon stocks. This approach addresses 418

uncertainty in our knowledge of carbon levels in different vegetation types, providing 419

accurate data that can usefully inform programs such as REDD+ on equitable price per 420

hectare. 421

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Conclusion 424

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We distinguish between three types of uncertainty/variation associated with carbon stock 426

assessments: differences between forest/vegetation types, including differences between 427

managed and unmanaged forest, in different areas; the spatial extent of forest/vegetation 428

types, and; measurement error. These factors will influence remuneration levels and the first 429

two should be incorporated into payment calculations. Effective training and management of 430

local field technicians is crucial to reduce measurement error and should be included in 431

baseline-setting MRV schemes. The field work and analysis carried out in the Rupununi 432

region demonstrates that on-the-ground forest measurements done by well-trained local 433

workers can make valuable contributions to carbon stock estimation across large areas. 434

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The results and findings of this project are of global importance, for example with regard to 436

the potential inclusion of forests on land held by indigenous peoples in REDD+ programs. 437

These programs are bilateral or international in nature, while it is unclear who owns the 438

carbon on indigenous lands. As we demonstrate here, indigenous people are capable of 439

assessing and monitoring carbon on their lands, and should therefore be partners in REDD+, 440

and similar, schemes. 441

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ACKNOWLEDGEMENTS 451

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Dedicated to the memory of Dr Kimberley (Kye) Epps, Stanford University, 1969-2011. We 453 thank the Guyana Environmental Protection Agency and the Ministry of Amerindian Affairs 454 for authorizing Project Fauna. The Gordon and Betty Moore Foundation, the National 455 Science Foundation (NSF; Grant BE/CNH 05 08094), and Stanford University School of 456 Earth Sciences provided funding. The Iwokrama International Centre for Rainforest 457 Conservation, the North Rupununi District Development Board, and the Deep South Toshaos 458 Council, acted as in-country collaborators and provided invaluable logistical support. We are 459 grateful to Mike Williams and Wilson Lorentino for invaluable help in negotiations. We 460 thank the Makushi, Wapishana and Wai-wai technicians whose hard work and dedication 461 made the research possible, as well as the leaders and members of all of our partner 462 communities for their participation, trust, push back, and innumerable contributions to the 463 project. We thank the graduate students, post- docs, data transcribers, and volunteers who are 464 not authors on this article but who contributed essential work and ideas to the project. We are 465 grateful to Kirsten Mariana Silvius, Peter Vitousek and Jeff Luzar for their helpful comments 466 on the manuscript. 467

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485

486

487

488

489

490

491

20

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560 Jindal, R., B. Swallow, and J. Kerr. (2008). Forestry-based carbon sequestration projects in Africa: 561

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_meeting_summary_for_website_clean.pdf. 2013 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648

23

649 Table 1: Description of vegetation types occurring along transects within the study region. 650 (Meter measurement refers to tree height). 651 652 Habitat type Description

High Forest Flooded Seasonally flooded forest (20 m – 30 m)

High Forest Upland Terra firme forest (20 m – 35 m)

Ite Swamp Mauritia flexuosa palm (≤ 20 m) dominated seasonal wetland

Low Forest Flooded Seasonally flooded forest (≤ 15 m)

Low Forest Upland Terra firme forest (≤ 15 m)

Muri Shrub Upland Terra firme scrub on white sand soils (≤ 10 m)

Savanna Flooded Seasonally flooded grassland with occasional small trees (≤ 5 m)

Savanna Upland Terra firme scrub with occasional small trees (≤ 5 m)

653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686

24

Table 2: Number of plots of each vegetation type, per site and region (‘N’=North Rupununi 687 and South Pakaraimas, ‘S’=South Rupununi), and total number of sampled transects and 688 plots by site. 689 690

Site Hig

h Fo

rest

Flo

oded

Hig

h Fo

rest

Upl

and

Ite S

wam

p

Low

For

est F

lood

ed

Low

For

est U

plan

d

Mur

i Shr

ub U

pand

Sava

nna

Floo

ded

Sava

nna

Upl

and

Num

ber o

f tra

nsec

ts

Num

ber o

f plo

ts

1N 8 22 6 12 8 48

2N 31 8 31

3N 15 1 6 1 6 26

4N 12 15 5 27

5N 3 8 4 3 15

6N 3 8 1 6 19 3 2 8 42

7N 10 16 9 6 8 41

8N 2 13 15 13 8 7 51

9N 4 9 17 14 4 7 48

10N 2 14 3 11 1 5 31

11N 8 33 3 12 8 56

12N 1 5 6 9 3 21

N total 38 181 1 66 125 16 0 7 76 437

14S 3 3 1 14 6 7 27

15S 6 2 1 3 9

16S 5 12 6 3 20

17S 5 13 3 18

18S 1 13 1 16 2 2 6 35

19S 5 1 1 4 7

20S 1 1 3 3 5

21S 10 36 6 46

S total 24 88 1 4 38 0 2 13 35 167

Total 62 269 2 70 163 16 2 20 111 604 691 692 693 694 695 696 697 698 699 700 701 702 703 704

25

Table 3a: T-test results for comparison of mean plot carbon biomass, by vegetation type. P 705 values are reported for significant differences. 706 707

HFF HFU LFF LFU MSU SU

HFF * ~ ~ ** *** HFU

** * *** ***

LFF

~ ** *** LFU

*** ***

MSU

~ SU

708 *P < 0.05 709 **P < 0.01 710 ***P < 0.001 711 ~ = no significant difference 712 713 714 715 716 717 718 719 720 721 Table 3b: T-test results for mean plot biomass, by vegetation type, north-south comparison. 722 723 vegetation type one-tail P two-tail P All plots (North-South) **0.029 *0.059 for veg types with more than two plots in each region High Forest Flooded 0.207 0.414 High Forest Upland 0.12 0.241 Low Forest Flooded 0.331 0.652 Low Forest Upland **0.019 **0.037 Savanna Upland *0.074 0.148

* P<0.1 724 **P<0.05 725 726 727 728 729 730 731 732 733 734 735 736 737 738

26

Table 4: Extent and total above ground carbon estimates of titled lands. Titled lands 3N and 739 5N fall within one large communal land title. 6N also shares its titled land with other villages 740 (not shown here). 741 742

titled land million MgC area (ha)

1N 3.01 21,925 3N 22.71 171, 275 5N 22.71 171, 275 6N 6.73 61,989 7N 7.24 48,502 8N 5.47 48,586 9N 7.00 48,658

10N 6.71 54,022 12N 0.90 24,442 14S 3.08 38,287 15S 4.25 36,183 16S 4.72 42,802 18S 4.30 34,599 19S 5.12 56,416 20S 5.88 53,544

743 744 745 746 747 748 749 750 751 752 753 754 755

27

756 757 Figure 1: Map of the Rupununi study region in Guyana. 758 759 760 761 762 763 764 765

28

766 767

768 769 Figure 2: Mean DBH by vegetation type (above) and sample site (below). N = north and S = 770 south. The control sites are represented by striped bars. Error bars represent standard error. 771 772

773 774 775 776 777 778 779 780 781 782 783 784

0

5

10

15

20

25

30

High ForestFlooded

High ForestUpland

Ite Swamp Low ForestFlooded

Low ForestUpland

Muri ShrubUpand

SavannaFlooded

SavannaUpland

DBH

(cm

)

vegetation type

0

5

10

15

20

25

30

1N 2N 3N 4N 5N 6N 7N 8N 9N 10N 11N 12N 14S 15S 16S 17S 18S 19S 20S 21S

DBH

(cm

)

Site

29

785 Figure 3: Mean carbon biomass (MgC per hectare) by vegetation type. Error bars represent 786 standard error. 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820

0

50

100

150

200

250

High ForestFlooded

High ForestUpland

Low ForestFlooded

Low ForestUpland

Muri ShrubUpland

SavannaFlooded

SavannaUpland

carb

on b

iom

ass (

MgC

per

ha)

vegetation type

carbon biomass by vegetation type

30

821 Figure 4: Average carbon biomass (MgC per hectare) by village titled land. The areas 822 encompassing control sites, included for comparison, are represented by striped bars. 823 824 825 826

0

50

100

150

200

250

300

1N 2N 3N 4N 5N 6N 7N 8N 9N 10N 11N 12N 14S 15S 16S 17S 18S 19S 20S 21S

biom

ass (

MgC

per

ha)

titled land/control sites

carbon biomass by titled land and control site