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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
107
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
359
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
398
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
404
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
435
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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_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
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