watershed rehabilitation, wet meadow restoration,...

Hartman et al. (2015)

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WATERSHED REHABILITATION, WET MEADOW RESTORATION, AND 1

INCREASED GREENNESS IN ANDEAN INDIGENOUS COMMUNITIES 2

Brett D. Hartmana1, Bodo Bookhagenß, Oliver A. Chadwicka, Carla D’Antonioγ 3 aDepartment of Geography, University of California Santa Barbara, Santa Barbara, CA 93106; 4

ßUniversity of Potsdam, Institute of Earth and Environmental Science, Karl-Liebknecht-Str. 24-25 14476 Potsdam-Golm, Germany 5 γDepartment of Ecology, Evolution and Marine Biology, University of California Santa Barbara, Santa Barbara, CA 93106 6

Abstract 7

Restoring degraded lands in rural environments that are heavily managed to meet subsistence 8

needs is a challenge due to high rates of disturbance and resource extraction. This paper investigates 9

the efficacy of erosion control structures as restoration tools in the context of a watershed rehabilitation 10

and wet meadow (bofedal) restoration program in the Bolivian Andes. In an effort to enhance water 11

security and increase grazing stability, Aymara indigenous communities built over 15,000 check dams, 12

9,100 terraces, 5,300 infiltration ditches, and 35 pasture improvement trials. Communities built erosion 13

control structures at different rates, and we compared vegetation change in the highest restoration 14

management intensity, lowest restoration management intensity, and non-project control communities. 15

We used line transects to measure changes in bofedal and standing water in gullies with check dams 16

and without check dams. We then related ground measurement to a time series (1986-2009) of 17

Normalized Difference Vegetation Index derived from Landsat TM5 images. Evidence suggests that 18

check dams increase bofedal vegetation and standing water at a local scale, and lead to increased 19

greenness at a basin scale when combined with other erosion control structures, grazing control, and 20

reduced land use pressure. Watershed rehabilitation enhances ecosystem services significant to local 21

communities (grazing stability, water security), and we discuss the importance of incorporating social 22

dynamics into land restoration conducted in rural development settings. 23

Keywords: Andes, bofedales, check dams, indigenous communities, land restoration, NDVI 24

Running head: Wet meadow restoration in indigenous communities 25

1To whom correspondence should be addressed. Email: [email protected]

Author contributions: B.D.H., B.B, O.A.C., and C.D. designed research; B.D.H. performed research; B.D.H. and B.B. analyzed data; and B.D.H., B.B, O.A.C., and C.D. wrote the paper.

mailto:[email protected]


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Highlights 26

We analyze the effectiveness of watershed restoration conducted in rural development settings 27

where resources are managed to meet subsistence needs. Results suggest that significant land 28

restoration can be achieved through a combination of intensive restoration management (erosion 29

control structures) and passive restoration (grazing control, reduced land use pressure). 30

We present a large-scale and long-term analysis of the effect of check dams and other erosion 31

control structures, indicating that significant ecosystem services (grazing stability, water security) 32

are enhanced at high levels of restoration management intensity. 33

Introduction 34

Significant portions of the world's tropics have been degraded by human use, with land 35

degradation concentrated in dryland montane areas managed by the rural poor (Bridges and 36

Oldeman 1999; Lambin et al. 2003; Lepers et al. 2005; Bai et al. 2008). Local and indigenous 37

people can be effective at ecosystem restoration, provided there is sufficient social coordination 38

and mobilization (e.g. Walters 2000; Long et al. 2003; Mingyi et al. 2003; Badola & Hussain 39

2005; Walton et al. 2006; Stringer et al. 2007; Blay et al. 2008). However, restoration efforts in 40

rural environments that are heavily managed to meet subsistence needs are often complicated by 41

high levels of disturbance from agriculture, grazing, fire, and biomass harvest (Brown & Lugo 42

1994; Lamb et al. 2005). There is a need to better understand land restoration dynamics in rural 43

development settings where land use pressure is high, and management objectives include 44

restoring ecosystem services important to local communities such as grazing stability and water 45

security. 46

One geographic region where intensive management by rural poor populations has led to 47

environmental degradation is the Central Andes of South America (Ellenberg 1979; Baied & 48


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Wheeler 1993; Chepstow-Lusty et al. 1998). The Central Andes are dominated by dry, tropical 49

montane Puna grasslands composed of bunchgrasses, tropical alpine rosette-forming herbs, and 50

dwarf shrubs in upland positions, and bofedal vegetation in seeps, springs, wet meadows, and 51

floodplains (Squeo et al. 2006). Large portions of the Central Andes have been degraded due to 52

population growth, changes in land management, and infrastructural development (Siebert 1983). 53

Degradation is characterized by reduced vegetative cover and productivity (Brandt & Townsend 54

2006), changes in species composition (Laegard 1992; Catorci et al. 2013), increased runoff and 55

erosion (Harden 1996, 2001; Valentin et. al. 2005), and deforestation of remnant Polylepis 56

woodlands (Kessler 2002; Sarmiento and Frolich 2002). 57

Bofedales are particularly sensitive to changes in hydrology induced by gully erosion or 58

prolonged drought (Earle et al. 2003; Moreau & Toan 2003; Squeo et al. 2006; Washington-59

Allen et al. 2008). Bofedal degradation impacts local livelihoods, as they are a vital source of dry 60

season grazing for llamas and sheep. In the absence of active management, degraded bofedales 61

may be slow to recover, remain suppressed at lower levels of productivity, or continue to 62

degrade (Beisner et al. 2003; Scheffer & Carpenter 2003; Avni 2005). However, bofedales can 63

be restored through watershed rehabilitation that includes erosion control and grazing 64

management (Lal 1992; Alemayehu et al. 2009). For example, check dams in gullies reduce 65

water flow velocity and increase sediment deposition, soil moisture, bank stability, and riparian 66

vegetation (Bombino et al. 2008; Boix-Fayos 2008; Zeng et al. 2009). Grazing management 67

increases biomass and productivity, improves species composition, and promotes infiltration 68

(Diaz et al. 1994; Preston et al. 2002; Buttolph & Coppock 2004; Chu et al. 2006; Catorci et al. 69

2013; Mekuria &Veldkamp 2012). Although the local effects of erosion control structures and 70


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grazing management are relatively well understood, their large-scale and long-term effectiveness 71

have not been evaluated. 72

This paper investigates the efficacy of erosion control structures as restoration tools in the 73

context of a watershed rehabilitation and bofedal restoration program in the Bolivian Andes. 74

Specifically, we evaluate the effect of check dams and other erosion control structures on bofedal 75

vegetation and Puna grasslands, and if there are any additive effects from increased restoration 76

management intensity. Evaluating long-term and large-scale responses to watershed management 77

requires a combination of ground measurement and remote sensing. We conducted ground 78

measurement of vegetation via line transects established in gullies with check dams and without 79

check dams. We then related ground measurement to a time-series (1986 – 2009) of Normalized 80

Difference Vegetation Index (NDVI) derived from Landsat TM5 images. NDVI is a robust tool 81

to measure relative changes in greenness that is correlated with biomass and productivity 82

(Anderson et al. 1993; Lu et al. 2004). NDVI has been used as an indicator of land degradation 83

in grassland and forest biomes (e.g. Thiam 2003; Wessels et al. 2004; Chen & Rao 2008; 84

Almeida-Filho & Carvalho 2010) and can be used as an indicator of ecosystem recovery 85

following restoration management (Kelly & Harwell 1990). NDVI derived from satellite imagery 86

is especially useful for post-Hoc analysis when pre-project baseline information is not available 87

(Malmstrom et al. 2008). The results indicate that check dams increase bofedal vegetation at a 88

local scale, but also lead to extensive areas of land restoration when combined with other erosion 89

control structures, grazing control, and reduced land use pressure. 90


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Study Area 91

Geographic setting 92

The study area is a traditional Aymara territory – the Ayllu Majasaya-Aransay-Urunsaya 93

– in the Tapacarí Province, Department of Cochabamba, Bolivia. Situated along the 94

Cochabamba-Oruro Highway on the Eastern Cordillera of the Andes (Figure 1), elevation ranges 95

from approximately 3,800 – 4,650 m. Mean annual precipitation is about 400 mm/yr, with 90% 96

of the rain falling between November and March (Oruro Station – Instituto Nacional de 97

Estadisticas de Bolivia, http://www.ine.gob.bo/). Daily temperature fluctuations are high, with 98

high levels of solar insolation during the day (x̄ Daily Max = 19.2ºC) alternating with cold nights 99

(x̄ Daily Min = -0.17ºC). Frosts generally occur in May and June. Dust, high winds and hypoxia 100

are also components of this environment. 101

Bofedal degradation 102

Land degradation in the study area is set within a context of population growth, rural-103

urban migration, and land use changes resulting from land reforms in 1952. Overgrazing and 104

cultivation on steep frost-prone slopes led to decreased vegetative cover, severe gully erosion, 105

altered hydrologic cycles, and reduced productivity. Bofedales in the study area were particularly 106

impacted by land degradation and gully erosion. Bofedales are vulnerable to changes in 107

hydrology (Squeo et al. 2006, Washington-Allen et al. 2008), and land degradation can impact 108

bofedales through the following mechanisms: 1) reduced vegetative cover, increased runoff, and 109

decreased infiltration rates reduce groundwater recharge rates, causing dry season water stress 110

for wetland plants; 2) gullies that incise through bofedales increase groundwater outflow rates, 111

also causing dry season water stress; 3) increased sediment transported from slopes can cover 112

http://www.ine.gob.bo/


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bofedales, causing plant mortality and increasing the soil elevation relative to the water table; 113

and 4) increased flow velocities in channels can increase vegetation scour, degrading bofedales 114

in channels and floodplains. 115

Moisture gradients and grazing intensity drive bofedal species composition (Bosman et 116

al. 1993, Ruthsatz 2012, Salvador et al. 2014). Cushion-forming species such as Distichlis 117

humilis (Poaceae), Plantago tubulosa (Plantaginaceae), and Ranunculus flagelliformis 118

(Ranunculaceae) are dominant in low-lying rivulets, pools and saturated areas. In mesic 119

hummocks and mounds, diminutive rosette species such as Hypochoeris taraxicoides 120

(Asteraceae), Hypsela reniformis (Campanulaceae), and Viola pygmaea (Violaceae) become 121

more common. If dry season water stress occurs due to erosion, grassland species such as 122

Festuca dolicophylla (Poaceae), Calamagrostis rigescens (Poaceae), Azorella biloba (Apiaceae) 123

and Lachemilla pinnata (Rosaceae) colonize the bofedales. Local people report that such 124

degraded bofedales support limited dry season grazing. 125

Watershed rehabilitation and wet meadow restoration 126

Land restoration efforts began in 1992 in a partnership between the Ayllu Majasaya-127

Aransya-Urunsaya and a non-governmental organization, the Dorothy Baker Environmental 128

Studies Center (CEADB). The project eventually expanded to include over 30 communities and 129

multiple resource management organizations. Local communities typically built check dams in 130

community work groups (aines), starting at the headwaters and working downstream as gullies 131

stabilized. In later stages, communities built terraces and infiltration ditches on slopes through a 132

food-for-work program. Local communities built a total of 15,000-20,000 check dams, 9,100 133


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terraces, 5,300 infiltration ditches, 36 gabions, 35 pasture improvement trials (enclosures), and 134

12 stock ponds, and planted 1,670 trees (CEADB project records). 135

Methods 136

Study basin selection 137

This research formed part of a broader study linking social variables with biophysical 138

indicators of restoration success conducted from 2010 to 2014. The sample design was based on 139

a comparative analysis of 4 High-Restoration Management Intensity (HighRMI), 4 Low-140

Restoration Management Intensity (LowRMI), and 4 NonProject control communities (Figure 2, 141

Table 1). Check dams, terraces, and infiltration ditches are the predominant watershed 142

management technology within the project area, and there is a high degree of variability in the 143

density of erosion control structures (ECSs) in project participant communities (from 14.7 – 144

225.3 ECSs/km2, CEADB project records). Within the context of the watershed rehabilitation 145

program at the Ayllu Majasaya-Aransaya-Urunsaya, Restoration Management Intensity was 146

defined as: 147

RMI = No ErosionControlStructures/km

2 148

HighRMI and LowRMI communities were identified from the project participant 149

communities after controlling for elevation range, geology and soils, vegetation types, 150

production methods, cultural norms and practices, and social structure. NonProject control 151

communities were selected along the same ridgeline as the project area, and they also exhibited 152

similar biophysical and social characteristics. However, control communities were not 153

immediately adjacent to the study area in order to reduce the potential for auto-correlation. As 154


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the NonProject control communities were in a different Ayllu, the level of interaction with 155

project area communities was minimal. There were two additional communities included in the 156

study (Thaya Laka and Chulpani) that had medium levels of RMI, and data from these 157

communities was included in the Pearson’s correlations as described below. 158

Study basins were delineated using a 30-m resolution ASTER Global DEM V2 159

downloaded from the NASA Land Process and Distributed Active Archive Center. The DEM 160

was re-projected to WGS-84 UTM Zone 19S with 30-m spatial resolution, consistent with the 161

Landsat TM5 images. Watersheds were defined using standard GIS techniques, and the basin 162

boundaries were delineated by merging polygons based on comparison with: 1) detailed sketch 163

maps created by local communities (CEADB project records), 2) GPS points of key landmarks, 164

and 3) ground-truthing with community informants in 2012. In general, the community 165

boundaries followed ridgelines, but if the community boundary was a river, the polygons were 166

clipped along the river centerline. 167

Line transects 168

Line transects were established in 5 gullies with check dams and in 5 gullies without 169

check dams to investigate potential changes in vegetation cover associated with check dams, and 170

to relate this information to NDVI. Gullies were selected at random after they were stratified, 171

using the following rules. First, the headwaters were between 10 – 25° hillslope angles (above 172

25° slopes, water flow velocities is such that all vegetation is scoured from gullies, and below 173

10° slopes water velocity is low enough that bofedal vegetation cover is high, regardless of 174

whether or not there are check dams). Second, the gully was located on the northeast side of the 175

ridgeline rather than on southwest slopes (this reduced aspect related variability due to solar 176


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insolation and evapotranspiration rates). Once a gully was selected, the transect start point was 177

randomly selected from 100 – 300 m below the confluence of the two feeder primary channels, 178

in the upper reaches of the secondary channels. Line transects consisted of placing a 25-m tape 179

measure down the center line of the gully. A total of four 25-m line transects were laid end-to-180

end in each sample gully (n=5x4x2). Due to the natural meander of the gullies, each 25-m 181

section of the tape measure crossed representative topographic positions, including the central 182

thalweg, the gully bottom, and portions of the side slopes that encroached on the line. The start 183

and end point of each cover type (bofedal vegetation, Puna grassland, standing water, and bare 184

areas) was measured to cm, and these values were converted to linear length and percent cover 185

for each 25-m section. The cover of different particle size classes in the bare areas was also noted 186

as follows: bedrock, cobbles, gravel, and sand. However, as the main interest was in changes in 187

vegetation cover, the bare areas were aggregated for the statistical analysis. 188

Remote sensing methods 189

A time series of Landsat TM5 images from 1986 – 2009 was constructed to evaluate 190

long-term trends in NDVI. A total of twelve Landsat TM5 scenes were acquired for the study 191

area. The images were acquired during the same time period in each sample year, on satellite 192

overpasses between May 12 and June 3. This period was selected in order to capture the dry-193

down immediately following the rainy season. Images were generally free of clouds and haze. 194

Images were acquired from USGS and from the Brazilian National Institute for Space Research 195

(INPE) receiving station. All images were projected in WGS-84 UTM Zone 19S and clipped to 196

the study area boundary (upper left 712425 E, 8073235 S; lower right 764895 E, 8017705 S), 197

and an image-to-image co-registration was performed using 28 – 100 automatically generated 198


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ground control points assisted by hand selected tie points. The resulting mean RMS error was 199

13.14 m. The 1989 image was part of the USGS Global Land Survey (GLS), therefore the 200

Landsat TM5 images were well aligned with the DEM. Following co-registration, all images 201

were radiometrically calibrated with a Dark Object Subtraction (Chavez 1988). 202

Normalized Difference Vegetation Index values were calculated for each 30-m resolution 203

pixel. Vegetation indices derived from remotely sensed data can be useful tools to measure 204

relative greenness and predicting green biomass (Carlson and Ripley 1997; Chen and Cihlar 205

1996). NDVI is a good metric across a wide variety of biomes, although it can saturate due to 206

high NIR reflectance from soils in some low biomass and high LAI systems. NDVI was 207

calculated using the red (RED) TM Band 3 (0.63 – 0.69 μm) and near-infrared (NIR) TM Band 4 208

(0.76 – 0.90 μm), where: 209

210

A 3x3 low pass filter was applied to smooth the NDVI values in each image to account 211

for inter-annual variability and any remaining pixel offset from the co-registration process. A 212

mask was created to account for areas within the study area that may have unreliable NDVI 213

values. A Maximum Likelihood Supervised Classification, using Regions of Interest (ROIs) 214

defined by areas of known cover types, was used to identify rock outcrops, sparsely vegetated 215

areas (convex shale outcrops with sparse vegetation), roads, shadow, and glint areas (pixels with 216

abnormally high DN values). As NDVI values in these areas were considered unreliable and 217

unaffected by watershed management, they were aggregated to create the RockOutcrop mask. 218

Some bare portions of river channels and gullies were misclassified as rock outcrop. Therefore, a 219


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terrain analysis was performed, using the DEM as input, and a second WetMask was created. 220

The WetMask was defined as any areas that were both concave and <10° hillslope angles, and 221

then added back into to the image to create a final mask. The resulting mask focused the sample 222

area primarily on the channels and adjacent valley bottoms, as well as gently sloping, well-223

vegetated Puna grassland slopes. Of the total of 262.57 km2 in the study basins, 96.29 km

2 was 224

masked out, leaving a total of 167.28 km2 or 63.47% of the study basins. 225

Points were selected in gullies with check dams in the HighRMI basins and in gullies 226

without check dams in the NonProject basins to focus the time series analysis on the effect of 227

check dams in gullies and to differentiate between local and basin scale effects (Figure 3). The 228

points were identified based on ground-truthing conducted in 2012 and from Google Earth® 229

imagery taken on August 8, 2009 and August 10. 2010. A total of 52 CheckDam points and 65 230

NoCheckDam points were identified. Note that the 5 line transects with check dams within the 231

project area were included as CheckDam points. A 3x3 cell grid was defined around each point, 232

in order to account for the linear nature of the gully feature, as well as a hypothesized 30–50 m 233

effect of check dams on either side of the gully, due to increased soil moisture and bank 234

stabilization. Following application of the mask, the sample size was 405 pixels (36.45 has) for 235

the CheckDam points and 501 pixels (45.09 has) for the NoCheckDam points. NDVI values 236

were extracted for these points for each year to construct a time series. A change detection 237

analysis was also conducted by taking the mean of the four pre-project years (1986 – 1992) and 238

the four post-project years (2005 – 2009). The ΔNDVI was then calculated by subtracting the 239

pre-project from the post-project mean NDVI values. 240


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A change detection analysis was also conducted at the basin level. An evaluation of the 241

ΔNDVI histograms revealed that between basin differences were greater at the tails than at the 242

median. Therefore, the ΔNDVI values were segregated by 12.5 percentiles. The number of pixels 243

in the upper and lower 12.5 percentiles were then converted into areas and percentage of land 244

surface. This allowed quantification of areas in each basin that were exhibiting a significant 245

increase in greenness, and areas that were either stagnant or exhibiting a decrease in greenness. 246

Pearson's correlations were performed between median ∆NDVI, the Lower 12.5 percentile, and 247

Upper 12.5 percentile and erosion control structures (RMI, Total No of check dams, terraces, 248

infiltration ditches). ∆NDVI data from two communities with medium levels of restoration 249

management intensity (Thaya Laka and Chulpani) was also included in the Pearson’s 250

correlations (n=14). 251

Results 252

Line transects 253

Bofedal percent cover was significantly higher in gullies with check dams compared to 254

gullies without check dams (Figure 4). There was no significant difference when comparing the 255

percent cover of grassland. Percent cover of standing water was significantly higher in gullies 256

with check dams, however standing water and soil saturation was present at the time of survey 257

(end of the dry season) in some gullies without check dams as well. The percent cover of bare 258

ground was significantly higher in gullies without check dams. Moreover, the disaggregated data 259

show that there was a significant difference in the particle size distribution between the two 260

groups, with greater proportions of bedrock, cobbles and gravel in gullies without check dams 261

and higher percent cover of smaller particles in gullies with check dams). 262


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Remote sensing 263

The NDVI time series showed a long-term, positive change in greenness across the study 264

area (Figure 5). The magnitude of change was different between basins, but a similar trend exists 265

across all basins, suggesting that a secular trend is driving a large part of the NDVI values. The 266

time series (1986 – 2009) of NDVI in the CheckDam pixels (n=405) and NoCheckDam pixels 267

(n=501) showed a similar pattern of general increase in NDVI (Figure 6). However, the increase 268

in NDVI in CheckDam points was greater, especially when examining the outliers on the upper 269

portion of the distribution. The CheckDam points have a significantly higher ∆NDVI (Figure 7), 270

with the mean difference=0.036, t(796.6)=26.97 (p<0.0001). Moreover, there appears to be a 271

skew in the distributions, insofar as the CheckDam points had a higher proportion of extremely 272

high ∆NDVI values. 273

A basin scale comparison of the ∆NDVI values segregated by percentiles (Table 2, 274

Figure 8) reveals an association between erosion control structures and the median, Lower 12.5 275

percentile and Upper 12.5 percentile values across all basins, but the variability is high. Based on 276

Pearson's correlations, the Total No check dams is the strongest predictor of median ∆NDVI 277

(r=0.576, p=0.016), followed by RMI (r=0.422, p=0.066). 278

Discussion 279

The line transects indicate that constructing check dams increases bofedal vegetation in 280

gullies. There is also an increase in standing water and a shift towards finer particle size 281

distribution in gullies with check dams, although standing water was present in some gullies 282

without check dams as well. The observed shift to smaller particle sizes and increased 283

colonization by riparian vegetation is consistent with other field-based studies (e.g. Bombino et. 284


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al. 2008). One interpretation is that check dams promote bofedal vegetation by decreasing flow 285

velocities, with increased soil moisture providing an additive or synergistic effect. As check 286

dams fill with sediment and flow velocities decrease, smaller particle sizes are deposited and 287

bofedal vegetation colonizes exposed mineral surfaces. Without check dams, the shallow rooted, 288

diminutive rosette species that form bofedal vegetation are easily scoured out. However, check 289

dams do not influence Puna grassland vegetation on the immediate banks of the central thalweg. 290

Puna grassland cover may increase at the top of the channel banks provided check dams 291

contribute to gully stabilization and decreased slumping, but this potential effect was not 292

measured. 293

The time series (1986 – 2009) of NDVI reveals a long-term positive increase across the 294

study area, characterized by high inter-annual variability due to drought and/or the El Niño 295

Southern Oscillation (ENSO). While the frequency of extreme precipitation events is increasing 296

in the eastern cordillera of the Central Andes (Thibeault et al. 2010; Boers et al. 2014), there has 297

been no significant increase in annual precipitation (Vuille et al. 2003). The long-term positive 298

increase in NDVI across the study area is more likely due to a temperature increase of about 299

0.15ºC per decade since 1950 (Vuille et al. 2003). High-elevation biomes are particularly 300

sensitive to increased temperature, with species or whole vegetation zones expected to migrate to 301

higher elevations (Beniston 2003) provided suitable soils are available (Lee et al. 2005). Local 302

people report that t'ola (Baccharis spp.), a common shrub in the region, has colonized high 303

elevation Puna grasslands in recent years, and this could lead to higher NDVI values. Additional 304

climate-related vegetation changes that may increase NDVI include increased bunchgrass 305

biomass and productivity, and earlier plant emergence times. 306


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The change-detection analysis indicated greater ∆NDVI in CheckDam points compared 307

to NoCheckDam points, consistent with the increased bofedal cover observed in the line 308

transects. Comparing ∆NDVI across basins, there is a correlation between check dams and the 309

median ∆NDVI, the Upper 12.5 percentile and the Lower 12.5 percentile. As NDVI correlates 310

with biomass and productivity (Anderson et al. 1993; Lu et al. 2004), we take the Upper 12.5 311

percentile to represent the areas where ecosystem restoration has occurred, and the Lower 12.5 312

percentile to represent areas of continued land degradation. The land restoration areas are very 313

large, from 46.9 - 610.6 hectares per community, and extend beyond the area of check dam 314

influence to include Puna grassland slopes. There are over 30 communities in the entire project 315

area, with an estimated 49.3 km2 of total land area affected by restoration activities after 316

accounting for regeneration rates in NonProject communities. 317

There is a high degree of variability when comparing the change in greenness across 318

basins. For example, one of the LowRMI communities (Japo), a large community with low 319

topographic variability located on the Cochabamba-Oruro highway, has the highest percent land 320

restoration of any basin. This indicates additional biophysical and social factors may help explain 321

the variance in ∆NDVI. Spatially, there is a strong association between the Cochabamba-Oruro 322

Highway and ∆NDVI, with a corridor of high ∆NDVI values that extends up to 1.2 km from the 323

central road. This is consistent with CEADB project records, which show that check dam 324

construction started in the most accessible areas along the main road. Work crews began building 325

check dams at the headwaters and worked progressively down slope as gullies were 326

consolidated. Efforts were later expanded to other areas and to other types of erosion controls 327

(e.g. terraces and infiltration ditches on slopes) but the highest density of erosion control 328


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structures remains along the central road. Other factors that may explain the variability in 329

∆NDVI include basin size, slope steepness, and decreased land use pressure due to off-farm 330

labor, outmigration, and grazing. For example, outmigration rates are high, ranging from 29.1% 331

of families in HighRMI communities to 70.3% of families in LowRMI communities (Hartman 332

2014). Moreover, when asked to evaluate ideal bofedal stocking rates, respondents cited 333

significantly lower rates in HighRMI basins (3.9 llamas/Ha) and LowRMI basins (8.0 llamas/Ha) 334

compared to NonProject basins (29.1 llamas/ha) (Hartman 2014). The relationships between 335

∆NDVI and social and biophysical factors will be explored in future research. 336

In conclusion, higher rates of watershed management leads to increased bofedal 337

vegetation and standing water at a local scale, and increased greenness at a basin scale. The 338

combination of active restoration (e.g. erosion control structures) and passive restoration (e.g. 339

grazing control, reduced land use pressure) can create additive or synergistic effects yielding 340

significant land restoration areas (approximately 50 km2). For local communities, land 341

restoration yields important ecosystem services (grazing stability, water security). Moreover, 342

building erosion control structures and implementing grazing management in indigenous 343

communities requires a high level of social investment in the form of conflict resolution and 344

institutional development (Hartman 2014). The extensive land area affected by these social 345

investments provides support for the argument that the next frontier in the field of restoration 346

ecology is increasing our understanding of the social dynamics that influence restoration success 347

(Temperton 2007; Shackelford et al. 2013; Hartman et al. 2015). 348

ACKNOWLEDGEMENTS 349

Funding was provided by the Inter-American Foundation Grassroots Development Fellowship, Leal Anne 350 Kerry Mertes Scholarship Award, Jack and Laura Dangermond Fund, and UCSB Academic Senate Doctoral Student 351


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Travel Grant. David Cleveland and Dan Montello provided invaluable comments on an earlier version of the 352 manuscript, and Diego Pedreros provided technical support. This research could not have been completed without 353

the support of the Ayllu Majasaya-Aransaya-Urunsaya, Reynaldo Hurtado and William Shoaie (Núr University, 354 Santa Cruz, Bolivia), and Dr. William Baker (CEADB, Cochabamba, Bolivia). 355

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Figure 1. Regional location of the study area on the Cochabamba-Oruro

Highway, eastern Cordillera of the South American Andes.


22

Figure 2. Location of the HighRMI, LowRMI, and Non-project control communities, watershed

rehabilitation and wet meadow (bofedal) restoration project in the Ayllu Majsaya-Aransaya-

Urunusaya. Study communities were selected after controlling for social and biophysical variables

and based on Restoration Management Intensity, defined as the density of erosion control

structures/km2.


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Table 1: Characteristics of the study communities, watershed rehabilitation and wet meadow (bofedal) restoration

project in the Ayllu Majsaya-Aransaya-Urunusaya.

Area Elevation Location

Land Tenancy

a

2006 Census

β Erosion Controls Structures (ECSs) RMI

γ

km2 m a.s.l. Latitude Longitude N

o

Families Check Dams

Terraces Infiltr. Ditches

Total No

ECSs (ECS/km

2)

HighRMI Communities

Lakolakoni 32.84 4,126-4,594 17⁰34’46”S 66⁰56’11”W Private 81 3400 2600 1400 7400 225.3

Qañaw Pallqa 17.76 3,846-4,397 17⁰44’31”S 66⁰44’50”W Ayanoka 52 500 1200 2200 3900 219.6

Yawri Totora 15.09 4,019-4,480 17⁰37’55”S 66⁰49’28”W Private 71 1200 1100 900 3200 212.1

Pasto Grande 14.41 4,027-4,480 17⁰39’32”S 66⁰47’40”W Private 38 2000 320 250 2570 178.4

LowRMI Communities

Waylla Tambo 7.21 3,934-4,384 17⁰44’10”S 66⁰47’26”W Ayanoka 38 300 120 110 530 73.5

Japo 28.84 4,074-4,390 17⁰41’45”S 66⁰45’50”W Private 87 1100 420 20 1540 53.4

Tola Marka 14.58 3,986-4,595 17⁰36’22”S 66⁰49’00”W Private 25 110 180 40 330 22.6

Tallija 63.32 3,821-4,430 17⁰40’45”S 66⁰43’49”W Ayanoka 129 580 320 30 930 14.7

NonProject Communities

Huaylla Marka 17.49 4,028-4,574 17⁰35’07”S 66⁰56’11”W Ayanoka --

Puitacani 12.57 4,074-4,654 17⁰34’10”S 66⁰56’27”W Ayanoka --

Lakalakani 11.97 4,149-4,624 17⁰32’27”S 66⁰57’33”W Ayanoka --

Qollpa 27.49 3,945-4,587 17⁰34’46”S 66⁰51’54”W Ayanoka --

aPrivate refers to communities that allocated titled landholdings (average size=40 has.) to individual families; Ayanoka refers to communities that have

maintained the Aymara communal system of land tenancy.

ßNo of Families resident in each community from a census conducted within the project area in 2006 to determine outmigration rates. Data not available for

the NonProject communities, as the census did not include NonProject communities.

γRMI = Total No of Erosion Control Structures (Total ECSs) per Km2; Total ECSs = sum total of check dams, terraces, and infiltration ditches in each project

community. RMI not calculated for the NonProject community.


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Figure 3. Location of the sample points in gullies with check dams (n=52) and in gullies without

check dams in the non-project area (n=62). Points were identified based on ground-truthing

conducted in 2012 and from Google Earth® imagery taken on August 8, 2009 and August 10, 2010.


25

p= <0.0001 0.502 0.049 <0.0001

Figure 4. Percent cover of bofedal vegetation, Puna grassland, water, and bare substrate (including

bedrock, sand and gravel, and cobbles) in gullies with check dams and gullies without check dams.

Data from 25-m line transects established in each sample group (n=20x2). p-values from a Brown-

Forsythe One-way ANOVA, following a Levene's F Test for Equality of Variances.


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Figure 5. Time series (1986 – 2009) of Normalized Difference Vegetation Index (x̄ ± SD) for the

study area, including the HighRMI, LowRMI, and NonProject basins. Data from Landsat TM5 images

taken from the same time period (May 12 – June 3) in each sample year.

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

1984 1988 1992 1996 2000 2004 2008 2012

Me

an

Norm

aliz

ed

Diffe

ren

ce

Ve

ge

tatio

n In

de

x


27

Figure 6. Time series (1986 – 2009) of NDVI in pixels with Check Dams (n=405)

and pixels with No Check Dams (n=501). The box-and-whisker plots show the

median, 75th percentile, 25th percentile, and maximum-minimum values, with

outliers symbolized with an (x).


28

Figure 7. Change in NDVI in points with CheckDams (n=405) and points with

NoCheckDams (n=501). Data is based on a change detection between the x̄

PreProject NDVI (1986 – 1992) and the x̄ PostProject NDVI (2005 - 2009). Mean

Difference = 0.036, t(796.6)=26.97, p<0.0001. Box-and-whisker plots display data

similar to Figure 6.


29

Figure 8. Change in in the study area. ΔNDVI based on a change detection between the x̄ PreProject

NDVI (1986 – 1992) and the x̄ PostProject NDVI (2005 - 2009). Data are segregated by 12.5

percentiles.


Table 2. Estimation of areas of land restoration/regeneration and continued degradation/arrested succession at the study communities. The

upper and lower 12.5 percentiles are based on segregation of the ∆NDVI values

Sample Areaa

∆ NDVI

RMI

Median Check Dams Upper 12.5%

Has. Percent Has. Percent Check Dams ECS/km2

HighRMI Communities 3400

Lakolakoni 2143.26 0.0667 500 7.68 551.43 25.73 3400 225.3

Qañaw Pallqa 957.15 0.0605 1200 13.94 125.1 13.07 500 219.6

Yawri Totora 941.94 0.0637 2000 6.01 94.59 10.04 1200 212.1

Pasto Grande 708.21 0.0642 7.88 113.94 16.09 2000 178.4

0.0638 8.88 16.23

LowRMI Communities

Waylla Tambo 362.97 0.0576 1100 20.21 27.54 7.59 300 73.5

Japo 1615.32 0.0720 110 5.68 610.56 37.80 1100 53.4

Tola Marka 991.71 0.0574 580 17.42 46.89 4.73 110 22.6

Tallija 4509.99 0.0619 11.09 522.18 11.58 580 14.7

0.0622 13.06 15.42

NonProject Communities

Huaylla Marka 969.57 0.0551 18.28 21.15 2.18 --

Puitacani 768.51 0.0590 15.95 78.21 10.18 --

Lakalakani 744.03 0.0618 12.33 112.59 15.13 --

Qollpa 2015.82 0.0590 13.19 91.62 4.55 --

0.0587

14.94 8.0

aSample area=the unmasked portion with each basin

ßOutmigration % based on data from the 2006 census and community records from 2012

γTotal off-farm labor is an aggregate of the No of days working in the city and the No of days working locally

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