Habitat evaluation and conservation framework of the newly ...
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Ren, Guo-Peng, et al. "Habitat evaluation and conservation framework of the newly discovered and
critically endangered black snub-nosed monkey." Biological Conservation 209 (2017): 273-279.
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Habitat evaluation and conservation framework of 1
the newly discovered and critically endangered black 2
snub-nosed monkey 3
Guo-Peng Ren a,c1,Yin Yang a,b,c1, Xiao-Dong He c,d, Guang-Song Li c,d, Ying Gao a,c, 4
Zhi-Pang Huang a,c, Chi Ma a,c, Wei Wang e*,Wen Xiao a,c* 5
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aInstitute of Eastern-Himalaya Biodiversity Research, Dali University, Dali, Yunnan 7
671003, China 8
b School of Archaeology & Anthropology, Australian National University, Canberra, 9
ACT 0200, Australia 10
c Collaborative Innovation Centre for Biodiversity and Conservation in the Three 11
Parallel Rivers Regionof China, Dali, Yunnan 671003, China 12
d Nujiang Bureau of Gaoligongshan National Nature Reserve, Nujiang, Yunnan 13
673100, China 14
e State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese 15
Research Academy of Environmental Sciences, Beijing 100012 China 16
1 Joint first authors/these authors contributed equally to this work 17
* Corresponding authors at: 18
Institute of Eastern-Himalaya Biodiversity Research, Dali University, Dali, Yunnan 19
671003, China. Email address: [email protected] (W.Xiao); 20
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese 21
Research Academy of Environmental Sciences, Beijing 100012 China (W.Wang). 22
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Abstract 26
The black snub-nosed monkey (Rhinopithecus strykeri) is an IUCN-Critically 27
Endangered primate, recently discovered on the northern Sino-Myanmar border. In 28
order to identify the most urgent gaps in the conservation of the black snub-nosed 29
monkey, a hierarchical process was employed to predict the distribution and 30
alterations in its habitat over the past 15 years. Our study showed that R. strykeri 31
appeared to inhabit a range from E98°20'-98°50', N25°40'- 26°50', including high 32
quality habitat at 1420 km2, medium quality habitat at 750 km2, and low quality 33
habitats at 1410 km2. Only 21.1% of the total habitat for R. strykeri is within 34
protected areas in China. Approximately 2.6% of the entire habitat has been lost in 35
the past 15 years, 96% of which has been in Myanmar. To save this species from 36
extinction, it is urgent to establish trans-boundary conservation and management 37
networks to address the loss of habitat, and to locate and preserve key wildlife 38
corridors to link fragmented habitats between Myanmar and China. 39
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Key words: Rhinopithecus strykeri, habitat alterations, trans-boundary conservation, 41
species distribution model 42
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1. Introduction 47
The recently described black snub-nosed monkey (Rhinopithecus strykeri), 48
alternatively known as the Myanmar or Nujiang snub-nosed monkey, is found in the 49
high altitude forests of north-eastern Kachin state, Myanmar (Geissmann et al., 2011) 50
as well as in the middle segment of the Gaoligong Mountains, Yunnan, China (Long 51
et al., 2012; Yang et al., 2016). There are up to 14 groups (10 in China and 4 in 52
Myanmar, < 950 individuals) of black snub-nosed monkeys living in the northern 53
Sino-Myanmar border area according to interviews (Geissmann et al., 2011; Ma et 54
al., 2014). As a slow breeding species, combined with the prevalence of hunting and 55
the extensive loss of habitat, this species is likely experiencing a rapid demographic 56
decline (Geissmann et al., 2011). With the booming economy and population 57
growth of the region, deforestation for agricultural cultivation and infrastructure 58
development pose potential perils to this IUCN-Critically Endangered primate. 59
To save this species from the brink of extinction, it is important to study its habitat 60
and its distribution, but, due to the extreme ruggedness of the terrain and the long 61
wet season, almost no information on population distribution and habitat status are 62
available for conservation planning. Where the data are poor in given study areas, 63
employing MAXENT can offer reliable assessments for a species’ possible 64
distribution, and can support conservation planning (Thorn et al., 2009; Peck et al., 65
2011; Ingberman et al., 2016) by prioritizing appropriate habitats for new protected 66
areas (Urbina-Cardona & Loyola, 2008; Campos & Jack, 2013) or guiding 67
prospective land use planning and management (Illera et al., 2010), by identifying 68
least-cost corridors for habitat connectivity (Liu et al., 2016; Luo et al., 2016; 69
Schaffer-Smith et al., 2016), and by pinpointing ideal reintroduction sites (Thorn et 70
al., 2009; Cilliers et al., 2013). The function of MAXENT is to generate a model 71
across one study area based on existing information concerning environmental 72
variables which is then used in another area to make predictions and inference of 73
maximum entropy occurrence under similar environmental constraints (Phillips & 74
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Dudík, 2008; Thorn et al., 2009; Araújo & Peterson, 2012). Compared to other 75
ecological niche algorithms, MAXENT as a present-only model can moderately 76
offset for imperfect, limited species occurrence data sets and reach near-maximum 77
accuracy levels under these circumstances (Hernandez et al., 2006, 2008; Giovanelli 78
et al., 2010). 79
Based on climatic variables and two sets of locality records obtained by 80
interview-based survey (Gessiman et al. 2011; Ma et al. 2014; Figure 1), a 81
MAXENT model was built up to map the habitat of R. strykeri. Forest cover maps 82
in the 2000s and 2015, based on Landsat images, were used to mask out 83
non-woodland areas from the habitat. We then assessed habitat changes from 2000 84
to 2015. According to the results, we identified additional areas where R. strykeri 85
may occur, proposed specific conservation priorities, and determined crucial areas 86
in which urgent protection measures should be taken to ensure the long survival of 87
this rare and little known primate. 88
2. Methods 89
2.1 Study area 90
The Gaoligong Mountains rise from the low altitude (183 m) drainage of the N'mai 91
River in Myanmar to an altitude of 6318 m in the southeast of Zayü County 92
(Chaplin, 2005). The altitudinal gradient yields a vertical zonation of climate, 93
soil composition and solar radiation, generating diverse vegetation types and a rich 94
flora and fauna diversity (ibid). The vegetation in the Gaoligong Mountains, from 95
the valleys to the peaks, goes from tropical monsoon forest (< 1000 m a.s.l.), 96
monsoon evergreen broad-leaved forest (1100-1800 m), semi moist evergreen 97
broad-leaved forest (1700-2500 m) and mid-montane moist evergreen broad-leaved 98
forest above (1900-2800 m), coniferous broadleaved mixed forest (2700-3500 m), to 99
the alpine bush zone (> 3400 m) (Li et al., 2000). 100
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According to Geissmann et al. (2011) and Ma et al. (2014), the known and potential 101
R. strykeri populations are restricted to a narrow range of 98°20'-98°49'E, 102
25°58'-26°31'N. Two insurmountable natural barriers, the N 'Mai Hka and Salween 103
Rivers, would appear to have physically blocked their expansion to other areas. 104
Thus, the study area for this project ranged from the China State Road 320 (named 105
Road NH3 in Myanmar) in the south to the provincial border between Yunnan and 106
the Tibet Autonomous Region in the north, and from the N' Mai Hka River in the 107
west to the Salween River in the east, with a total area of about 41,350 km2 108
(97.05-98.91E, 24.02-28.40N, Fig. 1). 109
2.2 Species Occurrence Extraction 110
Interview-based survey can cost-effectively and statistically access the number and 111
distribution of rare and poorly known large animals, especially in the case of primates 112
with a distinctive appearance such as the black snub-nosed monkey (Meijaard et al., 113
2011; Cui et al., 2015; Ma et al. 2015; Turvey et al. 2015). Between April 2011 and 114
December 2012, our research mission visited 68 villages near the high-altitude forests 115
and interviewed 358 old hunters or mountain villagers who entered the forests 116
frequently in Gongshan, Fugong and Lushui counties, on the west bank of the 117
Salween River in China (Ma et al., 2014). As a result, a total of 72 valid distributional 118
records was obtained from 67 respondents, and two groups of black snub nosed 119
monkeys then were confirmed in Pianma (Ma et al. 2014) and Luoma (Yang et al., 120
2016). These 72 records, which were further merged into 46 polygons with sizes 121
ranging from 16 to 1727 ha, were used as species occurrence data in China since R. 122
strykeri is not easily mistaken for any other primate given its peculiar appearance 123
(Fig.1). Three possible distribution records reported by Geissmann et al. (2011) in the 124
Maw River areas of Myanmar, with areas of 3854 ha, 4126 ha, and 6108 ha, were also 125
included in the species occurrence data. 126
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2.3 Data acquisition 127
Data on both climate and vegetation were used to predict the distribution of the 128
black snub-nosed monkey. Thirty two Landsat ETM+/OLI images were downloaded 129
from USGS Earth Explorer (http://earthexplorer.usgs.gov/) to map forest cover over 130
the study area (see Table A.1 in Supporting Information). Climatic data which we 131
used include monthly maximum, minimum and mean temperature, monthly 132
precipitation, and 19 variables (see Table A.2). All these climatic data were acquired 133
from the WorldClim (http://www.worldclim.org, see Hijmans et al. 2005). As with 134
the Landsat images, the WorldClim (30 arcsec spatial resolution, equal to 0.00833°) 135
were transformed to a Universal Transverse Mercator coordinate system (UTM, 136
WGS84 datum, Zone 47 N) and spatially interpolated to 30 m resolution using 137
bilinear interpolation. 138
2.4 Forest mapping 139
510 sets of 30 × 30 m squares (9 squares per set) were systematically sampled as 140
“ground-truth data” from the study area by visual interpretation of high resolution 141
images on Google Earth (Google Inc, Table 1, Fig. A.1). Each square was deemed 142
woodland if the tree cover was higher than 40%; otherwise, it was determined as 143
non-woodland. The whole project area was divided into woodland area and 144
non-woodland area, based on Landsat TM/ETM+/OLI images (resolution = 30 m) 145
using a random forest algorithm with the “ground-truth data” (Breiman, 2001). The 146
classification process was implemented using R Statistics software 147
(http://www.r-project.org/) with the Random Forest package (Liaw & Wiener, 2002) 148
and Raster package (Hijmans, 2016). 149
2.5 Species distribution modelling 150
MAXENT is a popular species distribution model, with a history of superior 151
performance (Boubli & Lima, 2009; Thorn et al., 2009; Peck et al., 2011; Chetan et 152
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al., 2014; Hernandez et al. 2006; 2008). Species occurrence sites and environmental 153
data are required to build a MAXENT model. At first, 10,000 pixels (30 m 154
resolution) were randomly selected from the study area as background data 155
(pseudo-absence data), 2000 samples of which were used as pseudo-absence data 156
for the MAXENT model, and the remaining 8000 samples were used for validation. 157
Two thirds of the species occurrence records, 33 of 49 distribution polygons, were 158
randomly selected as a training polygon, and the remaining 16 polygons were used 159
as test polygons. All the 49 distribution polygons were aggregated and rasterized at 160
30 m resolution. No > 100 pixels or 50% of the total pixels were systematically 161
sampled from each of the 49 distribution polygons. In total, 2772 samples were 162
obtained from the 33 training polygons, and 1008 samples were obtained from the 163
test polygons as test data. 164
A MAXENT model was built with the 2772 presence samples and 2000 background 165
samples based on the 67 climate variables with the default parameters setting in the 166
R package dismo (Hijmans et al. 2016). As a result, we obtained a climatic 167
suitability layer of the study area. Four levels of climatic suitability were quantified 168
according to relevant parameters of the MAXENT model for identifying core, 169
medium, low quality and none habitat (Table 2). 170
The Black snub-nosed monkey is arboreal and inhabits mainly subtropical 171
broad-leafed forest and mixed temperate forest. Forests provide shelter and ample 172
food for them; non-woodland areas could, therefore, be regarded as non-habitat. 173
We obtained habitat maps in both stages (2000s and 2015s) by masking out 174
non-woodland areas from the climatic suitability layer using forest cover maps. 175
Habitat changing from 2000 to 2015 was estimated by simply comparison of the 176
habitat maps in 2000 and 2015. 177
The AUC (area under curve) of ROC (receiver operating characteristic curves) was 178
employed to measure the accuracies of the habitat maps. The AUC values were 179
0.964 and 0.957, which indicated that our habitat evaluation models were accurate 180
(Araújo et al., 2005) 181
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All the above procedures were performed using R Statistics software (version 3. 3. 1, 182
R Core Team, 2016) with some key packages, such as raster (Hijmans, 2016) and 183
dismo (Hijmans et al. 2016). 184
3. Results 185
3.1 Habitat distribution for the black snub-nosed monkey 186
Based on results generated from the hierarchical model, the current geographical 187
distribution for R. strykeri is predicted to be in the range of E98°20'-98°50', 188
N25°40'- 26°50', with a total area of approximately 3575 km2, of which the core 189
habitat is 1420 km2, medium quality habitat is 750 km2, and low quality habitats are 190
1405 km2 (Fig. 2). Among these suitable habitats, 2444 km2 (68.4%) are in 191
Myanmar, and 1131 km2 (31.6%) in China. Only 21.1% of the total habitat is 192
located in existing protected areas of China. The largest patch of core habitat 193
harboring most R. strykeri groups covered 1280 km2 and crossed the 194
China-Myanmar border within a range of 50 km, including 450 km2 (35.2%) in 195
China and 830 km2 (64.8%) in Myanmar. The vertical range of core habitat is 196
estimated to be 2330 to 3240 m in both countries. 197
3.2 Habitat alterations in the past fifteen years 198
In 2000 there was an estimated 3670 km2 of habitat in the Sino-Myanmar border 199
region, but by 2015 this figure had declined by 95 km2 (a destruction of 2.6% over 200
time or a mean of 0.17% yr-1). Correspondingly, the core, medium and low quality 201
habitats decreased by 50, 20, 25 km2. Of the total habitat loss, 96% was in northern 202
Myanmar. The loss of habitat within Myanmar from 2000 to 2015 was estimated to 203
be 97 km2, or 3.8% of the entire habitat of the species in Myanmar for the year 2000 204
(Fig. 3), while in China, forest cover loss (4 km2) was lower than the amount of 205
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forest rehabilitation (6 km2); the destruction occurred mostly outside the Gaoligong 206
Mountains National Nature Reserve. 207
4. Discussion 208
4.1 Urgent conservation needs and applications 209
In general, the entire habitat area was reduced between 2000 and 2015, although the 210
overall speed of loss and recovery was irregular (Fig. 3). In Myanmar, R. strykeri 211
faces intense pressure from habitat loss and hunting, as there are no conservation 212
measures to date. Habitat loss is due not only to shifting cultivation near human 213
settlements in the valley, but also extends along criss-crossing roads because of 214
commercial logging, dam construction and mining into the central core habitat area, 215
and even peaks (Momberg et al., 2010; Geissmann et al., 2012), which may have 216
affected population continuity because of habitat fragmentation and disruption of 217
habitat connectivity (see Fig. 3, right images). In addition, hunting of this species is 218
still very severe, even after the species’ discovery in 2010 (Meyer et al., 2015); 219
combined with habitat loss it is projected that this may lead to a species decline of > 220
80% in the next fifteen years in Myanmar (Geissmann et al., 2012). In China, more 221
than half of the habitat (753 km2, 66.6%) is located outside the reserve and there has 222
been depletion of forest resources caused by human population growth. 223
Occasionally hunting for bush meat and traditional medicine has provided further 224
pressure (Ma et al., 2014; Fig. A.2.F). Considering all these factors, we propose the 225
three following major conservation procedures: 226
First, conservation gaps should be filled by the establishment of new protected areas 227
(Fig. 2). The habitat of R. strykeri slightly recovered in the Gaoligong Mountains 228
National Nature Reserve, and this indicates that the creation of new protected areas 229
might be an efficient method to maintain the habitat in this region. Long et al. (2012) 230
recommended Sino-Myanmar cooperation in delineating a trans-boundary wildlife 231
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sanctuary to protect this species. Support by the relevant institutions, the Myanmar 232
Ministry of Environmental Conservation and Forestry and the National Democratic 233
Army of Kachin (a pro-government militia), has already led to agreement to create a 234
protected area, named Imawbum National Park, in Sawlaw Township (pers. comm. 235
Fauna & Flora International, 2016). We propose that the range of Imawbum 236
National Park should border the Gaoligong Mountains National Nature Reserve in 237
China (see Fig. 2), which covers an area of 1934 km2. At present, the proclamation 238
of new national parks in China is proceeding rapidly. The Program for 239
National Park Development in Yunnan Province 2009-2020 under the Yunnan 240
Provincial Government proposed to create a national park in Nujiang Autonomous 241
Prefecture. We hence suggest setting up the range of Gaoligong Mountains National 242
Park (328 km2) to link the southern and central segments of the Giaoligong 243
Mountains National Nature Reserve in Lushui County (Fig. 2). 244
Second, management plans need to consider the linkages between the protected 245
areas on both sides of the border and the importance of maintaining habitat corridors 246
that presently connect them. Based on the habitat distribution pattern, two wildlife 247
corridors with management measures are proposed here to contribute habitat 248
connection and connectivity maintenance. In particular, the most important 249
trans-boundary habitat corridor should be located in the northern edge of the 250
fragmented habitats to connect the two largest core habitats in Myanmar (Fig. 2, 251
black circled area C1) in order to enable population dispersal and gene flow and 252
increase the probability of the species’ persistence. Timber extraction, shifting 253
cultivation and road construction are imminent threats to this corridor. Any further 254
deforestation should be prohibited there, and the lost habitats must be speedily 255
restored by natural rehabilitation. We subsequently propose that the remaining 256
vegetation in Myanmar must be strictly protected where the area borders the habitat 257
at Gangfang Village of Pianma Township in China (Fig. 2, black circled area C2). 258
The habitat there was dramatically disjointed by commercial harvesting, shifting 259
cultivation, road construction and mining; a small cross-border population, however, 260
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may inhabit this general area (Ma et al., 2014; a newly dead adult female specimen 261
was collected from a hunter in Gangfang of Myanmar in November, 2015). Natural 262
recovery assisted by artificial restoration of trees in this area will quickly restore a 263
habitat corridor on the Sino-Myanmar border and increase the chances for survival 264
for the threatened population. 265
Third, we urgently recommend a Sino-Myanmar joint-patrol law enforcement team 266
for implementation of the trans-boundary zoning and management plan to combat 267
hunting and illegal trans-boundary logging activities. Thus, we can ensure that 268
effective National Park management will be in place and that both forest 269
departments can enforce the law against hunting and illegal logging in the border 270
area, and to avoid a “paper park” becoming established like many others in 271
Myanmar (see Rao et al., 2002; Aung, 2007). As the Sino-Myanmar border is a 272
relatively poor and backward remote area, hunting wildlife has become a source of 273
protein supplementation and income for local minorities; it is therefore important to 274
China and Myanmar to set up an intergovernmental investment fund for payments 275
for ecosystem services, developing sustainable alternative livelihoods, and 276
community co-management projects, improving financial support to the protected 277
areas for their effective operation and capacity building, and enhancing 278
environmental education for local people and park staffs. 279
As a supplementary note we also suggest that the distribution maps produced here 280
should be superimposed with existing and future land-use maps for both the 281
development departments of China and Myanmar to prevent future damage of the 282
stretches of habitat that are key for the conservation of R. strykeri. 283
4.2 Determining new investigation areas 284
Another contribution of our study is to determine where the monkeys may occur for 285
future field survey. Further population investigations should be conducted in the 286
core habitats which have not been investigated and monitored. According to 287
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MAXENT, for example, field census or interview based surveys should be 288
conducted in the core habitat between the proposed corridors of C1 and C2 (Fig. 2) 289
which has a great potential to sustain some R. strykeri populations. Besides, the 290
peripheral population in marginal habitats are probably more subject to habitat loss 291
and more vulnerable to extinction before conservationists can find them. 292
Accordingly, the northernmost habitat (Fig. 2, black circled area NO. M1), the 293
Imawbum Mountain (black circled area NO. M2), and the southernmost habitat 294
(black circled area NO. M4) need immediate field censuses to confirm the potential 295
existence of peripheral populations. 296
4.3 Approach caveats 297
There probably are some prediction errors resulting from insufficient information on 298
habitat. The elevational range of the core habitats is strongly associated with the 299
mid-montane moist evergreen broad-leaved forest and coniferous broadleaved 300
mixed forest (Li et al., 2000). We found that R. strykeri prefers the abovementioned 301
forest types but avoids secondary deciduous broad-leaved forest, secondary bamboo 302
forest, artificial pine forest and coppice lands. These non-preferred forests have 303
mostly been degraded and fragmented by large scale logging, road creation and 304
burning or Lisu and Law Waw peoples' forest-crop rotation on these lands (eg. Fig. 305
3, black circled area NO. M3). We did not consider any of these forests in this study 306
due to the limitation of ground-truth data for forest mapping. Additionally, the 307
woodland being used in forest mapping includes shrub land, thus contains some 308
non-habitats for the arboreal R. strykeri. Therefore, the entire habitat range could 309
eventually be overrated and the habitat loss rate underestimated. A fine-scale 310
vegetation type map of the distribution of R. strykeri, where possible, should be 311
considered. Moreover, very informative materials are also essential to catch the 312
typically patchy distribution of intense anthropogenic impacts, which in our study 313
case include dam construction, mining sites, road opening and human settlements. 314
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5. Conclusion 316
As the black snub-nosed monkey is endemic to the junction of two biodiversity 317
hotspot regions, Indo-Burma and Mountains of Southwest China (Marchese, 2015), 318
we believe that, to protect this critically endangered species, all of its actual and 319
potential distribution area should be strictly protected by banning hunting, logging, 320
agricultural reclamation and other improper activities. As an umbrella and global 321
flagship species, the effective conservation of R. strykeri will also protect other 322
sympatrically threatened biota (see Tab. A.3). Results from this work highlighted 323
that strengthening current conservation practices should ensure the long survival of 324
R. strykeri in China while establishment of cross-border conservation and the rapid 325
elimination of the threat in Myanmar are necessary for re-emergence of this species 326
in that country and across the border. The conservation framework proposed in this 327
paper therefore can serve as a basic prop for conservation department to formulate 328
more specific strategies to protect R. strykeri. 329
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ACKNOWLEDGMENTS 338
This research complied with the policies and guidelines of the Australian National 339
University and Dali University for the ethical treatment of primates. Funding was 340
provided by the Zoological Society for the Conservation of Species and Populations 341
(Germany, 7.Rhinopithecus.MMR.2015) and National Science Foundation of China 342
(31560599). The authors were supported during this project by the Nujiang Forestry 343
Department and the Gaoligong Mountains National Nature Reserve. Thanks are due 344
to Emeritus Professor Colin Groves for his valuable suggestions on this paper and his 345
help in English editing; three anonymous reviewers provided constructive comments 346
to improve the original manuscript, and Frank Momberg and Guy Michael Williams 347
(FFI, Myanmar) shared with us their knowledge and research outcomes on the species 348
in Myanmar. Our field guides, Mr. Dong Shaohua and Mr. Kenhuaniuba, contributed 349
materially to this study. 350
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19
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Basin, China. Oryx 1-3. doi:10.1017/s0030605316000934 474
Biosketches 475
Guopen REN is a College Associate Professor based at the Institute of 476
Eastern-Himalaya Biodiversity Research, Dali University. His work specializes in 477
remote sensing, GIS, spatial ecology and their applications in biodiversity and 478
conservation management. 479
Yin YANG is a PhD student at the School of Archaeology & Anthropology, 480
Australian National University with research interests in understanding the ecology 481
and conservation needs for the black snub-nosed monkey in the Gaoligong Mountains 482
Region. 483
Author contributions: WX designed the study. GPR, YY, WW and GY conducted 484
data analysis. XDH, GSL, ZPH and CM collected species occurrence data. YY wrote 485
20
the first draft, WX, GPR and YY revised the manuscript, and all authors made 486
extensive contributions to the text. 487
488
Table 1. Hierarchical definition of forests
Name Definition
Fs: forest + shurb Tree crown cover degree > 40%,including forest and shrub.
Fw: forest = Fc + Fo Tree crown height >= 3m
Fc: closed-forest Tree crown cover degree >= 70%
Fo: open-forest 40% =< Tree crown cover degree < 70%
Sh: shrub Plant cover degree >= 40%, but not fulfilling the other
conditions of forests
489
490
491
492
493
494
495
496
497
498
499
500
501
502
21
503
504
505
Table 2. Habitat quality levels description of black snub-nosed monkeys
Quality Predicted
value Description
Core > 0.50 Equal training sensitivity and specificity (0.50)
Medium 0.41-0.50 Maximum training sensitivity plus specificity (0.41)
Edge 0.04-0.41 Minimum training presence data omission (0.04)
Non-habitat < 0.04 Less than 0.1% of training presence data predicted (0.04)
506
507
508
509
510
511
22
512
Figure 1. Map of the study area and distribution of R. strykeri in the Sino-Myanmar 513
border region. 514
515
516
517
518
519
520
521
23
522
Figure 2. Predictive habitat distribution and quality map with proposed 523
conservation and management areas for R. strykeri in the Sino-Myanmar border 524
region according to climate niche analysis, forest cover in 2015 and MAXENT. 525
526
527
24
528
Figure 3. Habitat alterations (2000-2015) in the Sino-Myanmar border region, 529
measured as the percentage of habitat cover change within 1 × 1 km2 grid cells. The 530
locations of habitat loss are marked with red colours and rehabilitation with blue 531
colours. The right-hand images show an example of habitat loss, fragmentation and 532
insularization in the study area during 2000 and 2015. 533
534
25
Supporting Information (When using data in this part, please cite the original 535
publication) 536
1. Modeling Setting: 537
The default setting was used for the climatic and species distribution parameters in 538
the R package dismo: 539
maxent.sample = maxent (x = trainvals.sample, p = species.sample) 540
in which, trainvals.sample is a 4772 × 67 matrix, and species.sample is a vector 541
of length 4772 . 542
543
Table Appendix 1. Path, row numbers and acquisition dates of Landsat images 544
used in this study. 545
Landsat 7, 2000s Landsat 8, 2015s
path row date path row date
132 40 2000360 132 40 2014022
132 41 2000360 132 41 2014022
132 42 2002013 132 42 2014022
132 43 2002013 132 43 2014022
133 40 2000047 133 40 2015016
133 41 2000047 133 41 2015016
133 42 2000047 133 42 2015016
133 43 2000047 133 43 2015048
132 40 2002333 132 40 2015105
132 41 2002333 132 41 2015105
132 42 2002333 132 42 2015073
132 43 2001330 132 43 2015073
133 40 2001337 133 40 2015080
133 41 1999300 133 41 2015080
133 42 1999316 133 42 2015080
133 43 1999316 133 43 2015080
546
26
Table Appendix 2. Bioclimatic variables used in the model
BIO1 Annual mean temperature
BIO2 Mean diurnal temperature range [mean of monthly (max
temp–min temp)]
BIO3 Isothermality (P2/P7) (×100)
BIO4 Temperature seasonality (standard deviation×100)
BIO5 Max temperature of warmest month
BIO6 Min temperature of coldest month
BIO7 Temperature annual range (P5–P6)
BIO8 Mean temperature of wettest quarter
BIO9 Mean temperature of driest quarter
BIO10 Mean temperature of warmest quarter
BIO11 Mean temperature of coldest quarter
BIO12 Annual precipitation
BIO13 Precipitation of wettest month
BIO14 Precipitation of driest month
BIO15 Precipitation seasonality (coefficient of variation)
BIO16 Precipitation of wettest quarter
BIO17 Precipitation of driest quarter
BIO18 Precipitation of warmest quarter
BIO19 Precipitation of coldest quarter
547
548
549
550
551
552
553
554
555
27
Table Appendix 3. List of threatened mammals monitored in Rhinopithecus 556
strykeri habitat in the Gaoligong Mountains. Location: MW, western slopes of 557
Gaoligong Mountain, northeastern Kachin state, Myanmar; WC, western slopes of 558
Gaoligong Mountain, Yunnan, China; EC: eastern slopes of Gaoligong Mountain, 559
Yunnan, China. Habitat types: LE: low land subtropical evergreen rain forest; SM: 560
semi moist evergreen broad-leaved forest; ME: mid-montane moist evergreen 561
broad-leaved forest; TC, temperate coniferous forest; BB, bamboo bushes. 562
Monitoring method: CT, camera trapping; FO, field observation; ST, specimens of 563
skulls and tails collected from hunters; IT, interview. IUCN RED List: CR, critically 564
endangered; EN, endangered; VU: vulnerable; NT, near threatened. CITES 565
Appendix: APP. I, Appendix I; APP. II, Appendix II. 566
Species Location
Elevation
(m) /
Habitat
types
Monitori
ng
method
Conserva
tion
status
Reference
Macaca
arctoides
MW, WC,
EC
2500-3100 /
ME, TC,
BB
CT, IT,
FO
VU,
APP. II
Momberg et al.,
2010; Chen et
al., 2016; Fig.
A.2.D
Macaca
assamensis
MW, WC,
EC
2500-3100 /
ME, TC,
BB
CT; FO,
ST
NT,
APP. II
Momberg et al.,
2010; Chen et
al., 2016; Fig.
A.2.C
Macaca leonina MW < 2700/ LE,
SM, ME IT, FO
VU,
APP. II
Momberg et al.,
2010
Macaca mulatta MW, EC
1300-3800/
LE, SM,
ME, TC
IT, ST APP. II
Momberg et al.,
2010; Xiao et
al., 2013a
Trachypithecus
phayrei
MW, WC,
EC
1200-2600
SM, ME FO; IT
EN,
APP.I
Meyer et al.,
2015; Xiao et
al,. 2013a
Trachypithecus
shortridgei MW, EC
500-2000 /
LE, SM,
ME
FO, ST EU,
APP. I
Meyer et al.,
2015;
Momberg et al.,
28
2010; Xiao et
al,. 2013a
Hoolock
tianxing MW, WC
400-2000 /
LE, SM,
ME
FO, ST,
IT
EN,
APP. I
Geissmann et
al., 2013; Fan
et al., 2016
Budorcas
taxicolor
MW, WC,
EC
2700-3200 /
ME, TC,
BB
CT, FO,
IT, ST
VN,
APP. II
Momberg et al.,
2010; Xiao et
al., 2013b
Elaphodus
cephalophus WC
2500-3300 /
ME, TC,
BB
CT, FO NT
Momberg et al.,
2010; Chen et
al., 2016
Capricornis
milneedwardsii
MW, WC,
EC
2500-3100 /
ME, TC,
BB
CT, ST NT,
APP. II
Chen et al.,
2016; Fig.
A.2.E
Ailurus styani MW, WC,
EC
2300-3500 /
ME, TC,
BB
CT, FO EN,
APP. I
Momberg et al.,
2010; Chen et
al., 2016; Fig.
A.2.B
Pardofelis cf.
marmorata EC
<2800 / SE,
ME CT
NT,
APP. I Fig. A.2.A
Ursus
thibetanus MW, WC
<3200 /
ME, TC,
BB
CT VN,
APP. I
Momberg et al.,
2010; Chen et
al., 2016
Helarctos
malayanus MW
< 2700 m /
SE, ME FO, ST
VN,
APP. I
Momberg et al.,
2010
Cuon alpinus MW Uncertain Iron Trap EN,
APP. II
Momberg et al.,
2010
References cited:
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Geissmann, T., Grindley, M., Lwin, N., Aung, SS., Aung, TN., Htoo, SB., Momberg,
29
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& German Primate Center, Göttingen, Germany. 38pp.
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567
568
569
570
571
572
573
574
575
576
577
578
579
30
580
Figure Appendix 1. High resolution images from Google Earth, aggregated to 30 m 581
resolution. The left-hand image has several land cover categories, including forest, 582
shrub, building and farmland. Zoom-in in image 1 shows scattered build-ups; 583
images 3 and 8 correspondingly show forest cover and dense shrubs; farmland is 584
presented in window 5. 585
586
587
588
589
590
591
592
593
594
595
596
31
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
Figure Appendix 2. Mammals and human threats were monitored by camera traps 612
at Rhinopithecus strykeri’s habitats on the eastern slope of the Gaoligong Mountains, 613
China from April 2015 to January 2017. A: Pardofelis marmorata (Credit to Wang 614
Bin); B: Ailurus styani; C: Macaca assamensis; D: Macaca arctoides; E: 615
Capricornis milneedwardsii; F: two hunters with a shotgun. 616
617
618
619
F
A B
C D
E F
32
620
Figure Appendix 3. Probability distribution curves (six examples: A-F) show how 621
climate variables affect the MAXENT prediction of habitat suitability for 622
Rhinopithecus strykeri in the study area. Blue areas correspond to climate variables 623
in the study area while red areas correspond to climate variables in the distribution 624
range of R. strykeri. Among 67 climate variables, the seasonal variation of 625
temperature (B: bio4), the temperature annual range (D: bio7), the average 626
precipitation in November (F: pre11) and the maximum temperature in May (E: 627
tmax5) contributed most to the MAXENT established in this study, reflecting a 628
A B
C D
E F