1 2Climate, human disturbance and geometric constraints drive the 3elevational richness pattern of birds in a biodiversity hotspot in 4south-western 5 6 7Xinyuan Pan1, Dan Liang1, Wei Zeng2, Yiming Hu3, Jianchao Liang4, Xinwen Wang5, 8Scott K. Robinson6, Xu Luo2*, Yang Liu1* 9 101 State Key Laboratory of Biocontrol, Department of Ecology, School of Life 11Sciences, Sun Yat-sen University, Guangzhou, 510275, China 122 Key Laboratory for Conserving Wildlife with Small Populations in , 13Southwest Forestry University, , 650224, China 143 School of Environmental Science and Engineering, Southern University of Science 15and Technology, Shenzhen, 518055, China 164 Guangdong Key Laboratory of Animal Conservation and Resource Utilization, 17Guangdong Public Laboratory of Wild Animal Conservation and Utilization, 18Guangdong Institute of Applied Biological Resources, Guangzhou, 510275, China 195 Management Bureau of Gaoligongshan National Nature Reserve, Lushui, 20673100, China. 216 Florida Museum of Natural History, Dickinson Hall, University of Florida, 22Gainesville, FL, USA 23 24Correspondences: [email protected], [email protected] 25Abstract 26To document the elevational richness patterns of birds in a biodiversity hotspot in 27south-western China, the Gaoligong Mountains and assess the role of different spatial 28factors, climatic factors and landscape composition in shaping the richness patterns. 29The east slope of the southern part of Gaoligong Mountains (24.79°N-26.49°N, 3098.65°E-98.93°E), which is the western-most part of the , 31China. We conducted field surveys of birds at each 300-m band from 700 to 3400 m 32a.s.l., and for the two bands from 3400 to 4000 m a.s.l., we obtained data from 33historical records. We obtained climatic recording data from local meteorological 34stations that were located in our study area and calculated the mean annual 35temperature and precipitation. We also calculated the area, MDE (the mid-domain 36effect), NDVI (the normalized difference vegetation index), habitat heterogeneity and 37human disturbance for each 300-m band. We then used multiple regression analysis to

38test the explanatory power of different factors for the elevational richness patterns of 39overall, endemic, non-endemic, large-ranged, and small-ranged birds. A total of 277 40breeding bird species were recorded. We found consistent hump-shaped patterns of 41species richness with elevation with varied peaks for different groups. The richness of 42endemic birds peaked at higher elevation than non-endemic birds. Temperature and 43human disturbance played important roles in shaping the richness patterns of most 44bird groups whereas MDE contributed to the richness pattern of large-ranged species. 45Although none of the seven factors (area, MDE, mean annual temperature, annual 46precipitation, NDVI, habitat heterogeneity and human disturbance) showed consistent 47explanatory power among different species groups, temperature and human 48disturbance correlated well with most bird groups, indicating that more studies are 49needed in this biodiversity hotspot to clarify the detailed influence of anthropogenic 50activities and climate change on elevational distributions of birds. 51 52Key words: climatic factors, elevational gradient, Gaoligong Mountains, human 53disturbance, landscape composition, spatial factors, species richness

54 55Short running title：Elevational richness pattern of birds 56 57Introduction 58 The exceptionally high biodiversity on mountain ranges is now under threat from 59rapid environmental change (Quintero & Jetz, 2018). Understanding geographic 60patterns of variation in species richness is critical for biodiversity conservation and for 61predicting the future consequences of environmental degradation and climate change 62(Vetaas & Grytnes, 2002; Wu, Yang, et al., 2013). Mountains represent ideal systems 63to study hypothesized mechanisms underlying changes in species richness because 64they contain steep environmental gradients in small areas where dispersal limitation 65(Hubbell, 2001) is unlikely to be a confounding variable. Elevational gradients can 66also be replicated in different locations and geographical regions, which is difficult to 67achieve in latitudinal gradients where the same environmental factors such as 68temperature changes, but over very large geographic areas where other confounding 69factors also vary (McCain, 2009). 70 Previous studies examining elevational richness patterns of montane species 71have shown that a hump-shaped pattern was the most common one with species 72richness reaching a peak at middle elevations (Rahbek 1995, 2005). Moreover, peaks 73of species richness varied among taxonomic groups, mountain ranges, spatial scales 74(Kluge et al., 2017; Mccain, 2009), and the percentage of an entire gradient that was 75sampled (Nogués-Bravo, Araújo, Romdal, & Rahbek, 2008). At least seven 76hypotheses have been proposed to explain the elevational richness patterns in 77mountain ranges, which can generally be grouped into three categories: spatial factors 78[species area relationships (SAR), mid-domain effects (MDE)], climatic factors [e.g. 79mean annual temperature (MAT), annual precipitation (AP) and their combined 80effect, productivity] and landscape composition [e.g. habitat heterogeneity (HH), 81human disturbance (HD)] (McCain & Grytnes, 2010). 82 The classical species-area relationship hypothesis (SAR) proposes that species 83richness and area are positively correlated (Rosenzweig, 1995). SAR predicts that 84elevational gradients covering more area on mountain regions should harbour more 85species (Rahbek, 1997). The MDE, which considers geometric boundary constraints 86when analyzing species richness, is defined as species ranges that tend to overlap in 87domain centres due to dispersal limitation, resulting in a mid-domain richness peak 88(Colwell & Hurtt, 1994; Colwell, Rahbek, & Gotelli, 2004). Subsequent studies found 89that MDE could explain a substantial proportion of the elevational variation in species 90richness (Hu et al., 2017; Rowe, 2009; Wu, Colwell, et al., 2013). 91 MAT and AP (a proxy for ambient water availability), which are the most 92important climatic factors for species richness (O’Brian 1998, 2006), can influence 93richness patterns both directly through species’ physiological tolerances and indirectly 94by affecting energy availability (Currie et al., 2004; Rowe, Heaney, & Rickart, 2015). 95The combined effect of temperature and water determine energy availability, and 96previous studies have reported a positive relationship between primary productivity 97(used as a proxy for energy availability and estimated using the normalized difference 98vegetation index [NDVI]) and species richness (Hurlbert & Haskell, 2003; Koh, Lee, 99& Lin, 2006; Pan et al., 2016; Wu, Colwell, et al., 2013). 100 High levels of habitat diversity and structural complexity have been 101hypothesized to increase species richness by increasing availability of resources and 102niches (the HH Hypothesis: MacArthur & MacArthur, 1961). Extensive HD, 103however, can modify or destroy some habitat types that are crucial for some species, 104which may decrease species richness (Koh et al., 2006). Alternatively, limited levels 105of human disturbance may increase local richness by adding species that depend upon 106disturbed habitats (Nogués-Bravo et al., 2008; Zhang, Kissling, & He, 2013) without 107necessarily eliminating species that depend upon undisturbed habitats. Thus, 108understanding the interaction of HH and HD and their effects on species richness is 109critical for biodiversity conservation. 110 Situated in southwest China, the Hengduan Mountains are the most biologically 111diverse areas in China (Lan & Dunbar, 2000), and are one of the world’s 34 112biodiversity hotspots (Mittermeier et al., 2005). Previous studies in this region have 113reported species richness patterns for mammals (Liu et al., 2005; Ma et al., 2010; Wu, 114Yang, et al., 2013), birds (He et al., 2019; Hu et al., 2016; Wu, Colwell, et al., 2013; 115Wu et al., 2014;), reptiles (Fu et al., 2007), and amphibians (Fu et al., 2006), revealing 116predominantly hump-shaped patterns with richness maxima occurring at different 117elevations. The hypothesized mechanisms shaping the hump-shaped richness patterns, 118however, varied among taxa. The Gaoligong Mountains (GM) comprise the western- 119most and most isolated range of the Hengduan Mountains. The geology, bioclimate, 120and evolutionary history are the most varied and diverse of the entire Hengduan 121Mountains, and therefore provide an excellent opportunity to study how these factors 122interact to affect patterns of biodiversity (Chaplin 2005). Yet, there are few studies of 123elevational richness patterns and underlying mechanisms along this gradient, except 124for one study of seed plants (Wang, Tang, & Fang, 2007). Birds are among the best- 125documented animal groups and ornithological data can help drive conservation 126priority-setting. Although ornithological surveys of the GM were conducted in the last 127three decades (Xue, 1995; Lan & Dunbar 2001; Dumbacher, Miller, Flannery, & 128Yang, 2011, Gao, Wang, He & Luo, 2017), an optimally designed collection of 129primary field data along an elevational gradient was still lacking. The goal of this 130study was to elucidate the patterns of species richness along an elevational gradient in 131the GM using standardized field sampling and to use these data to explore the relative 132importance of the seven hypothesized mechanisms underlying patterns of species 133richness described above (SAR, MDE, MAT, AP, NDVI, HH, and HD). 134 135Materials and Methods 136Study area 137 We conducted field surveys on the east slope of the central part of GM (24.79°N- 13826.49°N, 98.65°E-98.93°E, Fig. 1). This area covers a total area of 2247.3 km2. Acute 139topographical relief has created high peaks and deep valleys, which present 140substantial barriers for species dispersal, leading to the formation and development of 141high endemism (Li, Guo, & Dao, 2000). The climate of this area is dominated by the 142southwest monsoon from the Indian Ocean. The wet season lasts from May to 143October, and the dry season occurs from November to April. Vegetation varies from 144the valley to the mountaintop. Below 1000 m a. s. l., there is monsoon forest, then 145evergreen broadleaf forest (1000-2600 m a. s. l.), coniferous broadleaf mixed forest 146(1000-3000 m a. s. l.), coniferous forest (2700 m-3500 m a. s. l.), alpine meadow and 147alpine tundra (above 3400 m a. s. l.), and the treeline is around 3500 m a. s. l. (Xue, 1481995). 149Bird sampling 150 We divided the entire gradient from the Nujiang valley (700 m. a. s. l.) to the 151mountain top (4000 m. a. s. l.) into 11 elevational bands of 300-m. In each of the 9 152bands from 700 to 3400 m a. s. l., we designed 2-5 transects that varied in length from 1531-4 km and could cover all the habitat types. To ensure that the sampling efforts were 154equally distributed across the gradient, the total length of all transects in each of the 9 155bands was restricted to 7 km. For all transects, bird surveys were conducted six times 156during the breeding season (April and May in 2016, 2017 and 2018). We used 157standard line transect methods (Bibby, Burgess, Hill, & Mustoe, 2000) to survey birds 158and record the presence and abundance of bird species together with their position 159information according to a handheld GPS. Surveys were only conducted during peak 160hours of bird activity, (the 4 hours after sunrise and 3 hours before sunset) and were 161not conducted at mid-day or during inclement weather. 162 We were unable to census the two highest elevational bands during this study 163because of the remoteness of the few sites that included these elevational bands and 164because the harsh, windy weather made standard line transects very difficult to 165conduct. For these reasons, we used data that we collected in previous years that used 166different methods that get around the problems with inaccessibility and chronic severe 167weather. For the highest (3700-4000 m a.s.l.) elevational band, we used data from 20 168camera traps set out from May 2015 to June 2016) (Gao et al 2017). For the 169elevational band between 3400-3700m a.s.l., we recorded the birds observed during in 170a single trip from Pianma Pass to Tingming Lake, a 22-km transect sampled in July of 1712014 (Liang et al., 2015). Birds were so scarce above 3400m a.s.l. that we do not feel 172that we missed any species that occurred above 3400m a.s.l. in spite of the different 173methods that we used. The taxonomic system followed the CBR Checklist of Birds of 174China v5.0 (2017). 175 For each band from 700 to 3400 m a. s. l., we used species accumulation curves 176to test whether species diversity was sampled adequately, and assumed an adequate 177survey of species if the species accumulation curves reach a plateau or an asymptote 178(Magurran & McGill, 2011). We used EstimateS 9.10 (Colwell, 2013) to randomize 179the accumulation order of individuals 50 times and obtained the individual-based 180rarefaction curves. Furthermore, we computed estimated species richness (MMMean 181and Chao2) for each band based on sample-based rarefaction in EstimateS. Then we 182performed regression of the observed species richness against the estimated species 183richness. 184 Although all bird species were recorded during field surveys, only breeding birds 185(defined as those birds that breed in the study area) were analyzed in this study, 186because migrants could lead to a bias in the elevational range size (McCain, 2009). To 187compute the whole range of birds, we used interpolation methods to compute the 188range of all bird species; these methods assume species to be present between their 189highest and lowest recorded elevational bands (Colwell & Hurtt, 1994; Colwell et al., 1902004). Although this interpolation method may create potential bias of observed 191patterns (Grytnes & Vetaas, 2002; Hu et al., 2016), gaps in recorded distribution are 192more likely due to incomplete sampling rather than to true gaps of species 193distribution. We further transformed the range size of each species to “n×300” (“n” 194means the number of elevational bands covered by the interpolated range of this 195species) for further analyses. 196Spatial factors 197 To explore the effects of SAR, we calculated the amount of three-dimensional 198surface area for each 300-m band of elevation using ArcGIS 10.2 (ESRI, Redlands, 199CA, USA). Calculations were based on the GDEM 30-m digital elevation data from 200the International Scientific & Technical Data Mirror Site, Computer Network 201Information Center, Chinese Academy of Sciences (http://www.gscloud.cn/). 202 To test the MDE hypothesis, we used RangeModel 5 (Colwell, 2006) to estimate 203predicted species richness under geometric constraints. We randomized the empirical 204species ranges within the bounded domain (without replacement, see Colwell & Lees, 2052000) and computed predicted richness and their 95% confidence intervals for each 206300-m band based on the mean of 5,000 simulations. 207Climatic factors 208 We obtained the climatic recording data from 21 selected meteorological stations 209located in our study area. We used the monthly records (for 3 years, 2013, 2014 and 2102015) to calculate MAT (using linear regression) and AP (using LOESS regression) 211for each 300-m elevational band. MAT and AP for the sites without records were 212linearly or curvilinearly estimated based on the data from nearby sites with records. 213 We used the NDVI as proxy for the above-ground net primary productivity. We 214averaged the NDVI data for the study area for each 300-band over 4 years (2011- 2152014), using ERDAS IMAGINE 9.2 (ERDAS, Norcross, GA, USA). The data were 216obtained from the Ministry of Environment Protection of the People’s Republic of 217China (http://www.zhb.gov.cn). 218Landscape composition 219 To calculate habitat heterogeneity and human disturbance, we downloaded land 220cover data from the GlobeLand30 product (http://www.globallandcover.com). Land 221cover types were classified into water bodies, wetland, artificial surfaces, tundra, 222permanent snow and ice, grasslands, barren lands, cultivated land, shrub lands, and 223forests and primarily reflect the anthropogenic land use and the different types of 224vegetation. HH was summarized using the Shannon diversity index of land cover 225types (Turner & Gardner, 2015). We combined the land cover data and a 30-m digital 226elevation model (DEM) of the study area to calculate the area for each land-cover 227type within each 300-m elevational band, using ArcGIS 10.2 (ESRI, Redlands, CA, 228USA). We calculated proportions of artificial surfaces and cultivated land in each 229elevational band to quantify HD (Zhang et al., 2013). 230Species grouping 231 To discern the richness patterns of different species groups of birds and whether 232they are influenced by different factors, we broke species down into several groups: 233(1) endemic species that were found only in the Hengduan Mountains and adjacent 234Himalayas, (2) large-ranged species “with ranges above median size (elevational 235range size 1, 650 m, and (3) “small-ranged” species with ranges below median size 236(following Wu, Yang, et al., 2013). 237Statistical analyses 238 We performed polynomial regressions to discern the elevational distribution 239pattern of interpolated species richness (for overall and species groupings) as a 240function of elevation along the gradient, guided by the corrected Akaike’s information 241criteria (AICc) with smaller AICc values indicating a better fit. 242 We computed multiple linear regressions to consider factors in combination to 243develop an understanding of how diversity patterns are structured. The best model 244was selected from the 127 models representing all possible combinations of 7 245variables (Area, MDE, MAT, AP, NDVI, HH, and HD), guided by lowest AICc 246(Anderson, Burnham, & White, 1998). However, sometimes it can be challenging to 247choose the best model due to nearly equivalent support for multiple models (i.e., 248ΔAICc < 2). We furthermore performed model-averaging to compare with the 249selected best model and assess the relative importance of different factors by 250standardized beta coefficients (Anderson & Burnham, 2002; Johnson & Omland, 2512004). It is not feasible to apply spatial autoregressive analyses with seven 252explanatory variables in the case of a limited sample size. Thus, no P-values were 253reported for the multiple regressions (Brehm, Colwell, & Kluge, 2007). The 254multicollinearity [as quantified by the variance inflation factor (VIF)] among 255explanatory variables would affect credibility of results (Graham, 2003). Thus, we 256used Spearman’s rank correlation to examine the relationships among the seven 257variables (Area, MDE, MAT, AP, NDVI, HH, and HD). To reduce the 258multicollinearity in the model, we further conducted multiple OLS models without 259Area, AP, NDVI and HD (considering the correlation between the seven factors, 260Table 1). 261 Polynomial regressions were performed in PAST 2.17 (Hammer, Harper, & 262Ryan, 2001). Spearman correlation and multiple regression analyses were performed 263in SAM 4.0 (Rangel, Diniz-Filho, & Bini, 2010). 264 265Results 266Elevational richness patterns 267 Overall, we recorded 296 species, including 277 breeding species and 19 268migrants. The breeding species were distributed among 14 orders and 55 families (see 269Appendix S1 in Supporting Information). Of all the breeding birds, 69 were endemic 270and 208 were non-endemic. Seventy-nine species were assigned to the “large-ranged” 271group and 198 species were assigned to the “small-ranged” group. For each band from 272700 to 3400 m. a. s. l, all the species accumulation curves reached a plateau or an 273asymptote (Fig. 2), which suggests that sampling was adequate to reflect species 274richness patterns along this gradient. A further indication that our sampling effort was 275adequate came from the close correlation of the observed species richness with the 276estimated species richness (MMMean, r2=0.920, P<0.01; Chao2, r2=0.877, P<0.01, 277see Appendix S2 in Supporting Information). 278 The interpolated species richness of all birds together and of each group 279separately showed hump-shaped patterns along the elevational gradient, indicated by 280the polynomial regressions (Table 2). Overall bird species richness nearly reached a 281plateau at 1300-1900 m and reached the peak at the 1600-1900 m elevational band 282(Fig. 3a). The species richness of endemic species reached a peak at a higher 283elevational band (2500-2800 m, Fig. 3b) than the non-endemic group (1300-1600 m, 284Fig. 3c). The richness of large-ranged species increased with elevation and then 285reached a plateau between elevation 1600-2500 m, and then decreased with elevation 286(Fig. 3d), whereas the richness of small-ranged species reached a peak at the 1300- 2871600 m elevational band (Fig. 3e). 288Regressions of Explanatory factors 289 The best-fit models showed that different species groups had different 290relationships with the seven explanatory factors (Table 3). Not all of the species 291groups were correlated with area. MDE best explained the richness pattern of large- 292ranged species whereas MAT best explained overall, endemic and non-endemic 293species richness. AP played a secondary role in shaping the richness pattern of large- 294ranged species but was the most important explanatory factor for the richness pattern 295of small-ranged species. NDVI only played a minor role in shaping the richness 296pattern of endemic species. HH didn’t play a key role in shaping species richness 297patterns of all the groupings. HD was negatively correlated with species richness of 298overall, non-endemic, and small-ranged birds whereas not correlated with species 299richness of endemic and large-ranged brids (Table 3). 300 The results of the model-averaging analysis were in some ways different from 301the best-fit models (Table 4). Generally, MAT best explained patterns of overall, 302large-ranged and small-ranged species richness. AP played an important role in 303shaping the richness patterns of non-endemic species. Species richness of large- 304ranged birds was negatively correlated with NDVI and HD (Table 4). 305 The multiple OLS regressions with three selected factors demonstrated some 306differences from the results of best-fit models and model-averaging analyses. Based 307on the standardized beta coefficient, MDE was a strong explanatory factor among all 308the bird groups. MAT played an important role in shaping the richness patterns of 309endemic and large-ranged species. HH best explained the richness pattern of small- 310ranged species (Table 5). 311 312Discussion 313The hump-shaped richness pattern 314 The elevational patterns of species richness for all bird groups (overall, endemic, 315non-endemic, large-ranged and small-ranged birds) are hump-shaped, which is 316consistent with most previous elevational diversity studies of birds in the Hengduan 317mountains and the adjacent (He et al., 2019; Liang et al., 2015; Pan et al., 3182016; Price et al., 2014; Wu, Colwell, et al., 2013; Wu et al., 2014), indicating that the 319hump-shaped pattern might be the dominant pattern in this region. Many studies have 320documented that species richness along elevational gradients for endemic species 321often peaked at higher elevation than that of non-endemic species (Fu et al., 2006; 322Grytnes & Vetaas, 2002; Kluge et al., 2017; Wu, Yang, et al., 2013). In our analysis, 323endemic species reached their maximum diversity at the 2500-2800 m elevational 324band (Fig. 3b), higher than the richness peak of non-endemic species (1300-1600 m 325band, Fig. 3c). Considering that isolation is an important factor for speciation, the 326more fragmented topography and isolation at higher elevations may thus lead to an 327increase of endemism with elevation (Lee, Ding, Hsu, & Geng, 2004). 328 Interpolation of species presence between their highest and lowest recorded 329elevational bands used in this study may also generate the hump-shaped pattern. One 330study of Himalayan plants (Grytnes & Vetaas, 2002) found that using the 331interpolation method tended to underestimate species richness at the endpoints 332because the richness towards the endpoints consists only of observed species whereas 333at the center they include both observed and interpolated species. However, compared 334to other taxa, birds are generally of high vagility and tend to be continuously 335distributed between their highest and lowest elevational record, implying that the 336effect of the interpolation method on the elevational richness patterns in our study 337may not be substantial and that pattern of species diversity described in this study may 338be a real feature of bird diversity. 339Explanatory factors 340 Previous studies showed mixed support for the SAR hypothesis. In some cases 341area showed a strong effect on species richness (Fu et al., 2007; McCain, 2010; 342Williams, Shoo, Henriod, & Pearson, 2010), whereas in other cases area showed no 343effect (Mccain, 2007; Wu, Colwell, et al., 2013) or even a negative relationship with 344species richness (Pan et al., 2016). In our study, the results of regression models 345showed that area was not an explanatory factor for the richness patterns. We propose 346that at small scales habitat diversity and the strong ties of particular species to habitat 347could drive local SAR (Rosenzweig, 1995). A region with larger area is expected to 348harbour more habitats and thus can support more species to coexist. The low 349elevational bands of GM harbored a larger area but there are more artificial surfaces, 350cultivated land and high human density, resulting in low proportion of suitable 351habitats. 352 In our study, MDE showed strong explanatory power for large-ranged birds, 353which was consistent with most previous studies (Colwell et al., 2004; Liu et al., 3542018; Pan et al., 2016). When accounting for multicollinearity, MDE became a strong 355explanatory factor for richness patterns of all bird groups, indicating that this area is 356strongly isolated and geometric boundary constraints played an important role in 357shaping the species richness patterns of birds. Furthermore, Colwell et al. (2004) 358proposed that when studying the effect of MDE, the limits of the domain should be 359“hard” boundaries for organisms (the limits of adaptation of the organisms). In the 360present case, the study area ranged from the bank of Nujiang River to the ridge of the 361mountains, both of which are hard elevational boundaries for birds. Our study adds 362support for the importance of sampling the whole gradient when evaluating the role of 363MDE on richness patterns. 364 Climatic variables appeared to be important explanatory factors for richness 365patterns of birds in our study (Table 3, 4, 5). Hawkins et al. (2003) proposed that at 366low latitudes water-related variables (e.g. precipitation) would show higher impact, 367whereas at high latitudes energy-related variables (e.g. temperature) could be more 368influential. This could match the same mechanistic explanations for the elevational 369gradient in our study. In the low elevational bands of the GM, the climate is hot and 370dry, thus water was the limiting factor. At the highest elevations, the climate is harsh 371(cold, wet, and windy); thus, temperature is likely to be the limiting factor. At mid- 372elevations, the climate is neither too cold nor too dry, resulting in highest bird species 373richness in this region. Our results also coincided with the conclusion of Kluge et al. 374(2017) that the temperatures supporting most richness were situated between extremes 375of energy input. Generally, temperature and water do not affect richness separately 376but interact in a way that under sufficient and stable moisture regimes, where water is 377the key resource, is regulated by temperature, known as the water-energy dynamic 378hypothesis (O’Brien, 1998, 2006; Vetaas, 2006). Productivity (NDVI) did not 379demonstrate consistent explanatory power across all bird groups but only played an 380important role in shaping the richness pattern of endemic birds. This could probably 381be attributed to fact that the endemic birds tend to live at high elevations, where 382productivity was low and thus was a limiting factor. 383 HH only showed weak explanatory power for bird richness patterns in our study. 384This might be attributed to two issues: (1) the data that we used here to classify 385habitats were too coarse to capture the critical microhabitat differences that might 386influence diversity; and (2) at low elevations, there were more habitat types and high 387HH, but most of the habitats were artificial surfaces, such as buildings and farmland, 388which were not suitable for most forest birds. Furthermore, in our study area, human 389disturbance was negatively correlated with richness patterns of most bird groups 390(Table 3 & 4). Like most regions in the Hengduan Mountians, in our study area most 391of the cities, villages, and farms were distributed at low elevations and have been for 392thousands of years. This long history of disturbance may have eliminated the natural 393bird community of this vegetation type—that of monsoonal forest (Lan & Dunbar, 3942000). This, the hump-shaped distributions of species along this gradient may reflect 395not just a MDE, but may also reflect the loss of some lowland forest species. 396 397Conservation implications 398 In the GM, nature reserves have been placed where human density is lowest, 399whereas areas with high density of human settlement (mainly from the low to middle 400elevations) are avoided (Lan & Dunbar, 2000). Thus, those lower elevational bands 401with high biodiversity are less conserved. For example, the overall bird richness 402reaches a plateau between 1300-1900 m elevation. However, there are many villages 403distributed in this region. Thus, there is pressing need for increasing conservation 404effort in the lower elevations. Besides, at elevations around 1000-1900, there are 405several bird hides where local people offer water, corn and meal worms to attract 406birds for photographing and birdwatching. This kind of ecotourism could reduce 407village hunting, and in some ways supply extra water and food for some bird species. 408However, the effect of these bird hides is currently unknown; the food they provide, 409for example, might favor some birds more than others. Long-term monitoring and 410applied research are needed to clarify whether and how might these bird hides affect 411the biodiversity and functional diversity in this area. 412 In this study, temperature played a major role in shaping the species richness 413patterns, indicating that the biodiversity in this area could be affected by climate 414change. Warming temperatures have been hypothesized to cause upslope shifts and 415the species restricted to mountaintops could face local extinctions (Colwell, Brehm, 416Cardelús, Gilman, & Longino, 2008, (Freeman, Scholer, Ruiz-Gutierrez, & 417Fitzpatrick, 2018). High elevation species are especially vulnerable to temperature 418increases (Freeman et al., 2018). We found that the endemic birds tend to be 419distributed at high elevations and that their richness was negatively correlated with 420temperature (Table 3, 4, 5); thus, the endemic birds in this area may be more 421threatened by global warming. In this context, it may be important that the GM and 422the Hengduan Mountains all run from south to the north. Such mountains may 423facilitate the northward movement of species (Dumbacher et al., 2011). However, it is 424yet fully understood that whether the endemic birds of high isolated grasslands (e.g. 425Sclater’s Monal Lophophorus sclateri, Fire-tailed Myzornis Myzornis pyrrhoura) will 426be able to shift range northward along the mountain ridge under climate warming 427because their habitat will become increasingly more fragmented and isolated. 428 Altogether, our results show that conserving birds in the GM Mountains will 429require plans that deal with several of the mechanisms that we have identified as 430important in this region. Monsoonal forests in the lowlands may have to be 431systematically restored to bring back their original bird communities. The very few 432species that inhabit high-elevation grasslands, may need to be actively relocated 433northward as warming temperatures reduce the remaining grassland patches to sizes 434that are too small to sustain their populations. Middle-elevation birds in the zones of 435maximal species richness may need to be protected by working with local 436communities to make bird conservation more profitable than expanding agriculture. 437Additional work to identify plant communities such as bamboo that contribute to HH 438may be critical for the conservation of some of the endemic birds of this region. The 439Gaoligongshan National Nature Reserve provides an extremely promising start for 440conserving birds in this region, especially as it preserved the best forest areas in the 441upper elevational bands. Expanding this reserve into the lower elevations will add 442greatly to its conservation value.

443 444Conclusion 445Our data suggest that many factors contribute to the species richness patterns 446observed in the GM, a global biodiversity hotspot. We found evidence that the MDE 447and MAT both correlate strongly with the hump-shaped distributions that dominate 448mountain ranges in these communities. Endemic birds, which are often of greatest 449conservation concern, tend to be concentrated at higher elevations. In these respects, 450our results are consistent with many other studies of patterns of species richness along 451elevational gradients in Asia. Yet, another factor that we identified, human 452disturbance, may also play a major role in creating the hump-shaped distributions that 453are so characteristic of the mountains of Asia. Nearly all elevational transects from 454Asian mountains are characterized by a scarcity of native vegetation and plant 455communities at lower elevations where large populations of humans have lived for 456several millenia (Elsen, Tingley, Kalyanaraman, Ramesh , & Wilcove, 2017). The 457natural forests of these elevational bands tend also to be drier, which might account 458for some of their reduced diversity (see Table 4), but monsoonal forests can have very 459high species richness during the wet breeding season when conditions are favorable 460(Lan & Dunbar, 2000). Monsoonal forests that have been converted to agriculture, on 461the other hand, likely only retain a relatively small fraction of the original bird 462community of this habitat. In Andean transects where human disturbance has been 463much less extreme in the lowlands, the mid-domain effect is also much less extreme 464and the diversity of lowland forests is much higher than it is anywhere along adjacent 465elevational transects in the Andes (Terborgh & Weske, 1975; Terborgh, Robinson, 466Parker, Munn, & Pierpont, 1990). Thus, it is essential to consider HD when 467interpreting elevational patterns of species richness in this region. 468

469 470Acknowledgements 471We thank Ge Gao, Wande Li, Xuejing Wang, Danjie Li, Yingying Feng, Guansheng 472Wang, Xi Chen, Binqiang Li, Haotian Bai, Linzhuang Bai, Teng Tang for their field 473assistance. We also thank Pengfei Fan and the stuff of the meteorological Bureau of 474Lushui county of Yunnan Province for supplying the climatic data. This research was 475supported by grants from the National Natural Science Foundation of China (No. 47631700350 to XYP.), the Fundamental Research Funds for the Central Universities 477(No.161gpy34 to YL), and the National Biodiversity Monitoring Network of Ministry 478of Environmental Protection, China (Bird 2016–2018, to LX). 479 480REFERENCE 481Anderson, D. R., & Burnham, K. P. (2002). Avoiding pitfalls when using 482 information-theoretic methods. Journal of Wildlife Management, 66(3), 912-918. 483Anderson, D. R., Burnham, K. P., & White, G. C. (1998). 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Gaoligong Mountain National Nature Reserve. Beijing: Chinese 665 Forestry Publishing House. 666Zhang, J., Kissling, W. D., & He, F. (2013). Local forest structure, climate and human 667 disturbance determine regional distribution of boreal bird species richness in 668 Alberta, Canada. Journal of Biogeography, 40(6), 1131-1142. 669 670Authors’ contributions 671X.P., X.L. and Y.L. conceived the idea of the work. X.P., D. L., W. Z., J. L. and X. 672W. conducted field works. X. P. carried out analyses and wrote the paper with inputs 673from D. L., Y. H., X. L., S. R. and Y.L. X. L and Y. L. supervised the work. 674 675Competing interests 676The authors declare that they have no competing interests.

677

678FIGURE LEGENDS

679Figure 1. Location of the study area, the Gaoligong Mountains in south-western 680China. 681 682 683Figure 2. Individual‐ based rarefaction curves for bird data sets for each elevational 684band in the Gaoligong Mountains. The numbers from 1 to 9 represent the nine 300-m 685elevational bands from 700 to 3400 m a. s. l. 686 687 688Figure 3. Elevational patterns of interpolated species richness (dark solid lines). (a): 689overall species; (b): endemic species; (c): non-endemic species; (d): large-ranged 690species; (e): small-ranged species. The gray solid lines indicate the predicted species 691richness under the assumption of MDE (the mid-domain effect) and the gray dotted 692lines indicate the upper and lower 95% confidence interval simulation limits. 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714Figure 1 715

716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735Figure 2

736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757Figure 3

758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 Table 1 Spearman correlation coefficients for the seven selected factors. Area MDE MAT AP NDVI HH Area MDE 0.509 MAT 0.736** 0.064 AP -0.727* -0.018 -0.964** NDVI 0.618* 0.973** 0.218 -0.191 HH 0.136 -0.436 0.691* -0.673* -0.327 HD 0.707* -0.023 0.977** -0.968** 0.126 0.726* 774MDE, the mid-domain effect; MAT, mean annual temperature; AP, annual 775precipitation; NDVI, normalized difference vegetation index; HH, habitat 776heterogeneity; HD, human disturbance. *P < 0.05; **P < 0.01. 777 778 779 780Table 2 Polynomial regressions of the interpolated species richness patterns along the 781elevational gradients for all the species groups. Regressions Overall Endemic Non-endemic Large-ranged Small- species Species species species ranged species First-order r2 0.497* 0.468* 0.697** 0.400* 0.507* AICc 8514.6 557.1 5642.5 3859.3 1640.8 Second-order r2 0.885** 0.956** 0.881** 0.947** 0.649* AICc 1960.9 54.9 2226.4 350.7 1173.3 Third-order r2 0.970** 0.977** 0.979** 0.971** 0.846** AICc 517.4 38.60 408.1 200.0 514.5

782AICc, corrected Akaike information criterion. *P < 0.05, **P < 0.01; Bold numbers 783indicate the best regression model with lowest AICc value. 784 785 786 787 788 789 790 791Table 3 Parameter estimates for the best-fit multiple regression models. Standard coefficient of the best model 2 Species groups Area MDE MAT AP NDVI HH HD r adj Overall birds 1.614 -1.135 0.949 Endemic species -0.976 0.771 0.972 Non-endemic species 1.517 -0.853 0.947 Large-ranged species 0.773 -0.646 0.969 Small-ranged species -1.327 -0.745 0.77 792MDE, the mid-domain effect; MAT, mean annual temperature; AP, annual 793precipitation; NDVI, normalized difference vegetation index; HH, habitat

2 2 794heterogeneity; HD, human disturbance. r adj is the adjusted r value for multiple 795regressions. For each species group (overall, endemic, non-endemic, large-ranged and 796small-ranged species), the best model was selected from the 127 models obtained by 797forming all possible combinations of seven variables (Area, MDE, MAT, AP, NDVI,

798HH, HD), guided by the lowest corrected Akaike information criterion value (AICc).

799All 127 models with their Δ AICc and AICc weights for all species groups are reported 800in Appendix Table S3 in Supporting Information. 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816Table 4 Parameter estimates averaged across 127 ordinary least squares (OLS) 817models. Standard coefficient of the model averaging 2 Species Area MD MA AP NDV HH HD r adj AICc groups E T I Overall - 0.70 1.589 - - 0.165 - -0.38 305.85 birds 0.091 0 0.737 0.232 1.117 6 Endemic - 0.67 - 0.671 0.772 - 0.321 - 281.15 species 0.309 5 0.969 0.369 1.383 1 Non- - 0.51 1.509 - - 0.218 - - 305.21 endemic 0.188 5 0.881 0.415 0.887 0.185 2 species Large- 0.593 0.94 1.205 - - - - 0.521 283.57 ranged 7 0.646 0.934 0.262 0.888 1 species Small- - 0.67 1.342 - 0.189 0.762 - - 299.26 ranged 0.704 1.173 0.851 2.867 5 species 818MDE, the mid-domain effect; MAT, mean annual temperature; AP, annual 819precipitation; NDVI, normalized difference vegetation index; HH, habitat

820heterogeneity; HD, human disturbance; AICc, corrected Akaike information criterion. 2 2 821r adj is the adjusted r value for multiple regressions. 822 823 824 825 826 827 828 829 830 831 832 833 834 835Table 5: Multiple ordinary least squares (OLS) regression of interpolated species 836richness against three selected factors for different species groups. Species groups MDE MAT HH r2 P Overall birds 0.775 0.330 0.438 0.916 <0.001 Endemic species 0.607 -0.459 -0.264 0.967 <0.001 Non-endemic species 0.594 0.429 0.475 0.917 <0.001 Large-ranged species 0.768 0.580 0.060 0.959 <0.001 Small-ranged species 0.684 -0.067 0.913 0.789 =0.009 837MDE, the mid-domain effect; MAT, mean annual temperature; HH, habitat 838heterogeneity. Bold numbers indicate the parameters for each multiple regression 839model that were significant at P < 0.05.

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