Nest-Site Microhabitat Association of Red-Billed Leiothrix in Subtropical

Journal of Natural History

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Nest-site microhabitat association of red-billed leiothrix in subtropical fragmented forest in central China: evidence for a reverse edge effect on nest predation risk?

Zhiqiang Zhang, Donghan Hou, Yuan Xun, Xuewen Zuo, Daode Yang & Zhengwang Zhang

To cite this article: Zhiqiang Zhang, Donghan Hou, Yuan Xun, Xuewen Zuo, Daode Yang & Zhengwang Zhang (2016): Nest-site microhabitat association of red-billed leiothrix in subtropical fragmented forest in central China: evidence for a reverse edge effect on nest predation risk?, Journal of Natural History, DOI: 10.1080/00222933.2015.1130869

To link to this article: http://dx.doi.org/10.1080/00222933.2015.1130869

Published online: 17 Feb 2016.

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Download by: [University of Montana] Date: 19 February 2016, At: 22:24 JOURNAL OF NATURAL HISTORY, 2016 http://dx.doi.org/10.1080/00222933.2015.1130869

Nest-site microhabitat association of red-billed leiothrix in subtropical fragmented forest in central China: evidence for a reverse edge eﬀect on nest predation risk? Zhiqiang Zhanga,b, Donghan Houb, Yuan Xunc, Xuewen Zuoc, Daode Yangb and Zhengwang Zhanga aMinistry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, China; bCollege of Forestry, Central South University of Forestry and Technology, Changsha, China; cConservation Institute of Daweishan Nature Reserve, Forestry Bureau of Liuyang City, Liuyang, China

ABSTRACT ARTICLE HISTORY Previous studies of nest-site selection on a fine scale may reveal Received 2 March 2015 limiting resources within habitat types. The red-billed leiothrix Accepted 20 November 2015 (Leiothrix lutea Scopoli, 1786) is a common bird species that lives KEYWORDS in the subtropical forests of Asia. Despite many reports of this Microhabitat; nest-site species from introduced populations, little information has been selection; edge effects; obtained from its native range. From 2011 to 2013, we studied red-billed leiothrix; China nest-site selection of red-billed leiothrix at micro-scales in Daweishan Nature Reserve, Hunan Province, central China. A total of 363 nests were found in five vegetation types. We measured the habitat variables and constructed nest-site selection models for nests found in the forest and scrub-grassland. Among the 18 variables measured in the forest, six variables were selected to construct the nest-site selection model: distance to forest edge (DTE), distance to water (DTW), vegetation comprehensive coverage, tree coverage, bamboo coverage and shrub height. According to Akaike’s information criterion, the best model consisted of five of these variables (excluding vegetation comprehensive coverage), and distance to forest edge, distance to water, tree coverage and bamboo coverage had negative effects on nest-site selection. In scrub-grassland, the DTE, DTW, and bush coverage (BUC) were selected from the 13 variables measured, and, accord- ingly, the best model consisted of DTE and BUC. Model averaging suggested that BUC had a positive effect on nest-site selection. In Downloaded by [University of Montana] at 22:24 19 February 2016 contrast, DTE has a reverse effect. In addition, DTE differed significantly between successful and failed nests in forest and scrub- grassland. More successful nests were found near the forest edge. Taken together, these findings emphasise the power of fine-scale habitat selection models in identifying relevant habitat variables with a significant effect on preferred habitat and eventually, breeding success.

CONTACT Zhengwang Zhang [email protected] Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, 100875, China. © 2016 Taylor & Francis 2 Z. ZHANG ET AL.

Introduction Birds’ preferred habitat associations are expected to maximise their fitness by increasing the reproduction of individuals via nest-site selection (Martin 1988). Previous studies have suggested that selection patterns are often based on the synthesis of multiple factors and cues (Kristan et al. 2007), such as the availability of food, water and mates; predators; climate; and vegetation structure including vegetation height, density and cover (Martin 1993a; van Gils et al. 2006; Tellería and Pérez-Tris 2007; Patthey et al. 2012; Li et al. 2015). Furthermore, habitat characteristics preferred by birds also occur simulta- neously on several specific scales (Manly et al. 2002). Different specific scales have their habitat characteristics (Apps et al. 2001), and thus, birds may shift their preferences among different scales for particular activities (Leopold and Hess 2013). For example, some passerines usually select microhabitats for nest establishment (Latif et al. 2011; Murray and Best 2014). The microhabitat at a finer scale may reveal the limiting resource with the real needs of the topographic features or vegetation characteristics in the breeding period (Apps et al. 2001). The red-billed leiothrix Leiothrix lutea (Scopoli, 1786) is a small babbler (Timallidae) that inhabits dense bush in evergreen broadleaf and pine forests (Collar and Robson 2007). Its native distribution range is in Asia, including Southern China, North East Pakistan, North India, Nepal, Bhutan, North Myanmar and North Vietnam (Collar and Robson 2007; Zheng 2011). Historically, this species was highly appreciated and kept in captivity universally due to its colourful plumage and melodious songs (Cheng 1963). Thus, it has been introduced to Hawaii (Fisher and Baldwin 1947), Japan (Eguchi and Masuda 1994), the Island of Réunion and a number of scattered localities in Europe including France, Italy, Germany and Spain (Herrando et al. 2010; Farina et al. 2013), where the life-history traits of the introduced populations have been studied and reported partially during the past 20 years (Eguchi and Masuda 1994; Ralph et al. 1998; Amano and Eguchi 2002a, 2002b; Eguchi and Amano 2008; Herrando et al. 2010; Farina et al. 2013; Tojo and Nakamura 2014; Yang et al. 2014). However, limited information has been obtained in the native range of Asia, except Ma et al. ( 2010) and Zhou et al. (2012b), who described the nesting habitats of this babbler briefly in China, and suggested that red-billed leiothrix nested in dwarf bamboo or short bushes shaded by trees or shrubbery on slopes or nearby farmland. Recently, compared with the habitat-selection theory and methodology in early Downloaded by [University of Montana] at 22:24 19 February 2016 animal studies (MacArthur and Pianka 1966; Verner et al. 1986), many researchers have been paying more attention to employing statistical models in Akaike’s information criterion (AIC) framework and the cross-validation criterion (CVC) framework to demon- strate habitat association patterns and reveal nest-site selection preferences (Horne and Garton 2006; Wang et al. 2012; Murray and Best 2014). Thus, more ecologists have been identifying the heterogeneity of environmental factors by comparing used habitat with unused habitat or available habitat (Jones 2001; Crampton and Sedinger 2011; Schmidt et al. 2014). Moreover, nesting near the habitat edge, as an obvious habitat characteristic of nest- sites preferred by the red-billed leiothrix, has been described in most previous studies (Amano and Eguchi 2002a; Herrando et al. 2010; Ma et al. 2010). However, birds nesting along the forest edges usually faced higher nest predation than those nesting in the JOURNAL OF NATURAL HISTORY 3

forest core area, which is known as the so-called edge effect (Gates and Gysel 1978; Batáry and Báldi 2004; Vetter et al. 2013). Many studies have shown that the edge effect was extremely obvious on avian breeding success in fragmented habitat in temperate forests (Söderström 1999; Batáry and Báldi 2004). However, an inverse edge effect on avian nest predation has been found in tropical fragmented forest on the basis of some experimental studies (Carlson and Hartman 2001; Spanhove et al. 2009; Sedláček et al. 2014). Some recent studies have indicated that several species of passerines also prefer to nest near the forest edge in the subtropical fragmented forest in Asia, such as Emei Shan liocichla Liocichla omeiensis (Riley, 1926;Fu2011), Chinese babax Babax lanceolatus (Verreaux, 1870; Xu et al. 2012), and yellow-throated bunting Emberiza elegans (Temminck and Laugier, 1835; Chen et al. 2015). Unfortunately, only a few studies have suggested an extremely obvious edge effect on avian nest predation in the subtropical forest. Thus, more research is needed to understand the habitat association and breeding success of subtropical forest birds, such as red-billed leiothrix in its native range (Amano and Eguchi 2002a; Herrando et al. 2010). From 2011 to 2013, we examined the nest-site selection and nest success of red-billed leiothrix in a fragmented forest at Daweishan Nature Reserve (DSNR) in central China, which is one of the important native ranges for this species. We expected to identify the nest-site microhabitat association pattern and cues preferred by the red-billed leiothrix and to determine whether the red-billed leiothrix preferred to nest near the habitat edge, and the presence or absence of an edge effect on nest predation risk.

Materials and methods Study site Field work was conducted at DSNR, a provincial nature reserve of 6681 ha in the northern section of the Luoxiao Mountains, which is located in the northeast of Liuyang County, Hunan Province, central China (28°20′54″–28°28′ 47″N, 114°01′51″– 114°12′52″E; Figure 1). DSNR has an east-to-west orientation consistent with the tectonic line, with an elevation disparity from 230 to 1608 m. It is in the humid monsoon climate zone in the central subtropical region (with an annual average temperature of 13°C, and annual rainfall of 1800–2000 mm). It has a short rainy and humid spring (Chen et al.

Downloaded by [University of Montana] at 22:24 19 February 2016 2004), followed by a cool and pleasant summer (the warmest month is July, with an average temperature of 22°C), a snowy and cold winter (the coldest month is January, with an average temperature of 0.5°C), and a long frost-free period (243 days). In DSNR, there are six vegetation types consisting of mixed bamboo-broadleaf forest (MBBF), scrub-grassland (SG), coniferous forest (CF), evergreen-deciduous broadleaf forest (EDBF), evergreen broadleaf forest (EBF) and mixed conifer-broadleaf forest (MBCF). According to the areas of vegetation measured, their area proportion in the entire reserve were 39.2, 35.1, 13.4, 11.1, 0.8 and 0.4%, respectively. Among them, the scrub- grassland occupies the mountain-top areas above 1400 m (Table A1), while mixed bamboo-broadleaf forest is the major forest type (Yang et al. 1998; Komar et al. 2005). In addition, DSNR is also a national forest park and national geological park of China, where ecological tourism has developed rapidly in recent years. Thus, the population of red-billed leiothrix at DSNR is facing a potential risk attributed to human disturbance 4 Z. ZHANG ET AL.

Figure 1. Study areas and vegetation types for nest-site selection of the red-billed leiothrix in Daweishan Nature Reserve (DSNR; 28°20′54″–28°28′47″N, 114°01′51″–114°12′52″E), Hunan Province,

Downloaded by [University of Montana] at 22:24 19 February 2016 China.

and habitat fragmentation, particularly during the breeding season when more visitors come and stay in the park.

Nest monitoring We systematically walked along eight line transects that passed through each type of vegetation at DSNR with an elevation ranging from 600 to 1600 m (Table A1) and searched for nests of red-billed leiothrix in the breeding season – that is, from early May to early October in 2011, from late March to early October in 2012 and from early April to early October in 2013. We searched the nests systematically by combing bushes one JOURNAL OF NATURAL HISTORY 5

by one using a bamboo pole. In addition, adult behaviour and songs were used as reference (Pobprasert and Gale 2010). If a nest was found, then it was marked with a numbered, coloured adhesive tape (Amano and Eguchi 2002a) and the location was recorded using a global positioning system (GPS; GPS60CS, GARMIN, USA). Additionally, we used infrared cameras (Ltl-6210MC/MG, SHIBAOJIA, China) and identified prey traces to confirm predators. We monitored each nest at 3- to 7-day intervals and recorded its fate. A nest was considered to be successfully breeding when at least one nestling fledged at the expected time of fledging. Failure was assumed when the nest contents disappeared or were damaged before the fledging date (Pobprasert and Gale 2010; Zhou et al. 2011; Segura and Reboreda 2012). We determined the expected breeding time of each nest according to the method used in previous studies (Ma et al. 2010; Zhou et al. 2012b) and our observations during field surveys. The laying date was estimated by the number of eggs in the nest prior to the start of incubation, as the red-billed leiothrix normally lays one egg per day in the breeding season. On the basis of our observations, the average clutch size, incubation and nestling periods of red-billed leiothrix were 4 eggs, 12 days and 11 days, respectively.

Nest-site microhabitat quantiﬁcation Nest-site microhabitat variables were measured using 5 m radius circular plots where the nest trees were the plot centres. The measurements were performed after the nests failed or the young ﬂedged from the nest, to minimise disturbance from human activities (Pobprasert and Gale 2010; Murray and Best 2014). For each nest-site sampled, we also obtained measurements of one paired random site, which was established using a random cardinal direction bearing (north, east, south or west) and set at a random distance of 25, 50 or 75 m. It is important to note that red-billed leiothrix nests were absent at each random site (Martin et al. 1997; Benson et al. 2009). Following previous studies of nest-site selection in birds (Martin 1993a;Amanoand Eguchi 2002a;Zhouetal.2011), the 18 microhabitat variables we measured were divided into three groups – vegetation (V), terrain (T) and distance (D) (Table A2) – and used to construct the nest-site selection models. Among the terrain variables, the altitude was measured using GPS with a 5-m error; the slope aspect and degree were measured using a

Downloaded by [University of Montana] at 22:24 19 February 2016 compass at 1° accuracy of measurement. We recorded short distances with a tape measure with a range of 0 ~ 50 m, and long distance values were obtained using ArcGIS 9.3 calculating between locations. Vegetation height and cover at both nests and random sites were measured by establishing 1 m radius sampling plots at the plot centre and 2.5 m away in each cardinal direction (Benson et al. 2010). We used a simple spherical densi- ometer to measure vegetation cover, or visually estimated it (Korhonen et al. 2006). Vegetation heights were measured using a steel tape measure with 0.1 cm accuracy of measurement, and a laser range ﬁnder (Nikon 550, Japan).

Statistical analysis Chi-square goodness-of-ﬁt and Bonferroni z-statistic tests were applied to analyse the preference for vegetation types of the red-billed leiothrix (Neu et al. 1974; Zeng et al. 6 Z. ZHANG ET AL.

Figure 2. Nest sites of red-billed leiothrix (a) in the forest and (b) in the scrub-grassland.

2013). Considering the absence of trees or Moso bamboos Phyllostachys edulis (Carrière, 1866) (Houzeau, 1906) in the nest-sites constructed in scrub-grasslands, the microhabitat variables were divided into two groups for difference testing and model fitting: one group for nests in the forest (Figure 2a) and another group for nests in the scrub- grassland (Figure 2b). The Shapiro–Wilk W-test was performed to examine the normality of variables of the nests and random sites. For normally distributed variables, we used paired-sample t-tests to test the differences in the variables between nest sites and random sites. Conversely, the Wilcoxon rank sum test was used to test for differences of abnormally distributed variables (Zar 2010; Zhou et al. 2011). Variables with significant differences were retained, and Spearman’s correlation matrix indicated variables with

strong correlations (|rs| > 0.6 and p < 0.05; LaHaye and Gutiérrez 1999). The best subset of candidate models was sifted out of all potential combinations utilising AIC (R PROC AICCMODAVG) to rank models with ΔAICc ≤ 2 (Burnham and Anderson 2002, 2004). Goodness-of-fit tests were performed on all models using a log- likelihood ratio x2 statistic to assess the candidate models’ closeness of fit. The relative importance, present odds ratios and unconditional 95% confidence intervals (CIs) of each variable were assessed on the basis of these model-averaged estimates (Burnham and Anderson 2002). Downloaded by [University of Montana] at 22:24 19 February 2016 To visualise the influential effects of variables that were included in the models with ΔAICc ≤ 2, we analysed the ecological responses of nest and random site selection using nonmetric multidimensional scaling (NMS; McCune and Grace 2002; Peck 2010; Zhou et al. 2012a). Before analysis we eliminated outliers that were different from the rest of our data (Peck 2010). Next, we executed the stress test to determine how many dimensions to ordinate the sample units with Euclidean (Pythagorean) distance under autopilot mode settings. The tests were run from a four-dimensional solution stepping down to a one-dimensional solution with random data by nonmetric multidimensional scaling (NMS) three times. We selected two dimensions as the final best solution on the basis of the results of the autopilot runs, and for a real-data run, we used the NMS procedure 250 times using the manual settings in autopilot. The NMS was indepen- dently run five times with the same manual settings to produce two axes of NMS JOURNAL OF NATURAL HISTORY 7

ordination representing the highest percentage of variance in the sample units. Next, we performed Pearson and Kendall correlation analyses to characterise the linearity relationship between nest-site selection microhabitat variables and NMS axes (Peck 2010). To interpret the variables affecting sample unit distribution for nest and random sites, we generated a joint plot of microhabitat variables correlating with ordination scores as vectors (joint plot cut-off r2 > 0.20; McCune and Grace 2002; Zhou et al. 2012a). Finally, we tested the difference for variables preferred by red-billed leiothrix between successful and failed nests employing the paired-sample t-tests or Wilcoxon rank sum test based on the normality of data, and compared the breeding success between tourist areas and natural areas without tourists. Next, we performed the outlier analyses and NMS using the software PC-ORD 5.0 for Windows (McCune and Mefford 1999), and other statistical analyses and figure charting were performed using the software R (V.2.15.3). Values are reported as the means ± standard error (SE).

Results Nest-site microhabitat characteristics A total of 363 nests of red-billed leiothrix were found during our field work in five vegetation types, with the exception of evergreen broadleaf forest: 113 nests in 2011, 155 nests in 2012 and 95 in 2013. The Chi-Square goodness-of-fit test showed that nest sites selected by the red-billed leiothrix differed significantly from the random expecta- tion (x2 = 182.22, df = 4, p < 0.01, Table A3). Evergreen-deciduous broadleaf forest, mixed bamboo-broadleaf forest and mixed conifer-broadleaf forest were selected significantly more often than expected from availability, while scrub-grassland was used significantly less often than expected. Using a minimum distance of 100 m for nests and random sites, the variables of 194 nest sites (123 nests and 71 nests in forest and scrub-grassland, respectively) and 194 random sites were measured and used for the nest-site selection analysis. There were significant differences for seven variables and four variables between nest sites and random sites measured in forest and scrub-grassland, respectively (Table 1). In contrast, nest sites had a significantly lower mean distance to forest edge (DTE), distance to water Downloaded by [University of Montana] at 22:24 19 February 2016 (DTW), vegetation comprehensive coverage (VCC), tree height, tree coverage (TRC), bamboo coverage and shrub height (SHH) than random sites in the forest did. Furthermore, nest sites had a significantly lower mean DTE, DTW, and shrub coverage than random sites, and significantly higher mean bush coverage (BUC) than random sites in scrub-grassland. According to Spearman’s correlation coefficient of variables, there was a significantly

positive correlation between tree height and TRC of nest sites in forest (rs = 0.921, p < 0.001), and, thus, TRC and five additional variables were finally selected for construction of the nest-site selection model considering the ecological significance of the variables. Similarly, because shrub coverage correlated significantly negatively with BUC

in scrub-grassland, (rs = −0.623, p < 0.001), bush coverage, DTE and DTW were selected for the construction of the ﬁnal nest-site selection model. 8 Z. ZHANG ET AL.

Table 1. Comparison of mircrohabitat variables between nest sites and random sites measured in forest and scrub-grassland for red-billed leiothrix in Daweishan Nature Reserve during 2011 and 2013. Values shown are means ± standard error (SE). Nests in forest (n = 123) Nests in scrub-grassland (n = 71) Variablesa Nest sites Random sites p Nest sites Random sites p ALT (m) 953.61 ± 21.43 951.76 ± 21.42 0.865 1357 ± 23.83 1364 ± 23.40 0.723 SLA (°) 178.85 ± 11.35 215.28 ± 10.39 0.284 176.99 ± 14.06 177.73 ± 13.84 0.68 SLD (°) 53.76 ± 2.66 63.19 ± 2.06 0.07 53.00 ± 3.40 51.24 ± 3.21 0.378 DTE (m) 5.67 ± 1.55 18.49 ± 2.98 < 0.001** 2.82 ± 0.59 15.67 ± 1.51 < 0.001** DTW (m) 11.28 ± 1.94 21.04 ± 2.13 < 0.001** 17.25 ± 3.46 24.03 ± 3.89 < 0.001** DTRA (m) 891 ± 31.35 891 ± 31.06 0.984 794 ± 48.07 792 ± 47.74 0.982 VCC (%) 77 ± 1.22 85 ± 1.08 < 0.001** 79 ± 1.64 83 ± 1.48 0.057 DBH (cm) 13.60 ± 1.08 14.57 ± 0.70 0.242 ––– TRH (m) 9.63 ± 0.40 10.60 ± 0.27 0.018* ––– TRC (%) 24 ± 2.84 39 ± 2.76 < 0.001** ––– BAH (m) 9.58 ± 0.33 10.28 ± 0.21 0.222 ––– BAC (%) 29 ± 3.13 44 ± 3.05 < 0.001** ––– SHH (m) 3.34 ± 0.06 3.73 ± 0.08 < 0.001** 3.59 ± 0.10 3.78 ± 0.11 0.241 SHC (%) 48 ± 2.38 48 ± 2.71 0.951 54 ± 3.71 72 ± 2.30 < 0.001** BUH (m) 1.89 ± 0.04 1.89 ± 0.03 0.886 1.96 ± 0.05 1.80 ± 0.05 0.096 BUC (%) 37 ± 1.93 36 ± 1.96 0.537 57 ± 3.34 41 ± 3.18 0.001** HEH (m) 0.43 ± 0.02 0.41 ± 0.01 0.641 0.50 ± 0.03 0.49 ± 0.03 0.433 HEC (%) 30 ± 1.77 29 ± 2.11 0.356 37 ± 3.29 35 ± 3.72 0.61 a ALT: Altitude; SLA: slope aspect; SLD: slope degree; DTE: distance to forest edge; DTW: distance to water; DTRA: distance to residential area; VCC: vegetation comprehensive coverage; DBH: diameter at breast height; TRH: tree height; TRC: tree coverage; BAH: moso bamboo height; BAC: moso bamboo coverage; SHH: shrub height; SHC: shrub coverage; BUH: bush height; BUC: bush coverage; HEH: herbage height; HEC: herbage coverage; -: a null value. *: Correlation signiﬁcant at the 0.05 level; **: Correlation signiﬁcant at the 0.01 level.

Nest-site microhabitat selection model construction and analyses We performed conditional binomial logistic regression analyses for the 123 used nests and their associated random sites in the forest, and computed the AIC values of 64 priori

models including the null model (no variables). In light of ΔAICc < 2, one set of four candidate models was selected from the potential ones in two habitat types (Table 2). The

results of the model selection based on AICc indicated that the model consisting of DTE, DTW, TRC, moso bamboo coverage (BAC), and SHH was the best, with the highest Wi (0.33). According to the model-averaged analysis (Table 3), the DTE and TRC were the most important variables to account for the probability of a nest site being selected. Because 95% CIs for the coefficients of the variables did not overlap with zero, the DTE, DTW, TRC and bamboo coverage showed significantly negative effects on nest-site selection. Downloaded by [University of Montana] at 22:24 19 February 2016 Three of eight apriorimodels were empirically supported (Table 2) for 71 used nests and their associated random sites in scrub-grassland, with the most support for the model that

Table 2. Model selection for predicting nest-site selection by red-billed leiothrix in Daweishan Nature Reserve during 2011 and 2013. Model selection based on AICc. a ID Model K log(L) AICc ΔAICc Wi Cum.Wt Nests in forest 1 DTE + DTW + TRC + BAC + SHH 6 −116.93 246.21 0 0.33 0.33 2 DTE + DTW + TRC + BAC 5 −118.14 246.52 0.31 0.28 0.61 3 TRC + VCC + BAC + DTW + DTE 6 −117.81 247.98 1.77 0.14 0.75 4 TRC + VCC + SHH + BAC + DTW + DTE 7 −116.81 248.09 1.89 0.13 0.88 Nests in scrub-grassland 5 DTE + BUC 3 −56.72 119.62 0 0.64 0.64 6 DTE + DTW + BUC 4 −56.54 121.37 1.75 0.27 0.91 a DTE: Distance to forest edge; DTW: distance to water; TRC: tree coverage; BAC: moso bamboo coverage; SHH: shrub height; VCC: vegetation comprehensive coverage; BUC: bush coverage. JOURNAL OF NATURAL HISTORY 9

Table 3. Model-averaged results for explaining the inﬂuence of microhabitat variables on nest-site selection by red-billed leiothrix in Daweishan Nature Reserve during 2011 and 2013. a Variables (j) W +(j) βj SE(βj) 95%CI Nests in forest DTE 1 −0.71 0.19 (−1.08, −0.34) DTW 0.9 −0.33 0.14 (−0.60, −0.07) VCC 0.3 −0.01 0.01 (−0.04, 0.02) TRC 1 −0.05 0.01 (−0.07, −0.02) BAC 0.99 −0.03 0.01 (−0.05, −0.01) SHH 0.52 −0.44 0.3 (−1.03, 0.15) Nests in scrub-grassland DTE 1 −1.57 0.25 (−2.06, −1.08) DTW 0.3 0.11 0.18 (−0.24, 0.46) BUC 0.9 0.03 0.01 (0.01, 0.06) a DTE: Distance to forest edge; DTW: distance to water; VCC: vegetation comprehensive coverage; TRC: tree coverage; BAC: moso bamboo coverage; SHH: shrub height; BUC: bush coverage.

included DTE and BUC (Wi = 0.64). The distance to edge was the most important variable for the models of nest-site selection (Table 3), and showed significantly negative effects on nest-site selection because its 95% CIs for the coefficients did not include zero. In contrast, BUC, as a second important variable, had a significantly positive effect on nest-site selection. We constructed the matrix of microhabitat variables for nest-site selection in forest, including 237 sample units and six response variables, after eliminating nine outliers from 246 sample units. NMS analyses revealed that NMS1 and NMS3 accounted for 76% of the original characteristics matrix (Table 4). Correlation analyses indicated that DTE, VCC, TRC and BAC were significantly correlated with NMS axes (Table 4). Vectors of the microhabitat variables showed that DTE, TRC and BAC were weighted more than the other variables (Figure 3). The joint plot indicated that most of the sample units for nest sites were negatively correlated with DTE, while more than half of the sample units for random sites were positively correlated with BAC and TRC. In scrub-grassland, the matrix of three microhabitat variables was constructed for nest-site selection with 134 sample units derived from 142 sample units. A final two- dimensional ordination space accounted for 99% of the original characteristics matrix

Table 4. Variation in nest-site characteristic distribution represented by the two axes of the nonmetric multidimensional scaling (NMS) ordination, and Pearson and Kendall correlation analyses between nest site variables of red-billed leiothrix and NMS axes in Daweishan Nature Reserve during Downloaded by [University of Montana] at 22:24 19 February 2016 2011 and 2013. Nests in forest Nests in scrub-grassland NMS 1 NMS 3 NMS 1 NMS 2 Variance explained (R2) Increment R2 0.297 0.463 0.434 0.552 Cumulative R2 0.297 0.76 0.434 0.986 a Correlation with nest site variables (rs) DTE 0.519* 0.01 0.572* −0.007 DTW 0.337 −0.22 0.875** −0.083 VCC 0.585* −0.143 –– TRC 0.671** −0.696** –– BAC 0.526* 0.822** –– SHH −0.097 −0.147 –– BUC ––−0.532* −0.981** a DTE: Distance to forest edge; DTW: distance to water; VCC: vegetation comprehensive coverage; TRC: tree coverage; BAC: moso bamboo coverage; SHH: shrub height; BUC: bush coverage. Values are coefficients of Pearson and Kendall correlation analysis. *: Correlation significant at the 0.05 level; **: correlation significant at the 0.01 level. -: a null value. 10 Z. ZHANG ET AL.

Figure 3. Nonmetric multidimensional scaling (NMS) ordination of 237 sample units of microhabitat characteristics in the forest, and the joint plot of NMS scores with important microhabitat variables (r2 > 0.2). The ﬁrst and third axes represent 30% and 46% of the total variation, respectively.

(Table 4). The joint plot illustrated that most of the sample units for nest sites were negatively correlated with DTE and DTW, while more than half of the sample units for random sites were positively correlated with BUC (Figure 4). Finally, two plots revealed that the responses of nest-site selection to microhabitat variables were consistent with the results of model-averaged estimates for nest-site microhabitat selection models in forest and scrub-grassland, respectively.

Downloaded by [University of Montana] at 22:24 19 February 2016 Comparison of nest-site microhabitat variables between successful and failed nests There was only one significant difference for six nest-site microhabitat variables between 45 cases of breeding success and 78 failed nests in the forest (Table 5). The mean distance for successful nests to forest edges was significantly less than for failed nests. We did not detect significant differences for three nest-site microhabitat variables between 22 successful and 49 failed nests in scrub-grassland (Table 5). In addition, among these 194 nests, 73 nests and 121 nests were found in tourist areas and natural areas without tourists, respectively. Furthermore, 34 nests for the former and 33 nests for the latter were successfully fledged, resulting in an apparent breeding success of 46.58 and 26.45%, respectively. JOURNAL OF NATURAL HISTORY 11

Figure 4. Nonmetric multidimensional scaling (NMS) ordination of 134 sample units of microhabitat characteristics in the scrub-grassland, and the joint plot of NMS scores with important microhabitat variables (r2 > 0.2). The ﬁrst and second axes represent 78% and 15% of the total variation, respectively.

Table 5. Comparison of microhabitat variables between successful and failed nests of red-billed leiothrix in Daweishan Nature Reserve during 2011 and 2013. Values shown are means ± standard error (SE). Nests in forest (n = 123) Nests in scrub-grassland (n = 71) Successful Failed nests Successful Failed nests Variablesa nests (n = 45) (n = 78) p nests (n = 22) (n = 49) p DTE (m) 2.27 ± 0.26 10.22 ± 3.78 0.017* 3.48 ± 1.72 2.53 ± 0.51 0.539 DTW (m) 7.69 ± 1.94 13.35 ± 2.83 0.431 16.00 ± 6.08 17.68 ± 4.25 0.485 VCC (%) 77 ± 2.16 78 ± 1.48 0.798 ––– TRC (%) 23 ± 4.54 25 ± 3.69 0.661 ––– BAC (%) 27 ± 5.43 29 ± 3.87 0.562 ––– SHH (m) 3.34 ± 0.100 3.34 ± 0.08 0.978 ––– BUC (%) ––0.094 61 ± 6.27 55 ± 3.96 0.419 a

Downloaded by [University of Montana] at 22:24 19 February 2016 DTE: Distance to forest edge; DTW: distance to water; VCC: vegetation comprehensive coverage; TRC: tree coverage; BAC: moso bamboo coverage; SHH: shrub height; BUC: bush coverage. *: Correlation signiﬁcant at the 0.05 level; -: a null value.

Discussion According to the model-averaged estimates and NMS analyses, the distance of nests to forest edges played a key role in the nest-site selection of red-billed leiothrix in the forest and scrub-grassland at Daweishan Nature Reserve (DSNR). It showed that this babbler in Daweishan preferred to nest in areas with microhabitats near forest edges. In particular, successful nests were closer to the edges than failed nests in the forest but not in scrub-grasslands. Our ﬁndings were consistent with the numerous studies indicat- ing that red-billed leiothrix preferred to nest near forest edges (Amano and Eguchi 2002a; Herrando et al. 2010). 12 Z. ZHANG ET AL.

Previous studies have found that nest predation severely reduced reproductive success for most passerine birds (Ricklefs 1969; Martin 1993b). Similarly to some documented studies in tropical fragmented forests, our study might provide empirical evidence of a reverse edge effect on avian nest predation in subtropical fragmented forest, and the nest predation of red-billed leiothrix was higher in interior forests than that near the forest edge. According to documented cases of the edge effect, avian nest predation is consequently affected by some biotic and abiotic factors, such as predator communities, habitat type, suitable nest sites, landscape structure and geographical locations (Chalfoun et al. 2002; Stephens 2003; Spanhove et al. 2009; Li et al. 2015). First, many surveys found that snakes were the main nest predator in New World forests (Weatherhead and Blouin-Demers 2004). In particular, snakes occurred frequently at edge habitats for thermoregulatory reasons (Blouin-Demers and Weatherhead 2001). In DSNR, snakes, including the king rat snake Elaphe carinata (Günther 1864), Chinese rat snake Zaocys dhumnades (Cantor 1842a), Mandarin snake E. mandarina (Cantor 1842b) and others, were the primary potential predators for the nests of passerine birds (Ma et al. 2010). Some surveys found that snakes can reduce their activities at edges on sunny days, considering the risk of overheating in areas without cover (Weatherhead and Blouin-Demers 2004), and human and vehicle activities might deport predators from the forest edges (Dyrcz and Nagata 2002). These situations were likely to obtain in our study area; for example, the breeding peak of red-billed leiothrix often occurred between June and July when the weather was very hot on sunny days in uncovered edge habitats, and a number of tourists and vehicles would have typically appeared in the forest edges. Unfortunately, we did not obtain quantitative data for further analysis to prove this hypothesis. Another recent study found that the edge effect was more obvious in the small forest remnants (Sedláček et al. 2014). In DSNR, the low degree of fragmentation of forest landscapes due to roads, tourism and housing resulted in a high degree of forest cover. Thus, there are luxurious shrubs, saplings and Miscanthus growing in the forest edge at DSNR, which provides abundantly available shady nest locations for the red-billed leiothrix. This may explain why the red-billed leiothrix may choose sites with or without trees or nearby bamboo. Furthermore, nests were rarely built in denser vegetation, which may help parents to maintain high temperatures for eggs and nestlings (Webb 1987). In addition, edge habitat could have more changes in food abundance and avail-

Downloaded by [University of Montana] at 22:24 19 February 2016 ability, particularly near roads (Fahrig and Rytwinski 2009; Jones and White 2012). According to field observations, the red-billed leiothrix has specific foraging techniques, such as jumping and effectively capturing aerial insects or agile invertebrates. This can help these birds to take advantage of food resources in the forest edge where more aerial insects might reside compared to the forest interior (Amano and Eguchi 2002b). Thus, it seems that edge effects on avian nest success may not yet have a significant effect in our study sites. It may be premature to conclude that the red-billed leiothrix is a forest edge specialist during the breeding season. Many studies have suggested that the potential range of the forest edge could be 50 m and may even extend to 100 m into the forest (McCollin 1998). Thus, we need to obtain more information on nest-site selection and breeding in interior habitats to perform in-depth comparative analysis with the data from edges. JOURNAL OF NATURAL HISTORY 13

In the present study, we found that the red-billed leiothrix preferred to nest near the water in the forest and scrub-grassland. Other studies have shown that there are beneﬁts in selecting nest sites near water sources rich in invertebrates (Amano and Eguchi 2002a). In DSNR, rainfall is abundant in the spring and summer with many streams and puddles along the edges or on paths and roads. Thus, nesting near water sources might save time and energy for the red-billed leiothrix and allow it to invest more in reproduction during the breeding period. It is important to compare the habitat selection between populations in the introduced and the native range of the red-billed leiothrix (Amano and Eguchi 2002a). Previous studies have suggested that the red-billed leiothrix prefers to nest in dwarf bamboo groves (Amano and Eguchi 2002a; Tojo and Nakamura 2004; Herrando et al. 2010). In this study, we found that the red-billed leiothrix was more variable in its selection of nest sites. Thus, red-billed leiothrix is not a specialist nesting in bamboo stalks in its native habitats (Long 1987; Collar and Robson 2007). However, bamboo groves had the second highest abundance of nests among vegetation types (23.7%, n = 86), next to mixed broadleaf-bamboo forest (37.5%, n = 136). The red-billed leiothrix could increase in abundance by nesting in bamboo thickets where few competitors exist and by specialising in nesting substrate and nest position (Amano and Eguchi 2002a)in the initial stage of introduction, and it could gradually expand its niche after successful establishment (Sol et al. 1997). In addition, nests were mostly made from bamboo leaves even if the nests were built in other types of vegetation. Red-billed leiothrix is one kind of useful birds in forests and an important economic resource in history; thus, it has a high reputation in the world. In particular, red-billed leiothrix had been evaluated as the provincial bird of Hunan province, and its conservation has attracted great public attention (Yue 2008). To promote red- billed leiothrix conservation, management should protect the bushes and bamboo groves near forest edges. Management of such areas should include the creation and maintenance of habitat natural characteristics during the breeding season. In areas where the breeding habitat has been destroyed, replanting bushes can help to increase viable habitat for this species. Although we have inferred that human and vehicle activities occurring near forest edges could scare away predators (as the apparent breeding success rate of red-billed leiothrix in tourist areas was greater than that in natural areas), it remains uncertain how intensely these activities can

Downloaded by [University of Montana] at 22:24 19 February 2016 positively or negatively affect the breeding success of the red-billed leiothrix. Moreover, the number of nests in tourist areas was significantly smaller than that in natural areas. Thus, in view of the rapidly developing ecotourism in DSNR, managers should regulate the behaviour of tourists at scenic spots to reduce the effect of tourists on birds during the breeding season. Further work is needed to evaluate the effects of tourists on the distribution and breeding performance of the birds in the reserve. Our results have advanced the understanding of nest-site selection by the red-billed leiothrix, specifically for its native range (Ma et al. 2010; Zhou et al. 2012b) compared with introduced populations (Eguchi and Masuda 1994; Amano and Eguchi 2002a; Herrando et al. 2010). In the future, we should evaluate the relationship between nest- site characteristics and nesting success and investigate the spatial and temporal varia- tions in nest survival in long-term studies. In addition, future studies should assess 14 Z. ZHANG ET AL.

whether forest fragmentation and human disturbance aﬀect nest-site selection and nest success.

Acknowledgements

We are grateful to Prof. Guangmei Zheng and Dr. Yiqiang Fu who helped with the experimental design. We thank Prof. Yong Wang, Prof. Jiliang Xu, Prof. Wei Liang, Prof. Xunlin Yu, Dr. Yang Liu, Dr. Daqing Zhou, Dr. Jiaxiang Li and Dr. Yongfu Xu for their helpful comments, and Prof. Andy Moller for improving our manuscript. Many thanks to Xiaoxiong Chen, Qingzheng Tang, He Chen, Fanglin Zhao, Wen Fan, Ye Cao, Xiupeng Liang, Chuan Liu and Bai Xiao for their help with the ﬁeld work. The comments and suggestions of the editor and anonymous reviewers greatly helped the revision process. We also thank the Forestry Department of Hunan Province and the Management Bureau of Daweishan National Forest Park for giving us approval and providing conveniences for our ﬁeld study.

Disclosure statement

No potential conﬂict of interest was reported by the authors.

Funding

The field study was financially supported by the Youth Scientific Research Foundation of Central South University of Forestry & Technology [QJ2011044B].

References

Amano HE, Eguchi K. 2002a. Nest-site selection of the red-billed leiothrix and Japanese bush warbler in Japan. Ornithol Sci. 1:101–110. Amano HE, Eguchi K. 2002b. Foraging niches of introduced red-billed leiothrix and native species in Japan. Ornithol Sci. 1:123–131. Apps CD, McLellan BN, Kinley TA, Flaa JP. 2001. Scale-dependent habitat selection by mountain caribou, Columbia Mountains, British Columbia. J Wildl Manage. 65:65–77. Batáry P, Báldi A. 2004. Evidence of an edge eﬀect on avian nest success. Conserv Biol. 18:389–400. Benson TJ, Anich NM, Brown JD, Bednarz JC. 2009. Swainson’s warbler nest-site selection in eastern Arkansas. Condor. 111:694–705. Benson TJ, Anich NM, Brown JD, Bednarz JC. 2010. Habitat and landscape eﬀects on brood

Downloaded by [University of Montana] at 22:24 19 February 2016 parasitism, nest survival, and fledgling production in Swainson’s warblers. J Wildl Manage. 74:81–93. Blouin-Demers G, Weatherhead PJ. 2001. Habitat use by black rat snakes (Elaphe obsoleta obsoleta) in fragmented forests. Ecology. 82:2882–2896. Burnham KP, Anderson DR. 2002. Model selection and multi-model inference: a practical information-theoretic approach. New York: Springer Verlag. Burnham KP, Anderson DR. 2004. Multimodel inference understanding AIC and BIC in model selection. Sociol Method Res. 33:261–304. Cantor T. 1842a. General features of Chusan, with remarks on the flora and fauna of that island [part 1]. Ann Mag Nat Hist. 9:265–278. Cantor T. 1842b. General features of Chusan, with remarks on the flora and fauna of that island [part 3]. Ann Mag Nat Hist. 9:481–493. Carlson A, Hartman G. 2001. Tropical forest fragmentation and nest predation – an experimental study in an Eastern Arc montane forest, Tanzania. Biodivers & Conserv. 10:1077–1085. Carrière EA. 1866. Plantes nouvelles, rares ou peu connues. Rev Hort. 37:380. JOURNAL OF NATURAL HISTORY 15

Chalfoun AD, Thompson FR, Ratnaswamy MJ. 2002. Nest predators and fragmentation: a review and meta-analysis. Conserv Biol. 16:306–318. Chen WK, Xie H, Shi XX, Fan QF. 2015. Breeding notes of yellow-throated bunting (Emberiza elegans). Chin J Zool. 50:621–627. Chen ZF, Liang CF, Xiao YL, Xiao HA, Lu SP. 2004. Characteristics of edible wild herbs resources in higher plant in Daweishan Area and its exploitation and utilization from Liuyang City of Hunan Province. Guihaia. 24:291–296. Cheng ZX. 1963. Economic fauna sinica of China Aves. Beijing: Science Press. Collar NJ, Robson C. 2007. Family Timaliidae (Babblers). In: del Hoyo J, Elliott A, Christie DA, editors. Handbook of the birds of the world. Vol. 12. Picathartes to tits and chichadees. Barcelona: Lynx Edicions; p. 70–291. Crampton LH, Sedinger JS. 2011. Nest-habitat selection by the Phainopepla: congruence across spatial scales but not habitat types. Condor. 113:209–222. Dyrcz A, Nagata H. 2002. Breeding ecology of the Eastern great reed warbler Acrocephalus arundinaceus orientalis at Lake Kasumigaura, central Japan: two populations of the eastern great reed warblers, one close to settlements and one far away, differed mainly in nest losses which were higher at the latter site. Bird Study. 49:166–171. Eguchi K, Amano HE. 2008. Indirect influences of the invasive red-billed leiothrix on the breeding of the Japanese bush warbler. Jpn J Ornithol. 57:3–10. Eguchi K, Masuda T. 1994. A report on the habitats of Peking robin Leiothrix lutea in Kyushu. Jpn J Ornithol. 43:91–100. Fahrig L, Rytwinski T. 2009.Effects of roads on animal abundance: an empirical review and synthesis. Ecol Soci. [Internet]. [cited 2014 Oct 20]; 14:21. Available from: http://www.ecolo- gyandsociety.org/vol14/iss1/art21/ Farina A, Pieretti N, Morganti N. 2013. Acoustic patterns of an invasive species: the red-billed leiothrix (Leiothrix lutea Scopoli 1786) in a Mediterranean shrubland. Bioacoustics. 22:175–194. Fisher HI, Baldwin PH. 1947. Notes on the red-billed leiothrix in Hawaii. Pac Sci. January:45–51. Fu YQ. 2011. Studies on the breeding ecology of Emei Shan liocichla (Liocichla Omeiensis). Beijing: Beijing Normal University; p. 31–39. Gates JE, Gysel LW. 1978. Avian nest dispersion and fledging success in field-forest ecotones. Ecology. 59:871–883. Günther A. 1864. The reptiles of British India. London: Taylor & Francis; xxvii + 452 pp. Herrando S, Llimona F, Brotons L, Quesada J. 2010. A new exotic bird in Europe: recent spread and potential range of red-billed leiothrix Leiothrix lutea in Catalonia (northeast Iberian Peninsula). Bird Study. 57:226–235. Horne JS, Garton EO. 2006. Likelihood cross-validation versus least-squares cross-validation for choosing the smoothing parameter in kernel home-range analysis. J Wildl Manage. 70:641–648. Houzeau J. 1906. Le bambou. Bot J. 1:39. Jones J. 2001. Habitat selection studies in avian ecology: a critical review. Auk. 118:557–562.

Downloaded by [University of Montana] at 22:24 19 February 2016 Jones SL, White GC. 2012. The eﬀect of habitat edges on nest survival of Sprague’s pipits. Wilson J Ornithol. 124:310–315. Komar O, Benz BW, Chen GJ. 2005. Late summer ornithological inventories of Mt. Shunhuang and Mt. Dawei in Hunan, China. Zool Res. 26:31–39. Korhonen L, Korhonen KT, Rautiainen M, Stenberg P. 2006. Estimation of forest canopy cover: a comparison of ﬁeld measurement techniques. Silva Fenn. 40:577–588. Kristan III WB, Johnson MD, Rotenberry JT. 2007. Choices and consequences of habitat selection for birds. Condor. 109:485–488. LaHaye WS, Gutiérrez RJ. 1999. Nest sites and nesting habitat of the northern spotted owl in northwestern California. Condor. 115:17–27. Latif QS, Heath SK, Rotenberry JT. 2011.An‘ecological trap’ for yellow warbler nest microhabitat selection. Oikos. 120:1139–1150. Leopold CR, Hess SC. 2013. Multi-scale habitat selection of the endangered Hawaiian goose. Condor. 115:17–27. 16 Z. ZHANG ET AL.

Li DL, Sun XH, Lloyd H, Que PJ, Liu Y, Wan DM, Zhang ZW. 2015. Reed parrotbill nest predation by tidal mudflat crabs: evidence for an ecological trap? Ecosphere. 6:20. Long ZY. 1987. On subspecificdifferentiation and distribution of leiothrix. Acta Zoot Sinica. 12:219–221. Ma Q, Xiao WF, Su HL. 2010. The preliminary study on breeding habit of Leiothrix lutea at Xingshan County, Hubei. Chin J Zool. 45:46–51. MacArthur RH, Pianka ER. 1966. On optimal use of a patchy environment. Am Nat. 100:603–609. Manly JJ, Jacobs DM, Touradji P, Small SA, Stern Y. 2002. Reading level attenuates differences in neuropsychological test performance between African American and White elders. J Int Neuropsychol Soc. 8:341–348. Martin TE. 1988. Processes organizing open-nesting bird assemblages: competition or nest predation? Evol Ecol. 2:37–50. Martin TE. 1993a. Nest predation and nest sites: new perspectives on old patterns. BioScience. 43:523–532. Martin TE. 1993b. Nest predation among vegetation layers and habitat types: revising the dogmas. Am Nat. 141:897–913. Martin TE, Paine CR, Conway CJ, Hochachka WM, Allen P, Jenkins WP. 1997. BBIRD field protocol. Missoula: Montana Cooperative Wildlife Research Unit, University of Montana. McCollin D. 1998. Forest edges and habitat selection in birds: a functional approach. Ecography. 21:247–260. McCune B, Mefford MJ. 1999. PC-ORD. Multivariate analysis of ecological data. Version 5.0. Gleneden Beach (OR): MjM Software Design. McCune ML, Grace JB. 2002. Analysis of ecological communities. Gleneden Beach (OR): MjM Software Design. Murray LD, Best LB. 2014. Nest-site selection and reproductive success of common yellowthroats in managed Iowa grasslands. Condor. 116:74–83. Neu CW, Byers CR, Peek JM. 1974. A technique for analysis of utilization availability data. J Wildl Manage. 38:541–545. Patthey P, Signorell N, Rotelli L, Arlettaz R. 2012. Vegetation structural and compositional heterogeneity as a key feature in Alpine black grouse microhabitat selection: conservation management implications. Eur J Wildl Res. 58:59–70. Peck JE. 2010. Multivariate analysis for community ecologists: step-by-step using PC-ORD. Gleneden Beach (OR): MjM Software Design. Pobprasert K, Gale GA. 2010. Nest-site selection by Abbott’s babblers Malacocincla abbotti in northeastern Thailand. Acta Ornithol. 45:67–74. Ralph CJ, Fancy SG, Male TD. 1998. Demography of an introduced red-billed leiothrix population in Hawaii. Condor. 100:468–473. Ricklefs RE. 1969. An analysis of nesting mortality in birds. Washington: Smithsonian institution Press.

Downloaded by [University of Montana] at 22:24 19 February 2016 Riley JH. 1926. A new species of Liocichla from the mountains of Szechwan, China. Proc Biol Soc Wash. 39:57–58. Schmidt JH, Flamme MJ, Walker J. 2014. Habitat use and population status of yellow-billed and Pacific loons in western Alaska, USA. Condor. 116:483–492. Scopoli GA. 1786. Deliciae Florae et Faunae insubricae seu Novae, aut minus cognitae species Plantarum et Animalium quas in Iinsubria Austriaca. Deliciae Florae et Faunae Insubricae. 2:90. Monastierii S. Salvatoris, Ticini. Sedláček O, Mikeš M, Albrecht T, Reif J, Hořák D. 2014. Evidence for an edge effect on avian nest predation in fragmented afromontane forests in the amenda-Banso Highlands, NW Cameroon. Trop Conserv Sci. 7:720–732. Segura LN, Reboreda JC. 2012. Nest survival rates of red-crested cardinals increase with nest age in south-temperate forests of Argentina. J Field Ornithol. 83:343–350. Söderström B. 1999. Artificial nest predation rates in tropical and temperate forests: a review of the effects of edge and nest site. Ecography. 22:455–463. JOURNAL OF NATURAL HISTORY 17

Sol D, Santos DM, Feria E, Clavell J. 1997. Habitat selection by the monk parakeet during colonization of a new area in Spain. Condor. 99:39–46. Spanhove T, Lehouck V, Lens L. 2009. Inverse edge effect on nest predation in a Kenyan forest fragment: an experimental case study. Bird Conserv Int. 19:367–378. Stephens SE. 2003. The influence of landscape characteristics on duck nesting success in the Missouri Coteau region of North Dakota. Bozeman (MT): Montana State University. Tellería JL, Pérez-Tris J. 2007. Habitat effects on resource tracking ability: do wintering blackcaps Sylvia atricapilla track fruit availability? Ibis. 149:18–25. Temminck CJ, Laugier MC. 1835. Nouveau recueil de planches coloriées d’oiseaux, pour servir de suite et de complement aux planches enluminées de Buffon. Planckes Color Livr Pl. 98:583. Tojo H, Nakamura S. 2004. Breeding density of exotic red-billed leiothrix and native bird species on Mt. Tsukuba, central Japan. Ornithol Sci. 3:23–32. Tojo H, Nakamura S. 2014. The first record of brood parasitism on the introduced red-billed leiothrix in Japan. Ornithol Sci. 13:47–52. van Gils JA, Spaans B, Dekinga A, Piersma T. 2006. Foraging in a tidally structured environment by red knots (Calidris canutus): ideal, but not free. Ecology. 87:1189–1202. Verner J, Morrison ML, Ralph CJ. 1986. Wildlife 2000: Modeling habitat elationships of terrestrial vertebrates. Madison: University of Wisconsin Press. Verreaux JP. 1870. Note sur les espèces nouvelles d’oiseaux recueillis par M. l’Abbé Armand David dans les montagnes du Thibet Chinois. Nouv Arch Mus Hist Nat Paris, 6, Bull: 363–40. Vetter D, Rücker G, Storch I. 2013. A meta-analysis of tropical forest edge effects on bird nest predation risk: edge effects in avian nest predation. Biol Conserv. 159:382–395. Wang Y, Xu JL, Carpenter JP, Zhang ZW, Zheng GM. 2012. Information-theoretic model selection affects home-range estimation and habitat preference inference: a case study of male Reeves’s pheasants Syrmaticus reevesii. Ibis. 154:273–284. Weatherhead PJ, Blouin-Demers G. 2004. Understanding avian nest predation: why ornithologists should study snakes. J Avian Biol. 35:185–190. Webb DR. 1987. Thermal tolerance of avian embryos: a review. Condor. 89:874–898. Xu X, Yang CC, Liang W. 2012. Nest-site selection of Chinese babax in Kuankuoshui Nature Reserve of Guizhou. J H N N U (Nat Sci). 25:442–445. Yang CC, Liu Y, Zeng LJ, Liang W. 2014. Egg color variation, but not egg rejection behavior, changes in a cuckoo host breeding in the absence of brood parasitism. Ecol & Evol. 4:2239– 2246. Yang DD, Wu XW, Song C, Wang DL, Li SL. 1998. The survey of bird resources and analysis of species diversity in Daweishan experimental forest farm. J C S For Univ. 18:69–77. Yue L. 2008. Chinese Dongting Lake international bird watching festival held at Yueyang in 2007. Hunan For Sci Tech. 35:38. Zar JH. 2010. Biostatistical analysis. 5th ed. Upper Saddle River (NJ): Prentice Hall; 944 pp. Zeng YJ, Xu JL, Wang Y, Zhou CF. 2013. Habitat association and conservation implications of ’ Downloaded by [University of Montana] at 22:24 19 February 2016 endangered Francois langur (Trachypithecus francoisi). PLoS ONE. 8:e75661. Zheng GM. 2011. A checklist on the classification and distribution of the birds of China. 2nd ed. Beijing: Science Press. Zhou DQ, Fung T, Chu LM. 2012a. Avian community structure of urban parks in developed and new growth areas: a landscape-scale study in Southeast Asia. Landscape Urban Plan. 108:91–102. Zhou DQ, Zhou CF, Kong XK, Deng WH. 2011. Nest-site selection and nesting success of grey- backed thrushes in northeast China. Wilson J Ornithol. 123:492–501. Zhou MQ, Yuan SB, Zhou CQ, Wang XL, Huang HS, Qi SF, Xiang M, Yan LB. 2012b. The preliminary study on breeding habit of Leiothrix lutea at Laojunshan Natural Reserve, Yibin. SC J Zool. 31:965-969+1014. 18 Z. ZHANG ET AL.

Table A1. Features of line transects for nest searching of red-billed leiothrix in Daweishan Nature Reserve during 2011 and 2013. Line Length Widtha Altitude Disturbance transects (km) (m) (m) areas Dominant vegetation species 1 4 50 1450~1600 Tourist Azalea (Rhododendron spp.), willow (Salix spp.), 2 4 25 1400~1450 Tourist meadow sweets (Spiraea spp.), arrow bamboo 3 4 50 1400~1590 Tourist/naturalb (Fargesia spathacea), Indocalamus (Indocalamus spp.), sandalwood (Symplocos paniculata), alder (Clethra kaipoensis), awn (Miscanthus sinensis), sedge (Carex spp.), fern (Pteridium spp.), etc. 4 2 25 1300~1590 Tourist Moso bamboo (Phyllostachys edulis), beech (Fagus 5 3 25 1375~1400 Natural longipetiolata), sweet chestnut (Castanea henryi), 6 8 25 950~1340 Natural green paliurus (Cyclocarya paliurus), linden (Tilia tuan), sweetgum (Liquidambar formosana), etc., with meadow sweets (Spiraea spp.), red fruit tree (Stranvaesia spp.), wild chloranthus (Stephanandra chinensis), Chinese cinquefoil herb (Potentilla chinensis) and some species of Leguminosae, saxifrage, Labiatae, Euphorbiaceae, Polygonaceae, etc. 7 2 25 600~930 Natural Moso bamboo (Phyllostachys edulis), oriental white oak 8 3 25 760~800 Tourist/naturalc (Cyclobalanopsis spp.), camphor tree (Cinnamomum spp.), common elaeocarpus (Elaeocarpus spp.), sweet chestnut (Castanea henryi), light birch (Betula luminifera), hackberry (Celtis sinensis), tallow (Sapium sebiferum) and wild jasmine (Styrax japonicus), rose (Rosa spp.), berries (Rubus spp.) and arrow bamboo (Fargesia spp.). a Width of line transects refers to the unilateral width. b Half of the line transect was in a tourist area. c One third of the line transect was in a tourist area.

Table A2. De finition and measurement of nest-site microhabitat variables for red-billed leiothrix in Daweishan Nature Reserve during 2011 and 2013. Class Variable Description Vegetation (V) Vegetation comprehensive coverage Measured as the proportion of the ground covered by the (VCC; %) projection of all vegetation Diameter at breast height (DBH; cm) Measured as the diameter at breast height, height and Tree height (TRH; m) proportion of the ground covered by the mean values for Tree coverage (TRC; %) five sample circles of 1 m radius Moso bamboo height (BAH; m) Measured as the height and proportion of the ground Moso bamboo coverage (BAC; %) covered by the mean values for five sample circles of 1 m Downloaded by [University of Montana] at 22:24 19 February 2016 radius Shrub height (SHH; m) Measured as the height and proportion of the ground Shrub coverage (SHC; %) covered by the mean values of the SHH between 2.5 and 5 m for five sample circles of 1 m radius Bush height (BUH; m) Measured as the height and proportion of the ground Bush coverage (BUC; %) covered by the mean values of the bush height under 2.5 m for five sample circles of 1 m radius Herbage height (HEH; m) Measured as the height and proportion of the ground Herbage coverage (HEC; %) covered by the mean values of the grasses height under 1moffive sample circles of 1 m radius Terrain (T) Altitude (ALT; m) Elevation of nest and random sites above sea level Slope aspect (SLA; °) Aspect of the slope at the sites ranging from 0 to 360 Slope degree (SLD; °) Degree of the slope at the sites ranging from 0 to 90 Distance (D) Distance to forest edge (DTE; m) Distance from the centre location of nest and random sites Distance to water (DTW; m) to the nearest forest edge or gap, the nearest water body, Distance to residential area and the nearest residential area (DTRA; m) JOURNAL OF NATURAL HISTORY 19

Table A3. Nest site occurrence of red-billed leiothrix among five vegetation types in Daweishan Nature Reserve during 2011 and 2013. Expected Confidence interval Total Proportion Number of number of Proportion for proportion of occurrence c area of total area nests nests observed in each (pi) (90% family confidence 2 a b Vegetation types (hm ) (pio) observed observed vegetation type coefficient)

Scrub-grassland 117 0.616 134 224 0.369 0.340 ≤ p1 ≤ 0.398 (SCG) Coniferous forest 20 0.105 41 38 0.113 0.078 ≤ p1 ≤ 0.148 (COF) Evergreen- 3 0.016 19 6 0.052 0.017 ≤ p1 ≤ 0.088 deciduous broadleaf forest (EDBF) Mixed bamboo- 45 0.237 136 86 0.375 0.346 ≤ p1 ≤ 0.404 broadleaf forest (MBBF) Mixed conifer- 5 0.026 33 9 0.091 0.056 ≤ p1 ≤ 0.126 broadleaf forest (MCBF) Total 190 363 363 a Proportion of total area represents the expected proportion of nests observed if nests were found in each vegetation type in exact proportion to availability. b Calculated by multiplying proportion pio × n, i.e., 0.616 × 363 = 224. c pi represents the theoretical proportion of occurrence and was compared to corresponding pio to conﬁrm whether the hypothesis of proportional use was accepted or rejected, i.e., pi = pio (Neu et al. 1974). Downloaded by [University of Montana] at 22:24 19 February 2016