Biodiversity Science ›› 2019, Vol. 27 ›› Issue (1): 13-23.doi: 10.17520/biods.2018193

• Original Papers • Previous Article     Next Article

Seasonal variation in the distribution of Elliot’s pheasant (Syrmaticus ellioti) in Gutianshan National Nature Reserve

Ren Peng1, Yu Jianping2, Chen Xiaonan2, Shen Xiaoli3, Song Xiao1, Zhang Tiantian1, Yu Yongquan2, Ding Ping1, *()   

  1. 1 College of Life Sciences, Zhejiang University, Hangzhou 310058
    2 Center of Ecology and Resources, Qianjiangyuan National Park, Kaihua, Zhejiang 324300
    3 State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093
  • Received:2018-07-15 Accepted:2018-10-12 Online:2019-03-15
  • Ding Ping E-mail:dingping@zju.edu.cn

Here we studied the seasonal variation in the distribution pattern of Elliot’s pheasant (Syrmaticus ellioti) in Gutianshan National Nature Reserve, in Zhejiang Province, China. From May 2014 to April 2016, Elliot’s pheasants were monitored with camera traps as part of the grid monitoring system. Elliot’s pheasants were detected in 44 1 km × 1 km survey blocks, 211 independent times. The observed sex ratio was F : M = 1 : 1.64. These results showed that Elliot’s pheasant is mainly distributed in the buffer and experimental zones. Within the reserve, the detection rate of Elliot’s pheasant decreased over the gradient from mixed evergreen and deciduous broad leaf forest, Cunninghamia lanceolata forest, mixed coniferous and broad leaf forest to artificial Camellia oleifera forest and evergreen broad leaf forest. Elliot’s pheasant mainly lived at altitudes of 600-800 m. In winter and spring, their activity intensity was lower and the active area of Elliot’s pheasant was relatively smaller compared with the summer and autumn. In short, the distribution between altitudinal intervals (F4,12 = 3.76, P < 0.05) and seasons (F3,12 = 3.34, P < 0.05) differed significantly. Performing a regression analysis on altitudinal intervals and climatic factors showed that the daily average temperature and altitudinal intervals both significantly influenced the presence of Elliot’s pheasant (P < 0.01). Both the monthly detection rate of Elliot’s pheasant and the altitude at which Elliot’s pheasant was detected had a significant positive correlation with the monthly mean temperature (P < 0.001), but had no significant linear relationship with the monthly mean rainfall (P > 0.05). These results showed that the presence of Elliot’s pheasant was largely influenced by altitude and temperature. Elliot’s pheasants tended to move to higher altitude as the average monthly temperature increased. According to the results of model selection and multimodel inference, the optimal model only included by the variable “source of water within 100 meters”, and the suboptimal model was “source of water within 100 meters × altitude”, with weights of 0.18 and 0.14. This means that “source of water within 100 meters” and “altitude” were important factors affecting the distribution of Elliot’s pheasant, whose importance values were 0.82 and 0.51, respectively. Overall, the distribution of Elliot’s pheasant was determined by various environmental variables, rather than one and/or several environmental variables. In addition, the changes in temperature and the range of altitudinal intervals led to the differing seasonal distribution pattern of Elliot’s pheasant.

Key words: camera-trapping, Elliot’s pheasant;, distribution pattern, climate, model selection and multimodel inference

Fig. 1

Distribution of Elliot’s pheasant in Gutianshan National Nature Reserve"

Fig. 2

Seasonal differences of the detection rate of Elliot’s pheasant"

Fig. 3

Seasonal patterns of Elliot’s pheasant distribution in Gutianshan National Nature Reserve"

Fig. 4

The detection rate of Elliot’s pheasant in different altitude sections"

Table 1

The results of logistic regression and linear regression analysis"

自变量 Independent variable 回归系数
Coefficients
标准误差
Standard error
z P
逻辑斯谛回归 Logistic regression, P < 0.0001
雌 + 雄 Female + male 海拔 Altitude -0.259 0.100 -2.592 0.0095**
日平均气温 Daily average temperature 0.643 0.108 5.937 0.0000***
雌 Female 海拔 Altitude -0.307 0.145 -2.119 0.0341*
日平均气温 Daily average temperature 0.563 0.156 3.6 0.0003***
雄 Male 海拔 Altitude -0.216 0.115 -1.874 0.061
日平均气温 Daily average temperature 0.685 0.129 5.314 0.0000***
线性回归 Linear regression, P < 0.0001
雌 + 雄 Female + male 月平均气温 Month average temperature 6.231 0.980 6.361 0.0000***
月平均降水量 Month average precipitation -1.104 0.980 -1.127 0.273
线性回归 Linear regression, P < 0.0001
海拔 Altitude 月平均气温 Month average temperature 6.107 0.413 14.803 0.0000***
月平均降水量 Month average precipitation 0.000 0.002 0.182 0.856

Table 2

Correlation coefficients among the characteristic parameters of habitat"

生境因子
Habitat factor
植被类型
Vegetation
type
森林起源
Forest origin
乔木郁闭度
Tree canopy
closure
灌木盖度
Shrub coverage
草本盖度
Herbaceous
coverage
100 m内水源
Source of water
in 100 meters
海拔
Altitude (m)
坡位
Position
森林起源 Forest origin -0.415***
乔木郁闭度
Tree canopy closure
0.135* -0.142*
灌木盖度 Shrub coverage 0.146** 0.018 0.111*
草本盖度
Herbaceous coverage
-0.059 0.142* -0.08 0.069
100 m内水源
Source of water in 100 meters
-0.125* 0.032 0.127* 0.082 -0.145**
海拔 Altitude (m) 0.244*** -0.452*** 0.087 0.041 -0.188*** 0.287***
坡位 Position 0.118* -0.092 0.043 -0.195*** -0.056 -0.421*** 0.185***
坡度 Gradient 0.041 0.003 0.181** 0.281*** -0.186*** 0.06 0.154** -0.095

Table 3

The result of combined the parameters of the model average"

模型组合
Model combination
自由度
df
似然对数
Log-Likelihood
AICc Delta 权重
Weight
e 2 -59.12 122.39 0.00 0.18
a × e 3 -58.26 122.80 0.42 0.14
b × e 3 -59.01 124.30 1.91 0.07
d × e 3 -59.01 124.30 1.91 0.07
a × d × e 4 -57.99 124.46 2.07 0.06
c × e 3 -59.12 124.53 2.14 0.06
a 2 -60.33 124.79 2.40 0.05
a × b × e 4 -58.24 124.96 2.57 0.05
a × c × e 4 -58.26 124.99 2.60 0.05
b × d × e 4 -58.85 126.16 3.77 0.03
a × d 3 -60.01 126.30 3.91 0.02
c × d × e 4 -59.00 126.47 4.09 0.02
b × c × e 4 -59.01 126.49 4.10 0.02
a × b × d × e 5 -57.94 126.60 4.22 0.02
a × c × d × e 5 -57.99 126.69 4.30 0.02
a × b 3 -60.26 126.80 4.41 0.02
a × c 3 -60.26 126.80 4.41 0.02
a × b × c × e 5 -58.24 127.20 4.81 0.02
d 2 -62.09 128.32 5.94 0.01
b × c × d × e 5 -58.82 128.36 5.97 0.01
c 2 -62.14 128.42 6.04 0.01
a × b × d 4 -59.98 128.43 6.04 0.01
a × c × d 4 -60.00 128.46 6.07 0.01
b 2 -62.18 128.50 6.11 0.01
a × b × c 4 -60.18 128.84 6.45 0.01
a × b × c × d × e 6 -57.93 128.88 6.49 0.01
c × d 3 -62.08 130.43 8.04 0.00
b × d 3 -62.09 130.46 8.08 0.00
b × c 3 -62.14 130.56 8.17 0.00
a × b × c × d 5 -59.96 130.63 8.24 0.00
b × c × d 4 -62.08 132.62 10.24 0.00

Table 4

Importance values, model regression coefficient and the P-value of variables"

模型平均 Model averaging 100 m内水源
Source of water in 100 meters
海拔
Altitude
坡度
Gradient
草本盖度
Herbaceous coverage
灌木盖度
Shrub coverage
重要值 Importance value 0.82 0.51 0.30 0.27 0.26
标准化回归系数β
Standardized regression coefficient β
1.026 -0.172 0.096 0.122 0.009
P 0.031* 0.044* 0.299 0.438 0.570
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