Interannual variation of rain-use efficiency of three coniferous forests and their response to meteorological factors in Liupan Mountains of Ningxia

doi:10.3724/SP.J.1258.2013.00093

Abstract

Abstract:

Aims Rain-use efficiency (RUE), the ratio of vegetation productivity to annual precipitation, is an important measure of water use and a key determinant of the net primary production (NPP) of forest ecosystems. Our objective is to investigate the interannual variation of RUE of three conifer forests and their response to the variation of meteorological factors.
Methods We calculated the biomass and NPP of three coniferous forests (Pinus armandii natural forest, Larix principis-rupprechtii plantation and Pinus tablaeformis plantation) in Liupan Mountains of Ningxia, China by dendroecological methods, empirical biomass equations and collected meteorological data including monthly air temperature, humidity and precipitation. Then we calculated the RUE of these forests and analyzed their correlation with meteorological factors.
Important finding The NPP and RUE of L. principis-rupprechtii plantation, having the values of 6.72 t·hm ^-2·a ^-1and 1.12 g·m ^-2·mm ^-1, respectively, were 1.53 and 1.49 times higher than the Pinus armandii natural forest and 0.17 and 0.15 times higher than the P. tablaeformis plantation, respectively. The RUE increased with forest age when it was younger than 32 years, but RUE varied among species. The interannual variation of RUE of P. armandii natural forest was relatively stable when aged 32-45 years and then decreased with age. With increasing annual precipitation, the RUE of P. armandii natural forest decreased, but the RUE of the two plantations increased at first and then decreased. The RUE of all three forests tended to be similar in the driest years and tended to have the same minimum value in the wettest years. RUE, which was affected by meteorological factors of the previous year, responded to meteorological factors differently among tree species, but it was mainly influenced by meteorological factors of the current year and its annual variation pattern. The RUE of P. armandii natural forest was significantly negatively correlated with precipitation in August of the previous year and that in September, October and November of the current year. The RUE of the two plantations was significantly correlated with precipitation in September of the previous year and that in April of the current year and, in the case of the L. principis-rupprechtii plantation, to precipitation in September of the current year. The RUE of all three tree species was significantly correlated with the mean air temperature in June and March of the current year and that in June of the previous year. In addition, the RUE of P. armandii natural forest was significantly correlated with air temperature in February of the previous year, and the RUE of the two plantations was significantly positively correlated with air temperature in April and May of the current year.

Key words: climate change, meteorological factors, net primary productivity (NPP), rain-use efficiency (RUE)

WANG Yun-Ni,XIONG Wei,WANG Yan-Hui,YU Peng-Tao,CAO Gong-Xiang,XU Li-Hong,ZUO Hai-Jun,HE Liang-Liang. Interannual variation of rain-use efficiency of three coniferous forests and their response to meteorological factors in Liupan Mountains of Ningxia[J]. Chin J Plant Ecol, 2013, 37(10): 901-911.

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URL: https://www.plant-ecology.com/EN/10.3724/SP.J.1258.2013.00093

https://www.plant-ecology.com/EN/Y2013/V37/I10/901

Figures/Tables 7

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植被类型 Vegetation type	海拔Elevation (m)	坡位Slope position	坡度Slope gradient	乔木层 Tree layer				草本层 Herbaceous layer		土壤厚度 Soil thickness (cm)
植被类型 Vegetation type	海拔Elevation (m)	坡位Slope position	坡度Slope gradient	郁闭度Canopy density	平均胸径Mean DBH (cm)	平均树高Mean tree height (m)	密度 Density (株·hm^-2)	优势种 Dominant species	盖度 Coverage (%)	土壤厚度 Soil thickness (cm)
华山松林 Pinus armandii forest	2 260	中 Middle	34	0.75	15.8	8.7	1 617	鬼灯檠 Rodgersia podophylla	9	<60
华北落叶松林 Larix principis- rupprechtii forest	2 174	下 Down	31	0.83	16.9	15.3	1 653	蕨 Pteridium aquilinum	49	>120
油松林 Pinus tabulaeformis forest	2 157	下 Down	6	0.78	17.9	9.9	1 492	东方草莓 Fragaria orientalis	42	80-100

植被类型 Vegetation type	海拔Elevation (m)	坡位Slope position	坡度Slope gradient	乔木层 Tree layer				草本层 Herbaceous layer		土壤厚度 Soil thickness (cm)
植被类型 Vegetation type	海拔Elevation (m)	坡位Slope position	坡度Slope gradient	郁闭度Canopy density	平均胸径Mean DBH (cm)	平均树高Mean tree height (m)	密度 Density (株·hm^-2)	优势种 Dominant species	盖度 Coverage (%)	土壤厚度 Soil thickness (cm)
华山松林 Pinus armandii forest	2 260	中 Middle	34	0.75	15.8	8.7	1 617	鬼灯檠 Rodgersia podophylla	9	<60
华北落叶松林 Larix principis- rupprechtii forest	2 174	下 Down	31	0.83	16.9	15.3	1 653	蕨 Pteridium aquilinum	49	>120
油松林 Pinus tabulaeformis forest	2 157	下 Down	6	0.78	17.9	9.9	1 492	东方草莓 Fragaria orientalis	42	80-100

树种 Tree species	生物量模型 Biomass model	备注 Note
华北落叶松 Larix principis-rupprechtii	lnW_n_地上 = 2.2235lnD_n - 1.9634	基于香水河20棵解析木数据 Based on data of 20 analytic trees
华北落叶松 Larix principis-rupprechtii	lnW_n_根 = 2.0885lnD_n - 3.4782	Luo et al. (2009)
华山松 Pinus armandii	lnW_n = 2.4119lnD_n - 2.2962	Cheng et al. (2007)
油松 Pinus tabulaeformis	lnW_n = 2.5444lnD_n - 2.5075	Cheng et al. (2007)

树种 Tree species	生物量模型 Biomass model	备注 Note
华北落叶松 Larix principis-rupprechtii	lnW_n_地上 = 2.2235lnD_n - 1.9634	基于香水河20棵解析木数据 Based on data of 20 analytic trees
华北落叶松 Larix principis-rupprechtii	lnW_n_根 = 2.0885lnD_n - 3.4782	Luo et al. (2009)
华山松 Pinus armandii	lnW_n = 2.4119lnD_n - 2.2962	Cheng et al. (2007)
油松 Pinus tabulaeformis	lnW_n = 2.5444lnD_n - 2.5075	Cheng et al. (2007)

月份	降水量 Precipitation			气温 Air temperature			空气湿度 Air humidity
Month	华山松林 PAF	华北落叶松林 LPF	油松林 PTF	华山松林 PAF	华北落叶松林 LPF	油松林 PTF	华山松林 PAF	华北落叶松林 LPF	油松林 PTF
-6	-0.06	0.11	-0.03	-0.42^*	0.48^*	0.46^*	0.16	-0.26	-0.42
-7	0.09	-0.05	-0.01	-0.21	0.21	0.26	0.02	-0.07	-0.15
-8	0.29^*	-0.21	-0.16	-0.14	0.18	0.26	0.11	0.13	0.05
-9	-0.17	0.47^*	0.52^*	-0.13	-0.10	-0.06	-0.04	0.28	0.21
-10	-0.13	0.25	0.15	-0.07	0.08	0.17	-0.14	0.24	0.06
-11	-0.27	-0.27	-0.06	-0.08	0.20	0.22	-0.15	0.23	0.31
-12	-0.13	-0.06	-0.26	0.14	-0.13	-0.15	-0.19	0.07	-0.06
1	0.10	0.02	-0.15	0.02	0.13	0.16	0.07	-0.03	-0.28
2	0.19	-0.08	-0.15	-0.28^*	0.23	0.41	0.25	-0.22	-0.40
3	0.19	-0.28	-0.05	-0.37^**	0.71^**	0.62^**	0.40^**	-0.60^**	-0.39
4	-0.11	-0.43^*	-0.51^*	-0.21	0.45^*	0.59^**	0.20	-0.54^*	-0.58^**
5	0.13	-0.09	-0.07	-0.23	0.46^*	0.45^*	0.20	-0.22	-0.22
6	-0.01	-0.08	-0.14	-0.40^**	0.63^**	0.63^**	0.25	-0.38	-0.41
7	-0.11	-0.15	-0.20	-0.21	0.39	0.32	-0.15	-0.15	-0.17
8	-0.14	-0.28	-0.39	-0.10	0.19	0.30	-0.13	-0.06	-0.10
9	-0.51^**	0.51^*	0.20	-0.03	-0.13	-0.08	-0.43^**	0.30	0.05
10	-0.43^**	-0.23	-0.17	0.05	0.40	0.26	-0.49^**	-0.01	-0.22
11	-0.34^*	-0.22	-0.25	-0.01	0.20	0.27	-0.29^*	0.02	-0.14
12	0.09	-0.06	-0.08	0	-0.18	-0.17	0	-0.07	0.03