Response of abundance distribution of five species of Quercus to climate change in northern China

doi:10.17521/cjpe.2018.0249

Abstract

Abstract:

Aims To develop a statistically appropriate species distribution model for the abundance of five species from Quercus in the northern China, and to predict the change of abundance under climate change.
Methods We surveyed abundance data of five Quercus species from 1 045 plots in the northern China, and then fit the abundance with climatic variables using random forest model (RF). We then predict the abundance of these five Quercus species in 2050 and 2070 under Representation Concentration Pathways (RCP) 2.6 and 8.5.
Important findings The change magnitudes of abundance for all 5 species under RCP 8.5 were larger than under RCP 2.6. Except for Quercus variabilis, abundances of other four species declined under climate change to 2050 and 2070 in more than half of the current distribution areas. Moreover, the northeastern part of Nei Mongol and the northern part of Heilongjiang will be the hotspots of decrease of abundance. Therefore, it is necessary to strengthen the monitoring and species protection in the areas mentioned above with the increasing threaten of climate change.

Key words: generalized linear model, generalized additive model, random forest model, species distribution model

ZHANG Xue-Jiao, GAO Xian-Ming, JI Cheng-Jun, KANG Mu-Yi, WANG Ren-Qing, YUE Ming, ZHANG Feng, TANG Zhi-Yao. Response of abundance distribution of five species of Quercus to climate change in northern China[J]. Chin J Plant Ecol, 2019, 43(9): 774-782.

Figures/Tables 7

Fig. 1 Plots distribution of five species of Quercus in northern China.

Table 1 Survey statistics of plots of five species in Quercus in the northern China

物种 Species	样方数 Plot number	平均密度 Mean density (Ind.·1000 m^-2)	样方的气温范围 Air temperature range of plots (℃)	样方的降水范围 Precipitation range of plots (mm)
栓皮栎 Q. variabilis	304	89 ± 101	6.8-15.9	529-1 431
麻栎 Q. acutissima	195	39 ± 55	4.1-15.8	451-1 614
槲栎 Q. aliena	188	22 ± 43	3.6-16.0	335-1 775
锐齿槲栎 Q. aliena	147	37 ± 34	1.4-11.6	337-1 502
蒙古栎 Q. mongolica	492	55 ± 68	0.1-14.4	335-1 006

Fig. 2 Relationship between observed and predicted species abundances for five species in Quercus with random forest model (RF), generalized additive model (GAM), generalized linear model (GLM) and comparison of the accuracy of five species based on general linear model and random forest. A, Q. variabilis (Qva). B, Q. acutissima (Qac). C, Q. aliena (Qal). D, Q. aliena var. acuteserrata (Qaa). E, Q. mongolica (Qm). F, Model fitness of the three models, differed lowercase letters indicated significant differences between the models (p < 0.05).

Fig. 3 Species abundance (number·1 000 m-2) distribution maps of the North China for five species produced by random forest model, based on bioclimatic variables at year 1960-1990. A, Quercus variabilis. B, Q. acutissima. C, Q. aliena. D, Q. aliena var. acuteserrata. E, Q. mongolica.

Fig. 4 Future distribution maps of rate of abundance change in the North China for five species produced by random forest model, based on bioclimatic variables under RCP 2.6 dispersal scenario (A-E) and RCP 8.5 dispersal scenario (F-J) in year 2050. A, F, Quercus variabilis. B, G, Q. acutissima. C, H, Q. aliena. D, I, Q. aliena var. acuteserrata. E, J, Q. mongolica.

Fig. 5 Future distribution maps of rate of abundance change in the North China for five species produced by Random Forest models, based on bioclimatic variables under RCP 2.6 dispersal scenario (A-E) and RCP 8.5 dispersal scenario (F-J) in year 2070. A, F, Quercus variabilis. B, G, Q. acutissima. C, H, Q. aliena. D, I, Q. aliena var. acuteserrata. E, J, Q. mongolica.

Fig. 6 Elevation distribution of abundance change ratio of different periods under different scenario in North China. A, Quercus variabilis. B, Q. acutissima. C, Q. aliena. D, Q. aliena var. acuteserrata. E, Q. mongolica.

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