Impacts of climate change on the potential geographical distribution of broadleaved Korean pine (<i>Pinus koraiensis</i>) forests

Xiang, 贾翔 Jia; Fangfang, 马芳芳 Ma; Wangming, 周旺明 Zhou; Li, 周莉 Zhou; Dapao, 于大炮 Yu; Jing, 秦静 Qin; Limin, 代力民 Dai

doi:10.5846/stxb201508101680

Cited by 8 publications

(12 citation statements)

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“…The potential distribution area of species was strongly correlated with environmental variables including temperature, rainfall, terrain and so on (Jia et al ., 2017; Zhang et al ., 2022). Among environmental variables, climate (temperature and rainfall) is among the most critical factors limiting the potential distribution of species(Zhang et al ., 2022).…”

Section: Discussionmentioning

confidence: 99%

“…Due to the certain correlation between various environmental factors, if directly applied to the model, there may be overfitting phenomenon thought as error source (Jaryan et al, 2013;Jia et al, 2017). In order to eliminate this negative effects on model building and establish a model that has better performance with fewer variables, these 22 environmental variables were extracted from the corresponding layers associated with 422 documented occurrence points in ArcGIS 10.2 and then cross-correlations analysis (Pearson correlation coeffificient, r) have been performed.…”

Section: Variables Selectionmentioning

confidence: 99%

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Identification of potential distribution area for the sweet cherry (Prunus avium) by the MaxEnt model

Li,

Peng,

Jiang

et al. 2023

Preprint

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The potential distribution of species offers important information for species management, especially for fruit trees with economic value. The sweet cherry is a tree species with higher economic value among deciduous fruit trees in north China. Herein, its potential distributions were modeled under current conditions. The performances of this model were excellent with AUCs of >0.9 for model training and testing. The key factors identified were the thermal factors (min temperature of coldest month (bio06) from -14.5 to 4.5°C, the mean temperature of warmest quarter (bio10) from 21.0 to 28.0°C), followed by water factor (the annual precipitation (bio12) from 500 to 1200 mm). Its suitable region in China mainly is located on 6 geographical regions including southwest China (eastern Sichuan, northeast and main urban areas of Chongqing, mid-western Guizhou and mid-northern Yunnan), northwest China (mid-southern Shaanxi, southern Ningxia mid-southern and eastern Gansu), northeast China (Coastal region of Liaoning), central China (most of Henan, mid-northern Hubei and central Hunan), north China (Beijing, Tianjing, mid-southern Shanxi), east China (Shanghai, Jiangsu, Shandong, central Zhejiang, central and northern Anhui and eastern Jiangxi) and south China (western Guangxi). Relative to their actual distributions, Hubei, Hunan and Anhui etc have been identified to have some potentially suitable areas for these sweet cherries. Among them, these 9 provinces or cities (Shaanxi, Beijing, Tianjing, Shanxi, Hebei, Henan, Shanghai, Jiangsu and Shandong) are main regions for its current development and utilization, followed by Sichuan, Guizhou,Yunnan, Gansu, Liaoning, Hubei, Zhejiang and Guangxi etc. The results can be adopted to identify the suitable areas for its introduction and cultivation to avoid the losses of human labor, material and financial resources due to improper decision, and to provide a scientific basis for its current introduction, cultivation and management.

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Section: Discussionmentioning

confidence: 99%

Section: Variables Selectionmentioning

confidence: 99%

Identification of potential distribution area for the sweet cherry (Prunus avium) by the MaxEnt model

Li,

Peng,

Jiang

et al. 2023

Preprint

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“…Anomalous changes in temperature and precipitation influence the geographical distribution of species [ 13 , 21 ]. To determine which environmental variables over the period 1970–2000 most strongly influenced the distribution of the PWN, we selected 20 environmental variables, including 19 bioclimatic variables and one altitude variable in our model (Table 2), with a spatial resolution of approximately 1 km 2 , downloaded from global climate data ( http://www.worldclim.org ).…”

Section: Methodsmentioning

confidence: 99%

“…Over the past decade, some scientists assessed the potential distribution of PWN in Chongqing City or Sichuan Province alone using known distribution records together with layers of environmental variables [ 11 , 14 ], but there is a lack of similar research focusing on Sichuan-Chongqing together. Therefore, these above-mentioned related studies are relatively incomplete, which are mainly reflected in: (1) the relatively small areas studied, because the MaxEnt model can only achieve higher accuracy on a large scale and has a large error on a small scale, which could be because a higher spatial scale means that more species information can be obtained [ 15 , 16 ]; (2) the lack of accurate location analysis of increases and decreases in distribution, resulting in difficulty in laying out the scientific investigations and control pest infestations [ 17 ]; (3) a lack of innovation in research methods, compared with our new method that uses the newly introduced maximum Youden index and the average habitat suitability based on 10 replicates by cross-validation [ 18 , 19 ]; (4) only one specific global climate model (GCM) is used, making it difficult to explain related uncertainties owing to a lack of experimental verification of prediction results from multiple GCMs [ 11 , 14 ]; and (5) a lack of multicollinearity analysis among environmental variables, which is regarded as an error source [ 20 , 21 ]. Moreover, climate change, in terms of temperature and precipitation, will affect the geographical distribution of animals and plants [ 13 ], increase the invasion of alien species, and increase biodiversity losses [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic changes in the suitable areas for the pinewood nematode in the Sichuan–Chongqing Region of China

Li,

Peng,

Jiang

et al. 2023

PLoS ONE

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The pine wood nematode (PWN), one of the largest alien forestry pests in China, has caused numerous deaths of conifer forests in Europe and Asia, and is spreading to other suitable areas worldwide. Information on the spatial distribution of the PWN can provide important information for the management of this species. Here, the current and future geographical distributions of PWN were simulated in the Sichuan–Chongqing region of China in detail based on the MaxEnt model. The results indicated excellent prediction performance, with an area under curve score of more than 0.9. The key factors selected were the altitude, maximum temperature of the warmest month, annual precipitation, precipitation of the wettest quarter, and minimum temperature of the coldest month, with thresholds of < 400 m, > 37.5 °C, 1100–1250 mm, 460–530 mm and > 4.0 °C, respectively, indicating that the PWN can live in low-altitude, warm, and humid areas. The suitable region for the PWN is mainly concentrated in the metropolitan area, northeast of Chongqing, and the southeastern and eastern parts of Sichuan Province. Most importantly, in addition to their actual distribution area, the newly identified suitably distribution areas A, B, C, and D for the coming years and E, F, G, and H for the period–2041–2060 (2050s) should be strictly monitored for the presence of PWNs. Altogether, the suitable distribution ranges of the PWN in the Sichuan-Chongqing region show an increasing trend; therefore, owing to its inability to disperse by itself, human activities involving pine trees and vectors of the Japanese pine sawyer should be intensively controlled to prevent the PWN from spreading to these newly discovered suitable areas.

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“…The multi-collinearity test (IBM SPSS Statistics 24) was performed on the 22 variables (Appendix 2). Only those variables with higher loading coefficients were included in the model when the absolute Pearson correlation coefficient |r| ≥ 0.8 (Yang et al, 2013;Jia et al, 2017). Finally, nine environmental variables, including six bioclimatic variables (Bio1 (annual mean temperature), Bio3 (isothermality), Bio5 (max temperature of warmest month), Bio7 (temperature annual range), Bio15 (precipitation seasonality) and Bio17 (precipitation of driest quarter)) and three topographic variables (elevation, slope and aspect) were selected for Maxent prediction (Appendix 2).…”

Section: Environmental Variable Selection and Processingmentioning

confidence: 99%

Prediction of Suitable Habitat for Lycophytes and Ferns in Northeast China: A Case Study on Athyrium brevifrons

Cao

et al. 2019

Chin. Geogr. Sci.

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Suitable habitat is vital for the survival and restoration of a species. Understanding the suitable habitat range for lycophytes and ferns is prerequisite for effective species resource conservation and recovery efforts. In this study, we took Athyrium brevifrons as an example, predicted its suitable habitat using a Maxent model with 67 occurrence data and nine environmental variables in Northeast China. The area under the curve (AUC) value of independent test data, as well as the comparison with specimen county areal distribution of A. brevifrons exhibited excellent predictive performance. The type of environmental variables showed that precipitation contributed the most to the distribution prediction, followed by temperature and topography. Percentage contribution and permutation importance both indicated that precipitation of driest quarter (Bio17) was the key factor in determining the natural distribution of A. brevifrons, the reason could be proved by the fern gametophyte biology. The analysis of high habitat suitability areas also showed the habitat preference of A. brevifrons: comparatively more precipitation and less fluctuation in the driest quarter. Changbai Mountains, covering almost all the high and medium habitat suitability areas, provide the best ecological conditions for the survival of A. brevifrons, and should be considered as priority areas for protection and restoration of the wild resource. The potential habitat suitability distribution map could provide a reference for the sustainable development and utilisation of A. brevifrons resource, and Maxent modelling could be valuable for conservation management planning for lycophytes and ferns in Northeast China.

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Impacts of climate change on the potential geographical distribution of broadleaved Korean pine (Pinus koraiensis) forests

Cited by 8 publications

References 0 publications

Identification of potential distribution area for the sweet cherry (Prunus avium) by the MaxEnt model

Identification of potential distribution area for the sweet cherry (Prunus avium) by the MaxEnt model

Dynamic changes in the suitable areas for the pinewood nematode in the Sichuan–Chongqing Region of China

Prediction of Suitable Habitat for Lycophytes and Ferns in Northeast China: A Case Study on Athyrium brevifrons

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