MaxEnt Modeling to Estimate the Impact of Climate Factors on Distribution of Pinus densiflora

Xiangguang Duan,Junqing Li,Shuhong Wu

doi:10.3390/f13030402

Abstract

Pinus densiflora is an important evergreen coniferous species with both economic and ecological value. It is an endemic species in East Asia. Global climate warming greatly interferes with species survival. This study explored the impact of climate change on the distribution of this species and the relationship between its geographical distribution and climate demand, so as to provide a theoretical basis for the protection of P. densiflora under the background of global warming. This research used 565 valid data points and 19 typical climatic environmental factors distributed in China, Japan, and South Korea. The potential distribution area of P. densiflora in East Asia under the last glacial maximum (LGM), mid-Holocene, the current situation and two scenarios (RCP 2.6 and RCP 8.5) in the future (2050s and 2070s) was simulated by the MaxEnt model. The species distribution model toolbox in ArcGIS software was used to analyze the potential distribution range and change of P. densiflora. The contribution rates, jackknife test and environmental variable response curves were used to assess the importance of key climate factors. The area under the receiver-operating characteristic curve (AUC) was used to evaluate model accuracy. The MaxEnt model had an excellent simulation effect (AUC = 0.982). The forecast showed that the Korean Peninsula and Japan were highly suitable areas for P. densiflora, and the area had little change. Moreover, during the LGM, there was no large-scale retreat to the south, and it was likely to survive in situ in mountain shelters. The results suggested that Japan may be the origin of P. densiflora rather than the Shandong Peninsula of China. The distribution area of P. densiflora in the mid-Holocene and future scenarios was reduced compared with the current distribution, and the reduction of future distribution was greater, indicating that climate warming will have certain negative impacts on the distribution of P. densiflora in the future. The precipitation of the warmest quarter (Bio18), temperature seasonality (Bio4), mean annual temperature (Bio1) and mean temperature of the wettest quarter (Bio8) had the greatest impact on the distribution area of P. densiflora.

Highlights

Since the Quaternary Period, the drastic changes in the earth’s climate had an important impact on the current distribution pattern of species [1]
The MaxEnt3.3.3k software was used to simulate the potential geographic distribution of P. densiflora in East Asia based on 565 current distribution record data points and 7 climatic variables
The precipitation of the warmest quarter (Bio18) (67.7%), temperature seasonality (Bio4) (13.9%), mean annual temperature (Bio1) (6.6%), mean temperature of the wettest quarter (Bio8) (5.6%) and isothermality (Bio3) (5.1%) had the greatest impacts on the distribution range of the P. densiflora population (Figure 3, Table 2). These results show that precipitation and temperature are the main environmental factors affecting the distribution of P. densiflora plants in East Asia

Summary

Introduction

Since the Quaternary Period (from 2.58 Ma to ), the drastic changes in the earth’s climate had an important impact on the current distribution pattern of species [1]. Climate has far-reaching impacts on the distribution range of species and is a key factor determining the distribution of species on a large scale [4]. Points out that the global climate is obviously warming [5]. Species distribution, population size and genetic diversity will change [6], which will lead to plant migration, endangerment and even extinction [7–12]. We should study the response of the plant distribution pattern to climate change and understand the climate demand of organisms and its relationship with the species geographical distribution. It is important to reveal the history of species formation, migration and diffusion and to put forward reasonable strategies for species diversity protection [13]

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Methods

Results

Discussion

Conclusion