秦岭大熊猫种群扩散格局及研究方法

种群扩散格局是研究种群扩散规律和机制的关键信息,也是制定物种保护对策的重要基础。大型动物种群扩散格局研究方法为扩散生态学研究的薄弱领域,并制约扩散生态学的发展。以秦岭大熊猫为研究对象,根据2000年以来的种群调查数据,基于大熊猫领域的特性,利用GIS的扩展区分析功能和景观分析方法研究了大熊猫种群分布区及动态;基于聚集的特性,利用GIS的核密度分析功能对大熊猫种群多度和聚集状况及空间变化进行了分析。发现2012年的秦岭大熊猫种群分布区较2000年增加5.5%(即15307.8hm~2),高密度种群聚集区从2处变成1处,种群聚集程度进一步增加、聚集格局的完整性大大提升,尤以中密度聚集区增长最显著,种群格局呈明显的分布区扩张、聚集度增加的态势。表明基于物种的生物学特性,立足于种群分布和多度格局变化,通过长期调查和监测可以有效掌握物种的种群扩散格局;大型动物可根据其生物学特性探索可行的方法与量化种群扩散的参数来研究其扩散格局,从而促进大型动物种群扩散研究的开展。

An exploration of giant panda population dispersal patterns and methodology in the Qinling Mountains

Population dispersal is an important life history trait that is influenced by environmental change, and it can alter the distribution, structure, and abundance of a population. In addition, population dispersal allows a species to actively adapt and ensure long-term survival. Patterns of population dispersal can provide key information about the rules and mechanisms of how populations disperse, and they are an important basis for conservation management. Methods for studying population dispersal in large animals are lacking, which therefore restricts the development and application of dispersal ecology. Two crucial issues that need to be taken into account when considering dispersal patterns are population distribution and abundance. Based on the factors of dispersal pattern and the giant panda characteristics of population and home range, this study intends to (1) reveal the dispersal patterns of giant pandas in the Qinling Mountains by comparing the change in their population distribution and aggregation from 2000 to 2012, and (2) explore methods for studying large animal population dispersal. Based on signs of giant pandas obtained from the third and fourth national surveys conducted by the Chinese Forestry Administration (completed in 2000 and 2012, respectively), a circular extension region with a giant panda sign as the center was produced using the buffer function in ArcGIS10.0. The average diameter of the home range of giant pandas was defined as 3 km. Subsequently, using the dissolve function in ArcGIS, we created polygons based on these circles, and established the primary population distribution area around the outer boundary of the polygons. We identified the population dispersal area based on the change in distribution range. Additionally, we mapped population aggregation densities in 2000 and 2012, and divided the population distribution range into areas with different aggregation densities by employing the kernel density analysis function of ArcGIS. We also revealed the population abundance and direction of population dispersal based on the variation in population aggregation. We found that the population distribution area of giant pandas increased by 15307.8 hm² in the Qinling Mountains since 2000, with an obvious expanding trend in the northwestern and southwestern regions of the study area. However, the population distribution decreased in the eastern and southern regions. Furthermore, the degree of population aggregation increased, especially for areas with medium aggregation density, and two patches of high-density aggregation became one. In addition, the integrity of the population aggregation pattern also greatly improved. In this way, the population pattern showed a detectable trend of expansion and an increase in population aggregation. This study revealed the area and direction of giant pandas dispersal in the Qinling Mountains since 2000, which has important implications for current population safety and conservation. Our study also showed that the population dispersal patterns of giant pandas could be effectively determined with the spatial variation in population distribution and abundance that is based on biological characteristics and long-term monitoring. The methodology developed in this study, combined with ongoing monitoring programs in Chinese nature reserves, can facilitate the study of population dispersal in large animals.