森林道路对大型哺乳动物行为的影响

IF 0.4 4区环境科学与生态学 Q4 ECOLOGY

Polish Journal of Ecology Pub Date : 2021-02-02 DOI:10.3161/15052249PJE2020.68.4.006

Yilmaz Turk, Selcuk Cometen, A. Keten

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引用次数: 4

摘要

森林道路是开发利用可再生自然资源森林的最重要的基础设施。它们对于对森林进行各种科学和技术干预具有重要意义。尽管林道有好处，但它们极有可能造成自然生境的退化和破碎化。本研究的目的是探讨森林道路技术特征和使用强度(交通量)对哺乳动物活动的影响。2017年8月至9月，在固定样地设置野生动物相机陷阱，对6条森林道路及其周围环境进行了观察。这项研究共确定了12种哺乳动物。总共从461张野生动物图像中计算出589只个体。金豺、野猪和赤狐是研究区内的优势种。野生哺乳动物种类组成与道路特征关系不大。司机和野生动物使用相同的区域，但使用时间不同。还确定野生动物将道路作为自然环境的一部分。

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Effects of Forest Roads on Large Mammal Behaviour

ABSTRACT Forest roads are the most important infrastructural facilities to exploit forests that are renewable natural resources. They are of great importance for all kinds of scientific and technical interventions to be made to the forest. Despite the benefits of forest roads, they have great potential to cause degradation and fragmentation of natural habitats. The aim of this study is to investigate the effect of forest road technical features and intensity of use (traffic volume) on movements of mammals. From August 2017 to September 2017, a total of six forest roads and their surroundings were observed from fixed plots with wildlife camera traps. The study identified a total of twelve species of mammals. All together 589 individuals were counted from 461 wild animal images. Golden jackal, wild boar, and red fox are dominant species in the study area. The species composition of wild mammals was not strongly related to road characteristics. Drivers and wild animals use the same area but differ in time of use. It was also determined that wild animals adopted the road as a part of natural surroundings.

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来源期刊

Polish Journal of Ecology 环境科学-生态学

CiteScore

1.10

自引率

0.00%

发文量

审稿时长

18-36 weeks

期刊介绍： POLISH JOURNAL OF ECOLOGY (formerly Ekologia polska) publishes original scientific research papers dealing with all aspects of ecology: both fundamental and applied, physiological ecology, evolutionary ecology, ecology of population, community, ecosystem, landscape as well as global ecology. There is no bias regarding taxons, ecosystems or geographical regions.