Literature DB >> 24202281

Patterns of livestock predation by carnivores: human-wildlife conflict in northwest Yunnan, China.

Xueyou Li1, Paul Buzzard, Yongchun Chen, Xuelong Jiang.   

Abstract

Alleviating human-carnivore conflict is central to large carnivore conservation and is often of economic importance, where people coexist with carnivores. In this article, we report on the patterns of predation and economic losses from wild carnivores preying on livestock in three villages of northern Baima Xueshan Nature Reserve, northwest Yunnan during a 2-year period between January 2010 and December 2011. We analyzed claims from 149 households that 258 head of livestock were predated. Wolves (Canis lupus) were responsible for 79.1 % of livestock predation; Asiatic black bears (Selenarctos thibetanus) and dholes (Cuon alpinus) were the other predators responsible. Predation frequency varied between livestock species. The majority of livestock killed were yak-cattle hybrids or dzo (40.3 %). Wolves killed fewer cattle than expected, and more donkeys and horses than expected. Wolves and bears killed more adult female and fewer adult male livestock than expected. Intensified predation in wet season coincided with livestock being left to graze unattended in alpine meadows far away from villages. On average, carnivore attacks claimed 2.1 % of range stock annually. This predation represented an economic loss of 17 % (SD = 14 %) of the annual household income. Despite this loss and a perceived increase in carnivore conflict, a majority of the herders (66 %) still supported the reserve. This support is primarily due to the benefits from the collection of nontimber resources such as mushrooms and medicinal plants. Our study also suggested that improvement of husbandry techniques and facilities will reduce conflicts and contribute to improved conservation of these threatened predators.

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Year:  2013        PMID: 24202281     DOI: 10.1007/s00267-013-0192-8

Source DB:  PubMed          Journal:  Environ Manage        ISSN: 0364-152X            Impact factor:   3.266


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