Effects of human disturbance on vegetation, prey and Amur tigers in Hunchun Nature Reserve, China

Zhilin Li, Aili Kang, Jiayin Gu, Yangang Xue, Yi Ren, Zhiwen Zhu, Peiqi Liu, Jianzhang Ma and Guangshun Jiang

Ecological Modelling, 2017, vol. 353, issue C, 28-36

Abstract: Multiple human disturbances influence the vegetation, ungulates and Amur tigers (Panthera tigris altaica) in Hunchun Nature Reserve (HNR) in northeastern China. In order to understand the influence and relative contribution of human disturbance on Amur tigers, prey and vegetation, we conducted the transect lines and plot surveys of human disturbance inside HNR from August to October 2013. We used generalized additive models, generalized liner models and structural equation models to explore the effects of human disturbance on vegetation, prey and Amur tigers. We then used hierarchical partitioning models to quantify the contribution of four main kinds of human disturbance. Our results suggest that all three models indicate that human disturbance can directly and indirectly affect prey and Amur tigers via the Bottom Up chains. Among the human disturbances, grazing activity and ginseng land encroachment impacted vegetation more than roads did; for prey, secondary roads had the greatest impact. Grazing activity, secondary roads and primary roads were the main factors disturbing Amur tigers. The generalized additive model had a stronger detection ability for disturbance prediction than generalized liner and structural equation models. The generalized additive model detected more complex nonlinear interaction relationships between predator and prey or predator, prey and habitat factors. Reducing or eliminating specific types of disturbance will be essential to recover Amur tiger populations and their habitats.

Keywords: Bottom-up effect; Transect survey; GAM; GLM; SEM (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:353:y:2017:i:c:p:28-36

DOI: 10.1016/j.ecolmodel.2016.08.014

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