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Scientia Silvae Sinicae ›› 2016, Vol. 52 ›› Issue (1): 71-79.doi: 10.11707/j.1001-7488.20160109

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Selection of Microhabitat of Carabid Beetles (Coleoptera:Carabidae) in Different Ecological Restored Habitats in the Hilly and Gully Area of Loess Plateau, Ningxia, China

Hang Jia1, Shi Yun1,2, An Jingjing1, He Dahan1,3   

  1. 1. School of Agriculture, Ningxia University Yinchuan 750021;
    2. College of Resources and Environment, Ningxia University Yinchuan 750021;
    3. State Key Laboratory for Restoration and Reconstruction of Degraded Ecosystem in North-Western China, Ningxia University Yinchuan 750021
  • Received:2014-04-11 Revised:2014-06-17 Online:2016-01-25 Published:2016-02-26

Abstract: [Objective] The aim of this study was to probe into the influences of ecological restoration on the diversity of carabid beetles (Coleoptera:Carabidae) and selection of microhabitat by these beetles in different habitats, which can beneficial to reveal the process of ecological restoration and provide scientific basis for biodiversity conservation in the hill and gully area of Loess Plateau, Ningxia Hui Autonomous Region.[Method] An investigation of carabid beetles was carried out using pitfall traps in six habitats, namely, shrub and grass mixed forest land(SG), tree and shrub mixed forest land(TS), ecological fuel-wood forest land(EF), ecological economic forestl and(EE), natural enclosed grassland(EG) and level terraced field(TF) from June to August in 2012 and 2013. Microhabitat selection of carabid beetles was analyzed by Logistic regression model, and further the key environmental factors with influencing microhabitat selection by carabid beetles were explored.[Result] The results showed that we trapped 3 236 individuals of 16 species, belonging to 9 genera, among which Amara gebleri, Carabus brandti and Dolichus halensis were the most dominant species and accounted for 40.64%, 24.13% and 20.02% of abundance in the study area, respectively. The number of individuals of carabid beetles and Shannon-Wiener diversity index all showed significant differences among habitats (df=5, P=0.011; df=5, P=0.021), and species richness was relatively higher in shrub and grass mixed forest than other ones, but the difference was not significant among habitats (df=5, P=0.062). There was a marked difference in Chao-1 richness estimation curves in each habitat. Species richness estimation approached an asymptote, and indicting that the almost all species were captured in shrub and grass mixed forest and ecological fuel-wood forest, and the most species were captured in natural enclosed grassland and level terraced field. In contrast, species richness at tree and shrub mixed forest and ecological economic forest showed no indication of approaching an asymptote, and there was considerale variation in the estimates of species richness. The occurrence probabilities of these dominant species were significantly different among habitats. Multiple logistic regression analyses displayed that biomass of herbs and soil water content were the critical habitat factors for microhabitat selection by Amara gebleri. Cover of canopy, cover of shrubs, biomass of herbs and soil water content were the importantly environmental factors for microhabitat selection by Carabus brandti. Cover of shrubs, cover of herbs, depth of leaf litter, biomass of herbs and soil water content were key factors for microhabitat selection by Dolichus halensis. [Conclusion] There was a marked difference in microhabitat selection of carabid beetles in this study, which could be an important bioindicator for assessment of ecological restoration.With the knowledge of habitats preference of these species, we can guild how to further strengthen the ecological restoration and habitat protection. This study is of practical importance for biodiversity conservation and ecological construction in the hill and gully area of Loess Plateau, Ningxia Hui Autonomous Region.

Key words: hilly and gully area of Loess Plateau, ecological restoration, carabid beetles, diversity, Chao-1 index, Logistic regression model

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