期刊论文详细信息
JOURNAL OF ENVIRONMENTAL MANAGEMENT 卷:214
Exploring the social dimension of sandy beaches through predictive modelling
Article
Dominguez-Tejo, Elianny1  Metternicht, Graciela1  Johnston, Emma L.2,3  Hedge, Luke3 
[1] Univ New South Wales, Sch Biol Earth & Environm Sci, Pangea Ctr, Biol Sci Bldg D26, Kensington, NSW 2052, Australia
[2] Univ New South Wales, Sch Biol Earth & Environm Sci, Evolut & Ecol Res Ctr, Biol Sci South, Kensington, NSW 2052, Australia
[3] Sydney Inst Marine Sci, Chowder Bay Rd, Mosman, NSW 2088, Australia
关键词: Coastal planning;    Recreation;    Community values;    Social system;    Marine spatial planning;    Ecosystem-based approach;   
DOI  :  10.1016/j.jenvman.2018.03.006
来源: Elsevier
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【 摘 要 】

Sandy beaches are unique ecosystems increasingly exposed to human-induced pressures. Consistent with emerging frameworks promoting this holistic approach towards beach management, is the need to improve the integration of social data into management practices. This paper aims to increase understanding of links between demographics and community values and preferred beach activities, as key components of the social dimension of the beach environment. A mixed method approach was adopted to elucidate users' opinions on beach preferences and community values through a survey carried out in Manly Local Government Area in Sydney Harbour, Australia. A proposed conceptual model was used to frame demographic models (using age, education, employment, household income and residence status) as predictors of these two community responses. All possible regression-model combinations were compared using Akaike's information criterion. Best models were then used to calculate quantitative likelihoods of the responses, presented as heat maps. Findings concur with international research indicating the relevance of social and restful activities as important social links between the community and the beach environment. Participant's age was a significant variable in the four predictive models. The use of predictive models informed by demographics could potentially increase our understanding of interactions between the social and ecological systems of the beach environment, as a prelude to integrated beach management approaches. The research represents a practical demonstration of how demographic predictive models could support proactive approaches to beach management. (C) 2018 Elsevier Ltd. All rights reserved.

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