Methodological approaches to exploring the spatial variation in social impacts of protected areas: An intercomparison of Bayesian regression modeling approaches and potential implications

Chrysovalantis Malesios; Nikoleta Jones; Alfie Begley; James McGinlay; Chrysovalantis Malesios; Nikoleta Jones; Alfie Begley; James McGinlay

doi:10.3934/mbe.2024170

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 3: 3816-3837. doi: 10.3934/mbe.2024170

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Methodological approaches to exploring the spatial variation in social impacts of protected areas: An intercomparison of Bayesian regression modeling approaches and potential implications

Institute for Global Sustainable Development, The Ramphal Building, University of Warwick, Coventry CV4 7AL, UK

Academic Editor: Yang Kuang

Received: 12 August 2023 Revised: 24 October 2023 Accepted: 06 November 2023 Published: 19 February 2024

Protected Areas (PAs) are widely used to conserve biodiversity by protecting and restoring ecosystems while also contributing to socio-economic priorities. An increasing number of studies aim to examine the social impacts of PAs on aspects of people's well-being, such as, quality of life, livelihoods, and connectedness to nature. Despite the increase in literature on this topic, there are still few studies that explore possible robust methodological approaches to capturing and assessing the spatial distribution of impacts in a PA. This study aims to contribute to this research gap by comparing Bayesian spatial regression models that explore links between perceived social impacts and the relative location of local residents and communities in a PA. We use primary data collected from 227 individuals, via structured questionnaires, living in or near the Peak District National Park, United Kingdom. By comparing different models we were able to show that the location of respondents influences their perception of social impacts and that neighboring communities within the national park can have similar perceptions regarding social impacts. Simulation based on existing data using the Bootstrap sub-sampling was also conducted to validate the association between social impacts and mutual proximity of residents. Our findings suggest that this type of data is better treated, in terms of accounting for potential spatial effects, using models that allow for proximity effects to be stronger between people living nearby, e.g. between neighbors in the same community and have minimum effects otherwise. Understanding the spatial clustering of perceived social impacts in and around PA, is key to understanding their causes and to managing and mitigating them. Our findings highlight therefore the need to develop new methodological approaches to assessing and predicting accurately the spatial distribution of social impacts when designating PAs. The findings in this paper will assist practitioners in this regard by proposing approaches to the consideration of the distribution of social impacts when designing the boundaries of PAs alongside typical ecological and socio-economic criteria.
- spatial,
- Bayesian modeling,
- social impacts,
- benefits,
- costs,
- designation,
- perception of social impacts,
- clustering,
- mapping
Citation: Chrysovalantis Malesios, Nikoleta Jones, Alfie Begley, James McGinlay. Methodological approaches to exploring the spatial variation in social impacts of protected areas: An intercomparison of Bayesian regression modeling approaches and potential implications[J]. Mathematical Biosciences and Engineering, 2024, 21(3): 3816-3837. doi: 10.3934/mbe.2024170

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Abstract

Protected Areas (PAs) are widely used to conserve biodiversity by protecting and restoring ecosystems while also contributing to socio-economic priorities. An increasing number of studies aim to examine the social impacts of PAs on aspects of people's well-being, such as, quality of life, livelihoods, and connectedness to nature. Despite the increase in literature on this topic, there are still few studies that explore possible robust methodological approaches to capturing and assessing the spatial distribution of impacts in a PA. This study aims to contribute to this research gap by comparing Bayesian spatial regression models that explore links between perceived social impacts and the relative location of local residents and communities in a PA. We use primary data collected from 227 individuals, via structured questionnaires, living in or near the Peak District National Park, United Kingdom. By comparing different models we were able to show that the location of respondents influences their perception of social impacts and that neighboring communities within the national park can have similar perceptions regarding social impacts. Simulation based on existing data using the Bootstrap sub-sampling was also conducted to validate the association between social impacts and mutual proximity of residents. Our findings suggest that this type of data is better treated, in terms of accounting for potential spatial effects, using models that allow for proximity effects to be stronger between people living nearby, e.g. between neighbors in the same community and have minimum effects otherwise. Understanding the spatial clustering of perceived social impacts in and around PA, is key to understanding their causes and to managing and mitigating them. Our findings highlight therefore the need to develop new methodological approaches to assessing and predicting accurately the spatial distribution of social impacts when designating PAs. The findings in this paper will assist practitioners in this regard by proposing approaches to the consideration of the distribution of social impacts when designing the boundaries of PAs alongside typical ecological and socio-economic criteria.

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