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Marine sanctuaries serve as popular destinations for ecotourism, natural resource exploration, and recreation across the US. While often positive, visitation in marine and coastal areas can cause ecological threats to these ecosystems. Increased visitation in marine environments has led to the need for management due to negative ecological and social impacts. Understanding environmental values, attitudes, and perceptions is important to the success of environmental protection. Using online surveys sent via Qualtrics asking questions regarding the users’ knowledge, attitudes, and perceptions of ocean resources, goods and services, this research focused on identifying user profiles and understanding their environmental perception associated with Gray’s Reef National Marine Sanctuary, an offshore marine protected area, and surrounding coastal Georgia. The results show that across multiple types of threats or phenomena, respondents are most concerned about threats to resources related to pollution. Furthermore, they support marine protection and are willing to adjust their consumption habits, such as recycling and energy use, to ensure the sustainable use of ocean resources. The inclusion of insights achieved through research about visitor perceptions into management decision making and planning can positively contribute to the success of environmental protection.
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Mapping and modeling the impact of climate change on recreational ecosystem services using machine learning and big data
Abstract The use of recreational ecosystem services is highly dependent on the surrounding environmental and climate conditions. Due to this dependency, future recreational opportunities provided by nature are at risk from climate change. To understand how climate change will impact recreation we need to understand current recreational patterns, but traditional data is limited and low resolution. Fortunately, social media data presents an opportunity to overcome those data limitations and machine learning offers a tool to effectively use that big data. We use data from the social media site Flickr as a proxy for recreational visitation and random forest to model the relationships between social, environmental, and climate factors and recreation for the peak season (summer) in California. We then use the model to project how non-urban recreation will change as the climate changes. Our model shows that current patterns are exacerbated in the future under climate change, with currently popular summer recreation areas becoming more suitable and unpopular summer recreation areas becoming less suitable for recreation. Our model results have land management implications as recreation regions that see high visitation consequently experience impacts to surrounding ecosystems, ecosystem services, and infrastructure. This information can be used to include climate change impacts into land management plans to more effectively provide sustainable nature recreation opportunities for current and future generations. Furthermore, our study demonstrates that crowdsourced data and machine learning offer opportunities to better integrate socio-ecological systems into climate impacts research and more holistically understand climate change impacts to human well-being.
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- Award ID(s):
- 1735040
- PAR ID:
- 10348808
- Date Published:
- Journal Name:
- Environmental Research Letters
- Volume:
- 17
- Issue:
- 5
- ISSN:
- 1748-9326
- Page Range / eLocation ID:
- 054025
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1088/1748-9326/ac65a3
