Mapping and assessing coastal recreation cultural ecosystem services supply, flow, and demand in Lithuania

Coastal recreation as a cultural ecosystem service (CES) is key to human wellbeing. However, anthropogenic impacts at the coast affect CES supply. Mapping and assessing CES can help achieve better coastal planning and management of the coast. Quantitative approaches for assessing and mapping CES are...

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Bibliographic Details
Main Author: Inácio, Miguel (author)
Other Authors: Gomes, Eduardo (author), Bogdzevič, Katažyna (author), Kalinauskas, Marius (author), Zhao, Wenwu (author), Pereira, Paulo (author)
Format: article
Language:eng
Published: 2022
Subjects:
Online Access:http://hdl.handle.net/10451/54689
Country:Portugal
Oai:oai:repositorio.ul.pt:10451/54689
Description
Summary:Coastal recreation as a cultural ecosystem service (CES) is key to human wellbeing. However, anthropogenic impacts at the coast affect CES supply. Mapping and assessing CES can help achieve better coastal planning and management of the coast. Quantitative approaches for assessing and mapping CES are lacking, especially in coastal areas. We develop three quantitative models to assess and map coastal recreation CES supply, flow, and demand. We applied the developed models in the coastal region of Lithuania. The coastal recreation CES supply model comprises natural (e.g., naturalness) and cultural (e.g., points of interest) components. The input variables were (1) analysed for multicollinearity, (2) normalised and (3) overlayed using ArcGIS 10.8. An online survey was undertaken to assess and map CES flow and demand based on locations chosen by respondents where they perform recreation at the coast and the number of activities performed when visiting the coast. The coastal recreation CES supply model results showed that natural recreation is close to the coastline, forest areas, waterlines, and protected areas, while cultural recreation is highest in coastal urban areas. The supply model was validated (r2 = 0.11) based on the respondents' chosen coastal locations for recreation. The low validation allowed us to identify the mismatch between model results and respondents' preferences occurring in Klaipėda urban area. When removing respondents’ points in Klaipeda urban area, the model validation increased (r2 = 0.36). CES flow results highlighted Palanga, Šventoji, and Nida as the prime coastal recreation locations. The results of CES demand show that respondents living in municipalities near the coastline (Baltic Sea and Curonian Lagoon) perform fewer activities when visiting the coast. For other municipalities, a demand pattern was not observed. Our results yielded important spatial information that can be useful for planners and decision-makers in the context of coastal management.