Basic Information

• Associated Study Title: Conservation Gap Analysis and the Business Case for Protecting Surf Ecosystems in West Sumatra, Indonesia
• Authors / Data Creators:
  • Ryan Anderson
  • Manuela Diaz
  • Sofia Gutierrez
  • Miranda Scalzo
  • Emma Tao
  • Hunter Lenihan (Faculty Advisor)
• Date of Data Collection: June 2024-March 2025
• Date of README Creation: May 2025
• Code Repository: Available on GitHub – contains all scripts and documentation used in data processing and analysis.
• License: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
• Contact: For questions about the data, please contact Ryan Anderson at rka@bren.ucsb.edu.

Overview of Study

Surf ecosystems are regions where marine and terrestrial environments intersect and produce waves suitable for surfing. These ecosystems support rich biodiversity and coastal communities, offering ecological, economic, and social benefits.

This project established a framework for understanding the costs and benefits associated with conservation of surf ecosystems in western Sumatra, Indonesia. This region was highlighted for its high-quality surf spots and limited infrastructure.

One novel strategy to advance surf conservation is the implementation of Surf Protected Areas (SPAs) using the LMMA (Locally Managed Marine Area) approach. SPAs support local communities and ecosystems by establishing legal and social mechanisms that enhance surf tourism and conservation.

To assess the potential impacts of SPAs, we:

1. Mapped and characterized the state and natural value of surf ecosystems in Indonesia,
2. Quantified monetary costs of protecting surf ecosystems and qualitatively analyzed perceived costs of surf development,
3. Analyzed and quantified the various benefits of protecting surf ecosystems, primarily those associated with coral reefs, and
4. Developed a business case for implementing SPAs in western Sumatra.

The business case synthesized the first three objectives to provide insight into scaling SPAs, identified potential funding mechanisms, and offered policy-aligned recommendations. Our analysis found that targeted surf breaks in Sumatra ranked high in biodiversity but lacked existing conservation efforts. Cost modeling suggested that LMMA support could cost roughly $30 per village resident—a practical benchmark for community-scale interventions.

Surveyed surfers supported the economic potential of surf tourism but raised concerns about its environmental impact. Conservation-led development, through tools like SPAs, can promote sustainable economic growth while protecting critical marine ecosystems.

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Dataset Inventory

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File Descriptions and Variable Metadata

data_salaries_indonesia.csv

Description:

Derived from queries to SalaryExpert.com for job titles relevant to coastal conservation in Indonesia. Job titles were selected using a combination of human judgment and AI assistance.

Variables:

• category: Role classification (e.g., Law and Policy, Youth Engagement)
• job_title: Job title as listed on SalaryExpert.com
• salaryexpert_url: URL of the data source page
• average_salary_local: Average annual salary (in Indonesian Rupiah)

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costs_scenario_full.csv

Description:

Outputs from a cost model estimating expenses for establishing and supporting LMMAs until self-sufficiency. Model parameters are explained in the associated paper.

Variables:

• pop: Population of the village
• time: Duration (in months/years) assumed to reach self-sufficiency
• salary_per_staff: Monthly staff salary (USD)
• pop_to_staff_ratio: Population-to-staff ratio
• cost_per_workshop: Cost per community workshop (USD)
• per_capita_meeting_frequency: Weekly meetings per individual
• total_cost: Final model output cost (USD)

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SCI_data_final_western_sumatra.csv

Description:
This dataset compiles spatial and ecological metrics used to calculate the Surf Conservation Index (SCI) for surf breaks in western Sumatra. Each row corresponds to a surf break and includes geospatial, ecological, infrastructure, and socio-demographic features calculated within a 5 km buffer radius. These features were normalized and/or reversed to support multi-criteria prioritization and to construct a composite SCI score that reflects relative conservation value.

Variables:

• OBJECTID: Unique, non-null integer identifier for each row (used in GIS operations)
• Shape: Geometry of the feature (shapefile polygon or point)
• Surfing_Sp: Name of the surf spot
• PrioritizationOrder: Rank assigned based on SCI value, with 1 being highest priority

Environmental and Infrastructure Metrics (within a 5 km buffer):

• Ramsar: Count of RAMSAR-designated wetlands
• Airport: Count of airports
• Hotel: Count of hotels
• Ports: Count of seaports
• Roads (km): Total length of roads in kilometers
• UNESCO: Count of UNESCO World Heritage sites
• Coastal Vulnerability: Qualitative assessment of shoreline susceptibility to sea level rise
• Density: Population density (people per km²)
• PopChangePerYear: Average annual change in population from 2019–2021

Conservation and Ecosystem Metrics:

• OECM Normalized: Normalized extent of Other Effective area-based Conservation Measures (OECMs)
• KBA Area: Normalized area of Key Biodiversity Areas (KBAs)
• Normalized Mangrove: Normalized mangrove coverage area
• Carbon: Normalized amount of irreversible carbon storage
• Coral: Normalized area of coral reefs
• Seagrass: Normalized area of seagrass beds
• Treecover: Normalized extent of tree canopy coverage

Reversed Variables (to support prioritization logic where lower values indicate lower conservation value):

• OECM Reversed, KBA Reversed, Mangrove Reversed, Carbon Reversed, Coral Reverse, Seagrass Reverse, Treecover Reverse: Inverse versions of the normalized ecological values
• Protected Area Reversed: Inverse of the normalized area under formal protection
• Surf Reverse: Inverse of the normalized surf quality value (to capture pressure or degradation risk)
• Reverse SCI: Inverse of the raw SCI score (used for comparison or tradeoff scenarios)

SCI Scores:

• SCI Values: Raw Surf Conservation Index score, calculated as an average of the normalized input metrics
• Normalized SCI: Final SCI score scaled relative to all surf breaks for comparison

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surf_survey_wtp.csv

Description:

Cleaned output from a survey assessing surfers’ willingness to pay (WTP) for conservation-related spending. This file is easier to analyze than the full raw dataset.

Variables (all in USD):

• wtp_expenditure: Total trip spending
• wtp_crowd: WTP for crowd control measures
• wtp_cons: WTP for conservation contributions

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survey_responses_raw.xlsx

Description:

Raw survey responses collected in both English and Spanish. Two tabs:

• Form Responses Eng_Survey
• Form Responses Spanish_Survey

Each column corresponds to a specific survey question. No further documentation is provided in this README, as questions are self-descriptive within the file.

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Ethical Considerations and Human Subjects Research

This study involved the collection of anonymous human subject data via surveys and was reviewed by the UCSB Human Subjects Committee. It was determined to be exempt from federal human subjects research regulations under 45 CFR 46.104(d), Category 2.

• Protocol Number: 11-25-0055
• Approval Date: February 3, 2025
• Lead Investigator: Hunter Lenihan
• Determination: Exempt – data collected anonymously; no identifying information is present. No explicit consent was federally required for publication of de-identified data.

Investigators adhered to the ethical principles of Justice, Beneficence, and Respect for Persons as outlined in the Belmont Report.

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Citation

Suggested citation:

Anderson, R., Diaz, M., Gutierrez, S., Scalzo, M., Tao, E., & Lenihan, H. (2025). Data for: Conservation Gap Analysis and the Business Case for Protecting Surf Ecosystems in Western Sumatra, Indonesia. Dryad Digital Repository. https://doi.org/DOI-to-be-added-upon-publication