Studies of ecosystem service (ES) trade-offs generally focus on modeling, mapping and graphically analyzing their supply and the interaction among functions at a fixed scale. However, few studies in applied ecology explore the role of human beneficiaries of ES in: perceiving, cognitively identifying, and differentiating among ecosystem functions across multiple scales. The adverse effects these functions, or ecosystem disservices (ED), have on a person’s wellbeing and preferences for specific functions is also little explored. Similarly, little is known if indeed restoration programs aimed at increasing ecosystem characteristics and the subsequent provision of ES do so equitably and effectively.
To explore this, we apply a novel type of choice experiment, best-worst-choice (BWC), which elicits tradeoffs and estimates the value of services, while reducing cognitive biases and survey fatigue. We study these tradeoff interactions in Florida, United States by integrating urban forest field data, on-line survey panels (n1=500 [response rate=27%]; n2=1,052 [response rate=40%]) and econometric methods. Specifically, we explore: 1) homeowner ability to differentiate tradeoffs and levels of ES-EDs across different scales and tenure; and 2) governance and equity issues (e.g. race, gender, education, income) regarding citizen perceptions and preferences for different tree functional traits and restoration program characteristics.
Results/Conclusions
Our finding show that beneficiaries do go through a cognitive tradeoff process when identifying their demand for, and the interactions among, ES-EDs. Spatial and cognitive scales, land tenure, and context (e.g. subtropical climate and hurricane frequency) also affected their preferences for ES-EDs bundles. Increased property value ESs were most preferred followed by tree condition, a structure proxy for minimizing hurricane-related ED, and tree shade, a functional proxy for temperature and energy use regulation. We also found that Latinos, African-Americans, and high-income preferred fruit trees, while White, high-income respondents preferred native trees. Low-income respondents perceived the greatest barriers towards participation in urban reforestation/afforestation programs.
Management and research objectives that value and map ES should account for multiple scales and EDs. Accounting for beneficiaries’ demand for context-specific ES, ED and functional trait versus landscape or species level analyses can provide more necessary and relevant information for policy uptake. Not accounting for socio-ecological dynamics can result in a mismatch in adaptive governance between objectives to equitably increase tree cover, by planting shade trees, versus people’s preference for other tree functional traits and socio-political and economic objectives.