Conserved lands provide valuable ecosystem services to billions of people worldwide. Nature-based recreation alone contributes to human physical, mental and cultural wellbeing [1] and generates more than US $600 billion annually for the global economy [2]. This value far exceeds contemporary conservation expenditures [3], yet mounting pressures from land use and climate changes continue to threaten conserved lands, their biodiversity [4] and contribution to human wellbeing [5]. In recognition, conservation scientists increasingly quantify ecosystem services provided by conserved lands and use this value to support management and spending decisions [6]. Compiling the evidence needed for this task requires understanding both the spatially-explicit landscape attributes underpinning ecosystem services, and how changes in land use and management affect their contribution to human wellbeing over time [7,8]. Despite this, current empirical evidence for the ecosystem service of nature-based recreation is scarce—only 23% of studies are spatially explicit and 17% are multi-temporal [9].
A wide range of landscape attributes underpin nature-based recreation. For example, past studies show visitor use depends on natural features of conserved lands and their surrounding environment (e.g. biodiversity, forest cover, water quality) and built capital providing people access to these recreational sites (e.g. roads, camp facilities) [10–13]. Visitation rates are also affected by the spatial distribution of these landscape attributes [14, 15], and their value depends on the characteristics of human beneficiaries, their demand for recreation, and preferences for different recreational activities [9]. As a result, the most important landscape attributes for enhancing nature-based recreation often differ between sites and studies. More evidence is needed to obtain a general understanding of how landscape attributes and human beneficiaries affect nature-based recreation—to aid conservation decisions in information-limited contexts.
Maximizing nature-based recreation also requires understanding how visitation rates respond to changes in landscape attributes over time [16]. However, these dynamics are often inferred from static relationships (i.e. studying variability in space) rather than quantified from time series data. For example, Keeler et al. [17] found that water clarity explains the spatial distribution of visits to lakes across Iowa and Minnesota and extrapolated this relationship to predict future visitation under scenarios of improved water clarity. Such extrapolation assumes human preferences for landscape attributes remain constant over time, which is unlikely true for many cultural ecosystem services [18]. Visits to conserved lands may be initially motivated by opportunities to view species, while subsequent visits may be motivated by past site experiences. Such space-for-time substitutions also ignore interactions among sites that produce patterns that emerge over time. For example, reducing ecosystem services in one place indirectly can affect its supply or use elsewhere; relative (rather than absolute) water quality may explain visits to lakes, thus changes in water quality in one lake may redistribute (rather than increase) recreational visits across the landscape. Failing to understand these temporal dynamics may have perverse outcomes for conservation investments and human wellbeing.
Quantifying spatial and temporal dynamics of socio-ecological systems—and specifically the ecosystem services provided by conserved lands and their contribution to human wellbeing—has been limited by data availability. This is particularly true for studies seeking to quantify impacts on nature-based recreation and other forms of cultural services, which require time-consuming and expensive survey data [18]. Over the past 5 years, social media datasets, such as geotagged photographs uploaded to photo sharing websites (e.g. Flickr), have been successfully used to predict visits to recreation sites and to indicate human preferences and decision-making processes in locations with sparse empirical data [17, 19]. Other forms of social media data have also been useful indicators in similar ways [20, 21]. To date, social media data have not been used to quantify changes in recreation over large spatial and temporal scales.
In this study, we investigate nature-based recreation within conserved lands in the state of Vermont, USA. We define conserved lands as areas legally protected for the purpose of environmental conservation, and address four specific questions:
- Can photographs uploaded to Flickr be used to indicate visits to conserved lands?
- Which landscape attributes explain the spatial distribution of visits to conserved lands?
- What is the value of conserved lands for the state tourism industry?
- Do changes in landscape attributes explain changes in the spatial distribution of visits over time?
Access the study here.
