Scale dependency in drivers of outdoor recreation in England

Abstract

1. Managing landscapes for multiple, sometimes conflicting, objectives requires an understanding of the trade-offs and synergies between ecosystem services (ES).These trade-offs and synergies are often the result of drivers acting at different scales. Therefore, in order to understand trade-offs and synergies it is important that we understand the scale dependency in drivers of ES. Here, we examine scale dependencies in the drivers of outdoor recreation inEngland to better understand trade-offs between different aspects of this ES. We focus on outdoor recreation because it is culturally and economically important; it is the result of a range of social and biophysical attributes which vary at different scales; and proxies that are independent of these drivers exist.3. First, we tested the hypothesis that a social media-based proxy (photographs from Flickr) represents 'destination' recreation (e.g. day trips and overnight visits). We did so by comparing to a survey-based proxy, which is known to represent 'dayto-day' recreation (e.g. dog walking, visiting local parks). Second, we examined the scale dependencies in the social and biophysical drivers of both types of outdoor recreation. Flickr data were best explained by variables capturing supply of recreation;whereas, the survey data were best explained by variables capturing demand for recreation. This confirms our hypothesis that Flickr data measure 'destination' recreation given that the survey data measure 'day-to-day' recreation. In both cases, the importance of demand variables increased with increasing spatial resolution. 5.Understanding what a proxy measures provides us with information about how to use it. We conclude that Flickr data may be useful to plan at broad scales, but that to plan for equitable day-to-day recreation, specially designed survey data may be more appropriate. Estimating the scale dependencies in drivers of outdoor recreation gets us a step closer to a mechanistic understanding of the social-ecological system. K E Y W O R D S ecosystem services, flickr, outdoor recreation, scale dependency, scaling | 407 People and Nature GRAHAM And EIGEnBROd

Next-generation visitation models using social media to estimate recreation on public lands

“…Performance is improved by including relationships between visitation and social media (Flickr, Instagram, and Twitter), even when these relationships are transferred from a different region (WWA, Model 2). These results are consistent with prior research findings that social media counts are correlated with on-site visitor counts from public lands 8 , 9 , 24 , 32 , and extend earlier findings by showing the potential for statistical models to estimate absolute numbers of visitors at unmonitored sites with parameters derived from social media. This is evidence of patterns in how visitors use and share social media.…”

“…Globally, the performance of social media as a visitation proxy varies geographically and by type of attraction 8 , 12 , 20 . This may be due to differences in who uses various platforms 21 , 22 , how users share content 23 , 24 , and changes in the popularity of platforms over time 15 , 25 , 26 . Some studies suggest that these issues may be partly overcome by combining data from multiple social media platforms 4 , 9 , 12 , 17 , 20 , 27 .…”

Using crowdsourced spatial data from Flickr vs. PPGIS for understanding nature's contribution to people in Southern Norway

“…In addition to the accuracy, the resolution of the data also affects the method. For example, Flickr has been shown to capture visitor distribution at coarse resolutions (several kilometres; Graham & Eigenbrod, 2019; Mancini, Coghill, & Lusseau, 2018; van Zanten et al, 2016), while PPGIS performs well at fine resolutions (Munro, Kobryn, Palmer, Bayley, & Moore, 2017).…”

“…While the use of social media data has been compared to visitor data on a regional scale previously (Graham & Eigenbrod, 2019; Tenkanen et al, 2017), spatial data and the values identified using passive and active crowdsourcing tools have not been extensively evaluated using the same location. One exception is Levin et al (2017) who compared the visitor density and values mapped by crowdsourcing tools in multiple protected areas.…”

Next-generation Visitation Models using Social Media to Estimate Recreation on Public Lands

“…Globally, the performance of social media as a visitation proxy varies geographically and by type of attraction 8,12,20 . This may be due to differences in who uses various platforms 21,22 , how users share content 23,24 , and changes in the popularity of platforms over time 15,25,26 . Some studies suggest that these issues may be partly overcome by combining data from multiple social media platforms 4,9,12,17,20,27 .…”