Uses and Limitations of Social Media to Inform Visitor Use Management in Parks and Protected Areas: A Systematic Review

The risk of contact between visitors and Borrelia burgdorferi-infected ticks is associated with fine-scale landscape features in a southeastern Canadian nature park

BACKGROUND

Infectious diseases are emerging across temperate regions of the world, and, for some, links have been made between landscapes and emergence dynamics. For tick-borne diseases, public parks may be important exposure sites for people living in urbanized areas of North America and Europe. In most cases, we know more about the ecological processes that determine the hazard posed by ticks as disease vectors than we do about how human population exposure varies in urban natural parks.

METHODS

In this study, infrared counters were used to monitor visitor use of a public natural park in southern Quebec, Canada. A risk index representing the probability of encounters between humans and infected vectors was constructed. This was done by combining the intensity of visitor trail use and the density of infected nymphs obtained from field surveillance. Patterns of risk were examined using spatial cluster analysis. Digital forest data and park infrastructure data were then integrated using spatially explicit models to test whether encounter risk levels and its components vary with forest fragmentation indicators and proximity to park infrastructure.

RESULTS

Results suggest that, even at a very fine scales, certain landscape features and infrastructure can be predictors of risk levels. Both visitors and Borrelia burgdorferi-infected ticks concentrated in areas where forest cover was dominant, so there was a positive association between forest cover and the risk index. However, there were no associations between indicators of forest fragmentation and risk levels. Some high-risk clusters contributed disproportionately to the risk distribution in the park relative to their size. There were also two high-risk periods, one in early summer coinciding with peak nymphal activity, and one in early fall when park visitation was highest.

CONCLUSIONS

Here, we demonstrate the importance of integrating indicators of human behaviour visitation with tick distribution data to characterize risk patterns for tick-borne diseases in public natural areas. Indeed, understanding the environmental determinants of human-tick interactions will allow organisations to deploy more effective risk reduction interventions targeted at key locations and times, and improve the management of public health risks associated with tick-borne diseases in public spaces.

What's in a name? Political and economic concepts differ in social media references to harmful algae blooms

Policies and management decisions in the marine environment are driven in part by public sentiment which can grow more intense during hazard events like Harmful Algae Blooms (HABs). The public conversations on social media sites like Twitter (before X) reveal the polarized nature of HABs through nuanced language and sentiment. This article uses mixed methods of machine learned topic modeling and inductive qualitative coding to describe the ways the long-term 2017-2019 Karenia brevis "red tide" bloom were politicized across Florida's South West coast. It finds that there are topical differences in keywords related to place (e.g. beach, Florida, coast), agent (individual or organization), and epistemic values (reliance on scientific and/or media reports). These topical differences demonstrate different levels of politicization and partisanship in qualitative analysis. Conceptually, this research demonstrates the ways different dimensions of a long-duration marine hazard can be polarized. Regarding management, this research provides insights to political and organizational stakeholders and the gaps in the discourse shaping marine hazards which can be used to strategically guide future social media engagement to manage politicization. What if all the careful work that resource and environmental managers do can be undone by simple, seemingly uncontroversial words? In an era of increased environmental and marine distress-coupled with short format communication-the ways environmental managers choose their words is crucial, even between ostensibly inconsequential nouns like "red tide" or "algae bloom." Policies and management decisions in the marine environment are driven in part by public sentiment which can grow more intense during hazard events like Harmful Algae Blooms (HABs). The public conversations on social media sites like Twitter (before X) reveal the polarized nature of HABs through nuanced language and sentiment. This article relies on mining social media posts, and uses mixed methods of machine-learned topic modeling and human-driven inductive qualitative coding to describe the ways the long-term 2017-2019 Karenia brevis "red tide" blooms were politicized across Florida's South West coast. It finds that there are topical differences in keywords related to place (e.g. beach, Florida, coast), agent (individual or organization), and epistemic values (reliance on scientific and/or media reports). These topical differences demonstrate different levels of politicization and partisanship in qualitative analysis. Conceptually, this research demonstrates the ways different dimensions of a long-duration marine hazard can be polarized. Regarding management, this research provides insights to political and organizational stakeholders and the gaps in the discourse shaping marine hazards which can be used to strategically guide future social media engagement to manage politicization.

Mapping ecosystem services in protected areas. A systematic review

Protected areas (PAs) supply ecosystem services (ES) essential for human wellbeing. Mapping is a critical exercise that allows an understanding of the spatial distribution of the different ES in PAs. This work aims to conduct a systematic literature review on mapping ES in PAs. In order to carry out this systematic review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method was applied. The results showed an increase in the number of works between 2012 and 2023, and they were especially conducted in Europe and Asia and less in North America, South America, and Oceania. Most studies were developed in terrestrial areas, and the International Union for Conservation of Nature classified them into types II and IV. Most of the works followed the Millennium Ecosystem Assessment classification and were mainly focused on the supply dimension. Regulating and maintenance and cultural ES were the most mapped dimensions in PAs. The most frequent provisioning ES mapped in PAs were Animals reared for nutritional purposes and Cultivated terrestrial plants grown for nutritional purposes. In regulating and maintenance, Maintaining nursery populations and habitats and Regulation of the chemical composition of the atmosphere and oceans were the most analysed. For cultural ES, Characteristics of living systems that enable activities promoting health, recuperation, or enjoyment through active or immersive interactions and Characteristics of living systems that enable aesthetic experiences were the most mapped ES in PAs. Most works followed a quantitative approach, although the number of qualitative studies is high. Finally, most of the works needed to be validated, which may hamper the credibility of mapping ES in PAs. Overall, this systematic review contributed to a global picture of studies distribution, the areas where they are needed, and the most popular dimensions and sections as the methodologies were applied.

Using cellular device location data to estimate visitation to public lands: Comparing device location data to U.S. National Park Service's visitor use statistics

Understanding human use of public lands is essential for management of natural and cultural resources. However, compiling consistently reliable visitation data across large spatial and temporal scales and across different land managing entities is challenging. Cellular device locations have been demonstrated as a source to map human activity patterns and may offer a viable solution to overcome some of the challenges that traditional on-the-ground visitation counts face on public lands. Yet, large-scale applicability of human mobility data derived from cell phone device locations for estimating visitation counts to public lands remains unclear. This study aims to address this knowledge gap by examining the efficacy and limitations of using commercially available cellular data to estimate visitation to public lands. We used the United States' National Park Service's (NPS) 2018 and 2019 monthly visitor use counts as a ground-truth and developed visitation models using cellular device location-derived monthly visitor counts as a predictor variable. Other covariates, including park unit type, porousness, and park setting (i.e., urban vs. non-urban, iconic vs. local), were included in the model to examine the impact of park attributes on the relationship between NPS and cell phone-derived counts. We applied Pearson's correlation and generalized linear mixed model with adjustment of month and accounting for potential clustering by the individual park units to evaluate the reliability of using cell data to estimate visitation counts. Of the 38 parks in our study, 20 parks had a correlation of greater than 0.8 between monthly NPS and cell data counts and 8 parks had a correlation of less than 0.5. Regression modeling showed that the cell data could explain a great amount of the variability (conditional R-squared = 0.96) of NPS counts. However, these relationships varied across parks, with better associations generally observed for iconic parks. While our study increased our confidence in using cell phone data to estimate visitation, we also became aware of some of the limitations and challenges which we present in the Discussion.

Increasing biodiversity knowledge through social media: A case study from tropical Bangladesh

Citizen science programs are becoming increasingly popular among naturalists but remain heavily biased taxonomically and geographically. However, with the explosive popularity of social media and the near-ubiquitous availability of smartphones, many post wildlife photographs on social media. Here, we illustrate the potential of harvesting these data to enhance our biodiversity understanding using Bangladesh, a tropical biodiverse country, as a case study. We compared biodiversity records extracted from Facebook with those from the Global Biodiversity Information Facility (GBIF), collating geospatial records for 1013 unique species, including 970 species from Facebook and 712 species from GBIF. Although most observation records were biased toward major cities, the Facebook records were more evenly spatially distributed. About 86% of the Threatened species records were from Facebook, whereas the GBIF records were almost entirely Of Least Concern species. To reduce the global biodiversity data shortfall, a key research priority now is the development of mechanisms for extracting and interpreting social media biodiversity data.

Snapshots of Nature-Based Recreation Across Rural Landscapes: Insights from Geotagged Photographs in the Northeastern United States

Nature-based recreation is of increasing economic importance to rural communities transitioning away from traditional natural resource extraction. Rural areas are rich in cultural ecosystem services (CES) that function as essential public goods, providing benefits to livelihoods and fostering conservation of large landscapes. Geotagged photographs from social media platforms capture detailed information on public use of natural areas that can be useful for stakeholders interested in promoting resilient social-ecological systems, but applications of such "big data" are often limited in either spatiotemporal or thematic scope. We integrated multiple aspects of crowdsourced image data to better understand human-environment interactions, focusing on the Northern Forest, a culturally and ecologically significant region of the northeastern United States. Images for the region were mined from Flickr, a crowdsourcing image platform, from 2012 to 2017 and assigned themes via a neural network-cluster analysis pipeline while geographic drivers of nature-based visitor engagement were assessed with random forest models to predict use of CES across the Northern Forest. Daily and seasonal patterns in photography were broadly consistent throughout the region, whereas annual photography trends were more variable. Eleven core themes were identified in images, with 70% of photographs depicting activities related to CES. Visitor engagement with nature was greater near roads and shorelines, and at higher elevations, reflecting tensions between accessibility and aesthetics for recreation. Automated image classification tools can rapidly extract relevant information from crowdsourced photography for exploring human-environment interactions, but researchers should consider multiple spatial and temporal scales in CES assessments of large social-ecological systems.

How the natural environment in downtown neighborhood affects physical activity and sentiment: Using social media data and machine learning

BACKGROUND

Natural environment might encourage physical exercise, hence enhancing human health and wellbeing. Social media offers an extensive repository of spatiotemporal data, containing details on the feelings and behaviors of individuals. However, investigations on physical activity and public sentiment in the natural environment of the downtown neighborhood are lacking in the existing literature.

METHODS

To extract environmental and behavioral information from social media data and other multi-source data, natural language processing, semantic segmentation, instance segmentation, and fully convolutional neural networks are employed. The research examines how neighborhood blue-green spaces and other health-promoting facilities affect physical activity and public sentiment.

RESULTS

The results reveal that blue space visibility, activity facilities, street furniture, and safety all have a favorable influence on physical activity with a social gradient. Amenities, perceived street safety and beauty positively correlated to public sentiment. The findings from social media about the environment and physical activity are consistent with traditional surveys from the same time period with a 0.588 kappa value.

CONCLUSION

According to our findings, social media data might be utilized to learn more about how urban environments influence people's physical activity patterns. Also, the health-promoting effects of blue space require more investigation.

Spatiotemporal behavior pattern differentiation and preference identification of tourists from the perspective of ecotourism destination based on the tourism digital footprint data

Tourist impact management in ecotourism destinations requires an accurate description of tourists' spatiotemporal behavior patterns and recreation preferences to minimize the ecological environmental impact and maximize the recreation experience. This study classified tourist behaviors into five typical behavior patterns based on the digital footprints of tourists visiting Ziwuyu of the Qinling Mountains, including 348 traveling tracks and 750 corresponding geotagged photographs: short-distance, traversing, reentrant, large loop, and small loop. Furthermore, each behavior pattern's recreation preference was identified using photograph analysis. Tourists with large-loop and reentrant behavior patterns have 89.8% and 30% chances of visiting Jianshanding, respectively. Key protected areas are faced with the risk of ecological environmental damage. Based on the behavior pattern differentiation and preference of tourists, this paper provides a decision-making basis for the classified management and guidance of tourists in ecotourism destinations. It has reference value for the management of similar ecotourism destinations.

Digital indicators of interest in natural world heritage sites

Due to their remoteness or boundless nature, activities at Natural Heritage Sites are difficult to monitor. In this study, two digital measures of the interest in Natural Word Heritage Sites are compared: one ex ante based on the number of Wikipedia page views of the site and another ex post derived from actual visitation as measured by the number of Instagram posts. The entire UNESCO database, which includes 248 Natural World Heritage Sites is linked to the 2.8 million Wikipedia page views, the 58 million Instagram posts and the Köppen extreme climate zone categories. Quantile regressions reveal that the main association in common for the two indicators is the risk of the site losing its inscription. Presence in the UNESCO Danger list is associated with reduced interest in a site, particularly in the number of Instagram posts and in the top quartile of Wikipedia views. Years since inscription is also an important explanatory variable, especially for the Instagram posts and the Wikipedia views in the top quartile. The UNESCO selection criterion of outstanding beauty only relates to the Instagram posts. Climate zone is mainly linked to the ex post variable and its upper quartile, where the sites with the most attention are found. Wikipedia views are also negatively associated with sites in Africa, the Arab countries and Latin America. Elevation, size of the area as well as kind of site are all variables not significant. There is a significant correlation between the two outcome variables with a coefficient of 0.5. While the Instagram posts relate clearly to actual visits, the Wikipedia page views is considered a possible leading indicator of future interest in a site.

Using smartphone-GPS data to quantify human activity in green spaces

Cities are growing in density and coverage globally, increasing the value of green spaces for human health and well-being. Understanding the interactions between people and green spaces is also critical for biological conservation and sustainable development. However, quantifying green space use is particularly challenging. We used an activity index of anonymized GPS data from smart devices provided by Mapbox (www.mapbox.com) to characterize human activity in green spaces in the Greater Toronto Area, Canada. The goals of our study were to describe i) a methodological example of how anonymized GPS data could be used for human-nature research and ii) associations between park features and human activity. We describe some of the challenges and solutions with using this activity index, especially in the context of green spaces and biodiversity monitoring. We found the activity index was strongly correlated with visitation records (i.e., park reservations) and that these data are useful to identify high or low-usage areas within green spaces. Parks with a more extensive trail network typically experienced higher visitation rates and a substantial proportion of activity remained on trails. We identified certain land covers that were more frequently associated with human presence, such as rock formations, and find a relationship between human activity and tree composition. Our study demonstrates that anonymized GPS data from smart devices are a powerful tool for spatially quantifying human activity in green spaces. These could help to minimize trade-offs in the management of green spaces for human use and biological conservation will continue to be a significant challenge over the coming decades because of accelerating urbanization coupled with population growth. Importantly, we include a series of recommendations when using activity indexes for managing green spaces that can assist with biomonitoring and supporting sustainable human use.

Exploring public values through Twitter data associated with urban parks pre- and post- COVID-19

Since school and business closures due to the evolving COVID-19 outbreak, urban parks have been a popular destination, offering spaces for daily fitness activities and an escape from the home environment. There is a need for evidence for parks and recreation departments and agencies to base decisions when adapting policies in response to the rapid change in demand and preferences during the pandemic. The application of social media data analytic techniques permits a qualitative and quantitative big-data approach to gain unobtrusive and prompt insights on how parks are valued. This study investigates how public values associated with NYC parks has shifted between pre- COVID (i.e., from March 2019 to February 2020) and post- COVID (i.e., from March 2020 to February 2021) through a social media microblogging platform -Twitter. A topic modeling technique for short text identified common traits of the changes in Twitter topics regarding impressions and values associated with the parks over two years. While the NYC lockdown resulted in much fewer social activities in parks, some parks continued to be valued for physical activity and nature contact during the pandemic. Concerns about people not keeping physical distance arose in parks where frequent human interactions and crowding seemed to cause a higher probability of the coronavirus transmission. This study demonstrates social media data could be used to capture park values and be specific per park. Results could inform park management during disruptions when use is altered and the needs of the public may be changing.

Geolocated social media data counts as a proxy for recreational visits in natural areas: A meta-analysis

Geolocated social media data counts are increasingly used as proxy for number of visits in natural areas, including their spatial and temporal distribution. This paper synthesizes the empirical evidence concerning the correlation of social media data counts and visits through multi-level meta-analytical models. Analysis of 355 correlations from 41 studies reveals a strong correlation for annual number of visits over multiple sites (pooled Pearson's r = 0.73) and for monthly visits in a single site (pooled Pearson's r = 0.84). Using data from multiple social media sources improves the correlation. Mixed results are obtained with regard to the effect of social media penetration rate and designation as national park on the correlation. Future studies should focus on broadening the scope of investigation to middle and low-income countries, developing a systematic approach toward the use of covariates, and comparing the results from social media data to those from other emerging monitoring techniques.

Analysis of Forest Landscape Preferences and Emotional Features of Chinese Forest Recreationists Based on Deep Learning of Geotagged Photos

Forest landscape preference studies have an important role and significance for forest landscape conservation, quality improvement and utilization. However, there are few studies on objective forest landscape preferences from the perspective of plants and using photos. This study relies on Deep Learning technology to select six case sites in China and uses geotagged photos of forest landscapes posted by the forest recreationists on the “2BULU” app as research objects. The preferences of eight forest landscape scenes, including look down landscape, look forward landscape, look up landscape, single-tree-composed landscape, detailed landscape, overall landscape, forest trail landscape and intra-forest landscape, were explored. It also uses Deepsentibank to perform sentiment analysis on forest landscape photos to better understand Chinese forest recreationists’ forest landscape preferences. The research results show that: (1) From the aesthetic spatial angle, people prefer the flat view, while the attention of the elevated view is relatively low. (2) From the perspective of forest scale and level, forest trail landscape has a high preference, implying that trail landscape plays an important role in forest landscape recreation. The landscape within the forest has a certain preference, while the preference of individual, detailed and overall landscape is low. (3) Although forest landscape photographs are extremely high in positive emotions and emotional states, there are also negative emotions, thus, illustrating that people’s preferences can be both positive and negative.

A Literature Review of Big Data-Based Urban Park Research in Visitor Dimension

Urban parks provide multiple benefits to human well-being and human health. Big data provide new and powerful ways to study visitors’ feelings, activities in urban parks, and the effect they themselves have on urban parks. However, the term “big data” has been defined variably, and its applications on urban parks have so far been sporadic in research. Therefore, a comprehensive review of big data-based urban park research is much needed. The review aimed to summarize the big data-based urban park research in visitor dimension by a systematic review approach in combination with bibliometric and thematic analyses. The results showed that the number of publications of related articles has been increasing exponentially in recent years. Users’ days data is used most frequently in the big data-based urban park research, and the major analytical methods are of four types: sentiment analysis, statistical analysis, and spatial analysis. The major research topics of big data-based urban park research in visitor dimension include visitors’ behavior, visitors’ perception and visitors’ effect. Big data benefits urban park research by providing low-cost, timely information, a people-oriented perspective, and fine-grained site information. However, its accuracy is insufficient because of coordinate, keyword classification and different kinds of users. To move forward, future research should integrate multiple big data sources, expand the application, such as public health and human–nature interactions, and pay more attention to the big data use for overcoming pandemic. This review can help to understand the current situation of big data-based urban park research, and provide a reference for the studies of this topic in the future.

Heterogeneity of recreationists in a park and protected area

Limited information and resources have caused many parks and protected areas (PPAs) to functionally manage recreationists as a single homogeneous group, despite potential negative social and ecological consequences. We aimed to evaluate the homogeneity of recreationists at the Valentine National Wildlife Refuge (NWR) by 1) quantifying frequencies of consumptive (i.e., hunting), intermediate-consumptive (i.e., fishing), and non-consumptive recreational-activity groups (e.g., wildlife viewing), and 2) evaluating sociodemographic differences among these groups. We used onsite surveys to determine that Valentine NWR supports heterogeneous groups of recreationists. The intermediate-consumptive group was most frequent (77% of all parties). All three recreational-activity groups varied in party size, distance traveled, household income, population type (urban or rural residence), and vehicle type (two-wheel or four-wheel drive). Tracking and accounting for diverse recreationists will equip managers with the ability to sustain recreational activities while also preserving ecological systems.

Biodiversity and infrastructure interact to drive tourism to and within Costa Rica

Tourism accounts for roughly 10% of global gross domestic product, with nature-based tourism its fastest-growing sector in the past 10 years. Nature-based tourism can theoretically contribute to local and sustainable development by creating attractive livelihoods that support biodiversity conservation, but whether tourists prefer to visit more biodiverse destinations is poorly understood. We examine this question in Costa Rica and find that more biodiverse places tend indeed to attract more tourists, especially where there is infrastructure that makes these places more accessible. Safeguarding terrestrial biodiversity is critical to preserving the substantial economic benefits that countries derive from tourism. Investments in both biodiversity conservation and infrastructure are needed to allow biodiverse countries to rely on tourism for their sustainable development.

Nature-based tourism has potential to sustain biodiversity and economic development, yet the degree to which biodiversity drives tourism patterns, especially relative to infrastructure, is poorly understood. Here, we examine relationships between different types of biodiversity and different types of tourism in Costa Rica to address three questions. First, what is the contribution of species richness in explaining patterns of tourism in protected areas and country-wide in Costa Rica? Second, how similar are the patterns for birdwatching tourism compared to those of overall tourism? Third, where in the country is biodiversity contributing more than other factors to birdwatching tourism and to overall tourism? We integrated environmental data and species occurrence records to build species distribution models for 66 species of amphibians, reptiles, and mammals, and for 699 bird species. We used built infrastructure variables (hotel density and distance to roads), protected area size, distance to protected areas, and distance to water as covariates to evaluate the relative importance of biodiversity in predicting birdwatching tourism (via eBird checklists) and overall tourism (via Flickr photographs) within Costa Rica. We found that while the role of infrastructure is larger than any other variable, it alone is not sufficient to explain birdwatching and tourism patterns. Including biodiversity adds predictive power and alters spatial patterns of predicted tourism. Our results suggest that investments in infrastructure must be paired with successful biodiversity conservation for tourism to generate the economic revenue that countries like Costa Rica derive from it, now and into the future.

Evaluating the benefits and risks of social media for wildlife conservation

Given its extensive volume and reach, social media has the potential to widely spread conservation messaging and be a powerful tool to mobilize social change for conserving biodiversity. We synthesized gray and primary academic literature to investigate the effects of social media on wildlife conservation, revealing several overarching benefits and risks. We found that social media can increase pro-conservation behaviours among the public, increase conservation funding, and incite policy changes. Conversely, social media can contribute to species exploitation and illegal trade, cause unprecedented increases in tourism in protected areas, and perpetuate anti-conservation behaviours via misinformation. In most cases, we found that content sharing on social media did not result in a detectable impact on conservation; in this paper, however, we focus on providing examples where conservation impact was achieved. We relate these positive and negative outcomes of social media to psychological phenomena that may influence conservation efforts and discuss limitations of our findings. We conclude with recommendations of best practices to social media administrators, public social media users, nongovernmental organizations, and governing agencies to minimize conservation risks while maximizing beneficial outcomes. By improving messaging, policing online misconduct, and providing guidance for action, social media can help achieve wildlife conservation goals.

Social media reveal ecoregional variation in how weather influences visitor behavior in U.S. National Park Service units

Daily weather affects total visitation to parks and protected areas, as well as visitors’ experiences. However, it is unknown if and how visitors change their spatial behavior within a park due to daily weather conditions. We investigated the impact of daily maximum temperature and precipitation on summer visitation patterns within 110 U.S. National Park Service units. We connected 489,061 geotagged Flickr photos to daily weather, as well as visitors’ elevation and distance to amenities (i.e., roads, waterbodies, parking areas, and buildings). We compared visitor behavior on cold, average, and hot days, and on days with precipitation compared to days without precipitation, across fourteen ecoregions within the continental U.S. Our results suggest daily weather impacts where visitors go within parks, and the effect of weather differs substantially by ecoregion. In most ecoregions, visitors stayed closer to infrastructure on rainy days. Temperature also affects visitors’ spatial behavior within parks, but there was not a consistent trend across ecoregions. Importantly, parks in some ecoregions contain more microclimates than others, which may allow visitors to adapt to unfavorable conditions. These findings suggest visitors’ spatial behavior in parks may change in the future due to the increasing frequency of hot summer days.