Visitor Counts Research Articles

Drone Insights: Unveiling Beach Usage through AI-Powered People Counting

Ocean beaches are a major recreational attraction in many coastal cities, requiring accurate visitor counts for infrastructure planning and value estimation. We developed a novel method to assess beach usage on the Gold Coast, Australia, using 507 drone surveys across 24 beaches. The surveys covered 30 km of coastline, accounting for different seasons, times of day, and environmental conditions. Two AI models were employed: one for counting people on land and in water (91–95% accuracy), and another for identifying usage types (85–92% accuracy). Using drone data, we estimated annual beach usage at 34 million people in 2022/23, with 55% on land and 45% in water—approximately double the most recent estimate from lifeguard counts, which are spatially limited and prone to human error. When applying similar restrictions as lifeguard surveys, drone data estimated 15 million visits, aligning closely with lifeguard counts (within 9%). Temporal (time of day, day of the week, season) and spatial (beach location) factors were the strongest predictors of beach usage, with additional patterns explained by weather variables. Our method, combining drones with AI, enhances the coverage, accuracy, and granularity of beach monitoring, offering a scalable, cost-effective solution for long-term usage assessment.

Bridging Language Barriers in COVID-19 Research: Descriptive Study of AccesoCovid.com's Reach and User Engagement.

The COVID-19 pandemic underscored the challenge of swiftly disseminating research findings to a global audience. Language barriers further exacerbated disparities in access to timely scientific information, particularly for non-English speaking communities. The majority of COVID-19 research was published in English, limiting accessibility for Spanish-speaking populations. This paper aims to assess the reach and effectiveness of AccesoCovid.com, a platform designed to disseminate up-to-date COVID-19 research in both English and Spanish, addressing the language gap in scientific communication. AccesoCovid.com was developed through a partnership between the University of California, San Francisco (UCSF) and Universidad Nacional Autónoma de México (UNAM). The website's performance and user engagement were evaluated using Google Analytics over a span of 2 years. Key metrics included user language preference, geographical distribution, and site traffic. The website summarized and translated 1032 articles on various COVID-19 topics, such as "Pharmaceutical Interventions and Vaccines." From February 2021 to February 2023, the platform attracted 57,000 users. Of the 43,000 unique new visitors, 84.2% (n=36,219) hailed from Spanish-speaking regions. The majority accessed the site organically through search engines, with 88.4% (n=38,000) of users arriving this way, while 5000 (11.6%) users accessed the site directly. Most users (n=30,894, 72.1%) preferred the Spanish version of the site. The website's most accessed category was "Pharmaceutical Interventions and Vaccines," followed by "Clinical Presentation and Management" and "Mental Health." Regarding language distribution, 72.1% (n=30,894) of users primarily used Spanish; 21.4% (n=9215) used English; and 6.7% (n=2891) spoke other languages, including Portuguese, Chinese, and German. Geographically, the website attracted visitors from 179 countries, with the highest visitor counts from Mexico (n=12,342, 28.7%), Spain (n=6405, 14.9%), the United States (n=4416, 10.3%), and Peru (n=3821, 8.9%). AccesoCovid.com successfully bridged a critical language gap in the dissemination of COVID-19 research. Its success underscores the pressing need for multilingual scientific resources. The platform demonstrated significant user engagement and reach, particularly in Spanish-speaking countries. This highlights the potential for similar platforms to ensure equitable access to scientific knowledge across diverse linguistic communities. Future efforts should focus on expanding to other languages and conducting formal evaluations to enhance user satisfaction and impact.

Human Mobility Patterns during the 2024 Total Solar Eclipse in Canada

This study explores human mobility patterns during the 2024 total solar eclipse in Canada, leveraging de-identified network mobility data from TELUS Communications. We compare travel patterns during the total solar eclipse with a baseline period by averaging the visitor counts from April 15th to 19th, then calculate the change in visitor counts during the total solar eclipse relative to this baseline period (hereafter adjusted visitor counts). Using these adjusted visitor counts, we estimate that 589,290 Canadians traveled to areas within the path of totality to observe the eclipse. The findings highlight significant inter-provincial travel, with major influxes of visitors to Ontario, particularly near Lake Erie. We found significant evidence of a distance decay effect in the adjusted traveller counts to the path of totality. This study demonstrates the utility of de-identified network mobility data in understanding the dynamics of human mobility during once-in-a-lifetime events.

Multigenerational spaces: the usage and activities of different age groups within six community parks in Victoria, Australia.

This study investigated how different spaces within multigenerational local parks are being used by older people and other age groups. Observation of park visitors occurred in six Victorian parks one month after park refurbishment. Parks were classified into six spaces based on equipment/amenities and associated expected activity. Observations were summarized descriptively, and negative binomial regression models were used to examine the association between visitor counts and classified targeted areas. A total of 12501 people visited the parks with 4.1% older visitors. The number of older visitors in each park area was consistently less than other age groups, with counts of older visitors being 50% less in mixed spaces (95% confidence interval [CI] 0.38, 0.65), 40% less in adult exercise equipment areas (95% CI 0.46, 0.77) and 59% less in walking paths (95% CI 0.31, 0.55). The number of older visitors engaging in physical activity were significantly greater in walking paths (incidence rate ratios 1.75; 95% CI 1.16, 2.64) compared with children's play spaces. The number of visitors across different age groups varied significantly between the park targeted areas. Most spaces were mainly used by the intended target age group/user, with no particular area used by all age groups in similar proportions.

Infrared visitor counts: data validation and algorithm development

ABSTRACT Visitor counts, while critical for tourism management, are plagued by methodological complexities that negatively impact reliability, particularly at sites with porous boundaries. The purpose of this study is to advance the understanding of visitor counting techniques by analyzing the accuracy challenges specific to infrared sensors installed in outdoor settings. University researchers tested the reliability of infrared counters installed in four diverse locales. Factors that influenced accuracy included sensor location, extraneous devices, entrance modalities, and technology failure. The results confirm the necessity of onsite validations and data algorithms, as adjustments resulted in significantly improved accuracy.

Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware.

This article introduces a novel middleware that utilizes cost-effective, low-power computing devices like Raspberry Pi to analyze data from wireless sensor networks (WSNs). It is designed for indoor settings like historical buildings and museums, tracking visitors and identifying points of interest. It serves as an evacuation aid by monitoring occupancy and gauging the popularity of specific areas, subjects, or art exhibitions. The middleware employs a basic form of the MapReduce algorithm to gather WSN data and distribute it across available computer nodes. Data collected by RFID sensors on visitor badges is stored on mini-computers placed in exhibition rooms and then transmitted to a remote database after a preset time frame. Utilizing MapReduce for data analysis and a leader election algorithm for fault tolerance, this middleware showcases its viability through metrics, demonstrating applications like swift prototyping and accurate validation of findings. Despite using simpler hardware, its performance matches resource-intensive methods involving audiovisual and AI techniques. This design's innovation lies in its fault-tolerant, distributed setup using budget-friendly, low-power devices rather than resource-heavy hardware or methods. Successfully tested at a historical building in Greece (M. Hatzidakis' residence), it is tailored for indoor spaces. This paper compares its algorithmic application layer with other implementations, highlighting its technical strengths and advantages. Particularly relevant in the wake of the COVID-19 pandemic and general monitoring middleware for indoor locations, this middleware holds promise in tracking visitor counts and overall building occupancy.

Predicting Fan Attendance at Mega Sports Events—A Machine Learning Approach: A Case Study of the FIFA World Cup Qatar 2022

Mega sports events generate significant media coverage and have a considerable economic impact on the host cities. Organizing such events is a complex task that requires extensive planning. The success of these events hinges on the attendees’ satisfaction. Therefore, accurately predicting the number of fans from each country is essential for the organizers to optimize planning and ensure a positive experience. This study aims to introduce a new application for machine learning in order to accurately predict the number of attendees. The model is developed using attendance data from the FIFA World Cup (FWC) Russia 2018 to forecast the FWC Qatar 2022 attendance. Stochastic gradient descent (SGD) was found to be the top-performing algorithm, achieving an R2 metric of 0.633 in an Auto-Sklearn experiment that considered a total of 2523 models. After a thorough analysis of the result, it was found that team qualification has the highest impact on attendance. Other factors such as distance, number of expatriates in the host country, and socio-geopolitical factors have a considerable influence on visitor counts. Although the model produces good results, with ML it is always recommended to have more data inputs. Therefore, using previous tournament data has the potential to increase the accuracy of the results.

Comparing crowding perception and sensor counts at the Ghent festivities

Little is known about crowding perception on events and the link with objective visitor counts and visitor’s behavior. This research aims to fill this gap by collecting WiFi sensor counts and comparing them with perception ratings of visitors and police and first aid data of Europe’s largest outdoor urban festival: the Ghent festivities. During 48 h visitors were asked to rate the human crowding, spatial crowding and pleasure on two event plazas and one passageway within the festivities zone. The results show that spatial crowding ratings are a better indicator for visitor counts than human crowding ratings. On two locations a pleasure drop occurred each night, which cannot be linked to the visitor counts, but it can be linked to people’s ratings of crowding as follows: pleasure dropped when the human crowding ratings exceeded 3 and if the spatial crowding ratings increased above 3 on a five-point rating scale during the pleasure drop. In a last step, two datasets on visitor behavior were included in the analysis, i.e., number of police interventions and first aid visits. The analysis of these datasets shows that behavior, crowding and emotional response are linked at the Ghent festivities (e.g., at Vlasmarkt most police interventions and almost exactly 50% of the first aid happened during the pleasure drop), but we cannot say anything about the causality. This research demonstrates both the need for and potential of sensing what lives within a crowd in addition to the use of visitor counting sensors.

Understanding the impact of COVID-19 on consumer mobility and recovery from a distance perspective: a mobile phone data application

研究目的在我们的研究中, 为了调查COVID-19大流行期间的旅游出行（例如：酒店访问）, 我们根据保护动机理论制定了三个目标：（1）研究在COVID-19大流行前后美国的旅行距离的变化, （2）在大流行期间识别不同地区的不同旅行模式; 以及（3）探讨影响旅游出行的威胁和应对因素。研究方法我们利用了两个主要数据源。首先, 来自SafeGraph的智能手机数据提供了63,610家美国酒店的酒店特定变量（例如位置和访客计数）以及旅行距离数据。其次, 代表与旅行距离相关的各种因素的州级数据来自COVID-19数据中心和美国人口普查局。我们分析了州级和地区级的旅行距离随时间的变化, 并调查了与这些变化相关的临床、政策和人口因素。研究发现我们的研究结果揭示了不同阶段的实际旅行动态和地区内的差异, 以及在公共卫生风险中塑造旅行模式的健康相关政策和其他外部因素的作用。研究创新我们的研究是第一个利用智能手机数据以及与COVID-19和人口统计数据相关的州级数据, 经验性地研究了旅行距离到酒店作为目的地的变化。我们的研究结果表明, 旅游企业和利益相关者可以通过考虑威胁评估和应对机制来主动调整他们的策略, 这两者都受到健康相关政策等外部因素的影响。

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.

Testing and Evaluation of Low-Cost Sensors for Developing Open Smart Campus Systems Based on IoT.

Urbanization has led to the need for the intelligent management of various urban challenges, from traffic to energy. In this context, smart campuses and buildings emerge as microcosms of smart cities, offering both opportunities and challenges in technology and communication integration. This study sets itself apart by prioritizing sustainable, adaptable, and reusable solutions through an open-source framework and open data protocols. We utilized the Internet of Things (IoT) and cost-effective sensors to capture real-time data for three different use cases: real-time monitoring of visitor counts, room and parking occupancy, and the collection of environment and climate data. Our analysis revealed that the implementation of the utilized hardware and software combination significantly improved the implementation of open smart campus systems, providing a usable visitor information system for students. Moreover, our focus on data privacy and technological versatility offers valuable insights into real-world applicability and limitations. This study contributes a novel framework that not only drives technological advancements but is also readily adaptable, improvable, and reusable across diverse settings, thereby showcasing the untapped potential of smart, sustainable systems.

Vulnerability of wilderness areas to day-use visits

SummaryProtected areas worldwide are impacted by human activities within their boundaries. Despite having the highest level of protection in the US, wilderness areas are still vulnerable to ecological impacts. We compiled population, population growth rate, median travel time, wilderness size, wilderness proximity, relative accessibility, trail density and an amenity index to generate a Day-Use Vulnerability Index (DUVI) for 722 wilderness areas in the continuous US (CONUS). Using DUVI, we found that the Mount Timpanogos wilderness area in Utah, the Glacier View wilderness area in Washington, the J.N. Ding Darling wilderness area in Florida, the Philip Burton wilderness area in California and the Birkhead Mountains wilderness area in North Carolina were most likely to have ecological impacts from high day-use. Our findings provide a system for evaluating daily use of wilderness areas that could be paired with visitor counts in the future to improve predictions. Growing human populations and recreation are worldwide issues, suggesting that this framework could also be of interest to stakeholders outside the CONUS.

The Relationship between Measured Visitor Density and Perceived Crowding Revisited: Predicting Perceived Crowding in Outdoor Recreation

Protected areas around the globe face increasing visitation numbers and crowding, a situation exacerbated by the COVID-19 pandemic. Although measured visitor density and perceived crowding are strongly related, analyses of this relationship are lacking. We argue that measured visitor density serves as a reliable and objective indicator of perceived crowding. This study therefore examined the effects of visitation numbers, perceived encounters, and expectations on perceived crowding in the Bavarian Forest National Park (Germany). Automated visitor counts at six park locations were linked to data from an on-site crowding survey conducted in 2021. A predictive model and visitation thresholds were calculated using a logistic regression, which showed that visitation numbers are a well-suited predictor of crowding. The results indicated a strong association between measured visitor density and perceived crowding, moderated by site-specific conditions. The calculated thresholds provide a valuable instrument that can be used to inform the public in advance about peak visiting times.

How weather impacts the citizens' activity patterns in southern China? Enlightenment from large-scale mobile phone signaling data of Guangzhou

In the realm of smart cities, understanding citizens' activity patterns is essential for effective urban planning and development. Weather condition plays a crucial role in shaping citizen's activities. This study investigates the impact of weather conditions on activity patterns by examining variations in visitor numbers at three types of sites in Guangzhou, China: parks, pedestrian streets and shopping malls, representing outdoor, semi-outdoor and indoor urban environment, respectively. Mobile phone signaling data and meteorological data were collected over two months, July and January 2022, representing the typical summer and winter conditions. With hourly visitor counts for the selected locations scaling to the magnitude of millions, the dataset offers significant representativeness. Our findings underscore that, predominantly temperature, exerts a marked influence on the choice of activities among Guangzhou's populace. As temperature rose, even during the hot summer month, the total number of visitors across the three types of locations increased. However, the frequency of park visitors exhibited heightened sensitivity to meteorological variations in comparison to their counterparts in pedestrian streets and shopping malls, with park visitor numbers in winter oscillating over a six-fold range due to climatic shifts. In contrast, the trend of visitor number to pedestrian streets and shopping malls tends to be less affected by weather conditions. Notably, the impact of weather on activity patterns varied among different groups. Males appeared more attuned to external conditions than females, with their presence in parks surging more noticeably with temperature increments—a gender ratio surpassing 1.7 in summer, further accentuating in winter. This starkly contrasts the gender ratios in pedestrian streets and malls, which aligned closely with the city's overall demographic distribution. Age emerged as another differentiating factor. Younger individuals (19–29 years) demonstrated a higher adaptability to the local high-temperature weather conditions, while during the winter season, a significant decrease in the number of visitors from this group was observed as the temperature dropped. Remarkably, within a narrow temperature bracket of 27 °C to 27.5 °C, the hourly count of visitors aged 19–29 on pedestrian streets experienced a six-fold variance, ranging from just over 0.1 million to nearly 0.7 million. This suggests that factors beyond weather play a significant role in influencing the mobility choices of young adults. In contrast, the 30–39 age bracket showcased more robust correlations with weather patterns. The number of older visitors remained relatively stable and did not change significantly with weather changes, especially those aged 60 and above. Drawing on robust evidence from large-scale mobile phone signaling data, this study can inform local policy-making in smart city development.

Use of social media data, online reviews and wikipedia page views to measure visitation patterns of outdoor attractions

This study explores the suitability of activity counts extracted from social media platforms (Twitter, Flickr), review portals (TripAdvisor, Google Maps) and Wikipedia article views to model official visitor counts at selected outdoor attractions in Florida (U.S.) and Carinthia (Austria). It applies correlation analysis, multiple regression, and time series analysis to identify which of these user-generated content (UGC) sources and their combinations best match official monthly visitor count patterns for an analysis period of three years (2019–2021). With travel activities being severely hampered during 2020 due to the COVID-19 pandemic, the analysis also aims to analyze to which extent reduced visitor counts are reflected in the respective UGC sources. Results show that the number of Google Maps reviews combined with Wikipedia pageviews best explain the variability of monthly official visitor counts in Ordinary Least Squares (OLS) regression in both study areas. While the comparison analysis was conducted for monthly counts, data from some UGC sources can reflect shorter term activity fluctuations. Time series analysis detected a seasonality of 12 months for Wikipedia pageviews, Google Maps reviews, and official visitor counts for Austria due a clearly distinct summer season. As opposed to this, for Florida, due to a climate that facilitates all-year round park visitations, periodograms yielded different seasonality frequencies for UGC sources and official visitor counts. A short-term drop in visitor counts due to COVID-19 was evident in Florida both in UGC sources and official visitor counts in spring 2020, whereas for Austria attractions reduced activity is only somewhat reflected in UGC sources but not in actual visitor counts due to attractions still being closed in April. Management implicationsThis research explores multiple UGC sources for modeling official visitor counts at outdoor attractions. A combination of several sources has the advantage that it can help mitigate known limitations of UGC from individual sources, such as sparsity of geodata, data retrieval restrictions, sociodemographic bias, and varying popularity across regions. This research also revealed a better model fit of combined UGC data with official visitor counts than using any of the UGC sources alone. Data-rich UGC sources offer daily or weekly activity counts that provide a more refined temporal resolution than typical official visitor counts which are often limited to monthly data aggregations.

Lakeshore vegetation: More resilient towards human recreation than we think?

AbstractLakes and their shoreline vegetation are rich in biodiversity and provide multiple functions and habitats for fauna and flora. Humans are attracted by the scenic beauty of these ecosystems and the possibilities for recreational activities they offer. However, the use of lakes for recreational activities can lead to disturbance of vegetation, threatening the integrity and functionality of shoreline areas. Recent literature reviews revealed that impacts of the seemingly harmless activities bathing and lingering on the shore on lakeshore vegetation are poorly understood. In this study, we analysed the effects of shoreline use connected with bathing on the structure, composition and diversity of lakeshore vegetation. Vegetation relevés were recorded in 10 bathing and 10 adjacent control sites in the nature park ‘Dahme‐Heideseen’ (Brandenburg, Germany). In addition visitor counts were performed. The species composition and the cover of herbaceous and shrub vegetation differed between bathing and control sites, but all sites had a high percentage of plant species not typical for the community. The vegetation parameters did not correlate with visitor counts. The results indicate that the present visitor intensity in the nature park does not impact the vegetation severely.

Modeling and forecasting percent changes in national park visitation using social media

AbstractNational parks have tremendous cultural, ecological, and economic value to societies. In order to manage and maintain these public spaces, decision‐makers rely on detailed information about park use and park condition. Many parks, however, lack precise visitor counts because of challenges associated with monitoring large and inaccessible areas with porous boundaries. To facilitate better management, we propose a method to estimate percentage changes in park visitation without using any on‐site visitor counts. Specifically, using 20 national parks in the United States, we develop a time series model for forecasting future monthly changes in visitation based on the volume of social media images shared by visitors to parks. Forecasts are generated from historic park‐level and national‐level photo‐user‐days (PUD) of images posted to Flickr, using singular spectrum analysis (SSA). We further propose an approach for augmenting existing on‐site visitation data collected by the US National Park Service. Our model evaluations indicate that the proposed model that only uses social media data achieves competitive performance to the models which partially or fully utilizes on‐site visitor counts.

The influence of the COVID-19 pandemic on the demand for different shades of green.

COVID‐19 has heightened the dependence of urban dwellers on cultural ecosystem services provided by urban green spaces (UGS), specifically in regard to the provision of recreational opportunities, and psychological and physical health benefits arising from their use.As different types and levels of cultural ecosystem services are provided by different types of UGS, people may seek out different UGS to satisfy personal needs over various phases of COVID‐19 mobility restrictions imposed by cities. We report on a study that took advantage of the different phases of COVID‐19 mobility restrictions to assess the demand for and perception of different types of UGS in Singapore.The study utilised four datasets to compare demand for and visitorship patterns of UGS before the pandemic (Pre‐Circuit Breaker), the duration of the strictest mobility restrictions (Circuit Breaker), and after the measures were relaxed (Post‐Circuit Breaker). We used Google Search trends as a proxy for UGS demand, Google mobility data for an overview of population visitorship trends, visitor counts for granular insights on actual visitorship trends, and qualitative data on perception of parks by park visitors after restrictions eased. Parks were categorised as manicured and less manicured UGS for analysis.Search interest for UGS overall fell by more than 50% from during Circuit Breaker but the post‐Circuit Breaker levels exceeded pre‐Circuit Breaker, with a 70.9% increase for less manicured UGS compared to 20.8% for manicured UGS. This corroborated with Google mobility and visitor counts, which showed a steep decrease in park use followed by a rapid increase in the same periods, and with increased visitorship in the less manicured UGS. The perception study also showed that more than 50% of respondents reported visiting parks that they have never visited before, and there was a greater appreciation and use of UGS after the pandemic and preference for less manicured and more naturalistic landscapes.The pandemic has heightened the demand for cultural ecosystem services provided by UGS. Our study showed that this demand is not uniform across different types of UGS, with an increase visitorship and preference for less manicured green spaces. Read the free Plain Language Summary for this article on the Journal blog.

Understanding the impact of the installation of outdoor fitness equipment and a multi-sports court on park visitation and park-based physical activity: A natural experiment.

This natural experiment examined the impact of the installation of outdoor fitness equipment and one multi-sports court in a park located in Melbourne, Australia on park visitation and park-based physical activity compared to a control park. Barriers and facilitators to use of this equipment among adult park visitors were also explored. Park visitation and moderate-vigorous physical activity (MVPA) in the two parks were assessed before (T1, 2016) and after the intervention (T2, 2019) using observations of park visitors from children to older adults across four days (7.30am-5.00pm). The relative change in observed counts in visitors from T1-T2 in the intervention park compared to the control park was used to assess the effect of the park refurbishment. Intercept surveys regarding barriers and facilitators were conducted with adult park visitors. Over the three-year period, total visitor counts increased by 16% and the number of park visitors observed engaging in MVPA increased by 24% in the intervention park. However, there were no significant intervention effects on total visitor counts (IRR=0.89, 95%CI: 0.62, 1.30) or MVPA (IRR=0.80, 95%CI: 0.53, 1.19) relative to the control park. Examples of frequently mentioned facilitators to increase use of the sports court and fitness equipment included having someone to use the facilities with and instructional signage, and common barriers included poor weather and lack of time. The impact of the park upgrade on visitation and MVPA was not significant. A better understanding of the broader appeal of outdoor fitness equipment and a multi-sports court among different segments of the population is needed.

Contrasting long-term population trends of beach-nesting shorebirds under shared environmental pressures

Identifying the drivers of long-term population change is a key goal of ecological studies. It is complicated by extrinsic and intrinsic factors that may covary with time and/or operate on a time lag. For migratory shorebirds that breed on the barrier islands of eastern North America, populations may be limited by the anthropogenic, climatic, biological environments they encounter throughout the annual cycle. Using three-decades (1989–2017) of breeding monitoring data collected by the National Park Service at two national seashores in North Carolina (Hatteras and Lookout), we examined the potential drivers of nesting piping plover (Charadrius melodus) and American oystercatcher (Haematopus palliates) populations. Hatteras had five times more annual visitors than Lookout, and our modelling revealed a strong negative relationship between the population size of breeding plovers and human activity and a positive relationship with protection efforts aimed at reducing disturbance. Breeding and wintering climatic conditions, population productivity, and nesting habitat availability showed only weak effects. Thus, a decade-long decline in plover numbers at both seashores starting in the mid-90s reversed as the parks' visitor counts decreased and stricter protections from potential disturbance were implemented. However, the two sympatric populations of oystercatchers showed the opposite population trends from each other at the neighboring seashores, increasing only on Lookout after a hurricane improved habitat and subsequently the reproductive output. Our study suggests a strong relationship between the anthropogenic environment and the population trend of a threatened species and, simultaneously, the important role of stochastic events in shaping populations of long-lived shorebird species.