Crowdsourced data can provide spatially explicit data on the contribution of nature to people. Spatial information is essential for effectively managing the diverse relationships that people have with nature, but the potential and limits of using crowdsourcing data to generate maps for conservation purposes need further research. Passive crowdsourcing tools include social media platforms where photos and user‐generated tags are shared among users, whereas active crowdsourcing, such as public participatory geographic information system (PPGIS), provides an online platform for mapping place attributes such as values, experiences and preferences. In this study, we assess the spatial information gained through using Flickr (a photo sharing platform) and PPGIS (an online mapping platform) platforms for conservation planning to understand differences and similarities on the spatial distribution of values captured by the two platforms, and to identify what environmental and infrastructure variables correlate best with the distribution of values. We test these tools in Southern Norway including protected areas and the surrounding zones. We analysed non‐spatial (using chi‐square and Spearman rank correlation) and spatial (using clustering, Maxent and distribution overlap) data to identify differences between the two datasets and the values represented therein. We found large differences in spatial distribution using these two datasets, with Flickr data concentrated outside the protected areas and near roads, whereas PPGIS provided more fine‐scale data on diverse values in locations inaccessible by roads within the protected areas. Flickr can be used for generating regional scale data of scenic landscapes or routes, but PPGIS performs better for management of nature qualities appreciated by different user groups within protected areas. We discuss the pros and cons of using each data source and when each dataset is more suitable to be used in protected area management. A freePlain Language Summarycan be found within the Supporting Information of this article.

Increased visitation to protected areas could have adverse impacts on the conservation values in the protected areas, and therefore effective visitor monitoring methods are needed to meet the complex management challenges that arise. Collecting data on human impacts is highly time consuming, thus requiring more effective tools that allow for high‐quality and long‐term measurements. In this study, we show how unmanned aerial vehicles (i.e. UAV or drones) could be used to monitor tourism impacts in protected areas. Tourism has boomed in national parks in Norway in recent years, such as in Jotunheimen National Park for which this study applies. We test the use of drones on a site where new tourist facilities will be established to set a baseline to identify future changes. We demonstrate how drones could help protected area management by monitoring visitor use patterns and commonly associated impacts such as trail condition (width and depth), vegetation structure and disturbances, informal trail proliferation, trampling, and trash and other impacts along the trails. We assessed accuracy and reliability compared with intensive field measurements of impacts and found low‐cost drones to be effective in mapping the study area with a resolution of 0.5 cm/pixel: drone derived trail measurements were comparable to traditional measurements with a negligible divergence on trail width measurements and a consistent 1.05 cm divergence on trail depth measurements that can be corrected with a few validation points. In addition, we created a high‐resolution vegetation classification map that could be used as a baseline for monitoring impacts. We conclude that drones can effectively contribute to visitor monitoring by reducing time spent in the field and by providing high‐resolution time series that could be used as baseline to measure tourism impacts on conservation values in protected areas.

Digital technologies, including participatory Internet mapping, social media and smartphones, provide new avenues for research in outdoor recreation and tourism. The potential to reach a greater audience and collect visitation data on a broader scale, with less costs than traditional paper surveys, are key advantages that have increased the use of these novel technologies. Using of mobile apps for data collection is still at the experimental stage. We evaluate previous attempts to use apps for monitoring recreation and tourism in protected areas, as an alternative to other in situ or online methods. We present a pilot study implemented in Jotunheimen National Park (Norway), where we developed a mobile app for visitor monitoring and real-time mapping of values and experiences. We present the lessons learned, give suggestions on how and for what apps can be used, and discuss the advantages and limitations of using smartphones for visitor monitoring in protected areas.

Protected area (PA) coverage is used as an indicator of biodiversity protection worldwide. The effectiveness of using PAs as indicators has been questioned due to the diversity of categories encompassed by such designations, especially in PAs established for purposes other than biodiversity protection. Although international standards have been developed by the International Union for Conservation of Nature (IUCN), the policies on the ground have been developed independently of the IUCN categories, thus making the IUCN categories dubious measures of biodiversity conservation. Management plans are crucial for the effective management of parks and for guidance on how biodiversity maintenance should be prioritized relative to other goals. We therefore analyzed the aims and regulations of the management plans of alpine PAs in Spain as a first step in evaluating conservation performance. We used content analysis and correspondence analysis of instrumental variables (CAiv) to assess how aims and regulations vary in relation to three explanatory factors: IUCN categories, vegetation zones and autonomous communities. We found that the aims of many parks were vague, without clear indications of how to prioritize biodiversity goals. Furthermore, only 50% of the parks studied had any management plan, which strengthens our argument concerning the lack of clear guidance in PA management. Although certain aims were correlated with the IUCN categories, the regulations showed no clear relationship to international policies, which indicates that these aims do not necessarily influence management practices. Devolution to autonomous communities could be one explanation for the large variation in management practices among parks. Further studies are needed to evaluate the impact of such management policies on biodiversity.

Decision makers and stakeholders need high-quality data to manage ecosystem services (ES) efficiently. Landscape-level data on ES that are of sufficient quality to identify spatial tradeoffs, co-occurrence and hotspots of ES are costly to collect, and it is therefore important to increase the efficiency of sampling of primary data. We demonstrate how ES could be assessed more efficiently through image-based point intercept method and determine the tradeoff between the number of sample points (pins) used per image and the robustness of the measurements. We performed a permutation study to assess the reliability implications of reducing the number of pins per image. We present a flexible approach to optimize landscape-level assessments of ES that maximizes the information obtained from 1 m² digital images. Our results show that 30 pins are sufficient to measure ecosystem service indicators with a crown cover higher than 5% for landscape scale assessments. Reducing the number of pins from 100 to 30 reduces the processing time up to a 50% allowing to increase the number of sampled plots, resulting in more management-relevant ecosystem service maps. The three criteria presented here provide a flexible approach for optimal design of landscape-level assessments of ES.