Identifying and Mapping Groups of Protected Area Visitors by Environmental Awareness

Protected areas worldwide receive billions of visitors annually. The positive impact nature on health and wellbeing, in addition to providing opportunities for cultural activities such as recreation aesthetic appreciation, is well documented. Management reduce negative impacts biodiversity conservation aims whilst amenities access important. Understanding environmental awareness their on-site spatial patterns can assist making effective management decisions within often constrained resources. However, there a lack strategies site-specific identification predictive mapping by awareness. Here, we demonstrate method map visitation latent groups based issues. On-site surveys participatory were used collect data bird nesting an upland moor northern England. Latent class analysis (LCA), structural equation model, was discover underlying awareness, with random forest (RF) modelling, machine learning technique, using range predictors (bioclimatic, land cover, elevation, viewshed, proximity paths freshwater) predict across the site each group. Visitors segmented into ‘aware’ ‘ambiguous’ potential mapped. Our results ability uncover users variety predictors. Spatial understanding movement differently environmentally aware efficient targeting education endeavours (i.e., signage, positioning staff, monitoring programmes, etc.), therefore maximising efficacy. Furthermore, anticipate this will be importance managers educators when deploying limited