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Humanity benefits immensely from nature, including through cultural ecosystem services. Geotagged crowdsourced data provide an opportunity to characterize these services at large scales. Flickr data, for example, have been widely used as an indicator of recreational value, while Wikipedia data are increasingly being used as a measure of public interest, potentially capturing often overlooked and less-tangible aspects of socio-cultural values (such as educational, inspirational, and spiritual values). So far, few studies have explored how various geotagged crowdsourced data complement each other, or how correlated these may be, particularly at national scales. To address this knowledge gap, we compare Flickr and Wikipedia datasets in their ability to help characterize the sociocultural value of designated areas in England and assess how this value relates to species richness. Our results show that there was at least one Flickr photo in 35% of all designated areas in England, and at least one Wikipedia page in 60% of them. The Wikipedia and Flickr data were shown not to be independent of each other and were significantly correlated. Species richness was positively and significantly associated with the presence of at least one geotagged Wikipedia page; more biodiverse designated areas, however, were not any more likely to have at least one Flickr photo within them. Our results highlight the potential for new, emerging datasets to capture and communicate the socio-cultural value of nature, building on the strengths of more established crowdsourced data. [ABSTRACT FROM AUTHOR]

An enduring challenge in ecology is to understand what drives spatial variation in the size and structure of communities. The ability to count the number of species present at a location is hindered by the fact that not all species are equally detectable, and invariably some go completely undetected. This makes comparing species richness across distinct spatial units (or regions) problematic as sources of error are usually unaccounted for in simple enumerations of species., Multi-species occupancy models explicitly incorporate a model for this observation uncertainty and provide a framework for estimating community size when detection is imperfect. Currently, however, the model is restricted to estimating the number of species at only a single region of interest. In this paper we extend the multi-species occupancy model to accommodate data collected across multiple regions of interest (e.g., reserves or biomes)., We report improved model performance of the joint multiregion approach when compared to the more traditional two-stage approach of modelling spatial variation in species richness using simulations. Then, applying the model to data collected from eight avian communities in northern Italy, we demonstrate how species richness can be modeled as a spatially varying function of habitat complexity., Extending the multi-species occupancy model to accommodate data collected across multiple regions of interest (e.g., reserves or biomes) allows for joint estimation of region-specific community size and permits species richness to be modeled as a function of region-specific covariates. Our approach provides a mechanism for testing hypotheses about why and how species richness varies across space. [ABSTRACT FROM AUTHOR]