Plant species and functional trait diversity have each been shown to improve green roof services. Species and trait differences that contribute to ecosystem services are the product of past evolutionary change and phylogenetic diversity (PD), which quantifies the relatedness among species within a community. In this study, we present an experimental framework to assess the contribution of plant community PD for green roof ecosystem service delivery, and data from one season that support our hypotheses that PD would be positively correlated with two services: building cooling and rainwater management. Using 28 plant species in 12 families, we created six community combinations with different levels of PD. Each of these communities was replicated at eight green roofs along an elevation gradient, as well as a ground level control. We found that the minimum and mean roof temperature decreased with increasing PD in the plant community. Increasing PD also led to an increase in the volume of rainwater captured, but not the proportion of water lost via evapotranspiration 48 hr following the rain event. Our findings suggest that considering these evolutionary relationships could improve functioning of green infrastructure and we recommend that understanding how to make PD (and other measures of diversity) serviceable for plant selection by practitioners will improve the effectiveness of design and ecosystem service delivery. Lastly, since no two green roof sites are the same and can vary tremendously in microclimate conditions, our study illustrates the importance of including multiple independent sites in studies of green roof performance.
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.
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