Using smartphone-GPS data to quantify human activity in green spaces.

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.

No evidence for environmental filtering of cavity-nesting solitary bees and wasps by urbanization using trap nests.

Spatial patterns in biodiversity are used to establish conservation priorities and ecosystem management plans. The environmental filtering of communities along urbanization gradients has been used to explain biodiversity patterns but demonstrating filtering requires precise statistical tests to link suboptimal environments at one end of a gradient to lower population sizes via ecological traits. Here, we employ a three-part framework on observational community data to test: (I) for trait clustering (i.e., phenotypic similarities among co-occurring species) by comparing trait diversity to null expectations, (II) if trait clustering is correlated with an urbanization graient, and (III) if species' traits relate to environmental conditions. If all criteria are met, then there is evidence that urbanization is filtering communities based on their traits. We use a community of 46 solitary cavity-nesting bee and wasp species sampled across Toronto, a large metropolitan city, over 3 years to test these hypotheses. None of the criteria were met, so we did not have evidence for environmental filtering. We do show that certain ecological traits influence which species perform well in urban environments. For example, cellophane bees (Hylaeus: Colletidae) secrete their own nesting material and were overrepresented in urban areas, while native leafcutting bees (Megachile: Megachilidae) were most common in greener areas. For wasps, prey preference was important, with aphid-collecting (Psenulus and Passaloecus: Crabronidae) and generalist spider-collecting (Trypoxylon: Crabronidae) wasps overrepresented in urban areas and caterpillar- and beetle-collecting wasps (Euodynerus and Symmorphus: Vespidae, respectively) overrepresented in greener areas. We emphasize that changes in the prevalence of different traits across urban gradients without corresponding changes in trait diversity with urbanization do not constitute environmental filtering. By applying this rigorous framework, future studies can test whether urbanization filters other nesting guilds (i.e., ground-nesting bees and wasps) or larger communities consisting of entire taxonomic groups.

Phylogenetic diversity and plant trait composition predict multiple ecosystem functions in green roofs.

Plant selection and diversity can influence the provision of key ecosystem services in extensive green roofs. While species richness does predict ecosystem services, functional and phylogenetic community structure may provide a stronger mechanistic link to such services than species richness alone. In this study, we assessed the relationship between community-weighted trait values from four key leaf and canopy functional traits (plant height, leaf area, specific leaf area, dry leaf matter content), functional diversity, and phylogenetic diversity to ten different green roof functions, including ecosystem multifunctionality, in experimental polycultures. Functional traits of dominant plant species were a major driver for indicators of multiple green roof functions, such as substrate nitrate-N, substrate phosphorus, aboveground biomass and ecosystem multifunctionality. In contrast, functional diversity alone increased substrate organic matter. Moreover, both functional/phylogenetic diversity and identity predicted canopy density, substrate cooling. This study highlights the first line of evidence that distinct aspects of phylogenetic and functional diversity play a major role in predicting multiple green roof services. Therefore, we provide further evidence that to maximize green roof functioning, a very careful selection of plant traits and polycultures are needed.

Manipulating plant phylogenetic diversity for green roof ecosystem service delivery.

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.