Search for: All records

Abstract Urban trees are increasingly used by cities for cooling and climate adaptation. However, efforts to increase tree cover across cities have neglected to account for the trees' health and function, which are known to control their associated environmental benefits but have been difficult to assess at scales relevant for management. Here, we use remotely sensed, high resolution canopy temperature as a proxy for tree health and function and evaluate its relation to the built environment across Minneapolis‐St. Paul (MSP) using machine learning analyses. We develop a new index that incorporates information on urban trees' health and function, in addition to their presence. This index, when applied across MSP, suggests that canopy benefits may not be distributed equally even in neighborhoods with similar canopy cover. Furthermore, accounting for tree health and function can yield more effective and equitable benefits by guiding the location and magnitude of intervention for urban tree management. 

Abstract Understanding the determinants of urban forest diversity and structure is important for preserving biodiversity and sustaining ecosystem services in cities. However, comprehensive field assessments are resource‐intensive, and landscape‐level approaches may overlook heterogeneity within urban regions. To address this challenge, we combined remote sensing with field inventories to comprehensively map and analyze urban forest attributes in forest patches across the Minneapolis‐St. Paul Metropolitan Area (MSPMA) in a multistep process. First, we developed predictive machine learning models of forest attributes by integrating data from forest inventories (from 40 12.5‐m‐radius plots) with Global Ecosystem Dynamics Investigation (GEDI) observations and Sentinel‐2‐derived land surface phenology (LSP). These models enabled accurate predictions of forest attributes, specifically nine metrics of plant diversity (tree species richness, tree abundance, and understory plant abundance), structure (average canopy height, dbh, and canopy density), and structural complexity (variability in canopy height, dbh, and canopy density) with relative errors ranging between 11% and 21%. Second, we applied these machine learning models to predict diversity metrics for 804 additional plots from GEDI and Sentinel‐2. Finally, we applied Bayesian multilevel models to the predicted diversity metrics to assess the influence of multiple factors—patch dimensions, landscape attributes, plot position, and jurisdictional agency—on these forest attributes across the 804 predicted plots. The models showed all predictors have some degree of effect on forest attributes, presenting varying explanatory power withR²values ranging from 0.071 to 0.405. Overall, plot characteristics (e.g., distance to nearest trail, proximity to forest edge) and jurisdictional agency explained a large portion of the variability across patches, whereas patch and landscape characteristics did not. The relative effect of plot versus management sets of predictors on the marginal ΔR²was heterogeneous across metrics and ecological subsections (an ecological classification designation). The multiplicity of determinants influencing urban forests emphasizes the intricate nature of urban ecosystems and highlights nuanced, heterogeneous relationships between urban ecological and anthropogenic factors that determine forest properties. Effectively enhancing biodiversity in urban forests requires assessments, management, and conservation strategies tailored for context‐specific characteristics. 

Abstract Urban tree canopy cover is often unequally distributed across cities such that more socially vulnerable neighborhoods often have lower tree canopy cover than less socially vulnerable neighborhoods. However, how the diversity and composition of the urban canopy affect the nature of social‐ecological benefits (and burdens), including the urban forest's vulnerability to climate change, remains underexamined. Here, we synthesize tree inventories developed by multiple organizations and present a species‐specific, geolocated database of more than 600,000 urban trees across the 7‐county Minneapolis‐St. Paul (MSP) metropolitan area in the Upper Midwest of the United States. We find that tree diversity across the MSP is variable yet dominated by a few species (e.g.,Fraxinus pennsylvanica,Acer platanoides, andGleditsia triacanthos), contributing to the vulnerability of the MSP urban forest to future climate change and disturbances. In contrast to tree canopy cover, tree diversity was not well predicted by socioeconomic or demographic factors. However, our analysis identified areas where both climate and social vulnerability are high. Our results add to a growing body of literature emphasizing the importance of considering how complex and interacting social and ecological factors drive urban forest diversity and composition when pursuing management objectives. 

Abstract Terrestrial water fluxes are substantially mediated by vegetation, while the distribution, growth, health, and mortality of plants are strongly influenced by the availability of water. These interactions, playing out across multiple spatial and temporal scales, link the disciplines of plant ecophysiology and ecohydrology. Despite this connection, the disciplines have provided complementary, but largely independent, perspectives on the soil‐plant‐atmosphere continuum since their crystallization as modern scientific disciplines in the late 20th century. This review traces the development of the two disciplines, from their respective origins in engineering and ecology, their largely independent growth and maturation, and the eventual development of common conceptual and quantitative frameworks. This common ground has allowed explicit coupling of the disciplines to better understand plant function. Case studies both illuminate the limitations of the disciplines working in isolation, and reveal the exciting possibilities created by consilience between the disciplines. The histories of the two disciplines suggest opportunities for new advances will arise from sharing methodologies, working across multiple levels of complexity, and leveraging new observational technologies. Practically, these exchanges can be supported by creating shared scientific spaces. This review argues that consilience and collaboration are essential for robust and evidence‐based predictions and policy responses under global change. 

Abstract Scholars have spent decades arguing that policy entrepreneurs, change agents who work individually and in groups to influence the policy process, can be crucial in introducing policy innovation and spurring policy change. How to identify policy entrepreneurs empirically has received less attention. This oversight is consequential because scholars trying to understand when policy entrepreneurs emerge, and why, and what makes them more or less successful, need to be able to identify these change agents reliably and accurately. This paper explores the ways policy entrepreneurs are currently identified and highlights issues with current approaches. We introduce a new technique for eliciting and distinguishing policy entrepreneurs, coupling automated and manual analysis of local news media and a survey of policy entrepreneur candidates. We apply this technique to the empirical case of unconventional oil and gas drilling in Pennsylvania and derive some tentative results concerning factors which increase entrepreneurial efficacy. 

Abstract Green infrastructure (GI) practices improve stormwater quality and reduce urban flooding, but as urban hydrology is highly controlled by its associated gray infrastructure (e.g., stormwater pipe network), GI's watershed‐scale performance depends on its siting within its associated watershed. Although many stormwater practitioners have begun considering GI's spatial configuration within a larger watershed, few approaches allow for flexible scenario exploration, which can untangle GI's interaction with gray infrastructure network and assess its effects on watershed hydrology. To address the gap in integrated gray‐green infrastructure planning, we used an exploratory model to examine gray‐green infrastructure performance using synthetic stormwater networks with varying degrees of flow path meandering, informed by analysis on stormwater networks from the Minneapolis‐St. Paul Metropolitan Area, MN, USA. Superimposed with different coverage and placements of GI (e.g., bioretention cells), these gray‐green stormwater networks are then subjected to different rainfall intensities within Environmental Protection Agency's Storm Water Management Model to simulate their hydrological benefits (e.g., peak flow reduction, flood reduction). Although only limited choices of green and gray infrastructure were explored, the results show that the gray infrastructure's spatial configuration can introduce tradeoffs between increased peak flow and increased flooding, and further interacts with GI coverage and placement to reduce peak flow and flooding at low rainfall intensity. However, as rainfall intensifies, GI ceases to reduce peak flow. For integrated gray‐green infrastructure planning, our results suggest that physical constraints of the stormwater networks and the range of rainfall intensities must be considered when implementing GI. 

Abstract Infiltration stormwater control measures (SCMs) have the potential to contribute towards mitigating the effects of urbanization on downstream receiving waters. Infiltration SCMs are most often successful when the in‐situ saturated hydraulic conductivity (K_sat) is well characterized. In this paper numerical solutions of the Richards’ equation are used to quantify the bias of seven infiltration measurement methods, removing natural variability and random error from the analysis. The methods evaluated in this study include the double ring infiltrometer, Saturo infiltrometer, modified Philip–Dunne infiltrometer, Turf‐Tec IN2‐W infiltrometer, USBR 7300‐89 well permeameter, Philip–Dunne permeameter, and the Guelph permeameter. Seven homogenous, isotropic soil textures were simulated at four initial soil moistures for the seven methods, resulting in a total of 196 simulations. The dimensionless bias is defined as the “measured”K_satdetermined by a given method divided by theK_satinput to the numerical experiments. The “measured”K_satis in quotations to identify the measurement occurs in a numeric experiment rather than in a physical experiment. In sand through silt loam soils that are typical of infiltration SCMs, the simulated methods have a bias in the range of 0.7–6.2. The Turf‐Tec was the only infiltrometer that produced a bias >2.5 for these soils. Initial effective saturation had a minimal contribution to bias for most methods. Methods that rely on a one‐dimensional (1D) flow assumption consistently overestimated theK_sat. Borehole methods produced results with bias similar to surface methods. Long duration methods did not consistently produce more accurate results than short duration methods. 

Abstract Roadsides are targeted for restoration of pollinator‐friendly plants. Yet, roads are sources of macronutrient, micronutrient and heavy metal pollution that may contaminate roadside plants. Adjacent landscape features such as railroads and agriculture provide additional macronutrient and heavy metal pollution that may exacerbate traffic effects. However, we lack perspective on how roads combine with rural landscape features to influence nutrition of roadside plants, which could have implications for pollinator health.We surveyed roadsides across Minnesota, USA and measured foliar levels of dietary macronutrients (nitrogen, phosphorous and potassium), a micronutrient (sodium) and metals (iron, zinc, copper, chromium, nickel, lead, aluminium and cadmium) in six abundant roadside forb species used by insect pollinators:Asclepias syriaca,Dalea purpurea,Monarda fistulosa,Ratibida pinnata,Solidagospp. andTrifolium pratense. We aimed to determine (1) how road variables (traffic volume and distance from road) combine with adjacent land use (railroad and agriculture) to influence element content of roadside forbs and (2) whether some forb species show consistent differences in their accumulation of potentially toxic heavy metals, which could inform selection of species to plant along roadsides.We found that foliar concentrations of nine elements increased with greater traffic volume (nitrogen, phosphorous, iron, zinc, copper, chromium, nickel, lead and aluminium), and concentrations of six elements declined with distance from the road (nitrogen, phosphorous, potassium, iron, zinc and copper). Leaves collected adjacent to railroad had less phosphorous, potassium, iron, nickel and aluminium than leaves collected from sites not adjacent to railroad. Additionally, leaves collected from sites adjacent to agriculture had lower copper levels than leaves from sites without adjacent agriculture. We found no evidence that particular ford species along roadsides consistently rank higher than other species in their accumulation of heavy metals.Our results show that traffic alters more elements in roadside plants than does adjacent landscape context, alleviating concerns that landscape features exacerbate pollutant levels in roadside pollinator habitat. However, nutrient contamination of most roadside plants is unlikely to reach toxic levels for insect pollinators. This work is consistent with the positive conservation potential of low to moderate traffic roadsides for pollinators.