Search for: All records

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.