Urbanization transforms environments in ways that alter biological evolution. We examined whether urban environmental change drives parallel evolution by sampling 110,019 white clover plants from 6169 populations in 160 cities globally. Plants were assayed for a Mendelian antiherbivore defense that also affects tolerance to abiotic stressors. Urban-rural gradients were associated with the evolution of clines in defense in 47% of cities throughout the world. Variation in the strength of clines was explained by environmental changes in drought stress and vegetation cover that varied among cities. Sequencing 2074 genomes from 26 cities revealed that the evolution of urban-rural clines was best explained by adaptive evolution, but the degree of parallel adaptation varied among cities. Our results demonstrate that urbanization leads to adaptation at a global scale.

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Urban structures facilitate human activities and interactions but are also a main source of air pollutants; hence, investigating the relationship between urban structures and air pollution is crucial. The lack of an acceptable general model poses significant challenges to investigations on the underlying mechanisms, and this gap fuels our motivation to analyze the relationships between urban structures and the emissions of four air pollutants, including nitrogen oxides, sulfur oxides, and two types of particulate matter, in Korea. We first conduct exploratory data analysis to detect the global and local spatial dependencies of air pollutants and apply Bayesian spatial regression models to examine the spatial relationship between each air pollutant and urban structure covariates. In particular, we use population, commercial area, industrial area, park area, road length, total land surface, and gross regional domestic product per person as spatial covariates of interest. Except for park area and road length, most covariates have significant positive relationships with air pollutants ranging from 0 to 1, which indicates that urbanization does not result in a one-to-one negative influence on air pollution. Findings suggest that the government should consider the degree of urban structures and air pollutants by region to achieve sustainable development.

Using artificial light data measured from satellites has the potential to change research methods in geography and urban planning. The Defense Meteorological Satellite Program Optical Linescan System (DMSP-OLS) night-time light datasets provided consistent and valuable data sources for investigating urbanization processes. This study intends to empirically investigate the relationship between night-time lights, population, and urban development patterns. A novel protocol was developed to integrate heterogeneous datasets into a standardized unit of analysis. Multivariate mixed-effects models were applied to detect correlations within and between provinces in South Korea. To capture physical variations of urban development, four landscape metrics were used and tested in the analyses. Diminishing returns of night-time lights to population were found in all models. In single landscape metric models, all coefficients of landscape metrics were positively related to night-time lights. In combination models, the aggregation index (AI) was no longer statistically significant. The protocol developed in this study provides an effective way to create analytical units for integrating heterogeneous forms of data. Creating standardized units of analyses will make it possible for researchers to compare their results with other studies. Landscape metrics used in this study for capturing the composition and configuration of urban development patterns will enrich the discussion in the future.