Introduction & Question Material & Methods Conclusion Leandro B. C. Menezes1, Marcos R. Severgnini1, Diogo B. Provete1,2,3 leandrobc.menezes@gmail.com, marcosrafaelsevergnini@gmail.com, diogo.provete@ufms.br 1 - Institute of Biosciences, Federal University of Mato Grosso do Sul, Brazil; 2 - German Centre for Integrative Biodiversity Research, Leipzig, Germany; 3 - Gothenburg Global Biodiversity Centre, Göteborg, Sweden. Effects of urbanization on intra- and interspecific variability of frog metacommunities in two tropical cities Figure 1. Maps of Campo Grande (Mato Grosso do Sul, Brazil) and São José dos Campos (São Paulo, Brazil) with the 20 sampling sites (ponds), showing the change in land use between 1985 and 2022. Data from MapBiomas V. 8.0. Changes in land use Changes in ecoevolutionary dynamics 40 ponds Left side measurement (mm) of males (5 adults per species per pond) Relative leg length (residuals of a linear model) Relative head length and width (log) Body size Results & Discussion landscapemetrics São José dos Campos, SP, Brazil Campo Grande, MS, Brazil Impervious surface Pervious surface Urban pond Buffer 500m Figure 2. Urbanization gradient in Campo Grande (MS) and São José dos Campos (SP) with data from 2022. Urbanization in 2022 (%) Pond ID 1,051 individuals measured from 44 species and 5 families in both cities Rate of change in radiance (% 2022 - % 2012) 10 years Rate of change in green areas (% 2022 - % 1985) 37 years Rate of change in urbanization (% 2022 - % 1985) 37 years Urban Heat Island in 2022 (Cº) Building density in 2022 (number of buildings) buffer area Figure 6. Relative contribution of environmental variables to the components of phenotypic variation (intraspecific and turnover). The vertical black lines represent each environmental variable. In Campo Grande, urbanization and radiance rate, and building density had a significant effect on leg size at interspecific scale, and rate of change in green areas on body size at the intraspecific scale. In São José dos Campos, radiance and building density had a significant effect on head length and width at the intraspecific scale. São José dos Campos, SP, Brazil Campo Grande, MS, Brazil Figure 3. Standardized effect size (SES) of the T statistics for phenotypic traits at distinct hierarchical levels in each city. Dots represent the observed data; boxes indicate the standard deviation of the SES obtained from the null model. In both cities, the population variation in relation to the community variation (TIP/IC) for all traits had a lower mean and variation than expected under the null model, differently of TIC/IR and TPC/PR. This demonstrates that interspecific and regional-scale variability were greater in the urban gradient. Figure 4. Partitioning of the phenotypic variability into hierarchical levels. All phenotypic traits had little variation between ponds (< 0.001) and appear as 0% in the figure. The variability for genus, species. and region is greater for all phenotypic traits. Relative leg length had high variability at the population scale (within) along the urban gradient. Long-time urbanized Recently urbanized Habitat fragmentation Low effect High effect Changes in phenotypic variability High genetic drift Decrease in population size Low gene flow Do urban environments restrict phenotypic variability phenotypic traits related to the impact and requirement niche? At Intraspecific and interespecific scales Phenotypic adaptive changes Genetic adaptive changes to urban environments São José dos Campos, SP, Brazil Campo Grande, MS, Brazil Environmental variables at the landscape scale raster sf gtools terra tidyr 37 years 2022 1985 T statistics IP = Intra-population variability IC = Intra-community variability IC = Intra-community variability IR = Intra-regional pool variability PC = Variability in population means PR = Variability in community means Null models (local and regional pool) Hierarchical levels (population, species, community, regiona