This study was focused on the metropolitan area of Florence in Tuscany (Italy) with the aim of mapping and evaluating thermal summer diurnal hot- and cool-spots in relation to the features of greening, urban surfaces, and city morphology. The work was driven by Landsat 8 land surface temperature (LST) data related to 2015–2019 summer daytime periods. Hot-spot analysis was performed adopting Getis-Ord Gi* spatial statistics applied on mean summer LST datasets to obtain location and boundaries of hot- and cool-spot areas. Each hot- and cool-spot was classified by using three significance threshold levels: 90% (LEVEL-1), 95% (LEVEL-2), and 99% (LEVEL-3). A set of open data urban elements directly or indirectly related to LST at local scale were calculated for each hot- and cool-spot area: (1) Normalized Difference Vegetation Index (NDVI), (2) tree cover (TC), (3) water bodies (WB), (4) impervious areas (IA), (5) mean spatial albedo (ALB), (6) surface areas (SA), (7) Shape index (SI), (8) Sky View Factor (SVF), (9) theoretical solar radiation (RJ), and (10) mean population density (PD). A General Dominance Analysis (GDA) framework was adopted to investigate the relative importance of urban factors affecting thermal hot- and cool-spot areas. The results showed that 11.5% of the studied area is affected by cool-spots and 6.5% by hot-spots. The average LST variation between hot- and cold-spot areas was about 10 °C and it was 15 °C among the extreme hot- and cool-spot levels (LEVEL-3). Hot-spot detection was magnified by the role of vegetation (NDVI and TC) combined with the significant contribution of other urban elements. In particular, TC, NDVI and ALB were identified as the most significant predictors (p-values < 0.001) of the most extreme cool-spot level (LEVEL-3). NDVI, PD, ALB, and SVF were selected as the most significant predictors (p-values < 0.05 for PD and SVF; p-values < 0.001 for NDVI and ALB) of the hot-spot LEVEL-3. In this study, a reproducible methodology was developed applicable to any urban context by using available open data sources.

Urbanisation significantly reshapes ecosystems, affecting biodiversity and profoundly altering wildlife behaviour. Nonetheless, our understanding of the environmental drivers of behavioural changes, as well as the concurrent effects upon interspecific interactions, remains limited. Here, we investigated the spatiotemporal adaptations of wildlife to urban environments, with a focus on predator–prey dynamics in response to urban‐specific environmental characteristics, adopting a year‐round camera‐trapping campaign within the metropolitan area of Florence, Italy. Temporal and spatial activity patterns of predators, prey, humans and domestic animals, as well as their relative overlaps, were quantified, revealing a marked increase in nocturnal behaviour amongst most wildlife species compared to their conspecifics in rural areas. A wide temporal overlap was recorded between predators and most of their prey (70%–93%), yet limited to night hours. Contrary to more natural settings, the effects of moon phases were negligible upon urban mammals, and the occurrence of humans was a strong driver of wildlife spatiotemporal behaviour. Species richness was also shaped by environmental factors, with artificial light negatively impacting diversity, whereas the presence of herbaceous edges enhanced the diversity of local mammal assemblages. Suburban regions emerged as biodiversity hotspots, highlighting the critical role of green corridors and thoughtful urban planning in mitigating biodiversity loss. Practical implication: Our findings analysed the complex interface between human activity and wildlife in urban landscapes, showing how urbanisation reshapes predator–prey dynamics and ecological interspecific interactions. Camera trapping proved to be a fundamental tool, addressing observational challenges and providing an unbiased, detailed perspective on urban wildlife adaptations.

Climate-change-related extreme events impact ecosystems, people, economy, and infrastructures, with important consequences on the real estate market as well. This study aims to investigate the variation of residential buildings’ real estate values in a historic Italian city in relation to the summer surface thermal anomaly pattern and urban features surrounding buildings. Open data from remote sensing products and the national database of the Revenue Agency of Italy were used. Real estate values of residential buildings were spatially analyzed in four urban belts, and the association with daytime summer surface hot- and cool-spot zones was studied through odds ratio (OR) statistic. Urban features (impervious area, tree cover, grassland area, and water body) surrounding residential buildings with different real estate values were also analyzed. Considering the whole Florentine municipality, 13.0% of residential buildings fell into hot-spot zones (only 0.6% into cool-spot ones), characterized by very low tree cover surfaces (generally <1%), most of which were in the central belt (37% of all buildings in central belt). Almost 10% of these buildings belonged to the highest market value class revealing a positive association (OR = 1.53) with hot-spot zones. This study provides useful information to plan targeted building interventions to avoid a probable decrease of the value of residential properties in high heat-related risk areas.

Green areas in urban and peri-urban landscapes are increasingly recognized as important habitats for wild bees, but the extent to which species adjust their reproductive behavior to local conditions in these environments remains poorly studied. We studied the reproductive output, offspring’s body size, sex allocation, and larval diet (assessed using pollen morphology and DNA metabarcoding) of the solitary bee Osmia caerulescens (Linnaeus, 1758) in a Mediterranean peri-urban environment using trap nests and local-scale environmental descriptors. Based on a total of 215 nests and 648 brood cells, females increased their reproductive output with greater floral resource availability and increasing openness of the habitat, but this was accompanied by a reduction in offspring body size (ITD) and a sex allocation skewed toward males. These findings are consistent with a trade-off between offspring number and per-offspring investment and contrary to general expectations that higher resource availability should favor larger and more female-biased offspring. Together, these responses indicate a shift toward quantity-oriented reproductive strategies, in contrast to patterns reported for this species in agricultural systems, where higher resource availability is typically associated with greater per-offspring investment and more female-biased sex ratios. Pronounced among-site variation in pollen diet, reflecting differences in local floral resource composition, further highlights how use of floral resources varies at a fine spatial scale and may shape reproductive strategies and, ultimately, population persistence. Our results show that the ecological value of urban habitats for solitary bees depends not only on the amount of floral resources available, but also on how local conditions influence species-specific reproductive responses. These findings highlight the value of species-specific reproductive analyses in abundant, functionally important solitary bees as a complementary approach to evaluating habitat quality at local scales in urban and peri-urban environments. Understanding how solitary bees adjust reproductive strategies to urban conditions is essential for supporting their conservation and long-term persistence in urban green spaces.