Urban mammals in Italy: how common species shape communities’ differentiation

Urban mammals in Italy: how common species shape communities’ differentiation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Urban mammals in Italy: how common species shape communities’ differentiation Laura Limonciello, Vasco Avramo, Enrico Mirone, Pushpinder Jamwal Singh, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8479625/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Urban areas, and the share of population inhabiting them, are rapidly expanding, with significant impacts on biodiversity. Nevertheless, multi-city comparative studies on urban wildlife –specifically mammal communities – remain scarce, particularly in Italy. Thus, we investigated how a standard evaluation of green cover and fragmentation, designed for a national biodiversity monitoring program, and regional context shape medium-and large-sized mammal assemblages in four Italian cities in Northern, Central, and Southern Italy (Milan, Rome, Florence, Campobasso). We deployed a total 48 camera traps across 1 km² grid cells covering two gradients of green cover and fragmentation. We accumulated 8,759 trapping days and 17,996 independent detections of 12 wild species. Species’ detections were generally consistent with their known national distribution, except for the unexpected occurrence of wild boar and wolf in Milan. Contrary to expectations, we found no significant effects of green areas extent or fragmentation on community composition, suggesting that alternative metrics or spatial scales may be more appropriate for capturing urban mammals’ distribution patterns. Nevertheless, our results revealed significant differences in community composition among cities (PERMANOVA, p=6e-04), with Milan showing the most distinct assemblage compared with Florence, Rome, and Campobasso. The Random Forest analysis identified the red fox (Vulpes vulpes) and the eastern cottontail rabbit (Sylvilagus floridanus) as the most influential species driving inter-city differences, followed by the wild boar (Sus scrofa), crested porcupine (Hystrix cristata), martens (Martes spp.), and the European hedgehog (Erinaceus europaeus). Overall, this work provides a baseline for further investigations of urban mammal ecology in Italy. PERMANOVA terrestrial mammals urban ecology camera traps Random Forest Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. INTRODUCTION Urban areas and human populations are continuously expanding. By 2030, nearly five billion people, which will be more than 60% of the global population, are expected to live in increasingly large cities (Seto et al. 2012 ; Girard et al. 2019 ) – with significant implications for biodiversity and ecosystem functioning. Urbanization stands as a significant type of human-driven land modification, causing the alteration of biogeochemical cycles, as well as habitat loss and fragmentation (Grimm et al. 2008 ). The negative impacts of urban sprawl may trigger cascading effects, beginning with the reduction of ecosystem connectivity and possibly leading to local extinction of native species (Radford et al. 2005 ; Mckinney 2008 ; Rastandeh et al. 2018 ). At the same time, urban environments provide new opportunities and resources to the species able to exploit them, as they possess specific traits suited for life in anthropic-dominated landscapes (Grunwald et al. 2024; Salek et al. 2014). Urban ecology studies have grown substantially in recent decades (Magle et al. 2012 ; Collins et al. 2021 ), reflecting the urgent need to unveil the dynamics operating within these expanding ecosystems. This understanding will help effectively integrate biodiversity conservation with human activities while mitigating urban wildlife conflicts, which often represent primary concerns for coexistence (Collins et al. 2021 ; Streicher et al. 2023 ). Beyond improving human health and well-being, and providing several ecosystem services in urban areas, greenspaces are also widely recognized as vital biodiversity refuges (Bratman et al. 2019 ; Rojas-Rueda et al. 2019 ; De La Hera et al. 2009 ; Nielsen et al. 2014 ; Beninde et al. 2015 ), aligning with the species–area relationship principle (Arrhenius 1921 ). Furthermore, green corridors have proven to increase species richness in cities (Beninde et al. 2015 ; Hernández Romero et al. 2024 ). On the other side, species usually found in urban environment share common characteristics, like a broad ecological niche and high adaptability. These characteristics increase the biotic homogenization of urban areas contexts (McKinney 2006 ; Lokatis and Jeschke 2022 ). Despite Magle et al ( 2012 ) report that mammals are the second most-studied taxonomic group in urban ecology, Lokatis and Jeschke ( 2022 ) found that only few studies tested and confirmed the Urban Biotic Homogenization (from now on UBH) hypothesis for mammal communities, revealing a substantial knowledge gap. Due to their high functional diversity and global distribution, mammals represent an interesting taxon for studying the ecological impacts of urbanization. Multi-city research showed that urbanization exerts complex and heterogeneous effects on larger mammal species, both spatially (Fidino et al. 2020 ) and temporally (Gallo et al. 2022 ). Among the threats for species in this environment, there are vehicle collision and habitat loss and fragmentation (Temple and Terry 2007 ; Rivera-Ortíz et al. 2015 ). Concerns about mammal species may also arise from human-wildlife conflicts, disease transmission, property damage, and attacks on domestic animals (Soulsbury and White 2016 ). Nonetheless, several species in this group resulted very successful at adapting to urban environments, often reaching high densities (Bateman et al 2012, Young et al. 2019 ; Rodriguez et al. 2021 ) and, in some cases, altering inter-specific dynamics (Parsons et al. 2019 ; Gallo et al. 2019 ). Italy is a recognized hotspot of mammal biodiversity (Gippoliti and Amori 2002 ), harbouring 48.23% of the species occurring in the European continent (Temple and Terry 2007 ), 10.5% of which are endemic or nearly endemic in Italy (Loy et al. 2019 ). However, recent data from the National System for Environmental Protection (Bianco et al. 2023 ) reveals that artificial land cover in the country reached 7.13% in 2021, compared to the average value of 4.2% of the EU. In the last years, some citizen science urban monitoring projects have been carried out (Fedrigotti et al. 2023 ; Bianco et al. 2023 ; CESAB 2025 ), as well as research initiatives pertaining to different taxa (Piano et al. 2020 ; Dondina et al. 2025 ; Alba et al. 2025 ). However, we still lack multi-taxa and multi-city long term monitoring studies identifying trends in larger mammal responses to landscape modifications linked to human settlements (Magle et al. 2019 ). To fill this gap, the Italian Ministry of Education and Research recently funded a national project aimed at monitoring biodiversity in different ecosystems, the National Biodiversity Future Centre (NBFC) - Spoke Urban https://www.nbfc.it ), that included a specific focus on urban biodiversity. The project specifically aims at evaluating the impact of both extension and fragmentation of green urban areas on plant and animal communities alike. The project set a sampling design based on 1 km 2 gridcells characterized by a gradient of green cover and fragmentation in six Italian cities differing in extension, landscape structure, and population density (Dondina et al. 2025 ). Following the national sampling design, we used camera trapping to characterize the medium and large-sized mammal communities in four out of the six Italian cities of the NBFC project i.e., Rome, Milan, Florence, and Campobasso, and to eventually confirm the UBH hypothesis. Our specific goals were i): assess the composition of urban mammal communities across the four cities; ii) evaluate the effectiveness of the green cover and fragmentation gradients in shaping these communities; iii) identify the mammal species that mostly drive differences among communities. Specifically, we hypothesize that i) irrespective of biogeographic location of the city, green cover and fragmentation primarily drive variations in species composition, with a higher frequency of species observed in greener and more connected sites; ii) in compliance with the UBH hypothesis, the four cities should share the same set of generalist species in the less green and most fragmented sites. Our results will likely inform urban planning and contribute to an adaptive management of green areas and corridors to benefit endangered and rare species, promote human-wildlife coexistence and reduce conflicts. 2. METHODS 2.1 Study area The work was carried out in the Italian Functional Urban Areas (FUAs, Dijkstra et al. 2019 ) of Milan (MI, Northern Italy), Florence (FI, North-Central Italy), Rome (RM, Central Italy) and Campobasso (CB, Southern Italy). The four cities span 3,114 km² (Milan), 1,853 km (Florence)², 6,156 km² (Rome) and 1,028 km² (Campobasso), and host populations of roughly 5 million, 0.8 million, 4.3 million and 95.2 thousand inhabitants, respectively (Fig. 1 ) (OECD 2019 ). 2.2 Sampling design and camera trapping We focused on medium and large sized mammals (mean weight > 1 kg, Lim and Pacheco 2016 ) regularly present in the Italian territory (Loy et al. 2019 ) and potentially occurring in the study areas, for a total 21 target species, belonging to the order Carnivora (10 species), Cetartiodactyla (6 species), Eulipotyphla (1 species), Lagomorpha (3 species) and Rodentia (1 species) (complete list in SUPP1). We adopted the sampling design recommended by the NBFC project, consisting of 1 × 1 km² gridcells characterized by a combination of green area extent and fragmentation gradients, resulting in 16 cell categories (SUPP2; Dondina et al. 2025 ). In each city, we set one a Coolife H953 camera trap in each of the cell categories occurring in the urban area. Camera traps were always deployed in native forested habitats, mainly characterized by Quercus spp. The final sampling design included 11 cells in Milan, 14 in Florence, 14 in Rome and 9 in Campobasso (Fig. 2 ). All the devices were installed after obtaining written authorization from the respective municipalities and private landowners. Informative labels were also displayed, describing the project’s aim and contact information, in compliance with EU and national regulations (Viviano et al. 2025 ). The camera traps were active from 8 to 24 months: in Milan, from early May to the end of December 2024; in Florence, from early January 2023 to December 2024, though with intermittent sampling; in Rome, from mid-March to December 2024; in Campobasso, from January to mid-November 2024. Camera traps were set to record one picture followed by a 10-seconds video. Data were stored on SD memory cards that were retrieved and changed every 3–6 weeks, along with a battery change. Out of 48 camera traps, six were stolen or irreparably damaged during the first two months. 2.3 Data processing Camera trap records were analysed with the software Timelapse (Greenberg and Godin 2015 ; Greenberg et al. 2019 ). Pictures and videos containing humans were removed from the database according to articles 13 and 14 of Regulation (EU) 2016/679 on privacy (Viviano et al. 2025 ). Whenever possible, wild species were identified at the species level. Otherwise, we used the lowest certain taxon (genus, family or order), or else the observation was set to “Unknown”. The minimum interval to ensure temporal independence of records was set at 10 minutes (Kays and Parsons 2014 ; Oberosler et al. 2017 ). The time-to-independence criteria was applied to minimise the risk of double counting the same individual. However, it was maintained as short as possible to prevent unnecessary loss of data. Species frequencies were calculated as the number of independent observations of a species divided by the number of active days per camera trap (“fr”, hereafter). Because of the frequent uncertainty in the identification of Martes martes (16.3% of total Martes observations) and Martes foina (0.9% of total Martes observations), these two species were always included as Martes spp. Although the two species may have different ecological requirements, our analysis focus on broader patterns of species composition in urban environments. Therefore, grouping them under a single taxonomic category was still appropriate for the aims of the study. Additionally, we removed species with less than 5 independent detections (see SUPP3 for the list of total independent detection for each species). 2.4 Data analyses First, to account for differences in sampling effort across cities, we calculated accumulation curves for the number of species detected by trapping efforts (number of trapping days, SUPP4). To evaluate the role of green cover and fragmentation in explaining composition differences among the sampled mammal communities, we tested for the differences in mean fr values employing a PERMANOVA approach. PERMANOVA is a non-parametric permutation technique that allows the comparison among multiple group centroids based on non-metric multidimensional scaling, if group dispersion does not differ significantly (Anderson 2017 ). Prior to running PERMANOVA, we tested for homogeneity of multivariate dispersion among groups using a Bray–Curtis dissimilarity matrix (Bray and Curtis 1957 ). Then, we performed separate PERMANOVA analyses to quantify differences in community composition when grouping cells according to four variables: Study Area, Green Cover, Green Fragmentation and Cell Category (Table 1 , see SUPP2 for information on categories assignment). We excluded groups represented by less than 3 cells for each separate analysis to ensure robust statistical inference. To further explore the most relevant differences among groups, we performed pairwise comparisons for group dispersion (Tukey HSD test) and centroid separation (pairwise PERMANOVA). Because these procedures involve multiple simultaneous comparisons, p-values from all pairwise PERMANOVA tests were adjusted using the Bonferroni correction, adopting a significance threshold of α = 0.05 after adjustment. All these analyses were implemented with the “vegan” (Oksanen et al. 2008 ) and “pairwiseAdonis” (Martinez Arbizu 2020 ) R packages. Table 1 Gridcells listed by ID with the 4 categorical variables included in the PERMANOVA analysis. Cells are sampled from four Study Areas (MI = Milan, FI = Florence, RM = Rome, CB = Campobasso) and each has been assigned to a category both for a green cover and a fragmentation gradient. For the green cover gradient, categories range from A (lowest cover; category included in the sampling design but not represented in this dataset) to D (highest cover). For the green fragmentation gradient, categories span from 1 (most fragmented) to 4 (least fragmented). These two categories are then combined in the “Cell Category” column. Cell ID Study Area Green Cover category Green Fragmentation category Cell Category 1184 MI B 1 B1 1282 MI B 2 B2 1041 MI B 3 B3 1191 MI B 4 B4 897 MI C 1 C1 1049 MI C 2 C2 1659 MI C 2 C2 1570 MI C 3 C3 893 MI C 4 C4 405 MI D 4 D4 1380 MI D 4 D4 1948 FI B 2 B2 3064 FI B 3 B3 3150 FI B 4 B4 2372 FI C 1 C1 1940 FI C 2 C2 2117 FI C 2 C2 2034 FI C 3 C3 2977 FI C 3 C3 1945 FI C 4 C4 2199 FI D 3 D3 2460 FI D 3 D3 1536 FI D 4 D4 2294 FI D 4 D4 2893 FI D 4 D4 2462 RM B 1 B1 3411 RM B 2 B2 4003 RM B 3 B3 3612 RM B 3 B3 2875 RM B 4 B4 4015 RM B 4 B4 3503 RM C 1 C1 3045 RM C 2 C2 3224 RM C 3 C3 3912 RM C 4 C4 3227 RM C 4 C4 3220 RM D 2 D2 3605 RM D 3 D3 2873 RM D 4 D4 1720 CB B 2 B2 1721 CB B 3 B3 1806 CB B 4 B4 1473 CB C 2 C2 1472 CB C 3 C3 1395 CB C 4 C4 1632 CB D 2 D2 1550 CB D 3 D3 1159 CB D 4 D4 Secondly, only for grouping variables that explained significant differences in mean fr values, we identified what were the most influential species driving group differentiation. For this purpose, we used a Random Forest approach, that is a machine learning algorithm based on the construction, bootstrapping, and aggregation of multiple decision trees (Breiman 2001 ). This approach is usually considered more flexible compared to other inferential techniques, especially when dealing with non-parametric data (Spradley et al. 2019 ). Specifically, we implemented Random Forest classification models including the grouping variable as the response variable and the mammal species fr as the covariates. After testing multiple model configurations, we optimized model performance by comparing R² and RMSE values. The final model was selected based on the best-performing parameters, and its classification accuracy was assessed using a 10-fold cross-validation procedure (Boehmke & Greenwell, 2020 ; more info on RF models in SUPP6). All Random Forest models were implemented in R using the h2o package (Fryda et al., 2014 ). 3. RESULTS We examined a total of 100601 pictures and videos and detected 24 (18 native, 3 domestic, and 3 exotic) mammal species (excluding humans) over 8759 trapping days. After applying species selection criteria and aggregating consecutive imagery into independent events, we obtained 17996 independent species events of 12 wild target mammals. The most abundant species were the wild boar ( Sus scrofa ) and the red fox ( Vulpes vulpes ), with 3978 and 3003 total independent sightings, respectively, while the rarest were the European polecat ( Mustela putorius ) and the grey wolf ( Canis lupus ), with 6 and 18 independent sighting, respectively (see Fig. 3 c and SUPP3). Species relative frequency distributions across cell categories and study areas are shown in Fig. 3 a and 3 b. PERMANOVA revealed no significant differences among cell categories (p = 0.6142). Similarly, no significant differences were detected when observations were grouped according to the green cover (p = 0.1015) and green fragmentation (p = 0.3217) gradients. The only significant differences were found among cities (p = 6e-04, Fig. 4 ). Pairwise comparisons highlighted Milan as the only significantly differing city (see SUPP5). Among the model configurations tested, the Random Forest with 120 trees, a maximum depth of 20, and a sample rate of 0.7 performed best and was therefore selected for further analysis (see SUPP6 for model comparison). Model classification accuracy was relatively high (R² = 0.729). The species predicted to be most influential in community differentiation were the red fox and the eastern cottontail rabbit ( Sylvilagus floridanus ), together accounting for 28% of the cumulative importance. Other notable species (relative importance around 10%) included the crested porcupine ( Hystrix cristata ), the wild boar, Martes sp., and the European hedgehog ( Erinaceus europaeus ), while the European polecat and the fallow deer ( Dama dama ) were identified as the least influential species (Fig. 5 ). The partial dependence plots (Fig. 6 ) show how species frequency influenced the probability of a new observation being classified within each study area. Rome was predominantly associated with the red fox, whose probability steadily rose with increasing frequency, reaching a plateau (p = 0.5) when fr > 1. The European hedgehog and wild boar also showed a strong relationship with Rome, with a descending trend for the European hedgehog (highest p = 0.45 when fr = 1.3), and an ascending trend for the wild boar, where p = 0.28 with fr > 3, reaching the same probability as Florence. The crested porcupine displayed a marked positive relationship with Rome, with probabilities approaching p = 0.5 as frequency increased, although slightly declining when fr was close to 1. Florence showed a strong association with martens, whose probability increased with frequency and reached a plateau (p = 0.4) when fr > 0.03. Similarly, the wild boar and crested porcupine displayed moderate probabilities (around a mean of 0.3) with an opposite trend between each other. In contrast, the eastern cottontail rabbit and red fox exhibited lower probabilities, indicating a weaker association with this urban area. Milan was characterized by a strong association with the eastern cottontail rabbit, whose probability sharply increased with fr > 0.5 and remained consistently high (p = 0.5). Conversely, other species, such as Martes spp. and the European hedgehog exhibited a marked decline in probability as their frequency increased. The red fox reaches a peak probability (p = 0.29) when fr = 0.4, indicating its limited presence in this study area. Campobasso was primarily characterized by the wild boar, which maintained the highest probability (mean p = 0.27) across most frequencies. The only other species with a probability higher than 0.2 was Martes spp. (mean p = 0.25 across all frequencies), showing a slightly decreasing trend contrasting the one in Rome. 4. DISCUSSION Despite the growing interest in urban wildlife ecology (Collins et al. 2021 ), researchers in Europe, and in Italy in particular, are just now starting to investigate mammal species ecology in urban areas (Magle et al. 2012 ). In addition to that, while works focusing on single cities started to unveil the complex dynamics at play in these novel ecosystems (Ancillotto et al. 2024 , 2025 ; Mori et al. 2025 ), multi-city experimental designs remain scarce, especially for medium and large-sized mammals (Dondina et al. 2025 ; Alba et al. 2025 ). Through a year-long camera trap survey, we provided a first extensive assessment of urban medium- and large-sized mammal communities and found significant differences across four Italian cities. Mammal assemblages mainly differed in the frequency of occurrence of the most common species, i.e. the red fox and, at a lesser extent, of the crested porcupine ( Hystrix cristata ), the wild boar, martens ( Martes spp.), and the hedgehog. The only exception was the eastern cottontail rabbit, an alien species significantly affecting specifically the mammal community in Milan. Despite urban green cover and fragmentation were often portrayed as primary drivers shaping medium- and large-sized mammal communities (Beninde et al. 2015 ), we did not find any significant difference across combined or individual gradients of green area cover and fragmentation, likely related to the scale at which these parameters were measured. 4.1 Species detections in the context of their national range To place the detected species in a broader biogeographic context, we discuss our findings in relation to their currently documented national distribution patterns (Loy et al. 2025 ). Overall, species detected in our work were largely consistent with their known Italian distributions, with a few notable exceptions. First, wild boar and grey wolf were detected for the first time in Milan, an area located at the edge of their documented Italian ranges (Loy et al. 2025 ). Both species are known for their ecological plasticity and for propensity to access urban areas to exploit easily accessible feeding resources (Bateman and Fleming 2012 ; Castillo-Contreras et al. 2021 ; Marin et al. 2024 ). Nevertheless, both species were found quite close to the city, in an area where their distribution is still patchy (Loy et al. 2025 ). The wild boar is a highly plastic species that readily adapts to urban areas (Stillfried et al. 2017b , a ) exhibiting larger body size, a diet richer in anthropogenic food (Castillo-Contreras et al. 2021 ) and changes in movement and diel activity patterns (Podgórski et al. 2013 ). Wild populations are increasing in Europe (Massei et al. 2015 ), a trend that is mirrored in many urban areas, e.g. Barcelona, Spain (Alabau et al. 2020 ), Berlin, Germany (Stillfried et al. 2017c ), and Kraków, Poland (Podgórski et al. 2013 ), with corresponding rising numbers of human-wild boar conflict events (Licoppe A. et al. 2013 ). In Italy, the species has followed this continental trend, (Monaco et al. 2010 ; Loy et al. 2025 ) to the extent of needing management plans in several protected areas to mitigate its social and ecological impacts (e.g Pierucci P. et al. 2015 ; Ente Parco PNGSL 2024 ). Its presence has been documented in several Italian cities, starting from Trieste and Udine in 2011 (Primi et al. 2016 ; Scillitani 2023). This aligns with our findings: the species was the most frequently detected in our dataset, with high detection rates in three out of four study areas. Surprisingly, we did not detect any wolves in Florence, despite well-documented and widespread presence in the surrounding areas (Loy et al. 2025 ). The only known records near Florence were from the southern periphery of the city, particularly in Lastra a Signa, Impruneta and Fiesole municipalities (Redazione ANSA 2024 ), suggesting a limited urban colonization. However, a more extensive survey run in Florence by one of the authors did reveal a more extensive occurrence of this species in the urban area of Florence (Mori et al. 2025 ). Following a dramatic decline in the 1970s, wolf population began to recover and expand in the Alpine range in the 1980s (Boitani 1984 ), reaching higher population densities in this region compared to the Apennine range (Galaverni et al. 2016 ). Currently, this species might be also found in urbanised landscapes (Zanni et al. 2023 ). Another interesting result is the very limited occurrence of Martes sp. in the urban area of Milan, especially considering that both the stone marten ( M. foina ) and pine marten ( M. martes ) are known to occur in the region (Loy et al. 2025 ) and that both species have been observed in urban environments. Stone martens are often observed in cities, often denning in building attics (Herr et al. 2010 ), while the congeneric pine marten is known to avoid anthropogenic environments (Bateman and Fleming 2012 , but see Ancillotto et al. 2024 , 2025 ; Grelli et al. 2014 ). Due to their elusive behaviour and the difficulty of distinguishing between the two species through camera trap images, clarifying their distribution pattern in urban areas will require further investigation – with a dedicated effort for species-level identification and eventually the aid of AI. 4.2 Community differentiation The PERMANOVA analysis revealed no significant difference in species composition across the green cover and fragmentation gradients provided by the NBFC project sampling design (Dondina et al 2025 ). This result is in contrast with numerous studies that documented the importance of green areas extension and fragmentation in determining species richness and composition in urban environments (Beninde et al. 2015 ; Aznarez et al. 2022 ). A possible explanation is that the spatial scale of the sampling design (1 km² gridcells) did not match the ecological requirements of our target species. Medium- and large-sized mammals are generally more mobile and have larger home ranges compared to other strictly terrestrial taxonomic groups such as e.g., small mammals (Tucker et al. 2018 ). Accordingly, we suggest the sampling design of the national project is not adequate to fully capture the effects of green cover and fragmentation on large and medium mammals’ communities in urban contexts. Future work will focus on integrating new measures for these variables at multiple scale, as well as the implementation of targeted sampling designs, to further understand their role in shaping the communities. Our results clearly showed that the four Italian cities differed in the composition of mammal communities. Particularly, Milan showed a unique species composition and a significant divergence from the other three cities. Absence of the crested porcupine - Milan is outside the species range (Mori et al. 2013 , 2021 , but see Torretta et al 2021 ) -, together with the unique and widespread occurrence of the alien Eastern cottontail – introduced from North America in the 1960s for hunting purposes (Bertolino et al. 2011 ) – could partially explain these differences. In general, the species accounting for higher differences among the four cities were not the rarest, but rather the most abundant and shared across all cities. This result contrasts with an expectation based on a presence–absence viewpoint, where species unique to one city (e.g. European polecat or fallow deer) might be expected to drive community differentiation. However, in analyses based on relative frequencies and Bray–Curtis dissimilarities, abundant species contribute disproportionately to multivariate differences (Ricotta and Podani 2017 ), whereas rare or sporadically detected species have limited influence. Accordingly, species with low detection frequency, including those occurring in only one city (e.g. grey wolf), were identified as least important in explaining among-city differences. Specifically, the Random Forest analysis identified red fox as one of the most influential species in determining differences among the four cities. The red fox is a native carnivore with high ecological plasticity that allows for a high population density in urban areas (Baker and Harris 2004 ), where the species can exploit anthropogenic resources (Contesse et al. 2004 ) and adapt to the novel environment (Lazzaroni et al. 2024 ). The strong association of red foxes with Rome is consistent with previous studies documenting its presence in urban environments (Bateman and Fleming 2012 ; Kimmig 2021 ; Ancillotto et al. 2024 ). In Milan - the most densely populated urban area in our study (OECD 2019 ) - the red fox was much less represented than in Rome. The lower representation of red foxes in Milan compared to Rome cannot be explained by the variables included in our analysis and therefore remains unresolved. It is worth noting that Rome is characterized by a mosaic of green areas of different sizes immersed in the urban matrix (Blasi C. 2007), whereas Milan presents a more continuous and densely built environment. Structural differences or city-specific factors, such as mean impervious cover and mean housing density, could alter the response of the species to urbanization in complex and unexpected ways, influencing species assemblages and relative species abundances (Fidino et al. 2020 ). Regional context remains a frequently overlooked aspect in urban ecological research, yet it is fundamental to reveal large scale patterns (Magle et al. 2019 ; Haight et al. 2023 ). Our findings provide mixed support for the UBH hypothesis. The checklist of species found in the four Italian investigated cities generally reflected the species composition found in extra urban areas. Some highly elusive and endangered species known to occur in the study areas, -- like the wildcat Felis silvestris in Rome, Florence or Campobasso --, or species more sensitive to anthropogenic pressures, -- like the pine marten in Milan and the European polecat in Rome, Florence and Milan --, were not observed in the city, confirming the UBH hypothesis and the idea of cities as filters (McKinney 2006 ; Lokatis and Jeschke 2022 ). However, despite sharing many species, the four cities showed distinct community compositions, at least in terms of relative frequencies among species, partially contradicting the classical UBH prediction of increasing similarity among urban areas. City-specific factors that we did not account for may be as important as local urban habitat characteristics and the reason why these communities resulted dissimilar. Additionally, functional traits exhibited by the most influential species, like a large litter size and a broad trophic niche, grant adaptation success to urban environments across various taxonomic groups (Santini et al. 2019 , Lokatis and Jeschke 2022 ). This indicates that, while urbanization may favour convergent ecological traits, complete biotic homogenization does not necessarily occur, supporting recent critiques of the universality of UBH patterns (Lokatis and Jeschke 2022 ). 4.3 Conclusions Our comprehensive multi-city camera trap work offers a coordinated assessment of urban terrestrial mammals in Italy, a country where urban wildlife ecology remains an emerging field. Our studies evidenced that urban mammal communities in Italy are more diverse than expected, mostly reflecting the mammal assemblages found in extra-urban areas. Our findings also suggest common species’ relative abundance, rather than presence/absence, plays a central role in shaping urban communities. This highlights the importance of shifting to quantitative population assessments for effective urban wildlife management. Future research should focus on understanding the main drivers of species abundance patterns and identifying population hotspots. For invasive species like the eastern cottontail, abundance data becomes critical for evaluating control program efficacy and detecting early population surges before they establish permanent populations. Many common urban species may also act as hosts of parasite-borne diseases, e.g. sarcoptic mange in red foxes and wild boars (Perrucci et al. 2016 ; Lizana et al. 2024 ), with potential risk of transmission to domestic animals and humans. Integrating data on species-specific connectivity and habitat and feeding resources availability will help predict and prevent human-wildlife conflicts, informing urban policy makers on how to manage both endangered and problematic species. We are also aware of limitations that should suggest caution when interpreting our results. We worked in compliance with European privacy regulations (EU General Data Protection Regulation 2016/679) related to videos and images of people recording; nonetheless, we faced considerable difficulties in obtaining authorizations to deploy camera traps in public urban areas. Identifying safe and suitable sites for camera placement required attentive on-site evaluations and consultations with local experts. We experienced several thefts and damages to our devices in all cities, with Florence and Rome showing the highest rate of vandalism (14% and 21%, respectively). Theft and damage to equipment are well-documented challenges, especially in urban ecology studies (Dyson et al. 2019 ); our devices were equipped with cable lock and explanatory tags with information of the study and reference contacts. Still, our rates were higher than those reported in similar studies in other geographical contexts (Magle et al. 2019 ; Herrera et al. 2021 ). Future efforts could benefit from consultations with the communities, the addition of more personal and relatable tag messages (Clarin et al. 2014 ), or improved concealment strategies. Our results highlight the complexity and specificity of Italian urban mammal assemblages, suggesting that biotic homogenization processes are not universal (Aronson et al. 2014 ). These findings emphasize the importance of contextualized conservation and management approaches that consider the ecological, biogeographical, and historical peculiarities of individual urban areas. Statements & Declarations ACKNOWLEDGMENTS We are grateful to Bruno Cignini for his invaluable support in obtaining the permits needed to deploy camera traps in Rome’s urban parks. We thank Luca Fegatelli, Director of Ente Regionale Parco Appia Antica , Emiliano Manari from of Ente Regionale Roma Natura , and Marina Mantella from ‘ Gestione Territoriale e Ambientale del Verde ’ of the Rome Municipality, for granting access permission and providing support during fieldwork in Rome. We also thank Olivia Dondina and Valerio Orioli for their hospitality and assistance during fieldwork in Milan. FUNDING Project funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 - Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union - NextGenerationEU. Award Number: Project code CN_00000033, Concession Decree No. 1034 of 17 June 2022 adopted by the Italian Ministry of University and Research, CUP: H43C22000530001. Laura Limonciello was supported by a PhD grant by the Doctorate of National Interest (DIN), Cycle 39th, coordinated by University of Palermo. COMPETING INTERESTS The authors declare no competing interests. AUTHORS CONTRIBUTION LL: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Software; Validation; Visualization; Writing – original draft; Writing – reviewing and editing. EMi: Investigation; Writing – reviewing and editing. VA: Data curation; Investigation; Writing – reviewing and editing. PJS: Formal analysis; Investigation; Writing – reviewing and editing. GI: Data curation; Investigation. CC: Data curation; Investigation. EMo: Investigation; Writing – reviewing and editing. LA: Data curation; Investigation; AV: Data curation; Investigation. MDF: Conceptualization; Methodology; Supervision; Writing – reviewing and editing. 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09:23:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8479625/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8479625/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100905297,"identity":"32c4a3c0-a266-442b-81d3-6a17437f0018","added_by":"auto","created_at":"2026-01-22 15:41:26","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6745145,"visible":true,"origin":"","legend":"","description":"","filename":"urbanmammalcommunitiesLimoncielloetal.docx","url":"https://assets-eu.researchsquare.com/files/rs-8479625/v1/a1b3dcb1e2bf1d1c601e771a.docx"},{"id":100905263,"identity":"84e8e284-e57b-4b7d-a30c-0171c9c9fcc7","added_by":"auto","created_at":"2026-01-22 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15:41:24","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":29993,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8479625/v1/24d02430b89af97b3c4980d8.png"},{"id":100905304,"identity":"7e6619fb-b57e-4527-95c7-1a20c837cc3d","added_by":"auto","created_at":"2026-01-22 15:41:36","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":38843,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8479625/v1/cd9e9b55318bacb6a461356c.png"},{"id":100905353,"identity":"72d7a68a-89dc-4dc2-b95b-a81c83c8f6d9","added_by":"auto","created_at":"2026-01-22 15:41:45","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":95910,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8479625/v1/d81b6556643464a3da6f6883.png"},{"id":100951697,"identity":"3dbe1ad1-7689-4748-8735-e543b7c83596","added_by":"auto","created_at":"2026-01-23 07:11:06","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":189610,"visible":true,"origin":"","legend":"","description":"","filename":"d652e8d6667a488d89e83dac4ea831e71structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8479625/v1/fd6650182bb1d0afe7592792.xml"},{"id":100905322,"identity":"5ed65624-e45d-45a5-b048-328f4a06fe30","added_by":"auto","created_at":"2026-01-22 15:41:39","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":204517,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8479625/v1/7a47c97f422ad5f2eec2f07c.html"},{"id":100905231,"identity":"181e82b7-cefb-4d3e-bb1e-0f8e522ff154","added_by":"auto","created_at":"2026-01-22 15:41:06","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":203187,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the 4 Functional Urban Areas (FUAs) where the work was carried out. From the North of Italy, Milan, Florence, Rome and Campobasso.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8479625/v1/2342e93f09cb834ad82e55ae.jpeg"},{"id":100905288,"identity":"de6b5aff-a15f-49b5-8451-a6975565d9b3","added_by":"auto","created_at":"2026-01-22 15:41:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2330708,"visible":true,"origin":"","legend":"\u003cp\u003eGridcells selected in each city, coloured according to their Gridcell category. Categories are derived from the combination of two ecological gradients: green cover (from B, lowest cover, to D, highest cover) and green fragmentation (from 1, most fragmented, to 4, least fragmented). Orange dots represent the camera trap location within each cell. The four maps are shown at different scales to allow complete visualization of all gridcells, as study areas differ in spatial extent.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8479625/v1/7b6e66bdc872dd250793c8eb.png"},{"id":100951344,"identity":"0c96910a-3440-48f9-a685-7c33b567f721","added_by":"auto","created_at":"2026-01-23 07:10:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":354516,"visible":true,"origin":"","legend":"\u003cp\u003eRelative percentage of species frequency across cell categories (\u003cstrong\u003e3a\u003c/strong\u003e), study areas (\u003cstrong\u003e3b\u003c/strong\u003e) and species absolute frequencies across study areas (\u003cstrong\u003e3c\u003c/strong\u003e). In \u003cstrong\u003eFigure 3a \u003c/strong\u003eand\u003cstrong\u003e 3b\u003c/strong\u003e, species are grouped by family (with the exception on ungulates, grouped by order), represented by different colours, and percentages are expressed relative to the total frequency of all species within each cell category or study area. In Figure \u003cstrong\u003e3c\u003c/strong\u003e, circle size represents species absolute frequency, with absences (no detection) indicated by black triangles percentage of species frequency are computed relative to the total number of observations in each city.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8479625/v1/993439bd5a47d8532f12dfd4.png"},{"id":100905261,"identity":"79240e76-6b87-42ed-899b-bf44b9c4cf37","added_by":"auto","created_at":"2026-01-22 15:41:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96497,"visible":true,"origin":"","legend":"\u003cp\u003eDispersion plot for the 48 sites, grouped by Study Area (Milan = MI, in red; Rome = RM, in yellow; Florence = FI, in dark green; Campobasso = CB, in light green)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8479625/v1/70fb9f4f8ad58c104cf72101.png"},{"id":100905369,"identity":"156d6a5e-394c-4084-b021-9226220c0562","added_by":"auto","created_at":"2026-01-22 15:41:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":121815,"visible":true,"origin":"","legend":"\u003cp\u003eRelative importance of species in differentiating mammal communities across cities, according to the best-performing Random Forest Model. The red fox and eastern cottontail rabbit were the most influential species in differentiating the study areas, followed by the crested porcupine, wild boar, \u003cem\u003eMartes\u003c/em\u003e sp., and the European hedgehog\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8479625/v1/3c2e7b804d64f90256e610f8.png"},{"id":100905190,"identity":"52510a7a-5eb6-443a-acfc-88e7265b0e0a","added_by":"auto","created_at":"2026-01-22 15:40:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":489426,"visible":true,"origin":"","legend":"\u003cp\u003ePartial Dependence Plots for species occurrence. The x-axis represents species frequency, calculated as the number of independent observations divided by the number of trap days. The y-axis indicates the probability that a new observation belongs to a specific study area. The four study areas are represented by different coloured lines (light green = Campobasso, dark green = Florence, red = Milan, yellow = Rome). The dashed line represents the observed probability, while the solid line corresponds to a smoothed LOESS estimate (α = 0.6)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8479625/v1/1387974988afcbb4b64ab194.png"},{"id":106396129,"identity":"48867459-1932-4351-a7cf-0f4c627f4bdd","added_by":"auto","created_at":"2026-04-08 07:59:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5373363,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8479625/v1/b9681262-5715-4d17-ae8a-75d52f7a475f.pdf"},{"id":100905230,"identity":"13b656e1-920e-4b42-a331-207dba898790","added_by":"auto","created_at":"2026-01-22 15:41:06","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1604564,"visible":true,"origin":"","legend":"","description":"","filename":"urbanmammalcommunitiesSUPPLEMENTARYLimoncielloetal.docx","url":"https://assets-eu.researchsquare.com/files/rs-8479625/v1/96982cdc86da7ede10943578.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Urban mammals in Italy: how common species shape communities’ differentiation","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eUrban areas and human populations are continuously expanding. By 2030, nearly five billion people, which will be more than 60% of the global population, are expected to live in increasingly large cities (Seto et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Girard et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) \u0026ndash; with significant implications for biodiversity and ecosystem functioning. Urbanization stands as a significant type of human-driven land modification, causing the alteration of biogeochemical cycles, as well as habitat loss and fragmentation (Grimm et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The negative impacts of urban sprawl may trigger cascading effects, beginning with the reduction of ecosystem connectivity and possibly leading to local extinction of native species (Radford et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Mckinney \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Rastandeh et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). At the same time, urban environments provide new opportunities and resources to the species able to exploit them, as they possess specific traits suited for life in anthropic-dominated landscapes (Grunwald et al. 2024; Salek et al. 2014).\u003c/p\u003e \u003cp\u003eUrban ecology studies have grown substantially in recent decades (Magle et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Collins et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), reflecting the urgent need to unveil the dynamics operating within these expanding ecosystems. This understanding will help effectively integrate biodiversity conservation with human activities while mitigating urban wildlife conflicts, which often represent primary concerns for coexistence (Collins et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Streicher et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Beyond improving human health and well-being, and providing several ecosystem services in urban areas, greenspaces are also widely recognized as vital biodiversity refuges (Bratman et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rojas-Rueda et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; De La Hera et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Nielsen et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Beninde et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), aligning with the species\u0026ndash;area relationship principle (Arrhenius \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1921\u003c/span\u003e). Furthermore, green corridors have proven to increase species richness in cities (Beninde et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hern\u0026aacute;ndez Romero et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). On the other side, species usually found in urban environment share common characteristics, like a broad ecological niche and high adaptability. These characteristics increase the biotic homogenization of urban areas contexts (McKinney \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Lokatis and Jeschke \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite Magle et al (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) report that mammals are the second most-studied taxonomic group in urban ecology, Lokatis and Jeschke (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that only few studies tested and confirmed the Urban Biotic Homogenization (from now on UBH) hypothesis for mammal communities, revealing a substantial knowledge gap. Due to their high functional diversity and global distribution, mammals represent an interesting taxon for studying the ecological impacts of urbanization. Multi-city research showed that urbanization exerts complex and heterogeneous effects on larger mammal species, both spatially (Fidino et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and temporally (Gallo et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Among the threats for species in this environment, there are vehicle collision and habitat loss and fragmentation (Temple and Terry \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Rivera-Ort\u0026iacute;z et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Concerns about mammal species may also arise from human-wildlife conflicts, disease transmission, property damage, and attacks on domestic animals (Soulsbury and White \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Nonetheless, several species in this group resulted very successful at adapting to urban environments, often reaching high densities (Bateman et al 2012, Young et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rodriguez et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and, in some cases, altering inter-specific dynamics (Parsons et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gallo et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eItaly is a recognized hotspot of mammal biodiversity (Gippoliti and Amori \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), harbouring 48.23% of the species occurring in the European continent (Temple and Terry \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), 10.5% of which are endemic or nearly endemic in Italy (Loy et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, recent data from the National System for Environmental Protection (Bianco et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reveals that artificial land cover in the country reached 7.13% in 2021, compared to the average value of 4.2% of the EU. In the last years, some citizen science urban monitoring projects have been carried out (Fedrigotti et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bianco et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; CESAB \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), as well as research initiatives pertaining to different taxa (Piano et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dondina et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Alba et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, we still lack multi-taxa and multi-city long term monitoring studies identifying trends in larger mammal responses to landscape modifications linked to human settlements (Magle et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To fill this gap, the Italian Ministry of Education and Research recently funded a national project aimed at monitoring biodiversity in different ecosystems, the National Biodiversity Future Centre (NBFC) - Spoke Urban \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nbfc.it\u003c/span\u003e\u003cspan address=\"https://www.nbfc.it\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), that included a specific focus on urban biodiversity. The project specifically aims at evaluating the impact of both extension and fragmentation of green urban areas on plant and animal communities alike. The project set a sampling design based on 1 km\u003csup\u003e2\u003c/sup\u003e gridcells characterized by a gradient of green cover and fragmentation in six Italian cities differing in extension, landscape structure, and population density (Dondina et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Following the national sampling design, we used camera trapping to characterize the medium and large-sized mammal communities in four out of the six Italian cities of the NBFC project i.e., Rome, Milan, Florence, and Campobasso, and to eventually confirm the UBH hypothesis.\u003c/p\u003e \u003cp\u003eOur specific goals were i): assess the composition of urban mammal communities across the four cities; ii) evaluate the effectiveness of the green cover and fragmentation gradients in shaping these communities; iii) identify the mammal species that mostly drive differences among communities. Specifically, we hypothesize that i) irrespective of biogeographic location of the city, green cover and fragmentation primarily drive variations in species composition, with a higher frequency of species observed in greener and more connected sites; ii) in compliance with the UBH hypothesis, the four cities should share the same set of generalist species in the less green and most fragmented sites. Our results will likely inform urban planning and contribute to an adaptive management of green areas and corridors to benefit endangered and rare species, promote human-wildlife coexistence and reduce conflicts.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe work was carried out in the Italian Functional Urban Areas (FUAs, Dijkstra et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) of Milan (MI, Northern Italy), Florence (FI, North-Central Italy), Rome (RM, Central Italy) and Campobasso (CB, Southern Italy). The four cities span 3,114 km\u0026sup2; (Milan), 1,853 km (Florence)\u0026sup2;, 6,156 km\u0026sup2; (Rome) and 1,028 km\u0026sup2; (Campobasso), and host populations of roughly 5\u0026nbsp;million, 0.8\u0026nbsp;million, 4.3\u0026nbsp;million and 95.2 thousand inhabitants, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (OECD \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sampling design and camera trapping\u003c/h2\u003e \u003cp\u003eWe focused on medium and large sized mammals (mean weight\u0026thinsp;\u0026gt;\u0026thinsp;1 kg, Lim and Pacheco \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) regularly present in the Italian territory (Loy et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and potentially occurring in the study areas, for a total 21 target species, belonging to the order Carnivora (10 species), Cetartiodactyla (6 species), Eulipotyphla (1 species), Lagomorpha (3 species) and Rodentia (1 species) (complete list in SUPP1). We adopted the sampling design recommended by the NBFC project, consisting of 1 \u0026times; 1 km\u0026sup2; gridcells characterized by a combination of green area extent and fragmentation gradients, resulting in 16 cell categories (SUPP2; Dondina et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In each city, we set one a Coolife H953 camera trap in each of the cell categories occurring in the urban area. Camera traps were always deployed in native forested habitats, mainly characterized by \u003cem\u003eQuercus\u003c/em\u003e spp. The final sampling design included 11 cells in Milan, 14 in Florence, 14 in Rome and 9 in Campobasso (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All the devices were installed after obtaining written authorization from the respective municipalities and private landowners. Informative labels were also displayed, describing the project\u0026rsquo;s aim and contact information, in compliance with EU and national regulations (Viviano et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe camera traps were active from 8 to 24 months: in Milan, from early May to the end of December 2024; in Florence, from early January 2023 to December 2024, though with intermittent sampling; in Rome, from mid-March to December 2024; in Campobasso, from January to mid-November 2024. Camera traps were set to record one picture followed by a 10-seconds video. Data were stored on SD memory cards that were retrieved and changed every 3\u0026ndash;6 weeks, along with a battery change. Out of 48 camera traps, six were stolen or irreparably damaged during the first two months.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data processing\u003c/h2\u003e \u003cp\u003eCamera trap records were analysed with the software Timelapse (Greenberg and Godin \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Greenberg et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Pictures and videos containing humans were removed from the database according to articles 13 and 14 of Regulation (EU) 2016/679 on privacy (Viviano et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Whenever possible, wild species were identified at the species level. Otherwise, we used the lowest certain taxon (genus, family or order), or else the observation was set to \u0026ldquo;Unknown\u0026rdquo;.\u003c/p\u003e \u003cp\u003eThe minimum interval to ensure temporal independence of records was set at 10 minutes (Kays and Parsons \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Oberosler et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The time-to-independence criteria was applied to minimise the risk of double counting the same individual. However, it was maintained as short as possible to prevent unnecessary loss of data.\u003c/p\u003e \u003cp\u003eSpecies frequencies were calculated as the number of independent observations of a species divided by the number of active days per camera trap (\u0026ldquo;fr\u0026rdquo;, hereafter). Because of the frequent uncertainty in the identification of \u003cem\u003eMartes martes\u003c/em\u003e (16.3% of total \u003cem\u003eMartes\u003c/em\u003e observations) and \u003cem\u003eMartes\u003c/em\u003e foina (0.9% of total \u003cem\u003eMartes\u003c/em\u003e observations), these two species were always included as \u003cem\u003eMartes\u003c/em\u003e spp. Although the two species may have different ecological requirements, our analysis focus on broader patterns of species composition in urban environments. Therefore, grouping them under a single taxonomic category was still appropriate for the aims of the study. Additionally, we removed species with less than 5 independent detections (see SUPP3 for the list of total independent detection for each species).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data analyses\u003c/h2\u003e \u003cp\u003eFirst, to account for differences in sampling effort across cities, we calculated accumulation curves for the number of species detected by trapping efforts (number of trapping days, SUPP4). To evaluate the role of green cover and fragmentation in explaining composition differences among the sampled mammal communities, we tested for the differences in mean fr values employing a PERMANOVA approach. PERMANOVA is a non-parametric permutation technique that allows the comparison among multiple group centroids based on non-metric multidimensional scaling, if group dispersion does not differ significantly (Anderson \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrior to running PERMANOVA, we tested for homogeneity of multivariate dispersion among groups using a Bray\u0026ndash;Curtis dissimilarity matrix (Bray and Curtis \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1957\u003c/span\u003e). Then, we performed separate PERMANOVA analyses to quantify differences in community composition when grouping cells according to four variables: Study Area, Green Cover, Green Fragmentation and Cell Category (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, see SUPP2 for information on categories assignment). We excluded groups represented by less than 3 cells for each separate analysis to ensure robust statistical inference.\u003c/p\u003e \u003cp\u003eTo further explore the most relevant differences among groups, we performed pairwise comparisons for group dispersion (Tukey HSD test) and centroid separation (pairwise PERMANOVA). Because these procedures involve multiple simultaneous comparisons, p-values from all pairwise PERMANOVA tests were adjusted using the Bonferroni correction, adopting a significance threshold of α\u0026thinsp;=\u0026thinsp;0.05 after adjustment. All these analyses were implemented with the \u0026ldquo;vegan\u0026rdquo; (Oksanen et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and \u0026ldquo;pairwiseAdonis\u0026rdquo; (Martinez Arbizu \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) R packages.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGridcells listed by ID with the 4 categorical variables included in the PERMANOVA analysis. Cells are sampled from four Study Areas (MI\u0026thinsp;=\u0026thinsp;Milan, FI\u0026thinsp;=\u0026thinsp;Florence, RM\u0026thinsp;=\u0026thinsp;Rome, CB\u0026thinsp;=\u0026thinsp;Campobasso) and each has been assigned to a category both for a green cover and a fragmentation gradient. For the green cover gradient, categories range from A (lowest cover; category included in the sampling design but not represented in this dataset) to D (highest cover). For the green fragmentation gradient, categories span from 1 (most fragmented) to 4 (least fragmented). These two categories are then combined in the \u0026ldquo;Cell Category\u0026rdquo; column.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudy Area\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreen Cover category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreen Fragmentation category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCell Category\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSecondly, only for grouping variables that explained significant differences in mean fr values, we identified what were the most influential species driving group differentiation. For this purpose, we used a Random Forest approach, that is a machine learning algorithm based on the construction, bootstrapping, and aggregation of multiple decision trees (Breiman \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). This approach is usually considered more flexible compared to other inferential techniques, especially when dealing with non-parametric data (Spradley et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Specifically, we implemented Random Forest classification models including the grouping variable as the response variable and the mammal species fr as the covariates. After testing multiple model configurations, we optimized model performance by comparing R\u0026sup2; and RMSE values. The final model was selected based on the best-performing parameters, and its classification accuracy was assessed using a 10-fold cross-validation procedure (Boehmke \u0026amp; Greenwell, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; more info on RF models in SUPP6). All Random Forest models were implemented in R using the \u003cem\u003eh2o\u003c/em\u003e package (Fryda et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003eWe examined a total of 100601 pictures and videos and detected 24 (18 native, 3 domestic, and 3 exotic) mammal species (excluding humans) over 8759 trapping days. After applying species selection criteria and aggregating consecutive imagery into independent events, we obtained 17996 independent species events of 12 wild target mammals. The most abundant species were the wild boar (\u003cem\u003eSus scrofa\u003c/em\u003e) and the red fox (\u003cem\u003eVulpes vulpes\u003c/em\u003e), with 3978 and 3003 total independent sightings, respectively, while the rarest were the European polecat (\u003cem\u003eMustela putorius\u003c/em\u003e) and the grey wolf (\u003cem\u003eCanis lupus\u003c/em\u003e), with 6 and 18 independent sighting, respectively (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec and SUPP3). Species relative frequency distributions across cell categories and study areas are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePERMANOVA revealed no significant differences among cell categories (p\u0026thinsp;=\u0026thinsp;0.6142). Similarly, no significant differences were detected when observations were grouped according to the green cover (p\u0026thinsp;=\u0026thinsp;0.1015) and green fragmentation (p\u0026thinsp;=\u0026thinsp;0.3217) gradients. The only significant differences were found among cities (p\u0026thinsp;=\u0026thinsp;6e-04, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Pairwise comparisons highlighted Milan as the only significantly differing city (see SUPP5).\u003c/p\u003e \u003cp\u003eAmong the model configurations tested, the Random Forest with 120 trees, a maximum depth of 20, and a sample rate of 0.7 performed best and was therefore selected for further analysis (see SUPP6 for model comparison).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eModel classification accuracy was relatively high (R\u0026sup2; = 0.729). The species predicted to be most influential in community differentiation were the red fox and the eastern cottontail rabbit (\u003cem\u003eSylvilagus floridanus\u003c/em\u003e), together accounting for 28% of the cumulative importance. Other notable species (relative importance around 10%) included the crested porcupine (\u003cem\u003eHystrix cristata\u003c/em\u003e), the wild boar, \u003cem\u003eMartes\u003c/em\u003e sp., and the European hedgehog (\u003cem\u003eErinaceus europaeus\u003c/em\u003e), while the European polecat and the fallow deer (\u003cem\u003eDama dama\u003c/em\u003e) were identified as the least influential species (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe partial dependence plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) show how species frequency influenced the probability of a new observation being classified within each study area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRome was predominantly associated with the red fox, whose probability steadily rose with increasing frequency, reaching a plateau (p\u0026thinsp;=\u0026thinsp;0.5) when fr\u0026thinsp;\u0026gt;\u0026thinsp;1. The European hedgehog and wild boar also showed a strong relationship with Rome, with a descending trend for the European hedgehog (highest p\u0026thinsp;=\u0026thinsp;0.45 when fr\u0026thinsp;=\u0026thinsp;1.3), and an ascending trend for the wild boar, where p\u0026thinsp;=\u0026thinsp;0.28 with fr\u0026thinsp;\u0026gt;\u0026thinsp;3, reaching the same probability as Florence. The crested porcupine displayed a marked positive relationship with Rome, with probabilities approaching p\u0026thinsp;=\u0026thinsp;0.5 as frequency increased, although slightly declining when fr was close to 1.\u003c/p\u003e \u003cp\u003eFlorence showed a strong association with martens, whose probability increased with frequency and reached a plateau (p\u0026thinsp;=\u0026thinsp;0.4) when fr\u0026thinsp;\u0026gt;\u0026thinsp;0.03. Similarly, the wild boar and crested porcupine displayed moderate probabilities (around a mean of 0.3) with an opposite trend between each other. In contrast, the eastern cottontail rabbit and red fox exhibited lower probabilities, indicating a weaker association with this urban area.\u003c/p\u003e \u003cp\u003eMilan was characterized by a strong association with the eastern cottontail rabbit, whose probability sharply increased with fr\u0026thinsp;\u0026gt;\u0026thinsp;0.5 and remained consistently high (p\u0026thinsp;=\u0026thinsp;0.5). Conversely, other species, such as Martes spp. and the European hedgehog exhibited a marked decline in probability as their frequency increased. The red fox reaches a peak probability (p\u0026thinsp;=\u0026thinsp;0.29) when fr\u0026thinsp;=\u0026thinsp;0.4, indicating its limited presence in this study area.\u003c/p\u003e \u003cp\u003eCampobasso was primarily characterized by the wild boar, which maintained the highest probability (mean p\u0026thinsp;=\u0026thinsp;0.27) across most frequencies. The only other species with a probability higher than 0.2 was \u003cem\u003eMartes\u003c/em\u003e spp. (mean p\u0026thinsp;=\u0026thinsp;0.25 across all frequencies), showing a slightly decreasing trend contrasting the one in Rome.\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eDespite the growing interest in urban wildlife ecology (Collins et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), researchers in Europe, and in Italy in particular, are just now starting to investigate mammal species ecology in urban areas (Magle et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In addition to that, while works focusing on single cities started to unveil the complex dynamics at play in these novel ecosystems (Ancillotto et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mori et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), multi-city experimental designs remain scarce, especially for medium and large-sized mammals (Dondina et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Alba et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThrough a year-long camera trap survey, we provided a first extensive assessment of urban medium- and large-sized mammal communities and found significant differences across four Italian cities. Mammal assemblages mainly differed in the frequency of occurrence of the most common species, i.e. the red fox and, at a lesser extent, of the crested porcupine (\u003cem\u003eHystrix cristata\u003c/em\u003e), the wild boar, martens (\u003cem\u003eMartes\u003c/em\u003e spp.), and the hedgehog. The only exception was the eastern cottontail rabbit, an alien species significantly affecting specifically the mammal community in Milan.\u003c/p\u003e \u003cp\u003eDespite urban green cover and fragmentation were often portrayed as primary drivers shaping medium- and large-sized mammal communities (Beninde et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), we did not find any significant difference across combined or individual gradients of green area cover and fragmentation, likely related to the scale at which these parameters were measured.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Species detections in the context of their national range\u003c/h2\u003e \u003cp\u003eTo place the detected species in a broader biogeographic context, we discuss our findings in relation to their currently documented national distribution patterns (Loy et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Overall, species detected in our work were largely consistent with their known Italian distributions, with a few notable exceptions. First, wild boar and grey wolf were detected for the first time in Milan, an area located at the edge of their documented Italian ranges (Loy et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Both species are known for their ecological plasticity and for propensity to access urban areas to exploit easily accessible feeding resources (Bateman and Fleming \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Castillo-Contreras et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Marin et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nevertheless, both species were found quite close to the city, in an area where their distribution is still patchy (Loy et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe wild boar is a highly plastic species that readily adapts to urban areas (Stillfried et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003ea\u003c/span\u003e) exhibiting larger body size, a diet richer in anthropogenic food (Castillo-Contreras et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and changes in movement and diel activity patterns (Podg\u0026oacute;rski et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Wild populations are increasing in Europe (Massei et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), a trend that is mirrored in many urban areas, e.g. Barcelona, Spain (Alabau et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Berlin, Germany (Stillfried et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2017c\u003c/span\u003e), and Krak\u0026oacute;w, Poland (Podg\u0026oacute;rski et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), with corresponding rising numbers of human-wild boar conflict events (Licoppe A. et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In Italy, the species has followed this continental trend, (Monaco et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Loy et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) to the extent of needing management plans in several protected areas to mitigate its social and ecological impacts (e.g Pierucci P. et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ente Parco PNGSL \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Its presence has been documented in several Italian cities, starting from Trieste and Udine in 2011 (Primi et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Scillitani 2023). This aligns with our findings: the species was the most frequently detected in our dataset, with high detection rates in three out of four study areas.\u003c/p\u003e \u003cp\u003eSurprisingly, we did not detect any wolves in Florence, despite well-documented and widespread presence in the surrounding areas (Loy et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The only known records near Florence were from the southern periphery of the city, particularly in Lastra a Signa, Impruneta and Fiesole municipalities (Redazione ANSA \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), suggesting a limited urban colonization. However, a more extensive survey run in Florence by one of the authors did reveal a more extensive occurrence of this species in the urban area of Florence (Mori et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Following a dramatic decline in the 1970s, wolf population began to recover and expand in the Alpine range in the 1980s (Boitani \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1984\u003c/span\u003e), reaching higher population densities in this region compared to the Apennine range (Galaverni et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Currently, this species might be also found in urbanised landscapes (Zanni et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother interesting result is the very limited occurrence of \u003cem\u003eMartes\u003c/em\u003e sp. in the urban area of Milan, especially considering that both the stone marten (\u003cem\u003eM. foina\u003c/em\u003e) and pine marten (\u003cem\u003eM. martes\u003c/em\u003e) are known to occur in the region (Loy et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and that both species have been observed in urban environments. Stone martens are often observed in cities, often denning in building attics (Herr et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), while the congeneric pine marten is known to avoid anthropogenic environments (Bateman and Fleming \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, but see Ancillotto et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Grelli et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Due to their elusive behaviour and the difficulty of distinguishing between the two species through camera trap images, clarifying their distribution pattern in urban areas will require further investigation \u0026ndash; with a dedicated effort for species-level identification and eventually the aid of AI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Community differentiation\u003c/h2\u003e \u003cp\u003eThe PERMANOVA analysis revealed no significant difference in species composition across the green cover and fragmentation gradients provided by the NBFC project sampling design (Dondina et al \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This result is in contrast with numerous studies that documented the importance of green areas extension and fragmentation in determining species richness and composition in urban environments (Beninde et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Aznarez et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA possible explanation is that the spatial scale of the sampling design (1 km\u0026sup2; gridcells) did not match the ecological requirements of our target species. Medium- and large-sized mammals are generally more mobile and have larger home ranges compared to other strictly terrestrial taxonomic groups such as e.g., small mammals (Tucker et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Accordingly, we suggest the sampling design of the national project is not adequate to fully capture the effects of green cover and fragmentation on large and medium mammals\u0026rsquo; communities in urban contexts. Future work will focus on integrating new measures for these variables at multiple scale, as well as the implementation of targeted sampling designs, to further understand their role in shaping the communities.\u003c/p\u003e \u003cp\u003eOur results clearly showed that the four Italian cities differed in the composition of mammal communities. Particularly, Milan showed a unique species composition and a significant divergence from the other three cities. Absence of the crested porcupine - Milan is outside the species range (Mori et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, but see Torretta et al \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) -, together with the unique and widespread occurrence of the alien Eastern cottontail \u0026ndash; introduced from North America in the 1960s for hunting purposes (Bertolino et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) \u0026ndash; could partially explain these differences.\u003c/p\u003e \u003cp\u003eIn general, the species accounting for higher differences among the four cities were not the rarest, but rather the most abundant and shared across all cities. This result contrasts with an expectation based on a presence\u0026ndash;absence viewpoint, where species unique to one city (e.g. European polecat or fallow deer) might be expected to drive community differentiation. However, in analyses based on relative frequencies and Bray\u0026ndash;Curtis dissimilarities, abundant species contribute disproportionately to multivariate differences (Ricotta and Podani \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), whereas rare or sporadically detected species have limited influence. Accordingly, species with low detection frequency, including those occurring in only one city (e.g. grey wolf), were identified as least important in explaining among-city differences.\u003c/p\u003e \u003cp\u003eSpecifically, the Random Forest analysis identified red fox as one of the most influential species in determining differences among the four cities. The red fox is a native carnivore with high ecological plasticity that allows for a high population density in urban areas (Baker and Harris \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), where the species can exploit anthropogenic resources (Contesse et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and adapt to the novel environment (Lazzaroni et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The strong association of red foxes with Rome is consistent with previous studies documenting its presence in urban environments (Bateman and Fleming \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kimmig \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ancillotto et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In Milan - the most densely populated urban area in our study (OECD \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) - the red fox was much less represented than in Rome. The lower representation of red foxes in Milan compared to Rome cannot be explained by the variables included in our analysis and therefore remains unresolved. It is worth noting that Rome is characterized by a mosaic of green areas of different sizes immersed in the urban matrix (Blasi C. 2007), whereas Milan presents a more continuous and densely built environment. Structural differences or city-specific factors, such as mean impervious cover and mean housing density, could alter the response of the species to urbanization in complex and unexpected ways, influencing species assemblages and relative species abundances (Fidino et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Regional context remains a frequently overlooked aspect in urban ecological research, yet it is fundamental to reveal large scale patterns (Magle et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Haight et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur findings provide mixed support for the UBH hypothesis. The checklist of species found in the four Italian investigated cities generally reflected the species composition found in extra urban areas. Some highly elusive and endangered species known to occur in the study areas, -- like the wildcat \u003cem\u003eFelis silvestris\u003c/em\u003e in Rome, Florence or Campobasso --, or species more sensitive to anthropogenic pressures, -- like the pine marten in Milan and the European polecat in Rome, Florence and Milan --, were not observed in the city, confirming the UBH hypothesis and the idea of cities as filters (McKinney \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Lokatis and Jeschke \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, despite sharing many species, the four cities showed distinct community compositions, at least in terms of relative frequencies among species, partially contradicting the classical UBH prediction of increasing similarity among urban areas. City-specific factors that we did not account for may be as important as local urban habitat characteristics and the reason why these communities resulted dissimilar. Additionally, functional traits exhibited by the most influential species, like a large litter size and a broad trophic niche, grant adaptation success to urban environments across various taxonomic groups (Santini et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Lokatis and Jeschke \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This indicates that, while urbanization may favour convergent ecological traits, complete biotic homogenization does not necessarily occur, supporting recent critiques of the universality of UBH patterns (Lokatis and Jeschke \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Conclusions\u003c/h2\u003e \u003cp\u003eOur comprehensive multi-city camera trap work offers a coordinated assessment of urban terrestrial mammals in Italy, a country where urban wildlife ecology remains an emerging field. Our studies evidenced that urban mammal communities in Italy are more diverse than expected, mostly reflecting the mammal assemblages found in extra-urban areas. Our findings also suggest common species\u0026rsquo; relative abundance, rather than presence/absence, plays a central role in shaping urban communities. This highlights the importance of shifting to quantitative population assessments for effective urban wildlife management.\u003c/p\u003e \u003cp\u003eFuture research should focus on understanding the main drivers of species abundance patterns and identifying population hotspots. For invasive species like the eastern cottontail, abundance data becomes critical for evaluating control program efficacy and detecting early population surges before they establish permanent populations. Many common urban species may also act as hosts of parasite-borne diseases, e.g. sarcoptic mange in red foxes and wild boars (Perrucci et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lizana et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with potential risk of transmission to domestic animals and humans. Integrating data on species-specific connectivity and habitat and feeding resources availability will help predict and prevent human-wildlife conflicts, informing urban policy makers on how to manage both endangered and problematic species.\u003c/p\u003e \u003cp\u003eWe are also aware of limitations that should suggest caution when interpreting our results. We worked in compliance with European privacy regulations (EU General Data Protection Regulation 2016/679) related to videos and images of people recording; nonetheless, we faced considerable difficulties in obtaining authorizations to deploy camera traps in public urban areas. Identifying safe and suitable sites for camera placement required attentive on-site evaluations and consultations with local experts. We experienced several thefts and damages to our devices in all cities, with Florence and Rome showing the highest rate of vandalism (14% and 21%, respectively). Theft and damage to equipment are well-documented challenges, especially in urban ecology studies (Dyson et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); our devices were equipped with cable lock and explanatory tags with information of the study and reference contacts. Still, our rates were higher than those reported in similar studies in other geographical contexts (Magle et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Herrera et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Future efforts could benefit from consultations with the communities, the addition of more personal and relatable tag messages (Clarin et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), or improved concealment strategies.\u003c/p\u003e \u003cp\u003eOur results highlight the complexity and specificity of Italian urban mammal assemblages, suggesting that biotic homogenization processes are not universal (Aronson et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These findings emphasize the importance of contextualized conservation and management approaches that consider the ecological, biogeographical, and historical peculiarities of individual urban areas.\u003c/p\u003e \u003c/div\u003e"},{"header":" Statements \u0026 Declarations","content":"\u003cp\u003eACKNOWLEDGMENTS\u003c/p\u003e\n\u003cp\u003eWe are grateful to Bruno Cignini for his invaluable support in obtaining the permits needed to deploy camera traps in Rome\u0026rsquo;s urban parks. We thank Luca Fegatelli, Director of \u003cem\u003eEnte Regionale Parco Appia Antica\u003c/em\u003e, Emiliano Manari from of \u003cem\u003eEnte Regionale Roma Natura\u003c/em\u003e, and Marina Mantella from \u0026lsquo;\u003cem\u003eGestione Territoriale e Ambientale del Verde\u003c/em\u003e\u0026rsquo; of the Rome Municipality, for granting access permission and providing support during fieldwork in Rome. We also thank Olivia Dondina and Valerio Orioli for their hospitality and assistance during fieldwork in Milan.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFUNDING\u003c/p\u003e\n\u003cp\u003eProject funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 - Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union - NextGenerationEU. Award Number: Project code CN_00000033, Concession Decree No. 1034 of 17 June 2022 adopted by the Italian Ministry of University and Research, CUP: H43C22000530001. \u0026nbsp;Laura Limonciello was supported by a PhD grant by the Doctorate of National Interest (DIN), Cycle 39th, coordinated by University of Palermo.\u003c/p\u003e\n\u003cp\u003eCOMPETING INTERESTS\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eAUTHORS CONTRIBUTION\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLL:\u003c/strong\u003e Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Software; Validation; Visualization; Writing \u0026ndash; original draft; Writing \u0026ndash; reviewing and editing. \u003cstrong\u003eEMi:\u003c/strong\u003e Investigation; Writing \u0026ndash; reviewing and editing. \u003cstrong\u003eVA:\u003c/strong\u003e Data curation; Investigation; Writing \u0026ndash; reviewing and editing. \u003cstrong\u003ePJS:\u003c/strong\u003e Formal analysis; Investigation; Writing \u0026ndash; reviewing and editing. \u003cstrong\u003eGI:\u003c/strong\u003e Data curation; Investigation. \u003cstrong\u003eCC:\u003c/strong\u003e Data curation; Investigation. \u0026nbsp;\u003cstrong\u003eEMo:\u003c/strong\u003e Investigation; Writing \u0026ndash; reviewing and editing. \u003cstrong\u003eLA:\u003c/strong\u003e Data curation; Investigation; \u003cstrong\u003eAV:\u003c/strong\u003e Data curation; Investigation. \u003cstrong\u003eMDF:\u003c/strong\u003e Conceptualization; Methodology; Supervision; Writing \u0026ndash; reviewing and editing. \u003cstrong\u003eAL:\u003c/strong\u003e Conceptualization; Funding acquisition; Project administration; Resources; Supervision; Writing \u0026ndash; reviewing and editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlabau E, Mentaberre G, Camarero PR, et al (2020) Accumulation of diastereomers of anticoagulant rodenticides in wild boar from suburban areas: Implications for human consumers. 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Anim Conserv 26:. https://doi.org/10.1111/acv.12858\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"PERMANOVA, terrestrial mammals, urban ecology, camera traps, Random Forest","lastPublishedDoi":"10.21203/rs.3.rs-8479625/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8479625/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Urban areas, and the share of population inhabiting them, are rapidly expanding, with significant impacts on biodiversity. Nevertheless, multi-city comparative studies on urban wildlife –specifically mammal communities – remain scarce, particularly in Italy. Thus, we investigated how a standard evaluation of green cover and fragmentation, designed for a national biodiversity monitoring program, and regional context shape medium-and large-sized mammal assemblages in four Italian cities in Northern, Central, and Southern Italy (Milan, Rome, Florence, Campobasso). We deployed a total 48 camera traps across 1 km² grid cells covering two gradients of green cover and fragmentation. We accumulated 8,759 trapping days and 17,996 independent detections of 12 wild species. Species’ detections were generally consistent with their known national distribution, except for the unexpected occurrence of wild boar and wolf in Milan. Contrary to expectations, we found no significant effects of green areas extent or fragmentation on community composition, suggesting that alternative metrics or spatial scales may be more appropriate for capturing urban mammals’ distribution patterns. Nevertheless, our results revealed significant differences in community composition among cities (PERMANOVA, p=6e-04), with Milan showing the most distinct assemblage compared with Florence, Rome, and Campobasso. The Random Forest analysis identified the red fox (Vulpes vulpes) and the eastern cottontail rabbit (Sylvilagus floridanus) as the most influential species driving inter-city differences, followed by the wild boar (Sus scrofa), crested porcupine (Hystrix cristata), martens (Martes spp.), and the European hedgehog (Erinaceus europaeus). Overall, this work provides a baseline for further investigations of urban mammal ecology in Italy.","manuscriptTitle":"Urban mammals in Italy: how common species shape communities’ differentiation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 15:39:53","doi":"10.21203/rs.3.rs-8479625/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"00c4f0e0-9e1b-4f9e-9e0c-1f07fcd0f240","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T07:58:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-22 15:39:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8479625","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8479625","identity":"rs-8479625","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}