Food or urbanization? Assessing the diversity of urban insectivorous bats in a Neotropical city

Food or urbanization? 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Assessing the diversity of urban insectivorous bats in a Neotropical city Rebeca Selene Miguel-Méndez, M. Cristina MacSwiney G., Víctor Rosas-Guerrero, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7908158/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Urbanization generally has a negative effect on wildlife, including bats. The decline in particular insectivorous bat species, richness and abundance in urban environments is well known; however, the effect of urbanization on diversity is poorly understood. To explain this, we evaluated the effect of urban noise (dB) and artificial light (lux) as a proxy to urbanization, and resource availability, on insectivorous bats. To evaluate which of these drivers best explains the richness, abundance and diversity of urban insectivorous bats, acoustic sampling of the bat community was conducted in Morelia, Michoacán, in three habitat types with varying degrees of urbanization (urban, urban park, transition). The results showed a diverse community (14 species and 3 genera), where the most abundant species, regardless of the degree of disturbance, were Promops centralis and the genus Molossus , which are insectivorous bats known for their adaptation to urban environments. The abundance of insectivorous bats was higher in urban parks, richness did not differ and diversity was higher in transition sites. Despite this, differences among conditions were evident in species composition. Abundance and evenness were best explained by resource availability, while richness and diversity, were equally explained by urbanization and resources availability, which would indicate that the join influence of both is an important condition that can modify urban bat communities. urban noise artificial light degrees of urbanization resource availability Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Urbanization is one of the primary and most extreme forms of environmental transformation (McKinney 2002 , 2008 ; Nielsen et al. 2014 ). As the human population continues to increase, natural habitats are diminishing and are replaced by urbanized areas. This type of habitat modification leaves only remnants of vegetation and wildlife communities altered compared to those found in natural areas (Ancillotto et al. 2022 ). Urbanization regularly leads to a decline in metrics of richness and diversity of fauna, including bats (McKinney 2008 ; Faeth et al. 2011 ). Both richness and abundance of urban insectivorous bats have been widely studied and has a general decline in cities compared to rural or more conserved areas (Linttot et al. 2016, Moretto & Francis 2017 , Rodríguez-Aguilar et al. 2017 , Jung & Threlfall 2016 , 2018 ). However, evidence of the impact of urbanization on insectivorous bat diversity is limited, although a negative effect of urbanization is generally observed (Kurta & Teramino 1992 , Duchamp & Swihart 2008 , Hourigan et al. 2010 , Coleman & Barclay 2012 ). The decline in richness, abundance, and diversity of insectivorous bats in cities could be explained as the result of factors associated with the urbanization of areas where urban boundaries are expanding. Considering urbanization as an explanation to the reduction in bats would posit that the decline in the abundance, richness, and diversity of urban insectivorous bats is driven by urban elements such as artificial lighting and urban noise. Urban noise is a regular component of the city’s acoustic environment, originated from vehicles and human activities, and is considered to be one of the factors influencing wildlife communication (Brumm 2004 , Siemers & Schaub 2011 , Slabbekoorn 2013 , Simmons & Narins 2018 ). Therefore, urban noise is an important factor that can hinder territory defense, courtship, and communication between individuals through the masking of acoustic signals (Warren et al. 2006 ). This masking can occur not only in acoustic properties of vocalization such as amplitude (Slabbekoorn & Peet 2003, Warren et al. 2006 ), but also frequency. Urban noise is dominated by low frequencies, with sounds ​​below 2 kHz (Slabbekoorn & Peet 2003, Warren et al. 2006 ); sounds occurring below this frequency limit can be masked. Although urban noise would not be expected to mask the vocalizations of insectivorous bats due to the high frequencies that characterize them (> 20KHz), some studies have documented that the high levels of urban noise can lead to a decrease in the recording and activity of insectivorous bat species such as Eptesicus fuscus , Lasiurus cinereus , Myotis volans, Myotis miotis , Tadarida brasiliensis , among others (Schaub et al. 2008 , Bunkley et al. 2015 , Finch et al. 2020 , Li et al. 2025 ). This reduction in the activity of insectivorous bats can be the result of the masking in foraging echolocation calls. Artificial light as an urbanization factor can also be one of the main drivers influencing bat diversity. Artificial light has a profound impact on ecological interactions and wildlife behavior, such as foraging, reproduction, and communication (Gaston et al. 2013 ). For insectivorous bats, artificial light has a species-specific differential effect (Stone et al. 2015 ). Artificial light sources in cities, such as streetlights, attract large numbers of insects, creating a localized food source for some species of insectivorous bats in the Molossidae family (Rodríguez-Aguilar et al. 2017 ) and some genera in the Vespertilionidae family ( Eptesicus , Nyctalus , Pipistrellus , and Vespertilio ) (Lewanzik & Voigt 2017 ). In contrast, some bat species of the genus Myotis may show decreased activity on illuminated roads compared to unlit areas (Lewanzik & Voigt 2017 , Voigt et al. 2021 ). Species that forage in narrow and edge spaces also exhibit this light-avoidance behavior (Kalko et al. 2008 , Lewanzik & Voigt 2017 ), possibly due to their strong association with vegetation. This may mean that open areas of the city or well-lit roads could limit the movement and restrict the presence of certain insectivorous bat species to urban green areas. Thus, both artificial light and urban noise can be environmental filters for bat diversity so that the presence and distribution of particular insectivorous bat species can be influenced depending on their foraging frequencies and strategies. This can lead to greater abundance and dominance of some insectivorous bat species (Avila-Flores & Fenton 2005 , Lewanzik & Voigt 2017 ) and influences changes in species richness and abundance. When taken together this highlights the actual impact of these two drivers on particular species; however, their impact on the diversity of urban insectivorous bats is unclear. A second, additional explanation for urbanization-associated factors influencing insectivorous bat diversity is food availability. This indicates that the decline in abundance, richness, and diversity of insectivorous bats in cities is driven by a decrease in food resources. Bats show a widespread coincidence of decreased bat abundance during the dry season and an increase during the rainy season due to fluctuations in some of their food resources in the wild, including arthropods (De Jong & Ahlén 1991 , Hayes 1997 , Klingbeil & Willig 2010 , Ferreira et al. 2017 , Williams et al. 2008). This indicates, on the one hand, the seasonal fluctuation of the food supply for insectivorous bats, and on the other hand that their dynamics are closely related to resource availability. In cities, a reduction in arthropod populations can occur due to the increase in impermeable areas such as streets and buildings (Threlfall et al. 2011 , Russo & Ancilotto 2015, Vas et al. 2023). In addition, arthropods resistant to urbanization are found in greater numbers in green areas within the city such as parks, median strips, forest remnants, private gardens and orchards, which could generate an environmental heterogeneity in the spatial distribution of food resources for insectivorous bats (Biella et al. 2024 ), coinciding with a greater abundance and richness of bats in these areas (Avila-Flores & Fenton 2005 , Lewanzik & Voigt 2017 ). Hence, urban elements such as urban noise and artificial light can affect the abundance and distribution of insects (Eisenbeis & Hänel 2009, Bunkley et al. 2017 , Owens & Lewis 2018 , Fenoglio et al. 2021 ). Due to the lack of information on urban elements and their impact on some aspects of the urban insectivorous bat community, the objective of this study was to evaluate the effect of urbanization, measured as artificial light and urban noise, and resource availability, on the abundance, richness, and diversity of urban insectivorous bats. This study also aimed to assess whether explanation considering urbanization or the availability of food resource would better explain the variability in diversity metrics of the urban insectivorous bat community in a Neotropical city. METHODS Study area This study was carried out in the city of Morelia, Michoacán, Mexico (19°52’ and 19°26’ N, 101°02’ and 101°31’ W). With a population of 849,063 inhabitants as of 2020 (INEGI 2020), and an urban area of ​​97.13 km 2 for ​​the municipality (INEGI 2020), Morelia can be considered a metropolis (OECD 2025). The city of Morelia is a Neotropical city, with a high degree of urbanization mainly in the central zone and towards the north of the city where there is a higher density of residential and industrial complexes, while the southern zone has a greater presence of green areas and native vegetation (Bollo-Manent et al. 2021). The city's surrounding area includes hills, foothills, mountain slopes, intermountain depressions, and river valleys with scrub, grassland, and open forest vegetation (Martínez-Serrano & Bollo-Manent 2017 ). The city is crossed by two rivers, the Río Grande and the Río Chiquito rivers (García 1987 ), where riparian vegetation is found along them (Medina-García & Rodríguez-Jiménez 1993). Pine forest, oak forest, and subtropical scrub can be also found in the city's peripheral areas. The climate is primarily temperate, with the rainy season occurring in the summer (Medina-García & Rodríguez-Jiménez 1993). Temperatures range between 5° − 29°C, and the wettest month averaging 148 mm (Weather Spark 2025). 91.6% of the city of Morelia is considered to be anthropogenic landscapes, where vegetation cover does not exceed 5% of the surface area (Bollo-Manent et al. 2022). The remaining 8.4% of the city's surface area corresponds to landscapes that still have natural cover but have been degraded by anthropogenic activity. Study sites The city of Morelia is a complex urban environment with different degrees of urbanization, of which we focused on three: a) urban, b) urban park, c) transition (Fig. 1 ). Urban sites are dominated by buildings and streets, and vegetation such as parks or private gardens are reduced within an area of ​​700 m 2 , limited to a few isolated trees. These sites were former residences within the historic center of the city. Urban parks are parks or green areas of at least 170,000 m 2 , immersed within the urban area, surrounded by strees, and with a water source. Trees are the dominant growth form in these areas. Transition sites are located on the periphery of the city, in-between the city and the rural area. These sites have less urban influence, as they are not delimited by buildings or streets and have large areas of vegetation. However, they are not considered natural due to their proximity to the urban area (Coleman & Barclay 2012 ). Three sites were selected from each of these levels of urbanization. Characterization of light and noise levels Artificial light intensity measurements were obtained using a professional lux meter (Benetech GM1020, accuracy: ±3% rdg, below 10,000 lux; ±4% rdg, above 10,000 lux). Background noise measurements in decibels were also taken using a digital sound meter (Benetech GM1357, accuracy: ±1.5 dB). Measurements of both artificial light and urban noise were taken once a month during the surveying period, at five different points randomly distributed within each site, every hour for five hours, starting one hour before dusk. The measurements were averaged to obtain the artificial light and urban noise measurements per site, per month. Food resource availability assessment Two Malaise traps were placed at each site, simultaneously during acoustic monitoring, to assess the availability of nocturnal arthropods. The traps were baited, one with white light and the other with ultraviolet light (MacSwiney et al. 2009 ). The traps were checked every half hour to manually collect arthropods, thus increasing capture success. The traps were kept open one night a month for five hours, starting one hour before dusk. The collected arthropods were identified to the order level, as this is the common taxonomic level used in studies with insectivorous bats (Johnson et al. 2007 , Aguiar & Antonini 2008 , Coleman & Barclay 2013 , Morais et al. 2023 ). Only specimens considered as potential prey for insectivorous bats reported in the literature (Mies et al. 1996 , Arroyo-Cabrales & Jones 1988, Rydell et al. 2002 , Johnson et al. 2007 , Aguiar & Antonini 2008 , Kalko et al. 2008 , Lacki & Dodd 2011 , López-Wilchis et al. 2020 , Morais et al. 2023 ) were included in the analysis. Potential prey were dried and weighed on an analytical balance (OHAUS Pioneer, accuracy: ± 0.0002 g) to obtain prey biomass. The dry weight of potential prey was assigned to five prey categories: coleoptera, diptera, Lepidoptera, and Trichoptera; the remaining less abundant orders were grouped into the "others" category. Assessment of the abundance, richness, and diversity of insectivorous bats To estimate the abundance, richness, and diversity of insectivorous bats, the nine sites were sampled once a month from September 2024 to August 2025, during a days before and after new moon. Recordings were made using autonomous ultrasonic detectors (Song Meter, SM4BAT FS) for ten hours, starting one hour before dusk. The recorder recording settings were: 12 dB gain, 16kHz high filter off, 500 kHz sampling rate, 1.5 ms minimum duration, no maximum duration, 12 dB trigger level, and a 3-s trigger window. Species Identification A list of insectivorous bat species potentially distributed geographically in Morelia, Michoacán, was compiled based on various sources (Núñez 2005 , Ceballos & Arroyo Cabrales 2012, Martínez-Mijares 2016, Ferreyra-García et al. 2017 , Ortega et al. 2022 , iNaturalistMX, 2024). Among these potential species, the species Natalus mexicanus was omitted, as it is a high-frequency species (> 135 kHz) and is considered a whispering species that is difficult to record because it requires vocalizations very close to the microphone for proper recording. The analyzed recordings included vocalizations in the 9–135 kHz range. Bat species identification from the recordings was performed automatically in two steps. First, we trained the model. For this a RandomForest analysis was performed using the RandomForest package (Liaw & Wiener 2002 ) for R (Version 4.2.2). This analysis was performed on the list of potentially distributed bat species using the recordings dataset from Zamora-Gutiérrez et al. (2006). From this source we used acoustic parameters similar those we obtained to characterize our field recordings: Fc, HiFreq, LowFreq, Bndwdth, FreqKnee, CallDuration, and StartSlope. The model was trained using 80% of the records from the reference dataset; the remaining 20% ​​of the data were used for model validation. The trained RandomForest model was built with 1000 trees using the acoustic parameters from Zamora-Gutiérrez et al. (2006). Three predictor variables were randomly sampled at each stage. The resampling method used was a 5-fold cross-validation, using the five groups previously defined by Zamora-Gutiérrez et al. (2006). We used automatic grid search, with 1974 as the seed set. The variable to be predicted with RandomForest was the identity of the bat species, using the 7 acoustic parameters as predictors. The predictive performance of the model for species identification was evaluated using the accuracy metric. The model presented an overall average accuracy of 62% in terms of correct classification; however, each species has an independent identification accuracy percentage (see Online Resource 1). As a second procedure, after obtaining the trained model, the species identity was predicted for each recording obtained from the field. The recordings obtained from the autonomous recorders were processed using Kaleidoscope Pro (Wildlife Acoustics). Seven acoustic parameters were obtained from each recording: constant frequency (Fc), maximum frequency (Fmax), minimum frequency (Fmin), bandwidth (Bndwdth), knee frequency (Fk), duration (Dur), and initial slope (S1). Records with fewer than 5 pulses were omitted (Miller 2001 , Zamora-Gutiérrez et al. 2006). Species with a correct identification percentage lower than 75% ( A. pallidus, Neoeptesicus brasiliensis, E. fuscus, Lasiurus ega, M. volans, Myotis yumanensis and Rhogeessa parvula ) were manually reviewed, they were species with fewer than 10 records. There are species that have a low performance in correct classification, such as the three species of the genus Molossus , two species of the genus Corynorhinus and five species of the genus Myotis , so they were grouped into the phonogroups Molossus, Coryno and Myotis, respectively (Pio et al. 2010 , Zamora-Gutierrez et al. 2016 ). Species abundance The frequency of occurrence of echolocation calls has been used as an index of activity or habitat use (Miller 2001 ); however, we used it as an abundance estimator, which was subsequently used to generate diversity indices (Coleman & Barclay 2012 ). Each monthly nocturnal sampling per site was divided into one-minute intervals, and the number of intervals with the presence of each species was counted to obtain the relative abundance of each species and phonogroup (Miller 2001 , Hourigan et al. 2010 ). This procedure helped reduce overestimation of species abundance. Data analysis To determine the differences in environmental variables across urbanization levels, Generalized Linear Mixed Effects Models (GLMMs) were run using the glmer function in the lme4 statistical package (Bates et al. 2015 ). The response variables were: 1) artificial light, 2) prey biomass, both with a gamma distribution; the response variable 3) urban noise level had a lognormal distribution. One model was run independently to evaluate each of these variables. The fixed factor for the artificial light and urban noise models was the degree of urbanization (with three levels: urban, urban park and transition), while for the prey biomass model, the fixed factor was the degree of urbanization and the season (with three levels: dry-cold, dry-warm, and rainy). The dry-cold season included the months of November, December, January and February; the dry-warm season included the months of March, April, May and June; and the rainy season included the months of July, August, September and October. In all models, site was considered a random factor. A PCA (Principal Component Analysis) was performed on the variables artificial light, urban noise and prey biomass to reduce the dimensionality of the data set. The first principal component showed a greater contribution from the variables artificial light and urban noise, so this component can be considered an indirect measure of an urbanization index. The second principal component showed a greater contribution from the variable biomass, so it could be considered an indirect measure of resource availability. Alluvial plots were generated using the dry weight of prey categories (coleoptera, diptera, lepidoptera, trichoptera and others) to visualize how prey biomass is distributed at the different degrees of urbanization. A community structuring analysis was performed using NMDS (Non-Metric Multidimensional Scaling) considering the composition and abundance of bat species per site per month, to graphically represent the similarity of communities between urbanization levels. The diversity of insectivorous bats was assessed using the three Hill numbers (Hill 1973 ) in iNEXT online (Chao et al. 2014 , Chao et al. 2016 ). We estimated q0, which corresponds to species richness. We also obtained q1, which expresses the effective number of abundant species, and q2, which evaluates the effective number of dominant species. Metrics q1 and q2 can be considered an indirect measure of diversity and evenness respectively. For each Hill number we generated Rarefaction curves, along with their confidence intervals, which were used to compare and determine the differences in the diversity metrics ​​(q0, q1 and q2) among the three degrees of urbanization (urban, urban park and transition). Confidence intervals can be used to establish the presence of significant differences in statistics. Significant differences are considered when the 95% confidence interval curves in the rarefaction curves do not overlap (MacGregor-Fors & Payton 2013 ). Rank-abundance curves per degree of urbanization were generated to represent the distribution of relative abundance of species (Whittaker 1965 ). To analyze the effect of urbanization (artificial light and urban noise) and resource availability on abundance and diversity, Generalized Linear Mixed Effects Models (GLMM) were performed. For total urban insectivorous bat abundance response variable, which had a negative binomial distribution, the glmmTMB function was used in the glmmTMB statistical package (Brooks et al. 2025). For Hill numbers, the glmer function was used in the lme4 statistical package (Bates et al. 2015 ). Hill numbers q0 and q1 had a gamma distribution, while Hill number q2 had a lognormal distribution. Two independent models were run separately to assess the influence of two groups of variables in explaining abundance and Hill numbers (q0, q1, and q2) as response variables. First, the urbanization model considered the degree of urbanization (with three levels: urban, urban park and transition), the season (with three levels: dry-cold, dry-warm and rainy), and the component from the PCA representing the urbanization index (PC1). The second model was a resource availability model; this model considered the degree of urbanization (with three levels: urban, urban park and transition), the season (with three levels: dry-cold, dry-warm and rainy), and the component form the PCA representing an indirect measure of resource availability (PC2) as fixed factors. For both models, the site was considered a random factor (see Online Resource 2). Both models can be plausible explanations for the fluctuation in the response variable, so we subsequently evaluated which of the two was the best model to explain the variability in diversity measurements. To do this, we evaluated the performance of the urbanization model and the resource availability model with bat abundance and each Hill number (q0, q1, q2). This analysis was performed using the compare_performance function in the Performance package (Lüdecke et al. 2021 ). The best model was the one with the highest Performance Score. The Performance Score is the mean of the normalization of the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Squared Error (RMS), and Sigma indices (see Online Resource 3). The focus of this comparative analysis is on hypothesis testing, not parameter estimation. RESULTS Environmental Characterization of the Sites The characterization of the sampling sites during the year indicated that urban sites had the highest levels of artificial light and urban noise (light: 22.65 ± 1.8 lux, noise: 62.19 ± 0.69 dB) compared to urban parks (light: 5.06 ± 0.83 lux; noise: 52.94 ± 0.94 dB), while transition sites had the lowest levels of artificial light and urban noise (light: 0.02 ± 0.02 lux, noise: 50.92 ± 0.39 dB) (Table 1 ) (see Online Resource 4). Resource availability showed significant differences in prey biomass, being lower in urban sites and in the dry-cold season (urban: 0.0035 ± 0.0032 gr., urban park: 0.45 ± 0.2 gr., in transition: 0.71 ± 0.3 gr.; dry-warm: 0.44 ± 0.18 gr., dry-cold: 0.06 ± 0.01 gr., rainy: 0.72 ± 0.32 gr.) (Table 1 , Fig. 2 ). Overall coleoptera was the most abundant order, followed by lepidoptera. While coleoptera was absent from urban sites, diptera and lepidoptera were poorly represented. Tricoptera was only present in transition sites (see Online Resource 5). Table 1 Ambiental models of the surveying sites in Morelia city, Michoacán, México. The artificial light model and the urban noise model show significant differences between urbanization levels. The prey biomass model included degree of urbanization and seasonality as fixed factors, because seasonality strongly influences resource availability. This model showed differences in urban sites and the dry-cold season, which is when the lowest levels of prey biomass occur. Artificial light Noise Prey Biomass Predictors Estimates ± Standard Error p Estimates ± Standard Error p Estimates ± Standard Error p (Intercept) 4.6 ± 3 < 0.001* 3.97 ± 0.01 < 0.001* 0.69 ± 0.3 0.231 Non-Urban < 0.001 ± 0.01 < 0.001* -0.04 ± 0.01 0.009* 1.51 ± 0.3 0.211 Urban 4.93 ± 5.6 0.008* 0.16 ± 0.01 < 0.001* 0.05 ± 0.4 < 0.001* Dry-warm season 0.64 ± 0.3 0.171 Dry-cold season 0.17 ± 0.3 < 0.001* Random Effects Marginal R 2 0.865 0.001 0.672 Values marked with * are significant values. In the artificial light and urban noise models the intercept contains the effect of urban park. In the prey biomass model the intercept contains the effect of urban park and rainy season. Insectivorous Bat Community A total sampling effort of 1,080 hours of recordings was obtained, yielding 18,714 records. Fourteen species of insectivorous bats were identified, in addition to the three phonogroups (Table 2 ). No urbanization level presents all species and phonogroups. The species E. fuscus was not recorded in transition sites and urban parks. The species Lasiurus frantzii was not recorded in urban sites, and the species Idionycteris phyllotis and Pteronotus mexicanus are found only in transition sites. The species P. centralis was the most represented in urban sites, followed by the Molossus phonogroup, and the species Eumops underwoodi and T. brasiliensis . The Molossus phonogroup had the greatest number of records in urban parks, followed by the species P. centralis . The best-represented species for the transition sites was P. centralis , followed by the Molossus phonogroup (see Online Resource 6). The NMDS analysis yielded a stress value of 0.133, indicating a good fit. This analysis indicated no clear segregation of species within the three urbanization conditions (see Online Resource 7). Table 2 Bat species and phonotype identified by degree of urbanization (urban, urban park and transition) in the study areas in Morelia city, Michoacán, México. Species or fonotype Urbanization level Balantiopteryx plicata Urban, Urban Park, Transition Mormoops megalophylla Urban, Urban Park, Transition Pteronotus fulvus Urban, Urban Park, Transition Pteronotus mexicanus Transition Coryno Urban, Urban Park, Transition Eptesicus fuscus Urban Neoeptesicus furinalis Urban, Urban Park, Transition Neoeptesicus brasiliensis Urban, Urban Park, Transition Idionycteris phyllotis Transition Lasiurus franzii Urban Park, Transition Lasiurus xanthinus Urban, Urban Park, Transition Myotis Urban, Urban Park, Transition Eumops underwoodi Urban, Urban Park, Transition Molossus Urban, Urban Park, Transition Nyctinomops macrotis Urban, Urban Park, Transition Promops centralis Urban, Urban Park, Transition Tadarida brasiliensis Urban, Urban Park, Transition Diversity estimators The degree of urbanization did not show an impact on species richness (q0) (urban = 14, urban parks = 14, transition = 16). However, the degree of urbanization influenced species diversity and species evenness, considered as the effective number of abundant species (q1) and the effective number of dominant species (q2), respectively. Transition sites had the highest values for q1 = 4.96 and q2 = 3.56, followed by urban parks (q1 = 3.66, q2 = 2.69) and urban sites (q1 = 3.21, q2 = 2.32) (Fig. 3 ). While in the abundance, species richness (q0) and effective number of abundant species (q1) models, differences are found in urban parks and during the cold-dry season, in the effective number of dominant species (q2) differences are found in urban parks and during the warm-dry season (Fig. 4 ) (see Online Resource 2). The performance evaluation of the two models indicated that the resource availability model best explains both abundance (Performance-Score: 80%) and the effective number of dominant species (q2) (Performance-Score: 87.5%). Both models show the same percentage of explanation (Performance-Score: 50%) for richness (q0) and the effective number of abundant species (q1) (Table 3 ). Table 3 The Performance analysis compared the urbanization index and resource availability index models. The resource availability index had a higher Performance-Score for abundance and number of dominant species, indicating that this hypothesis best explains these diversity parameters. Both theories had the same Performance Score for q0 and q1. Conditional R 2 Marginal R 2 AIC weights Performance-Score Abundance Resource availability index 0.29 0.162 0.735 80.00% Urbanization index 0.282 0.146 0.265 20.00% q0 Urbanization index 0.314 0.22 0.51 50.00% Resource availability index 0.316 0.22 0.49 50.00% q1 Urbanization index 0.381 0.22 0.488 50.00% Resource availability index 0.38 0.221 0.512 50.00% q2 Resource availability index 0.083 0.041 0.644 87.50% Urbanization index 0.082 0.04 0.356 12.50% DISCUSSION We found that the selected sites within the city exhibit distinct levels of disturbance. The urban sites exhibit the highest levels of artificial light and urban noise, transition sites exhibit the lowest levels of artificial light and urban noise, while urban parks exhibited the most moderate levels of urbanization. This suggests that surveyed sites vary across defined urbanization levels along the urbanization gradient, with the highest diversity occurring in transition sites, followed by urban parks, and lowest values ​​in urban sites. However, our community structure analysis shows that the community structure of insectivorous bat species does not vary across site types, indicating a homogeneous pattern of diversity across the city. The analysis also indicates that species richness does not present differences between the degree of urbanization. This means that there is a homogenization in bat richness across conditions in the city of Morelia, which has also been reported in the study by Threlfall et al. ( 2011 ). They attributed this homogenization in richens to the forested composition near the urban area in their study site, since it offers trees and caves as resting sites chosen not only by bat species less tolerant to urbanization but also those that usually forage in urban areas. Despite this homogenization in species richness, differences between urbanization levels occur in terms of species composition as E. fuscus is found only in urban sites, while Lasiurus franzii is absent. Likewise, I. phyllotis and P. mexicanus were two exclusive bat species in transition sites. These species could be limited to occur exclusively in transition sites due to their foraging strategy, as these two narrow space foragers can exhibit evasive behavior towards artificial light (Kalko et al. 2008 , Lewanzik & Voigt 2017 ), and then occur in site with low levels of artificial light such as transition sites. Furthermore, I. phyllotis is a substrate forager (Czaplewski 1983 ), locating its food by the sound of prey. This means that the high noise levels found at urban sites could be masking the sound of preys (Schaub et al. 2008 , Bunkley et al. 2015 , Moretto & Francis 2017 ) and therefore avoiding more urbanized sites. Differences in the availability of food resources by condition could be an additional driving factor influencing the species composition. One of the most important elements in the diet of I. phyllotis and P. mexicanus is the order Lepidoptera (Black 1974 , Kalko et al. 2008 , Salinas-Ramos et al. 2015 ), which may be more abundant in transition sites (personal observations), which would explain their presence only in transition sites. Although the species Mormoops megalophylla is also considered a Lepidopteran specialist (Smotherman & Guillén-Servent 2008 ), it is an open-space forager (Rezsutek & Cameron, 1993 ). Unlike I. phyllotis and P. mexicanus which are narrow space foragers (Czaplewski 1983 , Kalko et al. 2008 ), so M. megalophylla would not be limited to these transition sites. Our comparative model analysis indicates that both the urbanization model and the resource availability model have the same capacity to explain species richness, so aversion to artificial light, urban noise, and food preference could be equally important in influencing the occurrence of these species. In our study, differences were found in the number of abundant effective species (q1) and the effective number of dominant species (q2) between the degrees of urbanization, with higher q1 and q2 values ​​in transition sites and lower diversity and evenness in urban sites. These results are consistent with those found by Kurta & Terramino (1992) and in the acoustic study by Coleman & Barclay ( 2012 ), where lower diversity and equity were found in urban areas. Coleman & Barclay ( 2012 ) attributed this decrease in diversity to the dominance of the genus Myotis in urban areas, since this species easily adapts to urban spaces, which can be used as refuge. However, Kurta & Terramino (1992) attributed this decrease to the decrease in food supplies in urban areas compared to areas outside the city. Coleman & Barclay ( 2012 ) found similar diversity between rural areas and the transition zone, and greater uniformity in the transition zone. They attribute this to the intermediate disturbance hypothesis (McKinney 2002 ), which proposes that these areas are heterogeneous landscapes that combine urban features, such as buildings that serve as refuges for some species tolerant to urbanization and natural habitat that favors other less tolerant species, allowing greater diversity. The rank-abundance graphs indicate that the most abundant and dominant species in the three degrees of urbanization were P. centralis and the phonotype Molossus. These two species have low echolocation frequencies (22.8 kHz and 25.6 kHz respectively), so they would be expected to be affected by the higher noise levels in urban sites (Bunkley et al. 2015 ). The distribution of these species in urban settings may indicate that urban noise is not influencing low-frequency species, as found by Bunkley et al. ( 2015 ). The absence of an effect of noise on species depending on the frequency of their vocalizations could be due to noise fluctuations in the city. The study by Bunkley et al. ( 2015 ) was conducted near a gas compressor plant, so the production of noise is constant and for long periods of time. In the city, noise fluctuates throughout the day, with peaks of greater intensity mainly during daylight hours and shortly after dusk. However, these levels decrease at night due to less vehicle activity than during the day. Therefore, the impact of urban noise could be primarily on crepuscular species or when they are emerging from their shelters on their way to foraging sites. However, Li et al. ( 2025 ) concludes that urban noise fluctuations, such as those generated by transportation and that originated from recreational activities, have a greater effect than constant industrial noise, because bats can adapt more easily to the latter. Despite this, it must be considered that not all bat species may be exposed to critical sound levels during dusk or respond in the same way to this stressor, and it could potentially still affect those species that cannot adapt. The observed impact of noise on insectivorous bats is due to the constant noise between 70 and 85 dB (Bunkley et al. 2015 ), while the characteristic noise of urban sites in Morelia is 46 to 72 dB, which suggests that the noise level present in Morelia is not as high to present a limitation on these species, which already proliferate in urban environments, and therefore does not affect the insectivorous bat community. P. centralis and the Molossus phonotype are also open foraging bats abundant in cities. These species are relatively large and have long, narrow wings, which enables them to fly quickly and feed on insects attracted to artificial light sources while avoiding predators (Kalko et al. 2008 , Jung & Threlfall 2018 ). The ability of these bats to exploit artificial light for foraging may explain their abundance and dominance in urban settings, where the highest levels of artificial light are present. Although these open-foraging species have less of an advantage in urban parks and transition sites than smaller, broad-winged, and short-maneuvering species, they are more efficient when foraging in vegetated areas (Kalko et al. 2008 , Jung & Kalko 2011 , Voigt et al. 2021 ), P. centralis and Molossus are also the most abundant and dominant species at these levels of urbanization. This is because, despite being species favored by urban structures that they take as refuges (Sampedro-Marín et al. 2008 , Ávila-Flores et al. 2023), they are also species found in non-urban areas (González-Terrazas et al. 2016 , Sánchez et al. 2025 ). Furthermore, these urban parks and transition sites are located in close proximity to urban areas, so these species may maintain their refuges in transition sites on the city's periphery and move to parks and urban areas to feed (Threlfall et al. 2011 ). Therefore, the presence of artificial light and urban noise could partially explain the abundance and dominance of these species. The presence of species that forage in open spaces in transition sites could be related to the availability of resources, since the species considered abundant and dominant in this degree of urbanization such as the Molossus phonotype, and Neoeptesicus furinalis consume a high percentage of Coleoptera and Lepidoptera (Mies et al. 1996 , Jennings et al. 2002 , Aguiar & Antonini 2008 , Kalko et al. 2008 , Morais et al. 2023 ), while M. megalophylla is considered a lepidopteran specialist bat (Smotherman & Guillén-Servent 2008 ), so the greater presence of this order of insects in transition sites could explain the dominance of these species, as indicated by the evaluation of models; while for abundance it is not very clear which hypothesis explains it best. CONCLUSION Bats are resilient mammals that manage to persist in urban environments; however, urbanization influences their communities. The city of Morelia, a medium-sized Neotropical metropolis, presents different degrees of urbanization that do not influence species richness, but do influence the abundance and diversity of insectivorous bats, decreasing in the most urbanized sites. Our results show that the availability of food resources better explains variations in abundance and dominance than urban factors such as artificial light and urban noise, supporting resource availability as a key driving factor. In the urban bat community of Morelia, differences in diversity are observed; however, species composition is very similar across the three degrees of urbanization, and particular species are making the difference. This indicates that a species-level analysis could provide a better picture of the effect of urbanization on the bat community. The current size of Morelia, coupled with the connections between the most urban and peripheral areas of the city, such as the vegetation of the rivers that runs through it and the urban green areas, allow bats to move throughout the city to feed and seek refuge. However, increasing urbanization, habitat fragmentation, and water pollution could pose growing risks to the conservation of this fauna. We recommend integrating this evidence into urban management through conservation strategies that include regulating levels of artificial lighting, preserving and restoring ecological corridors, and promoting native vegetation in urban parks. Statements and Declarations Acknowledgements We would like to thank the Secretaría de Ciencia, Humanidades, Tecnología e Innovación (Secihti) for the scholarship (No. 924500) to RSM-M. We also thank the Jardin Botánico “Melchor Ocampo”, Comunidad Educativa Villa Montessori, Club Campestre Morelia, Parque Zoológico Benito Juárez, Museo de Historia Natural (MUHNA) and Procuraduria Federal de Protección al Ambiente (PROFEPA) for providing access to their facilities for sampling. Funding This work was supported by Coordinación de la Investigación Científica-Universidad Michoacana de San Nicolas de Hidalgo and the Instituto de Ciencia, Tecnología e Innovación del Estado de Michoacán (ICTI) (Grant number PICIR22-087-C) to AS-M. The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Conflicts of interest/Competing interests The authors declare they have no financial interests to disclose. Contributions RSM-M leaded the conception, design, data collection, data analysis, drafting, and writing of the manuscript. AS-M contributed to the conception and design, data analysis and drafting and writing of the manuscript. MAS-M contributed with data analysis. MCMG contributed to the data analysis and revised the manuscript. VRG contributed revising the manuscript. YHD contributed revising the manuscript. Author information Authors and Affiliations Facultad de Biología, Universidad Michoacana de San Nicolás de Hidalgo C.P. 58004. Morelia, Michoacán de Ocampo, México. Rebeca Selene Miguel-Méndez, [email protected] . Alejandro Salinas-Melgoza, [email protected] . Yvonne Herrerías-Diego, [email protected] . Miguel Angel Salinas-Melgoza, [email protected] . Centro de Investigaciones Tropicales, Universidad Veracruzana. Xalapa, Veracruz. 91000, México. M. Cristina MacSwiney G., [email protected] . Escuela Superior en Desarrollo Sustentable, Universidad Autónoma de Guerrero, Tecpan de Galeana, Guerrero, 40900, México. Victor Rosas-Guerrero, [email protected] . 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Methods Ecol Evol 7(9):1082-1091. https://doi.org/10.1111/2041-210X.12556 Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYRESOURCE.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 08 Jan, 2026 Reviews received at journal 18 Dec, 2025 Reviews received at journal 04 Dec, 2025 Reviewers agreed at journal 28 Nov, 2025 Reviews received at journal 26 Nov, 2025 Reviewers agreed at journal 15 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers invited by journal 03 Nov, 2025 Editor assigned by journal 29 Oct, 2025 Submission checks completed at journal 27 Oct, 2025 First submitted to journal 20 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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08:28:25","extension":"html","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":209438,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7908158/v1/14582628e22d5bca90784db4.html"},{"id":95793204,"identity":"7333d60e-acb6-46f6-a8d6-53d90569e867","added_by":"auto","created_at":"2025-11-13 07:12:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":349876,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area and location of study sites in Morelia, Michoacán, México. The Vegetation index, based on the NDVI, measures vegetation density and condition (INEGI, 2024). On the map, the green color indicates denser vegetation, while the red color indicates elements such as water, clouds, bare ground or buildings.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7908158/v1/bfe6af64fef120a514313b68.png"},{"id":95802183,"identity":"47abead4-5024-4f5a-99f6-99d274005fb3","added_by":"auto","created_at":"2025-11-13 08:27:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":121816,"visible":true,"origin":"","legend":"\u003cp\u003eIn the principal component analysis (PCA), the variables of artificial light and urban noise are mainly associated with PC1, while the variable of prey biomass is mainly associated with PC2. The prey biomass was lower during the dry-cold season. Urban sites have the highest levels of artificial light and urban noise and the lowest prey biomass, as well as the least variation between them. Transition sites and urban parks have similar light and noise values and higher prey biomass values.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7908158/v1/693dd0b68e65ee7f58641092.png"},{"id":95802809,"identity":"b82a515e-afd1-4a7b-97f8-457314ee14df","added_by":"auto","created_at":"2025-11-13 08:28:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72282,"visible":true,"origin":"","legend":"\u003cp\u003eRarefaction curves of Hill numbers (q0, q1, q2) at different degree of urbanization. There are no differences between the degree of urbanization in q0, indicated by the confidence intervals overlap. However, there are differences between the degree of urbanization in q1 and q2.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7908158/v1/f6bed696f64fc84df6cd5bc7.png"},{"id":95802600,"identity":"a77c081f-71f3-490e-8e47-91128a213319","added_by":"auto","created_at":"2025-11-13 08:28:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":14253,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of expected bat abundance and expected Hill numbers (q0, q1 and q2) by models across degree of urbanization (urban, urban park and transition) and seasons (rainyl, dry-warm and dry-cold). Bat abundance is higher in urban parks. Hill numbers generally show lower numbers in urban sites and during the cold-dry season.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7908158/v1/99bf58245fcd430d226ee4da.png"},{"id":95805559,"identity":"666f96a9-d229-4740-8291-3b6ef77bef2a","added_by":"auto","created_at":"2025-11-13 08:42:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1374468,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7908158/v1/14726747-e1bc-4de1-9eef-b334601c9270.pdf"},{"id":95801335,"identity":"e1ec5dea-54fa-471e-b2cd-610dcc1d217f","added_by":"auto","created_at":"2025-11-13 08:25:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":128375,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYRESOURCE.docx","url":"https://assets-eu.researchsquare.com/files/rs-7908158/v1/74e73be15cf834eff8b88234.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Food or urbanization? Assessing the diversity of urban insectivorous bats in a Neotropical city","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eUrbanization is one of the primary and most extreme forms of environmental transformation (McKinney \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Nielsen et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As the human population continues to increase, natural habitats are diminishing and are replaced by urbanized areas. This type of habitat modification leaves only remnants of vegetation and wildlife communities altered compared to those found in natural areas (Ancillotto et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUrbanization regularly leads to a decline in metrics of richness and diversity of fauna, including bats (McKinney \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Faeth et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Both richness and abundance of urban insectivorous bats have been widely studied and has a general decline in cities compared to rural or more conserved areas (Linttot et al. 2016, Moretto \u0026amp; Francis \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Rodr\u0026iacute;guez-Aguilar et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Jung \u0026amp; Threlfall \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, evidence of the impact of urbanization on insectivorous bat diversity is limited, although a negative effect of urbanization is generally observed (Kurta \u0026amp; Teramino \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1992\u003c/span\u003e, Duchamp \u0026amp; Swihart \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Hourigan et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Coleman \u0026amp; Barclay \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe decline in richness, abundance, and diversity of insectivorous bats in cities could be explained as the result of factors associated with the urbanization of areas where urban boundaries are expanding. Considering urbanization as an explanation to the reduction in bats would posit that the decline in the abundance, richness, and diversity of urban insectivorous bats is driven by urban elements such as artificial lighting and urban noise. Urban noise is a regular component of the city\u0026rsquo;s acoustic environment, originated from vehicles and human activities, and is considered to be one of the factors influencing wildlife communication (Brumm \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Siemers \u0026amp; Schaub \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Slabbekoorn \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Simmons \u0026amp; Narins \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, urban noise is an important factor that can hinder territory defense, courtship, and communication between individuals through the masking of acoustic signals (Warren et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This masking can occur not only in acoustic properties of vocalization such as amplitude (Slabbekoorn \u0026amp; Peet 2003, Warren et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), but also frequency.\u003c/p\u003e\u003cp\u003eUrban noise is dominated by low frequencies, with sounds ​​below 2 kHz (Slabbekoorn \u0026amp; Peet 2003, Warren et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2006\u003c/span\u003e); sounds occurring below this frequency limit can be masked. Although urban noise would not be expected to mask the vocalizations of insectivorous bats due to the high frequencies that characterize them (\u0026gt;\u0026thinsp;20KHz), some studies have documented that the high levels of urban noise can lead to a decrease in the recording and activity of insectivorous bat species such as \u003cem\u003eEptesicus fuscus\u003c/em\u003e, \u003cem\u003eLasiurus cinereus\u003c/em\u003e, \u003cem\u003eMyotis volans, Myotis miotis\u003c/em\u003e, \u003cem\u003eTadarida brasiliensis\u003c/em\u003e, among others (Schaub et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Bunkley et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Finch et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Li et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This reduction in the activity of insectivorous bats can be the result of the masking in foraging echolocation calls.\u003c/p\u003e\u003cp\u003eArtificial light as an urbanization factor can also be one of the main drivers influencing bat diversity. Artificial light has a profound impact on ecological interactions and wildlife behavior, such as foraging, reproduction, and communication (Gaston et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). For insectivorous bats, artificial light has a species-specific differential effect (Stone et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Artificial light sources in cities, such as streetlights, attract large numbers of insects, creating a localized food source for some species of insectivorous bats in the Molossidae family (Rodr\u0026iacute;guez-Aguilar et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and some genera in the Vespertilionidae family (\u003cem\u003eEptesicus\u003c/em\u003e, \u003cem\u003eNyctalus\u003c/em\u003e, \u003cem\u003ePipistrellus\u003c/em\u003e, and \u003cem\u003eVespertilio\u003c/em\u003e) (Lewanzik \u0026amp; Voigt \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In contrast, some bat species of the genus \u003cem\u003eMyotis\u003c/em\u003e may show decreased activity on illuminated roads compared to unlit areas (Lewanzik \u0026amp; Voigt \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Voigt et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Species that forage in narrow and edge spaces also exhibit this light-avoidance behavior (Kalko et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Lewanzik \u0026amp; Voigt \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), possibly due to their strong association with vegetation. This may mean that open areas of the city or well-lit roads could limit the movement and restrict the presence of certain insectivorous bat species to urban green areas. Thus, both artificial light and urban noise can be environmental filters for bat diversity so that the presence and distribution of particular insectivorous bat species can be influenced depending on their foraging frequencies and strategies. This can lead to greater abundance and dominance of some insectivorous bat species (Avila-Flores \u0026amp; Fenton \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Lewanzik \u0026amp; Voigt \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and influences changes in species richness and abundance. When taken together this highlights the actual impact of these two drivers on particular species; however, their impact on the diversity of urban insectivorous bats is unclear.\u003c/p\u003e\u003cp\u003eA second, additional explanation for urbanization-associated factors influencing insectivorous bat diversity is food availability. This indicates that the decline in abundance, richness, and diversity of insectivorous bats in cities is driven by a decrease in food resources. Bats show a widespread coincidence of decreased bat abundance during the dry season and an increase during the rainy season due to fluctuations in some of their food resources in the wild, including arthropods (De Jong \u0026amp; Ahl\u0026eacute;n \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1991\u003c/span\u003e, Hayes \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1997\u003c/span\u003e, Klingbeil \u0026amp; Willig \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Ferreira et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Williams \u003cem\u003eet al.\u003c/em\u003e 2008). This indicates, on the one hand, the seasonal fluctuation of the food supply for insectivorous bats, and on the other hand that their dynamics are closely related to resource availability. In cities, a reduction in arthropod populations can occur due to the increase in impermeable areas such as streets and buildings (Threlfall et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Russo \u0026amp; Ancilotto 2015, Vas \u003cem\u003eet al.\u003c/em\u003e 2023). In addition, arthropods resistant to urbanization are found in greater numbers in green areas within the city such as parks, median strips, forest remnants, private gardens and orchards, which could generate an environmental heterogeneity in the spatial distribution of food resources for insectivorous bats (Biella et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), coinciding with a greater abundance and richness of bats in these areas (Avila-Flores \u0026amp; Fenton \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Lewanzik \u0026amp; Voigt \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Hence, urban elements such as urban noise and artificial light can affect the abundance and distribution of insects (Eisenbeis \u0026amp; H\u0026auml;nel 2009, Bunkley et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Owens \u0026amp; Lewis \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Fenoglio et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDue to the lack of information on urban elements and their impact on some aspects of the urban insectivorous bat community, the objective of this study was to evaluate the effect of urbanization, measured as artificial light and urban noise, and resource availability, on the abundance, richness, and diversity of urban insectivorous bats. This study also aimed to assess whether explanation considering urbanization or the availability of food resource would better explain the variability in diversity metrics of the urban insectivorous bat community in a Neotropical city.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy area\u003c/h2\u003e\u003cp\u003eThis study was carried out in the city of Morelia, Michoac\u0026aacute;n, Mexico (19\u0026deg;52\u0026rsquo; and 19\u0026deg;26\u0026rsquo; N, 101\u0026deg;02\u0026rsquo; and 101\u0026deg;31\u0026rsquo; W). With a population of 849,063 inhabitants as of 2020 (INEGI 2020), and an urban area of ​​97.13 km\u003csup\u003e2\u003c/sup\u003e for ​​the municipality (INEGI 2020), Morelia can be considered a metropolis (OECD 2025). The city of Morelia is a Neotropical city, with a high degree of urbanization mainly in the central zone and towards the north of the city where there is a higher density of residential and industrial complexes, while the southern zone has a greater presence of green areas and native vegetation (Bollo-Manent \u003cem\u003eet al.\u003c/em\u003e 2021). The city's surrounding area includes hills, foothills, mountain slopes, intermountain depressions, and river valleys with scrub, grassland, and open forest vegetation (Mart\u0026iacute;nez-Serrano \u0026amp; Bollo-Manent \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The city is crossed by two rivers, the R\u0026iacute;o Grande and the R\u0026iacute;o Chiquito rivers (Garc\u0026iacute;a \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1987\u003c/span\u003e), where riparian vegetation is found along them (Medina-Garc\u0026iacute;a \u0026amp; Rodr\u0026iacute;guez-Jim\u0026eacute;nez 1993). Pine forest, oak forest, and subtropical scrub can be also found in the city's peripheral areas. The climate is primarily temperate, with the rainy season occurring in the summer (Medina-Garc\u0026iacute;a \u0026amp; Rodr\u0026iacute;guez-Jim\u0026eacute;nez 1993). Temperatures range between 5\u0026deg; \u0026minus;\u0026thinsp;29\u0026deg;C, and the wettest month averaging 148 mm (Weather Spark 2025). 91.6% of the city of Morelia is considered to be anthropogenic landscapes, where vegetation cover does not exceed 5% of the surface area (Bollo-Manent \u003cem\u003eet al.\u003c/em\u003e 2022). The remaining 8.4% of the city's surface area corresponds to landscapes that still have natural cover but have been degraded by anthropogenic activity.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy sites\u003c/h3\u003e\n\u003cp\u003eThe city of Morelia is a complex urban environment with different degrees of urbanization, of which we focused on three: a) urban, b) urban park, c) transition (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Urban sites are dominated by buildings and streets, and vegetation such as parks or private gardens are reduced within an area of ​​700 m\u003csup\u003e2\u003c/sup\u003e, limited to a few isolated trees. These sites were former residences within the historic center of the city. Urban parks are parks or green areas of at least 170,000 m\u003csup\u003e2\u003c/sup\u003e, immersed within the urban area, surrounded by strees, and with a water source. Trees are the dominant growth form in these areas. Transition sites are located on the periphery of the city, in-between the city and the rural area. These sites have less urban influence, as they are not delimited by buildings or streets and have large areas of vegetation. However, they are not considered natural due to their proximity to the urban area (Coleman \u0026amp; Barclay \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Three sites were selected from each of these levels of urbanization.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eCharacterization of light and noise levels\u003c/h3\u003e\n\u003cp\u003eArtificial light intensity measurements were obtained using a professional lux meter (Benetech GM1020, accuracy: \u0026plusmn;3% rdg, below 10,000 lux; \u0026plusmn;4% rdg, above 10,000 lux). Background noise measurements in decibels were also taken using a digital sound meter (Benetech GM1357, accuracy: \u0026plusmn;1.5 dB). Measurements of both artificial light and urban noise were taken once a month during the surveying period, at five different points randomly distributed within each site, every hour for five hours, starting one hour before dusk. The measurements were averaged to obtain the artificial light and urban noise measurements per site, per month.\u003c/p\u003e\n\u003ch3\u003eFood resource availability assessment\u003c/h3\u003e\n\u003cp\u003eTwo Malaise traps were placed at each site, simultaneously during acoustic monitoring, to assess the availability of nocturnal arthropods. The traps were baited, one with white light and the other with ultraviolet light (MacSwiney et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The traps were checked every half hour to manually collect arthropods, thus increasing capture success. The traps were kept open one night a month for five hours, starting one hour before dusk. The collected arthropods were identified to the order level, as this is the common taxonomic level used in studies with insectivorous bats (Johnson et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Aguiar \u0026amp; Antonini \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Coleman \u0026amp; Barclay \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Morais et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Only specimens considered as potential prey for insectivorous bats reported in the literature (Mies et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, Arroyo-Cabrales \u0026amp; Jones 1988, Rydell et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Johnson et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Aguiar \u0026amp; Antonini \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Kalko et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Lacki \u0026amp; Dodd \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, L\u0026oacute;pez-Wilchis et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Morais et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) were included in the analysis. Potential prey were dried and weighed on an analytical balance (OHAUS Pioneer, accuracy: \u0026plusmn; 0.0002 g) to obtain prey biomass. The dry weight of potential prey was assigned to five prey categories: coleoptera, diptera, Lepidoptera, and Trichoptera; the remaining less abundant orders were grouped into the \"others\" category.\u003c/p\u003e\n\u003ch3\u003eAssessment of the abundance, richness, and diversity of insectivorous bats\u003c/h3\u003e\n\u003cp\u003eTo estimate the abundance, richness, and diversity of insectivorous bats, the nine sites were sampled once a month from September 2024 to August 2025, during a days before and after new moon. Recordings were made using autonomous ultrasonic detectors (Song Meter, SM4BAT FS) for ten hours, starting one hour before dusk. The recorder recording settings were: 12 dB gain, 16kHz high filter off, 500 kHz sampling rate, 1.5 ms minimum duration, no maximum duration, 12 dB trigger level, and a 3-s trigger window.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSpecies Identification\u003c/h2\u003e\u003cp\u003eA list of insectivorous bat species potentially distributed geographically in Morelia, Michoac\u0026aacute;n, was compiled based on various sources (N\u0026uacute;\u0026ntilde;ez \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Ceballos \u0026amp; Arroyo Cabrales 2012, Mart\u0026iacute;nez-Mijares 2016, Ferreyra-Garc\u0026iacute;a et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Ortega et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, iNaturalistMX, 2024). Among these potential species, the species \u003cem\u003eNatalus mexicanus\u003c/em\u003e was omitted, as it is a high-frequency species (\u0026gt;\u0026thinsp;135 kHz) and is considered a whispering species that is difficult to record because it requires vocalizations very close to the microphone for proper recording. The analyzed recordings included vocalizations in the 9\u0026ndash;135 kHz range.\u003c/p\u003e\u003cp\u003eBat species identification from the recordings was performed automatically in two steps. First, we trained the model. For this a RandomForest analysis was performed using the RandomForest package (Liaw \u0026amp; Wiener \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) for R (Version 4.2.2). This analysis was performed on the list of potentially distributed bat species using the recordings dataset from Zamora-Guti\u0026eacute;rrez \u003cem\u003eet al.\u003c/em\u003e (2006). From this source we used acoustic parameters similar those we obtained to characterize our field recordings: Fc, HiFreq, LowFreq, Bndwdth, FreqKnee, CallDuration, and StartSlope. The model was trained using 80% of the records from the reference dataset; the remaining 20% ​​of the data were used for model validation. The trained RandomForest model was built with 1000 trees using the acoustic parameters from Zamora-Guti\u0026eacute;rrez \u003cem\u003eet al.\u003c/em\u003e (2006). Three predictor variables were randomly sampled at each stage. The resampling method used was a 5-fold cross-validation, using the five groups previously defined by Zamora-Guti\u0026eacute;rrez \u003cem\u003eet al.\u003c/em\u003e (2006). We used automatic grid search, with 1974 as the seed set. The variable to be predicted with RandomForest was the identity of the bat species, using the 7 acoustic parameters as predictors. The predictive performance of the model for species identification was evaluated using the accuracy metric. The model presented an overall average accuracy of 62% in terms of correct classification; however, each species has an independent identification accuracy percentage (see Online Resource 1).\u003c/p\u003e\u003cp\u003eAs a second procedure, after obtaining the trained model, the species identity was predicted for each recording obtained from the field. The recordings obtained from the autonomous recorders were processed using Kaleidoscope Pro (Wildlife Acoustics). Seven acoustic parameters were obtained from each recording: constant frequency (Fc), maximum frequency (Fmax), minimum frequency (Fmin), bandwidth (Bndwdth), knee frequency (Fk), duration (Dur), and initial slope (S1). Records with fewer than 5 pulses were omitted (Miller \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, Zamora-Guti\u0026eacute;rrez \u003cem\u003eet al.\u003c/em\u003e 2006). Species with a correct identification percentage lower than 75% (\u003cem\u003eA. pallidus, Neoeptesicus brasiliensis, E. fuscus, Lasiurus ega, M. volans, Myotis yumanensis\u003c/em\u003e and \u003cem\u003eRhogeessa parvula\u003c/em\u003e) were manually reviewed, they were species with fewer than 10 records. There are species that have a low performance in correct classification, such as the three species of the genus \u003cem\u003eMolossus\u003c/em\u003e, two species of the genus \u003cem\u003eCorynorhinus\u003c/em\u003e and five species of the genus \u003cem\u003eMyotis\u003c/em\u003e, so they were grouped into the phonogroups Molossus, Coryno and Myotis, respectively (Pio et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Zamora-Gutierrez et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSpecies abundance\u003c/h3\u003e\n\u003cp\u003eThe frequency of occurrence of echolocation calls has been used as an index of activity or habitat use (Miller \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2001\u003c/span\u003e); however, we used it as an abundance estimator, which was subsequently used to generate diversity indices (Coleman \u0026amp; Barclay \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Each monthly nocturnal sampling per site was divided into one-minute intervals, and the number of intervals with the presence of each species was counted to obtain the relative abundance of each species and phonogroup (Miller \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, Hourigan et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This procedure helped reduce overestimation of species abundance.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eTo determine the differences in environmental variables across urbanization levels, Generalized Linear Mixed Effects Models (GLMMs) were run using the glmer function in the lme4 statistical package (Bates et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The response variables were: 1) artificial light, 2) prey biomass, both with a gamma distribution; the response variable 3) urban noise level had a lognormal distribution. One model was run independently to evaluate each of these variables. The fixed factor for the artificial light and urban noise models was the degree of urbanization (with three levels: urban, urban park and transition), while for the prey biomass model, the fixed factor was the degree of urbanization and the season (with three levels: dry-cold, dry-warm, and rainy). The dry-cold season included the months of November, December, January and February; the dry-warm season included the months of March, April, May and June; and the rainy season included the months of July, August, September and October. In all models, site was considered a random factor.\u003c/p\u003e\u003cp\u003eA PCA (Principal Component Analysis) was performed on the variables artificial light, urban noise and prey biomass to reduce the dimensionality of the data set. The first principal component showed a greater contribution from the variables artificial light and urban noise, so this component can be considered an indirect measure of an urbanization index. The second principal component showed a greater contribution from the variable biomass, so it could be considered an indirect measure of resource availability. Alluvial plots were generated using the dry weight of prey categories (coleoptera, diptera, lepidoptera, trichoptera and others) to visualize how prey biomass is distributed at the different degrees of urbanization.\u003c/p\u003e\u003cp\u003eA community structuring analysis was performed using NMDS (Non-Metric Multidimensional Scaling) considering the composition and abundance of bat species per site per month, to graphically represent the similarity of communities between urbanization levels. The diversity of insectivorous bats was assessed using the three Hill numbers (Hill \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1973\u003c/span\u003e) in iNEXT online (Chao et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Chao et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). We estimated q0, which corresponds to species richness. We also obtained q1, which expresses the effective number of abundant species, and q2, which evaluates the effective number of dominant species. Metrics q1 and q2 can be considered an indirect measure of diversity and evenness respectively. For each Hill number we generated Rarefaction curves, along with their confidence intervals, which were used to compare and determine the differences in the diversity metrics ​​(q0, q1 and q2) among the three degrees of urbanization (urban, urban park and transition). Confidence intervals can be used to establish the presence of significant differences in statistics. Significant differences are considered when the 95% confidence interval curves in the rarefaction curves do not overlap (MacGregor-Fors \u0026amp; Payton \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Rank-abundance curves per degree of urbanization were generated to represent the distribution of relative abundance of species (Whittaker \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e1965\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo analyze the effect of urbanization (artificial light and urban noise) and resource availability on abundance and diversity, Generalized Linear Mixed Effects Models (GLMM) were performed. For total urban insectivorous bat abundance response variable, which had a negative binomial distribution, the glmmTMB function was used in the glmmTMB statistical package (Brooks \u003cem\u003eet al.\u003c/em\u003e 2025). For Hill numbers, the glmer function was used in the lme4 statistical package (Bates et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Hill numbers q0 and q1 had a gamma distribution, while Hill number q2 had a lognormal distribution. Two independent models were run separately to assess the influence of two groups of variables in explaining abundance and Hill numbers (q0, q1, and q2) as response variables. First, the urbanization model considered the degree of urbanization (with three levels: urban, urban park and transition), the season (with three levels: dry-cold, dry-warm and rainy), and the component from the PCA representing the urbanization index (PC1). The second model was a resource availability model; this model considered the degree of urbanization (with three levels: urban, urban park and transition), the season (with three levels: dry-cold, dry-warm and rainy), and the component form the PCA representing an indirect measure of resource availability (PC2) as fixed factors. For both models, the site was considered a random factor (see Online Resource 2). Both models can be plausible explanations for the fluctuation in the response variable, so we subsequently evaluated which of the two was the best model to explain the variability in diversity measurements. To do this, we evaluated the performance of the urbanization model and the resource availability model with bat abundance and each Hill number (q0, q1, q2). This analysis was performed using the compare_performance function in the Performance package (L\u0026uuml;decke et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The best model was the one with the highest Performance Score. The Performance Score is the mean of the normalization of the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Squared Error (RMS), and Sigma indices (see Online Resource 3). The focus of this comparative analysis is on hypothesis testing, not parameter estimation.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eEnvironmental Characterization of the Sites\u003c/h2\u003e\n \u003cp\u003eThe characterization of the sampling sites during the year indicated that urban sites had the highest levels of artificial light and urban noise (light: 22.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 lux, noise: 62.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69 dB) compared to urban parks (light: 5.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83 lux; noise: 52.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94 dB), while transition sites had the lowest levels of artificial light and urban noise (light: 0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 lux, noise: 50.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39 dB) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) (see Online Resource 4). Resource availability showed significant differences in prey biomass, being lower in urban sites and in the dry-cold season (urban: 0.0035\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0032 gr., urban park: 0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 gr., in transition: 0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 gr.; dry-warm: 0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18 gr., dry-cold: 0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 gr., rainy: 0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32 gr.) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Overall coleoptera was the most abundant order, followed by lepidoptera. While coleoptera was absent from urban sites, diptera and lepidoptera were poorly represented. Tricoptera was only present in transition sites (see Online Resource 5).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAmbiental models of the surveying sites in Morelia city, Michoac\u0026aacute;n, M\u0026eacute;xico. The artificial light model and the urban noise model show significant differences between urbanization levels. The prey biomass model included degree of urbanization and seasonality as fixed factors, because seasonality strongly influences resource availability. This model showed differences in urban sites and the dry-cold season, which is when the lowest levels of prey biomass occur.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eArtificial light\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eNoise\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ePrey Biomass\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePredictors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEstimates\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEstimates\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEstimates\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.93\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDry-warm season\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDry-cold season\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eRandom Effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarginal R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eValues marked with * are significant values. In the artificial light and urban noise models the intercept contains the effect of urban park. In the prey biomass model the intercept contains the effect of urban park and rainy season.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eInsectivorous Bat Community\u003c/h2\u003e\n \u003cp\u003eA total sampling effort of 1,080 hours of recordings was obtained, yielding 18,714 records. Fourteen species of insectivorous bats were identified, in addition to the three phonogroups (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). No urbanization level presents all species and phonogroups. The species \u003cem\u003eE. fuscus\u003c/em\u003e was not recorded in transition sites and urban parks. The species \u003cem\u003eLasiurus frantzii\u003c/em\u003e was not recorded in urban sites, and the species \u003cem\u003eIdionycteris phyllotis\u003c/em\u003e and \u003cem\u003ePteronotus mexicanus\u003c/em\u003e are found only in transition sites. The species \u003cem\u003eP. centralis\u003c/em\u003e was the most represented in urban sites, followed by the Molossus phonogroup, and the species \u003cem\u003eEumops underwoodi\u003c/em\u003e and \u003cem\u003eT. brasiliensis\u003c/em\u003e. The Molossus phonogroup had the greatest number of records in urban parks, followed by the species \u003cem\u003eP. centralis\u003c/em\u003e. The best-represented species for the transition sites was \u003cem\u003eP. centralis\u003c/em\u003e, followed by the Molossus phonogroup (see Online Resource 6). The NMDS analysis yielded a stress value of 0.133, indicating a good fit. This analysis indicated no clear segregation of species within the three urbanization conditions (see Online Resource 7).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBat species and phonotype identified by degree of urbanization (urban, urban park and transition) in the study areas in Morelia city, Michoac\u0026aacute;n, M\u0026eacute;xico.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSpecies or fonotype\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eUrbanization level\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBalantiopteryx plicata\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban, Urban Park, Transition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMormoops megalophylla\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban, Urban Park, Transition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePteronotus fulvus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban, Urban Park, Transition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePteronotus mexicanus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoryno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban, Urban Park, Transition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEptesicus fuscus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNeoeptesicus furinalis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban, Urban Park, Transition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNeoeptesicus brasiliensis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban, Urban Park, Transition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIdionycteris phyllotis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLasiurus franzii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban Park, Transition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLasiurus xanthinus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban, Urban Park, Transition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMyotis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban, Urban Park, Transition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEumops underwoodi\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban, Urban Park, Transition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMolossus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban, Urban Park, Transition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNyctinomops macrotis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban, Urban Park, Transition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePromops centralis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban, Urban Park, Transition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTadarida brasiliensis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban, Urban Park, Transition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eDiversity estimators\u003c/h2\u003e\n \u003cp\u003eThe degree of urbanization did not show an impact on species richness (q0) (urban\u0026thinsp;=\u0026thinsp;14, urban parks\u0026thinsp;=\u0026thinsp;14, transition\u0026thinsp;=\u0026thinsp;16). However, the degree of urbanization influenced species diversity and species evenness, considered as the effective number of abundant species (q1) and the effective number of dominant species (q2), respectively. Transition sites had the highest values for q1\u0026thinsp;=\u0026thinsp;4.96 and q2\u0026thinsp;=\u0026thinsp;3.56, followed by urban parks (q1\u0026thinsp;=\u0026thinsp;3.66, q2\u0026thinsp;=\u0026thinsp;2.69) and urban sites (q1\u0026thinsp;=\u0026thinsp;3.21, q2\u0026thinsp;=\u0026thinsp;2.32) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eWhile in the abundance, species richness (q0) and effective number of abundant species (q1) models, differences are found in urban parks and during the cold-dry season, in the effective number of dominant species (q2) differences are found in urban parks and during the warm-dry season (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) (see Online Resource 2). The performance evaluation of the two models indicated that the resource availability model best explains both abundance (Performance-Score: 80%) and the effective number of dominant species (q2) (Performance-Score: 87.5%). Both models show the same percentage of explanation (Performance-Score: 50%) for richness (q0) and the effective number of abundant species (q1) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe Performance analysis compared the urbanization index and resource availability index models. The resource availability index had a higher Performance-Score for abundance and number of dominant species, indicating that this hypothesis best explains these diversity parameters. Both theories had the same Performance Score for q0 and q1.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eConditional R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMarginal R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAIC weights\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePerformance-Score\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbundance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResource availability index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrbanization index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eq0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrbanization index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResource availability index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eq1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrbanization index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResource availability index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eq2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResource availability index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrbanization index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eWe found that the selected sites within the city exhibit distinct levels of disturbance. The urban sites exhibit the highest levels of artificial light and urban noise, transition sites exhibit the lowest levels of artificial light and urban noise, while urban parks exhibited the most moderate levels of urbanization. This suggests that surveyed sites vary across defined urbanization levels along the urbanization gradient, with the highest diversity occurring in transition sites, followed by urban parks, and lowest values ​​in urban sites. However, our community structure analysis shows that the community structure of insectivorous bat species does not vary across site types, indicating a homogeneous pattern of diversity across the city.\u003c/p\u003e\u003cp\u003eThe analysis also indicates that species richness does not present differences between the degree of urbanization. This means that there is a homogenization in bat richness across conditions in the city of Morelia, which has also been reported in the study by Threlfall et al. (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). They attributed this homogenization in richens to the forested composition near the urban area in their study site, since it offers trees and caves as resting sites chosen not only by bat species less tolerant to urbanization but also those that usually forage in urban areas.\u003c/p\u003e\u003cp\u003eDespite this homogenization in species richness, differences between urbanization levels occur in terms of species composition as \u003cem\u003eE. fuscus\u003c/em\u003e is found only in urban sites, while \u003cem\u003eLasiurus franzii\u003c/em\u003e is absent. Likewise, \u003cem\u003eI. phyllotis\u003c/em\u003e and \u003cem\u003eP. mexicanus\u003c/em\u003e were two exclusive bat species in transition sites. These species could be limited to occur exclusively in transition sites due to their foraging strategy, as these two narrow space foragers can exhibit evasive behavior towards artificial light (Kalko et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Lewanzik \u0026amp; Voigt \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and then occur in site with low levels of artificial light such as transition sites. Furthermore, \u003cem\u003eI. phyllotis\u003c/em\u003e is a substrate forager (Czaplewski \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1983\u003c/span\u003e), locating its food by the sound of prey. This means that the high noise levels found at urban sites could be masking the sound of preys (Schaub et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Bunkley et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Moretto \u0026amp; Francis \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and therefore avoiding more urbanized sites.\u003c/p\u003e\u003cp\u003eDifferences in the availability of food resources by condition could be an additional driving factor influencing the species composition. One of the most important elements in the diet of \u003cem\u003eI. phyllotis\u003c/em\u003e and \u003cem\u003eP. mexicanus\u003c/em\u003e is the order Lepidoptera (Black \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1974\u003c/span\u003e, Kalko et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Salinas-Ramos et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which may be more abundant in transition sites (personal observations), which would explain their presence only in transition sites. Although the species \u003cem\u003eMormoops megalophylla\u003c/em\u003e is also considered a Lepidopteran specialist (Smotherman \u0026amp; Guill\u0026eacute;n-Servent \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), it is an open-space forager (Rezsutek \u0026amp; Cameron, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Unlike \u003cem\u003eI. phyllotis\u003c/em\u003e and \u003cem\u003eP. mexicanus\u003c/em\u003e which are narrow space foragers (Czaplewski \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1983\u003c/span\u003e, Kalko et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), so \u003cem\u003eM. megalophylla\u003c/em\u003e would not be limited to these transition sites. Our comparative model analysis indicates that both the urbanization model and the resource availability model have the same capacity to explain species richness, so aversion to artificial light, urban noise, and food preference could be equally important in influencing the occurrence of these species.\u003c/p\u003e\u003cp\u003eIn our study, differences were found in the number of abundant effective species (q1) and the effective number of dominant species (q2) between the degrees of urbanization, with higher q1 and q2 values ​​in transition sites and lower diversity and evenness in urban sites. These results are consistent with those found by Kurta \u0026amp; Terramino (1992) and in the acoustic study by Coleman \u0026amp; Barclay (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), where lower diversity and equity were found in urban areas. Coleman \u0026amp; Barclay (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) attributed this decrease in diversity to the dominance of the genus \u003cem\u003eMyotis\u003c/em\u003e in urban areas, since this species easily adapts to urban spaces, which can be used as refuge. However, Kurta \u0026amp; Terramino (1992) attributed this decrease to the decrease in food supplies in urban areas compared to areas outside the city. Coleman \u0026amp; Barclay (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) found similar diversity between rural areas and the transition zone, and greater uniformity in the transition zone. They attribute this to the intermediate disturbance hypothesis (McKinney \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), which proposes that these areas are heterogeneous landscapes that combine urban features, such as buildings that serve as refuges for some species tolerant to urbanization and natural habitat that favors other less tolerant species, allowing greater diversity.\u003c/p\u003e\u003cp\u003eThe rank-abundance graphs indicate that the most abundant and dominant species in the three degrees of urbanization were \u003cem\u003eP. centralis\u003c/em\u003e and the phonotype Molossus. These two species have low echolocation frequencies (22.8 kHz and 25.6 kHz respectively), so they would be expected to be affected by the higher noise levels in urban sites (Bunkley et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The distribution of these species in urban settings may indicate that urban noise is not influencing low-frequency species, as found by Bunkley et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The absence of an effect of noise on species depending on the frequency of their vocalizations could be due to noise fluctuations in the city. The study by Bunkley et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) was conducted near a gas compressor plant, so the production of noise is constant and for long periods of time. In the city, noise fluctuates throughout the day, with peaks of greater intensity mainly during daylight hours and shortly after dusk. However, these levels decrease at night due to less vehicle activity than during the day. Therefore, the impact of urban noise could be primarily on crepuscular species or when they are emerging from their shelters on their way to foraging sites. However, Li et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) concludes that urban noise fluctuations, such as those generated by transportation and that originated from recreational activities, have a greater effect than constant industrial noise, because bats can adapt more easily to the latter. Despite this, it must be considered that not all bat species may be exposed to critical sound levels during dusk or respond in the same way to this stressor, and it could potentially still affect those species that cannot adapt. The observed impact of noise on insectivorous bats is due to the constant noise between 70 and 85 dB (Bunkley et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), while the characteristic noise of urban sites in Morelia is 46 to 72 dB, which suggests that the noise level present in Morelia is not as high to present a limitation on these species, which already proliferate in urban environments, and therefore does not affect the insectivorous bat community.\u003c/p\u003e\u003cp\u003e\u003cem\u003eP. centralis\u003c/em\u003e and the Molossus phonotype are also open foraging bats abundant in cities. These species are relatively large and have long, narrow wings, which enables them to fly quickly and feed on insects attracted to artificial light sources while avoiding predators (Kalko et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Jung \u0026amp; Threlfall \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The ability of these bats to exploit artificial light for foraging may explain their abundance and dominance in urban settings, where the highest levels of artificial light are present. Although these open-foraging species have less of an advantage in urban parks and transition sites than smaller, broad-winged, and short-maneuvering species, they are more efficient when foraging in vegetated areas (Kalko et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Jung \u0026amp; Kalko \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Voigt et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), P. \u003cem\u003ecentralis\u003c/em\u003e and Molossus are also the most abundant and dominant species at these levels of urbanization. This is because, despite being species favored by urban structures that they take as refuges (Sampedro-Mar\u0026iacute;n et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, \u0026Aacute;vila-Flores et al. 2023), they are also species found in non-urban areas (Gonz\u0026aacute;lez-Terrazas et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, S\u0026aacute;nchez et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, these urban parks and transition sites are located in close proximity to urban areas, so these species may maintain their refuges in transition sites on the city's periphery and move to parks and urban areas to feed (Threlfall et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Therefore, the presence of artificial light and urban noise could partially explain the abundance and dominance of these species.\u003c/p\u003e\u003cp\u003eThe presence of species that forage in open spaces in transition sites could be related to the availability of resources, since the species considered abundant and dominant in this degree of urbanization such as the Molossus phonotype, and \u003cem\u003eNeoeptesicus furinalis\u003c/em\u003e consume a high percentage of Coleoptera and Lepidoptera (Mies et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, Jennings et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Aguiar \u0026amp; Antonini \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Kalko et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Morais et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), while \u003cem\u003eM. megalophylla\u003c/em\u003e is considered a lepidopteran specialist bat (Smotherman \u0026amp; Guill\u0026eacute;n-Servent \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), so the greater presence of this order of insects in transition sites could explain the dominance of these species, as indicated by the evaluation of models; while for abundance it is not very clear which hypothesis explains it best.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eBats are resilient mammals that manage to persist in urban environments; however, urbanization influences their communities. The city of Morelia, a medium-sized Neotropical metropolis, presents different degrees of urbanization that do not influence species richness, but do influence the abundance and diversity of insectivorous bats, decreasing in the most urbanized sites. Our results show that the availability of food resources better explains variations in abundance and dominance than urban factors such as artificial light and urban noise, supporting resource availability as a key driving factor. In the urban bat community of Morelia, differences in diversity are observed; however, species composition is very similar across the three degrees of urbanization, and particular species are making the difference. This indicates that a species-level analysis could provide a better picture of the effect of urbanization on the bat community.\u003c/p\u003e\u003cp\u003eThe current size of Morelia, coupled with the connections between the most urban and peripheral areas of the city, such as the vegetation of the rivers that runs through it and the urban green areas, allow bats to move throughout the city to feed and seek refuge. However, increasing urbanization, habitat fragmentation, and water pollution could pose growing risks to the conservation of this fauna. We recommend integrating this evidence into urban management through conservation strategies that include regulating levels of artificial lighting, preserving and restoring ecological corridors, and promoting native vegetation in urban parks.\u003c/p\u003e"},{"header":"Statements and Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the Secretar\u0026iacute;a de Ciencia, Humanidades, Tecnolog\u0026iacute;a e Innovaci\u0026oacute;n (Secihti) for the scholarship (No. 924500) to RSM-M. We also thank the Jardin Bot\u0026aacute;nico \u0026ldquo;Melchor Ocampo\u0026rdquo;, Comunidad Educativa Villa Montessori, Club Campestre Morelia, Parque Zool\u0026oacute;gico Benito Ju\u0026aacute;rez, Museo de Historia Natural (MUHNA) and Procuraduria Federal de Protecci\u0026oacute;n al Ambiente (PROFEPA) for providing access to their facilities for sampling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Coordinaci\u0026oacute;n de la Investigaci\u0026oacute;n Cient\u0026iacute;fica-Universidad Michoacana de San Nicolas de Hidalgo and the Instituto de Ciencia, Tecnolog\u0026iacute;a e Innovaci\u0026oacute;n del Estado de Michoac\u0026aacute;n (ICTI) (Grant number PICIR22-087-C) to AS-M. \u003cem\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare they have no financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRSM-M leaded the conception, design, data collection, data analysis, drafting, and writing of the manuscript. AS-M contributed to the conception and design, data analysis and drafting and writing of the manuscript. MAS-M contributed with data analysis. MCMG contributed to the data analysis and revised the manuscript. VRG contributed revising the manuscript. YHD contributed revising the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003eFacultad de Biolog\u0026iacute;a, Universidad Michoacana de San Nicol\u0026aacute;s de Hidalgo C.P. 58004. Morelia, Michoac\u0026aacute;n de Ocampo, M\u0026eacute;xico. Rebeca Selene Miguel-M\u0026eacute;ndez, [email protected]. Alejandro Salinas-Melgoza, [email protected]. Yvonne Herrer\u0026iacute;as-Diego, [email protected]. Miguel Angel Salinas-Melgoza, [email protected].\u003c/p\u003e\n\u003cp\u003eCentro de Investigaciones Tropicales, Universidad Veracruzana. Xalapa, Veracruz. 91000, M\u0026eacute;xico. M. Cristina MacSwiney G., [email protected].\u003c/p\u003e\n\u003cp\u003eEscuela Superior en Desarrollo Sustentable, Universidad Aut\u0026oacute;noma de Guerrero, Tecpan de Galeana, Guerrero, 40900, M\u0026eacute;xico. Victor Rosas-Guerrero, [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAguiar L, Antonini Y (2008) Diet of two sympatric insectivores bats (Chiroptera: Vespertilionidae) in the Cerrado of Central Brazil. Rev Bras Zool 25(1). https://doi.org/10.1590/S0101-81752008000100005\u003c/li\u003e\n \u003cli\u003eAncillotto L, Coleman JL, Gibellini AM, Russo D (2022) Human Dimensions of Bats in the City. In: Moretto L, Coleman JL, Davy CM, Fenton MB, Korine C, Patriquin KJ (eds) Urban Bats. Springer, Cham, pp 139-152\u003c/li\u003e\n \u003cli\u003eArroyo-Cabrales JY, Ceballos G (2012) Lista actualizada de los mam\u0026iacute;feros de M\u0026eacute;xico 2012. 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Methods Ecol Evol 7(9):1082-1091. https://doi.org/10.1111/2041-210X.12556\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"urban-ecosystems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ueco","sideBox":"Learn more about [Urban Ecosystems](https://www.springer.com/journal/11252)","snPcode":"11252","submissionUrl":"https://submission.nature.com/new-submission/11252/3","title":"Urban Ecosystems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"urban noise, artificial light, degrees of urbanization, resource availability","lastPublishedDoi":"10.21203/rs.3.rs-7908158/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7908158/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrbanization generally has a negative effect on wildlife, including bats. The decline in particular insectivorous bat species, richness and abundance in urban environments is well known; however, the effect of urbanization on diversity is poorly understood. To explain this, we evaluated the effect of urban noise (dB) and artificial light (lux) as a proxy to urbanization, and resource availability, on insectivorous bats. To evaluate which of these drivers best explains the richness, abundance and diversity of urban insectivorous bats, acoustic sampling of the bat community was conducted in Morelia, Michoac\u0026aacute;n, in three habitat types with varying degrees of urbanization (urban, urban park, transition). The results showed a diverse community (14 species and 3 genera), where the most abundant species, regardless of the degree of disturbance, were \u003cem\u003ePromops centralis\u003c/em\u003e and the genus \u003cem\u003eMolossus\u003c/em\u003e, which are insectivorous bats known for their adaptation to urban environments. The abundance of insectivorous bats was higher in urban parks, richness did not differ and diversity was higher in transition sites. Despite this, differences among conditions were evident in species composition. Abundance and evenness were best explained by resource availability, while richness and diversity, were equally explained by urbanization and resources availability, which would indicate that the join influence of both is an important condition that can modify urban bat communities.\u003c/p\u003e","manuscriptTitle":"Food or urbanization? 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