Historical and contemporary genetic structure reveals recent fragmentation without loss of diversity in Mediterranean House Sparrow populations

Historical and contemporary genetic structure reveals recent fragmentation without loss of diversity in Mediterranean House Sparrow populations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Historical and contemporary genetic structure reveals recent fragmentation without loss of diversity in Mediterranean House Sparrow populations Javier Quesada, Marta Martín-Huete, Javier Oliver, Joan Calderón-Llobera, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8669756/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Understanding how human-modified environments shape long-term genetic diversity is crucial for conservation, particularly in species experiencing widespread population declines. The House Sparrow Passer domesticus has declined markedly across Europe, yet its long-term genetic dynamics remain poorly understood, especially in Mediterranean landscapes. We assessed spatiotemporal patterns of neutral genetic variation by integrating contemporary samples from 13 populations distributed along an agricultural–urban gradient in Catalonia (NE Spain) with historical museum specimens collected between 1919 and 1950. Using twelve short-amplicon microsatellite loci suitable for degraded DNA, we quantified changes in genetic diversity, population structure, and demographic signals, and evaluated their association with recent population trends. Historical populations were genetically homogeneous and formed a single cluster, consistent with larger effective population sizes and higher connectivity in the past. In contrast, contemporary populations exhibited increased genetic structuring, positive inbreeding coefficients, and heterozygote deficiencies, with subdivision into multiple clusters not clearly associated with habitat categories. Signals of recent demographic bottlenecks were detected in several contemporary populations, particularly in urban areas, indicating localized population contractions linked to landscape intensification. Despite these changes, overall neutral genetic diversity remained largely stable across the past century, and genetic diversity metrics were not associated with recent population trends. Together, our results suggest that Mediterranean House Sparrow populations have experienced recent fragmentation and demographic contractions without pervasive genetic erosion, consistent with historical population declines followed by demographic stabilization or adjustment to novel anthropogenic environments. This study highlights the value of historical DNA for reconstructing long-term genetic baselines in conservation genetics. Bottleneck Historical DNA Genetic diversity Passer domesticus Population structure Urban-agricultural gradient Figures Figure 1 Figure 2 Figure 3 Introduction Global change is influencing the population dynamics of numerous terrestrial species (e.g., Bellard et al. 2014 ; Wan et al. 2022 ). Key drivers of biodiversity loss include climate change, ecosystem alteration, habitat fragmentation from urbanization, agricultural intensification, deforestation, and the introduction of alien species (Swanson, 1995 ; Farrer et al. 2014 ; Sage, 2020 ). The impact of these changes on terrestrial species and ecosystems varies significantly across different regions, but in some areas, it is intense enough to affect even formerly successful species, both within their natural habitats and in recently colonized urban environments (Møller 2013 ; Mohring et al. 2021 ). The direct impacts of global change extend beyond reducing population sizes, threatening fundamental components of biodiversity, particularly the genetic diversity within populations (Lovejoy and Hannah 2006 ; Sage 2020 ). Genetic diversity underpins long-term species survival, as evolutionary processes such as natural selection depend on this variation to drive adaptation and resilience (Pauls et al. 2013 ). Species face genetic diversity loss driven by both natural selective forces and stochastic processes—some closely linked to population size (e.g., genetic drift)—as well as by anthropogenic pressures on ecosystems (Monroy-Vilchis 2005). These pressures often cause population declines, which reduce effective population sizes and accelerate genetic erosion over time (Fourcade et al. 2020 ). To ensure effective conservation, management strategies must integrate data on genetic composition, including the geographic distribution of diversity, population connectivity, and temporal changes in genetic structure (Kekkonen et al. 2011 ). A prime example of a once globally abundant and highly adaptable species that is now experiencing a population decline is the House Sparrow ( Passer domesticus ). This species is a model for understanding the demographic decline of common, highly adaptable species. Until the late 20th century, it was one of the most widespread and abundant birds globally (Anderson 2006 ). Native to the Middle East and parts of Asia, it naturally expanded across Eurasia over millennia through its close association with humans (Ravinet et al. 2018 ) and later introduced by humans to North America, parts of southern Africa, and Australia (Anderson, 2006 ). Despite its adaptability and broad geographic range, global population of the House Sparrow has significantly declined over the past four decades (Mohring et al. 2021 ). Surveys in the United States (Berigan et al. 2020 ), Australia (reviewed in Lenz 2018 ), Western Europe (PECBMS 2025 ), and India (Sharma and Binner 2020 ) all document marked population declines, including the introduced areas. The causes of the House Sparrow decline remain debated, but there is general agreement that multiple interacting factors affect both rural and urban populations (Summers-Smith 2003 ). In rural areas, declines appear linked to human depopulation and shifts in agricultural practices, particularly the increased use of pesticides and more efficient grain collection, which reduces available food resources (Summers-Smith 1999 ; Hole et al. 2002 ). Pesticides negatively affect nesting sites and diminish invertebrate populations critical for feeding offspring (Dröscher, 1992 ; Hallmann et al. 2014 ). Urban populations face similar challenges, including increased pesticide use, fewer green spaces, and reduced availability or low-quality of food (Heij 1985 ; Hole et al. 2002 ; Murgui and Macías 2010 ; Peach et al. 2014 ; Guallar et al. 2025 ). Additionally, urban factors such as fewer breeding cavities due to modern building designs (Bernat-Ponce et al. 2024 ) and potential physiological impacts from electromagnetic radiation (Everaert and Bauwens 2007 ; but see Nath et al. 2022 ) may further exacerbate population declines. Oxidative stress (Herrera-Dueñas et al. 2017 ) and elevated disease prevalence, such as avian malaria (Dadam et al. 2019 ), could also contribute to reduced reproductive success and long-term population viability (Siriwardena et al. 1999 ). Collectively, these pressures have already led to the extinction of local populations across several European countries (Ringsby et al. 2006 ; De Laet and Summers-Smith 2007 ; Dadam et al. 2019 ). Monitoring populations is therefore critical for detecting the severity of population declines before local extinction occurs. Genetic diversity and its geographic distribution are key variables for developing and implementing successful conservation strategies (Escudero et al. 2003 ; Scribner et al. 2005 ; Hu et al. 2021 ). In the House Sparrow, short postnatal dispersal distances of 1-1.7 km (Vangestel et al. 2012 ) suggest spatially structured populations with limited gene flow, increasing the risk of local extinction and reducing the potential for recovery from genetic loss. However, some studies (e.g., Kekkonen et al. 2011 ) indicate that this species can be panmictic and genetically homogeneous despite limited dispersal, sufficient to maintain population connectivity. In such cases, geography rather than population dynamics plays a greater role in genetic differentiation. Yet, this approach has not been applied in Mediterranean areas, which constitute biodiversity hotspots owing to their complex hydro-climatic, morphological, geographical, and historical heterogeneity, compounded by long-standing anthropogenic pressures. This multifaceted environmental mosaic has shaped distinctive ecological adaptations and population dynamics across taxa (Aurelle et al. 2022 ), potentially increasing population genetic structure or promoting local adaptations. Given these knowledge gaps, it remains unclear whether House Sparrow populations in the Mediterranean exhibit similar genetic patterns to those reported in temperate zones, or whether reduced dispersal, habitat fragmentation, and local declines have led to greater genetic structuring and diversity loss. This fact is important, as recent evidence indicates that population-level assessments of Mediterranean House Sparrow remain limited, particularly regarding potential conservation implications, which have not yet been fully explored (Bernat-Ponce et al. 2025 ). To address some of these uncertainties, the objectives of this study were: a) to describe genetic diversity and structure of the House Sparrow House Sparrow in Catalonia, a Mediterranean region in northeast Iberia characterized by a mosaic of diverse land uses, b) to assess whether land-use (urban vs. rural) and negative population trends are linked to genetic diversity loss or genetic structuring; and c) analyze the temporal changes in genetic diversity by comparing current populations with those from the pre-decline period (< 1950). Our initial hypotheses are: 1) House Sparrow populations in Catalonia are highly structured due to limited dispersal, the species' sedentary behavior and land use; 2) declining populations exhibit lower levels of genetic diversity than stable or increasing populations; and 3) older populations established before the decline may retain higher genetic diversity than current populations. To achieve our objectives, we analyzed the population dynamics and genetic structure of 13 House Sparrow populations distributed across both agricultural and urban–periurban land-use types, which represent the main habitats occupied by the species in Catalonia (Quesada and Calderón-Llobera 2021 ). Prospective analyses of population trends indicated that populations in urbanized areas (including both urban and peri-urban sites) exhibit a steeper average decline compared to those in rural, agricultural landscapes (Calderón-Llobera and Quesada unpublished data). We complemented this spatial analysis with a temporal comparison by incorporating genetic data from historical specimens collected between 1919 and 1950 from zoological collections. Finally, we examined the relationship between genetic diversity parameters and the slope of population trends derived from long-term monitoring data, allowing us to assess whether demographic changes are associated with shifts in genetic variation. Materials and Methods Samples for population genetics analyses In order to describe genetic diversity and structure of the House Sparrow in Catalonia, we selected sampling sites (hereafter, contemporary populations) that must accomplish several criteria: a) populations should assign to agricultural or some degree of urbanized areas (Urban, Peri-Urban), b) trends of the area where a studied population was selected should be known (see SOCC population monitoring scheme trends below), and c) selected populations should have genetic information before the House Sparrow major decline in Europe (i.e., before 1950) were detected (PECBMS 2025 ). To obtain genetic information before the House Sparrow declines period, we conducted an exhaustive search for historical samples (hereafter OLD samples) of sparrows collected in Catalonia from Spanish museums and private collections, with collection data equal or prior to 1950, and repositories such as GBIF or Vertnet. Only individuals with complete metadata about the sampling location (at least at county level) and capture date were considered for analyses. In total, we obtained 24 old samples from specimens captured between 1919 and 1950, in different locations throughout Catalonia. All of them belonged to the collection of the Museum of Natural Sciences of Barcelona (Supplementary information, Table S1 ). From each specimen a small sample of toe pad skin was carefully extracted to minimize damage to these valuable materials. These old specimens were compared with those fresh specimens from the current locations (see more details below about the sampling collection) where they were collected (N Localities =9) (Table S1 , Fig. 1 ). Since the number of populations was low, and to evaluate whether regressive or expansion trends in population size influenced genetic parameters, we included four additional localities representing positive (LLA), stable (LLS, TAR), and regressive trends (BCN), thereby capturing a more comprehensive variance in populations dynamics (N Localities =13, Fig. 1 ). Living House sparrows were captured with mist nets during the spring-summer of 2014 and 2015 (7:00–13:00 h) by a single sampler (JO). All animals were ringed and measured for further studies, and 25–50µl of blood was extracted with a hematocrit capillary. Blood samples were preserved in absolute ethanol and stored at -20°C in the laboratory until further processing. DNA extraction and genotyping of samples Total genomic DNA was extracted from fresh blood of contemporary samples (n = 235) and OLD (N = 24) samples (see Table 1 ) with the NucleoSpinTM kit ( www.cultek.com ), following the protocol of the product. Due to the low amount of DNA obtained from the old samples, the protocol was modified for these samples by extending the tissue digestion overnight. After extraction, DNA purity, integrity and quantity were checked in Nanodrop (Thermo Fisher, www.thermofisher.com ), agarose electrophoresis gels, a Qubit DNA HS assay (Life Technologies, www.thermofisher.com ), respectively. Whereas fresh samples of blood retained high DNA quantity and quality, DNA extractions from the skin of the old samples were at extremely low concentrations, and the DNA appeared highly fragmented. Because the DNA quality and quantity of the archived skin samples was insufficient for population genomic analyses using high-throughput sequencing, we instead genotyped all samples using 12 previously described nuclear microsatellite markers for this species. This approach enabled a robust comparison between historical and contemporary samples despite differences in DNA preservation and quality. The microsatellites used were: Pdo1 and Pdo3 (Neumann and Wetton 1996 ); Pdo5 (Griffith et al. 1999 ); Pdo 10 and Ase18 (Griffith et al. 2007 ); and Pdo16, Pdo17, Pdo19, Pdo27, Pdo30, Pdo44 and Pdo47 (Dawson et al. 2012 ). For microsatellites genotyping, fragments were amplified with specific primers for each microsatellite following the protocol and PCR conditions described by Hermansen et al. ( 2011 ). Once amplified, the microsatellite fragments (alleles) were measured in an automatic sequencer from the company Macrogen® ( www.macrogen.com ) with an internal GENESCANTM 400HD ROXTM standard. 7 (Applied Biosystems) using the software Peak-Scanner (Thermo Fisher scientific, http:/ www.thermofisher.com ). Allele scoring was graphically performed using the R package MsatAllele (Alberto 2009 ). Table 1 Genetic diversity of P. domesticus . OLD samples (1919–1950) and 13 contemporary populations: Use: Land use (Ag: agricultural and Ur.: Urban-periurban); N: Number of individuals analyzed; N a : Total number of alleles observed across loci; A r : Allelic richness, a measure of genetic diversity normalized for sample size; H o : Observed heterozygosity, representing the proportion of heterozygotes in the population; H e : Expected heterozygosity under Hardy-Weinberg equilibrium, indicating the genetic diversity expected in an idealized population;. F IS : Inbreeding coefficient, measuring the departure from random mating (values near zero indicate random mating, while positive values suggest inbreeding). (* p < 0.05). Population Use N N a A r pA H o H e F IS OLD - 24 10.917 ± 0.621 7.062 0.583 0.676 ± 0.05 0.805 ± 0.023 0.159 ± 0.056* AM Ag. 19 11.167 ± 0.815 7.672 0.167 0.687 ± 0.039 0.806 ± 0.03 0.144 ± 0.041* BCN Ur. 20 10.75 ± 0.993 7.589 0.250 0.617 ± 0.063 0.816 ± 0.03 0.242 ± 0.075* BLA Ur. 20 12.083 ± 1.118 7.853 0.833 0.593 ± 0.044 0.798 ± 0.036 0.231 ± 0.073* CAS Ur. 20 11.667 ± 1.054 7.898 0.500 0.703 ± 0.043 0.821 ± 0.025 0.143 ± 0.049* CER Ag. 12 9.083 ± 0.645 7.591 0.250 0.611 ± 0.064 0.797 ± 0.032 0.236 ± 0.067* DE Ag. 14 9.25 ± 0.77 7.647 0.083 0.694 ± 0.047 0.814 ± 0.024 0.146 ± 0.053* FOL Ag. 18 9.5 ± 0.691 7.303 0.333 0.509 ± 0.057 0.828 ± 0.015 0.385 ± 0.066* GAV Ag. 15 8.25 ± 0.808 6.653 0.250 0.647 ± 0.044 0.774 ± 0.031 0.161 ± 0.054* LLA Ag. 22 11.25 ± 0.77 7.419 0.417 0.64 ± 0.051 0.814 ± 0.02 0.209 ± 0.067* LLS Ur. 17 11.25 ± 0.629 8.316 0.250 0.554 ± 0.05 0.85 ± 0.015 0.346 ± 0.059* PLA Ag. 18 10.667 ± 0.711 7.63 0.250 0.75 ± 0.035 0.824 ± 0.018 0.086 ± 0.043* SAN Ur. 20 11.000 ± 0.707 7.626 0.583 0.623 ± 0.053 0.81 ± 0.026 0.217 ± 0.069* TAR Ag. 20 10.917 ± 0.830 7.47 0.250 0.66 ± 0.048 0.795 ± 0.037 0.168 ± 0.047* Population genetics and diversity analyses We calculated genetic diversity parameters per population and microsatellite loci, including the number of alleles (N a ), allelic richness standardized to the smallest sample size (A r ), mean number of private alleles (pA), expected heterozygosity (H e ), and observed heterozygosity (H o ), using the Adegenet R package (Jombart 2008 ). The inbreeding coefficient ( F IS ) and deviations from Hardy-Weinberg equilibrium were computed in Genodive 3.04 (Meirmans 2020 ), with p-values derived from 100,000 permutations. To assess genetic divergence among populations, we conducted analyses of pairwise genetic differentiation, Discriminant Analysis of Principal Components (DAPC), and Bayesian clustering. We additionally measured individual-level relatedness. We calculated genetic distances between pairs of populations, independently of the land use, using the F ST statistic in Arlequin v. 3.5.2.2 (Excoffier and Lischer, 2010 ). P-values were obtained through 10,100 permutations and corrected for multiple testing using the p.adjust function from the stats package in R (R Core Team 2025 ). False Discovery Rate (FDR) corrections were applied following the Benjamini–Yekutieli procedure (Benjamini and Yekutieli, 2001 ). To account for the potential impact of null alleles on F ST values, we used FreeNA to perform internal corrections based on the proportion of null alleles (Chapuis and Estoup, 2007 ). The F ST values were graphically represented in a heatmap using the pheatmap function in the pheatmap 1.0.12 R package (Kolde 2019 ). To assess the potential genetic isolation of populations due to geographic distance in contemporary populations, we performed a Mantel test comparing linearized genetic distances ( F ST / [1 – F ST ]) with geographic distances between localities (in km) in Arlequin v. 3.5.2.2. Statistical significance was evaluated using 10,000 permutations. For the Mantel test OLD samples were excluded. A Bayesian clustering approach was applied using STRUCTURE 2.3.4 (Pritchard et al. 2000 ) to infer population genetic structure and determine the optimal number of clusters (K). The analysis was run with 200,000 Markov chain Monte Carlo (MCMC) iterations, following a burn-in period of 80,000, with 10 replicates for each of the 16 K values. The optimal number of clusters was determined using STRUCTURE Harvester version 0.6.94 (Earl and vonHoldt 2012 ), based on the ΔK statistic (Evanno et al. 2005 ). The discriminant analysis of principal components (DAPC) (Jombart et al. 2010 ) was performed for all samples and populations using the populations as groups with the adegenet 1.3 package in R (Jombart and Ahmed 2011 ). DAPC combines PCA with discriminant analysis to summarize genetic differentiation between populations. The optimal number of clusters for the DAPC was chosen based on the Bayesian information criterion (BIC), and the optimal number of principal components (PCs) retained from the PCA step was determined using cross-validation. This was achieved by comparing a-scores across an increasing range of PCs, followed by spline interpolation with the a-score function. To explore the relationships among individual genotypes, we constructed a minimum spanning network that visualizes distances among multilocus genotypes, based on Bruvo’s dissimilarity distances, a genetic distance measure designed for microsatellites (Bruvo et al. 2004 ). The network was constructed incorporating all individuals (old and fresh samples) from all sites in Poppr v2.9.2 package (Kamvar et al. 2014 ) in R and represented in igraph (Csardi and Nepusz 2006 ). To examine specifically the effect of land use on the genetic divergence of populations, we conducted an AMOVA analysis in Arlequin v. 3.5.2.2. We only used fresh samples that were grouped into two categories (see Table 1 ): urban-periurban (5 sites: BCN, BLA, CAS, LLS and SAN) and agricultural (8 sites: AM, CER, DE, FOL, GAV, LLA, PLA and TAR). We then assessed genetic variation partitioned across three hierarchical levels: (1) between land-use groups, (2) between populations within each group, and (3) within populations. Effect of population trends on genetic characteristics House Sparrow population dynamics were estimated using data from the SOCC monitoring program (Common Bird Monitoring in Catalonia). This citizen science initiative, active since 2002, involves approximately 3 km of transects distributed throughout Catalonia, which are surveyed four times annually (twice in winter and twice during the breeding season) (Quesada et al. 2010 ). For this study, we utilized breeding season data from 2002 to 2024. We considered the maximum abundance recorded during the two breeding visits as a proxy for population abundance (Guallar et al. 2025 ). As this monitoring program relies on citizen science, some transects typically have missing data for certain years because surveys were not conducted. To account for this, we used RStudio (R Core Team 2025 ) and the TRIM (Trends and Indices in Monitoring Data) software, implemented via the rtrim 2.1.1 package (Bogaart et al. 2020 ), to impute missing values. TRIM uses loglinear Poisson regressions, allowing us to calculate imputed values, long-term trends, and annual population indices. We used data where house sparrows were consistently recorded in 334 of the 433 transects. We applied a model 2 with a switching linear trend, overdispersion, autocorrelation, and stepwise change-point selection to produce a parsimonious model (Pannekoek and Van Strien 2005 ). Once the missing values were imputed, we investigated whether declining populations exhibited lower genetic diversity than stable or increasing populations. To do this, we first quantified the magnitude of population change for each transect by fitting an exponential population growth model: ln(Nt​)=α + r⋅t where Nt​ is abundance in year t , and r is the instantaneous growth rate. From r , we calculated λ = e r , representing the annual growth rate (λ > 1 indicates growth; λ < 1 indicates decline) (Gotelli 2008 ). The model was fitted using linear regression on log-transformed abundances. For each locality, we report R² and p-value for the slope. These growth rates (r and λ) were then used as explanatory variables in ordinary least squares (OLS) regressions to test for associations with genetic diversity metrics (N a , R a , pA, H o , H e , and F IS ). Finally, we tested for recent reductions in effective population size (bottlenecks) at all contemporary sampling sites using the program BOTTLENECK v1.2.02 (Piry et al. 1999 ). This software evaluates whether the observed heterozygosity is higher than expected under mutation–drift equilibrium, which would indicate a recent bottleneck due to the faster loss of rare alleles compared to heterozygosity. We analyzed each population under two mutation models: the Infinite Alleles Model (IAM) and the Stepwise Mutation Model (SMM), although the second one is considered more appropriated for microsatellites. The significance of the results was tested with a sign test that evaluates the proportion of loci with heterozygosity excess or deficiency, and a Wilcoxon signed-rank test (a non-parametric test recommended for < 20 loci). Additionally, we examined the mode-shift indicator, which detects deviations from the expected L-shaped allele frequency distribution in stable populations. A shifted distribution indicates a recent bottleneck. Results Genetic diversity descriptors All loci were highly polymorphic across all populations, with the total number of alleles ranging from 20 to 35 (Supplementary information, Table S2). Additionally, all loci exhibited higher expected heterozygosity (H e ) values compared to observed heterozygosity (H o ) (Table S2), leading to significant and positive inbreeding coefficient values. The number of alleles per population ranged from 8 and 12, whereas allelic richness (A r ) per population ranged from 6.65 to 8.32. In all populations, expected heterozygosity (H e ) values exceeded observed heterozygosity (H o ), indicating a notable heterozygous deficit (Table 1 ). Accordingly, F IS values were positive in all populations, and significant deviations from Hardy-Weinberg equilibrium were detected (p-value < 0.001). To evaluate the suitability of the OLD samples for population genetic analysis, we compared their genetic diversity parameters with those of contemporary populations. Expected heterozygosity (H e ) varied between populations, ranging from the highest value in LLS to the lowest in GAV population (Table 1 ). Notably, H e value of the OLD samples exceeded that of four current populations (BLA, CER, GAV, and TAR) (Table 1 ). Similarly, observed heterozygosity (H o ) differed across populations, with the highest value observed in the PLA population and the lowest in FOL. The OLD samples showed H o values lower than those of PLA, DE, CAS, and AM, but higher than the remaining populations. In terms of allelic richness (A r ), the pattern of genetic diversity mirrored that of He, with LLS showing the highest value and GAV the lowest. The A r of the OLD samples was higher than that of the GAV population, although four populations surpassed the OLD samples in H e . The estimated frequency of null alleles (Supplementary information, Table S3) revealed that FOL had the highest null allele frequency (0.1747), while PLA had the lowest. Interestingly, the frequency of null alleles in the OLD samples was lower than almost all other populations, except AM and PLA. Thus, the genetic data from OLD samples appears to be of comparable quality, if not better than most of the analyzed populations, supporting the use of these samples in population genetic studies. 3.2. Populations divergence in House Sparrow In general, we found genetic divergence among sampling sites, although each of the analyses performed provides complementary information. Divergence estimations obtained with FREENA were consistent with our original calculations in Arlequin, indicating that missing data and potential null alleles had no detectable effect on population differentiation; therefore, we used the original F ST values in subsequent analyses. Pairwise F ST values ranged from 0.086 to 0.001. Most pairwise comparisons were statistically significant, indicating populations divergence among most sampling sites (Fig. 2 A and Supplementary information, Table S4). The lowest value was observed between AM and DE, two geographically close localities. Interestingly, the OLD population exhibited the highest F ST value among all populations (Fig. 2 A and Table S4), suggesting a distinct genetic structure compared to contemporary populations. While the OLD samples demonstrated similar genetic diversity to current populations, their genetic structure differed, indicating both spatial genetic variation among current populations and temporal genetic shifts between recent and historical populations. For contemporary populations, the Mantel test revealed a significant positive correlation between genetic and geographic distances (r = 0.334, p-value = 0.021), indicating evidence of isolation by distance among populations of the species, and approximately 11.1% of the variation in genetic distances was explained by geographic separation. In STRUCTURE, the optimal number of genetic clusters for the House Sparrow was four, according to the Delta K criterium (see Supplementary information, Fig. S1 ), although seven and thirteen clusters also showed high Delta K values (Supplementary information, Fig. S2). For four clusters, individuals from the OLD samples were mostly assigned to a single cluster (dark green), displaying large homogeneity among most individuals. In the remaining populations, cluster distribution did not appear to be influenced by land use, yet substantial differences were observed among populations in the relative contribution of each cluster to their overall genetic structure (Fig. 2 C). Individuals from BLA and CAS (urban) and TAR (agricultural) showed a higher probability of assignment to the orange cluster, despite being geographically distant (see also Fig. 1 ). A similar pattern was observed for FOL (agricultural), SAN (urban), and LLS (urban), which were more likely to belong to the red cluster. The same trend was found for the light green cluster, which included BCN and AM (urban), as well as LLA, GAV, and PLA (agricultural). The findings from the discriminant analysis of principal components (DAPC) (Fig. 2 B) were consistent with those obtained using STRUCTURE. The OLD samples were clearly distinct from the modern populations along the Y-axis, forming a distant group. Meanwhile, the remaining populations showed some degree of mixing, though certain clusters could still be identified: a group comprising BLA, CAS, and TAR to the right; another group with LLS, SAN, and FOL at the bottom; and the remaining populations forming a central group. Again, no clustering of populations influenced by land use was observed. The Minimum Spanning Network including all samples revealed an interesting pattern. Most OLD grouped in a cluster with some contemporary individuals but separated from most of the contemporary populations. While certain individuals from the same population cluster together, the overall pattern reveals a scattered distribution across the network, with the OLD samples standing out as the only consistent exception. This supports the differentiation observed in STRUCTURE and DAPC analyses, indicating that historical individuals share a homogeneous genetic background that has diverged from contemporary populations (Fig. 3 ). That is particularly relevant given that OLD population contain individuals which are presumably the ancestors of several (N = 9) contemporary populations (e.g. see AM cluster and PLA cluster). The results of the AMOVA, in which populations were grouped based on land use (urban-periurban and agricultural), aligns with the STRUCTURE and DAPC analysis. No significant differences were detected between the urban-periurban and agricultural groups (variation 0%; F CT = -0.15; p = 0.668) but most of the molecular variance occurred within individuals (74,39%; F IT = 0.2560; p-value < 0.001), among individuals within populations (22,79%; F IS = 0.2345; p-value < 0.001), and among populations within groups (2,97%; F SC = 0.0296; p-value < 0.001)(Table 2 ). Table 2 AMOVA results in grouping populations according to land use (excluding OLD samples). Variation was tested among groups (urban-periurban vs agricultural), among populations within groups, among individuals within populations and within individuals Source of variation d.f Squares summ Variance components Variation percentage Fixation index P-value Among groups 1 10.12 -0.01 -0.15 F CT = -0.001 0.668 Among populations within groups 11 127.19 0.15 2.97 F SC = 0.0296 0.000 Among individuals within populations 222 1358.92 1.16 22.79 F IS = 0.2345 0.000 Within individuals 235 892.00 3.80 74.39 F IT = 0.2560 0.000 Total 469 2388.23 5.10 . Genetic Diversity and Population Trends Population growth rates (r) estimated from exponential models ranged from − 0.0358 to + 0.0153, corresponding to annual rates (λ) between 0.9649 and 1.0154 (Table 3 ). All urban sites, except Lleida Seròs (LLS), showed negative r values. The steepest decline was observed at the urban–periurban site of Sant Vicenç de Calders (SAN) (r = − 0.0358; λ = 0.9649; p-value < 0.001). Other urban sites, such as Barcelona (BCN) (r = − 0.0191; λ = 0.9810; p-value < 0.05) and Castellbisbal (CAS) (r = − 0.0169; λ = 0.9832; p-value = 0.056), also showed negative trends. The locality of Blanes (BLA) exhibited a moderate but non-significant decline (r = − 0.0139; λ = 0.9862; p-value = 0.0796). Table 3 Population growth rates (r), annual growth factor (λ), coefficient of determination (R²), and p-values for Passer domesticus populations across Catalonia. Growth rates were estimated using exponential models fitted to breeding season data (2002–2019). Values of λ > 1 indicate population growth, whereas λ < 1 indicate population decline (* p-value < 0.05; *** p-value < 0.001) Population r λ R² p-value AM 0.0031 1.0031 0.0040 0.7614 BCN -0.0191 0.9810 0.2350 * BLA -0.0139 0.9862 0.1390 0.0796 CAS -0.0169 0.9832 0.1630 0.0562 CER 0.0074 1.0075 0.0130 0.5996 DE 0.0002 1.0002 0.0000 0.9937 FOL 0.0066 1.0066 0.0040 0.7672 GAV -0.0314 0.9691 0.5570 *** LLA 0.0153 1.0154 0.2460 * LLS 0.0063 1.0063 0.0230 0.4886 PLA -0.0209 0.9793 0.5280 *** SAN -0.0358 0.9649 0.5540 *** TAR 0.0049 1.0049 0.0050 0.7583 In contrast, most agricultural sites showed stable or slightly positive trends. Notably, Lleida agricultural (LLA) presented a significant increase (r = + 0.0153; λ = 1.0154; p-value < 0.05). An exception was Pla de Santa Maria (PLA) (r = − 0.0209; λ = 0.9793; p-value < 0.001) and Gavà (GAV) (r = − 0.0314; λ = 0.9691; p-value < 0.001), the latter located within the matrix of the Metropolitan Area of Barcelona. Overall, urban-periurban areas exhibited stronger population declines compared to agricultural landscapes. When testing for associations between population growth rates and genetic diversity metrics (N a , A r , pA, H o , H e , F IS ), no significant relationships were detected (all p-value > 0.24, Table 4 ). This suggests that, despite demographic declines in some localities, genetic diversity has remained relatively stable during the study period. Table 4 Pearson correlation coefficients (R) between genetic diversity parameters (N a , A r , pA, H o , H e , F IS ) and population trends estimated using λ (population growth rate) and r (log-transformed annual rate of change) across House Sparrow populations in Catalonia. λ r R-pearson p-value R-pearson p-value N a 0.03401 0.91218 0.03612 0.90673 A r -0.16176 0.59751 -0.15888 0.60415 pA -0.32766 0.27444 -0.32839 0.27331 H o -0.27387 0.36523 -0.27219 0.36830 H e 0.29113 0.33452 0.29304 0.33121 FI s 0.34899 0.24251 0.34786 0.24415 BOTTLENECK results, under both IAM and SMM models, revealed no significant evidence of recent bottlenecks in most populations, as supported by non-significant Sign and Wilcoxon tests and the consistent L-shaped allele frequency distributions. However, four populations showed signals compatible with bottleneck events: GAV displayed marginal significance under IAM ( Wilcoxon p-value = 0.034), BCN exhibited significant heterozygosity excess under both IAM and SMM ( Wilcoxon p-value = 0.003 and 0.110, respectively), and CAS and BLA showed significant departures under SMM ( Wilcoxon p-value = 0.042 and 0.013, respectively). Despite these results, the mode-shift test indicated normal L-shaped distributions in all populations, suggesting no strong or widespread evidence of recent bottlenecks. As showed in Table 5 , three of the four populations showing bottleneck signals corresponded to urban–periurban areas (BCN, CAS, BLA), and three of them (whether urban–periurban or agricultural) were located within the intensified matrix of the Barcelona Metropolitan Area (BCN, CAS, GAV). Table 5 Results of bottleneck tests for 13 populations of House Sparrow in Catalonia. The table shows the number of loci with heterozygosity excess versus deficiency under the Infinite Allele Model (IAM) and Stepwise Mutation Model (SMM), along with associated p-values from Sign and Wilcoxon tests. Mode-shift column indicates the allele frequency distribution pattern, where an L-shaped distribution suggests demographic stability. * p-value < 0.05, ** p-value < 0.01 Population Sign Test IAM (Excess/Def) p-value (IAM) Sign Test SMM (Excess/Def) p- value (SMM) Wilcoxon IAM p-value (two-tail) Wilcoxon SMM p-value (two-tail) Mode-Shift AM 7/5 0.412 5/7 0.483 0.432 0.502 L-shaped DE 8/4 0.322 6/6 0.388 0.317 0.412 L-shaped LLA 9/3 0.210 5/7 0.412 0.294 0.390 L-shaped LLS 7/5 0.402 4/8 0.351 0.421 0.372 L-shaped FOL 6/6 0.498 5/7 0.439 0.462 0.398 L-shaped TAR 8/4 0.335 5/7 0.427 0.395 0.389 L-shaped CER 9/3 0.215 6/6 0.374 0.308 0.371 L-shaped PLA 7/5 0.398 5/7 0.442 0.415 0.387 L-shaped SAN 8/4 0.337 6/6 0.362 0.392 0.372 L-shaped GAV 10/2 0.087 6/6 0.356 * 0.791 L-shaped BCN 11/1 * 4/8 0.066 ** 0.110 L-shaped CAS 8/4 0.455 2/10 ** 0.301 * L-shaped BLA 6/6 0.315 3/9 * 0.970 * L-shaped Discussion This study aimed to assess the spatio-temporal genetic diversity and population structure of House Sparrow in Catalonia, a region characterized by heterogeneous landscapes and varying degrees of urbanization (Herrando et al. 2011 ). Specifically, we examined whether: (1) populations exhibit genetic structuring due to limited dispersal and philopatric behavior, (2) older populations maintain higher genetic diversity compared to contemporary ones, and (3) declining populations show reduced genetic diversity. Our analysis focused on the primary habitat types occupied by this species, namely urban-periurban and agricultural environments. Our results revealed significant genetic structuring among populations partially explained by isolation by geographic distance, even over small spatial scales (e.g. DE-AM, CAS-BCN, Fig. 1 ), likely reflecting restricted dispersal and site fidelity. Yet, historical samples displayed greater genetic homogeneity, indicating temporal changes in population structure. Despite observed declines in some populations, overall genetic diversity remained relatively stable across habitats. Notably, all populations exhibited heterozygote deficiencies and positive inbreeding coefficients. However, habitat type (agricultural versus urban) did not significantly explain the observed genetic variation, although three of the four populations showing evidence of recent bottlenecks were in urban–periurban areas. Genetic diversity and populations’ divergence The genetic analysis carried out in this study revealed a general deficiency of heterozygotes within House Sparrow populations compared to what would be expected for populations in Hardy-Weinberg equilibrium. Such a deficiency in heterozygotes is typically attributed to a combination of non-exclusive factors, including population substructure (Wahlund effect), inbreeding, the influence of null alleles on statistical calculations, selection against heterozygotes, or a combination of these effects (Pérez-Portela et al. 2015; De Meeûs 2018 ). As highlighted by Szulkin and Sheldon ( 2008 ) in their study on Great tit ( Parus major ), inbreeding is closely linked to the dispersal ability of a species, which could also be the case for the house sparrow. The House Sparrow is known for its philopatric behavior and restricted movement, typically following a “stepping-stone” dispersal model (Anderson 2006 ; Vangestel et al. 2012 ), which may elevate inbreeding levels and contribute to the observed genetic structure. The loss of these intermediate populations is, hence, pivotal to maintain the connectivity among populations. For example, Kekkonen et al. ( 2011 ) reported low genetic differentiation among Finnish populations of House Sparrow, suggesting high connectivity and panmixia. This pattern likely reflects the flat topography and relatively continuous habitat matrix in Finland, which facilitates dispersal. Yet, similarly to our results, Liu et al. ( 2013 ) and Geue et al. ( 2016 ) found moderate structuring in other parts of Europe with heterogenous habitats, reinforcing the idea that geographic and environmental context strongly influences population connectivity. Catalonia presents a classic Mediterranean landscape, characterized by a hilly topography, a complex mosaic of land uses, and long-standing anthropogenic pressures (Herrando et al. 2011 ). Environmental fragmentation likely exacerbates population isolation, contributing to the genetic differentiation observed even among geographically close populations. The significant Mantel test supports isolation by distance, indicating that genetic divergence increases with geographic separation. These results suggest that geographic barriers and local demographic dynamics in Catalonia create distinct genetic clusters. Although the House sparrow’s dispersal pattern should facilitate movement in stable environments, regions experiencing population declines or habitat fragmentation may lose intermediate populations that act as genetic bridges. This could be the case in a Mediterranean region like Catalonia, where sparrow numbers have declined over recent decades, despite the species’ overall distribution range remaining largely unchanged (Quesada and Calderón-Llobera 2021 ). Hence, the loss of these “stepping-stone” populations may further reduce connectivity and accelerate genetic isolation. Our first hypothesis proposed that House Sparrow populations in Catalonia are genetically structured, a structure partially explained by geographic distances, but regardless of land uses. Contrary to our expectations, the AMOVA results indicated that most genetic variation occurs within populations and individuals, with no significant differentiation between urban-periurban and agricultural areas. This finding contradicts expectations that urbanization would drive genetic divergence due to habitat-specific pressures (Fulgione et al. 2000 ; Brewer et al. 2020 ). It is also possible that no genetic differences exist between the groups, as our results do not differ from those of other studies on genetic diversity and structure associated with urbanization gradients, both in House Sparrow (Vangestel et al. 2012 ) and in other passerine species, such as the Song Sparrow Melospiza melodia (Brewer et al. 2020 ). Other factors like isolation, genetics drift, or other environmental drivers could play a major role than urbanization in shaping genetic structure (Orsini et al. 2013 , Geue et al. 2016 ). In this sense, conducting temporal monitoring analyses of genetic structure shifts would be crucial for developing conservation plans for this species, depending on the habitat where its individuals are found. Hence, our results indicate that in Mediterranean human-modified landscapes, genetic structure and the progressive erosion of genetic connectivity can emerge from fine-scale fragmentation and the loss of stepping-stone populations, even when overall genetic diversity remains high and broad land-use categories fail to predict genetic differentiation (Bustillo de la Rosa et al. 2022) House Sparrow population declines and genetics Our second hypothesis stated that declining populations exhibit lower levels of genetic diversity than stable or increasing populations. Widespread declines of House Sparrow populations have been reported across Europe (PECBMS 2025 ). Our results showed that urban and periurban populations are declining more steeply than agricultural ones in the Mediterranean human-modified landscape. Four of the five urban populations showed significant declines in comparison to agricultural areas. This suggests that, if sustained, these trends could lead to local extirpation within decades. Conversely, agricultural sites showed slight but significant increases to stable dynamics, suggesting that rural habitats remain demographic strongholds for the species. Most agricultural sites exhibited stable or slightly positive demographic trends but, interestingly Gavà showed negative trends. This agricultural area is embedded within the highly fragmented and urbanized matrix in the Barcelona Metropolitan Area, which could partially explain this trend (see below). Yet, although House Sparrow populations have experienced marked declines in Mediterranean urban areas (Ramos-Elvira et al. 2023 ), our results indicate that these demographic changes have not yet translated into a generalized loss of neutral genetic diversity. This temporal decoupling between population size and genetic diversity is consistent with the concept of a genetic erosion lag, whereby reductions in effective population size may only affect genome-wide diversity after several generations (Monnahan et al. 2023 ; Pinsky et al. 2025 ). This temporal lag may explain why even populations with strong negative trends still maintain moderate genetic variability. However, signals of heterozygote deficiency and inbreeding detected in our study (see genetic diversity descriptors in Results) could indicate early genetic consequences of fragmentation and reduced dispersal (Vangestel et al. 2012 ). Furthermore, alternative analyses identified potential bottlenecks in four House Sparrow populations, which suggest that effective population size may already be shrinking. This last result is consistent with the marked demographic declines in some urban areas identified in previous studies of the species (Calderón-Llobera 2019 ; Mohring et al. 2021 ). Hence, these findings highlight the importance of long-term monitoring to detect early signs of genetic deterioration before they become irreversible, and stress that conservation strategies should not only track population numbers but also anticipate potential genetic consequences of continued demographic decline. Historical trends of House Sparrow Our analysis, which included historical (OLD) samples (1919–1950), revealed comparable levels of genetic diversity to those observed in contemporary populations, particularly with respect to expected heterozygosity and allelic richness. However, historical populations appeared more genetically homogeneous, likely reflecting larger effective population sizes and greater connectivity at that time, consistent with metapopulation dynamics. This interpretation is consistent with metapopulation dynamics operating under landscapes with fewer barriers to dispersal and greater availability of stepping-stone habitats (Saura et al. 2014 ). Clustering analyses further supported this interpretation, as most historical individuals grouped into a single, homogeneous cluster despite originating from different localities. This pattern indicates temporal shifts in population structure, probably driven by recent habitat fragmentation and local extinctions (Ringsby et al. 2006 ; De Laet and Summers-Smith 2007 ). Despite these structural changes, we did not observe a clear reduction in overall neutral genetic diversity in contemporary populations compared to historical ones (Table 1 ). This suggests that, although population connectivity has decreased and genetic structuring has increased over the past century, genetic variation has remained relatively stable. Such a pattern is consistent with historical population contractions followed by demographic stabilization rather than ongoing genetic erosion (e.g. Bustillo-de la Rosa et al. 2022). The detection of bottleneck signatures in several urban populations (Barcelona, Blanes, Castellbisbal) and in one agricultural population (Gavà) (Fig. 1 ) raises important questions regarding the timing of these demographic contractions. In Catalonia, House Sparrow populations have been systematically monitored only since 2002, and recent trends from the SOCC program indicate a consistent but not abrupt decline (ICO 2025 ). This suggests that the bottlenecks detected here likely predate contemporary monitoring efforts and may have occurred during earlier phases of landscape transformation. Two complementary lines of evidence support this interpretation. First, long-term European monitoring schemes initiated in the 1980s indicate that the steepest declines in House Sparrow populations occurred between 1980 and 2000 (long-term reduction: −38%), followed by a more moderate decrease in subsequent decades (10-year reduction: −8%; PECBMS 2025 ). Similar temporal dynamics have also been documented in other parts of the species’ range, such as Canada (Smith et al. 2024 ). Unfortunately, the absence of standardized monitoring data in Catalonia prior to 2002 prevents direct evaluation of whether comparable declines occurred locally. Second, the populations showing bottleneck signatures coincide spatially with regions that experienced intense landscape transformation driven by urban expansion, tourism development, and infrastructure growth between the 1960s and 2000s (Pintó et al. 2018 ). The occurrence of bottlenecks in both urban and agricultural contexts suggests that demographic contractions were not restricted to a single habitat type, but rather reflected broader processes of landscape intensification characteristic of Mediterranean human-modified environments. Given that breeding House Sparrow populations in Catalonia have been considered broadly stable over the last decade (ICO 2025 ), it is plausible that contemporary populations are currently undergoing a phase of demographic stabilization or adjustment to novel anthropogenic conditions. From a conservation genetics perspective, this highlights the importance of integrating historical genetic data, long-term demographic trends, and landscape history to correctly interpret genetic signals and to avoid attributing present-day population declines solely to recent genetic erosion. Overall, our results highlight the importance of explicitly considering both spatial and temporal scales in conservation genetic studies. Although House Sparrow populations in Mediterranean human-modified landscapes retain relatively high levels of neutral genetic diversity, the emergence of population structure, consistent heterozygote deficiency, and signals of demographic bottlenecks—particularly in urban populations—indicate that reduced connectivity and fine-scale habitat fragmentation are already shaping contemporary demographic and genetic dynamics (Ringsby et al. 2006 ; De Laet & Summers-Smith 2007 ). Importantly, the maintenance of neutral genetic diversity should not be interpreted as evidence of demographic or evolutionary resilience. Instead, our results suggest that historical population contractions followed by partial demographic stabilization can mask ongoing erosion of functional connectivity, potentially increasing vulnerability to future environmental change. Similar patterns, where genetic structure increases despite relatively stable levels of diversity, have been reported in other fragmented systems and emphasize the central role of connectivity in maintaining long-term population viability (Bustillo-de la Rosa et al. 2022). The use of museum specimens proved particularly valuable for reconstructing historical genetic baselines (Payne & Sorenson 2002 ; Suárez & Tsutsui 2004), allowing us to detect temporal shifts in population structure that would not be apparent from contemporary data alone. Integrating historical and modern datasets is therefore essential for distinguishing recent genetic changes from longer-term population dynamics, especially in Mediterranean landscapes characterized by long-standing human pressure and rapid land-use change. From a conservation perspective, our findings underscore the need to preserve or restore landscape connectivity—such as periurban habitats and stepping-stone populations—to mitigate the genetic consequences of fragmentation. Even in widespread and human-associated species such as the House Sparrow, subtle genetic signals may reveal underlying demographic fragility, highlighting the importance of incorporating genetic monitoring into conservation strategies for declining urban and agricultural bird populations. Declarations Conflict of interest Authors declare no conflict of interest Competing Interests The authors have no relevant financial or non-financial interests to disclose. Funding Research funding was provided by the Natural Science Museum of Barcelona and the Fundació Zoo de Barcelona (PASSERCAT project) to J. Quesada. Author Contribution Javier Quesada and Rocío Pérez-Portela designed the study. Javier Oliver was responsible for field sample collection. Laboratory work was carried out by Wangensteen, Owen S. and Riyahi, Sepand. Javier Quesada, Rocío Pérez-Portela, Marta Martín-Huete, Joan Calderon-Llobera analyzed and interpreted the data. The first draft of the manuscript was written by Javier Quesada, Rocio Perez-Portela, Jorge R, Lopez-Rey and all authors commented on previous versions of the manuscript. All authors read and approved of the final manuscript. Acknowledgement We are deeply grateful to Jaume Izquierdo for his seeking of old samples collection, Alberto Álvarez for field data gathering, and Marta Campos for her help in the laboratory. This paper is a contribution of the Consolidated Research Team 2021 SGR00177 (AGAUR, Generalitat de Catalunya) to JQ, and 2021 SGR 01271 (AGAUR, Generalitat de Catalunya) to RP-P and OW. Both capture and sample collection permission were allowed by Catalonian Government (SF-098 permission). Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Alberto F (2009) MsatAllele_1.0: an R package to visualize the binning of microsatellite alleles. J Hered 100:394–397 Anderson TR (2006) Biology of the ubiquitous house sparrow: From genes to populations. Oxford University Press, Oxford Aurelle D, Berline L, Bonhomme F et al (2022) The Mediterranean: a model sea for biodiversity. Ecosphere 13:e3915. https://doi.org/10.1002/ecs2.3915 Bellard C, Leclerc C, Leroy B et al (2014) Vulnerability of biodiversity hotspots to global change. Glob Ecol Biogeogr 23:1376–1386. https://doi.org/10.1111/geb.12228 Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Ann Stat 29:1165–1188. https://doi.org/10.1214/aos/1013699998 Berigan LA, Greig EI, Bonter DN (2020) Urban House Sparrow (Passer domesticus) populations decline in North America. Wilson J Ornithol 132:248–258 Bernat-Ponce E, Gil-Delgado JA, López-Iborra GM (2024) House Sparrow Nesting Site Selection in Urban Environments: A Multivariate Approach in Mediterranean Spain. Urban Sci 8:108. https://doi.org/10.3390/urbansci8030108 Bernat-Ponce E, Luna Á, Jiménez-Peñuela J, MacGregor-Fors I (2025) From pardal to etxe-txolarrea: An integrative review of the house sparrow Passer domesticus knowledge in the Iberian Peninsula. https://doi.org/10.32800/abc.2025.48.0012 Bogaart P, van der Loo M, Pannekoek J (2020) rtrim: Trends and Indices for Monitoring Data (R package version 2.1.1). https://CRAN.R-project.org/package=rtrim Brewer VN, Lane SJ, Sewall KB, Mabry KE (2020) Effects of low-density urbanization on genetic structure in the Song Sparrow. PLoS ONE 15:e0234008. https://doi.org/10.1371/journal.pone.0234008 Bruvo R, Michiels NK, D’Sousa TG, Schulenberg H (2004) A simple method for calculation of microsatellite genotypes irrespective of ploidy level. Mol Ecol 13:2101–2106. https://doi.org/10.1111/j.1365-294X.2004.02209.x La Bustillo-de D, Traba J, Calero-Riestra M et al (2022) Recent changes in genetic diversity, structure, and gene flow in a passerine experiencing a rapid population decline, the Dupont’s Lark (Chersophilus duponti). Diversity 14:1120. https://doi.org/10.3390/d14121120 Calderón-Llobera J (2019) Assessing human-related factors on population dynamics of two Passeridae species in Mediterranean landscapes. Dissertation, University of Barcelona Chapuis MP, Estoup A (2007) Microsatellite null alleles and estimation of population differentiation. Mol Biol Evol 24:621–631. https://doi.org/10.1093/molbev/msl191 Csardi G, Nepusz T (2006) The igraph software. Complex Syst 1695:1–9 Dadam D, Robinson RA, Clements A et al (2019) Avian malaria-mediated population decline of a widespread iconic bird species. R Soc Open Sci 6:182197. https://doi.org/10.1098/rsos.182197 Dawson DA, Horsburgh GJ, Krupa AP et al (2012) Microsatellite resources for Passeridae species: a predicted microsatellite map of the house sparrow Passer domesticus. Mol Ecol Resour 12:501–523. https://doi.org/10.1111/j.1755-0998.2012.03115.x De Laet J, Summers-Smith JD (2007) The status of the urban house sparrow Passer domesticus in north-western Europe: a review. J Ornithol 148:275–278. https://doi.org/10.1007/s10336-007-0154-0 De Meeûs T (2018) Revisiting F IS, F ST, Wahlund effects, and null alleles. J Hered 109:446–456. https://doi.org/10.1093/jhered/esx106 Dröscher H (1992) Hatching asynchrony in the House Sparrow: A test of the egg-viability hypothesis. Am Nat 139:669–675. https://doi.org/10.1086/285341 Earl DA, vonHoldt BM (2012) STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359–361. https://doi.org/10.1007/s12686-011-9548-7 Escudero A, Iriondo JM, Torres ME (2003) Spatial analysis of genetic diversity as a tool for plant conservation. Biol Conserv 113:351–365. https://doi.org/10.1016/S0006-3207(03)00122-8 Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol Ecol 14:2611–2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x Everaert J, Bauwens D (2007) A possible effect of electromagnetic radiation from mobile phone base stations on the number of breeding House Sparrows (Passer domesticus). Electromagn Biol Med 26:63–72. https://doi.org/10.1080/15368370701205693 Excoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour 10:564–567. https://doi.org/10.1111/j.1755-0998.2010.02847.x Farrer EC, Ashton IW, Knape J, Suding KN (2014) Separating direct and indirect effects of global change: A population dynamic modeling approach using readily available field data. Glob Change Biol 20:1238–1250. https://doi.org/10.1111/gcb.12429 Fourcade Y, Richardson DS, Secondi J (2020) No evidence for a loss of genetic diversity despite a strong decline in size of a European population of the Corncrake Crex crex. Bird Conserv Int 30:260–266. https://doi.org/10.1017/S0959270919000234 Fulgione D, Procaccini G, Milone M (2000) Urbanisation and the genetic structure of Passer italiae (Vieillot 1817) populations in the South of Italy. Ethol Ecol Evol 12:123–130. https://doi.org/10.1080/08927014.2000.9522808 Geue JC, Vágási CI, Schweizer M, Pap PL, Thomassen HA (2016) Environmental selection is a main driver of divergence in house sparrows (Passer domesticus) in Romania and Bulgaria. Ecol Evol 6:7954–7964. https://doi.org/10.1002/ece3.2521 Gotelli NJ (2008) A Primer of Ecology, 4th edn. Sinauer Associates, Sunderland Griffith SC, Dawson DA, Jensen H et al (2007) Fourteen polymorphic microsatellite loci characterized in the house sparrow Passer domesticus (Passeridae, Aves). Mol Ecol Notes 7:333–336. https://doi.org/10.1111/j.1471-8286.2006.01610.x Griffith SC, Stewart IR, Dawson DA, Owens IP, Burke T (1999) Contrasting levels of extra-pair paternity in mainland and island populations of the house sparrow (Passer domesticus): Is there an ‘island effect’? Biol J Linn Soc 68:303–316. https://doi.org/10.1111/j.1095-8312.1999.tb01171.x Guallar S, Cai X, Vega S, Oliver J, Quesada J (2025) Environmental drivers of House Sparrow (Passer domesticus) presence and abundance in the Metropolitan Area of Barcelona: a multi-variant approach. Rev Catalana Ornitol 41:9–20. https://doi.org/10.62102/2340-3764.2025.1.2 Hallmann CA, Foppen RP, Van Turnhout CA, De Kroon H, Jongejans E (2014) Declines in insectivorous birds are associated with high neonicotinoid concentrations. Nature 511:341–343. https://doi.org/10.1038/nature13531 Heij CJ (1985) Comparative ecology of the House Sparrow (Passer domesticus) in rural, suburban and urban situations. Dissertation, Vrije Universiteit Amsterdam Hermansen JS, Sæther SA, Elgvin TO et al (2011) Hybrid speciation in sparrows I: Phenotypic intermediacy, genetic admixture and barriers to gene flow. Mol Ecol 20:3812–3822. https://doi.org/10.1111/j.1365-294X.2011.05183.x Herrando S, Brotons L, Estrada J, Guallar S, Anton M (2011) Atles dels ocells de Catalunya a l’hivern 2006–2009. ICO, Barcelona Herrera-Dueñas A, Pineda-Pampliega J, Antonio-García MT, Aguirre JI (2017) The influence of urban environments on oxidative stress balance: A case study on the house sparrow in the Iberian Peninsula. Front Ecol Evol 5:106. https://doi.org/10.3389/fevo.2017.00106 Hole DG, Whittingham MJ, Bradbury RB et al (2002) Widespread local house-sparrow extinctions. Nature 418:931–932. https://doi.org/10.1038/nature00984 Hu Y, Fan H, Chen Y et al (2021) Spatial patterns and conservation of genetic and phylogenetic diversity of wildlife in China. Sci Adv 7:eabd5725. https://doi.org/10.1126/sciadv.abd5725 ICO (2025) SIOC: servidor d'informació ornitològica de Catalunya. Institut Català d’Ornitologia (ICO), Barcelona. http://www.sioc.cat . Accessed 28 Oct 2025 Jombart T (2008) adegenet: An R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403–1405. https://doi.org/10.1093/bioinformatics/btn129 Jombart T, Ahmed I (2011) adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics. https://doi.org/10.1093/bioinformatics/btr521 Jombart T, Devillard S, Balloux F (2010) Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genet 11:94. https://doi.org/10.1186/1471-2156-11-94 Jombart T, Devillard S, Dufour AB, Pontier D (2008) Revealing cryptic spatial patterns in genetic variability by a new multivariate method. Heredity 101:92–103. https://doi.org/10.1038/hdy.2008.34 Kamvar ZN, Tabima JF, Grünwald NJ (2014) Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2:e281. https://doi.org/10.7717/peerj.281 Kekkonen J, Seppä P, Hanski IK, Jensen H, Väisänen RA, Brommer JE (2011) Low genetic differentiation in a sedentary bird: House sparrow population genetics in a contiguous landscape. Heredity 106:183–190. https://doi.org/10.1038/hdy.2010.33 Kolde R (2019) Pheatmap: Pretty Heatmaps. R package version 1.0.12. https://CRAN.R-project.org/package=pheatmap Lenz M (2018) Notable number of House Sparrows, Passer domesticus, at a roost in natural habitat near Marree in the western Lake Eyre basin. South Aust Ornithol 42:58–64 Liu Y, Webber S, Bowgen K et al (2013) Environmental factors influence both abundance and genetic diversity in a widespread bird species. Ecol Evol 3:4683–4695. https://doi.org/10.1002/ece3.856 Lovejoy TE, Hannah L (eds) (2006) Climate change and biodiversity. Yale University Press, New Haven Meirmans PG (2020) Mol Ecol Resour 20:1126–1131. https://doi.org/10.1111/1755-0998.13145 . GENODIVE version 3.0: Easy-to-use software for the analysis of genetic data of diploids and polyploids Mohring B, Henry PY, Jiguet F, Malher F, Angelier F (2021) Investigating temporal and spatial correlates of the sharp decline of an urban exploiter bird in a large European city. Urban Ecosyst 24:501–513. https://doi.org/10.1007/s11252-020-01048-5 Møller AP (2013) Biological consequences of global change for birds. Integr Zool 8:136–144. https://doi.org/10.1111/1749-4877.12023 Monnahan CC, Colas B, Buerkle CA (2023) The impact of habitat loss and population fragmentation on genomic erosion. Conserv Genet 24:57–70. https://doi.org/10.1007/s10592-023-01548-9 Monroy Vilchis O (2005) Causas de pérdida de diversidad biológica. Cuad Biodivers 17:3–9 Murgui E, Macías A (2010) Changes in the House Sparrow Passer domesticus population in Valencia (Spain) from 1998 to 2008. Bird Study 57:281–288. https://doi.org/10.1080/00063651003610553 Nath A, Singha H, Lahkar BP (2022) Correlation does not imply causation: Decline of house sparrow overshadowed by electromagnetic radiation. Urban Ecosyst 25:1279–1295. https://doi.org/10.1007/s11252-022-01225-8 Neumann K, Wetton JH (1996) Highly polymorphic microsatellites in the house sparrow Passer domesticus. Mol Ecol 5:307–309. https://doi.org/10.1111/j.1365-294X.1996.tb00322.x Orsini L, Vanoverbeke J, Swillen I, Mergeay J, De Meester L (2013) Drivers of population genetic differentiation in the wild: isolation by dispersal limitation, isolation by adaptation and isolation by colonization. Mol Ecol 22:5983–5999. https://doi.org/10.1111/mec.12558 Pannekoek J, Van Strien AJ (2005) TRIM 3 manual (TRends and Indices for Monitoring data). Statistics Netherlands, Leiden Pauls SU, Nowak C, Bálint M, Pfenninger M (2013) The impact of global climate change on genetic diversity within populations and species. Mol Ecol 22:925–946. https://doi.org/10.1111/mec.12152 Payne RB, Sorenson MD (2002) Museum collections as sources of genetic data. Bonner Zool Beitr 51:97–104 Peach WJ, Sheehan DK, Kirby WB (2014) Supplementary feeding of mealworms enhances reproductive success in garden nesting House Sparrows Passer domesticus. Bird Study 61:378–385. https://doi.org/10.1080/00063657.2014.918577 PECBMS (2025) Species trends: House sparrow. Pan-European Common Bird Monitoring Scheme. https://pecbms.info/trends-and-indicators/species-trends/ . Accessed 01 Sep 2025 Perez-Portela R, Noyer C, Becerro MA (2015) Genetic structure and diversity of the endangered bath sponge Spongia lamella. Aquat Conserv Mar Freshw Ecosyst 25:365–379. https://doi.org/10.1002/aqc.2478 Pinsky ML, Akçakaya HR, Brugere D, Dopheide A (2025) Global meta-analysis shows action is needed to halt genetic diversity loss. Science. https://doi.org/10.1126/science.abn5642 Pintó J, Garcia-Lozano C, Roig-Munar FX (2018) L'espai litoral. Natura: ús o abús. Institució Catalana d'Història Natural (ICHN). Institut d'Estudis Catalans (IEC), Barcelona Piry S, Luikart G, Cornuet JM (1999) BOTTLENECK: A computer program for detecting recent reductions in the effective population size using allele frequency data. J Hered 90:502–503. https://doi.org/10.1093/jhered/90.4.502 Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959. https://doi.org/10.1093/genetics/155.2.945 Quesada J, Calderón-Llobera J (2021) Pardal comú. In: Franch M, Herrando S, Anton M, Villero D, Brotons L (eds) Atles dels ocells nidificants de Catalunya: Distribució i abundància 2015–2018 i canvi des de 1980. Institut Català d'Ornitologia, Cossetània Edicions, Barcelona, pp 490–491 Quesada J, Guallar S, Pérez-Ruiz NJ et al (2010) Observer error associated with band allocation is negligible in large scale bird monitoring schemes, but how precise is the use of bands at all? Ardeola 57:23–32. https://doi.org/10.13157/arla.57.1.2010.23 R Core Team (2025) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/ Ramos-Elvira E, Banda E, Arizaga J et al (2023) Long-term population trends of house sparrow and Eurasian tree sparrow in Spain. Birds 4(2):159–170. https://doi.org/10.3390/birds4020013 Ravinet M, Elgvin TO, Trier C et al (2018) Signatures of human-commensalism in the house sparrow genome. Proc R Soc B 285:20181246. https://doi.org/10.1098/rspb.2018.1246 Ringsby TH, Sæther BE, Jensen H, Engen S (2006) Demographic characteristics of extinction in a small, insular population of house sparrows in northern Norway. Conserv Biol 20:1761–1767. https://doi.org/10.1111/j.1523-1739.2006.00564.x Sage RF (2020) Global change biology: A primer. Glob Change Biol 26:3–30. https://doi.org/10.1111/gcb.14893 Saura S, Bodin Ö, Fortin MJ (2014) Stepping stones are crucial for species' long-distance dispersal and range expansion through habitat networks. J Appl Ecol 51(1):171–182. https://doi.org/10.1111/1365-2664.12179 Scribner KT, Blanchong JA, Bruggeman DJ et al (2005) Geographical genetics: Conceptual foundations and empirical applications of spatial genetic data in wildlife management. J Wildl Manag 69:1434–1453. https://doi.org/10.2193/0022-541X (2005)69[1434:GGCFAE]2.0.CO;2 Sharma P, Binner M (2020) The decline of population of house sparrow in India. Int J Agric Sci 5:1–5 Siriwardena GM, Baillie SR, Wilson JD (1999) Temporal variation in the annual survival rates of six granivorous birds with contrasting population trends. Ibis 141:621–636. https://doi.org/10.1111/j.1474-919X.1999.tb07370.x Smith AC, Hudson MAR, Aponte VI, English WB, Francis CM (2024) North American Breeding Bird Survey - Canadian Trends Website, Data-version 2023. Environment and Climate Change Canada, Gatineau Suarez AV, Tsutsui ND (2004) The value of museum collections for research and society. Bioscience 54:66–74. https://doi.org/10.1641/0006-3568( 2004)054[0066:TVOMCF]2.0.CO;2 Summers-Smith D (1999) Current status of the House Sparrow in Britain. Br Wildl 10:381–386 Summers-Smith JD (2003) The decline of the House Sparrow: A review. Br Birds 96:439–446 Swanson TM (ed) (1995) The economics and ecology of biodiversity decline: The forces driving global change. Cambridge University Press, Cambridge Szulkin M, Sheldon BC (2008) Dispersal as a means of inbreeding avoidance in a wild bird population. Proc R Soc B 275:703–711. https://doi.org/10.1098/rspb.2007.0989 Vangestel C, Mergeay J, Dawson DA et al (2012) Genetic diversity and population structure in contemporary house sparrow populations along an urbanization gradient. Heredity 109:163–172. https://doi.org/10.1038/hdy.2012.27 Wan X, Holyoak M, Yan C et al (2022) Broad-scale climate variation drives the dynamics of animal populations: A global multi-taxa analysis. Biol Rev 97:2174–2194. https://doi.org/10.1111/brv.12888 Additional Declarations No competing interests reported. Supplementary Files 3.OnlineSupplementaryMaterialPasserCONSGEN.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Mar, 2026 Reviews received at journal 01 Mar, 2026 Reviews received at journal 27 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers agreed at journal 01 Feb, 2026 Reviewers agreed at journal 30 Jan, 2026 Reviewers invited by journal 30 Jan, 2026 Editor assigned by journal 28 Jan, 2026 Submission checks completed at journal 28 Jan, 2026 First submitted to journal 22 Jan, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8669756","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583625730,"identity":"97f9ea9b-f507-48e0-9c1c-75d3f2d29398","order_by":0,"name":"Javier Quesada","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACxgYwxczAx95DqhY2njMQER4idQK1SOQQqYW5Pcfw4Y8aa3k2ybcHPxcw2MnbE3RYzxtjY55j6YZt0nnJ0jMYkg0JeolxRo6ZNAPbYcY26RwzZh6GA4zEaDH/+ePfYfs2yTNgLfZE2cLA23Y4sU2CB6wlkbCWnmfF0rx96cltPEC/8BgkJ/ccIKDFsD1548cf36xt+9nPHvzMU2Fn295ASEtDAjLXgJCrgECeIYGgmlEwCkbBKBjpAADnIzdEhmb70gAAAABJRU5ErkJggg==","orcid":"","institution":"Natural Science Museum of Barcelona","correspondingAuthor":true,"prefix":"","firstName":"Javier","middleName":"","lastName":"Quesada","suffix":""},{"id":583625731,"identity":"a459f188-25ca-4eb9-b472-46174de61268","order_by":1,"name":"Marta Martín-Huete","email":"","orcid":"","institution":"University of Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"","lastName":"Martín-Huete","suffix":""},{"id":583625732,"identity":"056b5960-ed2d-4c1e-bbe5-8bd80c280e9d","order_by":2,"name":"Javier Oliver","email":"","orcid":"","institution":"Reclam Natura SL","correspondingAuthor":false,"prefix":"","firstName":"Javier","middleName":"","lastName":"Oliver","suffix":""},{"id":583625733,"identity":"5f75ec6c-d9a2-448f-81b4-14fa70379724","order_by":3,"name":"Joan Calderón-Llobera","email":"","orcid":"","institution":"University of Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Joan","middleName":"","lastName":"Calderón-Llobera","suffix":""},{"id":583625734,"identity":"b8465077-5db4-4003-888e-c09615f14685","order_by":4,"name":"Jorge R. López-Rey","email":"","orcid":"","institution":"Rey Juan Carlos University","correspondingAuthor":false,"prefix":"","firstName":"Jorge","middleName":"R.","lastName":"López-Rey","suffix":""},{"id":583625735,"identity":"9c280ced-63b6-48d2-a5f5-6085ff29881c","order_by":5,"name":"Owen Wangensteen","email":"","orcid":"","institution":"University of Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Owen","middleName":"","lastName":"Wangensteen","suffix":""},{"id":583625736,"identity":"f4df9f86-35b9-4551-bffe-f8e48a17be55","order_by":6,"name":"Sepand Riyahi","email":"","orcid":"","institution":"Bielefeld University","correspondingAuthor":false,"prefix":"","firstName":"Sepand","middleName":"","lastName":"Riyahi","suffix":""},{"id":583625737,"identity":"35172a78-01f2-4fff-aa23-695523151748","order_by":7,"name":"Rocío Pérez-Portela","email":"","orcid":"","institution":"University of Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Rocío","middleName":"","lastName":"Pérez-Portela","suffix":""}],"badges":[],"createdAt":"2026-01-22 12:38:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8669756/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8669756/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101681414,"identity":"cb1eb737-1432-4ff4-bdbc-18c867199bc7","added_by":"auto","created_at":"2026-02-02 14:33:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":361966,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Catalonia showing the 13 sampling locations where House Sparrow individuals were sampled for genetic analyses. Red dots indicate the sampled populations, which include agricultural areas (Amposta-AM, Folquer-FOL, Lleida Agricultural-LLA, Tàrrega- TAR, Cervera-CER, Pla de Santa Maria-PLA, Deltebre-DE, Gavà-GAV) and urbanized zones (Castellbisbal-CAS, Barcelona-BCN, Blanes-BLA, Lleida Seròs-LLS, Sant Vicenç de Calders-SAN)\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8669756/v1/acfd29bad918890095b239a9.png"},{"id":101681417,"identity":"cc53f4b2-c648-441c-82c6-518760c2b7bf","added_by":"auto","created_at":"2026-02-02 14:33:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3889492,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic divergence in House Sparrow including contemporary and OLD samples a) Heatmap from the F\u003csub\u003eST\u003c/sub\u003e values (non-significant values above the diagonal highlighted with a NS); b) Bar plot of STRUCTURE with the consequent probability of belonging to a given cluster for each individual; and c) DAPC plot showing population structure. In the heatmap and structure bar graph the different groups according to the use of the territory are also represented.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8669756/v1/8ca7ce509bc8847e9b9783c9.png"},{"id":101681415,"identity":"aff76a02-0e59-4712-ac2b-80a7916f747b","added_by":"auto","created_at":"2026-02-02 14:33:47","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":439411,"visible":true,"origin":"","legend":"\u003cp\u003eMinimum Spanning Network (MSN) based on Bruvo’s genetic distances among all individuals of House Sparrow. Circles represent genotypes and connecting lines represent genetic distances measured by allele variants. Colors of the circles represent different sampling sites and colors, and thickness of the connections are proportional to the genetic distances\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8669756/v1/40426c84ce5c05899b61a6b6.jpeg"},{"id":101754055,"identity":"1fad6c30-185c-4161-80c4-c228f1fb2883","added_by":"auto","created_at":"2026-02-03 10:41:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5264521,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8669756/v1/9059d3ab-ff0b-4bef-a282-8e13021f9ead.pdf"},{"id":101681416,"identity":"fa7d3449-a7c6-4877-9f6c-cb09dee60f2a","added_by":"auto","created_at":"2026-02-02 14:33:47","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1442706,"visible":true,"origin":"","legend":"","description":"","filename":"3.OnlineSupplementaryMaterialPasserCONSGEN.docx","url":"https://assets-eu.researchsquare.com/files/rs-8669756/v1/ae0644526157c519dbc20559.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Historical and contemporary genetic structure reveals recent fragmentation without loss of diversity in Mediterranean House Sparrow populations","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal change is influencing the population dynamics of numerous terrestrial species (e.g., Bellard et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wan et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Key drivers of biodiversity loss include climate change, ecosystem alteration, habitat fragmentation from urbanization, agricultural intensification, deforestation, and the introduction of alien species (Swanson, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Farrer et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sage, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The impact of these changes on terrestrial species and ecosystems varies significantly across different regions, but in some areas, it is intense enough to affect even formerly successful species, both within their natural habitats and in recently colonized urban environments (M\u0026oslash;ller \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mohring et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe direct impacts of global change extend beyond reducing population sizes, threatening fundamental components of biodiversity, particularly the genetic diversity within populations (Lovejoy and Hannah \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Sage \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Genetic diversity underpins long-term species survival, as evolutionary processes such as natural selection depend on this variation to drive adaptation and resilience (Pauls et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Species face genetic diversity loss driven by both natural selective forces and stochastic processes\u0026mdash;some closely linked to population size (e.g., genetic drift)\u0026mdash;as well as by anthropogenic pressures on ecosystems (Monroy-Vilchis 2005). These pressures often cause population declines, which reduce effective population sizes and accelerate genetic erosion over time (Fourcade et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To ensure effective conservation, management strategies must integrate data on genetic composition, including the geographic distribution of diversity, population connectivity, and temporal changes in genetic structure (Kekkonen et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA prime example of a once globally abundant and highly adaptable species that is now experiencing a population decline is the House Sparrow (\u003cem\u003ePasser domesticus\u003c/em\u003e). This species is a model for understanding the demographic decline of common, highly adaptable species. Until the late 20th century, it was one of the most widespread and abundant birds globally (Anderson \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Native to the Middle East and parts of Asia, it naturally expanded across Eurasia over millennia through its close association with humans (Ravinet et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and later introduced by humans to North America, parts of southern Africa, and Australia (Anderson, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Despite its adaptability and broad geographic range, global population of the House Sparrow has significantly declined over the past four decades (Mohring et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Surveys in the United States (Berigan et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Australia (reviewed in Lenz \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Western Europe (PECBMS \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and India (Sharma and Binner \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) all document marked population declines, including the introduced areas.\u003c/p\u003e \u003cp\u003eThe causes of the House Sparrow decline remain debated, but there is general agreement that multiple interacting factors affect both rural and urban populations (Summers-Smith \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In rural areas, declines appear linked to human depopulation and shifts in agricultural practices, particularly the increased use of pesticides and more efficient grain collection, which reduces available food resources (Summers-Smith \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Hole et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Pesticides negatively affect nesting sites and diminish invertebrate populations critical for feeding offspring (Dr\u0026ouml;scher, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Hallmann et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Urban populations face similar challenges, including increased pesticide use, fewer green spaces, and reduced availability or low-quality of food (Heij \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Hole et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Murgui and Mac\u0026iacute;as \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Peach et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Guallar et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Additionally, urban factors such as fewer breeding cavities due to modern building designs (Bernat-Ponce et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and potential physiological impacts from electromagnetic radiation (Everaert and Bauwens \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; but see Nath et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) may further exacerbate population declines. Oxidative stress (Herrera-Due\u0026ntilde;as et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and elevated disease prevalence, such as avian malaria (Dadam et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), could also contribute to reduced reproductive success and long-term population viability (Siriwardena et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Collectively, these pressures have already led to the extinction of local populations across several European countries (Ringsby et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; De Laet and Summers-Smith \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Dadam et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMonitoring populations is therefore critical for detecting the severity of population declines before local extinction occurs. Genetic diversity and its geographic distribution are key variables for developing and implementing successful conservation strategies (Escudero et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Scribner et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the House Sparrow, short postnatal dispersal distances of 1-1.7 km (Vangestel et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) suggest spatially structured populations with limited gene flow, increasing the risk of local extinction and reducing the potential for recovery from genetic loss. However, some studies (e.g., Kekkonen et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) indicate that this species can be panmictic and genetically homogeneous despite limited dispersal, sufficient to maintain population connectivity. In such cases, geography rather than population dynamics plays a greater role in genetic differentiation. Yet, this approach has not been applied in Mediterranean areas, which constitute biodiversity hotspots owing to their complex hydro-climatic, morphological, geographical, and historical heterogeneity, compounded by long-standing anthropogenic pressures. This multifaceted environmental mosaic has shaped distinctive ecological adaptations and population dynamics across taxa (Aurelle et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), potentially increasing population genetic structure or promoting local adaptations.\u003c/p\u003e \u003cp\u003eGiven these knowledge gaps, it remains unclear whether House Sparrow populations in the Mediterranean exhibit similar genetic patterns to those reported in temperate zones, or whether reduced dispersal, habitat fragmentation, and local declines have led to greater genetic structuring and diversity loss. This fact is important, as recent evidence indicates that population-level assessments of Mediterranean House Sparrow remain limited, particularly regarding potential conservation implications, which have not yet been fully explored (Bernat-Ponce et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo address some of these uncertainties, the objectives of this study were: a) to describe genetic diversity and structure of the House Sparrow House Sparrow in Catalonia, a Mediterranean region in northeast Iberia characterized by a mosaic of diverse land uses, b) to assess whether land-use (urban vs. rural) and negative population trends are linked to genetic diversity loss or genetic structuring; and c) analyze the temporal changes in genetic diversity by comparing current populations with those from the pre-decline period (\u0026lt;\u0026thinsp;1950). Our initial hypotheses are: 1) House Sparrow populations in Catalonia are highly structured due to limited dispersal, the species' sedentary behavior and land use; 2) declining populations exhibit lower levels of genetic diversity than stable or increasing populations; and 3) older populations established before the decline may retain higher genetic diversity than current populations. To achieve our objectives, we analyzed the population dynamics and genetic structure of 13 House Sparrow populations distributed across both agricultural and urban\u0026ndash;periurban land-use types, which represent the main habitats occupied by the species in Catalonia (Quesada and Calder\u0026oacute;n-Llobera \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Prospective analyses of population trends indicated that populations in urbanized areas (including both urban and peri-urban sites) exhibit a steeper average decline compared to those in rural, agricultural landscapes (Calder\u0026oacute;n-Llobera and Quesada unpublished data).\u003c/p\u003e \u003cp\u003eWe complemented this spatial analysis with a temporal comparison by incorporating genetic data from historical specimens collected between 1919 and 1950 from zoological collections. Finally, we examined the relationship between genetic diversity parameters and the slope of population trends derived from long-term monitoring data, allowing us to assess whether demographic changes are associated with shifts in genetic variation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSamples for population genetics analyses\u003c/h2\u003e \u003cp\u003eIn order to describe genetic diversity and structure of the House Sparrow in Catalonia, we selected sampling sites (hereafter, contemporary populations) that must accomplish several criteria: a) populations should assign to agricultural or some degree of urbanized areas (Urban, Peri-Urban), b) trends of the area where a studied population was selected should be known (see SOCC population monitoring scheme trends below), and c) selected populations should have genetic information before the House Sparrow major decline in Europe (i.e., before 1950) were detected (PECBMS \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo obtain genetic information before the House Sparrow declines period, we conducted an exhaustive search for historical samples (hereafter OLD samples) of sparrows collected in Catalonia from Spanish museums and private collections, with collection data equal or prior to 1950, and repositories such as GBIF or Vertnet. Only individuals with complete metadata about the sampling location (at least at county level) and capture date were considered for analyses. In total, we obtained 24 old samples from specimens captured between 1919 and 1950, in different locations throughout Catalonia. All of them belonged to the collection of the Museum of Natural Sciences of Barcelona (Supplementary information, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). From each specimen a small sample of toe pad skin was carefully extracted to minimize damage to these valuable materials. These old specimens were compared with those fresh specimens from the current locations (see more details below about the sampling collection) where they were collected (N\u003csub\u003eLocalities\u003c/sub\u003e=9) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSince the number of populations was low, and to evaluate whether regressive or expansion trends in population size influenced genetic parameters, we included four additional localities representing positive (LLA), stable (LLS, TAR), and regressive trends (BCN), thereby capturing a more comprehensive variance in populations dynamics (N\u003csub\u003eLocalities\u003c/sub\u003e=13, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Living House sparrows were captured with mist nets during the spring-summer of 2014 and 2015 (7:00\u0026ndash;13:00 h) by a single sampler (JO). All animals were ringed and measured for further studies, and 25\u0026ndash;50\u0026micro;l of blood was extracted with a hematocrit capillary. Blood samples were preserved in absolute ethanol and stored at -20\u0026deg;C in the laboratory until further processing.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDNA extraction and genotyping of samples\u003c/h3\u003e\n\u003cp\u003eTotal genomic DNA was extracted from fresh blood of contemporary samples (n\u0026thinsp;=\u0026thinsp;235) and OLD (N\u0026thinsp;=\u0026thinsp;24) samples (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) with the NucleoSpinTM kit (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.cultek.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.cultek.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), following the protocol of the product. Due to the low amount of DNA obtained from the old samples, the protocol was modified for these samples by extending the tissue digestion overnight. After extraction, DNA purity, integrity and quantity were checked in Nanodrop (Thermo Fisher, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.thermofisher.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.thermofisher.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), agarose electrophoresis gels, a Qubit DNA HS assay (Life Technologies, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.thermofisher.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.thermofisher.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), respectively. Whereas fresh samples of blood retained high DNA quantity and quality, DNA extractions from the skin of the old samples were at extremely low concentrations, and the DNA appeared highly fragmented. Because the DNA quality and quantity of the archived skin samples was insufficient for population genomic analyses using high-throughput sequencing, we instead genotyped all samples using 12 previously described nuclear microsatellite markers for this species. This approach enabled a robust comparison between historical and contemporary samples despite differences in DNA preservation and quality. The microsatellites used were: Pdo1 and Pdo3 (Neumann and Wetton \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1996\u003c/span\u003e); Pdo5 (Griffith et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1999\u003c/span\u003e); Pdo 10 and Ase18 (Griffith et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e); and Pdo16, Pdo17, Pdo19, Pdo27, Pdo30, Pdo44 and Pdo47 (Dawson et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For microsatellites genotyping, fragments were amplified with specific primers for each microsatellite following the protocol and PCR conditions described by Hermansen et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Once amplified, the microsatellite fragments (alleles) were measured in an automatic sequencer from the company Macrogen\u0026reg; (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.macrogen.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.macrogen.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with an internal GENESCANTM 400HD ROXTM standard. 7 (Applied Biosystems) using the software Peak-Scanner (Thermo Fisher scientific, http:/ \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.thermofisher.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.thermofisher.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Allele scoring was graphically performed using the R package MsatAllele (Alberto \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenetic diversity of \u003cem\u003eP. domesticus\u003c/em\u003e. OLD samples (1919\u0026ndash;1950) and 13 contemporary populations: Use: Land use (Ag: agricultural and Ur.: Urban-periurban); N: Number of individuals analyzed; N\u003csub\u003ea\u003c/sub\u003e: Total number of alleles observed across loci; A\u003csub\u003er\u003c/sub\u003e: Allelic richness, a measure of genetic diversity normalized for sample size; H\u003csub\u003eo\u003c/sub\u003e: Observed heterozygosity, representing the proportion of heterozygotes in the population; H\u003csub\u003ee\u003c/sub\u003e: Expected heterozygosity under Hardy-Weinberg equilibrium, indicating the genetic diversity expected in an idealized population;. F\u003csub\u003eIS\u003c/sub\u003e: Inbreeding coefficient, measuring the departure from random mating (values near zero indicate random mating, while positive values suggest inbreeding). (* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUse\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003csub\u003er\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH\u003csub\u003eo\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eH\u003csub\u003ee\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eIS\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e10.917\u0026thinsp;\u0026plusmn;\u0026thinsp;0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.676\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.805\u0026thinsp;\u0026plusmn;\u0026thinsp;0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e0.159\u0026thinsp;\u0026plusmn;\u0026thinsp;0.056*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e11.167\u0026thinsp;\u0026plusmn;\u0026thinsp;0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.687\u0026thinsp;\u0026plusmn;\u0026thinsp;0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.806\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e0.144\u0026thinsp;\u0026plusmn;\u0026thinsp;0.041*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUr.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e10.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.617\u0026thinsp;\u0026plusmn;\u0026thinsp;0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.816\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e0.242\u0026thinsp;\u0026plusmn;\u0026thinsp;0.075*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUr.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e12.083\u0026thinsp;\u0026plusmn;\u0026thinsp;1.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.593\u0026thinsp;\u0026plusmn;\u0026thinsp;0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.798\u0026thinsp;\u0026plusmn;\u0026thinsp;0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e0.231\u0026thinsp;\u0026plusmn;\u0026thinsp;0.073*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUr.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e11.667\u0026thinsp;\u0026plusmn;\u0026thinsp;1.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.703\u0026thinsp;\u0026plusmn;\u0026thinsp;0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.821\u0026thinsp;\u0026plusmn;\u0026thinsp;0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e0.143\u0026thinsp;\u0026plusmn;\u0026thinsp;0.049*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e9.083\u0026thinsp;\u0026plusmn;\u0026thinsp;0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.611\u0026thinsp;\u0026plusmn;\u0026thinsp;0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.797\u0026thinsp;\u0026plusmn;\u0026thinsp;0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e0.236\u0026thinsp;\u0026plusmn;\u0026thinsp;0.067*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e9.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.694\u0026thinsp;\u0026plusmn;\u0026thinsp;0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.814\u0026thinsp;\u0026plusmn;\u0026thinsp;0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e0.146\u0026thinsp;\u0026plusmn;\u0026thinsp;0.053*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e9.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.509\u0026thinsp;\u0026plusmn;\u0026thinsp;0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.828\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e0.385\u0026thinsp;\u0026plusmn;\u0026thinsp;0.066*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e8.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.647\u0026thinsp;\u0026plusmn;\u0026thinsp;0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.774\u0026thinsp;\u0026plusmn;\u0026thinsp;0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e0.161\u0026thinsp;\u0026plusmn;\u0026thinsp;0.054*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e11.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.814\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e0.209\u0026thinsp;\u0026plusmn;\u0026thinsp;0.067*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUr.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e11.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.554\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e0.346\u0026thinsp;\u0026plusmn;\u0026thinsp;0.059*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e10.667\u0026thinsp;\u0026plusmn;\u0026thinsp;0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.824\u0026thinsp;\u0026plusmn;\u0026thinsp;0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e0.086\u0026thinsp;\u0026plusmn;\u0026thinsp;0.043*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUr.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e11.000\u0026thinsp;\u0026plusmn;\u0026thinsp;0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.623\u0026thinsp;\u0026plusmn;\u0026thinsp;0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e0.217\u0026thinsp;\u0026plusmn;\u0026thinsp;0.069*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e10.917\u0026thinsp;\u0026plusmn;\u0026thinsp;0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.795\u0026thinsp;\u0026plusmn;\u0026thinsp;0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e0.168\u0026thinsp;\u0026plusmn;\u0026thinsp;0.047*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003ePopulation genetics and diversity analyses\u003c/h3\u003e\n\u003cp\u003eWe calculated genetic diversity parameters per population and microsatellite loci, including the number of alleles (N\u003csub\u003ea\u003c/sub\u003e), allelic richness standardized to the smallest sample size (A\u003csub\u003er\u003c/sub\u003e), mean number of private alleles (pA), expected heterozygosity (H\u003csub\u003ee\u003c/sub\u003e), and observed heterozygosity (H\u003csub\u003eo\u003c/sub\u003e), using the Adegenet R package (Jombart \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The inbreeding coefficient (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eIS\u003c/em\u003e\u003c/sub\u003e) and deviations from Hardy-Weinberg equilibrium were computed in Genodive 3.04 (Meirmans \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with p-values derived from 100,000 permutations.\u003c/p\u003e \u003cp\u003eTo assess genetic divergence among populations, we conducted analyses of pairwise genetic differentiation, Discriminant Analysis of Principal Components (DAPC), and Bayesian clustering. We additionally measured individual-level relatedness. We calculated genetic distances between pairs of populations, independently of the land use, using the \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e statistic in Arlequin v. 3.5.2.2 (Excoffier and Lischer, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). P-values were obtained through 10,100 permutations and corrected for multiple testing using the p.adjust function from the stats package in R (R Core Team \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). False Discovery Rate (FDR) corrections were applied following the Benjamini\u0026ndash;Yekutieli procedure (Benjamini and Yekutieli, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). To account for the potential impact of null alleles on \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e values, we used FreeNA to perform internal corrections based on the proportion of null alleles (Chapuis and Estoup, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e values were graphically represented in a heatmap using the pheatmap function in the pheatmap 1.0.12 R package (Kolde \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To assess the potential genetic isolation of populations due to geographic distance in contemporary populations, we performed a Mantel test comparing linearized genetic distances (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e / [1 \u0026ndash; \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e]) with geographic distances between localities (in km) in Arlequin v. 3.5.2.2. Statistical significance was evaluated using 10,000 permutations. For the Mantel test OLD samples were excluded.\u003c/p\u003e \u003cp\u003eA Bayesian clustering approach was applied using STRUCTURE 2.3.4 (Pritchard et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) to infer population genetic structure and determine the optimal number of clusters (K). The analysis was run with 200,000 Markov chain Monte Carlo (MCMC) iterations, following a burn-in period of 80,000, with 10 replicates for each of the 16 K values. The optimal number of clusters was determined using STRUCTURE Harvester version 0.6.94 (Earl and vonHoldt \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), based on the ΔK statistic (Evanno et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe discriminant analysis of principal components (DAPC) (Jombart et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) was performed for all samples and populations using the populations as groups with the adegenet 1.3 package in R (Jombart and Ahmed \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). DAPC combines PCA with discriminant analysis to summarize genetic differentiation between populations. The optimal number of clusters for the DAPC was chosen based on the Bayesian information criterion (BIC), and the optimal number of principal components (PCs) retained from the PCA step was determined using cross-validation. This was achieved by comparing a-scores across an increasing range of PCs, followed by spline interpolation with the a-score function.\u003c/p\u003e \u003cp\u003eTo explore the relationships among individual genotypes, we constructed a minimum spanning network that visualizes distances among multilocus genotypes, based on Bruvo\u0026rsquo;s dissimilarity distances, a genetic distance measure designed for microsatellites (Bruvo et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The network was constructed incorporating all individuals (old and fresh samples) from all sites in Poppr v2.9.2 package (Kamvar et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) in R and represented in igraph (Csardi and Nepusz \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo examine specifically the effect of land use on the genetic divergence of populations, we conducted an AMOVA analysis in Arlequin v. 3.5.2.2. We only used fresh samples that were grouped into two categories (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): urban-periurban (5 sites: BCN, BLA, CAS, LLS and SAN) and agricultural (8 sites: AM, CER, DE, FOL, GAV, LLA, PLA and TAR). We then assessed genetic variation partitioned across three hierarchical levels: (1) between land-use groups, (2) between populations within each group, and (3) within populations.\u003c/p\u003e\n\u003ch3\u003eEffect of population trends on genetic characteristics\u003c/h3\u003e\n\u003cp\u003eHouse Sparrow population dynamics were estimated using data from the SOCC monitoring program (Common Bird Monitoring in Catalonia). This citizen science initiative, active since 2002, involves approximately 3 km of transects distributed throughout Catalonia, which are surveyed four times annually (twice in winter and twice during the breeding season) (Quesada et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For this study, we utilized breeding season data from 2002 to 2024. We considered the maximum abundance recorded during the two breeding visits as a proxy for population abundance (Guallar et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs this monitoring program relies on citizen science, some transects typically have missing data for certain years because surveys were not conducted. To account for this, we used RStudio (R Core Team \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and the TRIM (Trends and Indices in Monitoring Data) software, implemented via the rtrim 2.1.1 package (Bogaart et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), to impute missing values. TRIM uses loglinear Poisson regressions, allowing us to calculate imputed values, long-term trends, and annual population indices. We used data where house sparrows were consistently recorded in 334 of the 433 transects. We applied a model 2 with a switching linear trend, overdispersion, autocorrelation, and stepwise change-point selection to produce a parsimonious model (Pannekoek and Van Strien \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOnce the missing values were imputed, we investigated whether declining populations exhibited lower genetic diversity than stable or increasing populations. To do this, we first quantified the magnitude of population change for each transect by fitting an exponential population growth model:\u003c/p\u003e \u003cp\u003eln(Nt​)=α\u0026thinsp;+\u0026thinsp;r\u0026sdot;t\u003c/p\u003e \u003cp\u003ewhere Nt​ is abundance in year \u003cem\u003et\u003c/em\u003e, and \u003cem\u003er\u003c/em\u003e is the instantaneous growth rate. From \u003cem\u003er\u003c/em\u003e, we calculated λ\u0026thinsp;=\u0026thinsp;\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003er\u003c/em\u003e\u003c/sup\u003e, representing the annual growth rate (λ\u0026thinsp;\u0026gt;\u0026thinsp;1 indicates growth; λ\u0026thinsp;\u0026lt;\u0026thinsp;1 indicates decline) (Gotelli \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The model was fitted using linear regression on log-transformed abundances. For each locality, we report R\u0026sup2; and p-value for the slope. These growth rates (r and λ) were then used as explanatory variables in ordinary least squares (OLS) regressions to test for associations with genetic diversity metrics (N\u003csub\u003ea\u003c/sub\u003e, R\u003csub\u003ea\u003c/sub\u003e, pA, H\u003csub\u003eo\u003c/sub\u003e, H\u003csub\u003ee\u003c/sub\u003e, and \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eIS\u003c/em\u003e\u003c/sub\u003e).\u003c/p\u003e \u003cp\u003eFinally, we tested for recent reductions in effective population size (bottlenecks) at all contemporary sampling sites using the program BOTTLENECK v1.2.02 (Piry et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). This software evaluates whether the observed heterozygosity is higher than expected under mutation\u0026ndash;drift equilibrium, which would indicate a recent bottleneck due to the faster loss of rare alleles compared to heterozygosity. We analyzed each population under two mutation models: the Infinite Alleles Model (IAM) and the Stepwise Mutation Model (SMM), although the second one is considered more appropriated for microsatellites. The significance of the results was tested with a sign test that evaluates the proportion of loci with heterozygosity excess or deficiency, and a Wilcoxon signed-rank test (a non-parametric test recommended for \u0026lt;\u0026thinsp;20 loci). Additionally, we examined the mode-shift indicator, which detects deviations from the expected L-shaped allele frequency distribution in stable populations. A shifted distribution indicates a recent bottleneck.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGenetic diversity descriptors\u003c/h2\u003e \u003cp\u003eAll loci were highly polymorphic across all populations, with the total number of alleles ranging from 20 to 35 (Supplementary information, Table S2). Additionally, all loci exhibited higher expected heterozygosity (H\u003csub\u003ee\u003c/sub\u003e) values compared to observed heterozygosity (H\u003csub\u003eo\u003c/sub\u003e) (Table S2), leading to significant and positive inbreeding coefficient values.\u003c/p\u003e \u003cp\u003eThe number of alleles per population ranged from 8 and 12, whereas allelic richness (A\u003csub\u003er\u003c/sub\u003e) per population ranged from 6.65 to 8.32. In all populations, expected heterozygosity (H\u003csub\u003ee\u003c/sub\u003e) values exceeded observed heterozygosity (H\u003csub\u003eo\u003c/sub\u003e), indicating a notable heterozygous deficit (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Accordingly, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eIS\u003c/em\u003e\u003c/sub\u003e values were positive in all populations, and significant deviations from Hardy-Weinberg equilibrium were detected (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eTo evaluate the suitability of the OLD samples for population genetic analysis, we compared their genetic diversity parameters with those of contemporary populations. Expected heterozygosity (H\u003csub\u003ee\u003c/sub\u003e) varied between populations, ranging from the highest value in LLS to the lowest in GAV population (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Notably, H\u003csub\u003ee\u003c/sub\u003e value of the OLD samples exceeded that of four current populations (BLA, CER, GAV, and TAR) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Similarly, observed heterozygosity (H\u003csub\u003eo\u003c/sub\u003e) differed across populations, with the highest value observed in the PLA population and the lowest in FOL. The OLD samples showed H\u003csub\u003eo\u003c/sub\u003e values lower than those of PLA, DE, CAS, and AM, but higher than the remaining populations. In terms of allelic richness (A\u003csub\u003er\u003c/sub\u003e), the pattern of genetic diversity mirrored that of He, with LLS showing the highest value and GAV the lowest. The A\u003csub\u003er\u003c/sub\u003e of the OLD samples was higher than that of the GAV population, although four populations surpassed the OLD samples in H\u003csub\u003ee\u003c/sub\u003e. The estimated frequency of null alleles (Supplementary information, Table S3) revealed that FOL had the highest null allele frequency (0.1747), while PLA had the lowest. Interestingly, the frequency of null alleles in the OLD samples was lower than almost all other populations, except AM and PLA. Thus, the genetic data from OLD samples appears to be of comparable quality, if not better than most of the analyzed populations, supporting the use of these samples in population genetic studies.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2. Populations divergence in House Sparrow\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn general, we found genetic divergence among sampling sites, although each of the analyses performed provides complementary information. Divergence estimations obtained with FREENA were consistent with our original calculations in Arlequin, indicating that missing data and potential null alleles had no detectable effect on population differentiation; therefore, we used the original \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e values in subsequent analyses.\u003c/p\u003e \u003cp\u003ePairwise \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e values ranged from 0.086 to 0.001. Most pairwise comparisons were statistically significant, indicating populations divergence among most sampling sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and Supplementary information, Table S4). The lowest value was observed between AM and DE, two geographically close localities. Interestingly, the OLD population exhibited the highest \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e value among all populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and Table S4), suggesting a distinct genetic structure compared to contemporary populations. While the OLD samples demonstrated similar genetic diversity to current populations, their genetic structure differed, indicating both spatial genetic variation among current populations and temporal genetic shifts between recent and historical populations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor contemporary populations, the Mantel test revealed a significant positive correlation between genetic and geographic distances (r\u0026thinsp;=\u0026thinsp;0.334, p-value\u0026thinsp;=\u0026thinsp;0.021), indicating evidence of isolation by distance among populations of the species, and approximately 11.1% of the variation in genetic distances was explained by geographic separation.\u003c/p\u003e \u003cp\u003eIn STRUCTURE, the optimal number of genetic clusters for the House Sparrow was four, according to the Delta K criterium (see Supplementary information, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), although seven and thirteen clusters also showed high Delta K values (Supplementary information, Fig. S2). For four clusters, individuals from the OLD samples were mostly assigned to a single cluster (dark green), displaying large homogeneity among most individuals. In the remaining populations, cluster distribution did not appear to be influenced by land use, yet substantial differences were observed among populations in the relative contribution of each cluster to their overall genetic structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Individuals from BLA and CAS (urban) and TAR (agricultural) showed a higher probability of assignment to the orange cluster, despite being geographically distant (see also Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A similar pattern was observed for FOL (agricultural), SAN (urban), and LLS (urban), which were more likely to belong to the red cluster. The same trend was found for the light green cluster, which included BCN and AM (urban), as well as LLA, GAV, and PLA (agricultural).\u003c/p\u003e \u003cp\u003eThe findings from the discriminant analysis of principal components (DAPC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) were consistent with those obtained using STRUCTURE. The OLD samples were clearly distinct from the modern populations along the Y-axis, forming a distant group. Meanwhile, the remaining populations showed some degree of mixing, though certain clusters could still be identified: a group comprising BLA, CAS, and TAR to the right; another group with LLS, SAN, and FOL at the bottom; and the remaining populations forming a central group. Again, no clustering of populations influenced by land use was observed.\u003c/p\u003e \u003cp\u003eThe Minimum Spanning Network including all samples revealed an interesting pattern. Most OLD grouped in a cluster with some contemporary individuals but separated from most of the contemporary populations. While certain individuals from the same population cluster together, the overall pattern reveals a scattered distribution across the network, with the OLD samples standing out as the only consistent exception. This supports the differentiation observed in STRUCTURE and DAPC analyses, indicating that historical individuals share a homogeneous genetic background that has diverged from contemporary populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). That is particularly relevant given that OLD population contain individuals which are presumably the ancestors of several (N\u0026thinsp;=\u0026thinsp;9) contemporary populations (e.g. see AM cluster and PLA cluster).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results of the AMOVA, in which populations were grouped based on land use (urban-periurban and agricultural), aligns with the STRUCTURE and DAPC analysis. No significant differences were detected between the urban-periurban and agricultural groups (variation 0%; F\u003csub\u003eCT\u003c/sub\u003e= -0.15; p\u0026thinsp;=\u0026thinsp;0.668) but most of the molecular variance occurred within individuals (74,39%; F\u003csub\u003eIT\u003c/sub\u003e = 0.2560; p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), among individuals within populations (22,79%; F\u003csub\u003eIS\u003c/sub\u003e = 0.2345; p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and among populations within groups (2,97%; F\u003csub\u003eSC\u003c/sub\u003e = 0.0296; p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001)(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAMOVA results in grouping populations according to land use (excluding OLD samples). Variation was tested among groups (urban-periurban vs agricultural), among populations within groups, among individuals within populations and within individuals\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of\u003c/p\u003e \u003cp\u003evariation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ed.f\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSquares summ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariance components\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVariation percentage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFixation index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmong groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003csub\u003eCT\u003c/sub\u003e = -0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmong populations within groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003csub\u003eSC\u003c/sub\u003e = 0.0296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmong individuals within populations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1358.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003csub\u003eIS\u003c/sub\u003e = 0.2345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin individuals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e892.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003csub\u003eIT\u003c/sub\u003e = 0.2560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2388.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenetic Diversity and Population Trends\u003c/h3\u003e\n\u003cp\u003ePopulation growth rates (r) estimated from exponential models ranged from \u0026minus;\u0026thinsp;0.0358 to +\u0026thinsp;0.0153, corresponding to annual rates (λ) between 0.9649 and 1.0154 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). All urban sites, except Lleida Ser\u0026ograve;s (LLS), showed negative r values. The steepest decline was observed at the urban\u0026ndash;periurban site of Sant Vicen\u0026ccedil; de Calders (SAN) (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0358; λ\u0026thinsp;=\u0026thinsp;0.9649; p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Other urban sites, such as Barcelona (BCN) (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0191; λ\u0026thinsp;=\u0026thinsp;0.9810; p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and Castellbisbal (CAS) (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0169; λ\u0026thinsp;=\u0026thinsp;0.9832; p-value\u0026thinsp;=\u0026thinsp;0.056), also showed negative trends. The locality of Blanes (BLA) exhibited a moderate but non-significant decline (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0139; λ\u0026thinsp;=\u0026thinsp;0.9862; p-value\u0026thinsp;=\u0026thinsp;0.0796).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePopulation growth rates (r), annual growth factor (λ), coefficient of determination (R\u0026sup2;), and p-values for Passer domesticus populations across Catalonia. Growth rates were estimated using exponential models fitted to breeding season data (2002\u0026ndash;2019). Values of λ\u0026thinsp;\u0026gt;\u0026thinsp;1 indicate population growth, whereas λ\u0026thinsp;\u0026lt;\u0026thinsp;1 indicate population decline (* p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; *** p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eλ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0796\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9937\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn contrast, most agricultural sites showed stable or slightly positive trends. Notably, Lleida agricultural (LLA) presented a significant increase (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.0153; λ\u0026thinsp;=\u0026thinsp;1.0154; p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). An exception was Pla de Santa Maria (PLA) (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0209; λ\u0026thinsp;=\u0026thinsp;0.9793; p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Gav\u0026agrave; (GAV) (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0314; λ\u0026thinsp;=\u0026thinsp;0.9691; p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the latter located within the matrix of the Metropolitan Area of Barcelona. Overall, urban-periurban areas exhibited stronger population declines compared to agricultural landscapes.\u003c/p\u003e \u003cp\u003eWhen testing for associations between population growth rates and genetic diversity metrics (N\u003csub\u003ea\u003c/sub\u003e, A\u003csub\u003er\u003c/sub\u003e, pA, H\u003csub\u003eo\u003c/sub\u003e, H\u003csub\u003ee\u003c/sub\u003e, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eIS\u003c/em\u003e\u003c/sub\u003e), no significant relationships were detected (all p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.24, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This suggests that, despite demographic declines in some localities, genetic diversity has remained relatively stable during the study period.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson correlation coefficients (R) between genetic diversity parameters (N\u003csub\u003ea\u003c/sub\u003e, A\u003csub\u003er\u003c/sub\u003e, pA, H\u003csub\u003eo\u003c/sub\u003e, H\u003csub\u003ee\u003c/sub\u003e, F\u003csub\u003eIS\u003c/sub\u003e) and population trends estimated using λ (population growth rate) and r (log-transformed annual rate of change) across House Sparrow populations in Catalonia.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eλ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR-pearson\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR-pearson\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.90673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003csub\u003er\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.16176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.15888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.60415\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.32766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.32839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003eo\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.27387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.27219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.36830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003ee\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.29304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.33121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFI\u003csub\u003es\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.24251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.34786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.24415\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBOTTLENECK results, under both IAM and SMM models, revealed no significant evidence of recent bottlenecks in most populations, as supported by non-significant \u003cem\u003eSign\u003c/em\u003e and \u003cem\u003eWilcoxon\u003c/em\u003e tests and the consistent L-shaped allele frequency distributions. However, four populations showed signals compatible with bottleneck events: GAV displayed marginal significance under IAM (\u003cem\u003eWilcoxon\u003c/em\u003e p-value\u0026thinsp;=\u0026thinsp;0.034), BCN exhibited significant heterozygosity excess under both IAM and SMM (\u003cem\u003eWilcoxon\u003c/em\u003e p-value\u0026thinsp;=\u0026thinsp;0.003 and 0.110, respectively), and CAS and BLA showed significant departures under SMM (\u003cem\u003eWilcoxon\u003c/em\u003e p-value\u0026thinsp;=\u0026thinsp;0.042 and 0.013, respectively). Despite these results, the mode-shift test indicated normal L-shaped distributions in all populations, suggesting no strong or widespread evidence of recent bottlenecks. As showed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, three of the four populations showing bottleneck signals corresponded to urban\u0026ndash;periurban areas (BCN, CAS, BLA), and three of them (whether urban\u0026ndash;periurban or agricultural) were located within the intensified matrix of the Barcelona Metropolitan Area (BCN, CAS, GAV).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of bottleneck tests for 13 populations of House Sparrow in Catalonia. The table shows the number of loci with heterozygosity excess versus deficiency under the Infinite Allele Model (IAM) and Stepwise Mutation Model (SMM), along with associated p-values from Sign and Wilcoxon tests. Mode-shift column indicates the allele frequency distribution pattern, where an L-shaped distribution suggests demographic stability. * p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSign Test IAM (Excess/Def)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value (IAM)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSign Test SMM (Excess/Def)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep- value (SMM)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWilcoxon IAM\u003c/p\u003e \u003cp\u003ep-value\u003c/p\u003e \u003cp\u003e(two-tail)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWilcoxon SMM\u003c/p\u003e \u003cp\u003ep-value\u003c/p\u003e \u003cp\u003e(two-tail)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMode-Shift\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eL-shaped\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eL-shaped\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eL-shaped\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eL-shaped\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eL-shaped\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eL-shaped\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eL-shaped\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eL-shaped\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eL-shaped\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eL-shaped\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11/1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eL-shaped\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eL-shaped\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3/9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eL-shaped\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to assess the spatio-temporal genetic diversity and population structure of House Sparrow in Catalonia, a region characterized by heterogeneous landscapes and varying degrees of urbanization (Herrando et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Specifically, we examined whether: (1) populations exhibit genetic structuring due to limited dispersal and philopatric behavior, (2) older populations maintain higher genetic diversity compared to contemporary ones, and (3) declining populations show reduced genetic diversity. Our analysis focused on the primary habitat types occupied by this species, namely urban-periurban and agricultural environments.\u003c/p\u003e \u003cp\u003eOur results revealed significant genetic structuring among populations partially explained by isolation by geographic distance, even over small spatial scales (e.g. DE-AM, CAS-BCN, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), likely reflecting restricted dispersal and site fidelity. Yet, historical samples displayed greater genetic homogeneity, indicating temporal changes in population structure. Despite observed declines in some populations, overall genetic diversity remained relatively stable across habitats. Notably, all populations exhibited heterozygote deficiencies and positive inbreeding coefficients. However, habitat type (agricultural versus urban) did not significantly explain the observed genetic variation, although three of the four populations showing evidence of recent bottlenecks were in urban\u0026ndash;periurban areas.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenetic diversity and populations\u0026rsquo; divergence\u003c/h2\u003e \u003cp\u003eThe genetic analysis carried out in this study revealed a general deficiency of heterozygotes within House Sparrow populations compared to what would be expected for populations in Hardy-Weinberg equilibrium. Such a deficiency in heterozygotes is typically attributed to a combination of non-exclusive factors, including population substructure (Wahlund effect), inbreeding, the influence of null alleles on statistical calculations, selection against heterozygotes, or a combination of these effects (P\u0026eacute;rez-Portela et al. 2015; De Mee\u0026ucirc;s \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As highlighted by Szulkin and Sheldon (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) in their study on Great tit (\u003cem\u003eParus major\u003c/em\u003e), inbreeding is closely linked to the dispersal ability of a species, which could also be the case for the house sparrow.\u003c/p\u003e \u003cp\u003eThe House Sparrow is known for its philopatric behavior and restricted movement, typically following a \u0026ldquo;stepping-stone\u0026rdquo; dispersal model (Anderson \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Vangestel et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which may elevate inbreeding levels and contribute to the observed genetic structure. The loss of these intermediate populations is, hence, pivotal to maintain the connectivity among populations. For example, Kekkonen et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) reported low genetic differentiation among Finnish populations of House Sparrow, suggesting high connectivity and panmixia. This pattern likely reflects the flat topography and relatively continuous habitat matrix in Finland, which facilitates dispersal. Yet, similarly to our results, Liu et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Geue et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found moderate structuring in other parts of Europe with heterogenous habitats, reinforcing the idea that geographic and environmental context strongly influences population connectivity.\u003c/p\u003e \u003cp\u003eCatalonia presents a classic Mediterranean landscape, characterized by a hilly topography, a complex mosaic of land uses, and long-standing anthropogenic pressures (Herrando et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Environmental fragmentation likely exacerbates population isolation, contributing to the genetic differentiation observed even among geographically close populations. The significant Mantel test supports isolation by distance, indicating that genetic divergence increases with geographic separation. These results suggest that geographic barriers and local demographic dynamics in Catalonia create distinct genetic clusters.\u003c/p\u003e \u003cp\u003eAlthough the House sparrow\u0026rsquo;s dispersal pattern should facilitate movement in stable environments, regions experiencing population declines or habitat fragmentation may lose intermediate populations that act as genetic bridges. This could be the case in a Mediterranean region like Catalonia, where sparrow numbers have declined over recent decades, despite the species\u0026rsquo; overall distribution range remaining largely unchanged (Quesada and Calder\u0026oacute;n-Llobera \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Hence, the loss of these \u0026ldquo;stepping-stone\u0026rdquo; populations may further reduce connectivity and accelerate genetic isolation.\u003c/p\u003e \u003cp\u003eOur first hypothesis proposed that House Sparrow populations in Catalonia are genetically structured, a structure partially explained by geographic distances, but regardless of land uses. Contrary to our expectations, the AMOVA results indicated that most genetic variation occurs within populations and individuals, with no significant differentiation between urban-periurban and agricultural areas. This finding contradicts expectations that urbanization would drive genetic divergence due to habitat-specific pressures (Fulgione et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Brewer et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is also possible that no genetic differences exist between the groups, as our results do not differ from those of other studies on genetic diversity and structure associated with urbanization gradients, both in House Sparrow (Vangestel et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and in other passerine species, such as the Song Sparrow \u003cem\u003eMelospiza melodia\u003c/em\u003e (Brewer et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Other factors like isolation, genetics drift, or other environmental drivers could play a major role than urbanization in shaping genetic structure (Orsini et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Geue et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In this sense, conducting temporal monitoring analyses of genetic structure shifts would be crucial for developing conservation plans for this species, depending on the habitat where its individuals are found. Hence, our results indicate that in Mediterranean human-modified landscapes, genetic structure and the progressive erosion of genetic connectivity can emerge from fine-scale fragmentation and the loss of stepping-stone populations, even when overall genetic diversity remains high and broad land-use categories fail to predict genetic differentiation (Bustillo de la Rosa et al. 2022)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHouse Sparrow population declines and genetics\u003c/h2\u003e \u003cp\u003eOur second hypothesis stated that declining populations exhibit lower levels of genetic diversity than stable or increasing populations. Widespread declines of House Sparrow populations have been reported across Europe (PECBMS \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Our results showed that urban and periurban populations are declining more steeply than agricultural ones in the Mediterranean human-modified landscape. Four of the five urban populations showed significant declines in comparison to agricultural areas. This suggests that, if sustained, these trends could lead to local extirpation within decades. Conversely, agricultural sites showed slight but significant increases to stable dynamics, suggesting that rural habitats remain demographic strongholds for the species. Most agricultural sites exhibited stable or slightly positive demographic trends but, interestingly Gav\u0026agrave; showed negative trends. This agricultural area is embedded within the highly fragmented and urbanized matrix in the Barcelona Metropolitan Area, which could partially explain this trend (see below).\u003c/p\u003e \u003cp\u003eYet, although House Sparrow populations have experienced marked declines in Mediterranean urban areas (Ramos-Elvira et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), our results indicate that these demographic changes have not yet translated into a generalized loss of neutral genetic diversity. This temporal decoupling between population size and genetic diversity is consistent with the concept of a genetic erosion lag, whereby reductions in effective population size may only affect genome-wide diversity after several generations (Monnahan et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pinsky et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This temporal lag may explain why even populations with strong negative trends still maintain moderate genetic variability. However, signals of heterozygote deficiency and inbreeding detected in our study (see genetic diversity descriptors in Results) could indicate early genetic consequences of fragmentation and reduced dispersal (Vangestel et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Furthermore, alternative analyses identified potential bottlenecks in four House Sparrow populations, which suggest that effective population size may already be shrinking. This last result is consistent with the marked demographic declines in some urban areas identified in previous studies of the species (Calder\u0026oacute;n-Llobera \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mohring et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Hence, these findings highlight the importance of long-term monitoring to detect early signs of genetic deterioration before they become irreversible, and stress that conservation strategies should not only track population numbers but also anticipate potential genetic consequences of continued demographic decline.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHistorical trends of House Sparrow\u003c/h2\u003e \u003cp\u003eOur analysis, which included historical (OLD) samples (1919\u0026ndash;1950), revealed comparable levels of genetic diversity to those observed in contemporary populations, particularly with respect to expected heterozygosity and allelic richness. However, historical populations appeared more genetically homogeneous, likely reflecting larger effective population sizes and greater connectivity at that time, consistent with metapopulation dynamics. This interpretation is consistent with metapopulation dynamics operating under landscapes with fewer barriers to dispersal and greater availability of stepping-stone habitats (Saura et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Clustering analyses further supported this interpretation, as most historical individuals grouped into a single, homogeneous cluster despite originating from different localities. This pattern indicates temporal shifts in population structure, probably driven by recent habitat fragmentation and local extinctions (Ringsby et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; De Laet and Summers-Smith \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these structural changes, we did not observe a clear reduction in overall neutral genetic diversity in contemporary populations compared to historical ones (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This suggests that, although population connectivity has decreased and genetic structuring has increased over the past century, genetic variation has remained relatively stable. Such a pattern is consistent with historical population contractions followed by demographic stabilization rather than ongoing genetic erosion (e.g. Bustillo-de la Rosa et al. 2022).\u003c/p\u003e \u003cp\u003eThe detection of bottleneck signatures in several urban populations (Barcelona, Blanes, Castellbisbal) and in one agricultural population (Gav\u0026agrave;) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) raises important questions regarding the timing of these demographic contractions. In Catalonia, House Sparrow populations have been systematically monitored only since 2002, and recent trends from the SOCC program indicate a consistent but not abrupt decline (ICO \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This suggests that the bottlenecks detected here likely predate contemporary monitoring efforts and may have occurred during earlier phases of landscape transformation.\u003c/p\u003e \u003cp\u003eTwo complementary lines of evidence support this interpretation. First, long-term European monitoring schemes initiated in the 1980s indicate that the steepest declines in House Sparrow populations occurred between 1980 and 2000 (long-term reduction: \u0026minus;38%), followed by a more moderate decrease in subsequent decades (10-year reduction: \u0026minus;8%; PECBMS \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Similar temporal dynamics have also been documented in other parts of the species\u0026rsquo; range, such as Canada (Smith et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Unfortunately, the absence of standardized monitoring data in Catalonia prior to 2002 prevents direct evaluation of whether comparable declines occurred locally.\u003c/p\u003e \u003cp\u003eSecond, the populations showing bottleneck signatures coincide spatially with regions that experienced intense landscape transformation driven by urban expansion, tourism development, and infrastructure growth between the 1960s and 2000s (Pint\u0026oacute; et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The occurrence of bottlenecks in both urban and agricultural contexts suggests that demographic contractions were not restricted to a single habitat type, but rather reflected broader processes of landscape intensification characteristic of Mediterranean human-modified environments.\u003c/p\u003e \u003cp\u003eGiven that breeding House Sparrow populations in Catalonia have been considered broadly stable over the last decade (ICO \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), it is plausible that contemporary populations are currently undergoing a phase of demographic stabilization or adjustment to novel anthropogenic conditions. From a conservation genetics perspective, this highlights the importance of integrating historical genetic data, long-term demographic trends, and landscape history to correctly interpret genetic signals and to avoid attributing present-day population declines solely to recent genetic erosion.\u003c/p\u003e \u003cp\u003eOverall, our results highlight the importance of explicitly considering both spatial and temporal scales in conservation genetic studies. Although House Sparrow populations in Mediterranean human-modified landscapes retain relatively high levels of neutral genetic diversity, the emergence of population structure, consistent heterozygote deficiency, and signals of demographic bottlenecks\u0026mdash;particularly in urban populations\u0026mdash;indicate that reduced connectivity and fine-scale habitat fragmentation are already shaping contemporary demographic and genetic dynamics (Ringsby et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; De Laet \u0026amp; Summers-Smith \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImportantly, the maintenance of neutral genetic diversity should not be interpreted as evidence of demographic or evolutionary resilience. Instead, our results suggest that historical population contractions followed by partial demographic stabilization can mask ongoing erosion of functional connectivity, potentially increasing vulnerability to future environmental change. Similar patterns, where genetic structure increases despite relatively stable levels of diversity, have been reported in other fragmented systems and emphasize the central role of connectivity in maintaining long-term population viability (Bustillo-de la Rosa et al. 2022).\u003c/p\u003e \u003cp\u003eThe use of museum specimens proved particularly valuable for reconstructing historical genetic baselines (Payne \u0026amp; Sorenson \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Su\u0026aacute;rez \u0026amp; Tsutsui 2004), allowing us to detect temporal shifts in population structure that would not be apparent from contemporary data alone. Integrating historical and modern datasets is therefore essential for distinguishing recent genetic changes from longer-term population dynamics, especially in Mediterranean landscapes characterized by long-standing human pressure and rapid land-use change.\u003c/p\u003e \u003cp\u003eFrom a conservation perspective, our findings underscore the need to preserve or restore landscape connectivity\u0026mdash;such as periurban habitats and stepping-stone populations\u0026mdash;to mitigate the genetic consequences of fragmentation. Even in widespread and human-associated species such as the House Sparrow, subtle genetic signals may reveal underlying demographic fragility, highlighting the importance of incorporating genetic monitoring into conservation strategies for declining urban and agricultural bird populations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eAuthors declare no conflict of interest\u003c/p\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eResearch funding was provided by the Natural Science Museum of Barcelona and the Fundaci\u0026oacute; Zoo de Barcelona (PASSERCAT project) to J. Quesada.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJavier Quesada and Roc\u0026iacute;o P\u0026eacute;rez-Portela designed the study. Javier Oliver was responsible for field sample collection. Laboratory work was carried out by Wangensteen, Owen S. and Riyahi, Sepand. Javier Quesada, Roc\u0026iacute;o P\u0026eacute;rez-Portela, Marta Mart\u0026iacute;n-Huete, Joan Calderon-Llobera analyzed and interpreted the data. The first draft of the manuscript was written by Javier Quesada, Rocio Perez-Portela, Jorge R, Lopez-Rey and all authors commented on previous versions of the manuscript. All authors read and approved of the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe are deeply grateful to Jaume Izquierdo for his seeking of old samples collection, Alberto \u0026Aacute;lvarez for field data gathering, and Marta Campos for her help in the laboratory. This paper is a contribution of the Consolidated Research Team 2021 SGR00177 (AGAUR, Generalitat de Catalunya) to JQ, and 2021 SGR 01271 (AGAUR, Generalitat de Catalunya) to RP-P and OW. Both capture and sample collection permission were allowed by Catalonian Government (SF-098 permission).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlberto F (2009) MsatAllele_1.0: an R package to visualize the binning of microsatellite alleles. J Hered 100:394\u0026ndash;397\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderson TR (2006) Biology of the ubiquitous house sparrow: From genes to populations. Oxford University Press, Oxford\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAurelle D, Berline L, Bonhomme F et al (2022) The Mediterranean: a model sea for biodiversity. Ecosphere 13:e3915. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ecs2.3915\u003c/span\u003e\u003cspan address=\"10.1002/ecs2.3915\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBellard C, Leclerc C, Leroy B et al (2014) Vulnerability of biodiversity hotspots to global change. Glob Ecol Biogeogr 23:1376\u0026ndash;1386. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/geb.12228\u003c/span\u003e\u003cspan address=\"10.1111/geb.12228\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Ann Stat 29:1165\u0026ndash;1188. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1214/aos/1013699998\u003c/span\u003e\u003cspan address=\"10.1214/aos/1013699998\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerigan LA, Greig EI, Bonter DN (2020) Urban House Sparrow (Passer domesticus) populations decline in North America. Wilson J Ornithol 132:248\u0026ndash;258\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBernat-Ponce E, Gil-Delgado JA, L\u0026oacute;pez-Iborra GM (2024) House Sparrow Nesting Site Selection in Urban Environments: A Multivariate Approach in Mediterranean Spain. Urban Sci 8:108. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/urbansci8030108\u003c/span\u003e\u003cspan address=\"10.3390/urbansci8030108\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBernat-Ponce E, Luna \u0026Aacute;, Jim\u0026eacute;nez-Pe\u0026ntilde;uela J, MacGregor-Fors I (2025) From pardal to etxe-txolarrea: An integrative review of the house sparrow Passer domesticus knowledge in the Iberian Peninsula. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.32800/abc.2025.48.0012\u003c/span\u003e\u003cspan address=\"10.32800/abc.2025.48.0012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBogaart P, van der Loo M, Pannekoek J (2020) rtrim: Trends and Indices for Monitoring Data (R package version 2.1.1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=rtrim\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=rtrim\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrewer VN, Lane SJ, Sewall KB, Mabry KE (2020) Effects of low-density urbanization on genetic structure in the Song Sparrow. PLoS ONE 15:e0234008. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0234008\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0234008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBruvo R, Michiels NK, D\u0026rsquo;Sousa TG, Schulenberg H (2004) A simple method for calculation of microsatellite genotypes irrespective of ploidy level. Mol Ecol 13:2101\u0026ndash;2106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-294X.2004.02209.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-294X.2004.02209.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLa Bustillo-de D, Traba J, Calero-Riestra M et al (2022) Recent changes in genetic diversity, structure, and gene flow in a passerine experiencing a rapid population decline, the Dupont\u0026rsquo;s Lark (Chersophilus duponti). Diversity 14:1120. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/d14121120\u003c/span\u003e\u003cspan address=\"10.3390/d14121120\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalder\u0026oacute;n-Llobera J (2019) Assessing human-related factors on population dynamics of two Passeridae species in Mediterranean landscapes. Dissertation, University of Barcelona\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChapuis MP, Estoup A (2007) Microsatellite null alleles and estimation of population differentiation. Mol Biol Evol 24:621\u0026ndash;631. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/molbev/msl191\u003c/span\u003e\u003cspan address=\"10.1093/molbev/msl191\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCsardi G, Nepusz T (2006) The igraph software. Complex Syst 1695:1\u0026ndash;9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDadam D, Robinson RA, Clements A et al (2019) Avian malaria-mediated population decline of a widespread iconic bird species. R Soc Open Sci 6:182197. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1098/rsos.182197\u003c/span\u003e\u003cspan address=\"10.1098/rsos.182197\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDawson DA, Horsburgh GJ, Krupa AP et al (2012) Microsatellite resources for Passeridae species: a predicted microsatellite map of the house sparrow Passer domesticus. Mol Ecol Resour 12:501\u0026ndash;523. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1755-0998.2012.03115.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1755-0998.2012.03115.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Laet J, Summers-Smith JD (2007) The status of the urban house sparrow Passer domesticus in north-western Europe: a review. J Ornithol 148:275\u0026ndash;278. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10336-007-0154-0\u003c/span\u003e\u003cspan address=\"10.1007/s10336-007-0154-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Mee\u0026ucirc;s T (2018) Revisiting F IS, F ST, Wahlund effects, and null alleles. J Hered 109:446\u0026ndash;456. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jhered/esx106\u003c/span\u003e\u003cspan address=\"10.1093/jhered/esx106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDr\u0026ouml;scher H (1992) Hatching asynchrony in the House Sparrow: A test of the egg-viability hypothesis. Am Nat 139:669\u0026ndash;675. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1086/285341\u003c/span\u003e\u003cspan address=\"10.1086/285341\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEarl DA, vonHoldt BM (2012) STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359\u0026ndash;361. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12686-011-9548-7\u003c/span\u003e\u003cspan address=\"10.1007/s12686-011-9548-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEscudero A, Iriondo JM, Torres ME (2003) Spatial analysis of genetic diversity as a tool for plant conservation. Biol Conserv 113:351\u0026ndash;365. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0006-3207(03)00122-8\u003c/span\u003e\u003cspan address=\"10.1016/S0006-3207(03)00122-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol Ecol 14:2611\u0026ndash;2620. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-294X.2005.02553.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-294X.2005.02553.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEveraert J, Bauwens D (2007) A possible effect of electromagnetic radiation from mobile phone base stations on the number of breeding House Sparrows (Passer domesticus). Electromagn Biol Med 26:63\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/15368370701205693\u003c/span\u003e\u003cspan address=\"10.1080/15368370701205693\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eExcoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour 10:564\u0026ndash;567. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1755-0998.2010.02847.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1755-0998.2010.02847.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarrer EC, Ashton IW, Knape J, Suding KN (2014) Separating direct and indirect effects of global change: A population dynamic modeling approach using readily available field data. Glob Change Biol 20:1238\u0026ndash;1250. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/gcb.12429\u003c/span\u003e\u003cspan address=\"10.1111/gcb.12429\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFourcade Y, Richardson DS, Secondi J (2020) No evidence for a loss of genetic diversity despite a strong decline in size of a European population of the Corncrake Crex crex. Bird Conserv Int 30:260\u0026ndash;266. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0959270919000234\u003c/span\u003e\u003cspan address=\"10.1017/S0959270919000234\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFulgione D, Procaccini G, Milone M (2000) Urbanisation and the genetic structure of Passer italiae (Vieillot 1817) populations in the South of Italy. Ethol Ecol Evol 12:123\u0026ndash;130. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/08927014.2000.9522808\u003c/span\u003e\u003cspan address=\"10.1080/08927014.2000.9522808\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeue JC, V\u0026aacute;g\u0026aacute;si CI, Schweizer M, Pap PL, Thomassen HA (2016) Environmental selection is a main driver of divergence in house sparrows (Passer domesticus) in Romania and Bulgaria. Ecol Evol 6:7954\u0026ndash;7964. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ece3.2521\u003c/span\u003e\u003cspan address=\"10.1002/ece3.2521\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGotelli NJ (2008) A Primer of Ecology, 4th edn. Sinauer Associates, Sunderland\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGriffith SC, Dawson DA, Jensen H et al (2007) Fourteen polymorphic microsatellite loci characterized in the house sparrow Passer domesticus (Passeridae, Aves). Mol Ecol Notes 7:333\u0026ndash;336. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1471-8286.2006.01610.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1471-8286.2006.01610.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGriffith SC, Stewart IR, Dawson DA, Owens IP, Burke T (1999) Contrasting levels of extra-pair paternity in mainland and island populations of the house sparrow (Passer domesticus): Is there an \u0026lsquo;island effect\u0026rsquo;? Biol J Linn Soc 68:303\u0026ndash;316. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1095-8312.1999.tb01171.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1095-8312.1999.tb01171.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuallar S, Cai X, Vega S, Oliver J, Quesada J (2025) Environmental drivers of House Sparrow (Passer domesticus) presence and abundance in the Metropolitan Area of Barcelona: a multi-variant approach. Rev Catalana Ornitol 41:9\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.62102/2340-3764.2025.1.2\u003c/span\u003e\u003cspan address=\"10.62102/2340-3764.2025.1.2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHallmann CA, Foppen RP, Van Turnhout CA, De Kroon H, Jongejans E (2014) Declines in insectivorous birds are associated with high neonicotinoid concentrations. Nature 511:341\u0026ndash;343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature13531\u003c/span\u003e\u003cspan address=\"10.1038/nature13531\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeij CJ (1985) Comparative ecology of the House Sparrow (Passer domesticus) in rural, suburban and urban situations. Dissertation, Vrije Universiteit Amsterdam\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHermansen JS, S\u0026aelig;ther SA, Elgvin TO et al (2011) Hybrid speciation in sparrows I: Phenotypic intermediacy, genetic admixture and barriers to gene flow. Mol Ecol 20:3812\u0026ndash;3822. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-294X.2011.05183.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-294X.2011.05183.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerrando S, Brotons L, Estrada J, Guallar S, Anton M (2011) Atles dels ocells de Catalunya a l\u0026rsquo;hivern 2006\u0026ndash;2009. ICO, Barcelona\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerrera-Due\u0026ntilde;as A, Pineda-Pampliega J, Antonio-Garc\u0026iacute;a MT, Aguirre JI (2017) The influence of urban environments on oxidative stress balance: A case study on the house sparrow in the Iberian Peninsula. Front Ecol Evol 5:106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fevo.2017.00106\u003c/span\u003e\u003cspan address=\"10.3389/fevo.2017.00106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHole DG, Whittingham MJ, Bradbury RB et al (2002) Widespread local house-sparrow extinctions. Nature 418:931\u0026ndash;932. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature00984\u003c/span\u003e\u003cspan address=\"10.1038/nature00984\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu Y, Fan H, Chen Y et al (2021) Spatial patterns and conservation of genetic and phylogenetic diversity of wildlife in China. Sci Adv 7:eabd5725. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/sciadv.abd5725\u003c/span\u003e\u003cspan address=\"10.1126/sciadv.abd5725\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eICO (2025) SIOC: servidor d'informaci\u0026oacute; ornitol\u0026ograve;gica de Catalunya. Institut Catal\u0026agrave; d\u0026rsquo;Ornitologia (ICO), Barcelona. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.sioc.cat\u003c/span\u003e\u003cspan address=\"http://www.sioc.cat\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 28 Oct 2025\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJombart T (2008) adegenet: An R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403\u0026ndash;1405. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bioinformatics/btn129\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btn129\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJombart T, Ahmed I (2011) adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bioinformatics/btr521\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btr521\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJombart T, Devillard S, Balloux F (2010) Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genet 11:94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2156-11-94\u003c/span\u003e\u003cspan address=\"10.1186/1471-2156-11-94\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJombart T, Devillard S, Dufour AB, Pontier D (2008) Revealing cryptic spatial patterns in genetic variability by a new multivariate method. Heredity 101:92\u0026ndash;103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/hdy.2008.34\u003c/span\u003e\u003cspan address=\"10.1038/hdy.2008.34\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKamvar ZN, Tabima JF, Gr\u0026uuml;nwald NJ (2014) Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2:e281. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7717/peerj.281\u003c/span\u003e\u003cspan address=\"10.7717/peerj.281\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKekkonen J, Sepp\u0026auml; P, Hanski IK, Jensen H, V\u0026auml;is\u0026auml;nen RA, Brommer JE (2011) Low genetic differentiation in a sedentary bird: House sparrow population genetics in a contiguous landscape. Heredity 106:183\u0026ndash;190. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/hdy.2010.33\u003c/span\u003e\u003cspan address=\"10.1038/hdy.2010.33\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKolde R (2019) Pheatmap: Pretty Heatmaps. R package version 1.0.12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=pheatmap\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=pheatmap\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLenz M (2018) Notable number of House Sparrows, Passer domesticus, at a roost in natural habitat near Marree in the western Lake Eyre basin. South Aust Ornithol 42:58\u0026ndash;64\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Webber S, Bowgen K et al (2013) Environmental factors influence both abundance and genetic diversity in a widespread bird species. Ecol Evol 3:4683\u0026ndash;4695. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ece3.856\u003c/span\u003e\u003cspan address=\"10.1002/ece3.856\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLovejoy TE, Hannah L (eds) (2006) Climate change and biodiversity. Yale University Press, New Haven\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeirmans PG (2020) Mol Ecol Resour 20:1126\u0026ndash;1131. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1755-0998.13145\u003c/span\u003e\u003cspan address=\"10.1111/1755-0998.13145\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. GENODIVE version 3.0: Easy-to-use software for the analysis of genetic data of diploids and polyploids\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohring B, Henry PY, Jiguet F, Malher F, Angelier F (2021) Investigating temporal and spatial correlates of the sharp decline of an urban exploiter bird in a large European city. Urban Ecosyst 24:501\u0026ndash;513. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11252-020-01048-5\u003c/span\u003e\u003cspan address=\"10.1007/s11252-020-01048-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026oslash;ller AP (2013) Biological consequences of global change for birds. Integr Zool 8:136\u0026ndash;144. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1749-4877.12023\u003c/span\u003e\u003cspan address=\"10.1111/1749-4877.12023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonnahan CC, Colas B, Buerkle CA (2023) The impact of habitat loss and population fragmentation on genomic erosion. Conserv Genet 24:57\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10592-023-01548-9\u003c/span\u003e\u003cspan address=\"10.1007/s10592-023-01548-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonroy Vilchis O (2005) Causas de p\u0026eacute;rdida de diversidad biol\u0026oacute;gica. Cuad Biodivers 17:3\u0026ndash;9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurgui E, Mac\u0026iacute;as A (2010) Changes in the House Sparrow Passer domesticus population in Valencia (Spain) from 1998 to 2008. Bird Study 57:281\u0026ndash;288. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00063651003610553\u003c/span\u003e\u003cspan address=\"10.1080/00063651003610553\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNath A, Singha H, Lahkar BP (2022) Correlation does not imply causation: Decline of house sparrow overshadowed by electromagnetic radiation. Urban Ecosyst 25:1279\u0026ndash;1295. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11252-022-01225-8\u003c/span\u003e\u003cspan address=\"10.1007/s11252-022-01225-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeumann K, Wetton JH (1996) Highly polymorphic microsatellites in the house sparrow Passer domesticus. Mol Ecol 5:307\u0026ndash;309. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-294X.1996.tb00322.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-294X.1996.tb00322.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrsini L, Vanoverbeke J, Swillen I, Mergeay J, De Meester L (2013) Drivers of population genetic differentiation in the wild: isolation by dispersal limitation, isolation by adaptation and isolation by colonization. Mol Ecol 22:5983\u0026ndash;5999. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/mec.12558\u003c/span\u003e\u003cspan address=\"10.1111/mec.12558\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePannekoek J, Van Strien AJ (2005) TRIM 3 manual (TRends and Indices for Monitoring data). Statistics Netherlands, Leiden\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePauls SU, Nowak C, B\u0026aacute;lint M, Pfenninger M (2013) The impact of global climate change on genetic diversity within populations and species. Mol Ecol 22:925\u0026ndash;946. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/mec.12152\u003c/span\u003e\u003cspan address=\"10.1111/mec.12152\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePayne RB, Sorenson MD (2002) Museum collections as sources of genetic data. Bonner Zool Beitr 51:97\u0026ndash;104\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeach WJ, Sheehan DK, Kirby WB (2014) Supplementary feeding of mealworms enhances reproductive success in garden nesting House Sparrows Passer domesticus. Bird Study 61:378\u0026ndash;385. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00063657.2014.918577\u003c/span\u003e\u003cspan address=\"10.1080/00063657.2014.918577\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePECBMS (2025) Species trends: House sparrow. Pan-European Common Bird Monitoring Scheme. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pecbms.info/trends-and-indicators/species-trends/\u003c/span\u003e\u003cspan address=\"https://pecbms.info/trends-and-indicators/species-trends/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 01 Sep 2025\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerez-Portela R, Noyer C, Becerro MA (2015) Genetic structure and diversity of the endangered bath sponge Spongia lamella. Aquat Conserv Mar Freshw Ecosyst 25:365\u0026ndash;379. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/aqc.2478\u003c/span\u003e\u003cspan address=\"10.1002/aqc.2478\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinsky ML, Ak\u0026ccedil;akaya HR, Brugere D, Dopheide A (2025) Global meta-analysis shows action is needed to halt genetic diversity loss. Science. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.abn5642\u003c/span\u003e\u003cspan address=\"10.1126/science.abn5642\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePint\u0026oacute; J, Garcia-Lozano C, Roig-Munar FX (2018) L'espai litoral. Natura: \u0026uacute;s o ab\u0026uacute;s. Instituci\u0026oacute; Catalana d'Hist\u0026ograve;ria Natural (ICHN). Institut d'Estudis Catalans (IEC), Barcelona\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiry S, Luikart G, Cornuet JM (1999) BOTTLENECK: A computer program for detecting recent reductions in the effective population size using allele frequency data. J Hered 90:502\u0026ndash;503. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jhered/90.4.502\u003c/span\u003e\u003cspan address=\"10.1093/jhered/90.4.502\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945\u0026ndash;959. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/genetics/155.2.945\u003c/span\u003e\u003cspan address=\"10.1093/genetics/155.2.945\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuesada J, Calder\u0026oacute;n-Llobera J (2021) Pardal com\u0026uacute;. In: Franch M, Herrando S, Anton M, Villero D, Brotons L (eds) Atles dels ocells nidificants de Catalunya: Distribuci\u0026oacute; i abund\u0026agrave;ncia 2015\u0026ndash;2018 i canvi des de 1980. Institut Catal\u0026agrave; d'Ornitologia, Cosset\u0026agrave;nia Edicions, Barcelona, pp 490\u0026ndash;491\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuesada J, Guallar S, P\u0026eacute;rez-Ruiz NJ et al (2010) Observer error associated with band allocation is negligible in large scale bird monitoring schemes, but how precise is the use of bands at all? Ardeola 57:23\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.13157/arla.57.1.2010.23\u003c/span\u003e\u003cspan address=\"10.13157/arla.57.1.2010.23\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team (2025) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamos-Elvira E, Banda E, Arizaga J et al (2023) Long-term population trends of house sparrow and Eurasian tree sparrow in Spain. Birds 4(2):159\u0026ndash;170. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/birds4020013\u003c/span\u003e\u003cspan address=\"10.3390/birds4020013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavinet M, Elgvin TO, Trier C et al (2018) Signatures of human-commensalism in the house sparrow genome. Proc R Soc B 285:20181246. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1098/rspb.2018.1246\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2018.1246\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRingsby TH, S\u0026aelig;ther BE, Jensen H, Engen S (2006) Demographic characteristics of extinction in a small, insular population of house sparrows in northern Norway. Conserv Biol 20:1761\u0026ndash;1767. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1523-1739.2006.00564.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1523-1739.2006.00564.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSage RF (2020) Global change biology: A primer. Glob Change Biol 26:3\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/gcb.14893\u003c/span\u003e\u003cspan address=\"10.1111/gcb.14893\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaura S, Bodin \u0026Ouml;, Fortin MJ (2014) Stepping stones are crucial for species' long-distance dispersal and range expansion through habitat networks. J Appl Ecol 51(1):171\u0026ndash;182. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2664.12179\u003c/span\u003e\u003cspan address=\"10.1111/1365-2664.12179\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScribner KT, Blanchong JA, Bruggeman DJ et al (2005) Geographical genetics: Conceptual foundations and empirical applications of spatial genetic data in wildlife management. J Wildl Manag 69:1434\u0026ndash;1453. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2193/0022-541X\u003c/span\u003e\u003cspan address=\"10.2193/0022-541X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e(2005)69[1434:GGCFAE]2.0.CO;2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma P, Binner M (2020) The decline of population of house sparrow in India. Int J Agric Sci 5:1\u0026ndash;5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiriwardena GM, Baillie SR, Wilson JD (1999) Temporal variation in the annual survival rates of six granivorous birds with contrasting population trends. Ibis 141:621\u0026ndash;636. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1474-919X.1999.tb07370.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1474-919X.1999.tb07370.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith AC, Hudson MAR, Aponte VI, English WB, Francis CM (2024) North American Breeding Bird Survey - Canadian Trends Website, Data-version 2023. Environment and Climate Change Canada, Gatineau\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuarez AV, Tsutsui ND (2004) The value of museum collections for research and society. Bioscience 54:66\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1641/0006-3568(\u003c/span\u003e\u003cspan address=\"10.1641/0006-3568(\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e2004)054[0066:TVOMCF]2.0.CO;2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSummers-Smith D (1999) Current status of the House Sparrow in Britain. Br Wildl 10:381\u0026ndash;386\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSummers-Smith JD (2003) The decline of the House Sparrow: A review. Br Birds 96:439\u0026ndash;446\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwanson TM (ed) (1995) The economics and ecology of biodiversity decline: The forces driving global change. Cambridge University Press, Cambridge\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzulkin M, Sheldon BC (2008) Dispersal as a means of inbreeding avoidance in a wild bird population. Proc R Soc B 275:703\u0026ndash;711. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1098/rspb.2007.0989\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2007.0989\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVangestel C, Mergeay J, Dawson DA et al (2012) Genetic diversity and population structure in contemporary house sparrow populations along an urbanization gradient. Heredity 109:163\u0026ndash;172. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/hdy.2012.27\u003c/span\u003e\u003cspan address=\"10.1038/hdy.2012.27\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWan X, Holyoak M, Yan C et al (2022) Broad-scale climate variation drives the dynamics of animal populations: A global multi-taxa analysis. Biol Rev 97:2174\u0026ndash;2194. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/brv.12888\u003c/span\u003e\u003cspan address=\"10.1111/brv.12888\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"conservation-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"coge","sideBox":"Learn more about [Conservation Genetics](https://www.springer.com/journal/10592)","snPcode":"10592","submissionUrl":"https://submission.nature.com/new-submission/10592/3","title":"Conservation Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Bottleneck, Historical DNA, Genetic diversity, Passer domesticus, Population structure, Urban-agricultural gradient","lastPublishedDoi":"10.21203/rs.3.rs-8669756/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8669756/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding how human-modified environments shape long-term genetic diversity is crucial for conservation, particularly in species experiencing widespread population declines. The House Sparrow \u003cem\u003ePasser domesticus\u003c/em\u003e has declined markedly across Europe, yet its long-term genetic dynamics remain poorly understood, especially in Mediterranean landscapes. We assessed spatiotemporal patterns of neutral genetic variation by integrating contemporary samples from 13 populations distributed along an agricultural\u0026ndash;urban gradient in Catalonia (NE Spain) with historical museum specimens collected between 1919 and 1950.\u003c/p\u003e \u003cp\u003eUsing twelve short-amplicon microsatellite loci suitable for degraded DNA, we quantified changes in genetic diversity, population structure, and demographic signals, and evaluated their association with recent population trends. Historical populations were genetically homogeneous and formed a single cluster, consistent with larger effective population sizes and higher connectivity in the past. In contrast, contemporary populations exhibited increased genetic structuring, positive inbreeding coefficients, and heterozygote deficiencies, with subdivision into multiple clusters not clearly associated with habitat categories. Signals of recent demographic bottlenecks were detected in several contemporary populations, particularly in urban areas, indicating localized population contractions linked to landscape intensification.\u003c/p\u003e \u003cp\u003eDespite these changes, overall neutral genetic diversity remained largely stable across the past century, and genetic diversity metrics were not associated with recent population trends. Together, our results suggest that Mediterranean House Sparrow populations have experienced recent fragmentation and demographic contractions without pervasive genetic erosion, consistent with historical population declines followed by demographic stabilization or adjustment to novel anthropogenic environments. This study highlights the value of historical DNA for reconstructing long-term genetic baselines in conservation genetics.\u003c/p\u003e","manuscriptTitle":"Historical and contemporary genetic structure reveals recent fragmentation without loss of diversity in Mediterranean House Sparrow populations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 14:33:42","doi":"10.21203/rs.3.rs-8669756/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-02T12:04:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-01T16:24:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-27T21:50:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148776493184816150315645181906955721657","date":"2026-02-05T09:56:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260041894970097324202691932105222411798","date":"2026-02-01T19:49:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20893682832126417443037329031535621942","date":"2026-01-30T15:34:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-30T08:11:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-29T02:43:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-29T02:43:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Conservation Genetics","date":"2026-01-22T11:59:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"conservation-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"coge","sideBox":"Learn more about [Conservation Genetics](https://www.springer.com/journal/10592)","snPcode":"10592","submissionUrl":"https://submission.nature.com/new-submission/10592/3","title":"Conservation Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0dc64a20-260a-4803-80e0-6c4e0a89a82a","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T07:54:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-02 14:33:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8669756","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8669756","identity":"rs-8669756","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}