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Article

The Genetic Response of Forest Birds to Urbanization: Variability in the Populations of Great and Blue Tits

by
Loreta Bisikirskienė
1,*,
Loreta Griciuvienė
2,
Asta Aleksandravičienė
2,
Gailenė Brazaitytė
1,
Algimantas Paulauskas
2 and
Gediminas Brazaitis
1
1
Faculty of Forest Sciences and Ecology, Vytautas Magnus University Agriculture Academy, Studentų Str. 11, LT-53361 Akademija, Lithuania
2
Faculty of Natural Sciences, Vytautas Magnus University, K. Donelaičio Str. 58, LT-44248 Kaunas, Lithuania
*
Author to whom correspondence should be addressed.
Forests 2024, 15(8), 1445; https://doi.org/10.3390/f15081445
Submission received: 11 July 2024 / Revised: 8 August 2024 / Accepted: 12 August 2024 / Published: 16 August 2024
(This article belongs to the Section Forest Biodiversity)
Browse Figures
Versions Notes

Abstract

:
Anthropogenic pressures such as over-urbanization, intensive agriculture/forestry practices, and the development of energy farms alter natural landscapes. Intensive urban development poses the greatest threat to natural ecosystems. Habitat degradation, fragmentation, and loss are among the key factors behind the current rise of biodiversity loss. In this study, we hypothesized that urbanization advances the adaptation of forest bird populations to relatively new urban ecosystems. The study was conducted in Kaunas, Lithuania, located in Eastern Europe. Genetic samples were collected in the city, representing urban landscapes, and its surrounding forests. In total, 160 nest boxes were erected, of which 80 were placed in the urban areas and 80 in the forests. Using a set of microsatellite markers, we investigated the genetic differentiation, genetic diversity, gene flow, and population structure of two common forest bird species of the Paridae family, the great tit (Parus major) and blue tit (Cyanistes caeruleus), in forests and urbanized areas. We observed low but significant differences between urban and forest great tit and blue tit populations, proving relatively high population genetic diversity. We determined that cities’ spatial structure and fragmented natural habitats can influence the formation of small and isolated bird populations (subpopulations). Urban blue tits had higher genetic differentiation and a higher tendency to form subpopulations. In conclusion, forest birds can inhabit urbanized landscapes but both great tits and blue tits respond differently to urbanization-related changes.

1. Introduction

The landscapes surrounding developed societies are rapidly changing in their type and spatial structure. The anthropogenic transformations of landscapes have reached unprecedented levels, driven by the increasing development of urban areas (grey infrastructure, including the establishment of roads and solar and wind energy parks), both intensive agriculture and forest management (including the drainage of land), and various other human activities that alter natural landscapes [1,2,3,4]. Thus, landscapes have become increasingly homogeneous, resulting in reduced species richness and the formation of altered and compromised ecosystems with low ecological values [5,6]. It is widely recognized that the rapid development of cities and towns poses the greatest threat to natural habitats, biodiversity, and their subsequent loss [7,8,9,10]. Urbanized landscapes also have large impervious surface coverages that exhibit dynamic shifts in energy, nutrient and hydrology cycles, and temperature balances that lead to increased pollution [11,12,13]. However, simultaneously, these novel ecosystems can offer shelter, refuge, and favorable conditions or habitat for birds. This includes suitable microclimates for feeding, breeding, and resting [14,15]. For instance, artificial lighting can provide opportunities for foraging or searching [16,17].
Birds inhabit a wide range of environments worldwide, including both natural and urban landscapes. Yet, birds are sensitive to habitat changes and are often regarded as excellent indicators for evaluating environmental conditions [18,19,20]. Birds play an important role in both forest and urban ecosystems, providing essential ecosystem services, and are detrimental to numerous ecosystem processes [21,22,23,24,25]. Previous studies have determined that certain birds have traits and niche positions (e.g., high environmental tolerance, high breeding success rates (reflected in high annual fecundity and adult survival rates), ability to inhabit building cavities, and diverse dietary requirements) that can provide advantages for using cities as suitable habitat [26,27,28]. Highly urbanized areas are dominated by urban-exploiter bird species. Invasive species may also fill this niche [28,29,30,31]. According to Møller and Díaz (2018) [32], urban-exploiter bird species richness or populations are steadily increasing. Observations of typical forest bird species (e.g., Paridae family) in cities also suggest that birds tend to fill vacant ecological niches in urbanized areas [33]. However, urbanized landscapes are recognized as not being very suitable habitats for specialized and rare species due to the scarcity of food resources, a lack of suitable nesting sites, and the absence of diverse landscape structural elements [34].
As urbanization continues to expand and encroach upon natural habitats, birds face increased pressures to adapt. Research has recognized that urban ecosystems exert potent evolutionary stimuli, serving as alternative selection pressure [35,36,37,38]. Environmental changes induce both phenotypic (non-hereditary) and genetic (hereditary) variability in organisms, enabling individuals to develop new characteristics for survival and adaptation. The diverse spectrum of phenotype changes observed in species serves as indicators of adaptive evolution in urban environments (e.g., morphological, physiological, behavioral, and phenological variations) [39,40,41,42].
The evolutionary response of bird populations to urbanization is not yet entirely understood. Genetic differentiation has been extensively studied in megacities (>1,000,000 inhabitants) in North America, Mexico, China, and Europe’s metropolitan areas [43,44,45]. However, in smaller cities or towns (i.e., <500,000 inhabitants), the genetic differential of birds is less known. Evaluating urban avifauna in smaller cities is, thus, an important missing link toward filling knowledge gaps that compare the genetic diversity of bird species along the urban–forest gradient.
The aim of this study is to explore the genetic variability of two generalist forest-dwelling bird species and their evolution to cope with urbanization using microsatellite markers. In this study, we propose the following hypotheses: (I) landscape impact hypothesis—urban landscapes influence the evolution of forest bird species populations; (II) distinctive ecotype hypothesis—different landscapes form distinct genetic differences in city and forest bird populations. We speculate that the urban population is fragmented into smaller, relatively isolated groups, which leads to changes in allele frequency, genetic structure, and gene flow, resulting in genetic differentiation.

2. Materials and Methods

2.1. Study Area

The study was conducted in two distinct landscapes, representing highly urbanized areas and natural forests (Figure 1). We selected the second largest city in Lithuania—Kaunas—to represent a highly urbanized landscape. Kaunas is a metropolitan city with an area of 157 km2 [46]. On a national scale, Kaunas is not only one of the largest but also one of the densest cities with a population of over 300 thou. people and 1919 inhabitants per km2 [46].
Kaunas has a diverse spatial structure with varying urbanization intensities, including the old town, the city center, commercial and industrial zones, residential areas with high-rise/low-rise apartments and low-density housing of single-family homes, construction sites, and green infrastructure such as city forests and parks. Within the city, we collected data in highly urbanized areas with a high population density (Urb). Kaunas neighborhoods have different prevalent spatial structures and ratios of urbanized/green infrastructure. Thus, we segregated Kaunas neighborhoods by urbanization intensity index (UII) based on Kaunas’s city municipality functional zoning plan [47]. The urbanization intensity index is regulated by national legislation. UII is determined based on the type of construction, the territory usage type, the maximum permitted construction density per plot (%), and regulated building height [48,49].
i.
City center (Urb1)—high-urbanization-intensity areas with allowed < 3.5 UII.
ii.
Šilainiai (Urb2), Dainava (Urb3)—high-urbanization-intensity residential areas with prevalent high-rise apartment buildings of <1.2 UII. The allowed construction height of high-rise buildings is up to 50 m. Dainava is one of the most densely populated residential areas in Kaunas, distinguished by lower apartment building heights (5–9-floor buildings) and an abundance of green spaces with mature woody vegetation.
iii.
Vilijampolė (Urb4)—moderate-urbanization-intensity residential area with prevalent low-rise apartment buildings of <0.8 UII. Low-rise residential housing is interspersed between apartment building units.
iv.
Žaliakalnis (Urb5)—moderate urbanization intensity with prevalent low-rise residential buildings of <0.8 UII. The maximum allowed height of buildings is up to 13 m. Žaliakalnis is one of the greenest areas situated close to the city center. The neighborhood has an abundance of tree alleys of mature and diverse species of trees, green spaces located close to the streets, broadleaf woodlands in slopes and screes, and the largest city oak park in Europe (60.83 ha). The development of this urban area was planned based on a popular European garden city concept.
Natural forest landscapes (For) were selected in three different forests—Padauguva, Dubrava, and Pravieniškės. These forests are located 10–30 km away from Kaunas in the north, east, and south directions, respectively. Each forest has an area of over 50 km2. The data were collected in commercially managed forest stands with varying soil fertility and humidity, tree species composition, age, and understory density. Selected stands represent the diverse forest landscapes of central Lithuania:
i.
Padauguva (For1) forest has an area of 57.20 km2. The prevalent soils are temporarily waterlogged and highly fertile. The majority of the forests are mixed with Norway spruce (Picea abies) (49.2%) and deciduous species such as birch (Betula spp.) (17.3%) and black alder (Alnus glutinosa) (16.7%). Less common mixtures include European aspen (Populus tremula) and grey alder (Alnus incana); 66.2% of the stands have dense understory vegetation.
ii.
Dubrava (For2) forest has an area of 57.50 km2. Its soils are infertile, dry, and sandy. The dominant tree species is Scots pine (Pinus sylvestris) (86.90%). Most of the stands are 101–120 years old; 27.96% of the stands have no understory layer, 28.59% have scarce shrubs, and moderately dense shrubs are observed in 43.45%.
iii.
Pravieniškės (For3) forest has an area of 50.70 km2. Most of the sites are fertile and temporarily waterlogged. The main tree species is Norway spruce (54.94%) while the remaining area is dominated by birch (41%); 45.98% of the stands have moderate-density understory vegetation.

2.2. Population Sampling and Genomic DNA Extraction

The multi-species approach was used by selecting two nest-boxing passerine bird species: great tit (Parus major Linnaeus 1758) and blue tit (Cyanistes caeruleus Linnaeus 1758) [50]. Both species are common and have a wide distribution range, exhibiting relatively sedentary behavior in both forest and urban environments with minimal seasonal migration [51]. These species have high plasticity, allowing them to migrate, adapt, and breed successfully in urban areas when faced with primary forest fragmentation and habitat loss [51,52]. Great tits have a preference for deciduous or mixed forest habitats while blue tits are deciduous forest specialists. Cities represent relatively new and secondary habitats for both species [53,54].
In September 2021, a total of 160 nest boxes were erected, of which 80 were placed in forests and 80 in highly urbanized areas. The distribution and abundance of both species populations were considered, as well as their preference for the hole diameter in the nest boxes. In total, 100 nest boxes were constructed with a 3.2 cm diameter hole and 60 nest boxes with a smaller 2.8 cm diameter hole. Blue tits can also inhabit nest boxes intended for great tits with larger diameter holes. Nest boxes with different diameter holes were equally distributed in all study areas. All nest boxes were of the same type and dimensions with removable, double front panels. They were mounted at equal intervals of 100 m from each other, considering the size of great tit and blue tit territory occupancy during the breeding season (about 50 m around the nest box) [55]. The placement of nest boxes aimed to prevent competition and minimize overlapping population occupancy. The study was conducted under the authorization of the National Environmental Protection Agency, permitting the use of protected species for scientific research (Permit No. (26)-SR-167). All feather samples were collected in accordance with relevant permissions and guidelines.
During the birds’ breeding season in 2022 (from 16 April to 18 July), we collected feather samples from nesting individuals inside the boxes. We observed two generations (adult birds and their chicks) of great tits and blue tits, sampling one feather from each bird. One tail feather was collected from the adult birds. The sampling of tail feathers was selected to minimize bird disturbance, as they are easier to pluck than the primary long flight feathers. Although, primary long feathers were collected from the chicks as their tail feathers had not yet developed. Feather calamus tips, keratin, and blood inside them are important for DNA extraction.
Samples were collected from 9 a.m. to 6 p.m. In the early mornings or during cold days, samples were not collected as relatively low air temperature poses a threat to critically lower the temperature inside the nest and cool down the clutch. After 6 p.m., nest boxes were not inspected, and no samples were collected to not disturb overnighting females. Nest boxes were inspected by removing double front panels and taking out birds inside them. From each bird, a single feather was collected without harm, releasing them quickly to minimize stress. Collected samples were put in sealed plastic bags, on which the collection date, study site, nest box number, and clutch size were written. Samples were refrigerated at −20 °C temperature. In total, in the For study areas, we collected 93 great tit and 26 blue tit samples. In the Urb study areas, we collected 34 great tit and 81 blue tit samples.
Many researchers practice a method of collecting and analyzing birds’ blood samples. We believe that DNA extraction from the feather sample is similar, although it does not require veterinary specialist knowledge of how to collect blood samples and how to lower the risk of sample contamination. The feather sampling method is non-invasive, and, thus, more suitable for chicks, which have no hostile reaction to humans yet. This methodology can be used instead of blood collection as a non-invasive genetic sampling.
DNA was extracted from the base of the calamus of freshly sampled feathers using a GeneJET Genomic DNA Purification Kit (Thermo Fisher Scientific, Vilnius, Lithuania) using the standard protocol and following the manufacturer’s instructions. The DNA quality and quantity were determined using a NanoDrop 2000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA). Prepared DNA samples were stored at −20 °C until PCR amplification.

2.3. Microsatellite Analysis

Ten microsatellites (PmaC25, PmaD22, PmaGAn27, PmaGAn30, PmaGAn40, PmaTAGAn71, PmaTAGAn86, PmaTGAn42, PmaTGAn45, and PmaCAn1), developed for tits, were used for genetic diversity and genetic structure analysis [56]. Each multiplex PCR mixture consisted of 1 µL of template DNA, 1X DreamTaq Green buffer, and primers in 0.2 µM concentration for each, and then, nuclease-free water was added to render a final volume of 20 μL. PCR was carried out under the following conditions: an initial denaturation step at 95 °C for 10 min, followed by 33 cycles of 30 s denaturation at 95°, 30 s annealing at 57 °C, and 75 s extension at 72 °C; there was a final extension of 72 °C for 7 min. Forward primers were labeled with 6-FAM and Cy3 fluorescent dyes. Fluorescently labeled PCR products were genotyped through capillary electrophoresis using a SeqStudio™ genetic analyzer (Thermo Fisher Scientific, Vacaville, CA, USA) and the GeneScan™ 500 LIZ™ size standard (Thermo Fisher Scientific, USA). The size of DNA fragments was analyzed manually in GeneMapper v. 4.0 (Applied Biosystems, Foster City, CA, USA).

2.4. Data Analysis

The genetic diversity of great tits and blue tits was analyzed by locus and subpopulation. The number of alleles (Na), the number of private alleles (Ap), observed heterozygosity (Ho), expected heterozygosity (He), and inbreeding coefficient (Fis) were calculated using GenAleX 6.51b2 software [57]. The Power Marker Software Version 3.25 was used to calculate the number of genotypes (NG) and the polymorphism information content (PIC) [58]. Allelic richness (Ar) was calculated using FSTAT, version 2.9.3.2 [59]. Deviations from the Hardy–Weinberg equilibrium (HWE) were evaluated using the Genepop v.4.0 software, employing the Markov chain algorithm with 10,000 dememorization steps, 100 batches, and 1000 iterations for each test [60]. The p-values for HWE were adjusted for multiple comparisons using a sequential Bonferroni correction, initially set at a probability of p = 0.05 [61]. The possible presence of null alleles was assessed with the software Microchecker V2.2.3 [62] and null allele frequencies were estimated with the Brookfield 1 estimator [63]. GenAlEx was further used to carry out an analysis of molecular variance (AMOVA) and F-statistics (Fst, Fis, and Fit). Estimates of pairwise population differentiation (Fst) and graphical representation were conducted using ARLEQUIN version 3.5.2.2 [64]. The analysis involved evaluating Fst between sampling sites using the distance method, with a significance level of p = 0.05. The exact test of population differentiation based on genotype frequencies was conducted using a Markov chain with 100,000 steps and 10,000 dememorization steps. Factorial correspondence analysis (FCA) was carried out using GENETIX version 4.05.2 [65] to display the genetic differentiation among all great tit and blue tit individual birds from forests and urban areas. In addition, population structure was estimated using a Bayesian Markov Chain Monte Carlo model (MCMC) implemented in STRUCTURE v2.3.4 [66]. Ten independent runs for each K = 1–10 involved a burn-in of 800,000 Markov Chain Monte Carlo (MCMC) iterations, followed by 200,000 replications. The optimal number of clusters (K) was determined with Structure Harvester [67] using the ΔK values (the rate of change in the log probability of data between successive K values) [68]. Based on the resulting values of K, CLUMPAK [69]. (Kopelman et al., 2015) was used to generate optimal alignment plots of the STRUCTURE results.

3. Results

3.1. SSR Marker Polymorphism

In total, 216 individual birds (124 great tits and 92 blue tits) were genotyped at 10 microsatellite loci, and a total of 254 alleles were detected. The number of alleles (Na) for each locus ranged from 5 to 19 for great tits and from 6 to 28 for blue tits (Table 1). The allelic richness (Ar) of great tits ranged from 2.622 to 8.916 and of blue tits from 2.548 to 7.740. Great tit observed heterozygosity values per locus (Ho) ranged from 0.294 (PmaGAn40) to 0.948 (PmaGAn27), while expected heterozygosity (He) ranged from 0.255 (PmaGAn40) to 0.849 (PmaGAn27). The Ho of blue tits ranged from 0.441 (PmaGAn40) to 1.000 (PmaCAn1), and He ranged from 0.415 (PmaGAn40) to 0.812 (PmaGAn27). The mean polymorphic information content (PIC) was similar for both great tits (0.748) and blue tits (0.752), ranging from 0.327 to 0.911 and from 0.517 to 0.934, respectively. Positive values of inbreeding coefficient (Fis) indicating excess homozygosity in blue tits were observed in two loci (PmaTAGAn86 and PmaD22) while in great tits, all negative values were observed. Significant deviations from HWE after Bonferroni correction were shown at three out of ten loci in great tits and at five loci in blue tits. The occurrence of null alleles at each locus was detected for great tits by low frequencies < 0.2. However, two loci in blue tits (PmaTAGAn86 and PmaD22) exhibited a higher null allele frequency, with one locus having a null allele frequency greater than 0.2 and the other exhibiting moderate null allele frequency (0.1 < fn < 0.2). Null alleles were present only in a subset of populations. Thus, loci were excluded from further analysis. The results of genetic diversity were not affected by the presence of null alleles.

3.2. Population Genetic Diversity

The assessment of great tit and blue tit genetic diversity across populations revealed comparable Ar, He, Ho, Fis, and PIC (Table 2). Although, the number of private alleles (Ap) in the blue tit population was higher than in the great tit population. The highest number of Ap was detected in the blue tit Urb5 population (20), while the lowest was in the great tit Urb2 and Urb3 populations (1). Significant deviations from Hardy–Weinberg after Bonferroni corrections were observed in three great tit sampling sites, resulting from heterozygote excess. Significant deviations from HWE were detected in four out of five blue tit sampling sites.

3.3. Genetic Differentiation and Structure

Analysis of molecular variance (AMOVA) revealed major genetic variations within individual birds, indicated by the high percentage of variation (Table 3 and Table 4). Both great tits and blue tits exhibited significant genetic differentiation among sampling sites, with 8% of total genetic variation (p < 0.001). Great tits displayed no noticeable genetic variation among individuals within sampling sites (0%, p = 0.955). Blue tits variation within sampling sites was low, and statistically insignificant (3%, p = 0.074). The overall Fst, Fis, and Fit fixation indices for great tits were 0.080, −0.024, and 0.058, and for blue tits, these were 0.080, 0.028, and 0.106, respectively.
Pairwise Fst revealed significant genetic differentiations among all pairs of great tit and blue tit subpopulations. The pairwise Fst of great tits varied from 0.028 (between For1 and For2) to 0.174 (between Urb3 and Urb5), and of blue tits from 0.035 (between Urb2 and For1) to 0.179 (between Urb1 and Urb4) (Figure 2). The lowest Fst of great tits was observed between For1 and For2, as well as between For1 and Urb2, while the highest Fst was estimated between Urb3 and Urb5. Similarly, the lowest Fst of blue tits was determined between For1 and Urb2, while the highest Fst was between Urb1 and Urb4.
Factorial correspondence analysis (FCA) was performed to study the degree of differentiation between all great tit and blue tit individual birds in subpopulations (Figure 3 and Figure 4). The FCA revealed the distinct clustering of both great tit and blue tit individual birds from various sampling sites and was consistent with the data obtained from Fst analysis.
The potential number of genetic clusters was determined in different sampling sites (Figure 5). The maximum delta K (∆K) was obtained at K = 2 in great tits and at K = 8 in blue tits. Great tits studied in For1, For2, and Urb5 were predominantly assigned to cluster 1 (green color), whereas cluster 2 (red color) consisted of For3, Urb1, Urb2, and Urb3 subpopulations. All blue tit population genetic clusters were present across most sampling sites ranging from low to high confidence. In Urb1 and Urb4, blue tits were mostly assigned to one dominant cluster.

4. Discussion

4.1. Genetic Diversity

Great tits and blue tits had a similar number of alleles (Na), allelic richness (Ar), number of genotypes (NG), expected heterozygosity (He), observed heterozygosity (Ho), inbreeding coefficient (Fis), and polymorphism information content (PIC). In contrast, the number of private alleles (Ap) was higher in blue tits (9.8) than in great tits (4.14). Similarly, a higher number of private alleles in blue tits was consistent with a recent study conducted in Poland [70]. The mean observed heterozygosity values in urban and forest areas were 0.736 and 0.763 for great tits and 0.687 and 0.760 for blue tits, respectively. The lower observed heterozygosity of blue tits in the forests may be attributed to the fact that individual samples were collected from only one site (For1). Correspondingly, Markowski et al. 2021 [70] determined similar observed heterozygosity of great tit and blue tit populations in both forests and urban areas, despite their sample size being two times greater. In the study by Postma et al. 2009 [71], similar observed and expected heterozygosity of great tits was determined in Vlieland, Netherlands. Furthermore, the observed heterozygosity was higher than the expected heterozygosity (excluding blue tit populations in forests) in both tits’ species. Heterozygosity can be influenced by the mating pattern known as heterozygosity-based assortative mating. This pattern can result from habitat preference, time of breeding, and mate selection based on phenotypic traits, e.g., the blue crown and yellow breast patch of blue tits [72,73]. Additionally, an excess of heterozygotes in populations indicated by a negative Individual Fixation Index (FIS < 0) may also depend on assortative mating patterns or the selection of the heterozygotes.
Significant deviations from the Hardy–Weinberg equilibrium (HWE) were observed in two great tit sampling sites in urban areas (Urb1 and Urb3) and in four blue tit sampling sites in forests (For1) and urban areas (Urb4, Urb1, and Urb5). However, we determined that blue tit populations had non-significant deviations from HWE while the deviation of great tit populations was significant. Similarly, significant deviations from HWE have also been reported in earlier studies of great tits [74,75]. Meanwhile, other studies indicated no deviations from HWE in both the blue tit and great tit populations [70,76]. The differences in population genetic diversity might arise from using different sets of markers or from variations in the number of samples collected. “Possibly, the observed significant deviations from HWE indicating an excess heterozygosity can be attributed to random sampling, null alleles, non-random mating, or ecological events (e.g., founder effects, habitat fragmentation) [72,77]”.
In this study, two pairs of primers with slightly greater frequencies of null alleles were identified, while others either had a very low frequency or inconsistent presence. Considering the unequal distribution of null alleles among populations, it is evident that other biological factors such as non-random mating could have contributed to these observed deviations.
In Kaunas, the genetic diversity of great tits and blue tits is relatively high, with significant gene flow and low levels of inbreeding. However, significant genetic differentiation is present between forest and urban populations, particularly in urban environments where barriers limiting gene flow are more pronounced. We hypothesized that urban landscapes could influence the evolution of forest bird species populations. Thus, we expected to determine more significant genetic differentiation between forest and urban bird populations. Additionally, we anticipated that urban populations would exhibit lower genetic diversity.

4.2. Population Differentiation and Structure

The genetic differentiation of great tit and blue tit populations ranged from low to high across the study sites. Low to moderate genetic differentiation was observed among tits in the forests surrounding Kaunas. Great tits in For1 and For2 exhibited low differentiation, which can be facilitated by the Nevėžis and Nemunas rivers, green spaces, and forest types as both species prefer deciduous or mixed forests [78,79,80]. However, the For2 Scots pine forests may not be a preferred habitat type for these species due to unfavorable conditions [81,82,83,84]. In contrast, great tits in For3 formed a subpopulation with moderate differentiation compared to the For1 and For2 populations. Despite the proximity of For3 to For2, the two forests are separated by the largest artificial waterbody in Lithuania—Kauno Marios—which likely acts as a barrier to gene flow [85,86]. The establishment of the For3 subpopulation could be further driven by the large nearby forest area that provides favorable nesting and wintering conditions, constant food resources, and a suitable landscape structure. As a result, a sedentary group of great tits may have formed, which could explain the low occupancy rate of nest boxes due to the abundance of natural cavities and tree hollows. Of the three forest sampling sites, blue tits most frequently inhabited the nest boxes in For1. The mixed coniferous–deciduous forests of this site are the most suitable for their breeding. In For2 and For3, only a few nest boxes were occupied by blue tits, but their sampling was unsuccessful. Thus, the sampling of blue tit feathers was limited to only one forest site. In future studies, we will aim to improve our sampling strategies as it is a detrimental factor that may affect results and our interpretations of the blue tit population structure.
In the urban neighborhoods, the genetic differentiation of tit populations was moderate to high. The highest genetic differentiation of great tits was observed in Urb3 and Urb5, indicating the presence of subpopulation. In Urb3, bird movement is likely restricted by the high urbanization intensity and the prevalence of high-rise residential buildings. The extensive urbanization, including high-rise constructions, road networks, and fragmented green spaces, develops barriers that restrict bird movement and dispersal, leading to the isolation of populations [87]. This does not apply to all bird species. The following scientific articles suggest that urbanization may facilitate the movement of anthrodependent bird species [44,88]. This isolation reduces genetic diversity and increases genetic differentiation. Thus, smaller populations are at risk of increased gene drift and inbreeding [36,88,89]. Although, public areas in Urb3, where woody vegetation covers about 40–50%, can improve breeding conditions. Similarly, favorable breeding conditions in Urb5 are promoted by green spaces. The highest genetic differentiation in urban blue tit populations was observed between Urb1 and Urb4. These adjacent neighborhoods are separated by the confluence of the Nemunas and Neris rivers, which significantly restricts bird movement. Such natural barriers can either facilitate dispersal by acting as gene migration corridors to surrounding forests or limit it [90,91]. It is evident that urbanization has a pronounced impact on blue tits, resulting in the formation of subpopulations. Higher genetic differentiation in urban blue tits supports our distinctive ecotype hypothesis. We suspected that different landscapes could form distinct genetic differences between urban and forest bird populations. Urban blue tit populations were fragmented into smaller, relatively isolated groups, which may lead to shifts in allele frequency and genetic structure due to restricted gene flow. In addition, Kaunas green infrastructure, dominated by a variety of deciduous tree species, has allowed blue tits to effectively adapt to these urban settings.
Bird mobility is influenced by urban historical contexts and the age of town or city establishments. Southern and Western Europe has an extensive urbanization history, which led us to suspect that adaptation of many bird species in Northern Europe is in the early stages. For example, common blackbirds (Turdus merula) and common wood pigeons (Columba palumbus) have successfully adapted to urban environments in Southern and Western Europe [28,92,93,94,95]. Although our study area—Kaunas—has a history of over 660 years, it is relatively “young” compared to many Southern and Western European metropolitan areas. Additionally, Kaunas green spaces cover about 32%, which increases gene flow between urban neighborhoods and surrounding forests [49] Our findings indicate that tit populations have low but significant genetic differentiation between urban areas and surrounding forests. This differentiation likely results from gene flow driven by bird migration patterns. Despite being largely sedentary, both tit species can migrate seasonally, covering distances of up to several hundred kilometers.
Our results revealed that the genetic differentiation of forest and urban tit populations ranged from low to high across different sites. The low differentiation between For1 and Urb2, located in the north-western part of the city, may be due to gene flow between forests and urban parks that act as migration corridors. Bird movement to urban habitats is common during winter, with studies indicating that 2.9 great tits/10 ha winter in forests while 3–14 great tits/10 ha prefer wintering in the cities [51]. Additional food resources, warmer microclimates, suitable habitats, and artificial lighting attract migrating birds during winter and encourage them to stay during the breeding season. Similarly, low differentiation between For1, For2, and Urb5 can be attributed to bird movements from forests to the greenest parts of the city. These patterns of genetic differentiation have also been observed in previous studies. Markowski et al. (2021) [70] determined low but significant genetic differentiation in great tit populations in Łódź and its surrounding forests. In Barcelona, Björklund et al. (2010) [74] estimated low to moderate genetic differentiation in urban and forest populations. Perrier et al. (2017) [96] observed low but significant great tit genetic differentiation between urban and rural populations in Montpellier. Salmón et al. (2021) [45] reported similar findings in nine European megacities, with low but significant differentiation in urban and rural great tit populations. Our results are consistent with these studies, revealing greater genetic differentiation across urban environments and lower in forests. These results are also consistent with low but significant blue tit genetic differentiation in central Spain [76]. Thus, the genetic differentiation in great tit and blue tit populations in Kaunas and its surrounding forests is comparable to that in Southern and Western European cities, indicating similar adaptation patterns.
Additionally, we identified two major lineages of great tits and eight of blue tits. Previous studies have similar results, with research in Poland [70] and Spain [74] identifying two genetic clusters for great tits. In contrast, Lemoine et al. (2016) [68] observed three clusters. For blue tits, the number of clusters varied: one in Poland [75], two in France [97], and two in Spain [76]. These differences can result from variations in geographic scope, genetic markers used, and sample sizes. Although, the Bayesian clustering program often overestimates the number of clusters [98]. In our study, great tit populations from forests and cities were classified based on the second highest ΔK value calculated through Fst and FCA. The nine surveyed blue tit populations were initially assigned to eight genetic clusters by the highest ΔK but to four clusters by the second highest ΔK. This suggests that eight clusters may be an overestimation, and blue tits are more likely to have four major lineages as indicated by the second highest ΔK.

5. Conclusions

First, great tits and blue tits have high genetic diversity in Kaunas and its surrounding forests. The negative or close-to-zero inbreeding coefficient (Fis) indicates intensive gene flow and heterozygote excess. This can result from the species’ characteristic high dispersal potential, which promotes random breeding of unrelated individual birds in genetically diverse populations.
Second, forest and urban populations had low but significant genetic differentiation. Tit populations had higher genetic differentiation between urban neighborhoods, forming subpopulations with distinct genetic structures. Urban blue tit populations had higher differentiation explained by lower gene flow. One great tit forest population was distinguished by its higher isolation of birds. In addition, both studied species in the forest (For1) and two neighborhoods within the city (Urb2, Urb5) had low differentiation. This pattern between For1 and Urb2 can result from gene flow between convenient geographical locations connecting natural areas, as well as due to the preference for deciduous forests. Gene flow in For1 and Urb5 can be attributed to green spaces with mature trees that attract tits from forests and allow them to settle. Although gene flow is present, we suspect that if great tit and blue tit population movement would be restricted by natural or anthropogenic obstacles, the tendency to form small subpopulations in the urban areas would increase.

Author Contributions

Conceptualization, L.B., G.B. (Gediminas Brazaitis) and A.P.; Field Sampling L.B.; Methodology, L.B., G.B. (Gediminas Brazaitis), A.A., L.G. and A.P.; Software, L.G. and A.A.; Data Curation, A.A., L.G. and A.P.; Writing—Original Draft Preparation, L.B., A.A., L.G., G.B. (Gailenė Brazaitytė) and A.P.; Writing—Review and Editing, L.B., G.B. (Gediminas Brazaitis), G.B. (Gailenė Brazaitytė), A.A., L.G. and A.P.; Visualization—Figures and Tables, L.B., A.A. and L.G.; Supervision, G.B. (Gediminas Brazaitis); Genetic Supervision, A.P.; Funding Acquisition, L.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Science Foundation of Vytautas Magnus University (MID-V5), under grant numbers P-A-22-06 and P-A-23-02.

Data Availability Statement

Data are contained within the article.

Acknowledgments

We wish to thank Vytautas Magnus University Agriculture Academy, Forest genetics laboratory (Laboratory of Climate Change Impact to Forest Ecosystems) for access to relevant equipment for genotyping collected samples.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.

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Figure 1. City of Kaunas and surrounding natural forest landscapes studied in Lithuania. Green areas correspond to natural forest landscapes (For); orange line indicates the highly urbanized area (Urb).
Figure 1. City of Kaunas and surrounding natural forest landscapes studied in Lithuania. Green areas correspond to natural forest landscapes (For); orange line indicates the highly urbanized area (Urb).
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Figure 2. Great tit (a) and blue tit (b) pairwise Fst distances between studied forest and urban landscapes. The color gradient represents a degree of genetic differentiation: low Fst < 0.05, moderate 0.05 < Fst < 0.15, high 0.15 < Fst < 0.25, and very high Fst > 0.25 (Wright 1978).
Figure 2. Great tit (a) and blue tit (b) pairwise Fst distances between studied forest and urban landscapes. The color gradient represents a degree of genetic differentiation: low Fst < 0.05, moderate 0.05 < Fst < 0.15, high 0.15 < Fst < 0.25, and very high Fst > 0.25 (Wright 1978).
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Figure 3. Great tit factorial correspondence analysis in seven sites.
Figure 3. Great tit factorial correspondence analysis in seven sites.
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Figure 4. Blue tit factorial correspondence analysis in five sites.
Figure 4. Blue tit factorial correspondence analysis in five sites.
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Figure 5. (A) Great tit population genetic structure: magnitude of delta K (ΔK) statistics based on 10 microsatellite loci (top); bar plots of individual Bayesian assignment probabilities of microsatellites generated by STRUCTURE for two clusters (bottom). (B) Blue tit population genetic structure: magnitude of delta K (ΔK) statistics based on 10 microsatellite loci (top); bar plots of individual Bayesian assignment probabilities of microsatellites generated by STRUCTURE for eight clusters (bottom). Each vertical line corresponds to an individual bird, with colors indicating the proportion of genetic clusters.
Figure 5. (A) Great tit population genetic structure: magnitude of delta K (ΔK) statistics based on 10 microsatellite loci (top); bar plots of individual Bayesian assignment probabilities of microsatellites generated by STRUCTURE for two clusters (bottom). (B) Blue tit population genetic structure: magnitude of delta K (ΔK) statistics based on 10 microsatellite loci (top); bar plots of individual Bayesian assignment probabilities of microsatellites generated by STRUCTURE for eight clusters (bottom). Each vertical line corresponds to an individual bird, with colors indicating the proportion of genetic clusters.
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Table 1. Great tit (Parus major) and blue tit (Cyanistes caeruleus) population microsatellite loci characteristics in Lithuania. Na—number of alleles, Ar—allelic richness, NG—number of genotypes, He—expected heterozygosity, Ho—observed heterozygosity, Fis—inbreeding coefficient, PIC—polymorphism information content, HWE p-value—exact test for HWE using Markov chain for all loci (significant, **: p < 0.01), fn—null allele frequency based on Brookfield 1 [63].
Table 1. Great tit (Parus major) and blue tit (Cyanistes caeruleus) population microsatellite loci characteristics in Lithuania. Na—number of alleles, Ar—allelic richness, NG—number of genotypes, He—expected heterozygosity, Ho—observed heterozygosity, Fis—inbreeding coefficient, PIC—polymorphism information content, HWE p-value—exact test for HWE using Markov chain for all loci (significant, **: p < 0.01), fn—null allele frequency based on Brookfield 1 [63].
Great tit, n = 126
LocusAllele Range, bpNaNGArHeHoFisPICHWE p-Valuefn
PmaTAGAn86142–21413286.0090.7900.833−0.0990.7990.6430.031
PmaTGAn42254–3109245.5740.7260.762−0.0870.7890.007 **−0.006
PmaCAn1107–13913306.6620.7040.833−0.2260.8100.0660.0199
PmaC25305–34614336.5660.7330.771−0.0980.8340.6280.030
PmaGAn30291–3097174.2190.6610.637−0.0090.6690.3020.041
PmaGAn40408–422582.6220.2550.294−0.1400.3270.5320.022
PmaTAGAn71174–2669204.9900.7010.748−0.1290.7460.6530.035
PmaD22392–48016477.9020.8410.896−0.1090.8790.080−0.007
PmaGAn27189–25819618.9160.8490.948−0.1670.9110.000 **−0.022
PmaTGAn45289–3608194.6060.6890.791−0.1910.7210.004 **−0.034
Mean 11.328.75.8070.6950.751−0.1250.748
Blue tit, n = 92
LocusAllele Range, bpNaNGArHeHoFisPICHWE p-Valuefn
PmaTAGAn86142–21413174.9250.6740.4640.1920.7590.000 **0.219
PmaTGAn42258–29012295.9030.7800.874−0.1690.8480.0447−0.004
PmaCAn1107–139762.5480.5431.000−0.8730.5060.000 **−0.289
PmaC25305–33711214.3350.6780.873−0.3470.7310.002 **−0.056
PmaGAn30295–3098123.6420.5520.550−0.0360.5670.004 **0.066
PmaGAn40414–4226113.2680.4150.441−0.0990.5170.06950.012
PmaTAGAn71174–20619416.8090.8280.873−0.1140.8950.86570.034
PmaD22392–45628487.7400.7940.6750.0580.9340.000 **0.157
PmaGAn27189–25814376.0850.8120.937−0.2140.8500.0419−0.026
PmaTGAn45289–31923337.2220.7710.765−0.0600.9090.17250.064
Mean-14.1255.2480.6850.745−0.1660.752
Table 2. Great tit and blue tit genetic diversity indices in forests and urbanized areas. N—sample size, Ar—allelic richness, Ap—number of private alleles, He—expected heterozygosity, Ho—observed heterozygosity, Fis—inbreeding coefficient, PIC—polymorphism information content, HWE p-value—exact test for HWE using Markov chain for all loci (significant, **: p < 0.01).
Table 2. Great tit and blue tit genetic diversity indices in forests and urbanized areas. N—sample size, Ar—allelic richness, Ap—number of private alleles, He—expected heterozygosity, Ho—observed heterozygosity, Fis—inbreeding coefficient, PIC—polymorphism information content, HWE p-value—exact test for HWE using Markov chain for all loci (significant, **: p < 0.01).
Sampling SitesNArApHeHoFisPICHWE p-Value
Great tit, n = 126
For1345.36670.7380.7430.0090.7150.120
For2365.327100.7570.770−0.0190.7240.357
For3174.52350.6600.694−0.0530.6240.117
Urb1104.22030.6090.746−0.2240.5790.000 **
Urb284.65410.7110.875−0.2490.6600.019
Urb3113.85010.6100.750−0.2080.5770.000 **
Urb584.35620.5980.680−0.1440.5780.686
Mean 4.6144.140.6690.751−0.1250.6370.000 **
Blue tit, n = 92
For1254.572140.6890.687−0.0170.6870.000 **
Urb172.86830.5830.857−0.4790.4990.000 **
Urb2304.49990.7210.725−0.0450.6870.969
Urb473.09330.5520.717−0.2960.5020.004 **
Urb5235.046200.7490.7400.0070.7400.003 **
Mean 4.2219.80.6590.745−0.1660.623
Table 3. Analysis of great tit molecular variance (AMOVA).
Table 3. Analysis of great tit molecular variance (AMOVA).
Source of VariationdfSum of SquaresVariance ComponentsPercentage of VariationF-StatisticsValuep-Value
Among sampling sites683.9860.3178%Fst0.080p < 0.001
Among individuals within sampling sites117414.2960.0000%Fis−0.0240.955
Within individuals124460.5003.71492%Fit0.058p < 0.001
Total247958.7824.030100%
Table 4. Analysis of blue tit molecular variance (AMOVA).
Table 4. Analysis of blue tit molecular variance (AMOVA).
Source of VariationdfSum of SquaresVariance ComponentsPercentage of VariationF-StatisticsValuep-Value
Among sampling sites458.6670.3188%Fst0.080p < 0.001
Among individuals within sampling sites87327.3330.1043%Fis0.0280.074
Within individuals92327.0003.55489%Fit0.106p < 0.001
Total183713.0003.976100%
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Bisikirskienė, L.; Griciuvienė, L.; Aleksandravičienė, A.; Brazaitytė, G.; Paulauskas, A.; Brazaitis, G. The Genetic Response of Forest Birds to Urbanization: Variability in the Populations of Great and Blue Tits. Forests 2024, 15, 1445. https://doi.org/10.3390/f15081445

AMA Style

Bisikirskienė L, Griciuvienė L, Aleksandravičienė A, Brazaitytė G, Paulauskas A, Brazaitis G. The Genetic Response of Forest Birds to Urbanization: Variability in the Populations of Great and Blue Tits. Forests. 2024; 15(8):1445. https://doi.org/10.3390/f15081445

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Bisikirskienė, Loreta, Loreta Griciuvienė, Asta Aleksandravičienė, Gailenė Brazaitytė, Algimantas Paulauskas, and Gediminas Brazaitis. 2024. "The Genetic Response of Forest Birds to Urbanization: Variability in the Populations of Great and Blue Tits" Forests 15, no. 8: 1445. https://doi.org/10.3390/f15081445

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