Anthropogenic habitat modification is linked with variation in dispersal dynamics, social kinship, and genetic structure in a cooperative breeder

Anthropogenic habitat modification can reshape dispersal dynamics, social organisation, and ultimately the genetic structure of populations. In cooperatively breeding species, where philopatry and helping are common, dispersal events are rare but critical for maintaining gene flow and reducing inbreeding. Using microsatellite data (N = 211 individuals) and long-term (1985 - 2023) social kinship data from the cooperatively breeding Arabian babblers (Argya squamiceps) living in natural and modified habitats in the hyper-arid Arava Valley (Israel), we examined how habitat modification influences the social and genetic structure. We combined spatial autocorrelation analyses, within-group relatedness estimates, and social kinship to assess patterns of dispersal, connectivity, and kinship across habitats. We found high gene flow across habitats and only weak differentiation among subpopulations, consistent with recent habitat changes that do not produce strong divergence. However, subtle but significant differences emerged between natural and modified habitats. Groups inhabiting modified areas showed shorter dispersal distances and lower within-group relatedness, suggesting higher turnover and increased immigration of unrelated individuals. In contrast, groups in natural habitats maintained stronger kin-based cohesion and broader positive spatial genetic structure. Our results indicate that habitat modification does not reduce connectivity in this system but reshapes the balance between philopatry and dispersal, favouring earlier dispersal and reduced kinship within groups. Modified habitats attract and concentrate individuals from multiple origins, while natural habitats function as demographic sources. These findings corroborate previous behavioural evidence of an accelerated pace of life in modified habitats and demonstrate that such life-history adjustments are detectable in the genetic and social structure. More broadly, our study highlights how anthropogenic changes in arid ecosystems can leave rapid genetic signatures in cooperative breeders, underscoring the importance of integrating behavioural, demographic, and genetic perspectives to understand population responses to environmental change.

Cooperative breeding is a social system in which individuals assist in raising the offspring of other group members (Koenig et al. 1992, Koenig and Dickinson 2004, Kappeler et al. 2019, Ben Mocha et al. 2023). The social structure of cooperative breeders varies widely among species, ranging from groups composed of closely related individuals with high reproductive skew to more complex systems with multiple breeders and weaker kin associations (Ben Mocha et al. 2023). In many cooperative breeders, strong kin structure arises from delayed dispersal, whereby juveniles remain in their natal groups and contribute as helpers in raising subsequent broods (Koenig et al. 1992, Ekman et al. 2001). Nevertheless, dispersal events do occur and can vary considerably in frequency, distance, and sex bias among species and populations (Brooker and Brooker 2002, Beck et al. 2008, Ridley 2012, Nelson-Flower et al. 2012, Ostreiher et al. 2025). Because dispersal determines patterns of relatedness among individuals and groups, the resulting genetic structure can provide valuable insights into dispersal dynamics and social organization in cooperatively breeding species. Indeed, many studies have shown that fine-scale spatial genetic structure in cooperative breeders reflects the restricted dispersal and high philopatry typical of kin-structured social systems (Woxvold et al. 2006, Haas et al. 2010, Nelson-Flower et al. 2012, Leedale et al. 2018, Leon et al. 2022, Rojas Ripari et al. 2022). In several species, sex-biased dispersal further shapes genetic structure, most commonly through female-biased dispersal, which reduces inbreeding within socially structured populations (Woxvold et al. 2006, Ribeiro et al. 2012, Nelson-Flower et al. 2012, Leedale et al. 2018, Leon et al. 2022). Natural dispersal patterns may be altered by anthropogenic habitat modification, which can drastically change ecological conditions through shifts in resource availability (e.g., food, water, or shelter) or by modifying mortality sources and survival rates (Mumme et al. 2000, Sillero et al. 2019, Cereghetti et al. 2019, Lewin et al. 2021, Catto et al. 2021, Oswald et al. 2024, 2025). Such changes may influence individual movement decisions and the success of dispersal, thereby reshaping patterns of connectivity among groups. Because dispersal plays a central role in determining patterns of relatedness and gene flow, environmental modification has the potential to alter population structure and the kin composition of social groups (Beck et al. 2008, Haas et al. 2010, Leon et al. 2022). For example, dispersal may become less successful in fragmented landscapes where survival during movement is reduced, potentially favouring reduced dispersal and leading to population isolation, decreased gene flow, and loss of genetic diversity (Haas et al. 2010, Han et al. 2019). Conversely, modified habitats may also increase resource availability or breeding opportunities, potentially attracting dispersers and reshaping patterns of social and genetic structure within populations. Despite the potential for habitat modification to alter dispersal and population connectivity, relatively few studies have examined how anthropogenic environmental change influences genetic structure in cooperatively breeding species. Existing work suggests that habitat modification can affect patterns of relatedness and gene flow among groups, but the underlying behavioural and demographic mechanisms remain poorly understood. For example, in the facultatively cooperative brown-headed nuthatch ( Sitta pusilla ), populations inhabiting fragmented landscapes showed reduced allelic richness and lower gene flow among habitat patches (Haas et al. 2010). Similarly, studies of the obligately cooperative white-winged chough ( Corcorax melanoramphos ) found lower intergroup relatedness in modified habitats compared with natural habitats, potentially associated with increased dispersal driven by higher mortality (Beck et al. 2008, Leon et al. 2022). Moreover, female choughs exhibited shifts in dispersal behaviour between drought and wet years, suggesting that environmental conditions can shape sex-specific dispersal strategies (Beck et al. 2008, Leon et al. 2022). However, few studies have explicitly linked variation in genetic structure with changes in social behaviour, dispersal dynamics, and life-history traits in response to anthropogenic habitat modification. This gap is particularly pronounced in arid ecosystems, where environmental conditions are extreme and human-modified habitats may substantially alter resource availability and climatic buffering, potentially reshaping both social organization and dispersal dynamics (Lewin et al. 2024). Here, we address this gap by examining the population genetic structure and social organization of the cooperatively breeding Arabian babbler ( Argya squamiceps ). This species has been extensively studied through long-term behavioural observations, which have documented patterns of dispersal, dominance acquisition, and social organization, but these patterns have not yet been tested using genetic or pedigree-based analyses, nor have their consequences for population structure and kin composition within groups been evaluated (Zahavi 1990, Ridley 2012, Ostreiher et al. 2022, 2025). Arabian babblers inhabit both natural desert habitats and nearby human-modified areas, which provide increased and more predictable resource availability and support higher group densities. Previous work has shown that individuals living in modified habitats exhibit a faster pace of life, characterized by earlier dispersal and dominance acquisition, reduced survival, and higher reproductive success (Alamán et al. 2024). Building on these findings, we tested how habitat modification influences patterns of dispersal, relatedness, and population genetic structure. Specifically, we predicted that (i) gene flow will be maintained between natural and modified habitats, as modified areas may attract individuals through increased breeding opportunities resulting from higher mortality and greater group densities; (ii) the higher density of groups in modified habitats will promote shorter dispersal distances and stronger fine-scale population structure compared with natural habitats; (iii) increased dispersal within modified habitats will lead to lower relatedness among group members compared with groups in natural habitats; and (iv) the faster turnover of breeding positions in modified habitats will disproportionately affect male dispersal, as Arabian babblers exhibit female-biased dispersal (Ridley 2012, Ostreiher et al. 2022, 2025), leading males from modified habitats to disperse more frequently than males from natural habitats. Study site and study species The Shezaf Nature Reserve (52.5 km²) is in the hyper-arid Arava Valley (30°43′N, 35°15′E). The region is characterised by extreme desert conditions, including intense solar radiation, high mean annual temperatures (> 23°C, with summer daily averages exceeding 38°C), and very limited rainfall averaging only 35 mm per year, falling between October and May (Anava et al. 2000, Goldreich and Karni 2001, Ginat et al. 2011). Despite the harsh conditions, a mosaic of microhabitats such as ephemeral riverbeds, springs, and patches of Acacia woodland supports a diverse community of plants and animals. In recent decades, however, road building, agricultural expansion, and urban development have intensified, reshaping water availability and altering natural vegetation and fauna distributions (Lewin et al. 2021, 2024). Arabian babblers ( Argya squamiceps ; hereafter babblers) have been continuously monitored at the study site since 1971 (Zahavi 1990, Zahavi and Zahavi 1997, Keynan and Ridley 2016, Dragić et al. 2022). These medium-sized (60-80 g) cooperative breeders are endemic to Middle Eastern deserts and live in groups of 2 - 13 adults (Alamán et al. 2024).Groups defend territories year-round and exhibit high reproductive skew, with most offspring (> 90%) produced by the dominant breeding pair (Lundy et al. 1998, Ben Mocha et al. 2025). Dispersal has been suggested to be female-biased, with females dispersing longer distances than males (Ostreiher et al. 2025). Juveniles reach sexual maturity at ~ 12 months, when sex is distinguishable by iris colouration: yellow in males and black in females (Ostreiher 1999, Ridley 2007). Each individual is ringed with a metal and three plastic rings for individual identification. The study population is habituated to human observers, enabling close-range behavioural monitoring without disruption (Dragić et al. 2022). Permits and protocols for this study were granted by the Israel Nature and Parks Authority (permit numbers: 2016/41453, 2018/41848, 2020/42538, 2022/43151). not-yet-known not-yet-known not-yet-known unknown Sample collection We captured 211 Arabian babblers between 2021 and 2023 and noted sex and age group (adult/fledgling) when possible. The habituated birds from the Shezaf Nature Reserve were trapped using a baited trap with a gravity door to minimize the effect of trapping on habituation. Birds from non-habituated groups were trapped using mist nets. Birds were inserted into cloth bags immediately after capture to reduce stress while waiting for handling. Nestlings were caught in the nest 8-10 days after hatching, when the tarsus had an appropriate length for ringing, and the risk of a premature fledging was low. From each individual, we collected ~ 70 μL of blood by venepuncture and capillary tube. Blood samples were placed in tubes containing buffer (100 mM Tris, pH 8; 100 mM Na2 EDTA; 10 mM NaCl; 2.0% SDS; White & Densmore III, 1992) and stored at room temperature in the field, then kept at -20 °C until analysis. If a bird was in poor physical condition, it was released without sampling. Microsatellite analysis We amplified nine microsatellites from 211 individuals across 43 social groups during the 2021 - 2023 breeding seasons (February - August). Following the manufacturer’s recommendations, we extracted genomic DNA using a gSYNC TM DNA extraction Kit (Geneaid Biotech). We examined the quantity and quality of genomic DNA using the Nanodrop 2000 Spectrophotometer (Thermo Scientific). We amplified the microsatellites with PCR reactions prepared in 20 μL volumes with HyTaq Ready Mix (Hylabs) and fluorescently labelled primers (Hylabs). We used the same primers as Nelson-Flower et al. (2011), which were designed for Pied babblers ( Turdoides bicolor ). PCR cycles were as follows: 3 min denaturation at 94° C followed by 30 cycles composed of 30 seconds denaturation at 92°C, 45 seconds annealing at 56°C (for primers Ase55, Pgm3, Ppi2, and PmaTGAn42_TB) or at 59°C (for primers Calex08, GCGATA10, GCGATA13, GCGATA15, Pij15ZFS_TB), and 45 seconds extension at 72°C. After 30 cycles, a final 10 min extension at 72°C was included. Three out of nine primers amplified polymorphic loci (GCGATA10, GCGATA13, GCGATA15, Table 1), while the other six amplified monomorphic loci. Loci were run by Hylabs (Rehovot, Israel) and scored using PeakScanner v1.0 (Applied Biosystems). Social kinship We constructed a multigenerational pedigree using long-term observational data collected since 1985 in our study population (Alamán et al. 2026). Parent–offspring relationships were assigned based on direct behavioural observations and long-term monitoring records. We compiled individual identity, sex, and the identities of known mothers and fathers. Individuals with incomplete parental information were retained in the pedigree; however, parental identities were set to missing when only one parent was known to avoid inconsistencies in the pedigree structure. We used the pedigree function implemented in the kinship2 package in R (Sinnwell et al. 2014, R Core Team 2022). Based on this pedigree, we calculated the pairwise kinship matrix using the kinship function. The kinship coefficient (k) represents the probability that two alleles randomly drawn (one from each individual) are identical by descent. Values range from 0 (unrelated individuals) to 0.5 (genetically identical individuals, e.g., clones or self-relatedness). Expected values for common relationships are 0.25 for parent–offspring and full siblings (first-order kinship) and 0.125 for half-siblings, grandparent–grandchild (second-order kinship). To ensure robust life-history information, subsequent analyses were restricted to individuals with known parental identities. Habitat classification We classified the habitat of the groups as described in Alamán et al. (2024). Briefly, we categorized group habitats (natural or modified) based on their territory characteristics defined by remote sensing imagery. For the groups lacking habitat classification, we used an approximation of our methodology: we defined a 200 m buffer around the modified habitats and another 700 m from the capture location. If the two buffers overlapped, the group was defined as modified; if not, it was natural. We used known groups with habitat classification for validating this method and compared the results with the methodology used in Alamán et al. (2024). To analyse the potential consequences of habitat modification on gene flow, we divided the samples into two geographical subpopulations (North and South) based on spatial proximity and the presence of potential dispersal barriers (i.e., modified habitats, Fig. 1). The northern subpopulation included groups inhabiting the modified habitats around the villages of Hazeva and Idan, as well as groups from the natural habitats of the Sahak, Gidron, and Idan wadi basins, which either cross or drain into modified areas. The southern subpopulation comprised groups from the modified habitats around the village of Ein Yahav and groups from the natural habitats of the Shezaf and Nekarot wadi basins. Statistical analysis All genetic analyses were performed using GenAlEx 6.5 (Peakall and Smouse 2006). The social kinship analyses were performed in R (R Core Team 2022). The ‘ lme4 ’ v1.1.27 package (Bates et al. 2015) was used to construct Generalized Linear Mixed Models (GLMMs). QGIS v3.22 was used for habitat classification and map creation (QGIS Development Team 2022). The rest of the figures were created with the R package ggplot2 ‘v3.4.4’ (R Core Team 2022). not-yet-known not-yet-known not-yet-known unknown Genetic variation We calculated the mean observed heterozygosity (H0), mean expected heterozygosity (HS) of the subpopulations, and the inbreeding coefficient within individuals (FIS), the subpopulation level fixation index (FST) relative to the total, and the individual inbreeding coefficient (FIT) relative to the rest of the population, using data from 211 individuals. We also calculated subpopulation FST by performing an analysis of molecular variance (AMOVA, Excoffier et al. 1992, Peakall et al. 1995). In addition to these analyses, we performed a parallel comparison of the same statistics between modified habitats and natural habitats for the whole population. For these analyses, we used the locations of individuals at the time of capture. We selected 50 randomly sampled individuals to study deviations from the Hardy-Weinberg equilibrium, avoiding the effect An error in the conversion from LaTeX to XML has occurred here. s of the familiar group structure. Spatial autocorrelation We explored the genetic structure of the population at a fine scale (from within-group relatedness to a maximum of 20 km distance) by a spatial autocorrelation analysis. We calculated pairwise genetic distances among individuals to explore the population’s genetic structure using multilocus spatial autocorrelation analyses (Smouse and Peakall 1999). These analyses are based on the genetic correlation coefficient (r spat ), which ranges from -1 to 1 (zero indicates no genetic correlation). We set a random permutation of the dataset (999 times) to test the significance against the null hypothesis of no correlation (Smouse and Peakall 1999, Peakall et al. 2003). We generated geographic and genetic distance matrices for all of the adults that were sampled in our study site. We also generated separate matrices by sex and habitat to explore the potential effects of sex- or habitat-specific traits on the spatial structure. The geographic distance was based on the location of the individual at capture. We then analysed the spatial autocorrelation using the Multiple Pops function in GenAlEx (Smouse and Peakall 1999, Peakall et al. 2003). To provide the distance classes with a biological significance, we explored the distance to the nearest neighbouring groups and the pairwise distance between groups (Beck et al. 2008). We first selected a distance class of 0 m to explore the within-group relatedness (WG). We then selected three distance classes based on the group’s nearest neighbours (NN): most of the groups (3 rd Quantile) had their NN under 1200 m (NN 1 ), the average distance between a group and its NN was 2800 m (NN 2 ), and the furthest distance separating a group and its NN was 4600 (NN 3 ). Finally, we selected three distance classes based on the global pairwise distance between groups (PD). The average pairwise distance between groups was 7200 m (PD 1 ). Pairwise distance for most of the groups (3 rd Quantile) was under 9900 m (PD 2 ), and the maximum distance between groups was 19700 m (PD 3 ). Within-group relatedness and social kinship We explored the within-group relatedness by habitat. We used estimated individual pairwise genetic relatedness using the Lynch & Ritland estimator (R, Lynch and Ritland 1999), and then we calculated the mean relatedness (r wit ) using the Pops Mean function in GenAlEx (Peakall and Smouse 2006) for each group. The 95 % confidence interval around the mean was estimated by bootstrapping. We set a random permutation of the dataset (999 times) to test the significance against the null hypothesis of no relatedness. For this analysis, we included only groups in which more than two-thirds of individuals were sampled and more than two individuals were sampled. We considered for this analysis the adult individuals who were part of each group on the 1 st of January of the respective sampling period (2021-2023). Finally, we tested for differences in relatedness between individuals of the same groups by comparing the relatedness values of the within-group dyads with a GLMM including the Lynch & Ritland estimator as the response variable and the habitat as the explanatory variable. The identity of the dyad member was included as a random factor. We used the observed pedigree to analyse the social kinship and within-group organisation. We first selected the individuals present at each breeding event between 2016 and 2023, corresponding to the years when the habitat use of the group was previously determined (Alamán et al. 2024). For this study, we considered breeding events as a nest with fledglings. For each breeding event, we calculated the social kinship of each member of the group with the nestlings, excluding the parents (dominant male and female). The social kinship with the nestlings was calculated as the average kinship of the individual with the dominant male and female. We then categorised the kin of the individuals as first-order kin (k ≥ 0.25), second-order kin (0.25 > k ≥ 0.125), and third-order kin (k < 0.125) or unrelated (Leedale et al. 2018b). We constructed binomial GLMMs for each category to address whether the habitat influenced the intra-group organisation. Each model included the habitat as the explanatory variable and the proportion of individuals in the category (measured as the number of individuals in the kinship category with respect to the total number of individuals in the group) as the response variable. The group and year were included as random factors. We repeated the analysis, separating the dataset by sex. not-yet-known not-yet-known not-yet-known unknown Results We present the results in terms of evidence rather than binary significance based on p-values (Muff et al. 2022). Genetic variation The mean observed heterozygosity across all individuals (H O = 0.649 ± 0.022) was slightly lower than the expected heterozygosity (H S = 0.686 ± 0.021; Table 1). When individuals were grouped by habitat type, observed heterozygosity was 0.680 in modified habitats and 0.663 in natural habitats, and the expected heterozygosity was 0.669 and 0.732 in modified and natural habitats, respectively (Table 1). The within-population inbreeding coefficient was positive (F IS = 0.063 ± 0.016), and the overall inbreeding coefficient was F IT = 0.081 ± 0.017. Genetic differentiation between habitats was low (F ST = 0.018 ± 0.001), and AMOVA indicated a statistically strong but weak structure (Φ PT = 0.031, p = 0.001; Table 1) and a high effective number of migrants per generation (Nm = 13.48). When individuals were grouped into geographical subpopulations (North and South subpopulations), observed heterozygosity varied from 0.618 in the north to 0.680 in the south, while expected heterozygosity ranged from 0.639 to 0.732 (Table 1). The inbreeding coefficient within subpopulations was F IS = 0.053 ± 0.017, and the overall inbreeding coefficient was F IT = 0.078 ± 0.012. Differentiation among geographical subpopulations was slightly higher than between habitats (F ST = 0.026 ± 0.006), and AMOVA provided strong evidence for a subtle structure (Φ PT = 0.045, p = 0.001; Table 4) and a lower gene flow between regions than between habitats (Nm = 10.27). Hardy–Weinberg equilibrium tests (performed on a random subset of 50 individuals to minimize family-structure effects) indicated that two loci (GCGATA10 and GCGATA15) conformed to expectations, whereas another (GCGATA13) did not. Spatial autocorrelation The spatial autocorrelation analyses revealed a positive genetic structure for the population (Table 2, Fig. 2a). Within-group relatedness was high (average [bootstrap confidence interval]: 0.253 [0.201, 0.311]) and the positive spatial structure was extended to the NN distance (NN 1 : 0.092 [0.036, 0.138], Table 2, Fig. 2a) and reached average values > 0 to the furthest neighbours (NN 2 : 0.092 [0.036, 0.138]; Fig. 2a). When analysed by habitats, within-group relatedness showed similar and high values for both habitats (WG: natural: 0.221 [0.160, 0.271]; modified: 0.277 [0.389, 0.148]; Table 2, Fig. 2b). in modified habitats the relatedness was higher for nearest neighbouring groups than for natural habitats (NN 1 : natural: 0.095 [0.029, 0.166]; modified: 0.207 [0.301, 0.112]; Table 2, Fig. 2b). The positive spatial genetic structure was absent for all distance classes beyond the nearest neighbouring groups (Table 2, Fig. 2b). The analysis of the spatial autocorrelation by sexes showed different patterns for males and females. In females, the within-group relatedness was slightly higher than in males (WG: females: 0.297 [0.238, 0.361]; males: 0.289 [0.208, 0.375]; Table 2, Fig. 2c). However, the spatial structure of the males was maintained for the nearest neighbours whereas in females, the relatedness was not distinct to 0 (NN 1 : females: 0.026 [-0.076, 0.162]; males: 0.161 [0.081, 0.239]; Table 2, Fig. 2c). In both sexes, the spatial structure was not maintained beyond the NN 1 (Table 2, Fig. 2c). Within-group relatedness and social kinship The analysis of within-group relatedness (r wit ) among years showed consistent values above 0 in natural habitats but not in modified habitats (Fig.3): 10 out of 15 groups (66.6 %) showed significant values of relatedness different than 0 in natural habitats, whereas 1 out of 7 (14.2% ) in modified habitats. We found strong evidence that individuals in modified habitats were less related to other group members than individuals from natural habitats (average ± SE; natural= 0.207 ± 0.023, modified = 0.010 ± 0.041, p = 0.03). The analysis of the within-group kinship revealed no evidence of the influence of habitat type on the proportion of helpers of first-order (average ± SE; natural= 42.2 ± 6.8 %, modified = 42.7 ± 4.6 %, p = 0.379) or second-order (natural= 52.8 ± 6.8 %, modified = 41.2 ± 4.5 %, p = 0.109). However, we found moderate evidence that groups in modified habitats had a higher proportion of unrelated or distant kin individuals then in natural habitats (natural= 4.9 ± 3.0 %, modified = 15.9 ± 3.3 %, p = 0.055). When only males were considered, we found no evidence of different proportions between habitats for first-order kinship (natural= 51.1 ± 9.0 %, modified = 45.8 ± 6.4 %, p = 0.548). However, we found very strong evidence of a higher proportion of second-order males (natural= 41.9 ± 9.0 %, modified = 34.1 ± 5.9 %, p = < 0.001) and a lower proportion of unrelated males (natural= 6.8 ± 4.7 %, modified = 20.0 ± 5 %, p = < 0.001) in natural habitats than in modified habitats. For females, no differences were found between habitats for first-order (natural= 44.7 ± 8.8 %, modified = 41.8 ± 6.5 %, p = 0.217), second-order (natural= 42.7 ± 8.7 %, modified = 32.2 ± 6.0 %, p = 0.127), or unrelated (natural= 12.5 ± 5.9 %, modified = 25.9 ± 5.7 %, p = 0.303) individuals’ proportions. not-yet-known not-yet-known not-yet-known unknown Discussion Our results show that habitat modification is linked with variation in dispersal behaviour and social organisation in Arabian babblers without substantially reducing population connectivity. Consistent with our first hypothesis, gene flow was maintained between natural and modified habitats, as indicated by the small genetic differences between habitats and subpopulations. In line with our second hypothesis, individuals in modified habitats exhibited stronger fine-scale spatial genetic structure, with higher relatedness between neighbouring groups. On the group level, as predicted in our third hypothesis, helpers in modified habitats were less related to other group members, and social groups contained a greater proportion of unrelated individuals. These patterns suggest that dispersal is more frequent within modified habitats, resulting in reduced intragroup kinship. Finally, consistent with our fourth hypothesis, habitat modification appeared to be more strongly linked to dispersal patterns in males than in females. Groups in modified habitats contained a higher proportion of unrelated male helpers, whereas a similar pattern was not detected in females. Our findings are consistent with reports from other cooperatively breeding species, where restricted dispersal and philopatry generate fine-scale spatial genetic structure and high relatedness among group members (Woxvold et al. 2006, Beck et al. 2008, Haas et al. 2010, Nelson-Flower et al. 2012, Leedale et al. 2018, Leon et al. 2022, Rojas Ripari et al. 2022). Additionally, we detected clear sex-specific differences in spatial genetic structure. Males showed higher relatedness with males from the nearest neighbouring groups, whereas this was not evident in females. Females dispersing more frequently and over longer distances than males has already been shown in Arabian babblers (Ostreiher et al. 2025). If males disperse, they are more likely to join nearby groups, maintaining stronger spatial genetic structure at short distances, whereas the broader dispersal distances of females dilute spatial genetic structure across the landscape. In addition, the higher proportion of unrelated females compared with males across groups supports the expectation of female-biased dispersal. Such sex-specific dispersal patterns likely contribute to reducing the probability of joining groups containing close relatives, thereby helping to avoid inbreeding (Nelson-Flower et al. 2012). While restricted dispersal and philopatry shape genetic structure within cooperative breeders, our results suggest that habitat modification does not necessarily reduce population connectivity. In contrast to expectations from studies of habitat fragmentation, where reduced dispersal often leads to increased genetic isolation and reduced gene flow (Haas et al. 2010, Han et al. 2019), genetic differentiation between natural and modified habitats in Arabian babblers remained low. Instead, differentiation was stronger among geographical subpopulations than between habitat types. In addition, estimates of migration indicated a higher number of migrants moving between habitat types than among some geographical subpopulations. Together, these patterns suggest that spatial configuration and landscape features (e.g., river basins) likely play a larger role in shaping genetic structure than habitat modification. At the same time, extensive areas of modified habitat separating natural populations may limit gene flow at broader spatial scales. For example, natural habitats located at opposite ends of the study area, such as the northern and southern subpopulations, are separated by continuous modified landscapes that may reduce dispersal between them. Thus, while connectivity appears to be maintained between nearby habitats, habitat modification still contributes to fragmentation among more distant natural populations. The ecological characteristics of modified habitats in arid environments provide a likely explanation for the dispersal patterns and genetic structure observed in our study. Compared with natural desert habitats, modified areas offer greater access to resources such as water, food, and shelter, but these resources are typically distributed in discrete patches rather than continuously across the landscape. Such spatial heterogeneity may shift the cost–benefit balance of group living and dispersal decisions (Shen et al. 2017). In modified habitats, dispersal may generate benefits for both the group and the dispersing individual. For the remaining group members, the departure of an individual can increase per-capita resource availability when resource-rich patches are defended by sufficiently large groups (Shen et al. 2017, Jungwirth et al. 2023, Shah and Rubenstein 2023). For the disperser, the potential benefits arise from increased opportunities to obtain a breeding position. Higher mortality rates and the faster turnover of dominant positions in modified habitats create more frequent breeding vacancies (Alamán et al. 2024). At the same time, the short distances between neighbouring groups increase the likelihood of successful dispersal by decreasing mortality (Brooker and Brooker 2002). In addition, groups with low recruitment rates may be more willing to accept immigrants to maintain an optimal group size. In our study system, modified habitats are highly heterogeneous at the micro-landscape scale, where low-suitability areas (e.g., greenhouses) are interspersed with resource-rich patches such as mango and date plantations (Alamán et al. 2024). Groups inhabiting modified habitats are also smaller and experience higher adult mortality than those in natural habitats (Alamán et al. 2024), conditions that are likely to increase both the incentives for dispersal and the probability that dispersers are accepted into neighbouring groups. These ecological dynamics are consistent with our genetic results, which revealed a greater proportion of unrelated adults within groups and stronger fine-scale spatial genetic structure in modified habitats. This increase in the presence of non-kin may also have important social consequences for group stability. In cooperative breeders, the presence of non-kin can increase within-group conflict and reduce cooperation, potentially leading to greater group instability and higher rates of group turnover (Hannon et al. 1985, Lazaro-Perea et al. 2000). Such dynamics could further contribute to indirect fitness effects and lead to the demographic differences between natural and modified habitats (Blumstein et al. 2023). Together, these findings suggest that behavioural responses to altered ecological conditions in modified habitats can rapidly translate into detectable changes in dispersal dynamics, group composition, and the resulting genetic structure of populations. Despite the variation in dispersal dynamics observed in modified habitats, differences in genetic diversity between modified and natural habitats were relatively small. Both observed and expected heterozygosity were slightly lower in modified habitats than in natural habitats, suggesting that populations partly rely on immigration from surrounding natural areas to maintain genetic diversity. Despite the apparent increase in dispersal within modified habitats, this movement does not appear to translate into higher levels of genetic diversity. One possible explanation is that, although individuals disperse into modified habitats, immigrants may not always successfully acquire breeding positions. Further work using higher-resolution genomic tools, such as genome-wide SNP genotyping (e.g., RAD-seq or ddRAD-seq) combined with landscape genomic analyses, and explicitly linking individual genotypes with social phenotypes and fitness outcomes will be required to better understand the evolutionary consequences of dispersal and habitat modification in this system. Our results show that habitat modification in arid environments can be related to dispersal behaviour, social organisation, and patterns of relatedness in cooperative breeders without substantially reducing population connectivity. In Arabian babblers, modified habitats appear to promote more frequent dispersal and a higher turnover of group members, resulting in groups that contain a greater proportion of unrelated individuals while maintaining gene flow across the landscape. These findings suggest that behavioural and demographic adjustments to altered ecological conditions can rapidly leave detectable signatures in the genetic structure of populations. More broadly, our study highlights how integrating behavioural, demographic, and genetic perspectives can provide a deeper understanding of how social species respond to anthropogenic environmental change. 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Number of samples (Nsamples), number of alleles (Nalleles), number of unique alleles (in parentheses), observed heterozygosity (HO), expected heterozygosity (HS), inbreeding coefficient (FIS), and fixation index (FST) for each subpopulation, habitat, and for the combined dataset (211 babblers). | North | 83 | 21 (5) | 0.618 ± 0.022 | 0.639 ± 0.005 | 0.033 ± 0.027 | - | | South | 128 | 20 (3) | 0.680 ± 0.030 | 0.732 ± 0.007 | 0.071 ± 0.046 | | | Total | 211 | 24 | 0.649 ± 0.022 | 0.686 ± 0.021 | 0.053 ± 0.017 | 0.026 ± 0.006 | | Habitat | N samples | N alleles | H O | H S | F IS | F ST | | Modified | 108 | 21 (3) | 0.633 ± 0.027 | 0.669 ± 0.013 | 0.055 ± 0.023 | - | | Natural | 103 | 21 (3) | 0.680 ± 0.017 | 0.732 ± 0.014 | 0.071 ± 0.027 | | | Total | 211 | 24 | 0.656 ± 0.018 | 0.701 ± 0.017 | 0.633 ± 0.027 | 0.018 ± 0.001 | Table 2. Outcomes of spatial genetic autocorrelation analysis. Separate results are provided for all individuals, modified habitats, natural habitats, females, and males. The correlation r spat and the upper U r and lower L r 95% error bounds about r as determined by bootstrap resampling are shown across seven distance classes, with zero representing within-group comparisons. The number of pairwise comparisons n, upper and lower bounds for the 95% confidence interval about the null hypothesis (H 0 ) of no spatial structure ( r = 0). | 0 | 1200 | 2800 | 4100 | 7200 | 9000 | 19700 | | | All individuals | ||||||| | n | 187 | 262 | 756 | 939 | 1883 | 1408 | 1235 | | r spat [CI 95%] | 0.25 [0.2,0.31] | 0.09 [0.03,0.13] | 0.02 [0,0.04] | 0 [-0.01,0.03] | -0.01 [-0.03,0] | -0.01 [-0.04,0] | -0.02 [-0.04,-0.01] | | H 0 | [-0.04,0.05] | [-0.03,0.03] | [-0.02,0.02] | [-0.02,0.01] | [-0.01,0.01] | [-0.01,0.01] | [-0.01,0.01] | | Habitat: Modified | ||||||| | n | 39 | 59 | 204 | 130 | 242 | 145 | 171 | | r spat [CI 95%] | 0.27 [0.14,0.38] | 0.2 [0.11,0.3] | 0.02 [-0.02,0.08] | -0.07 [-0.13,-0.01] | -0.07 [-0.1,-0.02] | -0.03 [-0.08,0.01] | 0.02 [-0.02,0.07] | | H 0 | [-0.09,0.12] | [-0.08,0.093] | [-0.04,0.03] | [-0.05,0.05] | [-0.03,0.03] | [-0.05,0.04] | [-0.04,0.03] | | Habitat: Natural | ||||||| | n | 148 | 155 | 336 | 375 | 446 | 520 | 505 | | r spat [CI 95%] | 0.22 [0.15,0.27] | 0.09 [0.02,0.16] | 0 [-0.05,0.03] | -0.04 [-0.08,0] | 0.01 [-0.01,0.04] | -0.02 [-0.05,0] | -0.04 [-0.07,-0.01] | | H 0 | [-0.04,0.06] | [-0.05,0.05] | [-0.03,0.03] | [-0.03,0.02] | [-0.02,0.02] | [-0.02,0.02] | [-0.02,0.01] | | Sex: Females | ||||||| | n | 32 | 44 | 163 | 197 | 406 | 273 | 211 | | r spat [CI 95%] | 0.29 [0.23,0.36] | 0.02 [-0.07,0.16] | 0.01 [-0.03,0.06] | 0 [-0.06,0.03] | -0.01 [-0.04,0.02] | 0 [-0.05,0.02] | -0.01 [-0.05,0.02] | | H 0 | [-0.1,0.13] | [-0.1,0.09] | [-0.04,0.04] | [-0.04,0.04] | [-0.03,0.02] | [-0.03,0.03] | [-0.03,0.03] | | Sex: Males | ||||||| | n | 73 | 93 | 184 | 282 | 531 | 433 | 420 | | r spat [CI 95%] | 0.28 [0.2,0.37] | 0.16 [0.08,0.23] | 0 [-0.05,0.05] | 0.01 [-0.02,0.05] | -0.01 [-0.04,0.01] | -0.03 [-0.06,0] | -0.03 [-0.06,0] | | H 0 | [-0.07,0.08] | [-0.06,0.07] | [-0.04,0.04] | [-0.03,0.03] | [-0.02,0.02] | [-0.03,0.02] | [-0.02,0.02] | not-yet-known not-yet-known not-yet-known unknown Figures not-yet-known not-yet-known not-yet-known unknown Figure 1. Map of Arabian babblers sampling locations. Each pie chart shows the location of a social group. The colour of the pie chart represents natural (orange) or modified (blue) habitat, and the percentage of males (light) vs. females (dark) sampled. Circle size represents the number of individuals sampled in the group (smaller = fewer individuals). The North (dashed line) and South (full line) subpopulations are encircled. Blue polygons cover modified habitats in the study region (farms and crops), while the lighter areas enclosing them represent a 200 m buffer around the modified habitats. The rest of the area is considered natural habitats. The white line crossing from north to south is a major highway. The dotted area is the Shezaf Nature Reserve’s protected land. Figure 2. Spatial genetic autocorrelation ( r spat ) across (a) the entire population, (b) between sexes (purple = females, green = males), and (c) between habitats (natural = orange, modified = blue) . Dots represent the mean value of the r spat coefficient of each distance class. Error bars represent the 95 % confidence interval of the mean. Shaded areas represent the upper and lower bounds for the 95% confidence interval about the null hypothesis of no spatial structure. Distance classes (x axis) were defined as within-group (WG = 0 m), very near neighbours (NN 1 = 1200 m), medium-distanced neighbours (NN 2 = 2800 m), far neighbours (NN F = 4100 m), and distant classes covering the average, Q 3, and furthest pairwise distance (PD 1 = 7200 m, PD 2 = 9900 m, and PD 3 = 19700 m respectively). Figure 3. a) Within-group relatedness (R) in natural (orange) and modified (blue) habitats. Dots represent the mean within-group relatedness. Group names and year are on the x-axis. Error bars represent the 95 % confidence intervals around the mean. Rectangles represent the top and bottom values of the bootstrap around the null hypothesis of no relatedness. The asterisks (*) mark the groups with relatedness higher than 0. b) Boxplot showing the Lynch & Ritland relatedness estimator (R) between within-group individual dyads from natural and modified habitats. Dots represent the Lynch & Ritland relatedness of dyads. c) Proportion of individuals by kin classes (first-order = k ≥ 0.25, second-order = 0.125 ≤ k < 0.25, third-order relatives or unrelated = k < 0.125 ) in groups from natural and modified habitats when: all the helpers are considered (left); only males are included (centre); and only females are included (right). In b a c) asterisks represent evidence found in differences between habitats (p ≤ 0.05). In all graphs, natural habitats are shown in orange and modified habitats in blue. Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 145views 91downloads Citations Download citation Alejandro Alamán, Devorah Matas, Rachel Ben-Shlomo, et al. Anthropogenic habitat modification is linked with variation in dispersal dynamics, social kinship, and genetic structure in a cooperative breeder. Authorea. 25 March 2026. DOI: https://doi.org/10.22541/au.177445479.90652982/v1 DOI: https://doi.org/10.22541/au.177445479.90652982/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.