Genetic structure and geneflow of Crowned Bullfrogs (Hoplobatrachus occipitalis) across ecological zones in Southwestern Nigeria

Genetic structure and geneflow of Crowned Bullfrogs (Hoplobatrachus occipitalis) across ecological zones in Southwestern Nigeria | 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 Genetic structure and geneflow of Crowned Bullfrogs (Hoplobatrachus occipitalis) across ecological zones in Southwestern Nigeria Oluwakayode Michael Coker, Isaac Overcast, Rayna C. Bell This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8663849/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 8 You are reading this latest preprint version Abstract Widespread amphibians are often assumed to be demographically resilient, yet increasing habitat modification and intensive harvesting may erode genetic connectivity. In Nigeria, edible frogs such as the crowned bullfrog ( Hoplobatrachus occipitalis ) are subject to intense and largely unregulated exploitation, despite limited information on population demography, genetic diversity, or population connectivity. Here, we combine mitochondrial and genomic data to evaluate patterns of genetic structure and gene flow in H. occipitalis across Southwestern Nigeria and within a broader African biogeographic framework. Mitochondrial haplotype analyses revealed a dominant, widely distributed haplotype shared across West, Central, and East Africa, consistent with a recent late-Quaternary expansion and weak phylogeographic structure. Analyses based on genome-wide nuclear SNP data showed weak but detectable habitat-associated structuring among savanna, rainforest, and mangrove populations. Nigerian populations exhibited moderate and relatively homogeneous nucleotide diversity (π = 0.0006–0.0015). Pairwise genetic differentiation was low overall, with the highest differentiation observed between Derived Savanna and Guinea Savanna populations (FST = 0.021). Effective migration surface analyses identified localized reductions in gene flow, particularly near urban and coastal centers, indicating that anthropogenic modification may constrain connectivity at fine spatial scales. These results demonstrate that H. occipitalis remains genetically cohesive at regional scales, yet locally vulnerable to habitat fragmentation, urbanization, and exploitation. These findings highlight the importance of maintaining breeding habitat connectivity and regulating harvest in rapidly developing landscapes to preserve genetic diversity in widespread amphibians. Conservation genomics Population connectivity Gene flow Genetic structure Landscape genetics Hoplobatrachus occipitalis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Globally, at least one third of amphibians are threatened with extinction (Stuart 2008 ) and overexploitation is one of the major causes of this decline (Gibbons et al. 2000 ; Halliday 2008 ). Large frog species belonging to the genera Conraua , Hoplobatrachus , Lithobates , and Fejervarya are extensively collected for human consumption, with millions of individuals traded annually across Asia, Africa, and South America (Stuart et al. 2004 ). In China and Southeast Asia, bullfrogs and other species are exported in high volumes for both domestic markets and international trade (Carpenter et al. 2014 ). Likewise, in parts of West Africa intensive harvesting of frogs has been documented in both rural and urban areas (Mohneke et al. 2009 ; Rödel et al. 2021 ; Keita et al. 2022 ). In Nigeria, several tons of edible frogs (mostly Hoplobatrachus sp and Ptychadena sp ) are sourced from the wild to meet local and international trade demands (Onadeko et al. 2011 ; Coker and Isong, 2019 ; Rödel et al. 2021 ). Local collectors report traveling greater distances to harvest target species, suggesting that populations are declining (Rödel et al. 2021 ). Over time this high level of extraction may lead to regional losses of anuran biodiversity with consequences that cascade across both aquatic and terrestrial ecosystems (Mohneke et al. 2009 ). Despite intense harvesting pressure of edible frogs in Nigeria, there is very little information on the population demography or population connectivity (e.g., structure, gene flow, diversity) of the targeted species. Emel and Storfer ( 2012 ), affirmed a need to understand the spatial distribution of genetic diversity in amphibian populations to aid conservation efforts, but the few studies that estimate amphibian population genetic structure and gene flow on a small spatial scale are mainly from temperate North America and Europe. Conservation genetic data are still under-represented for African amphibians, and collecting genetic data for these taxa can provide valuable information for conservation more quickly than longitudinal demographic studies (Storfer et al. 2009 ). In particular, landscape-level estimates of gene flow that reveal how landscape features impact population connectivity can be a powerful tool for informing conservation action to re-connect fragmented populations (Baguette et al. 2013 ). This approach has been used to inform management strategies to halt or slow down amphibian decline around the world. For instance, Anoop and George ( 2023 ) used both mitochondrial and nuclear gene sequences to examine the population genetic and demographic structure of the Karaavali Skittering Frog (Dicroglossidae: Phrynoderma karaavali ) in India to quantify how populations responded to a ban on exploitation of the species. The Crowned Bullfrog, Hoplobatrachus occipitalis (Dicroglossidae), is a widespread and adaptable species found across much of sub-Saharan Africa (Hirschfeld and Rödel, 2011 ). Previous studies have identified tetraploid populations in Liberia (Bogart and Tandy 1976, 1981) and cryptic genetic diversity in Mauritania (Gonçalves and Brito, 2019 ), but the taxonomy remains unchanged and Crowned Bullfrogs are presently considered a single, widespread species. Across their range, H. occipitalis are associated with seasonal pools, floodplains and irrigated crops (e.g., rice; Lea et al. 2003 ), and can tolerate disturbed sites including farmlands and urban edges (Efenakpo et al. 2025 ). Reproduction is associated with the onset of the rainy season during which populations exhibit explosive breeding behavior (Anoop and George 2023 ) and breed in temporary water bodies. The adaptability of H. occipitalis to different vegetation zones and breeding habitats across its vast distribution suggest it is resilient, though local population pressures may vary significantly. In Nigeria, although populations of H. occipitalis appear robust across a range of ecological zones from lowland humid forests to arid savannas, high levels of extraction for food and traditional medicine may pose a risk to the longer-term persistence of the species. Evidence from market/trade studies indicates intensive collection in northern and central Nigeria with subsequent shipping to large southern urban markets in Ibadan and Lagos (Efenakpo et al. 2015 ; Aminu and Anele 2024 ). Therefore, harvesting pressure is predicted to be high in: (a) northern/central states which are in turn located within the Guinea/Sudan Savanna, (b) rice-growing Middle Belt landscapes with abundant seasonal breeding sites; and (c) southwestern city catchments that drive demand. In the present study we quantify genetic structure, genetic diversity, and gene flow among populations of H. occipitalis across a range of habitat types in Southwestern Nigeria. First, we place the Nigerian samples into the broader biogeographic context of H. occipitalis by combining our data with the continental sampling of Gonçalves and Brito ( 2019 ). Second, we test the hypothesis that the underlying genetic structure of this species in Southwest Nigeria is stratified with respect to the dominant habitats in this region, which range from dry savanna habitats in the north to tropical rainforest and mangrove forest in the south. Third, we test the hypothesis that sites with increased anthropogenic pressure exhibit lower genetic diversity and reduced gene flow with neighboring regions. Collectively these results provide important insights as to the historical demography of this economically significant species and identify populations that may require conservation management efforts. Materials and Methods Sampling Design We sampled four vegetation zones (Guinea Savanna, Derived Savanna, Rainforest and Mangrove Forest) in Southwestern Nigeria (Fig. 1 ) between May and November 2022. Frogs were captured with hand nets and identified using a field guide (Channing and Rödel 2019 ). Toe clips were taken from ten individuals per location and preserved in 70% ethanol in the field. Ethical approval and clearance was obtained from the University of Ibadan Animal Care and Use Research Ethics Committee (ACUREC). DNA Extraction and Mitochondrial DNA (mtDNA) Dataset Genomic DNA was extracted using the Zymo Research Quick-DNA Miniprep Plus Kit. Extracted DNA samples were quantified using Nanodrop Spectrophotometer. We amplified the 16S mitochondrial gene using polymerase chain reaction (PCR) with primers 16SA and 16SB (Palumbi et al. 1991 ). Each reaction was carried out in a volume of 18µl containing: 1µl template DNA, 0.18µl DreamTaq DNA polymerase, 2.4µl dNTPs, 0.75µl of each primer, 2µl 10X DreamTaq Buffer, and 10.92µl molecular grade water. Amplifications were carried out with initial denaturation for 5 min at 94°C, followed by 35 cycles consisting of 60s denaturation at 94°C, 60s annealing at 48°C, 60s extension at 72°C and a final extension at 72°C for 5 min. PCR products were visualized using gel electrophoresis, purified using ExoSAP-IT (USB Corp., Cleveland, OH), and sequenced using a BigDye Terminator Cycle Sequencing Kit v3.1 (Applied Biosystems, Foster City, CA, USA) on an ABI Automated 3730x1 Genetic Analyzer (Applied Biosystems). Sequences were edited using Geneious Prime 2021.1 ( https://www.geneious.com ) and were deposited in NCBI Genbank (Table S1 ). mtDNA Haplotype Diversity A total of 49 mitochondrial 16S rRNA gene sequences of H. occipitalis were used for haplotype analysis. Seventeen of these sequences were obtained from a representative subset of our samples collected across the different vegetation zones in Nigeria (Mangrove Forest, Rainforest, Derived Savanna, and Guinea Savanna). The remaining 32 sequences were retrieved from the National Center for Biotechnology Information (NCBI) GenBank database to provide a broader geographic context of the species across West, Central, and East Africa (Table S1 ). We aligned the combined dataset using the ClustalW algorithm implemented in MEGA 11 (Tamura et al. 2021 ). Aligned sequences were exported in FASTA format and imported into DnaSP v6 (Rozas et al. 2017 ) to identify the number of unique haplotypes. The haplotype data file was exported in Nexus format for network construction. A haplotype network was constructed in POPART (Leigh et al. 2015 ) using the haplotype data file and a trait file (used to assign individuals to country or vegetation zones), allowing visualization of geographic clustering and haplotype distribution patterns. ddRADSeq Sequencing and Bioinformatic Assembly (dup: abstract ?) Double-digest restriction-site associated DNA (ddRADseq; Peterson et al. 2012 ; Streicher et al. 2016 ; Severn-Ellis et al. 2020 ) data were generated for 95 H. occipitalis samples, selecting 8–10 individuals per sampling site to maximize geographic coverage across all focal vegetation zones. We requantified DNA extracts with a Qubit 2.0 Fluorometer (ThermoFisher Scientific) to ensure DNA concentrations greater than 2ng/uL. Double-digest RADSeq libraries were prepared by Tangled Bank Conservation, LLC (Asheville, NC) following the 3RAD protocol of Bayona-Vasquez et al. (2019) using restriction enzymes BamHI and ClaI . Samples were pooled and sent to Azenta Life Sciences for paired-end 150-bp sequencing on an Illumina NovaSeq X Plus system. Raw Illumina data was demultiplexed to samples by the sequencing facility, and resulting sample fastq files were evaluated for quality using fastqc (Andrews 2010 ) to inspect per base sequence quality and adapter contamination levels. All further downstream bioinformatics related to RADSeq data assembly and filtering were performed with ipyrad (Eaton & Overcast, 2020 ), unless otherwise specified. Briefly, demultiplexed reads were subject to an initial round of quality control to remove adapter contamination and reads with too many low quality bases (`max_low_qual_bases` = 5). After an initial run of ipyrad step 2 using the default value for filtering short fragments (`filter_min_trim_len` = 35), we inspected the fragment length distribution with custom python code and determined that there were an excess of short fragments being retained. Because short fragments have reduced information content and also increase assembly runtime, we re-ran step 2 and set the `filter_min_trim_len` parameter to 140bp, to screen out all but the longest, most informative fragments. We used the same conservative value of 0.9 for the `clust_threshold` parameter, for clustering reads both within and across samples (ipyrad steps 3 & 6). We then applied a final set of filters for locus quality using default ipyrad parameters, notably including the default `min_samples_locus` of 4. We generated output files in several common formats for downstream analysis, as well as assembly-level summary statistics to inspect assembly quality. Nuclear Population Structure, Genetic Diversity, and Gene Flow We proceeded with an exploratory analysis to determine the nature and extent of population genetic structure within H. occipitalis within our focal region by applying principal component analysis (Reich et al. 2008 ; McVean 2009 ) to our assembled RADSeq data. To perform this analysis we utilized the 'PCA' module of the ipyrad analysis tools (Eaton and Overcast 2020 ), and imported the ipyrad-generated `snps.hdf5` file, which contains a compact representation of the SNP data. Prior to calculating the PCA we filtered to remove sites with indels and invariant as well as non-biallelic sites, as it is not possible to properly encode these for this analysis. We additionally applied a global minimum coverage filter to retain only sites with data in 60% of the samples or more. Additionally, because PCA can not be calculated in the presence of missing data, we imputed missing values based on observed allele frequencies, which is the default imputation scheme in the ipyrad PCA analysis tool. Finally, to reduce the effect of linkage disequilibrium, we randomly sampled one SNP per locus. We then calculated the principal components and projected values of the first two PCs (which we denote PC0 and PC1, for agreement with ipyrad notation) into a two-dimensional space to visualize genetic relationships among samples, and evaluated the information content of the data by quantifying the percent of variance explained along each PC axis. After determining through PCA analysis that populations were structured primarily by habitat type (see Results) we quantified nuclear genetic diversity within each of the genetic clusters. To this end we created 'population' files for each habitat type which contained sample IDs for all individuals from a given habitat. We then used the vcftools package (Danecek et al. 2011 ) to calculate nucleotide diversity (π; Nei and Li 1979 ) per site, and averaged these values across the total length of sequenced data to obtain π values per base, a standard practice in population genetics. We additionally used vcftools to calculate pairwise Fst values (Weir and Cockerham, 1984 ) among all habitat types, to quantify genetic divergence among populations, using a similar scheme to translate raw values into average Fst values per site. Finally, in order to investigate on a more fine grained spatial scale the impact of human land use and proximity of sites to centers of human population density, we created population files for each sampling site and calculated nucleotide diversity within each sampling site, again averaging over total sequence length to report site-specific nucleotide diversity values per base. To investigate gene flow and genetic connectivity for H. occipitalis across the landscape of southwestern Nigeria we constructed effective migration surfaces using FEEMS (Marcus et al. 2021 ). FEEMS is a fast python version of EEMS (Petkova et al. 2016 ) which implements a stepping stone model of migration among demes and quantifies deviations from a model of strict isolation by distance (IBD). We used GPS coordinates obtained for all samples, visually determined a bounding polygon for our focal region using a freely available online resource provided by BirdTheme.org ( https://www.birdtheme.org/useful/v3tool.html ) , and constructed a dense triangular lattice within this region using the GeoPandas function `delaunay_triangles` (Jordahl et al. 2021 ). We loaded the `snps.hdf5` file from ipyrad and converted it into a numpy matrix of raw genotype values, followed by imputation of missing values using the `SimpleImputer` function of scikit-learn (Pedregosa et al. 2011 ). We used the FEEMS `prepare_graph_inputs` function to pre-process the outer bounding polygon, the triangular grid, and the grid edge matrix for downstream analysis. One of the more important parameters in FEEMS is lambda (λ), which determines the strength of penalization of differences in migration rates among neighboring edges on the lattice. With large values of λ migration rates will be more homogeneous across the landscape, and with small values of λ they will vary over shorter spatial scales. We implemented a cross-validation procedure to select the optimal λ value, as recommended by Marcus et al. ( 2021 ), testing ten log10-uniformly distributed values of λ between 0.001 and 100. We then fit the FEEMS `SpatialGraph` to the input geographic and genetic data using the best value of λ determined by cross-validation to generate our migration surface. Finally, we visualized our best-fit migration surface using the FEEMS `Viz` module utilizing an equidistant conic projection constructed with the python `cartopy` package (Elson et al. 2023 ). Results mtDNA Haplotype Diversity Seventeen haplotypes were detected across the full dataset with three haplotypes unique to Nigeria and one haplotype shared across Nigeria and throughout West, Central, and East Africa (Fig. 2 ). Of the three unique Nigerian haplotypes, two were associated with Swamp Forest habitats and one with Rainforest habitats. The widespread haplotype was present in all four of the habitat types in Nigeria. ddRADSeq Sequencing and Bioinformatic Assembly In this study we generated paired-end RADSeq data for 95 H. occipitalis individuals. We removed one sample (HAB9) with insufficient DNA concentration prior to sequencing, and three samples (HOOC0024, HOOC0039, HOOC0047) which failed in sequencing, resulting in very low numbers of sequenced reads. Of those remaining we recovered an average of 13 million reads per sample (std +/- 2.5 million reads). After an initial quality control step to trim adapters, and filter reads by cutoffs for maximum number of low-quality bases and minimum read length we retained an average of 6.4 million reads per sample (std +/- 2.5 million reads). Clustering reads by our chosen sequence similarity of 0.9 produced an average of 383,685 clusters per sample (std +/- 85,878 clusters), and after applying the default read depth cutoff (6 reads per cluster) we retained mean 156,045 high-depth clusters per sample (std +/- 38,999 clusters). After clustering across samples and applying a final set of filters to retain loci which included at least four samples (a permissive setting which is the ipyrad default), and to remove paralogs and loci which did not pass cutoffs for maximum numbers of SNPs, indels, or shared heterozygous sites, on average each sample contained 105,911 loci (std +/- 28,579) in the final assembly. The final sequence matrix contained 87,815,468bp (70.46% missing sites) and the final SNP matrix contained 934,813 SNPs (62.67% missing sites). Nuclear Population Structure, Genetic Diversity, and Gene Flow We first explored population genetic structure by applying PCA to our ddRADSeq dataset. Our initial input data contained 934,813 SNPs, and our filtering removed 82,165 sites with indels, 122,160 multi-allelic sites, 2,025 subsample invariant sites, and 631,957 sites that did not pass our minimum coverage criteria. Of the remaining 273,784 sites we selected one SNP per RAD locus, ultimately retaining 77,476 unlinked bi-allelic SNPs for this analysis. After calculating the PCA we visualized the results by assigning individuals to the habitat types from which they were sampled. The final PCA plot shows identifiable population structuring by habitat type (Fig. 3 ) with 6.2% of genetic variance explained by PC0 and 4.3% explained by PC1, indicating weak but quantifiable population differentiation among these samples. One individual from the Mangrove Forest habitat (HOOC0074 sampled from Epe) clustered with samples from the sole Guinea Savanna habitat site (Igbeti). Nucleotide diversity was lower for the Guinea Savanna (π = 0.0012) and Mangrove Forest (π = 0.0018) populations, and higher for the Rainforest (π = 0.0020) and Derived Savanna (π = 0.0021) populations (all π values are indicated per base). Fst values among populations were on average quite low (mean = 0.011, std = 0.009), with the highest values between Derived Savanna and Guinea Savanna populations (Fst = 0.021), indicating relatively weak (though still measurable) population structure throughout the sampled region. Looking at nucleotide diversity on a per sampling site basis, we found that Iwo had by far the lowest genetic diversity (π = 0.0006), with Ibadan, Igbeti, Ado, Ikorodu, and Soku having moderate levels of genetic diversity (π = ~0.0012), and Ifetedo and Badagry having the highest levels of genetic diversity (π = ~0.0015; Table 1 ). Table 1 Estimated nucleotide diversity for each sampling locality. Diversity estimates (π; Nei & Li, 1979 ) are reported per site averaged across the total length of sequenced data. Habitat Population Nucleotide Diversity (π) Guinea Savanna Igbeti 0.001181 Derived Savanna Ado 0.001181 Iwo 0.000597 Shoku 0.001222 Rainforest Abeokuta 0.001323 Ibadan 0.001170 Ifetedo 0.001473 Mangrove Forest Badagry 0.001477 Epe 0.001302 Ikorodu 0.001201 Cross-validation analysis showed that our best-fit value of the FEEMS smoothing parameter (λ) was equal to 10. We then ran a full FEEMS analysis with λ = 10 to generate an effective migration surface for H. occipitalis across southwest Nigeria (Fig. 4 ). In general we found elevated migration rates across the landscape, indicating genetic connectivity among our sampling sites that exceeds what would be expected under a model of isolation by distance. The peripheral zone of reduced migration is almost certainly a sampling artifact, reflecting that our sampling does not encompass the complete distribution of the species. Because of this we elect not to over-interpret this area as a barrier to gene flow for this species. Within the region where our samples are concentrated, however, we found one patch of significantly reduced migration on the southern coast between Epe and Ikorodu. This indicates that samples from these two sites are more genetically distinct than their geographic proximity would suggest. Likewise, the FEEMS analysis indicates genetic isolation of H. occipitalis in Ibadan, signified by the reduced gene flow edges connecting it to the surrounding matrix. The reduced migration the west of Ado, south of Abeokuta and south of Shoku may reflect genetic isolation of these populations but these results are more challenging to interpret given our available sampling. Discussion The mtDNA haplotype network for Hoplobatrachus occipitalis is dominated by a single, centrally positioned haplotype that is widely distributed across West, Central, and East Africa and occurs in all four Nigerian vegetation zones. The recovery of this same continental haplotype in Nigerian populations directly supports the interpretation of Gonçalves and Brito ( 2019 ), who proposed a rapid, late Quaternary expansion of H. occipitalis across sub-Saharan Africa. The lack of geographic segregation among major African regions in the network further indicates weak phylogeographic structure and extensive historical connectivity, likely facilitated by the species’ high dispersal capacity and exceptional reproductive output (Duellman and Trueb 1994 ; Vences and Wake 2007 ; Alam et al. 2012 ). Similar continent-wide haplotype sharing has been reported in other savanna-adapted amphibians, including Sclerophrys xeros (Froufe et al. 2009 ). Despite this overall homogeneity, the network also reveals three private haplotypes restricted to Nigeria, two associated with swamp forest habitats and one with rainforest habitat. Their peripheral placement and low frequencies suggest recent local mutations rather than long-term isolation, consistent with expectations under a rapid expansion model where new haplotypes arise at the range margins or in ecologically stable microhabitats (Excoffier et al. 2009 ). While the association of these private haplotypes with forested habitats may hint at fine-scale ecological influences on genetic variation, the shallow divergence observed precludes strong inferences about population structuring based on mitochondrial data alone. As mitochondrial markers reflect only matrilineal history, the apparent panmixia observed here may mask more subtle patterns of restricted gene flow or local adaptation. Integrating nuclear genomic data across the species’ range would therefore be essential to disentangle ongoing gene flow from the historical signal of expansion and to test whether Nigeria’s heterogeneous ecological zones contribute to population differentiation at biparentally inherited loci. Patterns of genome-wide genetic structure in H. occipitalis across southwestern Nigeria indicate weak but detectable habitat-associated differentiation. The PCA of nuDNA SNPs shows broad clustering of individuals by dominant habitat type, separating savanna, rainforest, and mangrove forest populations. However, the small proportion of variance explained by the principal component axes and the low mean pairwise F ST values demonstrate that this structuring does not reflect strong genetic isolation among habitats. Instead, these results suggest that although ecological differences along the north–south gradient influence genetic variation, they do not constitute major barriers to gene flow. This pattern is consistent with the framework proposed by Vences and Wake ( 2007 ), who argued that amphibians with high dispersal ability and generalist ecologies often exhibit shallow genetic structure across heterogeneous environments. The modest differentiation observed between Derived Savanna and Guinea Savanna populations (F ST = 0.021) may reflect subtle ecological or demographic contrasts, including differences in hydroperiod, vegetation structure, and breeding-site permanence. Amphibian genetic structure is frequently shaped by larval habitat characteristics, particularly the predictability and connectivity of breeding water bodies (Beebee 2005 ; Smith and Green 2005 ). In southwestern Nigeria, savanna habitats are typically associated with seasonal and ephemeral pools, whereas rainforest and mangrove zones tend to support more permanent or semi-permanent water bodies, potentially influencing local recruitment dynamics and effective population sizes. The clustering of one mangrove forest individual with Guinea Savanna populations in the PCA likely reflects recent dispersal rather than long-term habitat association. While natural movement facilitated by seasonal flooding, riverine corridors, or landscape connectivity may contribute to such patterns, anthropogenic translocation cannot be excluded. Market and trade studies document intensive collection of H. occipitalis in northern and central Nigeria, with subsequent transport to major southern urban markets such as Ibadan and Lagos (Efenakpo et al. 2015 ; Aminu and Anele 2024 ). Accidental release or escape of transported individuals near coastal or mangrove-associated environments could therefore introduce savanna-derived genotypes into southern populations. Overall, the distribution of genetic variation in H. occipitalis across southwestern Nigeria suggests populations that are broadly resilient and well connected, yet locally impacted by anthropogenic pressures. Overall nucleotide diversity was moderate and relatively homogeneous among sites, consistent with a widespread, ecologically plastic amphibian capable of exploiting a wide range of breeding habitats across savanna, rainforest, and mangrove zones (Emel and Storfer 2012 ). Similar patterns of shallow genetic structure and demographic connectivity have been reported for H. occipitalis across the western Sahel, where recent demographic expansion and high dispersal capacity appear to counteract strong regional differentiation (Gonçalves and Brito 2019 ). However, notable exceptions in the present study (including reduced nucleotide diversity in Iwo and localized reductions in effective migration inferred from FEEMS around Ibadan, Epe, Abeokuta, and Ado) indicate that contemporary human activities may be influencing population connectivity and genetic variation. These findings support our third hypothesis that increased anthropogenic pressure can lead to reduced genetic diversity and constrained gene flow, even in species with high dispersal potential (Allentoft & O’Brien 2010 ). The reduced effective migration inferred around Ibadan, Abeokuta, and Ado may reflect landscape modification associated with rapid urbanization. As state capitals, these localities experience disproportionately high levels of infrastructure development, road density, industrial activity, and deforestation compared to surrounding areas. Urban-associated landscape features are well known to reduce functional connectivity among amphibian populations by fragmenting aquatic breeding habitats and increasing mortality during dispersal, even when geographic distances are small (Beebee 2005 ; Baguette et al. 2013 ). Similarly, reduced migration inferred for Epe may be linked to ongoing coastal sand filling and land reclamation activities that are progressively fragmenting mangrove and swamp forest habitats and disrupting hydrological connectivity along the coast. Hydrological alteration and wetland loss have been widely documented as drivers of reduced amphibian connectivity and local genetic erosion (Storfer et al. 2009 ; Allentoft & O’Brien 2010 ). Although H. occipitalis is ecologically flexible, these forms of landscape resistance may be sufficient to reduce effective gene flow locally, contributing to the heterogeneous connectivity patterns observed across southwestern Nigeria. Beyond harvesting, several natural and anthropogenic landscape features likely interact to shape population connectivity in this region. Riverine corridors and seasonal flooding may facilitate dispersal between savanna and forest zones, helping to maintain overall genetic cohesion. Conversely, extensive urban development, road networks, and agricultural expansion around cities such as Ibadan, Ado, and Epe may act as semi-permeable barriers to movement, reducing effective migration even where geographic separation is limited (Beebee 2005 ; Storfer et al. 2009 ). Together, these results highlight how landscape modification and exploitation pressure can generate fine-scale heterogeneity in genetic connectivity within an otherwise well-connected amphibian species. Collectively our results indicate that although populations of H. occipitalis in southwestern Nigeria are connected, certain populations may warrant targeted conservation attention due to localized pressures. Sites exhibiting reduced nucleotide diversity (Iwo) or constrained effective migration (Ibadan, Abeokuta, Ado and Epe) appear most vulnerable to ongoing anthropogenic impacts. These populations are embedded in landscapes experiencing rapid urbanization, wetland loss, coastal modification, and intense exploitation (Tijani et al. 2012 ; Odigie and Obayagbona 2025 ). These factors are likely to erode genetic diversity and functional connectivity over time despite the species’ high dispersal capacity. Conservation management should therefore prioritize the protection and restoration of breeding habitats in urban areas, maintenance of hydrological connectivity (especially along riverine and coastal systems), and monitoring and regulation of harvest and trade near major market hubs. In addition, safeguarding forested and swamp habitats that harbor private haplotypes may help preserve locally generated genetic variation that could be important for long-term adaptive potential. Future work should integrate genome-wide nuclear markers, finer-scale landscape data, and demographic monitoring to disentangle contemporary gene flow from historical expansion signals, assess the evolutionary significance of habitat-associated variation (Homola et al. 2019 ; Manel et al. 2003 ), and evaluate whether continued anthropogenic pressure risks pushing currently resilient populations toward genetic erosion (Garrett et al. 2025 ). Statements and Declarations Competing interests The authors have no relevant financial or non-financial interests to disclose. Ethics approval Ethical approval for this study was obtained from the University of Ibadan Animal Care and Use Research Ethics Committee (ACUREC), and all procedures complied strictly with approved ethical guidelines (Approval No. UI-ACUREC/001-0422/4). Funding This work was supported by the International Foundation for Science (Grant No. I1-B-6548-1) and a Lakeside Fellowship from the California Academy of Sciences, both awarded to OMC. Author Contribution All authors contributed to the study conception, design, analyses, and data visualization. OMC collected field samples and performed laboratory work. OMC wrote the first draft of the manuscript with contributions from IO and RCB. All authors read and approved the final manuscript. Acknowledgement We are grateful for support in computational analysis which was performed on servers maintained by the Center for Comparative Genomics at the California Academy of Sciences. Special thanks to Mr A. O. Fatoki, Mr M. O. Shittu and Mr S. J. Eludodun for their support during the fieldwork. We also thank Alex Krohn of Tangled Bank Conservation, LLC (Asheville, NC), for his technical support during and post laboratory procedures. Data Availability Illumina short read sequence data will be deposited in NCBI SRA upon acceptance of the manuscript under BioProject PRJXXXXXXXX. Final ipyrad assembly files including .hdf5, and .vcf formats are deposited in Zenodo at DOI 10.5281/zenodo.18273487. All scripts and jupyter notebooks sufficient to recreate assembly and analysis are publicly available in the project github repository at the following URL: [https://github.com/liftingup2003/Hoplobatrachus\_occipitalisddRAD\_Project] References Alam MS, Islam MM, Khan MMR, Hasan M, Wanichanon R, Sumida M (2012) Postmating isolation in six species of three genera ( Hoplobatrachus , Euphlyctis and Fejervarya ) from family Dicroglossidae (Anura), with special reference to spontaneous production of allotriploids. Zool Sci 29 :743–752 Allentoft M, O’Brien J (2010) Global amphibian declines, loss of genetic diversity and fitness: a review. 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Heredity 95 :423–427.https://doi.org/10.1038/sj.hdy.6800736 Bogart JP, Tandy M (1981a) Chromosome lineages in African ranoid frogs. Ital J Zool 15 :55–91. https://doi.org/10.1080/03749444.1981.10736629 Bogart JP, Tandy M (1981b) Polyploid amphibians: three more diploid–tetraploid cryptic species of frogs. Science 193 :334–335 Carpenter AI, Andreone F, Moore RD, Griffiths RA (2014) A review of the international trade in amphibians: the types, levels and dynamics of trade in CITES-listed species. Oryx 48 :565–574 Channing A, Rödel MO (2019) Field guide to the frogs and other amphibians of Africa . Penguin Random House, Cape Town Coker OM, Isong OM (2019) Status report of amphibian conservation in Nigeria. Afr Conserv Telegr 14 :1–8 Danecek P, Auton A, Abecasis G, et al (2011) The variant call format and VCFtools. Bioinformatics 27 :2156–2158 Duellman WE, Trueb L (1994) Biology of amphibians . Johns Hopkins University Press, Baltimore Eaton DA, Overcast I (2020) ipyrad: interactive assembly and analysis of RADseq datasets. Bioinformatics 36 :2592–2594 Efenakpo OD, Agbons AM, Eniang EA (2015) Assessment of frog meat trade and nutritional composition of selected Anura species in Ibadan, Nigeria. Prod Agric Technol 11 :203–218 Efenakpo OD, Agbons AM, Eniang EA (2025) Diversity patterns and community structure of amphibians in a West African suburban landscape. Next Res 100357 Elson P, Sales De Andrade E, Lucas G, May R, Hattersley R, Campbell E, ... & Hedley M (2023). SciTools/cartopy: v0. 21.0. Zenodo. Emel SL, Storfer A (2012) A decade of amphibian population genetic studies: synthesis and recommendations. Conserv Genet 13 :1685–1689 Excoffier L, Foll M, Petit RJ (2009) Genetic consequences of range expansions. Annu Rev Ecol Evol Syst 40 :481–501 Froufe E, Brito JC, Harris DJ (2009) Phylogeography of North African Amietophrynus xeros estimated from mitochondrial DNA sequences. Afr Zool 44 :208–215 Garrett MJ, Conway CJ, Waits LJ, Hohenlohe PA (2025) Genetic variation and metapopulation structure inform recovery goals in a threatened species. Genes 16 :694. Gibbons JW, Scott DE, Ryan TJ, et al (2000) The global decline of reptiles, déjà vu amphibians. BioScience 50 :653–666 Gonçalves DV, Brito JC (2019) Second Sahelian amphibian endemism suggested by phylogeography of groove-crowned bullfrog ( Hoplobatrachus occipitalis ) in western Sahel, and hints of polyploid species formation. J Zool Syst Evol Res 58 :262–274 Halliday TR (2008) Why amphibians are important. Int Zoo Yearb 42 :1–8 Hirschfeld M, Rödel MO (2011) The diet of the African tiger frog, Hoplobatrachus occipitalis , in northern Benin. Salamandra 47 :125–132 Homola JJ, Loftin CS, Cammen KS, Helbing CC, Birol I, Schultz TF, Kinnison MT (2019) Replicated landscape genomics identifies evidence of local adaptation to urbanization in wood frogs. J Hered 110 :707–719. https://doi.org/10.1093/jhered/esz041 Jordahl, K., Van den Bossche, J., Wasserman, J., McBride, J., Fleischmann, M., Gerard, J., ... & Bilogur, A. (2021). geopandas/geopandas: v0. 7.0. Zenodo. Keita G, Assemian NE, Zadou ZDA (2022) Status of harvesting, consumption and wild stocks of the edible frog Hoplobatrachus occipitalis in Daloa, Côte d’Ivoire. J Entomol Zool Stud 10 :190–196 Lea J, Politano E, Luiselli L (2003) Changes in the herpetofauna of a freshwater river in southern Nigeria after 20 years of development. Russ J Herpetol 10 :191–198 Leigh JW, Bryant D, Nakagawa S (2015) POPART: full-feature software for haplotype network construction. Methods Ecol Evol 6 :1110–1116 Manel S, Schwartz MK, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol 18 :189–197. https://doi.org/10.1016/S0169-5347(03)00008-9 Marcus J, Ha W, Barber RF, Novembre J (2021) Fast and flexible estimation of effective migration surfaces. eLife 10 :e61927 McVean G (2009) A genealogical interpretation of principal components analysis. PLoS Genet 5 :e1000686 Mohneke M, Onadeko AB, Rödel MO (2009) Exploitation of frogs—a review with a focus on West Africa. Salamandra 45 :193–202 Nei M, Li WH (1979) Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc Natl Acad Sci USA 76 :5269–5273 Odigie O, Obayagbona NO (2025) Continuous expansion of urban sprawls versus sustainable management of wetlands in Lagos State, Nigeria: implications for wetland loss and biodiversity decline. Dutse J Pure Appl Sci 11 :200–210 Onadeko AB, Egonmwan RI, Saliu JK (2011) Edible amphibian species: local knowledge of their consumption in southwest Nigeria and their nutritional value. West African Journal of Applied Ecology 19:67–76 Palumbi SR, Martin A, Romano S, McMillan WO, Stice L, Grabowski G (1991) The simple fool’s guide to PCR , version 2.0. University of Hawaii, Honolulu Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay É (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12 :2825–2830 Peterson BK, Weber JN, Kay EH, Fisher HS, Hoekstra HE (2012) Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS ONE 7 :e37135 Petkova D, Novembre J, Stephens M (2016) Visualizing spatial population structure with estimated effective migration surfaces. Nat Genet 48 :94–100 Reich D, Price AL, Patterson N (2008) Principal component analysis of genetic data. Nat Genet 40 :491–492.https://doi.org/10.1038/ng0508-491 Rödel MO, Adum GB, Aruna E, Assemian NE, Barej MF, Bell RC, Burger M, Demare G, Doherty-Bone T, Doumbia J, Ernst R, Gonwouo NL, Hillers A, Hirschfeld M, Jongsma GFM, Kouamé NG, Kpan TF, Mohneke M, Nago SGA, Ofori-Boateng C, Onadeko A, Pauwels OSG, Sandberger-Loua L, Segniagbeto GH, Tchassem Fokoua AM, Tobi E, Tohé B, Zimkus BM, Penner J. (2021) Diversity, threats and conservation of western and central African amphibians. In Status and Threats of Afrotropical Amphibians , H. Heatwole and M-O Rödel, eds. Edition Chimaira, Frankfurt, pp 11¬–101. Rozas J, Ferrer-Mata A, Sánchez-DelBarrio JC, Guirao-Rico S, Librado P, Ramos-Onsins SE, Sánchez-Gracia A (2017). DnaSP 6: DNA sequence polymorphism analysis of large data sets. Molecular biology and evolution, 34(12), 3299-3302. Severn-Ellis AA, Scheben A, Neik TX, Saad NSM, Pradhan A, Batley J (2020) Genotyping for species identification and diversity assessment using ddRAD-seq. Methods Mol Biol 2107 :163–179. https://doi.org/10.1007/978-1-0716-0223-2_11 Smith MA, Green DM (2005) Dispersal and the metapopulation paradigm in amphibian ecology and conservation: are all amphibian populations metapopulations? Ecography 28, 110 128. Stella OO, Philip OE (2019) Health effects of charcoal production as perceived by the rural dwellers in rainforest and guinea savannah agro-ecological zones of Nigeria. Journal of Scientific Research and Reports . 26;22(5):1-2. Storfer A, Eastman JM, Spear SF (2009) Modern molecular methods for amphibian conservation. BioScience 59 :559–571 Streicher JW, McEntee JP, Drzich LC, et al. (2016) Genetic surfing, not allopatric divergence, explains spatial sorting of mitochondrial haplotypes in venomous coralsnakes. Evolution 70:1435–1449 Stuart SN, editor. Threatened amphibians of the world. Lynx Edicions; 2008. Stuart SN, Chanson JS, Cox NA, et al. (2004) Status and trends of amphibian declines and extinctions worldwide. Science 306:1783–1786 Tamura K, Stecher G, Kumar S (2021) MEGA11: molecular evolutionary genetics analysis version 11. Molecular Biology and Evolution 38:3022–3027 Tijani MN, Olaleye AO, Olubanjo OO (2012) Impact of urbanization on wetland degradation: A case study of Eleyele wetland, Ibadan, South West, Nigeria. COLERM Proceedings. 2012 May 4;2:434-56. Vences M, Wake DB (2007) Speciation, species boundaries and phylogeography of amphibians. Proc Natl Acad Sci USA 104 :11303–11310 Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38 :1358–1370 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1Cokeretal..docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 08 May, 2026 Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 15 Feb, 2026 Reviewers invited by journal 12 Feb, 2026 Editor assigned by journal 22 Jan, 2026 Submission checks completed at journal 22 Jan, 2026 First submitted to journal 21 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. 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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-8663849","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591746415,"identity":"57fb4304-a04d-470a-9a9d-bb264c8934ee","order_by":0,"name":"Oluwakayode Michael Coker","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACPmbGBwwJIBZ7Y8PBDxVABjNzA14tbMzMhg1gLTyHDz6WOAPSwkhACwNQC5glkZZswNsGYhHSws7M/uDhjtrE7Qw5ZhKS82qj+duBWn5UbMPnMMaGxDPHE3c2nDGTKNx2PHfGYcYGxp4zt/Fo4T/YkNh2LHHDwR6gLduO5TYAtTAztuHTArIFpOUwj5kE75xjufOJ1FKTuOEYG9D7DTW5G4jRMiOx7YDxzh5mYCAfO5C7EajlID6/8PMfZvj4s61Odrv8Q2BU1tTlzjt/+OCDHxW4tUDBYQYDGAMEDhBSDwR1MC11RCgeBaNgFIyCkQYAdZ5fHFHVZZoAAAAASUVORK5CYII=","orcid":"","institution":"University of Ibadan","correspondingAuthor":true,"prefix":"","firstName":"Oluwakayode","middleName":"Michael","lastName":"Coker","suffix":""},{"id":591746416,"identity":"2ef91edb-b63a-4a40-8a96-30493b3ff0ad","order_by":1,"name":"Isaac Overcast","email":"","orcid":"","institution":"California Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Isaac","middleName":"","lastName":"Overcast","suffix":""},{"id":591746417,"identity":"ff4afbf3-676d-40b8-985c-b5d705f4244b","order_by":2,"name":"Rayna C. Bell","email":"","orcid":"","institution":"California Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Rayna","middleName":"C.","lastName":"Bell","suffix":""}],"badges":[],"createdAt":"2026-01-21 23:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8663849/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8663849/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102921632,"identity":"dc5badf1-545c-4994-a3d4-084c15198eeb","added_by":"auto","created_at":"2026-02-18 12:45:07","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":287481,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic location of sampling sites within the four focal habitat types within the Southwest region of Nigeria. Vegetation zone data from Stella and Philip (2019)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8663849/v1/641eed3525f641d0bb3e2b54.jpeg"},{"id":102921634,"identity":"ae88772e-d777-4416-bfee-e4cb176e5af1","added_by":"auto","created_at":"2026-02-18 12:45:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109976,"visible":true,"origin":"","legend":"\u003cp\u003eMitochondrial (16S) haplotype diversity of \u003cem\u003eH. occipitalis\u003c/em\u003e across West, Central, and East Africa. Samples from Nigeria are colored according to the savanna and forest habitats in which they were collected. Note that samples of the endemic Sahel lineage (Gonçalves \u0026amp; Brito, 2019) were not included.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8663849/v1/95a845b0597752b1f2cdd16d.png"},{"id":102963874,"identity":"67c5fd05-50ec-41ae-93ed-d94d3be194f3","added_by":"auto","created_at":"2026-02-19 04:20:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":79306,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis of nuclear ddRADSeq data (77,476 unlinked bi-allelic SNPs) for \u003cem\u003eH. occipitalis\u003c/em\u003esampled from southwestern Nigeria. Each sample point is styled according to the habitat type from which it was sampled. This low dimensional representation shows results along the first two principal component axes (PC0 and PC1, to correspond with ipyrad analysis tools labeling). Proximity of samples within this 2-dimensional space is an indication of genetic similarity.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8663849/v1/846ef003d242f6782fa9fa86.png"},{"id":102921636,"identity":"880eb845-ed0a-430f-8208-0b1ec946ea16","added_by":"auto","created_at":"2026-02-18 12:45:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1148178,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated effective migration surface for \u003cem\u003eH. occipitalis\u003c/em\u003e samples within the focal region of southwestern Nigeria. Sampling localities are plotted on the landscape using the same habitat associated color and shape markers as in Figure 1. The background lattice shows the inferred effective migration rates among nodes of the graph which correspond to demes. Edges closer to white in color follow an isolation by distance model, where genetic similarity decays with geographic distance. Brown edges show areas that are barriers to gene flow, or areas where genetic similarity is less than expected given geographic distance. Blue edges show corridors of connectivity, or areas where genetic similarity is greater than expected given geographic distance. Darker edge colors indicate larger (blue) or smaller (brown) magnitudes of effective migration.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8663849/v1/855d7d24e6d72bf462b2f060.png"},{"id":103049768,"identity":"f95b41fb-d56d-4ca6-bbb7-6fb6f6f0acd2","added_by":"auto","created_at":"2026-02-20 07:45:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2304116,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8663849/v1/230ffe6b-2411-4167-aa5b-1373c40a7a96.pdf"},{"id":102963631,"identity":"66dddc74-c1d2-4f2a-b17e-0eeb91ed6772","added_by":"auto","created_at":"2026-02-19 04:19:32","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":32156,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1Cokeretal..docx","url":"https://assets-eu.researchsquare.com/files/rs-8663849/v1/d06d8c6f977109f187bc32b0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic structure and geneflow of Crowned Bullfrogs (Hoplobatrachus occipitalis) across ecological zones in Southwestern Nigeria","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, at least one third of amphibians are threatened with extinction (Stuart \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and overexploitation is one of the major causes of this decline (Gibbons et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Halliday \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Large frog species belonging to the genera \u003cem\u003eConraua\u003c/em\u003e, \u003cem\u003eHoplobatrachus\u003c/em\u003e, \u003cem\u003eLithobates\u003c/em\u003e, and \u003cem\u003eFejervarya\u003c/em\u003e are extensively collected for human consumption, with millions of individuals traded annually across Asia, Africa, and South America (Stuart et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In China and Southeast Asia, bullfrogs and other species are exported in high volumes for both domestic markets and international trade (Carpenter et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Likewise, in parts of West Africa intensive harvesting of frogs has been documented in both rural and urban areas (Mohneke et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; R\u0026ouml;del et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Keita et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In Nigeria, several tons of edible frogs (mostly \u003cem\u003eHoplobatrachus sp\u003c/em\u003e and \u003cem\u003ePtychadena sp\u003c/em\u003e) are sourced from the wild to meet local and international trade demands (Onadeko et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Coker and Isong, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; R\u0026ouml;del et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Local collectors report traveling greater distances to harvest target species, suggesting that populations are declining (R\u0026ouml;del et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Over time this high level of extraction may lead to regional losses of anuran biodiversity with consequences that cascade across both aquatic and terrestrial ecosystems (Mohneke et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite intense harvesting pressure of edible frogs in Nigeria, there is very little information on the population demography or population connectivity (e.g., structure, gene flow, diversity) of the targeted species. Emel and Storfer (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), affirmed a need to understand the spatial distribution of genetic diversity in amphibian populations to aid conservation efforts, but the few studies that estimate amphibian population genetic structure and gene flow on a small spatial scale are mainly from temperate North America and Europe. Conservation genetic data are still under-represented for African amphibians, and collecting genetic data for these taxa can provide valuable information for conservation more quickly than longitudinal demographic studies (Storfer et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In particular, landscape-level estimates of gene flow that reveal how landscape features impact population connectivity can be a powerful tool for informing conservation action to re-connect fragmented populations (Baguette et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This approach has been used to inform management strategies to halt or slow down amphibian decline around the world. For instance, Anoop and George (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) used both mitochondrial and nuclear gene sequences to examine the population genetic and demographic structure of the Karaavali Skittering Frog (Dicroglossidae: \u003cem\u003ePhrynoderma karaavali\u003c/em\u003e) in India to quantify how populations responded to a ban on exploitation of the species.\u003c/p\u003e \u003cp\u003eThe Crowned Bullfrog, \u003cem\u003eHoplobatrachus occipitalis\u003c/em\u003e (Dicroglossidae), is a widespread and adaptable species found across much of sub-Saharan Africa (Hirschfeld and R\u0026ouml;del, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Previous studies have identified tetraploid populations in Liberia (Bogart and Tandy 1976, 1981) and cryptic genetic diversity in Mauritania (Gon\u0026ccedil;alves and Brito, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), but the taxonomy remains unchanged and Crowned Bullfrogs are presently considered a single, widespread species. Across their range, \u003cem\u003eH. occipitalis\u003c/em\u003e are associated with seasonal pools, floodplains and irrigated crops (e.g., rice; Lea et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), and can tolerate disturbed sites including farmlands and urban edges (Efenakpo et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Reproduction is associated with the onset of the rainy season during which populations exhibit explosive breeding behavior (Anoop and George \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and breed in temporary water bodies. The adaptability of \u003cem\u003eH. occipitalis\u003c/em\u003e to different vegetation zones and breeding habitats across its vast distribution suggest it is resilient, though local population pressures may vary significantly. In Nigeria, although populations of \u003cem\u003eH. occipitalis\u003c/em\u003e appear robust across a range of ecological zones from lowland humid forests to arid savannas, high levels of extraction for food and traditional medicine may pose a risk to the longer-term persistence of the species. Evidence from market/trade studies indicates intensive collection in northern and central Nigeria with subsequent shipping to large southern urban markets in Ibadan and Lagos (Efenakpo et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Aminu and Anele \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, harvesting pressure is predicted to be high in: (a) northern/central states which are in turn located within the Guinea/Sudan Savanna, (b) rice-growing Middle Belt landscapes with abundant seasonal breeding sites; and (c) southwestern city catchments that drive demand.\u003c/p\u003e \u003cp\u003eIn the present study we quantify genetic structure, genetic diversity, and gene flow among populations of \u003cem\u003eH. occipitalis\u003c/em\u003e across a range of habitat types in Southwestern Nigeria. First, we place the Nigerian samples into the broader biogeographic context of \u003cem\u003eH. occipitalis\u003c/em\u003e by combining our data with the continental sampling of Gon\u0026ccedil;alves and Brito (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Second, we test the hypothesis that the underlying genetic structure of this species in Southwest Nigeria is stratified with respect to the dominant habitats in this region, which range from dry savanna habitats in the north to tropical rainforest and mangrove forest in the south. Third, we test the hypothesis that sites with increased anthropogenic pressure exhibit lower genetic diversity and reduced gene flow with neighboring regions. Collectively these results provide important insights as to the historical demography of this economically significant species and identify populations that may require conservation management efforts.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSampling Design\u003c/h2\u003e \u003cp\u003eWe sampled four vegetation zones (Guinea Savanna, Derived Savanna, Rainforest and Mangrove Forest) in Southwestern Nigeria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) between May and November 2022. Frogs were captured with hand nets and identified using a field guide (Channing and R\u0026ouml;del \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Toe clips were taken from ten individuals per location and preserved in 70% ethanol in the field. Ethical approval and clearance was obtained from the University of Ibadan Animal Care and Use Research Ethics Committee (ACUREC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDNA Extraction and Mitochondrial DNA (mtDNA) Dataset\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was extracted using the Zymo Research Quick-DNA Miniprep Plus Kit. Extracted DNA samples were quantified using Nanodrop Spectrophotometer. We amplified the 16S mitochondrial gene using polymerase chain reaction (PCR) with primers 16SA and 16SB (Palumbi et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Each reaction was carried out in a volume of 18\u0026micro;l containing: 1\u0026micro;l template DNA, 0.18\u0026micro;l \u003cem\u003eDreamTaq\u003c/em\u003e DNA polymerase, 2.4\u0026micro;l dNTPs, 0.75\u0026micro;l of each primer, 2\u0026micro;l 10X \u003cem\u003eDreamTaq\u003c/em\u003e Buffer, and 10.92\u0026micro;l molecular grade water. Amplifications were carried out with initial denaturation for 5 min at 94\u0026deg;C, followed by 35 cycles consisting of 60s denaturation at 94\u0026deg;C, 60s annealing at 48\u0026deg;C, 60s extension at 72\u0026deg;C and a final extension at 72\u0026deg;C for 5 min. PCR products were visualized using gel electrophoresis, purified using ExoSAP-IT (USB Corp., Cleveland, OH), and sequenced using a BigDye Terminator Cycle Sequencing Kit v3.1 (Applied Biosystems, Foster City, CA, USA) on an ABI Automated 3730x1 Genetic Analyzer (Applied Biosystems). Sequences were edited using Geneious Prime 2021.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.geneious.com\u003c/span\u003e\u003cspan address=\"https://www.geneious.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e and were deposited in NCBI Genbank (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003emtDNA Haplotype Diversity\u003c/h3\u003e\n\u003cp\u003eA total of 49 mitochondrial 16S rRNA gene sequences of \u003cem\u003eH. occipitalis\u003c/em\u003e were used for haplotype analysis. Seventeen of these sequences were obtained from a representative subset of our samples collected across the different vegetation zones in Nigeria (Mangrove Forest, Rainforest, Derived Savanna, and Guinea Savanna). The remaining 32 sequences were retrieved from the National Center for Biotechnology Information (NCBI) GenBank database to provide a broader geographic context of the species across West, Central, and East Africa (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). We aligned the combined dataset using the ClustalW algorithm implemented in MEGA 11 (Tamura et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Aligned sequences were exported in FASTA format and imported into DnaSP v6 (Rozas et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) to identify the number of unique haplotypes. The haplotype data file was exported in Nexus format for network construction. A haplotype network was constructed in POPART (Leigh et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) using the haplotype data file and a trait file (used to assign individuals to country or vegetation zones), allowing visualization of geographic clustering and haplotype distribution patterns.\u003c/p\u003e\n\u003ch3\u003eddRADSeq Sequencing and Bioinformatic Assembly (dup: abstract ?)\u003c/h3\u003e\n\u003cp\u003eDouble-digest restriction-site associated DNA (ddRADseq; Peterson et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Streicher et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Severn-Ellis et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) data were generated for 95 \u003cem\u003eH. occipitalis\u003c/em\u003e samples, selecting 8\u0026ndash;10 individuals per sampling site to maximize geographic coverage across all focal vegetation zones. We requantified DNA extracts with a Qubit 2.0 Fluorometer (ThermoFisher Scientific) to ensure DNA concentrations greater than 2ng/uL. Double-digest RADSeq libraries were prepared by Tangled Bank Conservation, LLC (Asheville, NC) following the 3RAD protocol of Bayona-Vasquez et al. (2019) using restriction enzymes \u003cem\u003eBamHI\u003c/em\u003e and \u003cem\u003eClaI\u003c/em\u003e. Samples were pooled and sent to Azenta Life Sciences for paired-end 150-bp sequencing on an Illumina NovaSeq X Plus system.\u003c/p\u003e \u003cp\u003eRaw Illumina data was demultiplexed to samples by the sequencing facility, and resulting sample fastq files were evaluated for quality using fastqc (Andrews \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) to inspect per base sequence quality and adapter contamination levels. All further downstream bioinformatics related to RADSeq data assembly and filtering were performed with \u003cem\u003eipyrad\u003c/em\u003e (Eaton \u0026amp; Overcast, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), unless otherwise specified. Briefly, demultiplexed reads were subject to an initial round of quality control to remove adapter contamination and reads with too many low quality bases (`max_low_qual_bases` = 5). After an initial run of ipyrad step 2 using the default value for filtering short fragments (`filter_min_trim_len` = 35), we inspected the fragment length distribution with custom python code and determined that there were an excess of short fragments being retained. Because short fragments have reduced information content and also increase assembly runtime, we re-ran step 2 and set the `filter_min_trim_len` parameter to 140bp, to screen out all but the longest, most informative fragments. We used the same conservative value of 0.9 for the `clust_threshold` parameter, for clustering reads both within and across samples (ipyrad steps 3 \u0026amp; 6). We then applied a final set of filters for locus quality using default ipyrad parameters, notably including the default `min_samples_locus` of 4. We generated output files in several common formats for downstream analysis, as well as assembly-level summary statistics to inspect assembly quality.\u003c/p\u003e\n\u003ch3\u003eNuclear Population Structure, Genetic Diversity, and Gene Flow\u003c/h3\u003e\n\u003cp\u003eWe proceeded with an exploratory analysis to determine the nature and extent of population genetic structure within \u003cem\u003eH. occipitalis\u003c/em\u003e within our focal region by applying principal component analysis (Reich et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; McVean \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) to our assembled RADSeq data. To perform this analysis we utilized the 'PCA' module of the ipyrad analysis tools (Eaton and Overcast \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and imported the ipyrad-generated `snps.hdf5` file, which contains a compact representation of the SNP data. Prior to calculating the PCA we filtered to remove sites with indels and invariant as well as non-biallelic sites, as it is not possible to properly encode these for this analysis. We additionally applied a global minimum coverage filter to retain only sites with data in 60% of the samples or more. Additionally, because PCA can not be calculated in the presence of missing data, we imputed missing values based on observed allele frequencies, which is the default imputation scheme in the ipyrad PCA analysis tool. Finally, to reduce the effect of linkage disequilibrium, we randomly sampled one SNP per locus. We then calculated the principal components and projected values of the first two PCs (which we denote PC0 and PC1, for agreement with ipyrad notation) into a two-dimensional space to visualize genetic relationships among samples, and evaluated the information content of the data by quantifying the percent of variance explained along each PC axis.\u003c/p\u003e \u003cp\u003eAfter determining through PCA analysis that populations were structured primarily by habitat type (see Results) we quantified nuclear genetic diversity within each of the genetic clusters. To this end we created 'population' files for each habitat type which contained sample IDs for all individuals from a given habitat. We then used the vcftools package (Danecek et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) to calculate nucleotide diversity (π; Nei and Li \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) per site, and averaged these values across the total length of sequenced data to obtain π values per base, a standard practice in population genetics. We additionally used vcftools to calculate pairwise Fst values (Weir and Cockerham, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) among all habitat types, to quantify genetic divergence among populations, using a similar scheme to translate raw values into average Fst values per site. Finally, in order to investigate on a more fine grained spatial scale the impact of human land use and proximity of sites to centers of human population density, we created population files for each sampling site and calculated nucleotide diversity within each sampling site, again averaging over total sequence length to report site-specific nucleotide diversity values per base.\u003c/p\u003e \u003cp\u003eTo investigate gene flow and genetic connectivity for \u003cem\u003eH. occipitalis\u003c/em\u003e across the landscape of southwestern Nigeria we constructed effective migration surfaces using FEEMS (Marcus et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). FEEMS is a fast python version of EEMS (Petkova et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) which implements a stepping stone model of migration among demes and quantifies deviations from a model of strict isolation by distance (IBD). We used GPS coordinates obtained for all samples, visually determined a bounding polygon for our focal region using a freely available online resource provided by \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBirdTheme.org\u003c/span\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.birdtheme.org/useful/v3tool.html\u003c/span\u003e\u003cspan address=\"https://www.birdtheme.org/useful/v3tool.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, and constructed a dense triangular lattice within this region using the GeoPandas function `delaunay_triangles` (Jordahl et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We loaded the `snps.hdf5` file from ipyrad and converted it into a numpy matrix of raw genotype values, followed by imputation of missing values using the `SimpleImputer` function of scikit-learn (Pedregosa et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). We used the FEEMS `prepare_graph_inputs` function to pre-process the outer bounding polygon, the triangular grid, and the grid edge matrix for downstream analysis. One of the more important parameters in FEEMS is lambda (λ), which determines the strength of penalization of differences in migration rates among neighboring edges on the lattice. With large values of λ migration rates will be more homogeneous across the landscape, and with small values of λ they will vary over shorter spatial scales. We implemented a cross-validation procedure to select the optimal λ value, as recommended by Marcus et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), testing ten log10-uniformly distributed values of λ between 0.001 and 100. We then fit the FEEMS `SpatialGraph` to the input geographic and genetic data using the best value of λ determined by cross-validation to generate our migration surface. Finally, we visualized our best-fit migration surface using the FEEMS `Viz` module utilizing an equidistant conic projection constructed with the python `cartopy` package (Elson et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003emtDNA Haplotype Diversity\u003c/h2\u003e \u003cp\u003eSeventeen haplotypes were detected across the full dataset with three haplotypes unique to Nigeria and one haplotype shared across Nigeria and throughout West, Central, and East Africa (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Of the three unique Nigerian haplotypes, two were associated with Swamp Forest habitats and one with Rainforest habitats. The widespread haplotype was present in all four of the habitat types in Nigeria.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eddRADSeq Sequencing and Bioinformatic Assembly\u003c/h3\u003e\n\u003cp\u003eIn this study we generated paired-end RADSeq data for 95 \u003cem\u003eH. occipitalis\u003c/em\u003e individuals. We removed one sample (HAB9) with insufficient DNA concentration prior to sequencing, and three samples (HOOC0024, HOOC0039, HOOC0047) which failed in sequencing, resulting in very low numbers of sequenced reads. Of those remaining we recovered an average of 13\u0026nbsp;million reads per sample (std +/- 2.5\u0026nbsp;million reads). After an initial quality control step to trim adapters, and filter reads by cutoffs for maximum number of low-quality bases and minimum read length we retained an average of 6.4\u0026nbsp;million reads per sample (std +/- 2.5\u0026nbsp;million reads). Clustering reads by our chosen sequence similarity of 0.9 produced an average of 383,685 clusters per sample (std +/- 85,878 clusters), and after applying the default read depth cutoff (6 reads per cluster) we retained mean 156,045 high-depth clusters per sample (std +/- 38,999 clusters). After clustering across samples and applying a final set of filters to retain loci which included at least four samples (a permissive setting which is the ipyrad default), and to remove paralogs and loci which did not pass cutoffs for maximum numbers of SNPs, indels, or shared heterozygous sites, on average each sample contained 105,911 loci (std +/- 28,579) in the final assembly. The final sequence matrix contained 87,815,468bp (70.46% missing sites) and the final SNP matrix contained 934,813 SNPs (62.67% missing sites).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eNuclear Population Structure, Genetic Diversity, and Gene Flow\u003c/h2\u003e \u003cp\u003eWe first explored population genetic structure by applying PCA to our ddRADSeq dataset. Our initial input data contained 934,813 SNPs, and our filtering removed 82,165 sites with indels, 122,160 multi-allelic sites, 2,025 subsample invariant sites, and 631,957 sites that did not pass our minimum coverage criteria. Of the remaining 273,784 sites we selected one SNP per RAD locus, ultimately retaining 77,476 unlinked bi-allelic SNPs for this analysis. After calculating the PCA we visualized the results by assigning individuals to the habitat types from which they were sampled. The final PCA plot shows identifiable population structuring by habitat type (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) with 6.2% of genetic variance explained by PC0 and 4.3% explained by PC1, indicating weak but quantifiable population differentiation among these samples. One individual from the Mangrove Forest habitat (HOOC0074 sampled from Epe) clustered with samples from the sole Guinea Savanna habitat site (Igbeti).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNucleotide diversity was lower for the Guinea Savanna (π\u0026thinsp;=\u0026thinsp;0.0012) and Mangrove Forest (π\u0026thinsp;=\u0026thinsp;0.0018) populations, and higher for the Rainforest (π\u0026thinsp;=\u0026thinsp;0.0020) and Derived Savanna (π\u0026thinsp;=\u0026thinsp;0.0021) populations (all π values are indicated per base). Fst values among populations were on average quite low (mean\u0026thinsp;=\u0026thinsp;0.011, std\u0026thinsp;=\u0026thinsp;0.009), with the highest values between Derived Savanna and Guinea Savanna populations (Fst\u0026thinsp;=\u0026thinsp;0.021), indicating relatively weak (though still measurable) population structure throughout the sampled region. Looking at nucleotide diversity on a per sampling site basis, we found that Iwo had by far the lowest genetic diversity (π\u0026thinsp;=\u0026thinsp;0.0006), with Ibadan, Igbeti, Ado, Ikorodu, and Soku having moderate levels of genetic diversity (π = ~0.0012), and Ifetedo and Badagry having the highest levels of genetic diversity (π = ~0.0015; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\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\u003eEstimated nucleotide diversity for each sampling locality. Diversity estimates (π; Nei \u0026amp; Li, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) are reported per site averaged across the total length of sequenced data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabitat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNucleotide Diversity (π)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuinea Savanna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgbeti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDerived Savanna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIwo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShoku\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainforest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbeokuta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIbadan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIfetedo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001473\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMangrove Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBadagry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEpe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIkorodu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001201\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\u003eCross-validation analysis showed that our best-fit value of the FEEMS smoothing parameter (λ) was equal to 10. We then ran a full FEEMS analysis with λ\u0026thinsp;=\u0026thinsp;10 to generate an effective migration surface for \u003cem\u003eH. occipitalis\u003c/em\u003e across southwest Nigeria (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In general we found elevated migration rates across the landscape, indicating genetic connectivity among our sampling sites that exceeds what would be expected under a model of isolation by distance. The peripheral zone of reduced migration is almost certainly a sampling artifact, reflecting that our sampling does not encompass the complete distribution of the species. Because of this we elect not to over-interpret this area as a barrier to gene flow for this species. Within the region where our samples are concentrated, however, we found one patch of significantly reduced migration on the southern coast between Epe and Ikorodu. This indicates that samples from these two sites are more genetically distinct than their geographic proximity would suggest. Likewise, the FEEMS analysis indicates genetic isolation of \u003cem\u003eH. occipitalis\u003c/em\u003e in Ibadan, signified by the reduced gene flow edges connecting it to the surrounding matrix. The reduced migration the west of Ado, south of Abeokuta and south of Shoku may reflect genetic isolation of these populations but these results are more challenging to interpret given our available sampling.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe mtDNA haplotype network for \u003cem\u003eHoplobatrachus occipitalis\u003c/em\u003e is dominated by a single, centrally positioned haplotype that is widely distributed across West, Central, and East Africa and occurs in all four Nigerian vegetation zones. The recovery of this same continental haplotype in Nigerian populations directly supports the interpretation of Gon\u0026ccedil;alves and Brito (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who proposed a rapid, late Quaternary expansion of \u003cem\u003eH. occipitalis\u003c/em\u003e across sub-Saharan Africa. The lack of geographic segregation among major African regions in the network further indicates weak phylogeographic structure and extensive historical connectivity, likely facilitated by the species\u0026rsquo; high dispersal capacity and exceptional reproductive output (Duellman and Trueb \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Vences and Wake \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Alam et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Similar continent-wide haplotype sharing has been reported in other savanna-adapted amphibians, including \u003cem\u003eSclerophrys xeros\u003c/em\u003e (Froufe et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Despite this overall homogeneity, the network also reveals three private haplotypes restricted to Nigeria, two associated with swamp forest habitats and one with rainforest habitat. Their peripheral placement and low frequencies suggest recent local mutations rather than long-term isolation, consistent with expectations under a rapid expansion model where new haplotypes arise at the range margins or in ecologically stable microhabitats (Excoffier et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). While the association of these private haplotypes with forested habitats may hint at fine-scale ecological influences on genetic variation, the shallow divergence observed precludes strong inferences about population structuring based on mitochondrial data alone. As mitochondrial markers reflect only matrilineal history, the apparent panmixia observed here may mask more subtle patterns of restricted gene flow or local adaptation. Integrating nuclear genomic data across the species\u0026rsquo; range would therefore be essential to disentangle ongoing gene flow from the historical signal of expansion and to test whether Nigeria\u0026rsquo;s heterogeneous ecological zones contribute to population differentiation at biparentally inherited loci.\u003c/p\u003e \u003cp\u003ePatterns of genome-wide genetic structure in \u003cem\u003eH. occipitalis\u003c/em\u003e across southwestern Nigeria indicate weak but detectable habitat-associated differentiation. The PCA of nuDNA SNPs shows broad clustering of individuals by dominant habitat type, separating savanna, rainforest, and mangrove forest populations. However, the small proportion of variance explained by the principal component axes and the low mean pairwise F\u003csub\u003eST\u003c/sub\u003e values demonstrate that this structuring does not reflect strong genetic isolation among habitats. Instead, these results suggest that although ecological differences along the north\u0026ndash;south gradient influence genetic variation, they do not constitute major barriers to gene flow. This pattern is consistent with the framework proposed by Vences and Wake (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), who argued that amphibians with high dispersal ability and generalist ecologies often exhibit shallow genetic structure across heterogeneous environments. The modest differentiation observed between Derived Savanna and Guinea Savanna populations (F\u003csub\u003eST\u003c/sub\u003e = 0.021) may reflect subtle ecological or demographic contrasts, including differences in hydroperiod, vegetation structure, and breeding-site permanence. Amphibian genetic structure is frequently shaped by larval habitat characteristics, particularly the predictability and connectivity of breeding water bodies (Beebee \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Smith and Green \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In southwestern Nigeria, savanna habitats are typically associated with seasonal and ephemeral pools, whereas rainforest and mangrove zones tend to support more permanent or semi-permanent water bodies, potentially influencing local recruitment dynamics and effective population sizes. The clustering of one mangrove forest individual with Guinea Savanna populations in the PCA likely reflects recent dispersal rather than long-term habitat association. While natural movement facilitated by seasonal flooding, riverine corridors, or landscape connectivity may contribute to such patterns, anthropogenic translocation cannot be excluded. Market and trade studies document intensive collection of \u003cem\u003eH. occipitalis\u003c/em\u003e in northern and central Nigeria, with subsequent transport to major southern urban markets such as Ibadan and Lagos (Efenakpo et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Aminu and Anele \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Accidental release or escape of transported individuals near coastal or mangrove-associated environments could therefore introduce savanna-derived genotypes into southern populations.\u003c/p\u003e \u003cp\u003eOverall, the distribution of genetic variation in \u003cem\u003eH. occipitalis\u003c/em\u003e across southwestern Nigeria suggests populations that are broadly resilient and well connected, yet locally impacted by anthropogenic pressures. Overall nucleotide diversity was moderate and relatively homogeneous among sites, consistent with a widespread, ecologically plastic amphibian capable of exploiting a wide range of breeding habitats across savanna, rainforest, and mangrove zones (Emel and Storfer \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Similar patterns of shallow genetic structure and demographic connectivity have been reported for \u003cem\u003eH. occipitalis\u003c/em\u003e across the western Sahel, where recent demographic expansion and high dispersal capacity appear to counteract strong regional differentiation (Gon\u0026ccedil;alves and Brito \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, notable exceptions in the present study (including reduced nucleotide diversity in Iwo and localized reductions in effective migration inferred from FEEMS around Ibadan, Epe, Abeokuta, and Ado) indicate that contemporary human activities may be influencing population connectivity and genetic variation. These findings support our third hypothesis that increased anthropogenic pressure can lead to reduced genetic diversity and constrained gene flow, even in species with high dispersal potential (Allentoft \u0026amp; O\u0026rsquo;Brien \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe reduced effective migration inferred around Ibadan, Abeokuta, and Ado may reflect landscape modification associated with rapid urbanization. As state capitals, these localities experience disproportionately high levels of infrastructure development, road density, industrial activity, and deforestation compared to surrounding areas. Urban-associated landscape features are well known to reduce functional connectivity among amphibian populations by fragmenting aquatic breeding habitats and increasing mortality during dispersal, even when geographic distances are small (Beebee \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Baguette et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Similarly, reduced migration inferred for Epe may be linked to ongoing coastal sand filling and land reclamation activities that are progressively fragmenting mangrove and swamp forest habitats and disrupting hydrological connectivity along the coast. Hydrological alteration and wetland loss have been widely documented as drivers of reduced amphibian connectivity and local genetic erosion (Storfer et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Allentoft \u0026amp; O\u0026rsquo;Brien \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Although \u003cem\u003eH. occipitalis\u003c/em\u003e is ecologically flexible, these forms of landscape resistance may be sufficient to reduce effective gene flow locally, contributing to the heterogeneous connectivity patterns observed across southwestern Nigeria. Beyond harvesting, several natural and anthropogenic landscape features likely interact to shape population connectivity in this region. Riverine corridors and seasonal flooding may facilitate dispersal between savanna and forest zones, helping to maintain overall genetic cohesion. Conversely, extensive urban development, road networks, and agricultural expansion around cities such as Ibadan, Ado, and Epe may act as semi-permeable barriers to movement, reducing effective migration even where geographic separation is limited (Beebee \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Storfer et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Together, these results highlight how landscape modification and exploitation pressure can generate fine-scale heterogeneity in genetic connectivity within an otherwise well-connected amphibian species.\u003c/p\u003e \u003cp\u003eCollectively our results indicate that although populations of \u003cem\u003eH. occipitalis\u003c/em\u003e in southwestern Nigeria are connected, certain populations may warrant targeted conservation attention due to localized pressures. Sites exhibiting reduced nucleotide diversity (Iwo) or constrained effective migration (Ibadan, Abeokuta, Ado and Epe) appear most vulnerable to ongoing anthropogenic impacts. These populations are embedded in landscapes experiencing rapid urbanization, wetland loss, coastal modification, and intense exploitation (Tijani et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Odigie and Obayagbona \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These factors are likely to erode genetic diversity and functional connectivity over time despite the species\u0026rsquo; high dispersal capacity. Conservation management should therefore prioritize the protection and restoration of breeding habitats in urban areas, maintenance of hydrological connectivity (especially along riverine and coastal systems), and monitoring and regulation of harvest and trade near major market hubs. In addition, safeguarding forested and swamp habitats that harbor private haplotypes may help preserve locally generated genetic variation that could be important for long-term adaptive potential. Future work should integrate genome-wide nuclear markers, finer-scale landscape data, and demographic monitoring to disentangle contemporary gene flow from historical expansion signals, assess the evolutionary significance of habitat-associated variation (Homola et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Manel et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), and evaluate whether continued anthropogenic pressure risks pushing currently resilient populations toward genetic erosion (Garrett et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e"},{"header":"Statements and Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval\u003c/h2\u003e \u003cp\u003e Ethical approval for this study was obtained from the University of Ibadan Animal Care and Use Research Ethics Committee (ACUREC), and all procedures complied strictly with approved ethical guidelines (Approval No. UI-ACUREC/001-0422/4).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the International Foundation for Science (Grant No. I1-B-6548-1) and a Lakeside Fellowship from the California Academy of Sciences, both awarded to OMC.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception, design, analyses, and data visualization. OMC collected field samples and performed laboratory work. OMC wrote the first draft of the manuscript with contributions from IO and RCB. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe are grateful for support in computational analysis which was performed on servers maintained by the Center for Comparative Genomics at the California Academy of Sciences. Special thanks to Mr A. O. Fatoki, Mr M. O. Shittu and Mr S. J. Eludodun for their support during the fieldwork. We also thank Alex Krohn of Tangled Bank Conservation, LLC (Asheville, NC), for his technical support during and post laboratory procedures.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eIllumina short read sequence data will be deposited in NCBI SRA upon acceptance of the manuscript under BioProject PRJXXXXXXXX. Final ipyrad assembly files including .hdf5, and .vcf formats are deposited in Zenodo at DOI 10.5281/zenodo.18273487. All scripts and jupyter notebooks sufficient to recreate assembly and analysis are publicly available in the project github repository at the following URL: [https://github.com/liftingup2003/Hoplobatrachus\\_occipitalisddRAD\\_Project]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlam MS, Islam MM, Khan MMR, Hasan M, Wanichanon R, Sumida M (2012) Postmating isolation in six species of three genera (\u003cem\u003eHoplobatrachus\u003c/em\u003e, \u003cem\u003eEuphlyctis\u003c/em\u003e and \u003cem\u003eFejervarya\u003c/em\u003e) from family Dicroglossidae (Anura), with special reference to spontaneous production of allotriploids. \u003cem\u003eZool Sci\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e:743\u0026ndash;752\u003c/li\u003e\n\u003cli\u003eAllentoft M, O\u0026rsquo;Brien J (2010) Global amphibian declines, loss of genetic diversity and fitness: a review. \u003cem\u003eDiversity\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e:47\u0026ndash;71.https://doi.org/10.3390/d2010047\u003c/li\u003e\n\u003cli\u003eAminu A, Anele E (2024) Survey of anuran species sold in Kano and Zaria markets in Nigeria. \u003cem\u003eAfr Herpetol News\u003c/em\u003e \u003cstrong\u003e84\u003c/strong\u003e:1\u0026ndash;9\u003c/li\u003e\n\u003cli\u003eAndrews S (2010) FastQC: a quality control tool for high throughput sequence data.http://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/li\u003e\n\u003cli\u003eAnoop VS, George S. 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COLERM Proceedings. 2012 May 4;2:434-56.\u003c/li\u003e\n\u003cli\u003eVences M, Wake DB (2007) Speciation, species boundaries and phylogeography of amphibians. \u003cem\u003eProc Natl Acad Sci USA\u003c/em\u003e \u003cstrong\u003e104\u003c/strong\u003e:11303\u0026ndash;11310\u003c/li\u003e\n\u003cli\u003eWeir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. \u003cem\u003eEvolution\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e:1358\u0026ndash;1370\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Conservation genomics, Population connectivity, Gene flow, Genetic structure, Landscape genetics, Hoplobatrachus occipitalis","lastPublishedDoi":"10.21203/rs.3.rs-8663849/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8663849/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWidespread amphibians are often assumed to be demographically resilient, yet increasing habitat modification and intensive harvesting may erode genetic connectivity. In Nigeria, edible frogs such as the crowned bullfrog (\u003cem\u003eHoplobatrachus occipitalis\u003c/em\u003e) are subject to intense and largely unregulated exploitation, despite limited information on population demography, genetic diversity, or population connectivity. Here, we combine mitochondrial and genomic data to evaluate patterns of genetic structure and gene flow in \u003cem\u003eH. occipitalis\u003c/em\u003e across Southwestern Nigeria and within a broader African biogeographic framework.\u003c/p\u003e \u003cp\u003eMitochondrial haplotype analyses revealed a dominant, widely distributed haplotype shared across West, Central, and East Africa, consistent with a recent late-Quaternary expansion and weak phylogeographic structure. Analyses based on genome-wide nuclear SNP data showed weak but detectable habitat-associated structuring among savanna, rainforest, and mangrove populations. Nigerian populations exhibited moderate and relatively homogeneous nucleotide diversity (π\u0026thinsp;=\u0026thinsp;0.0006\u0026ndash;0.0015). Pairwise genetic differentiation was low overall, with the highest differentiation observed between Derived Savanna and Guinea Savanna populations (FST\u0026thinsp;=\u0026thinsp;0.021). Effective migration surface analyses identified localized reductions in gene flow, particularly near urban and coastal centers, indicating that anthropogenic modification may constrain connectivity at fine spatial scales.\u003c/p\u003e \u003cp\u003eThese results demonstrate that \u003cem\u003eH. occipitalis\u003c/em\u003e remains genetically cohesive at regional scales, yet locally vulnerable to habitat fragmentation, urbanization, and exploitation. These findings highlight the importance of maintaining breeding habitat connectivity and regulating harvest in rapidly developing landscapes to preserve genetic diversity in widespread amphibians.\u003c/p\u003e","manuscriptTitle":"Genetic structure and geneflow of Crowned Bullfrogs (Hoplobatrachus occipitalis) across ecological zones in Southwestern Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 12:45:02","doi":"10.21203/rs.3.rs-8663849/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-08T10:02:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T06:37:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279999697263113497167862107559175758901","date":"2026-03-18T15:37:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99555247186936439939560364381264690912","date":"2026-02-15T21:36:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-12T21:48:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-22T10:42:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-22T10:36:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Conservation Genetics","date":"2026-01-21T23:21:00+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":"108d8121-4551-4d8e-88a1-ab5146848552","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-08T10:02:16+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T10:13:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 12:45:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8663849","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8663849","identity":"rs-8663849","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}