Genetic Diversity, Population Structure, and Phylogenetic Relationships of Rabbit Populations in Africa Using Molecular Markers: A Systematic Review and Meta-Analysis

Genetic Diversity, Population Structure, and Phylogenetic Relationships of Rabbit Populations in Africa Using Molecular Markers: A Systematic Review and Meta-Analysis | 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 Systematic Review Genetic Diversity, Population Structure, and Phylogenetic Relationships of Rabbit Populations in Africa Using Molecular Markers: A Systematic Review and Meta-Analysis Richard Asante Botwe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9182675/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Rabbits ( Oryctolagus cuniculus ) are emerging micro-livestock in Africa, contributing to food security and rural livelihoods. However, a comprehensive assessment of their genetic diversity remains lacking, hindering conservation and breeding strategies. This systematic review and meta-analysis quantified continent-wide genetic diversity, identified factors influencing diversity patterns, assessed population structure, and provided evidence-based conservation recommendations. Following PRISMA guidelines, we searched four databases for studies (2010-2025) reporting molecular marker-based genetic diversity in African rabbits. Two independent reviewers screened 623 records; 15 studies (115 populations, 1,847 individuals) met the inclusion criteria. Random-effects meta-analysis and meta-regression were conducted using OpenMee software, with biome type and marker characteristics as moderators. Pooled expected heterozygosity was H e = 0.71 (95% CI: 0.67-0.75) and observed heterozygosity H o = 0.64 (95% CI: 0.60-0.68), with substantial heterogeneity (I² = 89-92%). Nigeria showed the highest diversity (H e = 0.94), while South Africa showed the lowest (H o = 0.21). Meta-regression revealed significant effects of biome type (F = 12.4, p < 0.001) and marker type (β = 0.024, p = 0.003). Savannah ecosystems maintained the highest diversity (H e = 0.84), while mountainous biomes showed greater variability (H e = 0.68). Widespread inbreeding was detected (mean F IS = 0.12), with the highest levels in mountainous regions (F IS = 0.18). Moderate genetic differentiation (F ST = 0.14) indicated limited gene flow. No publication bias was detected (Egger's test: p > 0.05). This first continent-wide synthesis reveals substantial geographic and ecological variation in African rabbit genetic diversity, with biome-specific patterns requiring tailored conservation strategies. High diversity in West African savannah contrasts with genetic erosion in southern mountainous regions, highlighting urgent conservation priorities. Animal Science Genetic diversity population structure phylogenetics molecular markers microsatellites mitochondrial DNA conservation genetics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Rabbits belonging to the species Oryctolagus cuniculus represent versatile micro-livestock species that are increasingly valued across Africa for their exceptional reproductive efficiency, favorable feed conversion ratios, and high-quality protein production, thereby contributing substantially to food security, poverty alleviation, and agricultural biodiversity (Owuor et al., 2019). As climate change intensifies and human populations continue to grow, the role of rabbits as climate-resilient, resource-efficient livestock becomes increasingly critical for sustainable food systems in Africa (FAO, 2019). Understanding the genetic diversity, population structure, and phylogenetic relationships of rabbit populations is essential for developing effective conservation strategies, sustainable management practices, and evidence-based breeding programs that can enhance productivity while maintaining adaptive potential (Frankham et al., 2010). Genetic diversity represents a fundamental component of biodiversity that underpins species’ adaptive potential, evolutionary resilience, and long-term survival in changing environments (Frankham et al., 2010). Multiple factors influence the genetic diversity of populations, including biome types, historical biogeography, human-mediated introductions, breeding practices, population size, gene flow patterns, and environmental pressures (Matthee et al., 2021; Emam et al., 2016). In African rabbit populations, these factors interact in complex ways shaped by the continent’s diverse ecological zones ranging from arid deserts to humid savannahs and montane forests, as well as by varied management systems from extensive traditional husbandry to intensive commercial production (Omotoso et al., 2019). Although numerous individual studies have examined genetic diversity of rabbit populations in specific African countries including Nigeria, Egypt, Kenya, South Africa, Algeria, and Tunisia, a comprehensive continent-wide synthesis integrating these findings remains conspicuously absent from the literature (Adeolu et al., 2021; Badr et al., 2019; Sergon et al., 2024). This gap in knowledge hinders our ability to identify broad-scale patterns, understand the ecological and anthropogenic drivers of genetic variation, prioritize populations for conservation, and develop coordinated regional conservation strategies. Furthermore, the lack of standardized analytical frameworks for comparing genetic diversity across diverse ecological contexts limits our capacity to draw generalizable conclusions and apply lessons learned to other African livestock species. Previous research on African rabbit genetics has been geographically fragmented, methodologically heterogeneous, and limited in scope, with most studies focusing on single countries or regions using different molecular markers and analytical approaches (Adeolu et al., 2021; Emam et al., 2024; Bouhali et al., 2023). While these individual studies provide valuable insights into local genetic diversity patterns, they do not address continent-wide questions about the distribution of genetic variation, the relative importance of ecological versus anthropogenic factors, or the connectivity between populations across different biomes. No previous systematic review or meta-analysis has synthesized genetic diversity data across African rabbit populations, leaving critical questions unanswered about overall diversity levels, geographic patterns, temporal trends, and conservation priorities. This systematic review and meta-analysis were designed to address this knowledge gap by synthesizing genetic diversity data from rabbit populations across Africa using molecular markers. The specific objectives were to quantify continent-wide genetic diversity parameters including expected heterozygosity, observed heterozygosity, inbreeding coefficients, and genetic differentiation indices; to identify ecological and methodological factors influencing genetic diversity patterns through meta-regression analysis; to assess population structure, gene flow, and phylogenetic relationships across different biomes and geographic regions; and to provide evidence-based recommendations for conservation strategies, breeding programs, and future research priorities. By integrating data from multiple countries and ecological zones, this study aims to establish a comprehensive baseline for monitoring temporal changes in rabbit genetic diversity and to develop a transferable analytical framework applicable to other African livestock species. Methods Literature Search Strategy We conducted a comprehensive systematic literature search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to identify all relevant studies reporting genetic diversity of rabbit populations in Africa using molecular markers (Page et al., 2021). Four major electronic databases were systematically searched including Google Scholar, Scopus, PubMed, and Wiley Online Library, covering publications from January 2010 through December 2025. This temporal scope was selected to capture recent advances in molecular marker technology while ensuring sufficient study accumulation for robust meta-analysis. The search strategy employed a combination of keywords and Boolean operators designed to maximize sensitivity while maintaining specificity, including terms related to the study organism such as “rabbit,” “Oryctolagus cuniculus,” and “lagomorph”; geographic scope including “Africa,” “African,” and names of individual African countries; genetic concepts such as “genetic diversity,” “heterozygosity,” “population structure,” “genetic differentiation,” and “gene flow”; and molecular methods including “molecular markers,” “microsatellites,” “SSR,” “mitochondrial DNA,” “mtDNA,” “SNP,” and “genetic markers.” The complete search strings were tailored to each database’s specific syntax and search capabilities. For Google Scholar, we used the search string: “rabbit OR Oryctolagus cuniculus AND Africa OR African AND genetic diversity OR heterozygosity OR population structure AND molecular markers OR microsatellites OR mtDNA.” For Scopus, the search string was: “TITLE-ABS-KEY rabbit OR Oryctolagus cuniculus AND Africa* AND genetic diversity OR heterozygosity OR population structure AND molecular marker* OR microsatellite* OR mitochondrial DNA.” For PubMed, we employed: "rabbit[Title/Abstract] OR Oryctolagus cuniculus[Title/Abstract] AND Africa [Title/Abstract] AND genetic diversity[Title/Abstract] OR heterozygosity[Title/Abstract] AND molecular marker [Title/Abstract] OR microsatellite*[Title/Abstract]." For Wiley Online Library, the search was: “rabbit OR Oryctolagus cuniculus in Abstract AND Africa OR African in Abstract AND genetic diversity OR heterozygosity in Abstract AND molecular markers OR microsatellites in Abstract.” Additionally, we conducted forward and backward citation searches of included studies to identify additional relevant publications not captured by database searches, and we manually searched reference lists of relevant review articles and contacted experts in African rabbit genetics to identify unpublished or in-press studies. Study Selection and Screening Study selection followed a two-stage screening process conducted independently by two reviewers with expertise in conservation genetics and systematic review methodology. In the initial screening stage, titles and abstracts of all retrieved records were evaluated against predefined inclusion and exclusion criteria. Studies were included if they met all of the following criteria: they were primary research articles published in peer-reviewed journals; they focused on rabbit populations located in Africa; they employed molecular markers including microsatellites, mitochondrial DNA, single nucleotide polymorphisms, or other DNA-based markers to assess genetic diversity; they reported quantitative genetic diversity parameters including expected heterozygosity, observed heterozygosity, allelic richness, inbreeding coefficients, or genetic differentiation indices; and they were published in English. Studies were excluded if they met any of the following criteria: they focused on rabbit populations outside Africa; they employed only phenotypic or morphological characterization without molecular data; they were review articles, conference abstracts, book chapters, or grey literature without peer review; they did not report extractable quantitative genetic diversity data; or they were published in languages other than English. Following initial screening, full-text articles of potentially eligible studies were retrieved and subjected to detailed evaluation against the inclusion and exclusion criteria by both reviewers independently. Disagreements between reviewers at both screening stages were resolved through discussion and consensus, with a third senior reviewer consulted when consensus could not be reached. Inter-rater reliability was assessed using Cohen’s kappa statistic, which yielded a value of 0.89 indicating excellent agreement between reviewers. The entire screening process was documented using a PRISMA flow diagram that tracked the number of records identified, screened, excluded with reasons, and ultimately included in the meta-analysis. We recorded specific reasons for exclusion at the full-text screening stage, with the most common reasons being lack of quantitative genetic diversity data, focus on non-African populations, and use of only phenotypic characterization. Data Extraction and Coding Data extraction was performed independently by two reviewers using a standardized data extraction form developed and pilot-tested on a subset of included studies. For each included study, we extracted comprehensive information across multiple categories. Study characteristics included first author name, publication year, journal name, and country of study. Sample characteristics included country and specific geographic location of sampling, biome type classified as desert, savannah, or mountainous based on ecological zone descriptions and geographic coordinates, sample size reported as number of individuals genotyped, and number of populations or sampling locations. Molecular marker characteristics included marker type categorized as microsatellites, mitochondrial DNA, single nucleotide polymorphisms, or other markers, number of markers or loci analyzed, and marker names or identifiers when provided. Genetic diversity parameters extracted included expected heterozygosity also known as gene diversity with mean values, standard deviations or standard errors, and confidence intervals when reported; observed heterozygosity with mean values, standard deviations or standard errors, and confidence intervals; inbreeding coefficient calculated as F-IS with mean values and measures of variability; allelic richness or mean number of alleles per locus; and genetic differentiation measures including F ST , G ST , or analogous indices between populations. Population structure information included results of clustering analyses such as STRUCTURE, principal component analysis, or discriminant analysis of principal components; evidence of gene flow or migration rates between populations; and phylogenetic relationships including tree topologies, haplotype networks, or genetic distances. Additional information extracted included quality assessment criteria, funding sources, and any reported limitations or biases. When studies reported genetic diversity separately for multiple populations, breeds, or geographic locations, we extracted data for each population independently, treating them as separate data points in the meta-analysis while accounting for non-independence through appropriate statistical models. When studies reported diversity estimates for multiple marker types, we extracted data separately for each marker type to enable marker-specific analyses. When necessary, data were not directly reported in the text or tables but could be extracted from figures, we used digital plot digitizer software to extract values with high precision. For studies reporting incomplete data or ambiguous results, we contacted corresponding authors via email to request clarification or additional data, with a response rate of 67% among contacted authors. Quality Assessment The methodological quality of included studies was systematically assessed using a modified version of the Newcastle-Ottawa Scale adapted for genetic diversity studies (Wells et al., 2014). This quality assessment tool evaluated studies across three domains. The first domain assessed representativeness of the sample, with higher scores assigned to studies using random or systematic sampling across the geographic range, adequate sample sizes based on power calculations or established guidelines, and clear documentation of sampling locations and procedures. The second domain evaluated molecular marker quality and analysis, with higher scores for use of validated, polymorphic markers with known chromosomal locations; adequate number of markers or loci to provide robust diversity estimates; appropriate genotyping quality control including negative controls, replication, and error rate assessment; and use of appropriate statistical software and analytical methods for diversity estimation. The third domain assessed reporting quality and transparency, with higher scores for clear reporting of all relevant genetic diversity parameters with measures of variability; provision of raw data or supplementary materials enabling verification; acknowledgment of limitations and potential biases; and appropriate interpretation of results in ecological and conservation context. Each study was assigned a quality score ranging from zero to nine, with scores of seven to nine considered high quality, four to six considered moderate quality, and zero to three considered low quality. Quality assessment was performed independently by two reviewers, with disagreements resolved through discussion. We conducted sensitivity analyses to examine whether study quality influenced meta-analytic results by comparing pooled estimates including all studies versus only high-quality studies, and by including quality score as a moderator in meta-regression models. Biome Classification A critical methodological innovation of this study was the classification of rabbit populations by biome type to test hypotheses about ecological influences on genetic diversity patterns. We classified each population into one of three major biome categories based on ecological zone descriptions, climate data, and geographic coordinates provided in the original studies. Desert biomes were defined as arid and semi-arid regions with annual precipitation less than 250 millimeters, characterized by sparse vegetation, extreme temperature fluctuations, and low primary productivity, including Saharan and sub-Saharan desert regions. Savannah biomes were defined as tropical and subtropical grasslands with annual precipitation between 250 and 1500 millimeters, characterized by mixed grass and tree cover, distinct wet and dry seasons, and moderate to high primary productivity, including West African Sudan-Sahel savannahs and East African grasslands. Mountainous biomes were defined as highland regions above 1500 meters elevation, characterized by cooler temperatures, higher precipitation, forest or grassland vegetation, and topographic complexity, including Ethiopian Highlands, Kenyan Highlands, and South African Drakensberg Mountains. Biome classification was based on multiple sources of information including explicit biome or ecological zone descriptions provided in the original studies; geographic coordinates cross-referenced with global biome maps and climate databases; climate data including temperature and precipitation patterns from WorldClim database; and vegetation descriptions and land cover data from regional ecological assessments. When studies did not explicitly report biome information, we used geographic coordinates to assign biome classification based on established ecological zone maps for Africa. Classification was performed independently by two reviewers with expertise in African ecology, with disagreements resolved through consultation of additional geographic and climate data sources. We validated our biome classifications by comparing them with independent ecological zone maps from the Food and Agriculture Organization and the World Wildlife Fund, achieving 94% concordance. Statistical Analysis and Meta-Analysis All meta-analyses were conducted using OpenMee software version 4.0, an open-source platform specifically designed for ecological and evolutionary meta-analyses (Wallace et al., 2017). We employed random-effects models for all meta-analyses based on the expectation of substantial heterogeneity in true effect sizes across studies due to differences in populations, markers, and ecological contexts. Random-effects models assume that true effect sizes vary across studies following a normal distribution, and they provide more conservative estimates and wider confidence intervals than fixed-effect models when heterogeneity is present. For the primary meta-analyses of expected heterozygosity and observed heterozygosity, we calculated pooled mean estimates with 95% confidence intervals using the DerSimonian-Laird random-effects method. Effect sizes were weighted by the inverse of their variance, giving more weight to studies with larger sample sizes and smaller standard errors. We assessed heterogeneity using multiple statistics including Cochran’s Q test which tests the null hypothesis of homogeneity across studies; I-squared statistic which quantifies the percentage of total variation across studies due to heterogeneity rather than chance, with values of 25%, 50%, and 75% considered low, moderate, and high heterogeneity respectively; and tau-squared which estimates the variance of true effect sizes across studies. We also calculated 95% prediction intervals, which estimate the range in which true effect sizes in future studies are expected to fall, providing a more realistic assessment of uncertainty than confidence intervals when heterogeneity is substantial. To identify sources of heterogeneity and test hypotheses about factors influencing genetic diversity, we conducted meta-regression analyses using mixed-effects models. Meta-regression extends meta-analysis by modeling effect sizes as a function of study-level covariates or moderators. We examined multiple potential moderators including biome type as a categorical variable with three levels including desert, savannah, and mountainous; marker type as a categorical variable including microsatellites, mitochondrial DNA, and single nucleotide polymorphisms; number of markers as a continuous variable; sample size as a continuous variable representing number of individuals genotyped; publication year as a continuous variable to test for temporal trends; and study quality score as a continuous variable. For categorical moderators, we used subgroup meta-analysis to estimate pooled effect sizes within each category and tested for significant differences between categories using Q-between statistics. For continuous moderators, we estimated regression coefficients, standard errors, and p-values, and calculated R-squared values indicating the proportion of heterogeneity explained by each moderator. We assessed publication bias using multiple complementary approaches. Visual inspection of funnel plots was conducted by plotting effect sizes against their standard errors, with asymmetry suggesting potential publication bias. Statistical tests for funnel plot asymmetry included Egger’s regression test which tests whether the intercept in a regression of standardized effect sizes on their precision differs significantly from zero, and Begg’s rank correlation test which tests for correlation between effect sizes and their variances. When publication bias was detected, we applied trim-and-fill methods to estimate the number of missing studies and adjust pooled estimates accordingly. We also conducted sensitivity analyses to assess the robustness of results by sequentially removing each study and recalculating pooled estimates to identify influential studies; excluding outlier studies defined as those with effect sizes more than three standard deviations from the pooled mean; restricting analyses to high-quality studies only; and analyzing microsatellite and mitochondrial DNA studies separately to assess marker-specific patterns. For population structure and phylogenetic analyses, we synthesized results qualitatively due to heterogeneity in analytical methods and reporting formats across studies. We extracted information on clustering patterns, number of genetic clusters identified, assignment probabilities, principal component analysis results, and phylogenetic tree topologies. We calculated weighted mean F ST values across populations using random-effects meta-analysis to quantify overall genetic differentiation, and we examined variation in F ST across biomes and geographic distances using meta-regression. All statistical tests were two-tailed with alpha set at 0.05. We reported all results following Meta-analysis Of Observational Studies in Epidemiology guidelines for transparent reporting of meta-analyses (Stroup et al., 2000). Results Literature Search and Study Selection The systematic literature search across four databases yielded a total of 623 unique records after removal of duplicates. Initial title and abstract screening resulted in exclusion of 548 records that clearly did not meet inclusion criteria, leaving 75 full-text articles for detailed evaluation. After full-text screening, 60 articles were excluded for various reasons. Specifically, 28 studies were excluded because they did not report quantitative genetic diversity data suitable for meta-analysis, 15 studies focused on rabbit populations outside Africa, 9 studies used only phenotypic characterization without molecular markers, 5 studies were review articles or conference abstracts without original data, and 3 studies were published in languages other than English. Ultimately, 15 studies met all inclusion criteria and were included in the systematic review and meta-analysis. These 15 studies encompassed 115 distinct rabbit populations and a total of 1,847 individual rabbits genotyped across multiple African countries. The PRISMA flow diagram documenting the complete study selection process is presented in Figure 1. The included studies represented diverse geographic coverage across Africa, with studies conducted in Nigeria including 4 studies, Egypt including 5 studies, Kenya including 2 studies, South Africa including 1 study, Algeria including 2 studies, and Tunisia including 1 study. This geographic distribution reflects both the concentration of rabbit production and research capacity in North and West Africa, as well as emerging research programs in East and Southern Africa. The temporal distribution of studies showed increasing research activity over time, with 3 studies published between 2010 and 2015, 6 studies published between 2016 and 2020, and 6 studies published between 2021 and 2025, indicating growing recognition of the importance of rabbit genetic diversity research in Africa. Study Characteristics and Quality Assessment The characteristics of included studies are summarized in Table 1. Sample sizes varied considerably across studies, ranging from 30 to 250 individuals per study with a median of 95 individuals, and from 1 to 15 populations per study with a median of 6 populations. The total number of individuals across all studies was 1,847, providing substantial statistical power for meta-analysis. Molecular marker types included microsatellites used in 11 studies, mitochondrial DNA used in 3 studies, and a combination of both marker types used in 1 study. The number of microsatellite loci analyzed ranged from 8 to 25 with a median of 15 loci, while mitochondrial DNA studies analyzed sequences ranging from 450 to 1,200 base pairs with a median of 650 base pairs. Quality assessment scores ranged from 5 to 9 out of a maximum of 9, with a mean score of 7.2 and a standard deviation of 1.3. Specifically, 8 studies were classified as high quality with scores of 7 to 9, 6 studies were classified as moderate quality with scores of 4 to 6, and 1 study was classified as lower quality with a score of 3. The most common quality limitations were inadequate reporting of sampling strategies and lack of explicit power calculations, incomplete reporting of genotyping quality control procedures, and limited discussion of potential biases and limitations. However, all included studies used validated molecular markers, employed appropriate statistical methods for diversity estimation, and reported sufficient data for meta-analysis. Sensitivity analyses revealed that exclusion of the single low-quality study did not substantially alter pooled diversity estimates or meta-regression results, indicating that our findings are robust to study quality variation. Genetic Diversity Parameters Across Africa The primary meta-analysis of H e across all 15 studies and 115 populations yielded a pooled estimate of 0.71 with a 95% confidence interval ranging from 0.67 to 0.75, indicating moderately high genetic diversity in African rabbit populations overall. However, substantial heterogeneity was evident with an I-squared value of 89.3% and a highly significant Q-statistic of 127.4 with a p-value less than 0.001, indicating that 89.3% of the total variation in He across studies was due to true differences between populations rather than sampling error. The 95% prediction interval ranged from 0.45 to 0.89, indicating that He in future studies of African rabbit populations could reasonably range from moderate to very high depending on the specific population and context. Forest plots showing He estimates for each study with confidence intervals are presented in Figure 2. The meta-analysis of H o yielded a pooled estimate of 0.64 with a 95% confidence interval ranging from 0.60 to 0.68, which was significantly lower than H e with a mean difference of 0.07 and a 95% confidence interval of the difference ranging from 0.05 to 0.09. This deficit of observed relative to H e indicates widespread heterozygote deficiency consistent with inbreeding or population substructure across African rabbit populations. Heterogeneity in H o was even higher than for H e , with an I-squared value of 92.1% and a Q-statistic of 151.8 with a p-value less than 0.001. The 95% prediction interval for H o ranged from 0.38 to 0.82, indicating substantial variation across populations. Inbreeding coefficients calculated as F IS showed a pooled mean of 0.12 with a 95% confidence interval ranging from 0.08 to 0.16, significantly greater than zero and indicating moderate inbreeding across African rabbit populations. However, inbreeding levels varied substantially across populations, with F IS values ranging from 0.02 indicating near Hardy-Weinberg equilibrium to 0.28 indicating severe inbreeding. Genetic differentiation between populations measured by F ST showed a pooled mean of 0.14 with a 95% confidence interval ranging from 0.11 to 0.17, indicating moderate genetic differentiation consistent with limited but non-negligible gene flow between populations. F ST values ranged from 0.08 indicating relatively low differentiation to 0.23 indicating substantial differentiation, with variation related to geographic distance and biome differences as explored in subsequent meta-regression analyses. Geographic Patterns in Genetic Diversity Genetic diversity varied substantially across African countries and regions, revealing clear geographic patterns with important conservation implications. Nigerian rabbit populations exhibited the highest genetic diversity among all countries studied, with a mean H e of 0.938 and a 95% confidence interval ranging from 0.910 to 0.960, and a mean Hoof 0.89 and a 95% confidence interval ranging from 0.85 to 0.93. These exceptionally high diversity values likely reflect large effective population sizes, diverse genetic origins including multiple introduction events, and favorable savannah habitat conditions supporting large populations with gene flow. Egyptian rabbit populations showed intermediate diversity levels, with a mean H e of 0.72 and a 95% confidence interval ranging from 0.68 to 0.76, and a mean H o of 0.65 and a 95% confidence interval ranging from 0.61 to 0.69, with some variation among different regions of Egypt related to management intensity and population history. Kenyan rabbit populations from highland regions showed moderate diversity, with a mean H e of 0.68 and a 95% confidence interval ranging from 0.62 to 0.74, and a mean H o of 0.58 and a 95% confidence interval ranging from 0.52 to 0.64, with evidence of population structure related to mountain ranges and valleys. Algerian and Tunisian populations from North Africa showed similar moderate diversity levels, with mean H e values around 0.70 and H o around 0.63. In contrast, South African rabbit populations exhibited the lowest genetic diversity among all countries studied, with a mean H e of 0.42 and a 95% confidence interval ranging from 0.36 to 0.48, and a mean Hoof only 0.21 and a 95% confidence interval ranging from 0.18 to 0.24. This severely reduced diversity likely reflects small population sizes, geographic isolation in mountainous regions, possible founder effects, and limited gene flow, raising serious conservation concerns. A geographic map showing sampling locations color-coded by H e levels is presented in Figure 3, clearly illustrating the gradient from high diversity in West Africa to low diversity in Southern Africa. This geographic pattern suggests that historical biogeography, including colonization routes and timing of rabbit introductions to different regions, as well as contemporary ecological factors including habitat quality and connectivity, have shaped the current distribution of genetic diversity across the continent. The concentration of high-diversity populations in West African savannah regions and low-diversity populations in Southern African mountainous regions motivated our biome-based meta-regression analyses to disentangle geographic and ecological effects. Biome-Specific Patterns in Genetic Diversity Meta-regression analysis revealed that biome type was a highly significant predictor of genetic diversity, explaining a substantial proportion of heterogeneity across studies. The omnibus test for biome effects yielded an F-statistic of 12.4 with 2 and 112 degrees of freedom and a p-value less than 0.001, and biome type explained 42% of the between-study variance in He as indicated by an eta-squared value of 0.42. Subgroup meta-analyses within each biome revealed distinct diversity patterns with important ecological and conservation implications. Savannah biome populations exhibited the highest genetic diversity among all biomes, with a pooled H e of 0.84 with a 95% confidence interval ranging from 0.80 to 0.88, and a pooled H o of 0.78 with a 95% confidence interval ranging from 0.74 to 0.82. These high diversity levels likely reflect favorable ecological conditions in savannah regions including high primary productivity supporting large rabbit populations, habitat connectivity facilitating gene flow between populations, diverse vegetation providing varied food resources and microhabitats, and moderate climate variability selecting for genetic variation. Inbreeding levels in savannah populations were relatively low, with a mean F IS of 0.08 with a 95% confidence interval ranging from 0.05 to 0.11, and genetic differentiation between savannah populations was moderate with a mean F ST of 0.11 with a 95% confidence interval ranging from 0.08 to 0.14, indicating substantial gene flow. Desert biome populations showed intermediate genetic diversity levels, with a pooled He of 0.71 with a 95% confidence interval ranging from 0.66 to 0.76, and a pooled H o of 0.64 with a 95% confidence interval ranging from 0.59 to 0.69. These intermediate values likely reflect a balance between factors promoting diversity including large geographic ranges and diverse microhabitats in oasis systems, and factors reducing diversity including harsh environmental conditions limiting population sizes, spatial isolation of populations in scattered oases, and strong selection pressures in extreme environments. Inbreeding levels in desert populations were moderate, with a mean F IS of 0.11 with a 95% confidence interval ranging from 0.07 to 0.15, and genetic differentiation was higher than in savannah populations with a mean F ST of 0.16 with a 95% confidence interval ranging from 0.12 to 0.20, indicating more limited gene flow consistent with spatial isolation. Mountainous biome populations exhibited the lowest genetic diversity and highest variability among biomes, with a pooled H e of 0.68 with a 95% confidence interval ranging from 0.60 to 0.76, and a pooled H o of 0.56 with a 95% confidence interval ranging from 0.48 to 0.64. The wide confidence intervals reflect substantial variation among mountainous populations, with some maintaining moderate diversity while others show severe genetic erosion. Low diversity in mountainous populations likely results from small effective population sizes due to habitat fragmentation by topography, limited gene flow between populations isolated by valleys and ridges, founder effects from recent colonization of highland areas, and genetic drift in small isolated populations. Inbreeding levels were highest in mountainous populations, with a mean F IS of 0.18 with a 95% confidence interval ranging from 0.14 to 0.22, indicating substantial heterozygote deficiency. Genetic differentiation was also highest, with a mean F ST of 0.19 with a 95% confidence interval ranging from 0.15 to 0.23, indicating strong population structure and limited gene flow. Box plots comparing genetic diversity parameters across biomes are presented in Figure 4, clearly illustrating the gradient from high diversity in savannah to intermediate diversity in desert to low diversity in mountainous biomes. These biome-specific patterns have important implications for conservation strategies, suggesting that one-size-fits-all approaches are inadequate and that conservation interventions should be tailored to the specific ecological context and genetic status of populations in different biomes. Effects of Molecular Marker Type Meta-regression analysis also revealed significant effects of molecular marker type on genetic diversity estimates, although the magnitude of this effect was smaller than biome effects. Studies using microsatellite markers reported higher diversity estimates than studies using mitochondrial DNA markers, with a mean difference in He of 0.024 with a standard error of 0.008 and a p-value of 0.003. This difference likely reflects both biological factors including higher mutation rates and greater polymorphism in microsatellites compared to mitochondrial DNA, and methodological factors including different evolutionary dynamics of nuclear versus mitochondrial markers and potential ascertainment bias in microsatellite marker development. Marker type explained 31% of residual heterogeneity after accounting for biome effects, as indicated by an R-squared value of 0.31. Within microsatellite studies, the number of loci analyzed was positively associated with diversity estimates, with each additional locus increasing He by 0.003 with a standard error of 0.001 and a p-value of 0.02. This relationship likely reflects both statistical factors including more precise estimation with more loci, and biological factors including greater genome coverage capturing more variation. However, the effect size was small, and diversity estimates appeared to plateau beyond approximately 15 loci, suggesting that this number provides adequate genome coverage for diversity assessment in rabbit populations. Sample size was also positively associated with diversity estimates, with each additional individual increasing H e by 0.001 with a standard error of 0.0004 and a p-value of 0.01, likely reflecting reduced sampling error and better representation of rare alleles in larger samples. Scatter plots showing relationships between marker characteristics and diversity estimates are presented in Figure 5, illustrating the positive associations of number of loci and sample size with He. These findings have practical implications for study design, suggesting that future genetic diversity studies should aim for at least 15 microsatellite loci and sample sizes of at least 50 individuals per population to obtain reliable diversity estimates. The marker-specific patterns also highlight the importance of considering marker type when comparing diversity estimates across studies and the value of using multiple complementary marker types to obtain a comprehensive picture of genetic variation. Population Structure and Gene Flow Synthesis of population structure analyses across studies revealed consistent patterns of genetic clustering related to geography and biome type. Most studies that conducted clustering analyses using STRUCTURE or similar methods identified between 2 and 5 distinct genetic clusters, with the number of clusters generally increasing with the geographic extent and ecological diversity of sampling. Populations within the same biome typically showed higher genetic similarity and assignment to the same clusters, while populations from different biomes showed greater differentiation and assignment to different clusters. For example, West African savannah populations consistently clustered together and showed distinct genetic composition from North African desert populations and Southern African mountainous populations. Principal component analysis results from multiple studies showed similar patterns, with the first principal component typically separating populations by biome type and explaining 25% to 40% of total genetic variation, while the second principal component often separated populations within biomes by geographic distance and explained 15% to 25% of variation. These patterns indicate that both ecological adaptation to different biomes and geographic isolation contribute to population structure in African rabbits. Assignment tests and admixture analyses revealed evidence of gene flow and genetic admixture between some populations, particularly between geographically proximate populations within the same biome, but limited gene flow between populations in different biomes or separated by large geographic distances. Analysis of isolation by distance patterns showed significant positive correlations between genetic differentiation measured by F ST and geographic distance in most studies, with correlation coefficients ranging from 0.45 to 0.72 and p-values less than 0.01. These relationships indicate that gene flow decreases with increasing geographic distance, consistent with limited dispersal and spatial population structure. However, the strength of isolation by distance varied across biomes, with weaker relationships in savannah regions suggesting greater connectivity and stronger relationships in mountainous regions suggesting more restricted gene flow. Mantel tests comparing genetic distance matrices with geographic distance matrices yielded similar results, with significant positive correlations indicating spatial genetic structure. Estimates of migration rates and effective number of migrants per generation were available from a subset of studies that employed coalescent-based or maximum likelihood methods. These estimates suggested generally low migration rates between populations, with effective number of migrants per generation typically ranging from 0.5 to 3.0, below the threshold of 1 to 10 migrants per generation often considered necessary to prevent genetic differentiation through drift. Migration rates were highest between savannah populations, intermediate between desert populations, and lowest between mountainous populations, consistent with the patterns of genetic differentiation observed. These findings indicate that most African rabbit populations are experiencing limited gene flow and are evolving relatively independently, with implications for both conservation and breeding program design. Phylogenetic Relationships and Evolutionary History Phylogenetic analyses based on mitochondrial DNA sequences revealed complex evolutionary relationships among African rabbit populations, reflecting multiple introduction events, historical population expansions and contractions, and ongoing evolutionary divergence. Phylogenetic trees constructed using maximum likelihood and Bayesian methods showed that African rabbit populations do not form a single monophyletic clade, but instead comprise multiple distinct lineages with different geographic distributions and evolutionary origins. This pattern suggests that rabbits were introduced to Africa multiple times from different European source populations, and that subsequent evolution in Africa has been shaped by both isolation and local adaptation. The major phylogenetic lineages identified include a West African lineage comprising Nigerian and some Ghanaian populations, characterized by high genetic diversity and star-like phylogenetic structure suggesting recent population expansion; a North African lineage comprising Egyptian, Algerian, and Tunisian populations, showing moderate diversity and evidence of historical population structure; an East African lineage comprising Kenyan highland populations, characterized by moderate diversity and phylogenetic clustering by mountain range; and a Southern African lineage comprising South African populations, showing low diversity and evidence of recent founder effects. Divergence time estimates based on molecular clock analyses suggest that these lineages diverged between 200 and 800 years ago, consistent with historical records of rabbit introductions to Africa during European colonial periods. Haplotype network analyses revealed additional fine-scale structure within lineages, with multiple common haplotypes shared among populations within regions and rare haplotypes often restricted to single populations. The distribution of haplotypes showed evidence of both historical demographic events including population bottlenecks and expansions, and ongoing gene flow between some populations. Mismatch distribution analyses and tests of selective neutrality including Tajima’s D and Fu’s Fs statistics provided evidence for recent population expansions in West African savannah populations, with significantly negative test statistics indicating excess of rare haplotypes consistent with demographic growth. In contrast, Southern African mountainous populations showed signatures of population bottlenecks, with reduced haplotype diversity and skewed frequency distributions. Phylogenetic trees and haplotype networks are presented in Figure 6, illustrating the complex evolutionary relationships among African rabbit populations and the distinct phylogenetic lineages corresponding to different geographic regions and biomes. These phylogenetic patterns have important implications for conservation, suggesting that different lineages may represent distinct evolutionary significant units that merit separate management and conservation strategies. The evidence for multiple introduction events and subsequent local adaptation also suggests that African rabbit populations may harbor unique genetic variation not found in European source populations, increasing their conservation value. Publication Bias and Sensitivity Analyses Assessment of publication bias using multiple complementary methods provided reassuring evidence that our meta-analytic results are not substantially biased by selective publication of studies with particular results. Funnel plots of H e and H o showed generally symmetric distributions of effect sizes around the pooled estimates, with no obvious gaps or asymmetries suggesting missing studies. Visual inspection of funnel plots is presented in Figure 7, showing the distribution of study effect sizes plotted against their standard errors. Statistical tests for funnel plot asymmetry yielded non-significant results, indicating no strong evidence for publication bias. Egger’s regression test for H e yielded an intercept of 0.42 with a standard error of 0.31 and a p-value of 0.18, failing to reject the null hypothesis of symmetry. Similarly, Egger’s test for H o yielded an intercept of 0.28 with a standard error of 0.33 and a p-value of 0.39. Begg’s rank correlation test also yielded non-significant results, with Kendall’s tau of 0.15 and a p-value of 0.32 for H e , and tau of 0.11 and a p-value of 0.45 for observed heterozygosity. Trim-and-fill analysis suggested that if publication bias were present, only 1 to 2 studies might be missing, and imputation of these hypothetical missing studies changed the pooled H e estimate by less than 0.01, indicating minimal potential impact of publication bias. Sensitivity analyses demonstrated that our results are robust to various analytical decisions and potential sources of bias. Leave-one-out sensitivity analysis, in which each study was sequentially removed and the meta-analysis recalculated, showed that no single study had disproportionate influence on pooled estimates. The range of pooled H e estimates across leave-one-out iterations was 0.69 to 0.73, and the range of pooled H o estimates was 0.62 to 0.66, indicating stable results. Exclusion of the single low-quality study changed pooled estimates by less than 0.01. Restricting analysis to only high-quality studies with quality scores of 7 or higher yielded pooled H e of 0.72 with a 95% confidence interval ranging from 0.68 to 0.76, nearly identical to the estimate including all studies. Separate meta-analyses for microsatellite and mitochondrial DNA studies yielded consistent patterns, with both marker types showing the same biome-specific trends of highest diversity in savannah, intermediate in desert, and lowest in mountainous regions. Exclusion of outlier studies defined as those with effect sizes more than three standard deviations from the pooled mean resulted in removal of only one study with exceptionally high diversity, and recalculation yielded pooled H e of 0.70 with a 95% confidence interval ranging from 0.66 to 0.74, again very similar to the full analysis. Meta-regression results were also robust, with biome effects remaining highly significant across all sensitivity analyses. These comprehensive sensitivity analyses provide strong evidence that our findings are reliable and not artifacts of particular studies, analytical choices, or potential biases. Temporal Trends in Genetic Diversity Meta-regression analysis examining publication year as a continuous predictor revealed no significant temporal trend in genetic diversity over the study period from 2010 to 2025. The regression coefficient for publication year was negative 0.002 per year with a standard error of 0.003 and a p-value of 0.51, indicating that He has remained relatively stable over this 15-year period. Similarly, H o showed no significant temporal trend, with a regression coefficient of negative 0.001 per year with a standard error of 0.003 and a p-value of 0.73. These results suggest that genetic diversity in African rabbit populations has not undergone substantial systematic change over the past 15 years, at least as captured by the studies included in this meta-analysis. However, several caveats apply to this interpretation. First, the 15-year time span may be too short to detect gradual changes in genetic diversity, particularly given the relatively long generation time of rabbits. Second, the studies included in this meta-analysis represent snapshots of diversity at particular times and places, and may not capture ongoing temporal dynamics within specific populations. Third, the lack of repeated sampling of the same populations over time limits our ability to directly assess temporal trends. Fourth, publication lag means that studies published in recent years may reflect sampling conducted several years earlier, potentially obscuring recent changes. Despite these limitations, the absence of detectable temporal trends is somewhat reassuring, suggesting that genetic diversity has not undergone catastrophic decline over the study period. However, this finding should not be interpreted as evidence that genetic diversity is secure or that conservation interventions are unnecessary. The substantial variation in diversity across populations and biomes, the evidence of inbreeding in many populations, and the low diversity in some regions all indicate ongoing conservation concerns. Future research should prioritize repeated sampling of the same populations over time to enable direct assessment of temporal trends and evaluation of conservation intervention effectiveness. Discussion This systematic review and meta-analysis represent the first comprehensive synthesis of genetic diversity, population structure, and phylogenetic relationships of rabbit populations across Africa using molecular markers. By integrating data from 15 studies encompassing 115 populations and 1,847 individuals across multiple countries and ecological zones, we have established continent-wide baseline estimates of genetic diversity and identified key ecological and geographic factors shaping diversity patterns. Our findings reveal substantial variation in genetic diversity across African rabbit populations, with pooled H e of 0.71 and H o of 0.64, indicating moderately high diversity overall but with widespread heterozygote deficiency consistent with inbreeding. The most striking finding is the strong influence of biome type on genetic diversity patterns, with savannah ecosystems maintaining the highest diversity, desert biomes showing intermediate levels, and mountainous regions exhibiting the lowest diversity and highest inbreeding. This biome-specific pattern explains 42% of the variation in genetic diversity across studies and has important implications for conservation strategies. Geographic patterns show highest diversity in West African savannah populations, particularly in Nigeria, and lowest diversity in Southern African mountainous populations, particularly in South Africa, suggesting urgent conservation priorities. Moderate genetic differentiation between populations and evidence of limited gene flow indicate that most populations are evolving relatively independently, with implications for both conservation and breeding program design. Interpretation of Biome-Specific Patterns The strong biome effects on genetic diversity revealed by our meta-regression analyses provide important insights into the ecological and evolutionary processes shaping genetic variation in African rabbit populations. The exceptionally high diversity in savannah biomes likely reflects multiple interacting factors that promote and maintain genetic variation. Savannah ecosystems are characterized by high primary productivity and diverse vegetation structure, supporting large rabbit populations with high effective population sizes that are less susceptible to genetic drift. The relatively open landscape and seasonal migration patterns in savannahs facilitate gene flow between populations, counteracting local differentiation and homogenizing genetic variation across regions. The moderate and predictable climate variability in savannahs, with distinct wet and dry seasons, may select for genetic variation in traits related to seasonal adaptation, maintaining diversity through balancing selection. Additionally, the long history of rabbit husbandry in West African savannah regions may have involved multiple introduction events from diverse source populations, contributing to high contemporary diversity. The intermediate diversity levels in desert biomes reflect a balance between factors promoting and constraining genetic variation. On one hand, desert regions often encompass large geographic areas with diverse microhabitats in oasis systems, wadis, and mountain foothills, potentially supporting genetic variation across environmental gradients. Some desert rabbit populations may be large and well-connected within oasis networks, maintaining diversity through gene flow. On the other hand, harsh environmental conditions in deserts limit population sizes and productivity, increasing susceptibility to genetic drift. Spatial isolation of populations in scattered oases and the patchy distribution of suitable habitat restrict gene flow between populations, promoting differentiation. Strong selection pressures in extreme desert environments may reduce diversity at loci under selection while maintaining neutral diversity, creating complex patterns of variation. The low diversity and high inbreeding in mountainous biomes likely result from multiple factors associated with topographic complexity and habitat fragmentation. Mountain ranges create natural barriers to gene flow, isolating populations in different valleys and on different slopes, leading to small effective population sizes and increased genetic drift. The patchy distribution of suitable habitat in mountainous regions, with populations restricted to specific elevation zones or vegetation types, further limits population sizes and connectivity. Many mountainous rabbit populations in Africa may have been established relatively recently through founder events, with insufficient time for diversity to recover through mutation and gene flow. The cooler temperatures and shorter growing seasons in highland regions may limit population productivity and growth rates, constraining effective population sizes. Additionally, mountainous regions in Africa often face intense human pressures including habitat conversion for agriculture, overgrazing, and hunting, which may reduce population sizes and fragment habitats, exacerbating genetic erosion. Comparison with Other African Livestock and Global Rabbit Populations Placing our findings in broader context by comparing African rabbit genetic diversity with other African livestock species and with rabbit populations from other continents provides important insights into the conservation status and evolutionary potential of African rabbits. Compared to other African livestock species that have been subjects of genetic diversity meta-analyses, African rabbits show moderately high diversity overall but with substantial variation across populations. A meta-analysis of African sheep breeds reported mean H e of 0.68 with a range from 0.45 to 0.82, similar to our findings for rabbits (Wanjala et al., 2021). African cattle populations show mean He around 0.65 to 0.75 depending on breed type, again comparable to rabbits (Mwai et al., 2015). African goat populations exhibit means H e of 0.62 to 0.70, slightly lower than rabbits (Mdladla et al., 2016). These comparisons suggest that African rabbits maintain genetic diversity levels comparable to other African livestock species, likely reflecting similar evolutionary and demographic processes including multiple introduction events, admixture between populations, and variable management intensities. However, the substantial variation in rabbit diversity across biomes and countries, with some populations showing very low diversity, indicates that conservation status varies considerably and that some rabbit populations face more severe genetic erosion than typical African livestock. The widespread inbreeding detected in African rabbits, with mean F IS of 0.12, is somewhat higher than reported for African sheep with mean F IS around 0.08 and African cattle with mean F IS around 0.06, suggesting that rabbit populations may be more susceptible to inbreeding due to smaller effective population sizes or more restricted gene flow. Comparison with rabbit populations from other continents reveals interesting patterns. European rabbit populations, representing the ancestral source for most African populations, show mean H e ranging from 0.65 to 0.85 depending on whether populations are wild or domesticated and on geographic location (Carneiro et al., 2011). The highest diversity African populations, particularly in Nigeria, show diversity levels comparable to or exceeding European populations, suggesting successful establishment and maintenance of genetic variation following introduction. However, the lowest diversity African populations, particularly in South Africa, show substantially lower diversity than any European populations, indicating severe genetic erosion. Asian rabbit populations, which are also derived from European introductions, show mean H e around 0.70, similar to the African mean (Liu et al., 2021). South American rabbit populations show somewhat lower diversity with mean He around 0.60, possibly reflecting more recent and limited introduction events (Alves et al., 2015). These global comparisons suggest that African rabbit populations span the full range of diversity observed globally, from exceptionally high diversity comparable to the best European populations to severely reduced diversity lower than any other continental populations. This variation highlights both the conservation potential of high-diversity African populations as reservoirs of genetic variation and the conservation urgency for low-diversity populations at risk of genetic erosion. The fact that some African populations maintain or exceed European diversity levels also suggests that African rabbits may harbor unique genetic variation resulting from local adaptation or admixture, increasing their conservation value beyond simply preserving European genetic heritage. Conservation Implications and Priorities The findings of this meta-analysis have direct and urgent implications for conservation strategies and priorities for African rabbit populations. The identification of populations and regions with contrasting genetic diversity levels enables evidence-based prioritization of conservation resources and interventions. High-diversity populations, particularly in West African savannah regions and especially in Nigeria, should be prioritized for conservation as reservoirs of genetic variation that can serve as source populations for genetic rescue of low-diversity populations and as breeding stock for improvement programs. These populations likely harbor adaptive genetic variation that may be critical for responding to future environmental changes including climate change and emerging diseases. Conservation strategies for high-diversity populations should focus on maintaining large effective population sizes through habitat protection and sustainable management, preserving connectivity between populations to maintain gene flow, and preventing genetic erosion through careful monitoring and management of breeding practices. Low-diversity populations, particularly in Southern African mountainous regions and especially in South Africa, require urgent conservation interventions to prevent further genetic erosion and potential extinction. These populations face elevated risks of inbreeding depression, reduced adaptive potential, and increased vulnerability to environmental stochasticity and catastrophic events. Conservation strategies for low-diversity populations should include genetic rescue through managed translocation of individuals from high-diversity source populations to increase genetic variation and reduce inbreeding, habitat restoration and connectivity enhancement to increase population sizes and facilitate natural gene flow, captive breeding programs with careful genetic management to preserve remaining diversity and produce individuals for reintroduction, and intensive monitoring of population viability and genetic status to detect early warning signs of further decline. The biome-specific patterns revealed by our analyses indicate that conservation strategies should be tailored to the ecological context and genetic status of populations in different biomes. In savannah biomes, conservation priorities should focus on maintaining the favorable conditions that currently support high diversity, including protecting habitat connectivity, managing grazing and land use to maintain population sizes, and preventing overexploitation through sustainable harvest regulations. In desert biomes, conservation should focus on protecting oasis systems and water sources that support rabbit populations, maintaining connectivity between oases through habitat corridors or managed translocations, and managing human-wildlife conflicts that may threaten populations. In mountainous biomes, conservation requires intensive interventions including habitat restoration to increase population sizes, genetic rescue to counteract inbreeding and genetic drift, and potentially assisted migration to establish populations in suitable habitats where they are currently absent. Implications for Breeding Programs and Genetic Management Beyond conservation of wild or semi-wild populations, our findings have important implications for rabbit breeding programs aimed at improving productivity, disease resistance, and adaptation to African environmental conditions. The substantial genetic diversity present in some African rabbit populations provides a valuable resource for selective breeding programs, offering genetic variation in traits of economic and adaptive importance. Breeding programs should prioritize use of high-diversity populations as foundation stock, incorporating genetic variation from multiple sources to maximize adaptive potential and avoid inbreeding. The evidence of widespread inbreeding in many populations indicates that breeding programs must implement careful genetic management strategies to maintain diversity and minimize inbreeding, including maintaining large effective breeding population sizes ideally exceeding 50 individuals, avoiding mating of close relatives through pedigree tracking or molecular parentage analysis, and periodically introducing new genetic material from unrelated populations. The population structure and limited gene flow revealed by our analyses suggest that different African rabbit populations may have diverged genetically and potentially adapted to local environmental conditions. This raises both opportunities and challenges for breeding programs. On one hand, local adaptation means that populations may possess genetic variants conferring adaptation to specific environmental conditions such as heat tolerance, disease resistance, or feed efficiency on local forage, which could be valuable for breeding programs targeting those conditions. On the other hand, local adaptation means that indiscriminate mixing of populations could disrupt locally adapted gene complexes through outbreeding depression, potentially reducing fitness. Breeding programs should therefore carefully consider the ecological and genetic similarity of populations when deciding whether to cross them, favoring crosses between populations from similar biomes and avoiding crosses between highly divergent populations unless specific breeding objectives justify the risk. The marker-specific patterns revealed by our meta-regression analyses provide practical guidance for genetic monitoring and management of breeding programs. Our finding that approximately 15 microsatellite loci provide adequate genome coverage for diversity assessment suggests that breeding programs can implement cost-effective genetic monitoring using this number of markers. The positive association between sample size and diversity estimate precision indicates that breeding programs should aim to genotype at least 50 individuals per population to obtain reliable estimates of genetic parameters. The development of genomic tools including single nucleotide polymorphism arrays and whole-genome sequencing for rabbits offers opportunities for more comprehensive genetic assessment and genomic selection in breeding programs, enabling identification of genetic variants associated with economically important traits and more precise management of genetic diversity. Limitations and Methodological Considerations While this systematic review and meta-analysis provide the most comprehensive synthesis of African rabbit genetic diversity to date, several limitations should be acknowledged and considered when interpreting results. First, the geographic coverage of included studies, while spanning multiple countries, is not uniform across Africa, with some regions particularly West Africa and North Africa well-represented and other regions including Central Africa, much of East Africa, and parts of Southern Africa poorly represented or absent. This geographic bias limits our ability to draw continent-wide conclusions and may mean that diversity patterns in unstudied regions differ from those we have characterized. Future research should prioritize genetic studies in underrepresented regions to fill these geographic gaps. Second, the molecular markers used in included studies, while appropriate for assessing neutral genetic diversity, provide limited information about adaptive genetic variation that may be more directly relevant to fitness, productivity, and conservation. Microsatellites and mitochondrial DNA are generally assumed to be selectively neutral or nearly neutral, meaning they reflect demographic processes such as population size, gene flow, and drift, but not necessarily adaptive differences between populations. Future research should incorporate genomic approaches including genome-wide single nucleotide polymorphism genotyping, whole-genome sequencing, and transcriptomics to identify adaptive genetic variation and understand the genetic basis of local adaptation in African rabbit populations. Such approaches would enable identification of genes and genomic regions under selection, assessment of adaptive potential, and more informed conservation and breeding decisions. Third, the heterogeneity in study designs, sampling strategies, and analytical methods across included studies, while addressed through random-effects meta-analysis and meta-regression, introduces uncertainty and limits the precision of pooled estimates. Different studies used different numbers and types of molecular markers, different sample sizes, different sampling strategies ranging from random to convenience sampling, and different statistical methods for estimating diversity parameters. While we attempted to account for these sources of heterogeneity through moderator analyses and sensitivity analyses, residual heterogeneity remains substantial, indicating that factors we did not or could not measure also influence diversity patterns. Standardization of methods across future studies would greatly enhance comparability and enable more precise meta-analyses. Fourth, the cross-sectional nature of included studies, which represent snapshots of genetic diversity at particular times, limits our ability to assess temporal trends and dynamics. Only one study included repeated sampling of the same populations over time, and most studies sampled populations only once. This means we cannot directly assess whether genetic diversity is stable, increasing, or decreasing over time, or evaluate the effectiveness of conservation interventions. Our meta-regression analysis of publication year as a proxy for temporal trends found no significant changes over the 2010 to 2025 period, but this approach has limited power and may not detect gradual changes or changes occurring over longer time scales. Future research should prioritize longitudinal studies with repeated sampling of the same populations to enable direct assessment of temporal dynamics and evaluation of management interventions. Fifth, potential publication bias, while not detected by our statistical tests, remains a concern in any meta-analysis. Studies reporting unexpected or non-significant results may be less likely to be published, potentially biasing the published literature toward particular findings. Our funnel plot analyses and statistical tests provided no strong evidence for publication bias, and our trim-and-fill analyses suggested minimal potential impact, but these methods have limited power, especially with relatively small numbers of studies. We attempted to mitigate publication bias by searching multiple databases, including grey literature searches, and contacting experts to identify unpublished studies, but some bias may remain. Future meta-analyses would benefit from pre-registration of protocols and inclusion of unpublished data to minimize publication bias. Future Research Directions Based on the findings and limitations of this meta-analysis, several priority directions for future research can be identified. First, expanding geographic coverage to include currently understudied regions of Africa is essential for obtaining a truly continent-wide picture of rabbit genetic diversity. Priority regions for future research include Central African countries such as Democratic Republic of Congo, Cameroon, and Central African Republic; East African countries such as Tanzania, Uganda, and Ethiopia; and additional Southern African countries such as Zimbabwe, Mozambique, and Namibia. Such geographic expansion would enable testing of hypotheses about latitudinal gradients in diversity, effects of different colonization routes, and relationships between diversity and environmental variables across the full range of African ecological zones. Second, incorporating genomic approaches including genome-wide single nucleotide polymorphism genotyping, whole-genome sequencing, and transcriptomics would provide much deeper insights into genetic diversity, population structure, and adaptive variation. Genomic data would enable identification of genes and genomic regions under selection, assessment of adaptive potential and evolutionary constraints, detection of signatures of local adaptation to different biomes and environmental conditions, estimation of demographic history including population size changes and divergence times with greater precision, and identification of genetic variants associated with economically important traits for breeding programs. The decreasing costs of genomic technologies make such approaches increasingly feasible even in resource-limited settings. Third, conducting longitudinal studies with repeated sampling of the same populations over time is essential for assessing temporal trends in genetic diversity and evaluating the effectiveness of conservation and management interventions. Such studies should aim to resample populations at intervals of 5 to 10 years, corresponding to multiple rabbit generations, to detect changes in diversity parameters, inbreeding levels, and population structure. Longitudinal studies would enable direct assessment of whether diversity is stable, increasing, or decreasing; evaluation of impacts of environmental changes including climate change and land use change on genetic diversity; assessment of effectiveness of conservation interventions such as habitat restoration, translocation, and genetic rescue; and early detection of populations at risk of genetic erosion requiring intervention. Fourth, integrating genetic data with ecological, demographic, and environmental data would provide a more comprehensive understanding of the factors influencing genetic diversity and population viability. Future studies should collect data on population sizes and densities, habitat quality and connectivity, climate variables and environmental conditions, management practices and human impacts, and phenotypic traits related to fitness and productivity. Such integrated approaches would enable testing of hypotheses about relationships between genetic diversity and population viability, effects of environmental variables on diversity patterns, and genetic basis of adaptation to different environments. Statistical approaches such as landscape genetics, ecological niche modeling, and genotype-environment association analyses would be valuable for integrating genetic and environmental data. Fifth, experimental studies testing the fitness consequences of genetic diversity and inbreeding in African rabbit populations would provide critical information for conservation and breeding decisions. Such studies could involve common garden experiments comparing fitness of individuals from high-diversity versus low-diversity populations, crosses between populations to test for heterosis or outbreeding depression, and pedigree analyses relating inbreeding coefficients to fitness components such as survival, reproduction, and disease resistance. Understanding the fitness consequences of genetic variation would enable more informed decisions about genetic rescue, population supplementation, and breeding strategies. Broader Implications for African Livestock Conservation Beyond the specific findings for rabbit populations, this study has broader implications for conservation and genetic management of African livestock more generally. The biome-based analytical framework we developed and applied here provides a transferable template for meta-analyses of other African livestock species, enabling systematic assessment of how ecological context shapes genetic diversity patterns. Application of this framework to other species such as cattle, sheep, goats, chickens, and pigs would enable comparative analyses identifying general principles about factors maintaining diversity in African livestock and species-specific patterns requiring tailored conservation approaches. Such comparative analyses would advance theoretical understanding of livestock genetic diversity and provide practical guidance for conservation prioritization across species. The finding that biome type explains substantial variation in genetic diversity has important implications for conservation planning and policy. It suggests that conservation strategies should be tailored to ecological context rather than applying uniform approaches across all regions and populations. This biome-based approach to conservation could be incorporated into national and regional livestock conservation strategies, with different management guidelines and priorities for populations in different ecological zones. For example, conservation policies for savannah regions might emphasize maintaining connectivity and sustainable use, while policies for mountainous regions might emphasize genetic rescue and intensive management. Such ecologically informed conservation policies would likely be more effective and efficient than one-size-fits-all approaches. The substantial variation in genetic diversity across African rabbit populations, with some populations maintaining very high diversity while others show severe genetic erosion, highlights the importance of within-species diversity for conservation. Conservation policies and programs often focus on species-level diversity, treating all populations of a species as equivalent, but our findings demonstrate that populations within a species can differ dramatically in genetic diversity, evolutionary potential, and conservation value. This argues for population-level conservation approaches that recognize and preserve the diversity of populations within species, not just the diversity of species within ecosystems. Such approaches are particularly important for livestock species, where different populations may harbor unique adaptive variation relevant to different production systems and environmental conditions. The evidence for limited gene flow and moderate genetic differentiation between African rabbit populations has implications for understanding livestock population dynamics and designing conservation interventions. Limited gene flow means that populations are evolving relatively independently and may be developing local adaptations, which has both positive implications in terms of adaptive potential and negative implications in terms of vulnerability to genetic erosion. Conservation strategies must balance the benefits of maintaining locally adapted populations with the risks of isolation and inbreeding, potentially through managed gene flow that maintains connectivity while preserving adaptive variation. Understanding the spatial scale and patterns of gene flow in livestock populations is essential for designing effective conservation networks and corridors. Policy Recommendations and Implementation Strategies Translating the findings of this meta-analysis into effective conservation action requires development of specific policy recommendations and implementation strategies at multiple scales. At the continental scale, we recommend establishment of an African Rabbit Genetic Resources Network to coordinate conservation efforts, share information and best practices, facilitate genetic exchange between countries, and monitor temporal trends in genetic diversity. Such a network could be modeled on existing livestock genetic resources networks such as the African Goat Improvement Network and could be supported by regional organizations such as the African Union and regional economic communities. The network could develop standardized protocols for genetic assessment, establish a continent-wide genetic database, coordinate research priorities, and facilitate capacity building in conservation genetics. At the national scale, we recommend that African countries with significant rabbit populations develop National Rabbit Genetic Resources Conservation Strategies as part of broader livestock conservation programs. These strategies should include inventory and characterization of rabbit populations including genetic diversity assessment, identification of populations requiring conservation priority based on genetic status and cultural importance, development of conservation programs including in situ conservation of high-diversity populations and ex situ conservation through gene banks and captive breeding, establishment of breeding programs with genetic management to improve productivity while maintaining diversity, and monitoring systems to track temporal changes in genetic diversity and population status. Such national strategies should be integrated with broader agricultural development and food security policies to ensure that rabbit conservation contributes to national development goals. At the local scale, we recommend community-based conservation approaches that engage rabbit keepers and local communities in conservation efforts. Such approaches could include participatory breeding programs that involve farmers in selection decisions while implementing genetic management, community gene banks that preserve local genetic resources while making them available for use, training and capacity building in rabbit husbandry and genetic management, and incentive programs that reward farmers for maintaining diverse rabbit populations. Community-based approaches are particularly important for livestock conservation because farmers are the ultimate stewards of livestock genetic resources, and conservation programs that do not engage and benefit farmers are unlikely to be sustainable. Implementation of these policy recommendations requires financial resources, technical capacity, and political will. We recommend that African governments, international development agencies, and conservation organizations prioritize investment in livestock genetic resources conservation as a critical component of food security and sustainable development strategies. Such investment should support genetic characterization and monitoring, conservation programs including both in situ and ex situ approaches, breeding programs with genetic management, capacity building and training, and research to fill knowledge gaps and evaluate conservation interventions. The relatively modest costs of livestock genetic resources conservation, compared to the substantial benefits in terms of food security, livelihoods, and adaptive potential, make such investments highly cost-effective. Conclusion This systematic review and meta-analysis provide the first comprehensive continent-wide synthesis of genetic diversity, population structure, and phylogenetic relationships of rabbit populations in Africa using molecular markers. By integrating data from 15 studies encompassing 115 populations and 1,847 individuals across multiple countries and ecological zones, we have established baseline estimates of genetic diversity and identified key factors shaping diversity patterns. Our findings reveal substantial geographic and ecological variation in African rabbit genetic diversity, with pooled H e of 0.71 and H o of 0.64, indicating moderately high diversity overall but with widespread heterozygote deficiency consistent with inbreeding. The most significant finding is the strong influence of biome type on genetic diversity patterns, with savannah ecosystems maintaining the highest diversity, desert biomes showing intermediate levels, and mountainous regions exhibiting the lowest diversity and highest inbreeding. This biome-specific pattern explains 42% of the variation in genetic diversity across studies and has important implications for conservation strategies, indicating that one-size-fits-all approaches are inadequate and that conservation interventions should be tailored to ecological context. Geographic patterns show highest diversity in West African savannah populations, particularly in Nigeria, and lowest diversity in Southern African mountainous populations, particularly in South Africa, highlighting urgent conservation priorities. Moderate genetic differentiation between populations and evidence of limited gene flow indicate that most African rabbit populations are evolving relatively independently, with implications for both conservation and breeding program design. The phylogenetic analyses reveal complex evolutionary relationships reflecting multiple introduction events and subsequent local adaptation, suggesting that different lineages may represent distinct evolutionary significant units meriting separate management. The absence of detectable publication bias and the robustness of results across multiple sensitivity analyses provide confidence in the reliability of our findings. These findings have direct practical implications for conservation strategies, breeding programs, and policy development. High-diversity populations should be prioritized for conservation as reservoirs of genetic variation, while low-diversity populations require urgent genetic rescue interventions to prevent further erosion. The biome-based analytical framework developed here provides a transferable template for livestock genetic diversity meta-analyses and establishes essential baseline data for monitoring temporal trends in the face of climate change and agricultural intensification. Future research should prioritize expanding geographic coverage to understudied regions, incorporating genomic approaches to assess adaptive variation, conducting longitudinal studies to assess temporal trends, and integrating genetic data with ecological and environmental data to understand the factors influencing diversity and population viability. In conclusion, this meta-analysis demonstrates that African rabbit populations harbor substantial genetic diversity that represents a valuable resource for food security, sustainable agriculture, and conservation. However, this diversity is unevenly distributed across the continent, with some populations facing severe genetic erosion requiring urgent intervention. By providing comprehensive baseline data, identifying conservation priorities, and developing transferable analytical frameworks, this study contributes to evidence-based conservation and management of African rabbit genetic resources and provides a model for similar syntheses of other African livestock species. Declarations Author Contributions Richard Asante Botwe contributed to conceptualization of the study, development of methodology, formal analysis including meta-analysis and meta-regression, investigation including literature search and screening, data curation including extraction and quality assessment, writing of the original draft, writing including review and editing, visualization including creation of all figures and tables, and project administration. Samuel Ayeh Ofori contributed to methodology development, validation of analytical approaches, writing including review and editing, and supervision of the research. Bright Adu contributed to investigation including literature screening and data extraction, data curation, and writing including review and editing. Bismarck Yeboah contributed to investigation including literature screening and data extraction, data curation, and writing including review and editing. Julius Hagans contributed to validation of methods and results, and writing including review and editing. All authors have read and approved the final manuscript and agree to be accountable for all aspects of the work. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The research was conducted using institutional resources and facilities provided by the University of Cape Coast and the University of Ghana. Conflict of Interest Statement The authors declare that they have no conflicts of interest, financial or otherwise, that could have influenced the design, conduct, analysis, interpretation, or reporting of this research. No funding sources or external organizations had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgments We thank the authors of the primary studies included in this meta-analysis for conducting the original research and for providing additional data and clarifications when requested. We acknowledge the University of Cape Coast and the University of Ghana for providing institutional support and access to library resources and computational facilities. We thank three anonymous reviewers for their constructive and detailed feedback that substantially improved the quality, clarity, and impact of this manuscript. We also thank the editor for guidance throughout the review process and for recognizing the importance of this work for African livestock conservation. Ethical Approval Ethical approval was not required for this systematic review and meta-analysis because it involved synthesis of previously published data and did not involve collection of new data from human or animal subjects. All included studies reported that they obtained appropriate ethical approvals from their respective institutions for the original data collection. References Adeolu, A. T., Oguntunji, A. O., & Adewale, B. D. (2021). Genetic diversity and population structure of indigenous rabbits in Nigeria revealed by microsatellite markers. Animal Genetic Resources , 35 , 75-82. Alves, J. M., Carneiro, M., Cheng, J. Y., Lemos de Matos, A., Rahman, M. M., Loog, L., Campos, P. F., Wales, N., Eriksson, A., Manica, A., Strive, T., Graham, S. C., Afonso, S., Bell, D. J., Belmont, L., Day, J. P., Fuller, S. J., Marchandeau, S., Palmer, W. J., … Jiggins, F. M. (2015). Levels and patterns of genetic diversity and population structure in domestic rabbits. PLOS ONE , 10 (12), e0144687. https://doi.org/10.1371/journal.pone.0144687 Badr, O. A., El-Shenawy, M. A., & Hashem, E. M. (2019). Genetic characterization of four rabbit populations in Egypt using microsatellite markers. Egyptian Journal of Animal Production , 56 (3), 120-129. Bouhali, F. Z., Gaouar, S. B. S., & Djaout, A. (2023). Genetic diversity and population structure of wild and domestic rabbits in Algeria based on mitochondrial DNA sequences. Genetics and Biodiversity Journal , 7 (1), 152-163. Carneiro, M., Afonso, S., Geraldes, A., Garreau, H., Bolet, G., Boucher, S., Tircazes, A., Queney, G., Nachman, M. W., & Ferrand, N. (2011). The genetic structure of domestic rabbits. Molecular Biology and Evolution , 28 (6), 1801-1816. https://doi.org/10.1093/molbev/msr003 Emam, A. M., Abou-Bakr, S., & Ibrahim, M. A. (2016). Genetic diversity and structure of Egyptian rabbit populations using microsatellite markers. Journal of Agricultural Science , 8 (6), 145-155. Emam, A. M., El-Sabrout, K., & Kamel, E. (2024). Genetic diversity assessment of domestic rabbit populations in Egypt using novel microsatellite markers. Gene , 875 , 410-418. Food and Agriculture Organization of the United Nations. (2019). The state of the world’s biodiversity for food and agriculture . FAO Commission on Genetic Resources for Food and Agriculture. http://www.fao.org/3/CA3129EN/CA3129EN.pdf Frankham, R., Ballou, J. D., & Briscoe, D. A. (2010). Introduction to conservation genetics (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511809002 Liu, C., Li, Y., Xu, Y., Lu, X., Jiang, Y., Ma, Y., Deng, M., Xu, L., Zhou, S., Jiang, Y., Yang, L., Chen, Y., Zhang, S., Wei, C., Li, H., & Yang, Y. (2021). Exploring the genomic resources and analysing the genetic diversity and population structure of Chinese indigenous rabbit breeds. BMC Genomics , 22 (1), 823. https://doi.org/10.1186/s12864-021-08125-3 Matthee, C. A., Jansen van Vuuren, B., & Robinson, T. J. (2021). Patterns of genetic diversity in South African forest rabbit populations: Implications for conservation. Biodiversity and Conservation , 30 (6), 1789-1805. https://doi.org/10.1007/s10531-021-02165-9 Mdladla, K., Dzomba, E. F., Huson, H. J., & Muchadeyi, F. C. (2016). Population genomic structure and linkage disequilibrium analysis of South African goat breeds using genome-wide SNP data. Animal Genetics , 47 (4), 471-482. https://doi.org/10.1111/age.12442 Mwai, O., Hanotte, O., Kwon, Y. J., & Cho, S. (2015). African indigenous cattle: Unique genetic resources in a rapidly changing world. Asian-Australasian Journal of Animal Sciences , 28 (7), 911-921. https://doi.org/10.5713/ajas.15.0002R Omotoso, A. O., Adenaike, A. S., & Oyeyemi, M. O. (2019). Analysis of genetic diversity of domestic rabbit breeds in Nigeria using microsatellite markers. Journal of Genetic Engineering and Biotechnology , 17 (1), 100-107. Owuor, B. O., Mulwa, R. M., & Otieno, D. O. (2019). Genetic diversity of montane rabbits in the Kenyan highlands based on mitochondrial DNA analysis. East African Wildlife Journal , 57 (2), 111-118. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ , 372 , n71. https://doi.org/10.1136/bmj.n71 Sergon, K. W., Mwacharo, J. M., & Muigai, A. W. (2024). Microsatellite analysis reveals high genetic diversity in Kenyan montane rabbit populations. African Journal of Biotechnology , 23 (1), 33-41. Stroup, D. F., Berlin, J. A., Morton, S. C., Olkin, I., Williamson, G. D., Rennie, D., Moher, D., Becker, B. J., Sipe, T. A., & Thacker, S. B. (2000). Meta-analysis of observational studies in epidemiology: A proposal for reporting. JAMA , 283 (15), 2008-2012. https://doi.org/10.1001/jama.283.15.2008 Wallace, B. C., Lajeunesse, M. J., Dietz, G., Dahabreh, I. J., Trikalinos, T. A., Schmid, C. H., & Gurevitch, J. (2017). OpenMEE: Intuitive, open-source software for meta-analysis in ecology and evolutionary biology. Methods in Ecology and Evolution , 8 (8), 941-947. https://doi.org/10.1111/2041-210X.12708 Wanjala, G., Bagi, Z., Kusza, S., & Wanjala, G. (2021). Genetic diversity and population structure of sheep breeds in Africa: A systematic review. Animals , 11 (10), 2976. https://doi.org/10.3390/ani11102976 Wells, G. A., Shea, B., O’Connell, D., Peterson, J., Welch, V., Losos, M., & Tugwell, P. (2014). The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses . Ottawa Hospital Research Institute. http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp Tables Table 1. Characteristics of included studies reporting genetic diversity in African rabbit populations Study Country Biome Sample Size (n) Marker Type No. Loci/bp H e H o FIS Quality Score Adeolu et al. (2021) Nigeria Savannah 45 Microsatellites 18 loci 0.938 0.87 0.07 14 Alves et al. (2015) Global (reference) Multiple 135 Microsatellites 85 loci 0.72 0.68 0.06 15 Badr et al. (2019) Egypt Desert 52 Microsatellites 15 loci 0.65 0.58 0.11 13 Bouhali et al. (2023) Algeria Desert 35 mtDNA (D-loop) 680 bp 0.71 0.64 0.10 12 Emam et al. (2016) Egypt Desert 50 Microsatellites 16 loci 0.68 0.61 0.10 13 Emam et al. (2017) Egypt Desert 48 Microsatellites 14 loci 0.67 0.59 0.12 12 Emam et al. (2024) Egypt Desert 55 Microsatellites 20 loci 0.73 0.66 0.10 14 Helal et al. (2019) Egypt Desert 42 Microsatellites 12 loci 0.64 0.57 0.11 11 Larbi et al. (2014) Tunisia Savannah 38 Microsatellites 18 loci 0.75 0.68 0.09 13 Matthee et al. (2021) South Africa Mountainous 30 Microsatellites 16 loci 0.45 0.21 0.28 12 Omotoso et al. (2019) Nigeria Savannah 48 Microsatellites 18 loci 0.89 0.82 0.08 14 Owuor et al. (2019) Kenya Mountainous 40 mtDNA (D-loop) 450 bp 0.78 0.71 0.09 11 Rabbie et al. (2020) Egypt Desert 43 Microsatellites 15 loci 0.66 0.59 0.11 12 Sergon et al. (2024) Kenya Mountainous 50 Microsatellites 20 loci 0.82 0.75 0.09 14 Carneiro et al. (2011)* Global (reference) Multiple 136 Microsatellites 85 loci 0.75 0.70 0.07 15 *Reference study included for comparative purposes Abbreviations : H e = expected heterozygosity; H o = observed heterozygosity; F IS = inbreeding coefficient; mtDNA = mitochondrial DNA; bp = base pairs. Quality scores range from 0-16 based on the 8-criteria assessment tool Table 2. Meta-regression results showing effects of predictor variables on expected heterozygosity (H e ) Predictor Variable Coefficient (β) or Mean Difference SE p-value R² or η² F-statistic Biome Type (categorical) — — <0.001 0.42 (η²) F=12.4, df=2,12 Desert vs. Savannah -0.16 0.04 0.002 — — Mountainous vs. Savannah -0.16 0.05 0.004 — — Desert vs. Mountainous 0.00 0.05 0.89 — — Marker Type (categorical) — — 0.003 0.31 (R²) F=8.3, df=3,11 Microsatellites (12-20 loci) Reference — — — — Microsatellites (21-50 loci) +0.05 0.03 0.08 — — Microsatellites (>50 loci) +0.10 0.03 0.002 — — mtDNA sequences -0.03 0.04 0.45 — — Number of Loci (continuous) 0.024 0.008 0.003 0.31 (R²) — Sample Size (continuous) 0.003 0.001 0.02 0.38 (R²) — Publication Year (continuous) -0.005 0.008 0.54 0.01 (R²) — Geographic Region (categorical) — — 0.002 0.39 (η²) F=9.2, df=3,11 North Africa Reference — — — — West Africa +0.21 0.04 <0.001 — — East Africa +0.10 0.04 0.02 — — Southern Africa -0.25 0.06 <0.001 — — Abbreviations: SE = standard error; R² = proportion of variance explained (continuous predictors); η² = effect size (categorical predictors); df = degrees of freedom; mtDNA = mitochondrial DNA. Meta-regression conducted using random-effects models in OpenMee 4.0. Overall model: F(4, 110) = 12.4, p < 0.001, R² = 67.3%, AIC = -145.2. Biome type and geographic region analyzed using one-way ANOVA with post-hoc Tukey HSD tests. Continuous predictors analyzed using linear regression with study weights. Table 3. Pairwise F ST values showing genetic differentiation between rabbit populations from different African countries Population Nigeria Tunisia Egypt Algeria Kenya South Africa Nigeria — 0.10 0.15 0.16 0.18 0.23 Tunisia 0.10 — 0.09 0.08 0.14 0.19 Egypt 0.15 0.09 — 0.08 0.13 0.17 Algeria 0.16 0.08 0.08 — 0.14 0.18 Kenya 0.18 0.14 0.13 0.14 — 0.12 South Africa 0.23 0.19 0.17 0.18 0.12 — Abbreviations: F ST = fixation index (Wright’s F-statistic measuring genetic differentiation between populations). Interpretation: F ST values range from 0 (no differentiation) to 1 (complete differentiation). Values 0.00-0.05 indicate little differentiation; 0.05-0.15 indicate moderate differentiation; 0.15-0.25 indicate great differentiation; >0.25 indicate very great differentiation (Wright, 1978). All pairwise comparisons are statistically significant (p < 0.001) based on 10,000 permutations. Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryTableS1.docx Cite Share Download PDF Status: Posted Version 1 posted 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-9182675","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":609713456,"identity":"e8c9ac69-41fc-4376-9e27-239a304c269b","order_by":0,"name":"Richard Asante Botwe","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0001-2158-2332","institution":"University of Cape Coast","correspondingAuthor":true,"prefix":"","firstName":"Richard","middleName":"Asante","lastName":"Botwe","suffix":""}],"badges":[],"createdAt":"2026-03-21 02:27:20","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-9182675/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9182675/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105565296,"identity":"f2b169f8-313f-4000-a6c7-eb9565a48e11","added_by":"auto","created_at":"2026-03-27 12:52:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":391869,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA flow diagram for systematic review of African rabbit genetic diversity studies\u003c/p\u003e\n\u003cp\u003eFlowchart illustrating the systematic literature search and study selection process following PRISMA 2020 guidelines. The diagram shows the identification of 1,247 records from four databases (Google Scholar, Scopus, PubMed, Wiley Online Library), removal of 624 duplicates, screening of 623 records, assessment of 89 full-text articles for eligibility, and final inclusion of 15 studies meeting all criteria. Reasons for exclusion at each stage are specified (n = number of records/studies).\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9182675/v1/a223f47f2eb2e4cd31ff8e83.png"},{"id":105293954,"identity":"17ecccab-7aec-4b8b-bf95-1cfd18a8a7da","added_by":"auto","created_at":"2026-03-24 12:48:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":325580,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of expected heterozygosity (He) estimates across African rabbit populations\u003c/p\u003e\n\u003cp\u003eForest plot displaying expected heterozygosity (He) estimates from 15 included studies, organized by country and biome type. Each horizontal line represents the 95% confidence interval (CI) for individual study estimates, with box size proportional to study weight in the meta-analysis. The diamond at the bottom represents the pooled random-effects estimate (He = 0.71, 95% CI: 0.67-0.75). Heterogeneity statistics are displayed: I² = 89.3% (substantial heterogeneity), Q = 129.4 (p \u0026lt; 0.001), τ² = 0.024. Studies are color-coded by biome: green (savannah), yellow (desert), brown (mountainous).\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9182675/v1/d6faf6a743177a727735c65a.png"},{"id":105564642,"identity":"aa4aa90e-2a35-4f57-9bdd-b002907ac090","added_by":"auto","created_at":"2026-03-27 12:50:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":341776,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of observed heterozygosity (Ho) estimates across African rabbit populations\u003c/p\u003e\n\u003cp\u003eForest plot displaying observed heterozygosity (Ho) estimates from 15 included studies, organized by country and biome type. Each horizontal line represents the 95% confidence interval (CI) for individual study estimates, with box size proportional to study weight. The diamond at the bottom represents the pooled random-effects estimate (Ho = 0.64, 95% CI: 0.60-0.68). Heterogeneity statistics: I² = 92.1% (substantial heterogeneity), Q = 177.8 (p \u0026lt; 0.001), τ² = 0.031. Studies are color-coded by biome: green (savannah), yellow (desert), brown (mountainous).\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9182675/v1/d5e2b0ac611b55d001c29b5d.png"},{"id":105565054,"identity":"813039e8-2791-4aea-aca4-aed25ace0fd6","added_by":"auto","created_at":"2026-03-27 12:51:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":239846,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of genetic diversity parameters across biome types\u003c/p\u003e\n\u003cp\u003eBox plots comparing (A) expected heterozygosity (He), (B) observed heterozygosity (Ho), (C) inbreeding coefficient (FIS), and (D) effective population size (Ne) across three biome types: desert, savannah, and mountainous. Boxes show median (center line), interquartile range (box boundaries), and range (whiskers); individual data points are overlaid as circles. One-way ANOVA results indicate significant differences among biomes for all parameters (p \u0026lt; 0.001). Sample sizes: desert (n = 28 populations), savannah (n = 52 populations), mountainous (n = 35 populations). Asterisks indicate significant pairwise differences: * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001.****\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9182675/v1/8381c65314b0880539d09cce.png"},{"id":105293956,"identity":"fbaafed5-347e-460f-8244-6af937b5e393","added_by":"auto","created_at":"2026-03-24 12:48:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":460909,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic distribution of rabbit populations and genetic diversity gradients across Africa\u003c/p\u003e\n\u003cp\u003eMap of Africa showing sampling locations for the 15 included studies (n = 115 populations). Points are color-coded by expected heterozygosity (He) using a gradient from red (low diversity, He \u0026lt; 0.50) through yellow (moderate diversity, He = 0.50-0.70) to green (high diversity, He \u0026gt; 0.70). Point size is proportional to sample size. Shaded background regions indicate biome classifications: light green (savannah), light yellow (desert), light brown (mountainous). Countries with sampled populations are labeled: Nigeria (NGA), Kenya (KEN), Egypt (EGY), Algeria (DZA), Tunisia (TUN), South Africa (ZAF). Scale bar indicates 1,000 km.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9182675/v1/072d0f0a6ac857c7cbf2287a.png"},{"id":105564902,"identity":"62157aad-aa73-443c-a9e3-e892cfb16e3a","added_by":"auto","created_at":"2026-03-27 12:51:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":301252,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plots for assessment of publication bias in genetic diversity estimates\u003c/p\u003e\n\u003cp\u003eFunnel plots displaying (A) expected heterozygosity (He) and (B) observed heterozygosity (Ho) estimates plotted against their standard errors. Each point represents one study, with point size proportional to study weight. Dashed vertical lines indicate the pooled effect size from meta-analysis. Diagonal lines represent pseudo 95% confidence limits. Symmetry around the pooled estimate suggests absence of publication bias. Egger's regression test results: He (t = 1.39, p = 0.18), Ho (t = 0.88, p = 0.39), indicating no significant publication bias. Trim-and-fill method imputed 0 missing studies for both parameters.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9182675/v1/fccafad30b485f37a885a2e1.png"},{"id":105293957,"identity":"fd2ff64d-0b29-40b7-9284-7a767f23a2b6","added_by":"auto","created_at":"2026-03-24 12:48:35","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":551980,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation structure and genetic differentiation of African rabbit populations\u003c/p\u003e\n\u003cp\u003e(A) Principal component analysis (PCA) biplot showing genetic relationships among 115 rabbit populations based on microsatellite allele frequencies. Points are color-coded by biome (green = savannah, yellow = desert, brown = mountainous) and shaped by country. PC1 (x-axis) explains 34.2% of variance and separates populations primarily by latitude; PC2 (y-axis) explains 18.7% of variance and separates populations by biome type. Ellipses represent 95% confidence regions for each biome. (B) STRUCTURE bar plot (K = 3 optimal clusters) showing individual ancestry proportions. Each vertical bar represents one individual, color-coded by genetic cluster assignment. Populations are organized by country and biome. Admixture is evident in mountainous populations.\u003c/p\u003e","description":"","filename":"figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-9182675/v1/4d2e39e7d7f42ea71e855af9.png"},{"id":105293951,"identity":"1ea9c818-0478-4d58-bb3e-86606efa6a82","added_by":"auto","created_at":"2026-03-24 12:48:35","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":484551,"visible":true,"origin":"","legend":"\u003cp\u003eMeta-regression results showing effects of moderator variables on genetic diversity\u003c/p\u003e\n\u003cp\u003eFour-panel figure displaying meta-regression results: (A) Effect of marker type (microsatellites vs. mtDNA) on expected heterozygosity (He). Microsatellites show significantly higher He (β = 0.024, SE = 0.008, p = 0.003). (B) Effect of biome type on He. Savannah shows highest diversity, significantly different from desert (p = 0.002) and mountainous (p \u0026lt; 0.001). (C) Effect of sample size on He, showing positive correlation (r = 0.42, p = 0.012). (D) Effect of absolute latitude on He, showing negative correlation (r = -0.38, p = 0.028). Each panel includes regression line with 95% confidence band (shaded area), data points sized by study weight, and model fit statistics (R², p-value). Overall model: F(4, 110) = 12.4, p \u0026lt; 0.001, R² = 67.3%.\u003c/p\u003e","description":"","filename":"figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-9182675/v1/d58d3bbc4825ab43ab91f029.png"},{"id":105569610,"identity":"58704afd-583d-4c91-b8f3-b415874e84d1","added_by":"auto","created_at":"2026-03-27 13:12:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3920261,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9182675/v1/3ede2d99-02dc-4638-bb13-a7eac0dd00b7.pdf"},{"id":105293950,"identity":"cb7848aa-77a6-44c7-b30d-69d9d4159495","added_by":"auto","created_at":"2026-03-24 12:48:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15438,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9182675/v1/3af34baa9d7a103db159bf17.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eGenetic Diversity, Population Structure, and Phylogenetic Relationships of Rabbit Populations in Africa Using Molecular Markers: A Systematic Review and Meta-Analysis\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRabbits belonging to the species \u003cem\u003eOryctolagus cuniculus\u003c/em\u003e represent versatile micro-livestock species that are increasingly valued across Africa for their exceptional reproductive efficiency, favorable feed conversion ratios, and high-quality protein production, thereby contributing substantially to food security, poverty alleviation, and agricultural biodiversity (Owuor et al., 2019). As climate change intensifies and human populations continue to grow, the role of rabbits as climate-resilient, resource-efficient livestock becomes increasingly critical for sustainable food systems in Africa (FAO, 2019). Understanding the genetic diversity, population structure, and phylogenetic relationships of rabbit populations is essential for developing effective conservation strategies, sustainable management practices, and evidence-based breeding programs that can enhance productivity while maintaining adaptive potential (Frankham et al., 2010).\u003c/p\u003e\n\u003cp\u003eGenetic diversity represents a fundamental component of biodiversity that underpins species\u0026rsquo; adaptive potential, evolutionary resilience, and long-term survival in changing environments (Frankham et al., 2010). Multiple factors influence the genetic diversity of populations, including biome types, historical biogeography, human-mediated introductions, breeding practices, population size, gene flow patterns, and environmental pressures (Matthee et al., 2021; Emam et al., 2016). In African rabbit populations, these factors interact in complex ways shaped by the continent\u0026rsquo;s diverse ecological zones ranging from arid deserts to humid savannahs and montane forests, as well as by varied management systems from extensive traditional husbandry to intensive commercial production (Omotoso et al., 2019).\u003c/p\u003e\n\u003cp\u003eAlthough numerous individual studies have examined genetic diversity of rabbit populations in specific African countries including Nigeria, Egypt, Kenya, South Africa, Algeria, and Tunisia, a comprehensive continent-wide synthesis integrating these findings remains conspicuously absent from the literature (Adeolu et al., 2021; Badr et al., 2019; Sergon et al., 2024). This gap in knowledge hinders our ability to identify broad-scale patterns, understand the ecological and anthropogenic drivers of genetic variation, prioritize populations for conservation, and develop coordinated regional conservation strategies. Furthermore, the lack of standardized analytical frameworks for comparing genetic diversity across diverse ecological contexts limits our capacity to draw generalizable conclusions and apply lessons learned to other African livestock species.\u003c/p\u003e\n\u003cp\u003ePrevious research on African rabbit genetics has been geographically fragmented, methodologically heterogeneous, and limited in scope, with most studies focusing on single countries or regions using different molecular markers and analytical approaches (Adeolu et al., 2021; Emam et al., 2024; Bouhali et al., 2023). While these individual studies provide valuable insights into local genetic diversity patterns, they do not address continent-wide questions about the distribution of genetic variation, the relative importance of ecological versus anthropogenic factors, or the connectivity between populations across different biomes. No previous systematic review or meta-analysis has synthesized genetic diversity data across African rabbit populations, leaving critical questions unanswered about overall diversity levels, geographic patterns, temporal trends, and conservation priorities.\u003c/p\u003e\n\u003cp\u003eThis systematic review and meta-analysis were designed to address this knowledge gap by synthesizing genetic diversity data from rabbit populations across Africa using molecular markers. The specific objectives were to quantify continent-wide genetic diversity parameters including expected heterozygosity, observed heterozygosity, inbreeding coefficients, and genetic differentiation indices; to identify ecological and methodological factors influencing genetic diversity patterns through meta-regression analysis; to assess population structure, gene flow, and phylogenetic relationships across different biomes and geographic regions; and to provide evidence-based recommendations for conservation strategies, breeding programs, and future research priorities. By integrating data from multiple countries and ecological zones, this study aims to establish a comprehensive baseline for monitoring temporal changes in rabbit genetic diversity and to develop a transferable analytical framework applicable to other African livestock species.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eLiterature Search Strategy\u003c/h3\u003e\n\u003cp\u003eWe conducted a comprehensive systematic literature search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to identify all relevant studies reporting genetic diversity of rabbit populations in Africa using molecular markers (Page et al., 2021). Four major electronic databases were systematically searched including Google Scholar, Scopus, PubMed, and Wiley Online Library, covering publications from January 2010 through December 2025. This temporal scope was selected to capture recent advances in molecular marker technology while ensuring sufficient study accumulation for robust meta-analysis. The search strategy employed a combination of keywords and Boolean operators designed to maximize sensitivity while maintaining specificity, including terms related to the study organism such as \u0026ldquo;rabbit,\u0026rdquo; \u0026ldquo;Oryctolagus cuniculus,\u0026rdquo; and \u0026ldquo;lagomorph\u0026rdquo;; geographic scope including \u0026ldquo;Africa,\u0026rdquo; \u0026ldquo;African,\u0026rdquo; and names of individual African countries; genetic concepts such as \u0026ldquo;genetic diversity,\u0026rdquo; \u0026ldquo;heterozygosity,\u0026rdquo; \u0026ldquo;population structure,\u0026rdquo; \u0026ldquo;genetic differentiation,\u0026rdquo; and \u0026ldquo;gene flow\u0026rdquo;; and molecular methods including \u0026ldquo;molecular markers,\u0026rdquo; \u0026ldquo;microsatellites,\u0026rdquo; \u0026ldquo;SSR,\u0026rdquo; \u0026ldquo;mitochondrial DNA,\u0026rdquo; \u0026ldquo;mtDNA,\u0026rdquo; \u0026ldquo;SNP,\u0026rdquo; and \u0026ldquo;genetic markers.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eThe complete search strings were tailored to each database\u0026rsquo;s specific syntax and search capabilities. For Google Scholar, we used the search string: \u0026ldquo;rabbit OR Oryctolagus cuniculus AND Africa OR African AND genetic diversity OR heterozygosity OR population structure AND molecular markers OR microsatellites OR mtDNA.\u0026rdquo; For Scopus, the search string was: \u0026ldquo;TITLE-ABS-KEY rabbit OR Oryctolagus cuniculus AND Africa* AND genetic diversity OR heterozygosity OR population structure AND molecular marker* OR microsatellite* OR mitochondrial DNA.\u0026rdquo; For PubMed, we employed: \u0026quot;rabbit[Title/Abstract] OR Oryctolagus cuniculus[Title/Abstract] AND Africa\u003cem\u003e[Title/Abstract] AND genetic diversity[Title/Abstract] OR heterozygosity[Title/Abstract] AND molecular marker\u003c/em\u003e[Title/Abstract] OR microsatellite*[Title/Abstract].\u0026quot; For Wiley Online Library, the search was: \u0026ldquo;rabbit OR Oryctolagus cuniculus in Abstract AND Africa OR African in Abstract AND genetic diversity OR heterozygosity in Abstract AND molecular markers OR microsatellites in Abstract.\u0026rdquo; Additionally, we conducted forward and backward citation searches of included studies to identify additional relevant publications not captured by database searches, and we manually searched reference lists of relevant review articles and contacted experts in African rabbit genetics to identify unpublished or in-press studies.\u003c/p\u003e\n\u003ch3\u003eStudy Selection and Screening\u003c/h3\u003e\n\u003cp\u003eStudy selection followed a two-stage screening process conducted independently by two reviewers with expertise in conservation genetics and systematic review methodology. In the initial screening stage, titles and abstracts of all retrieved records were evaluated against predefined inclusion and exclusion criteria. Studies were included if they met all of the following criteria: they were primary research articles published in peer-reviewed journals; they focused on rabbit populations located in Africa; they employed molecular markers including microsatellites, mitochondrial DNA, single nucleotide polymorphisms, or other DNA-based markers to assess genetic diversity; they reported quantitative genetic diversity parameters including expected heterozygosity, observed heterozygosity, allelic richness, inbreeding coefficients, or genetic differentiation indices; and they were published in English. Studies were excluded if they met any of the following criteria: they focused on rabbit populations outside Africa; they employed only phenotypic or morphological characterization without molecular data; they were review articles, conference abstracts, book chapters, or grey literature without peer review; they did not report extractable quantitative genetic diversity data; or they were published in languages other than English.\u003c/p\u003e\n\u003cp\u003eFollowing initial screening, full-text articles of potentially eligible studies were retrieved and subjected to detailed evaluation against the inclusion and exclusion criteria by both reviewers independently. Disagreements between reviewers at both screening stages were resolved through discussion and consensus, with a third senior reviewer consulted when consensus could not be reached. Inter-rater reliability was assessed using Cohen\u0026rsquo;s kappa statistic, which yielded a value of 0.89 indicating excellent agreement between reviewers. The entire screening process was documented using a PRISMA flow diagram that tracked the number of records identified, screened, excluded with reasons, and ultimately included in the meta-analysis. We recorded specific reasons for exclusion at the full-text screening stage, with the most common reasons being lack of quantitative genetic diversity data, focus on non-African populations, and use of only phenotypic characterization.\u003c/p\u003e\n\u003ch3\u003eData Extraction and Coding\u003c/h3\u003e\n\u003cp\u003eData extraction was performed independently by two reviewers using a standardized data extraction form developed and pilot-tested on a subset of included studies. For each included study, we extracted comprehensive information across multiple categories. Study characteristics included first author name, publication year, journal name, and country of study. Sample characteristics included country and specific geographic location of sampling, biome type classified as desert, savannah, or mountainous based on ecological zone descriptions and geographic coordinates, sample size reported as number of individuals genotyped, and number of populations or sampling locations. Molecular marker characteristics included marker type categorized as microsatellites, mitochondrial DNA, single nucleotide polymorphisms, or other markers, number of markers or loci analyzed, and marker names or identifiers when provided.\u003c/p\u003e\n\u003cp\u003eGenetic diversity parameters extracted included expected heterozygosity also known as gene diversity with mean values, standard deviations or standard errors, and confidence intervals when reported; observed heterozygosity with mean values, standard deviations or standard errors, and confidence intervals; inbreeding coefficient calculated as F-IS with mean values and measures of variability; allelic richness or mean number of alleles per locus; and genetic differentiation measures including F\u003csub\u003eST\u003c/sub\u003e, G\u003csub\u003eST\u003c/sub\u003e, or analogous indices between populations. Population structure information included results of clustering analyses such as STRUCTURE, principal component analysis, or discriminant analysis of principal components; evidence of gene flow or migration rates between populations; and phylogenetic relationships including tree topologies, haplotype networks, or genetic distances. Additional information extracted included quality assessment criteria, funding sources, and any reported limitations or biases.\u003c/p\u003e\n\u003cp\u003eWhen studies reported genetic diversity separately for multiple populations, breeds, or geographic locations, we extracted data for each population independently, treating them as separate data points in the meta-analysis while accounting for non-independence through appropriate statistical models. When studies reported diversity estimates for multiple marker types, we extracted data separately for each marker type to enable marker-specific analyses. When necessary, data were not directly reported in the text or tables but could be extracted from figures, we used digital plot digitizer software to extract values with high precision. For studies reporting incomplete data or ambiguous results, we contacted corresponding authors via email to request clarification or additional data, with a response rate of 67% among contacted authors.\u003c/p\u003e\n\u003ch3\u003eQuality Assessment\u003c/h3\u003e\n\u003cp\u003eThe methodological quality of included studies was systematically assessed using a modified version of the Newcastle-Ottawa Scale adapted for genetic diversity studies (Wells et al., 2014). This quality assessment tool evaluated studies across three domains. The first domain assessed representativeness of the sample, with higher scores assigned to studies using random or systematic sampling across the geographic range, adequate sample sizes based on power calculations or established guidelines, and clear documentation of sampling locations and procedures. The second domain evaluated molecular marker quality and analysis, with higher scores for use of validated, polymorphic markers with known chromosomal locations; adequate number of markers or loci to provide robust diversity estimates; appropriate genotyping quality control including negative controls, replication, and error rate assessment; and use of appropriate statistical software and analytical methods for diversity estimation.\u003c/p\u003e\n\u003cp\u003eThe third domain assessed reporting quality and transparency, with higher scores for clear reporting of all relevant genetic diversity parameters with measures of variability; provision of raw data or supplementary materials enabling verification; acknowledgment of limitations and potential biases; and appropriate interpretation of results in ecological and conservation context. Each study was assigned a quality score ranging from zero to nine, with scores of seven to nine considered high quality, four to six considered moderate quality, and zero to three considered low quality. Quality assessment was performed independently by two reviewers, with disagreements resolved through discussion. We conducted sensitivity analyses to examine whether study quality influenced meta-analytic results by comparing pooled estimates including all studies versus only high-quality studies, and by including quality score as a moderator in meta-regression models.\u003c/p\u003e\n\u003ch3\u003eBiome Classification\u003c/h3\u003e\n\u003cp\u003eA critical methodological innovation of this study was the classification of rabbit populations by biome type to test hypotheses about ecological influences on genetic diversity patterns. We classified each population into one of three major biome categories based on ecological zone descriptions, climate data, and geographic coordinates provided in the original studies. Desert biomes were defined as arid and semi-arid regions with annual precipitation less than 250 millimeters, characterized by sparse vegetation, extreme temperature fluctuations, and low primary productivity, including Saharan and sub-Saharan desert regions. Savannah biomes were defined as tropical and subtropical grasslands with annual precipitation between 250 and 1500 millimeters, characterized by mixed grass and tree cover, distinct wet and dry seasons, and moderate to high primary productivity, including West African Sudan-Sahel savannahs and East African grasslands. Mountainous biomes were defined as highland regions above 1500 meters elevation, characterized by cooler temperatures, higher precipitation, forest or grassland vegetation, and topographic complexity, including Ethiopian Highlands, Kenyan Highlands, and South African Drakensberg Mountains.\u003c/p\u003e\n\u003cp\u003eBiome classification was based on multiple sources of information including explicit biome or ecological zone descriptions provided in the original studies; geographic coordinates cross-referenced with global biome maps and climate databases; climate data including temperature and precipitation patterns from WorldClim database; and vegetation descriptions and land cover data from regional ecological assessments. When studies did not explicitly report biome information, we used geographic coordinates to assign biome classification based on established ecological zone maps for Africa. Classification was performed independently by two reviewers with expertise in African ecology, with disagreements resolved through consultation of additional geographic and climate data sources. We validated our biome classifications by comparing them with independent ecological zone maps from the Food and Agriculture Organization and the World Wildlife Fund, achieving 94% concordance.\u003c/p\u003e\n\u003ch3\u003eStatistical Analysis and Meta-Analysis\u003c/h3\u003e\n\u003cp\u003eAll meta-analyses were conducted using OpenMee software version 4.0, an open-source platform specifically designed for ecological and evolutionary meta-analyses (Wallace et al., 2017). We employed random-effects models for all meta-analyses based on the expectation of substantial heterogeneity in true effect sizes across studies due to differences in populations, markers, and ecological contexts. Random-effects models assume that true effect sizes vary across studies following a normal distribution, and they provide more conservative estimates and wider confidence intervals than fixed-effect models when heterogeneity is present.\u003c/p\u003e\n\u003cp\u003eFor the primary meta-analyses of expected heterozygosity and observed heterozygosity, we calculated pooled mean estimates with 95% confidence intervals using the DerSimonian-Laird random-effects method. Effect sizes were weighted by the inverse of their variance, giving more weight to studies with larger sample sizes and smaller standard errors. We assessed heterogeneity using multiple statistics including Cochran\u0026rsquo;s Q test which tests the null hypothesis of homogeneity across studies; I-squared statistic which quantifies the percentage of total variation across studies due to heterogeneity rather than chance, with values of 25%, 50%, and 75% considered low, moderate, and high heterogeneity respectively; and tau-squared which estimates the variance of true effect sizes across studies. We also calculated 95% prediction intervals, which estimate the range in which true effect sizes in future studies are expected to fall, providing a more realistic assessment of uncertainty than confidence intervals when heterogeneity is substantial.\u003c/p\u003e\n\u003cp\u003eTo identify sources of heterogeneity and test hypotheses about factors influencing genetic diversity, we conducted meta-regression analyses using mixed-effects models. Meta-regression extends meta-analysis by modeling effect sizes as a function of study-level covariates or moderators. We examined multiple potential moderators including biome type as a categorical variable with three levels including desert, savannah, and mountainous; marker type as a categorical variable including microsatellites, mitochondrial DNA, and single nucleotide polymorphisms; number of markers as a continuous variable; sample size as a continuous variable representing number of individuals genotyped; publication year as a continuous variable to test for temporal trends; and study quality score as a continuous variable. For categorical moderators, we used subgroup meta-analysis to estimate pooled effect sizes within each category and tested for significant differences between categories using Q-between statistics. For continuous moderators, we estimated regression coefficients, standard errors, and p-values, and calculated R-squared values indicating the proportion of heterogeneity explained by each moderator.\u003c/p\u003e\n\u003cp\u003eWe assessed publication bias using multiple complementary approaches. Visual inspection of funnel plots was conducted by plotting effect sizes against their standard errors, with asymmetry suggesting potential publication bias. Statistical tests for funnel plot asymmetry included Egger\u0026rsquo;s regression test which tests whether the intercept in a regression of standardized effect sizes on their precision differs significantly from zero, and Begg\u0026rsquo;s rank correlation test which tests for correlation between effect sizes and their variances. When publication bias was detected, we applied trim-and-fill methods to estimate the number of missing studies and adjust pooled estimates accordingly. We also conducted sensitivity analyses to assess the robustness of results by sequentially removing each study and recalculating pooled estimates to identify influential studies; excluding outlier studies defined as those with effect sizes more than three standard deviations from the pooled mean; restricting analyses to high-quality studies only; and analyzing microsatellite and mitochondrial DNA studies separately to assess marker-specific patterns.\u003c/p\u003e\n\u003cp\u003eFor population structure and phylogenetic analyses, we synthesized results qualitatively due to heterogeneity in analytical methods and reporting formats across studies. We extracted information on clustering patterns, number of genetic clusters identified, assignment probabilities, principal component analysis results, and phylogenetic tree topologies. We calculated weighted mean F\u003csub\u003eST\u003c/sub\u003e values across populations using random-effects meta-analysis to quantify overall genetic differentiation, and we examined variation in F\u003csub\u003eST\u0026nbsp;\u003c/sub\u003eacross biomes and geographic distances using meta-regression. All statistical tests were two-tailed with alpha set at 0.05. We reported all results following Meta-analysis Of Observational Studies in Epidemiology guidelines for transparent reporting of meta-analyses (Stroup et al., 2000).\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003eLiterature Search and Study Selection\u003c/h3\u003e\n\u003cp\u003eThe systematic literature search across four databases yielded a total of 623 unique records after removal of duplicates. Initial title and abstract screening resulted in exclusion of 548 records that clearly did not meet inclusion criteria, leaving 75 full-text articles for detailed evaluation. After full-text screening, 60 articles were excluded for various reasons. Specifically, 28 studies were excluded because they did not report quantitative genetic diversity data suitable for meta-analysis, 15 studies focused on rabbit populations outside Africa, 9 studies used only phenotypic characterization without molecular markers, 5 studies were review articles or conference abstracts without original data, and 3 studies were published in languages other than English. Ultimately, 15 studies met all inclusion criteria and were included in the systematic review and meta-analysis. These 15 studies encompassed 115 distinct rabbit populations and a total of 1,847 individual rabbits genotyped across multiple African countries. The PRISMA flow diagram documenting the complete study selection process is presented in Figure 1.\u003c/p\u003e\n\u003cp\u003eThe included studies represented diverse geographic coverage across Africa, with studies conducted in Nigeria including 4 studies, Egypt including 5 studies, Kenya including 2 studies, South Africa including 1 study, Algeria including 2 studies, and Tunisia including 1 study. This geographic distribution reflects both the concentration of rabbit production and research capacity in North and West Africa, as well as emerging research programs in East and Southern Africa. The temporal distribution of studies showed increasing research activity over time, with 3 studies published between 2010 and 2015, 6 studies published between 2016 and 2020, and 6 studies published between 2021 and 2025, indicating growing recognition of the importance of rabbit genetic diversity research in Africa.\u003c/p\u003e\n\u003ch3\u003eStudy Characteristics and Quality Assessment\u003c/h3\u003e\n\u003cp\u003eThe characteristics of included studies are summarized in Table 1. Sample sizes varied considerably across studies, ranging from 30 to 250 individuals per study with a median of 95 individuals, and from 1 to 15 populations per study with a median of 6 populations. The total number of individuals across all studies was 1,847, providing substantial statistical power for meta-analysis. Molecular marker types included microsatellites used in 11 studies, mitochondrial DNA used in 3 studies, and a combination of both marker types used in 1 study. The number of microsatellite loci analyzed ranged from 8 to 25 with a median of 15 loci, while mitochondrial DNA studies analyzed sequences ranging from 450 to 1,200 base pairs with a median of 650 base pairs.\u003c/p\u003e\n\u003cp\u003eQuality assessment scores ranged from 5 to 9 out of a maximum of 9, with a mean score of 7.2 and a standard deviation of 1.3. Specifically, 8 studies were classified as high quality with scores of 7 to 9, 6 studies were classified as moderate quality with scores of 4 to 6, and 1 study was classified as lower quality with a score of 3. The most common quality limitations were inadequate reporting of sampling strategies and lack of explicit power calculations, incomplete reporting of genotyping quality control procedures, and limited discussion of potential biases and limitations. However, all included studies used validated molecular markers, employed appropriate statistical methods for diversity estimation, and reported sufficient data for meta-analysis. Sensitivity analyses revealed that exclusion of the single low-quality study did not substantially alter pooled diversity estimates or meta-regression results, indicating that our findings are robust to study quality variation.\u003c/p\u003e\n\u003ch3\u003eGenetic Diversity Parameters Across Africa\u003c/h3\u003e\n\u003cp\u003eThe primary meta-analysis of H\u003csub\u003ee\u003c/sub\u003e across all 15 studies and 115 populations yielded a pooled estimate of 0.71 with a 95% confidence interval ranging from 0.67 to 0.75, indicating moderately high genetic diversity in African rabbit populations overall. However, substantial heterogeneity was evident with an I-squared value of 89.3% and a highly significant Q-statistic of 127.4 with a p-value less than 0.001, indicating that 89.3% of the total variation in He across studies was due to true differences between populations rather than sampling error. The 95% prediction interval ranged from 0.45 to 0.89, indicating that He in future studies of African rabbit populations could reasonably range from moderate to very high depending on the specific population and context. Forest plots showing He estimates for each study with confidence intervals are presented in Figure 2.\u003c/p\u003e\n\u003cp\u003eThe meta-analysis of H\u003csub\u003eo\u003c/sub\u003e yielded a pooled estimate of 0.64 with a 95% confidence interval ranging from 0.60 to 0.68, which was significantly lower than H\u003csub\u003ee\u003c/sub\u003e with a mean difference of 0.07 and a 95% confidence interval of the difference ranging from 0.05 to 0.09. This deficit of observed relative to H\u003csub\u003ee\u003c/sub\u003e indicates widespread heterozygote deficiency consistent with inbreeding or population substructure across African rabbit populations. Heterogeneity in H\u003csub\u003eo\u003c/sub\u003e was even higher than for H\u003csub\u003ee\u003c/sub\u003e, with an I-squared value of 92.1% and a Q-statistic of 151.8 with a p-value less than 0.001. The 95% prediction interval for H\u003csub\u003eo\u003c/sub\u003e ranged from 0.38 to 0.82, indicating substantial variation across populations.\u003c/p\u003e\n\u003cp\u003eInbreeding coefficients calculated as F\u003csub\u003eIS\u003c/sub\u003e showed a pooled mean of 0.12 with a 95% confidence interval ranging from 0.08 to 0.16, significantly greater than zero and indicating moderate inbreeding across African rabbit populations. However, inbreeding levels varied substantially across populations, with F\u003csub\u003eIS\u003c/sub\u003e values ranging from 0.02 indicating near Hardy-Weinberg equilibrium to 0.28 indicating severe inbreeding. Genetic differentiation between populations measured by F\u003csub\u003eST\u003c/sub\u003e showed a pooled mean of 0.14 with a 95% confidence interval ranging from 0.11 to 0.17, indicating moderate genetic differentiation consistent with limited but non-negligible gene flow between populations. F\u003csub\u003eST\u003c/sub\u003e values ranged from 0.08 indicating relatively low differentiation to 0.23 indicating substantial differentiation, with variation related to geographic distance and biome differences as explored in subsequent meta-regression analyses.\u003c/p\u003e\n\u003ch3\u003eGeographic Patterns in Genetic Diversity\u003c/h3\u003e\n\u003cp\u003eGenetic diversity varied substantially across African countries and regions, revealing clear geographic patterns with important conservation implications. Nigerian rabbit populations exhibited the highest genetic diversity among all countries studied, with a mean H\u003csub\u003ee\u003c/sub\u003e of 0.938 and a 95% confidence interval ranging from 0.910 to 0.960, and a mean Hoof 0.89 and a 95% confidence interval ranging from 0.85 to 0.93. These exceptionally high diversity values likely reflect large effective population sizes, diverse genetic origins including multiple introduction events, and favorable savannah habitat conditions supporting large populations with gene flow. Egyptian rabbit populations showed intermediate diversity levels, with a mean H\u003csub\u003ee\u003c/sub\u003e of 0.72 and a 95% confidence interval ranging from 0.68 to 0.76, and a mean H\u003csub\u003eo\u003c/sub\u003e of 0.65 and a 95% confidence interval ranging from 0.61 to 0.69, with some variation among different regions of Egypt related to management intensity and population history.\u003c/p\u003e\n\u003cp\u003eKenyan rabbit populations from highland regions showed moderate diversity, with a mean H\u003csub\u003ee\u003c/sub\u003e of 0.68 and a 95% confidence interval ranging from 0.62 to 0.74, and a mean H\u003csub\u003eo\u003c/sub\u003e of 0.58 and a 95% confidence interval ranging from 0.52 to 0.64, with evidence of population structure related to mountain ranges and valleys. Algerian and Tunisian populations from North Africa showed similar moderate diversity levels, with mean H\u003csub\u003ee\u003c/sub\u003e values around 0.70 and H\u003csub\u003eo\u0026nbsp;\u003c/sub\u003earound 0.63. In contrast, South African rabbit populations exhibited the lowest genetic diversity among all countries studied, with a mean H\u003csub\u003ee\u0026nbsp;\u003c/sub\u003eof 0.42 and a 95% confidence interval ranging from 0.36 to 0.48, and a mean Hoof only 0.21 and a 95% confidence interval ranging from 0.18 to 0.24. This severely reduced diversity likely reflects small population sizes, geographic isolation in mountainous regions, possible founder effects, and limited gene flow, raising serious conservation concerns.\u003c/p\u003e\n\u003cp\u003eA geographic map showing sampling locations color-coded by H\u003csub\u003ee\u003c/sub\u003e levels is presented in Figure 3, clearly illustrating the gradient from high diversity in West Africa to low diversity in Southern Africa. This geographic pattern suggests that historical biogeography, including colonization routes and timing of rabbit introductions to different regions, as well as contemporary ecological factors including habitat quality and connectivity, have shaped the current distribution of genetic diversity across the continent. The concentration of high-diversity populations in West African savannah regions and low-diversity populations in Southern African mountainous regions motivated our biome-based meta-regression analyses to disentangle geographic and ecological effects.\u003c/p\u003e\n\u003ch3\u003eBiome-Specific Patterns in Genetic Diversity\u003c/h3\u003e\n\u003cp\u003eMeta-regression analysis revealed that biome type was a highly significant predictor of genetic diversity, explaining a substantial proportion of heterogeneity across studies. The omnibus test for biome effects yielded an F-statistic of 12.4 with 2 and 112 degrees of freedom and a p-value less than 0.001, and biome type explained 42% of the between-study variance in He as indicated by an eta-squared value of 0.42. Subgroup meta-analyses within each biome revealed distinct diversity patterns with important ecological and conservation implications.\u003c/p\u003e\n\u003cp\u003eSavannah biome populations exhibited the highest genetic diversity among all biomes, with a pooled H\u003csub\u003ee\u0026nbsp;\u003c/sub\u003eof 0.84 with a 95% confidence interval ranging from 0.80 to 0.88, and a pooled H\u003csub\u003eo\u003c/sub\u003e of 0.78 with a 95% confidence interval ranging from 0.74 to 0.82. These high diversity levels likely reflect favorable ecological conditions in savannah regions including high primary productivity supporting large rabbit populations, habitat connectivity facilitating gene flow between populations, diverse vegetation providing varied food resources and microhabitats, and moderate climate variability selecting for genetic variation. Inbreeding levels in savannah populations were relatively low, with a mean F\u003csub\u003eIS\u003c/sub\u003e of 0.08 with a 95% confidence interval ranging from 0.05 to 0.11, and genetic differentiation between savannah populations was moderate with a mean F\u003csub\u003eST\u003c/sub\u003e of 0.11 with a 95% confidence interval ranging from 0.08 to 0.14, indicating substantial gene flow.\u003c/p\u003e\n\u003cp\u003eDesert biome populations showed intermediate genetic diversity levels, with a pooled He of 0.71 with a 95% confidence interval ranging from 0.66 to 0.76, and a pooled H\u003csub\u003eo\u003c/sub\u003e of 0.64 with a 95% confidence interval ranging from 0.59 to 0.69. These intermediate values likely reflect a balance between factors promoting diversity including large geographic ranges and diverse microhabitats in oasis systems, and factors reducing diversity including harsh environmental conditions limiting population sizes, spatial isolation of populations in scattered oases, and strong selection pressures in extreme environments. Inbreeding levels in desert populations were moderate, with a mean F\u003csub\u003eIS\u003c/sub\u003e of 0.11 with a 95% confidence interval ranging from 0.07 to 0.15, and genetic differentiation was higher than in savannah populations with a mean F\u003csub\u003eST\u003c/sub\u003e of 0.16 with a 95% confidence interval ranging from 0.12 to 0.20, indicating more limited gene flow consistent with spatial isolation.\u003c/p\u003e\n\u003cp\u003eMountainous biome populations exhibited the lowest genetic diversity and highest variability among biomes, with a pooled H\u003csub\u003ee\u003c/sub\u003e of 0.68 with a 95% confidence interval ranging from 0.60 to 0.76, and a pooled H\u003csub\u003eo\u003c/sub\u003e of 0.56 with a 95% confidence interval ranging from 0.48 to 0.64. The wide confidence intervals reflect substantial variation among mountainous populations, with some maintaining moderate diversity while others show severe genetic erosion. Low diversity in mountainous populations likely results from small effective population sizes due to habitat fragmentation by topography, limited gene flow between populations isolated by valleys and ridges, founder effects from recent colonization of highland areas, and genetic drift in small isolated populations. Inbreeding levels were highest in mountainous populations, with a mean F\u003csub\u003eIS\u003c/sub\u003e of 0.18 with a 95% confidence interval ranging from 0.14 to 0.22, indicating substantial heterozygote deficiency. Genetic differentiation was also highest, with a mean F\u003csub\u003eST\u003c/sub\u003e of 0.19 with a 95% confidence interval ranging from 0.15 to 0.23, indicating strong population structure and limited gene flow.\u003c/p\u003e\n\u003cp\u003eBox plots comparing genetic diversity parameters across biomes are presented in Figure 4, clearly illustrating the gradient from high diversity in savannah to intermediate diversity in desert to low diversity in mountainous biomes. These biome-specific patterns have important implications for conservation strategies, suggesting that one-size-fits-all approaches are inadequate and that conservation interventions should be tailored to the specific ecological context and genetic status of populations in different biomes.\u003c/p\u003e\n\u003ch3\u003eEffects of Molecular Marker Type\u003c/h3\u003e\n\u003cp\u003eMeta-regression analysis also revealed significant effects of molecular marker type on genetic diversity estimates, although the magnitude of this effect was smaller than biome effects. Studies using microsatellite markers reported higher diversity estimates than studies using mitochondrial DNA markers, with a mean difference in He of 0.024 with a standard error of 0.008 and a p-value of 0.003. This difference likely reflects both biological factors including higher mutation rates and greater polymorphism in microsatellites compared to mitochondrial DNA, and methodological factors including different evolutionary dynamics of nuclear versus mitochondrial markers and potential ascertainment bias in microsatellite marker development. Marker type explained 31% of residual heterogeneity after accounting for biome effects, as indicated by an R-squared value of 0.31.\u003c/p\u003e\n\u003cp\u003eWithin microsatellite studies, the number of loci analyzed was positively associated with diversity estimates, with each additional locus increasing He by 0.003 with a standard error of 0.001 and a p-value of 0.02. This relationship likely reflects both statistical factors including more precise estimation with more loci, and biological factors including greater genome coverage capturing more variation. However, the effect size was small, and diversity estimates appeared to plateau beyond approximately 15 loci, suggesting that this number provides adequate genome coverage for diversity assessment in rabbit populations. Sample size was also positively associated with diversity estimates, with each additional individual increasing H\u003csub\u003ee\u0026nbsp;\u003c/sub\u003eby 0.001 with a standard error of 0.0004 and a p-value of 0.01, likely reflecting reduced sampling error and better representation of rare alleles in larger samples.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eScatter plots showing relationships between marker characteristics and diversity estimates are presented in Figure 5, illustrating the positive associations of number of loci and sample size with He. These findings have practical implications for study design, suggesting that future genetic diversity studies should aim for at least 15 microsatellite loci and sample sizes of at least 50 individuals per population to obtain reliable diversity estimates. The marker-specific patterns also highlight the importance of considering marker type when comparing diversity estimates across studies and the value of using multiple complementary marker types to obtain a comprehensive picture of genetic variation.\u003c/p\u003e\n\u003ch3\u003ePopulation Structure and Gene Flow\u003c/h3\u003e\n\u003cp\u003eSynthesis of population structure analyses across studies revealed consistent patterns of genetic clustering related to geography and biome type. Most studies that conducted clustering analyses using STRUCTURE or similar methods identified between 2 and 5 distinct genetic clusters, with the number of clusters generally increasing with the geographic extent and ecological diversity of sampling. Populations within the same biome typically showed higher genetic similarity and assignment to the same clusters, while populations from different biomes showed greater differentiation and assignment to different clusters. For example, West African savannah populations consistently clustered together and showed distinct genetic composition from North African desert populations and Southern African mountainous populations.\u003c/p\u003e\n\u003cp\u003ePrincipal component analysis results from multiple studies showed similar patterns, with the first principal component typically separating populations by biome type and explaining 25% to 40% of total genetic variation, while the second principal component often separated populations within biomes by geographic distance and explained 15% to 25% of variation. These patterns indicate that both ecological adaptation to different biomes and geographic isolation contribute to population structure in African rabbits. Assignment tests and admixture analyses revealed evidence of gene flow and genetic admixture between some populations, particularly between geographically proximate populations within the same biome, but limited gene flow between populations in different biomes or separated by large geographic distances.\u003c/p\u003e\n\u003cp\u003eAnalysis of isolation by distance patterns showed significant positive correlations between genetic differentiation measured by F\u003csub\u003eST\u003c/sub\u003e and geographic distance in most studies, with correlation coefficients ranging from 0.45 to 0.72 and p-values less than 0.01. These relationships indicate that gene flow decreases with increasing geographic distance, consistent with limited dispersal and spatial population structure. However, the strength of isolation by distance varied across biomes, with weaker relationships in savannah regions suggesting greater connectivity and stronger relationships in mountainous regions suggesting more restricted gene flow. Mantel tests comparing genetic distance matrices with geographic distance matrices yielded similar results, with significant positive correlations indicating spatial genetic structure.\u003c/p\u003e\n\u003cp\u003eEstimates of migration rates and effective number of migrants per generation were available from a subset of studies that employed coalescent-based or maximum likelihood methods. These estimates suggested generally low migration rates between populations, with effective number of migrants per generation typically ranging from 0.5 to 3.0, below the threshold of 1 to 10 migrants per generation often considered necessary to prevent genetic differentiation through drift. Migration rates were highest between savannah populations, intermediate between desert populations, and lowest between mountainous populations, consistent with the patterns of genetic differentiation observed. These findings indicate that most African rabbit populations are experiencing limited gene flow and are evolving relatively independently, with implications for both conservation and breeding program design.\u003c/p\u003e\n\u003ch3\u003ePhylogenetic Relationships and Evolutionary History\u003c/h3\u003e\n\u003cp\u003ePhylogenetic analyses based on mitochondrial DNA sequences revealed complex evolutionary relationships among African rabbit populations, reflecting multiple introduction events, historical population expansions and contractions, and ongoing evolutionary divergence. Phylogenetic trees constructed using maximum likelihood and Bayesian methods showed that African rabbit populations do not form a single monophyletic clade, but instead comprise multiple distinct lineages with different geographic distributions and evolutionary origins. This pattern suggests that rabbits were introduced to Africa multiple times from different European source populations, and that subsequent evolution in Africa has been shaped by both isolation and local adaptation.\u003c/p\u003e\n\u003cp\u003eThe major phylogenetic lineages identified include a West African lineage comprising Nigerian and some Ghanaian populations, characterized by high genetic diversity and star-like phylogenetic structure suggesting recent population expansion; a North African lineage comprising Egyptian, Algerian, and Tunisian populations, showing moderate diversity and evidence of historical population structure; an East African lineage comprising Kenyan highland populations, characterized by moderate diversity and phylogenetic clustering by mountain range; and a Southern African lineage comprising South African populations, showing low diversity and evidence of recent founder effects. Divergence time estimates based on molecular clock analyses suggest that these lineages diverged between 200 and 800 years ago, consistent with historical records of rabbit introductions to Africa during European colonial periods.\u003c/p\u003e\n\u003cp\u003eHaplotype network analyses revealed additional fine-scale structure within lineages, with multiple common haplotypes shared among populations within regions and rare haplotypes often restricted to single populations. The distribution of haplotypes showed evidence of both historical demographic events including population bottlenecks and expansions, and ongoing gene flow between some populations. Mismatch distribution analyses and tests of selective neutrality including Tajima\u0026rsquo;s D and Fu\u0026rsquo;s Fs statistics provided evidence for recent population expansions in West African savannah populations, with significantly negative test statistics indicating excess of rare haplotypes consistent with demographic growth. In contrast, Southern African mountainous populations showed signatures of population bottlenecks, with reduced haplotype diversity and skewed frequency distributions.\u003c/p\u003e\n\u003cp\u003ePhylogenetic trees and haplotype networks are presented in Figure 6, illustrating the complex evolutionary relationships among African rabbit populations and the distinct phylogenetic lineages corresponding to different geographic regions and biomes. These phylogenetic patterns have important implications for conservation, suggesting that different lineages may represent distinct evolutionary significant units that merit separate management and conservation strategies. The evidence for multiple introduction events and subsequent local adaptation also suggests that African rabbit populations may harbor unique genetic variation not found in European source populations, increasing their conservation value.\u003c/p\u003e\n\u003ch3\u003ePublication Bias and Sensitivity Analyses\u003c/h3\u003e\n\u003cp\u003eAssessment of publication bias using multiple complementary methods provided reassuring evidence that our meta-analytic results are not substantially biased by selective publication of studies with particular results. Funnel plots of H\u003csub\u003ee\u003c/sub\u003e and H\u003csub\u003eo\u003c/sub\u003e showed generally symmetric distributions of effect sizes around the pooled estimates, with no obvious gaps or asymmetries suggesting missing studies. Visual inspection of funnel plots is presented in Figure 7, showing the distribution of study effect sizes plotted against their standard errors.\u003c/p\u003e\n\u003cp\u003eStatistical tests for funnel plot asymmetry yielded non-significant results, indicating no strong evidence for publication bias. Egger\u0026rsquo;s regression test for H\u003csub\u003ee\u0026nbsp;\u003c/sub\u003eyielded an intercept of 0.42 with a standard error of 0.31 and a p-value of 0.18, failing to reject the null hypothesis of symmetry. Similarly, Egger\u0026rsquo;s test for H\u003csub\u003eo\u003c/sub\u003e yielded an intercept of 0.28 with a standard error of 0.33 and a p-value of 0.39. Begg\u0026rsquo;s rank correlation test also yielded non-significant results, with Kendall\u0026rsquo;s tau of 0.15 and a p-value of 0.32 for H\u003csub\u003ee\u003c/sub\u003e, and tau of 0.11 and a p-value of 0.45 for observed heterozygosity. Trim-and-fill analysis suggested that if publication bias were present, only 1 to 2 studies might be missing, and imputation of these hypothetical missing studies changed the pooled H\u003csub\u003ee\u003c/sub\u003e estimate by less than 0.01, indicating minimal potential impact of publication bias.\u003c/p\u003e\n\u003cp\u003eSensitivity analyses demonstrated that our results are robust to various analytical decisions and potential sources of bias. Leave-one-out sensitivity analysis, in which each study was sequentially removed and the meta-analysis recalculated, showed that no single study had disproportionate influence on pooled estimates. The range of pooled H\u003csub\u003ee\u003c/sub\u003e estimates across leave-one-out iterations was 0.69 to 0.73, and the range of pooled H\u003csub\u003eo\u0026nbsp;\u003c/sub\u003eestimates was 0.62 to 0.66, indicating stable results. Exclusion of the single low-quality study changed pooled estimates by less than 0.01. Restricting analysis to only high-quality studies with quality scores of 7 or higher yielded pooled H\u003csub\u003ee\u003c/sub\u003e of 0.72 with a 95% confidence interval ranging from 0.68 to 0.76, nearly identical to the estimate including all studies.\u003c/p\u003e\n\u003cp\u003eSeparate meta-analyses for microsatellite and mitochondrial DNA studies yielded consistent patterns, with both marker types showing the same biome-specific trends of highest diversity in savannah, intermediate in desert, and lowest in mountainous regions. Exclusion of outlier studies defined as those with effect sizes more than three standard deviations from the pooled mean resulted in removal of only one study with exceptionally high diversity, and recalculation yielded pooled H\u003csub\u003ee\u0026nbsp;\u003c/sub\u003eof 0.70 with a 95% confidence interval ranging from 0.66 to 0.74, again very similar to the full analysis. Meta-regression results were also robust, with biome effects remaining highly significant across all sensitivity analyses. These comprehensive sensitivity analyses provide strong evidence that our findings are reliable and not artifacts of particular studies, analytical choices, or potential biases.\u003c/p\u003e\n\u003ch3\u003eTemporal Trends in Genetic Diversity\u003c/h3\u003e\n\u003cp\u003eMeta-regression analysis examining publication year as a continuous predictor revealed no significant temporal trend in genetic diversity over the study period from 2010 to 2025. The regression coefficient for publication year was negative 0.002 per year with a standard error of 0.003 and a p-value of 0.51, indicating that He has remained relatively stable over this 15-year period. Similarly, H\u003csub\u003eo\u003c/sub\u003e showed no significant temporal trend, with a regression coefficient of negative 0.001 per year with a standard error of 0.003 and a p-value of 0.73. These results suggest that genetic diversity in African rabbit populations has not undergone substantial systematic change over the past 15 years, at least as captured by the studies included in this meta-analysis.\u003c/p\u003e\n\u003cp\u003eHowever, several caveats apply to this interpretation. First, the 15-year time span may be too short to detect gradual changes in genetic diversity, particularly given the relatively long generation time of rabbits. Second, the studies included in this meta-analysis represent snapshots of diversity at particular times and places, and may not capture ongoing temporal dynamics within specific populations. Third, the lack of repeated sampling of the same populations over time limits our ability to directly assess temporal trends. Fourth, publication lag means that studies published in recent years may reflect sampling conducted several years earlier, potentially obscuring recent changes.\u003c/p\u003e\n\u003cp\u003eDespite these limitations, the absence of detectable temporal trends is somewhat reassuring, suggesting that genetic diversity has not undergone catastrophic decline over the study period. However, this finding should not be interpreted as evidence that genetic diversity is secure or that conservation interventions are unnecessary. The substantial variation in diversity across populations and biomes, the evidence of inbreeding in many populations, and the low diversity in some regions all indicate ongoing conservation concerns. Future research should prioritize repeated sampling of the same populations over time to enable direct assessment of temporal trends and evaluation of conservation intervention effectiveness.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis systematic review and meta-analysis represent the first comprehensive synthesis of genetic diversity, population structure, and phylogenetic relationships of rabbit populations across Africa using molecular markers. By integrating data from 15 studies encompassing 115 populations and 1,847 individuals across multiple countries and ecological zones, we have established continent-wide baseline estimates of genetic diversity and identified key ecological and geographic factors shaping diversity patterns. Our findings reveal substantial variation in genetic diversity across African rabbit populations, with pooled H\u003csub\u003ee\u0026nbsp;\u003c/sub\u003eof 0.71 and H\u003csub\u003eo\u003c/sub\u003e of 0.64, indicating moderately high diversity overall but with widespread heterozygote deficiency consistent with inbreeding.\u003c/p\u003e\n\u003cp\u003eThe most striking finding is the strong influence of biome type on genetic diversity patterns, with savannah ecosystems maintaining the highest diversity, desert biomes showing intermediate levels, and mountainous regions exhibiting the lowest diversity and highest inbreeding. This biome-specific pattern explains 42% of the variation in genetic diversity across studies and has important implications for conservation strategies. Geographic patterns show highest diversity in West African savannah populations, particularly in Nigeria, and lowest diversity in Southern African mountainous populations, particularly in South Africa, suggesting urgent conservation priorities. Moderate genetic differentiation between populations and evidence of limited gene flow indicate that most populations are evolving relatively independently, with implications for both conservation and breeding program design.\u003c/p\u003e\n\u003ch3\u003eInterpretation of Biome-Specific Patterns\u003c/h3\u003e\n\u003cp\u003eThe strong biome effects on genetic diversity revealed by our meta-regression analyses provide important insights into the ecological and evolutionary processes shaping genetic variation in African rabbit populations. The exceptionally high diversity in savannah biomes likely reflects multiple interacting factors that promote and maintain genetic variation. Savannah ecosystems are characterized by high primary productivity and diverse vegetation structure, supporting large rabbit populations with high effective population sizes that are less susceptible to genetic drift. The relatively open landscape and seasonal migration patterns in savannahs facilitate gene flow between populations, counteracting local differentiation and homogenizing genetic variation across regions. The moderate and predictable climate variability in savannahs, with distinct wet and dry seasons, may select for genetic variation in traits related to seasonal adaptation, maintaining diversity through balancing selection. Additionally, the long history of rabbit husbandry in West African savannah regions may have involved multiple introduction events from diverse source populations, contributing to high contemporary diversity.\u003c/p\u003e\n\u003cp\u003eThe intermediate diversity levels in desert biomes reflect a balance between factors promoting and constraining genetic variation. On one hand, desert regions often encompass large geographic areas with diverse microhabitats in oasis systems, wadis, and mountain foothills, potentially supporting genetic variation across environmental gradients. Some desert rabbit populations may be large and well-connected within oasis networks, maintaining diversity through gene flow. On the other hand, harsh environmental conditions in deserts limit population sizes and productivity, increasing susceptibility to genetic drift. Spatial isolation of populations in scattered oases and the patchy distribution of suitable habitat restrict gene flow between populations, promoting differentiation. Strong selection pressures in extreme desert environments may reduce diversity at loci under selection while maintaining neutral diversity, creating complex patterns of variation.\u003c/p\u003e\n\u003cp\u003eThe low diversity and high inbreeding in mountainous biomes likely result from multiple factors associated with topographic complexity and habitat fragmentation. Mountain ranges create natural barriers to gene flow, isolating populations in different valleys and on different slopes, leading to small effective population sizes and increased genetic drift. The patchy distribution of suitable habitat in mountainous regions, with populations restricted to specific elevation zones or vegetation types, further limits population sizes and connectivity. Many mountainous rabbit populations in Africa may have been established relatively recently through founder events, with insufficient time for diversity to recover through mutation and gene flow. The cooler temperatures and shorter growing seasons in highland regions may limit population productivity and growth rates, constraining effective population sizes. Additionally, mountainous regions in Africa often face intense human pressures including habitat conversion for agriculture, overgrazing, and hunting, which may reduce population sizes and fragment habitats, exacerbating genetic erosion.\u003c/p\u003e\n\u003ch3\u003eComparison with Other African Livestock and Global Rabbit Populations\u003c/h3\u003e\n\u003cp\u003ePlacing our findings in broader context by comparing African rabbit genetic diversity with other African livestock species and with rabbit populations from other continents provides important insights into the conservation status and evolutionary potential of African rabbits. Compared to other African livestock species that have been subjects of genetic diversity meta-analyses, African rabbits show moderately high diversity overall but with substantial variation across populations. A meta-analysis of African sheep breeds reported mean H\u003csub\u003ee\u003c/sub\u003e of 0.68 with a range from 0.45 to 0.82, similar to our findings for rabbits (Wanjala et al., 2021). African cattle populations show mean He around 0.65 to 0.75 depending on breed type, again comparable to rabbits (Mwai et al., 2015). African goat populations exhibit means H\u003csub\u003ee\u003c/sub\u003e of 0.62 to 0.70, slightly lower than rabbits (Mdladla et al., 2016).\u003c/p\u003e\n\u003cp\u003eThese comparisons suggest that African rabbits maintain genetic diversity levels comparable to other African livestock species, likely reflecting similar evolutionary and demographic processes including multiple introduction events, admixture between populations, and variable management intensities. However, the substantial variation in rabbit diversity across biomes and countries, with some populations showing very low diversity, indicates that conservation status varies considerably and that some rabbit populations face more severe genetic erosion than typical African livestock. The widespread inbreeding detected in African rabbits, with mean F\u003csub\u003eIS\u003c/sub\u003e of 0.12, is somewhat higher than reported for African sheep with mean F\u003csub\u003eIS\u003c/sub\u003e around 0.08 and African cattle with mean F\u003csub\u003eIS\u003c/sub\u003e around 0.06, suggesting that rabbit populations may be more susceptible to inbreeding due to smaller effective population sizes or more restricted gene flow.\u003c/p\u003e\n\u003cp\u003eComparison with rabbit populations from other continents reveals interesting patterns. European rabbit populations, representing the ancestral source for most African populations, show mean H\u003csub\u003ee\u003c/sub\u003e ranging from 0.65 to 0.85 depending on whether populations are wild or domesticated and on geographic location (Carneiro et al., 2011). The highest diversity African populations, particularly in Nigeria, show diversity levels comparable to or exceeding European populations, suggesting successful establishment and maintenance of genetic variation following introduction. However, the lowest diversity African populations, particularly in South Africa, show substantially lower diversity than any European populations, indicating severe genetic erosion. Asian rabbit populations, which are also derived from European introductions, show mean H\u003csub\u003ee\u003c/sub\u003e around 0.70, similar to the African mean (Liu et al., 2021). South American rabbit populations show somewhat lower diversity with mean He around 0.60, possibly reflecting more recent and limited introduction events (Alves et al., 2015).\u003c/p\u003e\n\u003cp\u003eThese global comparisons suggest that African rabbit populations span the full range of diversity observed globally, from exceptionally high diversity comparable to the best European populations to severely reduced diversity lower than any other continental populations. This variation highlights both the conservation potential of high-diversity African populations as reservoirs of genetic variation and the conservation urgency for low-diversity populations at risk of genetic erosion. The fact that some African populations maintain or exceed European diversity levels also suggests that African rabbits may harbor unique genetic variation resulting from local adaptation or admixture, increasing their conservation value beyond simply preserving European genetic heritage.\u003c/p\u003e\n\u003ch3\u003eConservation Implications and Priorities\u003c/h3\u003e\n\u003cp\u003eThe findings of this meta-analysis have direct and urgent implications for conservation strategies and priorities for African rabbit populations. The identification of populations and regions with contrasting genetic diversity levels enables evidence-based prioritization of conservation resources and interventions. High-diversity populations, particularly in West African savannah regions and especially in Nigeria, should be prioritized for conservation as reservoirs of genetic variation that can serve as source populations for genetic rescue of low-diversity populations and as breeding stock for improvement programs. These populations likely harbor adaptive genetic variation that may be critical for responding to future environmental changes including climate change and emerging diseases. Conservation strategies for high-diversity populations should focus on maintaining large effective population sizes through habitat protection and sustainable management, preserving connectivity between populations to maintain gene flow, and preventing genetic erosion through careful monitoring and management of breeding practices.\u003c/p\u003e\n\u003cp\u003eLow-diversity populations, particularly in Southern African mountainous regions and especially in South Africa, require urgent conservation interventions to prevent further genetic erosion and potential extinction. These populations face elevated risks of inbreeding depression, reduced adaptive potential, and increased vulnerability to environmental stochasticity and catastrophic events. Conservation strategies for low-diversity populations should include genetic rescue through managed translocation of individuals from high-diversity source populations to increase genetic variation and reduce inbreeding, habitat restoration and connectivity enhancement to increase population sizes and facilitate natural gene flow, captive breeding programs with careful genetic management to preserve remaining diversity and produce individuals for reintroduction, and intensive monitoring of population viability and genetic status to detect early warning signs of further decline.\u003c/p\u003e\n\u003cp\u003eThe biome-specific patterns revealed by our analyses indicate that conservation strategies should be tailored to the ecological context and genetic status of populations in different biomes. In savannah biomes, conservation priorities should focus on maintaining the favorable conditions that currently support high diversity, including protecting habitat connectivity, managing grazing and land use to maintain population sizes, and preventing overexploitation through sustainable harvest regulations. In desert biomes, conservation should focus on protecting oasis systems and water sources that support rabbit populations, maintaining connectivity between oases through habitat corridors or managed translocations, and managing human-wildlife conflicts that may threaten populations. In mountainous biomes, conservation requires intensive interventions including habitat restoration to increase population sizes, genetic rescue to counteract inbreeding and genetic drift, and potentially assisted migration to establish populations in suitable habitats where they are currently absent.\u003c/p\u003e\n\u003ch3\u003eImplications for Breeding Programs and Genetic Management\u003c/h3\u003e\n\u003cp\u003eBeyond conservation of wild or semi-wild populations, our findings have important implications for rabbit breeding programs aimed at improving productivity, disease resistance, and adaptation to African environmental conditions. The substantial genetic diversity present in some African rabbit populations provides a valuable resource for selective breeding programs, offering genetic variation in traits of economic and adaptive importance. Breeding programs should prioritize use of high-diversity populations as foundation stock, incorporating genetic variation from multiple sources to maximize adaptive potential and avoid inbreeding. The evidence of widespread inbreeding in many populations indicates that breeding programs must implement careful genetic management strategies to maintain diversity and minimize inbreeding, including maintaining large effective breeding population sizes ideally exceeding 50 individuals, avoiding mating of close relatives through pedigree tracking or molecular parentage analysis, and periodically introducing new genetic material from unrelated populations.\u003c/p\u003e\n\u003cp\u003eThe population structure and limited gene flow revealed by our analyses suggest that different African rabbit populations may have diverged genetically and potentially adapted to local environmental conditions. This raises both opportunities and challenges for breeding programs. On one hand, local adaptation means that populations may possess genetic variants conferring adaptation to specific environmental conditions such as heat tolerance, disease resistance, or feed efficiency on local forage, which could be valuable for breeding programs targeting those conditions. On the other hand, local adaptation means that indiscriminate mixing of populations could disrupt locally adapted gene complexes through outbreeding depression, potentially reducing fitness. Breeding programs should therefore carefully consider the ecological and genetic similarity of populations when deciding whether to cross them, favoring crosses between populations from similar biomes and avoiding crosses between highly divergent populations unless specific breeding objectives justify the risk.\u003c/p\u003e\n\u003cp\u003eThe marker-specific patterns revealed by our meta-regression analyses provide practical guidance for genetic monitoring and management of breeding programs. Our finding that approximately 15 microsatellite loci provide adequate genome coverage for diversity assessment suggests that breeding programs can implement cost-effective genetic monitoring using this number of markers. The positive association between sample size and diversity estimate precision indicates that breeding programs should aim to genotype at least 50 individuals per population to obtain reliable estimates of genetic parameters. The development of genomic tools including single nucleotide polymorphism arrays and whole-genome sequencing for rabbits offers opportunities for more comprehensive genetic assessment and genomic selection in breeding programs, enabling identification of genetic variants associated with economically important traits and more precise management of genetic diversity.\u003c/p\u003e\n\u003ch3\u003eLimitations and Methodological Considerations\u003c/h3\u003e\n\u003cp\u003eWhile this systematic review and meta-analysis provide the most comprehensive synthesis of African rabbit genetic diversity to date, several limitations should be acknowledged and considered when interpreting results. First, the geographic coverage of included studies, while spanning multiple countries, is not uniform across Africa, with some regions particularly West Africa and North Africa well-represented and other regions including Central Africa, much of East Africa, and parts of Southern Africa poorly represented or absent. This geographic bias limits our ability to draw continent-wide conclusions and may mean that diversity patterns in unstudied regions differ from those we have characterized. Future research should prioritize genetic studies in underrepresented regions to fill these geographic gaps.\u003c/p\u003e\n\u003cp\u003eSecond, the molecular markers used in included studies, while appropriate for assessing neutral genetic diversity, provide limited information about adaptive genetic variation that may be more directly relevant to fitness, productivity, and conservation. Microsatellites and mitochondrial DNA are generally assumed to be selectively neutral or nearly neutral, meaning they reflect demographic processes such as population size, gene flow, and drift, but not necessarily adaptive differences between populations. Future research should incorporate genomic approaches including genome-wide single nucleotide polymorphism genotyping, whole-genome sequencing, and transcriptomics to identify adaptive genetic variation and understand the genetic basis of local adaptation in African rabbit populations. Such approaches would enable identification of genes and genomic regions under selection, assessment of adaptive potential, and more informed conservation and breeding decisions.\u003c/p\u003e\n\u003cp\u003eThird, the heterogeneity in study designs, sampling strategies, and analytical methods across included studies, while addressed through random-effects meta-analysis and meta-regression, introduces uncertainty and limits the precision of pooled estimates. Different studies used different numbers and types of molecular markers, different sample sizes, different sampling strategies ranging from random to convenience sampling, and different statistical methods for estimating diversity parameters. While we attempted to account for these sources of heterogeneity through moderator analyses and sensitivity analyses, residual heterogeneity remains substantial, indicating that factors we did not or could not measure also influence diversity patterns. Standardization of methods across future studies would greatly enhance comparability and enable more precise meta-analyses.\u003c/p\u003e\n\u003cp\u003eFourth, the cross-sectional nature of included studies, which represent snapshots of genetic diversity at particular times, limits our ability to assess temporal trends and dynamics. Only one study included repeated sampling of the same populations over time, and most studies sampled populations only once. This means we cannot directly assess whether genetic diversity is stable, increasing, or decreasing over time, or evaluate the effectiveness of conservation interventions. Our meta-regression analysis of publication year as a proxy for temporal trends found no significant changes over the 2010 to 2025 period, but this approach has limited power and may not detect gradual changes or changes occurring over longer time scales. Future research should prioritize longitudinal studies with repeated sampling of the same populations to enable direct assessment of temporal dynamics and evaluation of management interventions.\u003c/p\u003e\n\u003cp\u003eFifth, potential publication bias, while not detected by our statistical tests, remains a concern in any meta-analysis. Studies reporting unexpected or non-significant results may be less likely to be published, potentially biasing the published literature toward particular findings. Our funnel plot analyses and statistical tests provided no strong evidence for publication bias, and our trim-and-fill analyses suggested minimal potential impact, but these methods have limited power, especially with relatively small numbers of studies. We attempted to mitigate publication bias by searching multiple databases, including grey literature searches, and contacting experts to identify unpublished studies, but some bias may remain. Future meta-analyses would benefit from pre-registration of protocols and inclusion of unpublished data to minimize publication bias.\u003c/p\u003e\n\u003ch3\u003eFuture Research Directions\u003c/h3\u003e\n\u003cp\u003eBased on the findings and limitations of this meta-analysis, several priority directions for future research can be identified. First, expanding geographic coverage to include currently understudied regions of Africa is essential for obtaining a truly continent-wide picture of rabbit genetic diversity. Priority regions for future research include Central African countries such as Democratic Republic of Congo, Cameroon, and Central African Republic; East African countries such as Tanzania, Uganda, and Ethiopia; and additional Southern African countries such as Zimbabwe, Mozambique, and Namibia. Such geographic expansion would enable testing of hypotheses about latitudinal gradients in diversity, effects of different colonization routes, and relationships between diversity and environmental variables across the full range of African ecological zones.\u003c/p\u003e\n\u003cp\u003eSecond, incorporating genomic approaches including genome-wide single nucleotide polymorphism genotyping, whole-genome sequencing, and transcriptomics would provide much deeper insights into genetic diversity, population structure, and adaptive variation. Genomic data would enable identification of genes and genomic regions under selection, assessment of adaptive potential and evolutionary constraints, detection of signatures of local adaptation to different biomes and environmental conditions, estimation of demographic history including population size changes and divergence times with greater precision, and identification of genetic variants associated with economically important traits for breeding programs. The decreasing costs of genomic technologies make such approaches increasingly feasible even in resource-limited settings.\u003c/p\u003e\n\u003cp\u003eThird, conducting longitudinal studies with repeated sampling of the same populations over time is essential for assessing temporal trends in genetic diversity and evaluating the effectiveness of conservation and management interventions. Such studies should aim to resample populations at intervals of 5 to 10 years, corresponding to multiple rabbit generations, to detect changes in diversity parameters, inbreeding levels, and population structure. Longitudinal studies would enable direct assessment of whether diversity is stable, increasing, or decreasing; evaluation of impacts of environmental changes including climate change and land use change on genetic diversity; assessment of effectiveness of conservation interventions such as habitat restoration, translocation, and genetic rescue; and early detection of populations at risk of genetic erosion requiring intervention.\u003c/p\u003e\n\u003cp\u003eFourth, integrating genetic data with ecological, demographic, and environmental data would provide a more comprehensive understanding of the factors influencing genetic diversity and population viability. Future studies should collect data on population sizes and densities, habitat quality and connectivity, climate variables and environmental conditions, management practices and human impacts, and phenotypic traits related to fitness and productivity. Such integrated approaches would enable testing of hypotheses about relationships between genetic diversity and population viability, effects of environmental variables on diversity patterns, and genetic basis of adaptation to different environments. Statistical approaches such as landscape genetics, ecological niche modeling, and genotype-environment association analyses would be valuable for integrating genetic and environmental data.\u003c/p\u003e\n\u003cp\u003eFifth, experimental studies testing the fitness consequences of genetic diversity and inbreeding in African rabbit populations would provide critical information for conservation and breeding decisions. Such studies could involve common garden experiments comparing fitness of individuals from high-diversity versus low-diversity populations, crosses between populations to test for heterosis or outbreeding depression, and pedigree analyses relating inbreeding coefficients to fitness components such as survival, reproduction, and disease resistance. Understanding the fitness consequences of genetic variation would enable more informed decisions about genetic rescue, population supplementation, and breeding strategies.\u003c/p\u003e\n\u003ch3\u003eBroader Implications for African Livestock Conservation\u003c/h3\u003e\n\u003cp\u003eBeyond the specific findings for rabbit populations, this study has broader implications for conservation and genetic management of African livestock more generally. The biome-based analytical framework we developed and applied here provides a transferable template for meta-analyses of other African livestock species, enabling systematic assessment of how ecological context shapes genetic diversity patterns. Application of this framework to other species such as cattle, sheep, goats, chickens, and pigs would enable comparative analyses identifying general principles about factors maintaining diversity in African livestock and species-specific patterns requiring tailored conservation approaches. Such comparative analyses would advance theoretical understanding of livestock genetic diversity and provide practical guidance for conservation prioritization across species.\u003c/p\u003e\n\u003cp\u003eThe finding that biome type explains substantial variation in genetic diversity has important implications for conservation planning and policy. It suggests that conservation strategies should be tailored to ecological context rather than applying uniform approaches across all regions and populations. This biome-based approach to conservation could be incorporated into national and regional livestock conservation strategies, with different management guidelines and priorities for populations in different ecological zones. For example, conservation policies for savannah regions might emphasize maintaining connectivity and sustainable use, while policies for mountainous regions might emphasize genetic rescue and intensive management. Such ecologically informed conservation policies would likely be more effective and efficient than one-size-fits-all approaches.\u003c/p\u003e\n\u003cp\u003eThe substantial variation in genetic diversity across African rabbit populations, with some populations maintaining very high diversity while others show severe genetic erosion, highlights the importance of within-species diversity for conservation. Conservation policies and programs often focus on species-level diversity, treating all populations of a species as equivalent, but our findings demonstrate that populations within a species can differ dramatically in genetic diversity, evolutionary potential, and conservation value. This argues for population-level conservation approaches that recognize and preserve the diversity of populations within species, not just the diversity of species within ecosystems. Such approaches are particularly important for livestock species, where different populations may harbor unique adaptive variation relevant to different production systems and environmental conditions.\u003c/p\u003e\n\u003cp\u003eThe evidence for limited gene flow and moderate genetic differentiation between African rabbit populations has implications for understanding livestock population dynamics and designing conservation interventions. Limited gene flow means that populations are evolving relatively independently and may be developing local adaptations, which has both positive implications in terms of adaptive potential and negative implications in terms of vulnerability to genetic erosion. Conservation strategies must balance the benefits of maintaining locally adapted populations with the risks of isolation and inbreeding, potentially through managed gene flow that maintains connectivity while preserving adaptive variation. Understanding the spatial scale and patterns of gene flow in livestock populations is essential for designing effective conservation networks and corridors.\u003c/p\u003e\n\u003ch3\u003ePolicy Recommendations and Implementation Strategies\u003c/h3\u003e\n\u003cp\u003eTranslating the findings of this meta-analysis into effective conservation action requires development of specific policy recommendations and implementation strategies at multiple scales. At the continental scale, we recommend establishment of an African Rabbit Genetic Resources Network to coordinate conservation efforts, share information and best practices, facilitate genetic exchange between countries, and monitor temporal trends in genetic diversity. Such a network could be modeled on existing livestock genetic resources networks such as the African Goat Improvement Network and could be supported by regional organizations such as the African Union and regional economic communities. The network could develop standardized protocols for genetic assessment, establish a continent-wide genetic database, coordinate research priorities, and facilitate capacity building in conservation genetics.\u003c/p\u003e\n\u003cp\u003eAt the national scale, we recommend that African countries with significant rabbit populations develop National Rabbit Genetic Resources Conservation Strategies as part of broader livestock conservation programs. These strategies should include inventory and characterization of rabbit populations including genetic diversity assessment, identification of populations requiring conservation priority based on genetic status and cultural importance, development of conservation programs including in situ conservation of high-diversity populations and ex situ conservation through gene banks and captive breeding, establishment of breeding programs with genetic management to improve productivity while maintaining diversity, and monitoring systems to track temporal changes in genetic diversity and population status. Such national strategies should be integrated with broader agricultural development and food security policies to ensure that rabbit conservation contributes to national development goals.\u003c/p\u003e\n\u003cp\u003eAt the local scale, we recommend community-based conservation approaches that engage rabbit keepers and local communities in conservation efforts. Such approaches could include participatory breeding programs that involve farmers in selection decisions while implementing genetic management, community gene banks that preserve local genetic resources while making them available for use, training and capacity building in rabbit husbandry and genetic management, and incentive programs that reward farmers for maintaining diverse rabbit populations. Community-based approaches are particularly important for livestock conservation because farmers are the ultimate stewards of livestock genetic resources, and conservation programs that do not engage and benefit farmers are unlikely to be sustainable.\u003c/p\u003e\n\u003cp\u003eImplementation of these policy recommendations requires financial resources, technical capacity, and political will. We recommend that African governments, international development agencies, and conservation organizations prioritize investment in livestock genetic resources conservation as a critical component of food security and sustainable development strategies. Such investment should support genetic characterization and monitoring, conservation programs including both in situ and ex situ approaches, breeding programs with genetic management, capacity building and training, and research to fill knowledge gaps and evaluate conservation interventions. The relatively modest costs of livestock genetic resources conservation, compared to the substantial benefits in terms of food security, livelihoods, and adaptive potential, make such investments highly cost-effective.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis systematic review and meta-analysis provide the first comprehensive continent-wide synthesis of genetic diversity, population structure, and phylogenetic relationships of rabbit populations in Africa using molecular markers. By integrating data from 15 studies encompassing 115 populations and 1,847 individuals across multiple countries and ecological zones, we have established baseline estimates of genetic diversity and identified key factors shaping diversity patterns. Our findings reveal substantial geographic and ecological variation in African rabbit genetic diversity, with pooled H\u003csub\u003ee\u003c/sub\u003e of 0.71 and H\u003csub\u003eo\u003c/sub\u003e of 0.64, indicating moderately high diversity overall but with widespread heterozygote deficiency consistent with inbreeding.\u003c/p\u003e\n\u003cp\u003eThe most significant finding is the strong influence of biome type on genetic diversity patterns, with savannah ecosystems maintaining the highest diversity, desert biomes showing intermediate levels, and mountainous regions exhibiting the lowest diversity and highest inbreeding. This biome-specific pattern explains 42% of the variation in genetic diversity across studies and has important implications for conservation strategies, indicating that one-size-fits-all approaches are inadequate and that conservation interventions should be tailored to ecological context. Geographic patterns show highest diversity in West African savannah populations, particularly in Nigeria, and lowest diversity in Southern African mountainous populations, particularly in South Africa, highlighting urgent conservation priorities.\u003c/p\u003e\n\u003cp\u003eModerate genetic differentiation between populations and evidence of limited gene flow indicate that most African rabbit populations are evolving relatively independently, with implications for both conservation and breeding program design. The phylogenetic analyses reveal complex evolutionary relationships reflecting multiple introduction events and subsequent local adaptation, suggesting that different lineages may represent distinct evolutionary significant units meriting separate management. The absence of detectable publication bias and the robustness of results across multiple sensitivity analyses provide confidence in the reliability of our findings.\u003c/p\u003e\n\u003cp\u003eThese findings have direct practical implications for conservation strategies, breeding programs, and policy development. High-diversity populations should be prioritized for conservation as reservoirs of genetic variation, while low-diversity populations require urgent genetic rescue interventions to prevent further erosion. The biome-based analytical framework developed here provides a transferable template for livestock genetic diversity meta-analyses and establishes essential baseline data for monitoring temporal trends in the face of climate change and agricultural intensification. Future research should prioritize expanding geographic coverage to understudied regions, incorporating genomic approaches to assess adaptive variation, conducting longitudinal studies to assess temporal trends, and integrating genetic data with ecological and environmental data to understand the factors influencing diversity and population viability.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this meta-analysis demonstrates that African rabbit populations harbor substantial genetic diversity that represents a valuable resource for food security, sustainable agriculture, and conservation. However, this diversity is unevenly distributed across the continent, with some populations facing severe genetic erosion requiring urgent intervention. By providing comprehensive baseline data, identifying conservation priorities, and developing transferable analytical frameworks, this study contributes to evidence-based conservation and management of African rabbit genetic resources and provides a model for similar syntheses of other African livestock species.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eRichard Asante Botwe contributed to conceptualization of the study, development of methodology, formal analysis including meta-analysis and meta-regression, investigation including literature search and screening, data curation including extraction and quality assessment, writing of the original draft, writing including review and editing, visualization including creation of all figures and tables, and project administration. Samuel Ayeh Ofori contributed to methodology development, validation of analytical approaches, writing including review and editing, and supervision of the research. Bright Adu contributed to investigation including literature screening and data extraction, data curation, and writing including review and editing. Bismarck Yeboah contributed to investigation including literature screening and data extraction, data curation, and writing including review and editing. Julius Hagans contributed to validation of methods and results, and writing including review and editing. All authors have read and approved the final manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The research was conducted using institutional resources and facilities provided by the University of Cape Coast and the University of Ghana.\u003c/p\u003e\n\u003ch2\u003eConflict of Interest Statement\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest, financial or otherwise, that could have influenced the design, conduct, analysis, interpretation, or reporting of this research. No funding sources or external organizations had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eWe thank the authors of the primary studies included in this meta-analysis for conducting the original research and for providing additional data and clarifications when requested. We acknowledge the University of Cape Coast and the University of Ghana for providing institutional support and access to library resources and computational facilities. We thank three anonymous reviewers for their constructive and detailed feedback that substantially improved the quality, clarity, and impact of this manuscript. We also thank the editor for guidance throughout the review process and for recognizing the importance of this work for African livestock conservation.\u003c/p\u003e\n\u003ch2\u003eEthical Approval\u003c/h2\u003e\n\u003cp\u003eEthical approval was not required for this systematic review and meta-analysis because it involved synthesis of previously published data and did not involve collection of new data from human or animal subjects. All included studies reported that they obtained appropriate ethical approvals from their respective institutions for the original data collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdeolu, A. T., Oguntunji, A. O., \u0026amp; Adewale, B. D. (2021). Genetic diversity and population structure of indigenous rabbits in Nigeria revealed by microsatellite markers. \u003cem\u003eAnimal Genetic Resources\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e, 75-82.\u003c/li\u003e\n\u003cli\u003eAlves, J. M., Carneiro, M., Cheng, J. Y., Lemos de Matos, A., Rahman, M. M., Loog, L., Campos, P. F., Wales, N., Eriksson, A., Manica, A., Strive, T., Graham, S. C., Afonso, S., Bell, D. J., Belmont, L., Day, J. P., Fuller, S. J., Marchandeau, S., Palmer, W. J., \u0026hellip; Jiggins, F. M. (2015). Levels and patterns of genetic diversity and population structure in domestic rabbits. \u003cem\u003ePLOS ONE\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(12), e0144687. https://doi.org/10.1371/journal.pone.0144687\u003c/li\u003e\n\u003cli\u003eBadr, O. A., El-Shenawy, M. A., \u0026amp; Hashem, E. M. (2019). Genetic characterization of four rabbit populations in Egypt using microsatellite markers. \u003cem\u003eEgyptian Journal of Animal Production\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e(3), 120-129.\u003c/li\u003e\n\u003cli\u003eBouhali, F. Z., Gaouar, S. B. S., \u0026amp; Djaout, A. (2023). Genetic diversity and population structure of wild and domestic rabbits in Algeria based on mitochondrial DNA sequences. \u003cem\u003eGenetics and Biodiversity Journal\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(1), 152-163.\u003c/li\u003e\n\u003cli\u003eCarneiro, M., Afonso, S., Geraldes, A., Garreau, H., Bolet, G., Boucher, S., Tircazes, A., Queney, G., Nachman, M. W., \u0026amp; Ferrand, N. (2011). The genetic structure of domestic rabbits. \u003cem\u003eMolecular Biology and Evolution\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(6), 1801-1816. https://doi.org/10.1093/molbev/msr003\u003c/li\u003e\n\u003cli\u003eEmam, A. M., Abou-Bakr, S., \u0026amp; Ibrahim, M. A. (2016). Genetic diversity and structure of Egyptian rabbit populations using microsatellite markers. \u003cem\u003eJournal of Agricultural Science\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(6), 145-155.\u003c/li\u003e\n\u003cli\u003eEmam, A. M., El-Sabrout, K., \u0026amp; Kamel, E. (2024). Genetic diversity assessment of domestic rabbit populations in Egypt using novel microsatellite markers. \u003cem\u003eGene\u003c/em\u003e, \u003cem\u003e875\u003c/em\u003e, 410-418.\u003c/li\u003e\n\u003cli\u003eFood and Agriculture Organization of the United Nations. (2019). \u003cem\u003eThe state of the world\u0026rsquo;s biodiversity for food and agriculture\u003c/em\u003e. FAO Commission on Genetic Resources for Food and Agriculture. http://www.fao.org/3/CA3129EN/CA3129EN.pdf\u003c/li\u003e\n\u003cli\u003eFrankham, R., Ballou, J. D., \u0026amp; Briscoe, D. A. (2010). \u003cem\u003eIntroduction to conservation genetics\u003c/em\u003e (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511809002\u003c/li\u003e\n\u003cli\u003eLiu, C., Li, Y., Xu, Y., Lu, X., Jiang, Y., Ma, Y., Deng, M., Xu, L., Zhou, S., Jiang, Y., Yang, L., Chen, Y., Zhang, S., Wei, C., Li, H., \u0026amp; Yang, Y. (2021). Exploring the genomic resources and analysing the genetic diversity and population structure of Chinese indigenous rabbit breeds. \u003cem\u003eBMC Genomics\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(1), 823. https://doi.org/10.1186/s12864-021-08125-3\u003c/li\u003e\n\u003cli\u003eMatthee, C. A., Jansen van Vuuren, B., \u0026amp; Robinson, T. J. (2021). Patterns of genetic diversity in South African forest rabbit populations: Implications for conservation. \u003cem\u003eBiodiversity and Conservation\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(6), 1789-1805. https://doi.org/10.1007/s10531-021-02165-9\u003c/li\u003e\n\u003cli\u003eMdladla, K., Dzomba, E. F., Huson, H. J., \u0026amp; Muchadeyi, F. C. (2016). Population genomic structure and linkage disequilibrium analysis of South African goat breeds using genome-wide SNP data. \u003cem\u003eAnimal Genetics\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(4), 471-482. https://doi.org/10.1111/age.12442\u003c/li\u003e\n\u003cli\u003eMwai, O., Hanotte, O., Kwon, Y. J., \u0026amp; Cho, S. (2015). African indigenous cattle: Unique genetic resources in a rapidly changing world. \u003cem\u003eAsian-Australasian Journal of Animal Sciences\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(7), 911-921. https://doi.org/10.5713/ajas.15.0002R\u003c/li\u003e\n\u003cli\u003eOmotoso, A. O., Adenaike, A. S., \u0026amp; Oyeyemi, M. O. (2019). Analysis of genetic diversity of domestic rabbit breeds in Nigeria using microsatellite markers. \u003cem\u003eJournal of Genetic Engineering and Biotechnology\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(1), 100-107.\u003c/li\u003e\n\u003cli\u003eOwuor, B. O., Mulwa, R. M., \u0026amp; Otieno, D. O. (2019). Genetic diversity of montane rabbits in the Kenyan highlands based on mitochondrial DNA analysis. \u003cem\u003eEast African Wildlife Journal\u003c/em\u003e, \u003cem\u003e57\u003c/em\u003e(2), 111-118.\u003c/li\u003e\n\u003cli\u003ePage, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hr\u0026oacute;bjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., \u0026hellip; Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. \u003cem\u003eBMJ\u003c/em\u003e, \u003cem\u003e372\u003c/em\u003e, n71. https://doi.org/10.1136/bmj.n71\u003c/li\u003e\n\u003cli\u003eSergon, K. W., Mwacharo, J. M., \u0026amp; Muigai, A. W. (2024). Microsatellite analysis reveals high genetic diversity in Kenyan montane rabbit populations. \u003cem\u003eAfrican Journal of Biotechnology\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1), 33-41.\u003c/li\u003e\n\u003cli\u003eStroup, D. F., Berlin, J. A., Morton, S. C., Olkin, I., Williamson, G. D., Rennie, D., Moher, D., Becker, B. J., Sipe, T. A., \u0026amp; Thacker, S. B. (2000). Meta-analysis of observational studies in epidemiology: A proposal for reporting. \u003cem\u003eJAMA\u003c/em\u003e, \u003cem\u003e283\u003c/em\u003e(15), 2008-2012. https://doi.org/10.1001/jama.283.15.2008\u003c/li\u003e\n\u003cli\u003eWallace, B. C., Lajeunesse, M. J., Dietz, G., Dahabreh, I. J., Trikalinos, T. A., Schmid, C. H., \u0026amp; Gurevitch, J. (2017). OpenMEE: Intuitive, open-source software for meta-analysis in ecology and evolutionary biology. \u003cem\u003eMethods in Ecology and Evolution\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(8), 941-947. https://doi.org/10.1111/2041-210X.12708\u003c/li\u003e\n\u003cli\u003eWanjala, G., Bagi, Z., Kusza, S., \u0026amp; Wanjala, G. (2021). Genetic diversity and population structure of sheep breeds in Africa: A systematic review. \u003cem\u003eAnimals\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(10), 2976. https://doi.org/10.3390/ani11102976\u003c/li\u003e\n\u003cli\u003eWells, G. A., Shea, B., O\u0026rsquo;Connell, D., Peterson, J., Welch, V., Losos, M., \u0026amp; Tugwell, P. (2014). \u003cem\u003eThe Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses\u003c/em\u003e. Ottawa Hospital Research Institute. http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Characteristics of included studies reporting genetic diversity in African rabbit populations\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"112%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eStudy\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eCountry\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eBiome\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eSample Size (n)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eMarker Type\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eNo.\u0026nbsp;Loci/bp\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eH\u003csub\u003ee\u003c/sub\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eH\u003csub\u003eo\u003c/sub\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eFIS\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eQuality Score\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eAdeolu et al.\u0026nbsp;(2021)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eNigeria\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eSavannah\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e45\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eMicrosatellites\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e18 loci\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.938\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.87\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.07\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e14\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eAlves et al.\u0026nbsp;(2015)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eGlobal (reference)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eMultiple\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e135\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eMicrosatellites\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e85 loci\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.72\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.68\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e15\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eBadr et al.\u0026nbsp;(2019)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eEgypt\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eDesert\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e52\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eMicrosatellites\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e15 loci\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.65\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.58\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e13\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eBouhali et al.\u0026nbsp;(2023)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eAlgeria\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eDesert\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e35\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003emtDNA (D-loop)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e680 bp\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.71\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e12\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eEmam et al.\u0026nbsp;(2016)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eEgypt\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eDesert\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e50\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eMicrosatellites\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e16 loci\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.68\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.61\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e13\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eEmam et al.\u0026nbsp;(2017)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eEgypt\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eDesert\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e48\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eMicrosatellites\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e14 loci\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.67\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.59\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e12\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eEmam et al.\u0026nbsp;(2024)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eEgypt\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eDesert\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e55\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eMicrosatellites\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e20 loci\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.73\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.66\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e14\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eHelal et al.\u0026nbsp;(2019)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eEgypt\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eDesert\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e42\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eMicrosatellites\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e12 loci\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.57\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e11\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eLarbi et al.\u0026nbsp;(2014)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eTunisia\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eSavannah\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e38\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eMicrosatellites\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e18 loci\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.75\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.68\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e13\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eMatthee et al.\u0026nbsp;(2021)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eSouth Africa\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eMountainous\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e30\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eMicrosatellites\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e16 loci\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.45\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.28\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e12\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eOmotoso et al.\u0026nbsp;(2019)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eNigeria\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eSavannah\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e48\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eMicrosatellites\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e18 loci\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.89\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e14\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eOwuor et al.\u0026nbsp;(2019)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eKenya\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eMountainous\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e40\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003emtDNA (D-loop)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e450 bp\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.78\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.71\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e11\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eRabbie et al.\u0026nbsp;(2020)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eEgypt\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eDesert\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e43\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eMicrosatellites\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e15 loci\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.66\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.59\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e12\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eSergon et al.\u0026nbsp;(2024)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eKenya\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eMountainous\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e50\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eMicrosatellites\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e20 loci\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.75\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e14\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eCarneiro et al.\u0026nbsp;(2011)*\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eGlobal (reference)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eMultiple\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e136\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eMicrosatellites\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e85 loci\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.75\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.70\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.07\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e15\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Reference study included for comparative purposes\u003c/p\u003e\n\u003cp\u003eAbbreviations\u003cstrong\u003e:\u003c/strong\u003e H\u003csub\u003ee\u003c/sub\u003e = expected heterozygosity; H\u003csub\u003eo\u003c/sub\u003e = observed heterozygosity; F\u003csub\u003eIS\u003c/sub\u003e = inbreeding coefficient; mtDNA = mitochondrial DNA; bp = base pairs. Quality scores range from 0-16 based on the 8-criteria assessment tool \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2.\u0026nbsp;Meta-regression results showing effects of predictor variables on expected heterozygosity (H\u003csub\u003ee\u003c/sub\u003e)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003ePredictor Variable\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eCoefficient (\u0026beta;) or Mean Difference\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eSE\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003ep-value\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eR\u0026sup2; or \u0026eta;\u0026sup2;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eF-statistic\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eBiome Type (categorical)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.42 (\u0026eta;\u0026sup2;)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eF=12.4, df=2,12\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; Desert vs. Savannah\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e-0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.002\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; Mountainous vs. Savannah\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e-0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.004\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; Desert vs. Mountainous\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.89\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eMarker Type (categorical)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.003\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.31 (R\u0026sup2;)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eF=8.3, df=3,11\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; Microsatellites (12-20 loci)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eReference\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; Microsatellites (21-50 loci)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e+0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; Microsatellites (\u0026gt;50 loci)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e+0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.002\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; mtDNA sequences\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e-0.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.45\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eNumber of Loci (continuous)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.024\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.008\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.003\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.31 (R\u0026sup2;)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eSample Size (continuous)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.003\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.02\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.38 (R\u0026sup2;)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003ePublication Year (continuous)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e-0.005\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.008\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.54\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.01 (R\u0026sup2;)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eGeographic Region (categorical)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.002\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.39 (\u0026eta;\u0026sup2;)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eF=9.2, df=3,11\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; North Africa\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eReference\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; West Africa\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e+0.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; East Africa\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e+0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.02\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; Southern Africa\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e-0.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: SE = standard error; R\u0026sup2; = proportion of variance explained (continuous predictors); \u0026eta;\u0026sup2; = effect size (categorical predictors); df = degrees of freedom; mtDNA = mitochondrial DNA. \u0026nbsp;Meta-regression conducted using random-effects models in OpenMee 4.0. Overall model: F(4, 110) = 12.4, p \u0026lt; 0.001, R\u0026sup2; = 67.3%, AIC = -145.2. Biome type and geographic region analyzed using one-way ANOVA with post-hoc Tukey HSD tests. Continuous predictors analyzed using linear regression with study weights.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Pairwise F\u003csub\u003eST\u003c/sub\u003e values showing genetic differentiation between rabbit populations from different African countries\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"0%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003ePopulation\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eNigeria\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eTunisia\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eEgypt\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eAlgeria\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eKenya\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eSouth Africa\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eNigeria\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.23\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eTunisia\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.19\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eEgypt\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eAlgeria\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eKenya\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.12\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eSouth Africa\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026mdash;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: F\u003csub\u003eST\u003c/sub\u003e = fixation index (Wright\u0026rsquo;s F-statistic measuring genetic differentiation between populations).\u003c/p\u003e\n\u003cp\u003eInterpretation: F\u003csub\u003eST\u003c/sub\u003e values range from 0 (no differentiation) to 1 (complete differentiation). Values 0.00-0.05 indicate little differentiation; 0.05-0.15 indicate moderate differentiation; 0.15-0.25 indicate great differentiation; \u0026gt;0.25 indicate very great differentiation (Wright, 1978). All pairwise comparisons are statistically significant (p \u0026lt; 0.001) based on 10,000 permutations.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Cape Coast","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Genetic diversity, population structure, phylogenetics, molecular markers, microsatellites, mitochondrial DNA, conservation genetics","lastPublishedDoi":"10.21203/rs.3.rs-9182675/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9182675/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRabbits (\u003cem\u003eOryctolagus cuniculus\u003c/em\u003e) are emerging micro-livestock in Africa, contributing to food security and rural livelihoods. However, a comprehensive assessment of their genetic diversity remains lacking, hindering conservation and breeding strategies.\u0026nbsp;This systematic review and meta-analysis quantified continent-wide genetic diversity, identified factors influencing diversity patterns, assessed population structure, and provided evidence-based conservation recommendations. Following PRISMA guidelines, we searched four databases for studies (2010-2025) reporting molecular marker-based genetic diversity in African rabbits. Two independent reviewers screened 623 records; 15 studies (115 populations, 1,847 individuals) met the inclusion criteria. Random-effects meta-analysis and meta-regression were conducted using OpenMee software, with biome type and marker characteristics as moderators.\u0026nbsp;Pooled expected heterozygosity was H\u003csub\u003ee\u003c/sub\u003e = 0.71 (95% CI: 0.67-0.75) and observed heterozygosity H\u003csub\u003eo\u003c/sub\u003e = 0.64 (95% CI: 0.60-0.68), with substantial heterogeneity (I² = 89-92%). Nigeria showed the highest diversity (H\u003csub\u003ee\u003c/sub\u003e = 0.94), while South Africa showed the lowest (H\u003csub\u003eo\u003c/sub\u003e = 0.21). Meta-regression revealed significant effects of biome type (F = 12.4,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e\u0026nbsp;\u0026lt; 0.001) and marker type (β = 0.024,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e\u0026nbsp;= 0.003). Savannah ecosystems maintained the highest diversity (H\u003csub\u003ee \u003c/sub\u003e= 0.84), while mountainous biomes showed greater variability (H\u003csub\u003ee\u003c/sub\u003e = 0.68). Widespread inbreeding was detected (mean F\u003csub\u003eIS\u003c/sub\u003e = 0.12), with the highest levels in mountainous regions (F\u003csub\u003eIS\u003c/sub\u003e = 0.18). Moderate genetic differentiation (F\u003csub\u003eST\u003c/sub\u003e = 0.14) indicated limited gene flow. No publication bias was detected (Egger's test:\u0026nbsp;\u003cem\u003ep\u003c/em\u003e\u0026nbsp;\u0026gt; 0.05). This first continent-wide synthesis reveals substantial geographic and ecological variation in African rabbit genetic diversity, with biome-specific patterns requiring tailored conservation strategies. High diversity in West African savannah contrasts with genetic erosion in southern mountainous regions, highlighting urgent conservation priorities.\u003c/p\u003e","manuscriptTitle":"Genetic Diversity, Population Structure, and Phylogenetic Relationships of Rabbit Populations in Africa Using Molecular Markers: A Systematic Review and Meta-Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 12:48:26","doi":"10.21203/rs.3.rs-9182675/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5f2913e9-3f57-4bdc-90e0-315cc9b1fe16","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64881162,"name":"Animal Science"}],"tags":[],"updatedAt":"2026-03-24T12:48:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 12:48:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9182675","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9182675","identity":"rs-9182675","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}