Maternal lineage diversity and genetic relationships of Sukuma chicken ecotype with other Tanzanian indigenous chickens based on mitochondrial DNA D-loop region

Understanding the genetic diversity, phylogenetic relationships, and demographic history of chickens is crucial for elucidating domestication processes, conserving indigenous genetic resources, and improving productivity and resilience in sustainable poultry production systems. This study investigates the genetic diversity, phylogenetic relationships, and demographic history of six Tanzanian indigenous chicken ecotypes, namely, Sukuma, Kuchi, Unguja, Pemba, Morogoro Medium, and Ching’wekwe, using mitochondrial DNA (mtDNA) D-loop sequences. A total of 100 mitochondrial DNA (mtDNA) D-loop sequences of 310 base pairs, representing six Tanzanian chicken ecotypes (Sukuma, Kuchi, Unguja, Pemba, Morogoro Medium, and Ching’wekwe), were analyzed. Genetic variation among the six Tanzanian chicken ecotypes was evaluated using several statistical approaches. Molecular diversity indices revealed substantial heterogeneity, with Sukuma chickens exhibiting the highest haplotype diversity (Hd = 0.95789) and nucleotide diversity (π = 0.30299), reflecting multiple maternal origins and deep evolutionary history. Conversely, Kuchi chickens showed the lowest diversity (Hd = 0.36316; π = 0.00158), consistent with founder effects. Analysis of molecular variance (AMOVA) indicated significant genetic differentiation (FST = 0.35452, p < 0.001), with 35.45% variation among populations and 64.55% within populations. Pairwise FST values and phylogenetic analyses revealed clear clustering, with Sukuma, Kuchi, and Morogoro Medium forming distinct clades, while coastal and Island ecotypes showed closer relationships. Neutrality tests suggested demographic stability in most ecotypes, except Kuchi, which showed signals of past expansion. These findings highlight Sukuma chickens as a critical genetic resource for breeding programs, while the low diversity of Kuchi chickens underscores the need for genetic management. The results provide a foundation for conservation strategies aimed at preserving the adaptive potential and genetic heritage of Tanzania’s indigenous chickens.

1 Introduction

Indigenous chickens are a critical genetic resource in sub-Saharan Africa, providing meat, eggs, income, and cultural value to rural households (Mtileni et al., 2011; FAO, 2015; Lyimo, 2025). In Tanzania, indigenous chicken ecotypes such as Sukuma, Kuchi, Pemba, Unguja, Morogoro Medium, and Ching’wekwe are reared under low-input, free-range systems and have adapted to diverse agro-ecological conditions, endemic diseases, and feed scarcity (Msoffe et al., 2004; Lyimo et al., 2013). These populations exhibit substantial phenotypic and genetic variation, making them vital for sustainable poultry improvement (Mtileni et al., 2011). Despite these potential, indigenous chicken populations are increasingly threatened by genetic erosion driven by uncontrolled crossbreeding, habitat changes, and the introduction of exotic breeds, which can undermine their unique adaptive traits. This necessitates a study on maternal lineage diversity and genetic relationships of the Sukuma chicken ecotype with other Tanzanian chickens. Critical insights based on the genetic diversity, phylogenetic relationships, and demographic history of chickens are vital for informative domestication processes, conserving indigenous genetic resources and improving productivity and resilience in sustainable poultry production systems. Historical and phylogeographic evidence indicates that domestic chickens reached Africa through at least two major introduction waves: one via Egypt during the Ptolemaic period (ca. 300 BC) and another through the Indian Ocean trade between the early and mid–1st millennium AD (MacDonald and Edward, 1993; Mwacharo et al., 2013; Lyimo, 2025). Archaeological remains from Egypt, Zanzibar, and the Horn of Africa, alongside linguistic and ethnographic records, point to complex dispersal patterns shaped by trade, migration, and cultural exchange. Genetic analyses support this by revealing a mixture of haplogroups from Southeast Asia, the Indian subcontinent, and possibly the Middle East (Williamson, 2000; Lyimo and Al-Qamashoui, 2022). Such multi-origin gene flow has contributed to the rich genetic landscape observed in Tanzanian ecotypes, including Sukuma chickens, which may have benefited from historical trade connections between inland Tanzania and coastal markets.

Sukuma chickens are also known as “Horashi” or “Horasi,” predominantly found in the Western and Lake Zone regions of Tanzania, which are characterized by a semiarid agro-ecological zone. They are highly valued for their distinctive features, including a larger body size, tall stature, long legs, elongated neck, abundant plumage, and docile temperament (Esatu et al., 2022). Oral histories from Sukuma livestock-keeping communities affirm that these chickens have long been integral to traditional farming systems. The name “Horashi,” derived from the Sukuma Bantu language, reflects this chicken’s physical attributes of larger body size, heavier weight, taller stature, and extended neck. Genetically, Sukuma chickens predominantly belong to haplogroup E, as reported by Liu et al. (2006), and they show close phenotypic resemblance to Asian Aseel chickens from the Indian subcontinent, which also cluster primarily within the same haplogroup (Kanakachari et al., 2023).

Mitochondrial DNA (mtDNA), particularly the hypervariable D-loop region, has proven to be a powerful marker for tracing maternal genetic diversity, population structure, and domestication history because of its rapid mutation rate and strict maternal inheritance (Saccone et al., 1987; Liu et al., 2006). Previous studies on African village chickens revealed multiple maternal origins, highlighting significant contributions from Asian lineages that reached the continent through Indian Ocean maritime exchange, Mediterranean trade, and inland dispersal routes (Mwacharo et al., 2013; Osman et al., 2016; Lawal and Hanotte, 2021; Lyimo, 2025). These diverse introductions produced distinct haplogroups that not only reflect the complex history of chicken domestication and dispersal but also strengthen the adaptive potential, resilience, and genetic richness of Tanzanian indigenous ecotypes.

Previous molecular studies on Tanzanian indigenous chickens using microsatellites and mtDNA D-loop sequences demonstrated clear genetic structuring among ecotypes, often clustering populations by geographical and cultural origin (Lyimo et al., 2013). For example, island populations (Unguja and Pemba) grouped together, mainland Bankiva-type ecotypes (Morogoro Medium and Ching’wekwe) formed another cluster, and game-type Kuchi remained genetically distinct patterns reflecting limited interbreeding and unique evolutionary histories. In this study, the inclusion of Sukuma chickens in such comparative frameworks allows for a deeper understanding of their maternal lineage diversity, potential adaptive traits, and their role in the broader genetic landscape of Tanzanian poultry. This knowledge is essential for designing conservation and breeding programs that will maintain productivity while safeguarding genetic heritage.

2 Materials and methods

2.1 Study area

The study involved investigating the maternal lineage diversity and genetic relationships of indigenous chicken ecotypes in Tanzania. Six indigenous chicken ecotypes, namely, Mwanza, Geita, Shinyanga, Tabora, Singida, Dodoma, and Morogoro, from five regions of mainland Tanzania were used in this study. Likewise, two indigenous chicken ecotypes from Unguja and Pemba Island were used (Figure 1).

Figure 1

Figure 1. Map showing the study areas.

2.2 Materials

2.2.1 mitochondrial DNA sequences

A total of 100 mtDNA sequences were used. Eighty sequences from Sukuma chickens were generated after the extraction of genomic mitochondrial DNA, and 20 sequences from five Tanzanian chicken ecotypes (Kuchi, Pemba, Unguja, Morogoro Medium, and Ching’wekwe), each being represented by four sequences, were retrieved from the NCBI GenBank database. Consequently, a total of 100 mtDNA D-loop sequences of 310 base pairs, representing six Tanzanian chicken ecotypes, were analyzed. Two of these, Pemba and Unguja, are named after their primary islands in the Zanzibar archipelago (Msoffe et al., 2004; Lyimo et al., 2013). The remaining four ecotypes originate from mainland Tanzania and include Sukuma from Mwanza; Shinyanga Kuchi from Mwanza, Shinyanga, Tabora, and Geita; Morogoro Medium from Morogoro; and Ching’wekwe chickens from Morogoro and Tanga. These ecotypes represent diverse geographic and cultural backgrounds, providing valuable insights into the genetic diversity, evolutionary history, and potential adaptive traits of indigenous chickens in the region.

2.2.2 Data collection

Fresh blood samples were collected from 100 Sukuma chicken ecotypes. Among these, 50 blood samples were collected from the Mwanza region, while the remaining 50 samples were collected from the Shinyanga region. Whole blood was obtained by bleeding the wing vein using a small sterile needle and then transferred immediately into vacutainer tubes containing ethylenediaminetetraacetic acid (EDTA), which was used as an anticoagulant. All samples were subsequently stored at 8°C to preserve DNA integrity for downstream molecular analysis.

2.2.3 DNA extraction

Genomic DNA was extracted from the blood using the Quick-DNA MiniPrep plus Kit (ZYMO Research, Co., USA, Cat. No. D4068) following the manufacturer’s protocol for nucleated blood samples. The extracted DNA was stored at −20°C until further analysis.

2.2.4 Amplification (polymerase chain reaction)

The primers L16750 (5′AGGACTACGGCTTGAAAAGC-3′) as forward primer and H547 (5′ATGTGCCTGACCGAGGAACCAG-3′) as reverse primer were used to amplify a fragment of 550 bp from the HV1 region of the Sukuma chicken mitochondrial genome. All polymerase chain reactions (PCRs) were carried out in a ProFlex™ 3 × 32-well PCR System (Thermo Fisher Scientific, Carlsbad, CA, USA) thermocycler. Each amplification reaction mix constituted a final volume of 25 μL, which included 12.5 μL of Quick-Load^®, oneTaq2X Master Mix with standard buffer (New England Biolabs, UK), 2 μL of BSA (New England Biolabs, UK), 0.5 μL of forward and reverse primers, 5.5 μL of nuclease-free water, and 4 μL of DNA template. The reaction profile was as follows: initial denaturation at 94°C for 2 min, followed by 35 cycles of 94°C for 30 s, 58°C for 30 s, and 68°C for 1 min, and a final extension step at 68°C for 10 min.

2.2.5 Gel electrophoresis and visualization

Agarose gel electrophoresis was performed using the HU10 Mini-Plus (10 cm) horizontal gel electrophoresis unit (Scie-Plas, Camlab, UK) together with the nanoPAC-300P Electrophoresis Power Supply (SLS Flowgen, UK). The amplicons/PCR products, along with Quick-Load 100 bp DNA ladder (New England Biolabs, UK), were electrophoresed on a 1.5% agarose gel pre-stained with ethidium bromide DNA staining dye in 1× TAE buffer. The electrophoresis setup was set at 80 V for 45 min. Gel visualization was done using the T 2201 Ultraviolet (UV) transilluminator (Sigma Chemical Co., USA).

2.2.6 Polymerase chain reaction results

Eighty samples showed a successful amplification of the 550-bp fragment of the HV1 region of the chicken mitochondrial genome. DNA bands were clearly visualized under UV light, confirming successful amplification of target fragments (Figure 2).

Figure 2

Figure 2. Gel image of representative amplicons produced by the primers used. M is the maker used (100 bp ladder); lanes 1 to 13 are positive samples.

2.2.7 mtDNA sequencing

The Sanger sequencing technique (the chain termination method) was employed for the determination of nucleotide sequences. A volume of 20 μL of each reaction was sent to the Macrogen Europe, Netherlands laboratory for Sanger dideoxynucleotide (ddNTP) sequencing, where each sample was sequenced in both directions, preceded by purification of the PCR products. PCR products from 80 samples, which are representatives from five districts of the study area, were sent for sequencing. Primers CR-forward 5′TCTATATTCCACATTTCTC3′ and CR-reverse 5′GCGAGCATAACCAAATGG-3′ were used for sequencing. Forward and reverse reads were manually edited and aligned to obtain consensus sequences using BioEdit v7.0.9.0 (Hall and Carlsbad, 2011).

2.2.8 Data analysis

Genetic variation among the six Tanzanian chicken ecotypes was evaluated using several statistical approaches. AMOVA and pairwise genetic differentiation (FST) were performed in Arlequin v3.5 to assess population structure and gene flow (Excoffier and Lischer, 2010). Pairwise FST genetic distance matrices were used to infer phylogenetic relationships, visualized in SplitsTree4 Version 4.14.2 (Huson and Bryant, 2006). A neighbor-joining phylogenetic tree was constructed in R using pairwise FST distances to visualize ecotype clustering patterns (Paradis and Schliep, 2019). Molecular diversity indices, including haplotype diversity (Hd), nucleotide diversity (Pi), number of segregating sites (S), and the average pairwise differences (k), were calculated using Arlequin and DNASP v6 (Rozas et al., 2017), with significance tested through 1,000 permutations. Demographic history and neutrality tests, including Tajima’s D, Fu’s Fs, Harpending’s raggedness index (r), and sum of squared deviation (SSD), were also computed to infer historical population dynamics and test for deviations from neutrality, providing insight into demographic stability or expansion trends.

3 Results

3.1 Population structure of Tanzanian indigenous chickens

AMOVA revealed significant genetic differentiation among the six ecotypes (FST = 0.35452, p < 0.001), partitioning 35.45% of variation among populations and 64.55% within populations (Table 1). The high and significant FST indicates strong population structure, while the larger within-population variance suggests considerable genetic diversity within each ecotype, reflecting rich genetic resources despite pronounced differentiation across groups.

Table 1

Table 1. AMOVA results showing genetic variation partitioning among and within populations.

3.2 Genetic distance metrics and gene flow estimates in Tanzania chicken populations

Pairwise FST values reveal varying levels of genetic differentiation among the six Tanzanian chicken ecotypes (Table 2), reflecting both genetic distance and historical gene flow. The greatest genetic distances occur between Morogoro Medium and Kuchi (0.42325), Kuchi and Sukuma (0.38158), and Morogoro Medium and Sukuma (0.37996), indicating limited gene flow. In contrast, minimal distances between Morogoro Medium and Unguja (0.01601) and between Pemba and Ching’wekwe (0.01101) suggest close genetic relationships and high recent gene flow. Overall, the results highlight pronounced genetic structuring among most ecotypes, with a few pairs maintaining substantial connectivity or sharing recent common ancestry.

Table 2

Table 2. Pairwise FST values indicating genetic differentiation among the six Tanzanian indigenous chicken ecotypes.

The phylogenetic tree constructed from pairwise FST distances (Figure 3) reveals distinct clustering patterns among the six Tanzanian chicken ecotypes, highlighting their genetic differentiation. The Sukuma, Kuchi, and Morogoro Medium ecotypes form clearly separated clades, indicating limited gene flow, suggesting long-term maternal isolation likely shaped by geographic barriers, cultural preferences, and localized breeding practices. In contrast, Pemba, Unguja, and Ching’wekwe cluster more closely, suggesting recent genetic exchange or shared ancestry. These relationships align with pairwise FST values, where high differentiation was observed among inland ecotypes, while lower values between island and coastal populations indicate recent gene flow. These patterns align with observed genetic distance metrics, highlighting both historical isolation and connectivity among Tanzania’s indigenous chicken populations. The distinct positioning of Sukuma chickens highlights their deep maternal lineage structure. The distinct positioning of Sukuma chickens highlights their deep maternal lineage structure, consistent with their high haplotype and nucleotide diversity.

Figure 3

Figure 3. Phylogenetic tree generated from FST pairwise difference, showing the clustering relationships among the six Tanzanian indigenous chicken populations.

3.3 Genetic diversity of Tanzanian indigenous chickens

The molecular diversity indices reveal marked genetic variation among the six Tanzanian chicken ecotypes (Table 3). Sukuma chickens exhibited the highest haplotype diversity (Hd = 0.95789) and nucleotide diversity (π = 0.30299), with 204 segregating sites (S) and an average pairwise difference (k) of 93.926, indicating rich maternal lineages and deep genetic structure. In contrast, Kuchi chickens showed the lowest diversity (Hd = 0.36316; π = 0.00158), with only 4 segregating sites and k = 0.489, suggesting past bottlenecks or founder effects. Unguja, Morogoro, Pemba, and Ching’wekwe displayed moderate variability (S = 10–11; k ≈ 3.9–4.6), likely reflecting admixture or localized selection. The pooled population diversity (Hd = 0.86765; π = 0.08756; S = 310; k = 27.144) highlights significant overall variation, essential for conservation and breeding programs aimed at maintaining adaptability and resilience.

Table 3

Table 3. Molecular diversity indices of the six Tanzanian chicken ecotypes.

3.4 Demographic history and neutrality analysis

The demographic history and neutrality test results in Table 4 suggest varied evolutionary dynamics among the six Tanzanian chicken ecotypes. Sukuma chickens show high Tajima’s D (2.63095, p = 0.993) and Fu’s Fs (5.20711, p = 0.981) values, both non-significant, indicating no strong deviation from neutrality and suggesting demographic stability or balancing selection. Unguja, Morogoro, and Pemba exhibit similarly high, non-significant positive values, reflecting potential population stability. Kuchi has negative Tajima’s D (−2.09760) and Fu’s Fs (−0.03325), also non-significant, possibly hinting at past expansion or purifying selection. Harpending’s raggedness and SSD p-values across ecotypes indicate generally good model fit for population stability rather than recent bottlenecks or expansions.

Table 4

Table 4. Demographic history and neutrality test parameters of six Tanzanian chicken ecotypes.

4 Discussion

The phylogenetic and molecular diversity analyses conducted in this study provide compelling evidence of substantial genetic structuring among six Tanzanian indigenous chicken ecotypes, namely, Sukuma, Kuchi, Unguja, Pemba, Morogoro Medium, and Ching’wekwe. This structuring reflects their diverse origins, evolutionary trajectories, and historical patterns of gene flow shaped by geographic isolation, human-mediated dispersal, and localized selection pressures (Lyimo et al., 2013; Lyimo, 2025). The observed FST value of 0.35452 is highly significant (p < 0.001), indicating strong genetic differentiation among the ecotypes. Such differentiation reinforces the role of maternal lineage diversity in preserving unique genetic resources essential for conservation, breeding programs, and sustainable improvement of Tanzania’s poultry sector (Mwacharo et al., 2013; Osman et al., 2016).

4.1 Genetic differentiation and population structure

The genetic differentiation observed among Tanzanian indigenous chicken ecotypes highlights both localized isolation and historical connections. AMOVA revealed that 35.45% of variation occurs among populations, with a high and significant fixation index (FST = 0.35452; p < 0.001), confirming strong structuring. Nevertheless, the substantial within-population variation (64.55%) highlights the richness of maternal lineages preserved within each ecotype, a pattern consistent with previous findings in African village chickens (MuChadeyi et al., 2008; Mtileni et al., 2011; Lyimo et al., 2013).

Pairwise FST comparisons indicate the greatest genetic differentiation between Morogoro Medium and Kuchi (0.42325), as well as between Kuchi and Sukuma (0.38158), suggesting long-term maternal isolation possibly reinforced by cultural preferences and geographic separation. Conversely, the lowest values, observed between Morogoro Medium and Unguja (0.01601) and between Pemba and Ching’wekwe (0.01101), reflect recent gene flow or shared ancestry likely mediated by interisland poultry exchange and coastal trade routes (Msoffe et al., 2004; Osman et al., 2016). These results align with the broader understanding that African village chickens exhibit a mosaic of differentiation shaped by both ancient introductions and contemporary management practices (Groeneveld et al., 2010). Such population structure highlights the need for conservation and breeding programs that safeguard both inter-ecotype distinctiveness and intrapopulation diversity critical for adaptation and resilience.

4.2 Phylogenetic relationships and maternal origins

The phylogenetic reconstruction based on FST values reveals distinct clades corresponding to Sukuma, Kuchi, and Morogoro Medium chickens, supporting their maternal divergence and relative isolation. These ecotypes likely retained unique lineages shaped by localized breeding systems and adaptation to inland environments. In contrast, the coastal and island ecotypes, Unguja and Pemba, cluster more closely, reflecting shared haplotypes and higher levels of maternal connectivity. Such relationships suggest stronger historical and contemporary exchanges facilitated by maritime and interisland trade networks (Mwacharo et al., 2013; Osman et al., 2016). Such clustering patterns align with the observed FST values, which indicate strong differentiation among most ecotypes yet closer relationships within the coastal and island groups. The Sukuma ecotype is particularly notable for its deep maternal structure and exceptionally high haplotype diversity, consistent with multiple maternal origins introduced through inland trade routes connecting western regions of Tanzania to coastal markets (Lyimo, 2025).

The observed phylogenetic topology reflects not only ecological and cultural selection but also the broader history of domestic chicken introduction and dispersal across Africa. Archaeological and historical evidence suggests that chickens entered the continent through multiple waves, including introductions via the Mediterranean and the Indian Ocean, with significant contributions from Southeast Asia and the Indian subcontinent (MacDonald and Edward, 1993; Mwacharo et al., 2013; Lyimo et al., 2013). These repeated introductions created a mosaic of maternal lineages, explaining the admixture found among coastal populations and the persistence of unique haplotypes within inland ecotypes. In particular, island and coastal groups show strong genetic connectivity, shaped by maritime trade and interisland poultry exchange, whereas inland populations such as the Sukuma ecotype retained distinct maternal signatures due to limited exposure to such external gene flow. These findings emphasize the importance of integrating phylogenetic evidence with archaeological and ethnographic records to trace the maternal history of African poultry and inform conservation strategies.

4.3 Molecular diversity patterns

Molecular diversity indices demonstrate substantial heterogeneity among the six Tanzanian indigenous chicken ecotypes. Sukuma chickens display the highest haplotype diversity (Hd = 0.95789), nucleotide diversity (π = 0.30299), segregating sites (S = 249), and average pairwise differences (k = 114.329), indicating multiple maternal lineages, long evolutionary history, and minimal bottleneck effects. Such high variability likely reflects historical trade connections and livestock exchanges, particularly in the western part of Tanzania and the Lake Zone, that introduced diverse maternal haplotypes (Lyimo et al., 2013).

The high haplotype diversity observed in Sukuma chickens may partly reflect breeding autonomy at the household level, supported by strong informal networks that enable the exchange of breeding stock. Such exchanges, though beneficial for genetic variation, can also risk genetic dilution if not complemented by formal conservation programs. In Tanzania, conservation and sustainable use of indigenous chicken ecotypes are influenced not only by genetic and ecological factors but also by the interplay between formal and informal institutions (Kapella et al., 2020). Formal institutions provide technical guidance, breeding policies, and research support, whereas informal structures such as traditional knowledge systems, local norms, and community networks play a critical role in maintaining breeding practices and genetic integrity at the grassroots level.

Kuchi chickens exhibited the lowest genetic diversity (Hd = 0.36316; π = 0.00158; S = 12; k = 0.873), reflecting founder effects, genetic drift, or strong directional selection. Their ancestry links to South Asian game fowl, traditionally bred for combat under rigorous selection (Lyimo et al., 2013). Mitochondrial analyses further showed that most Kuchi individuals clustered within a single haplogroup previously identified in Shamo game birds, underlining their narrow maternal base. This restricted variability parallels patterns observed in recently introduced or intensively selected populations, highlighting potential vulnerability to inbreeding and reduced adaptability (Liu et al., 2006).

Unguja, Pemba, Morogoro Medium, and Ching’wekwe populations exhibit intermediate diversity (Hd = 0.76842–0.90526; π = 0.00378–0.16249), reflecting a balance between localized adaptation and sporadic introgression. Such patterns indicate moderate gene flow among coastal and island ecotypes, likely mediated by interisland poultry trade and cultural exchange (Osman et al., 2016). This exchange has promoted genetic connectivity while retaining ecotype-specific variation. For these populations, sustaining current levels of gene flow while protecting distinct haplotypes is crucial for maintaining adaptive potential and ensuring long-term resilience under changing production environments (Grant and Bowen, 1998; Tajima, 1989; Fu, 1997).

4.4 Demographic history and neutrality tests

Neutrality test results provide valuable insights into the demographic history of Tanzanian indigenous chicken ecotypes. Most ecotypes, including Sukuma, Unguja, Morogoro Medium, and Pemba, exhibited high, non-significant positive Tajima’s D and Fu’s Fs values, suggesting demographic stability or balancing selection rather than recent population expansion. In particular, Sukuma’s high Tajima’s D (2.63095) and Fu’s Fs (5.20711) reflect the long-term maintenance of diverse haplotypes, possibly reinforced by cultural preferences for phenotypic variation and the retention of multiple maternal lineages (Mwacharo et al., 2013; Mtileni et al., 2011; Lyimo et al., 2013). Such stability may also be linked to their geographic isolation and adaptive significance in local environments.

In contrast, Kuchi chickens showed a negative Tajima’s D (−2.09760) and Fu’s Fs (−0.03325), though non-significant, which may suggest a historical expansion following a founder event, a scenario consistent with their low diversity and known derivation from Southeast Asian game fowl lineages (Lyimo et al., 2013; Liu et al., 2006). The narrow genetic base of the Kuchi ecotype signals vulnerability to disease and environmental change, necessitating strategies to broaden their genetic pool. Complementing these demographic signals, neutrality and diversity analyses offer deeper insights into the evolutionary forces shaping Tanzanian chicken ecotypes. Harpending’s raggedness index and SSD p-values across ecotypes further support a stable demographic model, with no strong evidence of recent bottlenecks. These patterns mirror observations in other African village chickens, where localized selection, limited long-distance dispersal, and occasional admixture shape long-term population structure (MuChadeyi et al., 2008).

4.5 Implications for conservation and breeding

The high genetic diversity in Sukuma (Hd = 0.95789; π = 0.30299) and Ching’wekwe chickens highlights their significance as reservoirs of adaptive traits valuable for breeding programs. This diversity enhances resilience to emerging diseases, climatic variability, and production challenges, aligning with the FAO (2015) guidelines on safeguarding livestock genetic resources. In Sukuma chickens, the deep maternal lineage structure suggests long-term adaptation to the Lake Zone’s agro-ecological conditions, making them ideal candidates for conservation-oriented breeding to maintain traits for resistance and environmental adaptability (Lyimo et al., 2013).

Conversely, the Kuchi ecotype possessing low diversity (Hd = 0.36316; π = 0.00158) reflects a narrow genetic base, increasing vulnerability to inbreeding depression and reduced adaptability. Targeted management, including genetic monitoring and controlled introgression from genetically compatible indigenous populations, could help mitigate these risks (Liu et al., 2006; Mtileni et al., 2011). Conservation strategies should combine in situ approaches by safeguarding traditional free-range management systems, with ex situ measures such as cryopreservation of germplasm. Breeding programs must integrate productivity goals with preservation of unique genetic signatures to avoid erosion of adaptive variation, ensuring both food security and cultural heritage preservation (FAO, 2015; Mwacharo et al., 2013). Selective breeding programs should aim to balance productivity traits with the conservation of unique genetic lineages to avoid erosion of adaptive diversity.

5 Conclusion

This study demonstrates marked genetic structuring among Tanzanian chicken ecotypes, shaped by historical trade routes, cultural selection, and geographical isolation. While certain populations exhibit signs of shared ancestry and recent gene flow, others, such as the Sukuma and Kuchi ecotypes, retain distinct genetic signatures that reflect their unique evolutionary paths. The exceptional diversity of Sukuma chickens positions them as a crucial genetic resource for breeding programs that may focus on resilience and adaptability. Contrarywise, Kuchi’s narrow genetic base emphasizes the need for strategies to broaden genetic variation and mitigate inbreeding risks. For ecotypes with intermediate diversity, maintaining existing gene flow while conserving unique haplotypes is essential to safeguarding the adaptive potential and long-term productivity of Tanzania’s indigenous chicken genetic heritage.

Data availability statement

The data presented in the study are deposited in the NCBI GenBank repository under accession numbers KP067445 – KP067545.

Ethics statement

The animal study was approved by Sokoine University of Agriculture. The study was conducted in accordance with the local legislation and institutional requirements.

Author contributions

ZM: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. SM: Conceptualization, Data curation, Project administration, Supervision, Validation, Visualization, Writing – review & editing. AN: Data curation, Methodology, Software, Supervision, Validation, Visualization, Writing – review & editing. EM: Methodology, Project administration, Supervision, Visualization, Writing – review & editing. CL: Conceptualization, Data curation, Methodology, Project administration, Software, Validation, Visualization, Writing – review & editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. The authors sincerely thank the Ministry of Livestock and Fisheries for funding this research.

Acknowledgments

ZM thanks the Ministry of Livestock and Fisheries for permitting him to pursue MSc studies at SUA. The authors are also grateful to the Genome Science Center Laboratory at the College of Veterinary Medicine and Biomedical Sciences from SUA for providing facilities, guidance, and support during molecular analyses, including DNA extraction and sequencing of the chicken samples.

Conflict of interest

The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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