Morus including promising functional varieties were chosen to represent the genetic spectrum from the whole germplasm collection. To estimate the GS, accessions were subjected to flow cytometry (FCM) analysis and the result suggested that four different ploidies  with GS ranging from 0.72±0.005pg (S-30) to 2.89±0.015pg (M. serrata), accounting~4.01 fold difference. The predicted polyploidy was further confirmed with metaphase chromosome count. In addition, the genetic variation was estimated by selecting a representative morphologically, diverse population of 82 accessions comprised of all ploidy variations using simple sequence repeats (SSR). The estimated average Polymorphism Information Content (PIC) and expected heterozygosity showed high levels of genetic diversity. Additionally, three populations were identified by the model-based population structure (k = 3) with a moderate level of correlation between the populations and different species of mulberry, which imply the effect of genetic variation instead of ploidy on trait plasticity that could be a consequence of the high level of heterozygosity imposed by natural cross-pollination. Further, the correlation between ploidies, especially diploid and triploid with selected phenotypic traits was identified, however, consistency could not be defined with higher ploidy levels (>3x). Moreover, incite gained here can serve as a platform for future omics approaches to the improvement of mulberry traits.Elucidation of genome size (GS), genetic and phenotypic variation is the fundamental aspect of crop improvement programs. Mulberry is a cross-pollinated, highly heterozygous tree eudicot, and comprised of huge ploidy variation with great adaptability across the world. However, because of inadequate information on GS, ploidy-associated traits, as well as the correlation between genetic and phenotypic variation hinder the further improvement of mulberry. In this present research, a core set of 157 germplasm accessions belonging to eight accepted species of  Morus spp.) has been commercially exploited as the host of monophagous pest silkworm, Bombyx mori L. , 2. It belongs to the Moraceae family comprised of 37 genera with more than 1,100 species . The genus Morus has over 10 species with more than 1,000 cultivated varieties spanning Asia, Europe, Africa, and the United States , 5. Efforts were made to classify Morus spp., however, to date, taxonomic nomenclature remains doubtful . Besides that, genetics of inheritance are also complicated in Morus spp. As an account of the occurrence of a higher level of heterozygosity as well as polyploidy , one of the most important evolutionary processes in higher plants , 8.Mulberry  to docosaploid (2n = 22x = 308) , 10, 11, for example, M. notabilis was reported as “haploid” in nature , and diploid (2n = 2x = 28) Morus spp. such as M. alba, M. atropurpurea, M. bombycis, M. indica, M. latifoliaand M. rotundiloba. The majority of triploids (2n = 3x = 42) and tetraploids (2n = 4x = 56) have been identified in M. laevigata. Hexaploid species (2n = 6x = 84), such as M. serrata and M. tiliaefolia are also recognized, and earlier reports also suggest that polyploidy can extend up to docosaploid (2n = 22x = 308) as in M. nigra, 11. The economically important species of mulberry available in India are M. alba, M. indica, M. atropurpurea, M. nigra, M. serrata, M. latifolia, and M. laevigata, 15. Moreover, metaphase chromosome count, detecting the ploidy status, size, and nature of chromosomes and behaviour in pairing and segregation during meiosis has been restricted toa few popular mulberry varieties and polyploid species only like M. serrata, M. tiliaefolia and M. nigra, 16–23. Despite our increasing recognition of the chromosomal number or ploidy level, we are still unaware of the biological effects of ploidy in mulberry. Application of FCM and GS estimations of a large collection of mulberry are missing, which is considered to be a preciseway to generate useful information for the analysis of the genetic relationship, phenotypic diversity, estimation of ploidy-associated traits, and other fundamental aspects of mulberry genomes.Cytology studies in mulberry are more complicated because of extreme variations in chromosome size and number Morus species , 25. Primarily, Xiang et al.  applied the Random amplified polymorphic DNA (RAPD) technique to illustrate variation among different species of Morus. Later Inter-Simple Sequence Repeats (ISSR) work has been reported on the genetic diversity analysis amongst the mulberry plants grown in India , 27. Additionally, several restrictions limit the estimation of the genetic diversity of mulberry as highlighted by earlier reports like-(a) lack of sufficient molecular markers in mulberry ; and (b) lack of codominant markers to construct the genetic map, which is considered to be a major constraint towards marker-assisted breeding for targeted trait improvement , 29. As mulberry has a highly cross-pollinated heterozygous genome, Simple Sequence Repeats (SSRs) are the most appropriate DNA markers to use for analyzing the genetic diversity of mulberries , 30–32. SSR-based approaches appear to be one of the best DNA markers for detecting genetic diversity within a species for several reasons, especially for revealing high levels of polymorphisms . In Morus spp. SSR markers developed from M. indica are used to illustrate genetic relationships and origin of cultivated mulberry . However, limited studies on SSR markers-based genetic diversity and model-based population structure analysis of a wide collection of mulberry germplasm have been conducted.In the last few decades, genetic relationship analysis of mulberry is an important component of crop improvement programs and molecular markers are the most popular to understand the diversity between Morus to develop a model-based population structure and identify ploidy-associated traits. The information generated in the present study may serve as a platform for future omics approaches for the improvement of mulberry traits.In brief, in the present study, we have tried to highlight a few fundamental aspects of mulberry core set collections like (1) variation of GS (FCM-based), (2) genetic diversity (SSR-based), and (3) phenotypic variation (growth-yield and reproductive trait-based) of a wide collection of cross-pollinated, highly heterozygous tree species of genus Morus species including popular varieties cultivated in India were used in this study, which was obtained from the core collection maintained at the a). Central Sericultural Germplasm Resources Centre (CSGRC) (csgrc.res.in), Hosur, India; b). Central Sericultural Research and Training Institute (CSRTI), Mysore, India; and3). Karnataka State Sericulture Research and Development Institute, Karnataka, India. The common name, accession number, species name, and place of origin (S1 Table). Accepted nomenclature for the synonymous and unresolved species was considered from The Plant List (theplantlist.org) database (description available in S1 Table). As per The Plant List (theplantlist.org) database, most of the studied germplasm accessions have belonged to accepted eight species viz. M. alba L., M. auastralis Poir., M. cathayana Hemsl., M. indica L., M. macroura Miq., M. nigra L., M. rubra L., and M. serrata Roxb. and remaining species like M. bombycis Koidz. , M. laevigata Wall. ex Brandis (synonym of M. macroura Miq.), M. latifolia Poir. , M. multicaulis Perri. , M. rotundiloba Koidz. (Unresolved), and M. sinensis Loudon. (unresolved) used in this study were identified and merged with the accepted eight species, to avoid taxonomical/classification-related constraints.A total of 157 accessions of the eight different . For analysis, nuclei were isolated from 40 mg of fresh young leaves (~5 mm2) of mulberry along with the internal reference standard Pisum sativum . The tissue sample was taken in a Petri dish comprised of 2 ml of buffer solution  of non-ionic detergent equivalent  containing 2 mg/mL RNase A) and immediately co-chopped to homogenate using a sharp razor blade for 60 sec at 4°C. The homogenate solution was filtered through a 37 μm nylon filter to remove large debris. Samples were incubated at 37°C for 30 min and FCM analysis was performed using BD LSR Fortessa™ cell analyzer (BD Biosciences). Signals of PI-stained nuclei were processed with an excitation filter and absorbance filter by the flow cytometer. For each accession, three samples were examined and analyzed using BD FACS Diva 8.0.3 Software. GS  was calculated according to the formulae given by Dolezel et al.  [Ploidy = Reference ploidy ×(mean position of the G1 sample peak/mean position of the G1 reference peak)] with the conversion of 1 pg equal to 980 Mbp.To estimate the GS, flow cytometry (FCM) analysis was performed by using the modified protocol as described by Galbraith et al. S2 Table) were collected at 9.30–10.00 AM in 1 ml of saturated p-dichloro benzyne (PDB) with 2 drops of 8-hydroxyquinoline (8HQ) in 1.5 ml Eppendorf. After the shoot collection, meristems were transferred to a 0°C freezer for 5 min and it is subjected to transfer to 4°C for 16-20h. After that, meristems were transferred to ice-cold 3 parts of 100% ethanol: 1 part of glacial acetic acid and incubated at RT for 1h. Then samples were transferred to 4°C for 3 days. After that, samples were incubated with the enzymatic solution comprised of pectinase (2.5% w/v), cellulase (1%), and pectolyase (2%) for 4-6h. Then, samples were transferred to 1% orcine stain for 24h at RT, and during microscopy 2 drops of Acetoorcine stain were used to squash the sample. Chromosome count was performed using Cilika  microscope.Besides FCM, metaphase chromosome count was performed to validate FCM data. In brief, shoot tips of 12selected accessions  gel electrophoresis and quantified using a Nanodrop DNA quantifier . Reliable and reproducible twenty polymorphic nSSR primer pairs were selected after the screening of 62 nSSR markers for molecular characterization as mentioned by Mathithumilan et al. ; and Pinto et al.  (Primers in S3 Table). The SSR genotyping was carried out in 10 μl of PCR reaction containing ~50 ng of DNA, 2x PCR Amplicon master mix, and 10 picomoles of SSR primers  using the PCR program: 94°C for 5 min, followed by 35 cycles of 94°C for 45sec, specific annealing temperature (50°C to 56°C) for 30 sec, 72°C for 45 sec for extension, and72°C for 8 min for the final extension using a thermal cycler . The final PCR product was separated on 3% agarose gel containing 0.5μgethidium bromide in 1X TBE buffer and gel images were documented in the gel documentation system . Twenty polymorphic SSR marker data were numerically coded as presence = 1, absence = 0, and missing data were coded as ‘-1’ as suggested in the GenAlExV6.5 user manual . Gel images of twenty polymorphic SSR markers of 49 representative accessions of mulberry are presented in S1 Fig along with the scored SSR data (S4 Table).Eighty-two germplasm were selected based on collection site, open/cross-pollinated hybrid, and breeding information  and different ploidy levels covering all available Na), the effective number of alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He), and Shannon’s information index (I) for each SSR marker were computed using POPGENE v. 1.32 , while Polymorphism Information Content (PIC) using CERVUS 3.0.7 . Power of discrimination (PD)  =  1 − ΣPi2, where Pi = frequency of genotype i, and probability of identity (PI)  =  1 − PD for 20 SSR markers was calculated as described by Huang et al. . Whereas, GenAlEx V6.5 was used for species-wise diversity measures i.e. percentage of polymorphic loci (%P), Na, Ne, I, h (diversity) and uh (unbiased diversity), pairwise population matrix of Nei’s genetic distance and pairwise PhiPT values.Genetic diversity parameters such as the observed number of alleles  ranging from 1 to 12 with 10 iterations for each value of K. The length of burn-in Markov Chain Monte Carlo (MCMC) replications was set to 500,000 and data were collected from over 500,000 MCMC replications in each run. We identified the optimal value of K using both the ad hoc procedure described by Pritchard et al.  and the method developed by Evanno et al.  with the help of Structure Harvester software Earl and Von . Mulberry accessions were assigned to the subpopulations based on the cluster assignment probability (Q). Further cluster analysis of 82 germ plasm was determined using DARWIN software, version 6.0.21 . Principle Coordinate Analysis (PCoA) and Analysis of Molecular Variance (AMOVA) was performed using the software GenAlEx V6.5.Mulberry germplasm individual molecular data were scored from SSRs banding patterns and STRUCTURE software plantauthority.gov.in/sites/default/files/mulberry.pdf) and FAO (fao.org/3/AD107E/ad107e0v.htm#TopOfPage). To estimate the inter-nodal distance , the total length of the longest shoot was divided by the total number of nodes after 90 days of pruning. For, leaf-lamina length  fully grown leaves from the 7th to 9th position in the longest shoot were selected, and measured the leaf blade length from the leaf base at the juncture of the petiole attachment to the leaf tip. Additionally, for leaf-lamina width , the width of the leaf was taken from the widest point on both sides of the leaf margins. The leaf area  of the mulberry was measured using a leaf area meter (Biovis PSM L3000). To measure Petiole length , the petiole portion was separated from the base of the leaf blade and measured, and for petiole width , the thickness was measured using Vernier caliper from the same petioles that were used for measuring PL.To correlate phenotypic traits with ploidies, morphologically unique 80 mulberry accessions were chosen to represent the genetic spectrum from the whole germplasm collection. The data was recorded for 9 characters viz. inter-nodal distance (cm), leaf-lamina length (cm), leaf-lamina width (cm), leaf area (sq. cm), petiole length (cm), petiole width (cm), mature inflorescence length (cm), mature fruit length (cm) and mature fruit width (cm) for the two seasons during 2020 and 2021. All the characters were recorded based on DUS test guidelines for Mulberry , fully bloomed inflorescence of male (before anthesis), female (at the receptive stage), and bisexual catkins depending upon the sex expression of the accession were used and measure the length including the pedicel of the inflorescence in a minimum of three inflorescence/plant and three plants/accession. For the measurement of mature fruit length  fruits fully ripened was collected and the length of the full fruit including the peduncle was recorded, whereas for mature fruit width  thickness was recorded using Vernier calipers. Parameters like Ind, LLA, LLW, LA, PA, and PW were measured after 90 days of pruning, whereas, MIL, MFL, and MFW were measured after 60 days of pruning.) and R Studio Team . The mean values were used for Spearman rank correlation, ploidy groups PCA, and ploidy-species annotated heatmap analysis with MVappa  and ClustVis  online tools, respectively.The normality test (Shapiro-Wilk test) and homogeneity of variance test (Levene’s test) were carried out . Kruskal-Wallis H test followed by Dunn’s post hoc tests (p < 0.05) was conducted to know the significance among the ploidy groups. All statistical analyses were carried out using IBM SPSS Statistics 23.0. The descriptive statistics  were calculated for replicated data. A box plot for ploidy groups was drawn for all characters using the R 3.5.3 version . Results indicate that-The GS estimation of the core collections available in India is not yet investigated so far. Therefore, the estimation of the GS of the 157 potential germplasm encompasses different species of M. serrata (hexaploid genome) and the least value of 0.72pg was recorded in S-30 (diploid genome) (S1 Table). The nDNA content in the replication of the same accession was almost the same .GS estimation signifying that the nDNA content of studied mulberry accessions ranged from 0.72 to 2.89pg, the highest amount of 2.89pgDNA was found in Fig 1A).The mean genome content of 0.86, 1.24, and 1.79pg was recorded for diploids, triploids, and tetraploids, respectively. The average GS ranges between 0.71 to 0.98pg for diploids, 1.16 to 1.29pg for triploids, 1.45 to 2.02pg for tetraploids, and 2.88 to 2.91pg for hexaploids are substantially different ploidy levels are differentiated as per the GS estimated . For example, 0.73pg and 0.98pg of GS were observed in SRDC-2 and Kokuso-20, which belongs to M. alba. Similarly, accessions of M. indica GS ranged between 0.72 to 0.98pg in diploids (S1 Table).The mulberry accession in the same species varied slightly .Among the predicted four ploidy, accessions of tetraploids showed the highest variability in GS, ranging from 1.45 to 2.02pg (±0.03) . For example, accessions of M. indica MI-0173 (1.27±0.031pg), MI-0652 (1.26±0.024pg) and MI-0799 (1.24±0.021pg) are considered to be triploid, whereas MI-0454 (1.69±0.050pg) is tetraploid (S1 Table). In M. alba, accessions MI-0050 (1.16±0.015pg) and ME-0092 (1.21±0.018pg) are predicted to be triploid, whereas ME-0149 (1.45±0.024pg) is tetraploid. Accessions of M. macroura like MI-0051(1.21±0.018pg), MI-0079(1.23±0.015pg), MI-0521 (1.28±0.035pg) and MI-0772 (1.25±0.025pg) considered to be triploid and MI-0247(1.53±0.048pg), MI-0365(1.91±0.027pg), and MI-0387 (2.02±0.036pg) are tetraploid in nature (S1 Table).Predicted ploidy level indicates wild collections of M. macroura, out of seventeen studied accessions, six are tested as higher ploidy levels .Additionally, FCM data indicates that with increasing ploidy levels, event count consistently decreased. The less event count might be the consequence of polyploidy-mediated increasing cell size with decreasing cell number  .The present cytological study validated and confirmed the chromosomal numbers of 12 selected mulberry accessions comprising different ploidy groups such as diploid varietiesV1, K2 , triploid Saranath-1 , tetraploid Lava forest-1  and hexaploid thick leaf Table 1). The value of Ho for SSR markers ranging from 0.024 to 0.890 with a mean of 0.477 was observed, while the value of He varied from 0.208 to 0.853 with a mean of 0.606. The average PIC value for SSRs was 0.556. Fifteen (75%) SSR markers in total were found to be highly informative with a PIC value ≥0.50, Four (20%) were moderately informative with PICs values ≥0.25 and <0.50 and the remaining one was least informative with a PIC value <0.25. The values of PI varied from 0.152 to 0.794, with an average of 0.398. Whereas, PD varied from 0.206 to 0.848, with an average of 0.602 (Table 1).A set of 20 (32.25%) polymorphic SSRs out of 62 SSR primer pairs was expedited tomeasure the genetic diversity inferences among the 82 mulberry accessions selected based on collection site, open/cross-pollinated hybrid, and breeding information . These 20 informative markers generated 94 SSR alleles with an average of 4.7 alleles per SSR marker ranging from 2 (M2SSR112A) to 7 . A mean Shannon’s Information index (I) value of 1.137 was observed for all the markers varying from 0.383 to 1.914 , while M. indica had at least one (80.85%). The maximum Na was observed in M. alba (1.777) with a mean of 1.686, while Ne (1.487) and I (0.425) were observed in Morus spp. with a mean of 1.462 and 0.413, respectively. The h (diversity) ranged from 0. 257 (M. indica) to 0.283 (Morus spp.) with an average of 0.273 whereas maximum uh (0.302) was recorded in Morus spp. with a mean of 0.288 (S5 Table).The species-wise diversity analysis revealed that in four species a mean of 83.78% of loci were polymorphic. S6 Table). The overall PhiPT was 0.058  suggesting a low level of genetic differentiation among studied Morusspecies. The pairwise population PhiPTand Nei’s genetic distance were analyzed to determine the genetic relationship among Morus species (S7 Table). The highest PhiPT (0.120) and Nei’s genetic distance (0.085) were observed between M. macroura and Morus spp. revealing that these species are more divergent. Whereas the lowest PhiPT (0.025) and Nei’s genetic distance (0.025) were observed between M. alba and M. indica, followed by M. alba and M. macroura with PhiPT (0.039) and Nei’s genetic distance (0.037). These results suggest that the pair of M. alba and M. indica and M. alba and M. macroura are genetically closer to each other as compared to other species (S7 Table).To evaluate the overall partitioning of genetic diversity among studied germplasm collections, an AMOVA was executed using the genetic distance matrix. The AMOVA revealed that 94% of molecular variance was partitioned withinspecies and only 6% among species , Unweighted Pair Group Method with Arithmetic Mean (UPGMA) based Neighbor-joining clustering, and Bayesian model-based complementary clustering approaches. The PCoA  revealed that Morus species were grouped into three major genetic clusters with a high level of intermixing as complemented by UPGMA-based NJ clustering. The first three coordinates of PCoA only explained 25.68% of the total variation .The SSR-basedclustering analysis of different Morus species representation was from M. alba (33%), M. indica (28%), and M. macroura (20%) respectively . The triploids  were grouped in Cluster III. These triploids were found very close to accession Vishwa, which is one of the parents used in breeding these triploids. Similarly, two accessions of ME-0018 and ME-0254 belonged to M. cathayana were grouped in Cluster III and exotic accessions ME-0113 and ME-0108 (Japan), ME-0178 (France), ME-0179 and Lun 40–2 (China) all belonged to M. alba and were groped in Cluster II-1 .The highest percentage of Morus accessions . According to the dendrogram analysis and dissimilarity coefficient measured using the Neighbor-joining method, it is depicted that the markers were successful in segregating among the mulberry accessions irrespective of species. The dendrogram grouped all accessions into three major genetic clusters-I, II, and III (See more detailed in S8 Table). Cluster I consists of 25 accessions and the majority of them were M. indica (13) and M. alba (9) conferring theirclose genetic relationships as depicted by pairwise population PhiPTand Nei’s genetic distance. ClusterII consists of 33 accessions and has two sub-clusters, Cluster II-1 and II-2 comprising 19 and 14 accessions respectively. Interestingly, sub-cluster II-1 comprises M. macroura (11) and M. alba (8) accessions conferring theirclose genetic relationships. Whereas, Cluster-III consists of 24 accessions of six mulberry species i.e. Morusspp. (7), M. alba (6), M. indica (5), M. macroura (3), M. cathayana (2), and M. serrata (1) and found to be the highest admixture cluster, since more accessions of which are mulberry varieties developed from two different species . Further, other than cluster I, higher ploidy level accessions are distributed in the entire groups projecting that polymorphic SSRs detected in our study were not able to discriminate amongdiploids, triploids, and tetraploids.Additionally, the UPGMA cluster analysis was used to describe the genetic relationships among the 82 Morus accessions was resolute using STRUCTURE analysis based on the determination of optimal K-value . The results indicated that Morus accessions were grouped into three clusters . The cluster-I represents 35 accessions and the majority of them belong to M. indica (16) and M. alba (11), cluster-II represents 17 accessionsmajority of them belong to M. macroura (8) and M. alba (8), while clusterIII represents 17 accessions majority of them belongs toM. indica (7), Morus spp. (7) and M. alba (8). Further, based on membership coefficients (q ≥ 0.70) only 40 (48.78%) accessions were assigned toa specific population structure cluster belonging to pure ancestry. While the remaining 51.22% of accessions represented a high level of genetic admixtures in three clusters with membership coefficients of <0.70. Among species, two accessions of M. cathayana (100%), eight Morus spp. (72.73%), fourteen M. indica (60.87%), twelve M. alba (44.44%), and four M. macroura (25%) accessions represent pure ancestry.The existence of population structure in S9 Table). From this study, we observed that-Variations in morphological characters among diploids, triploids, tetraploids, and hexaploids were examined and analysed .The internodal length (InD), leaf-lamina length (LLL), leaf area (LA), and mature inflorescence length (MIL) were greater in triploids compared to diploids and tetraploids (Leaf-lamina width (LLW) and petiole width (PW) had higher mean values in tetraploids (17.8±6.62 cm and 0.36±0.1 cm) compared to diploids  and triploids .Petiole length (PL) was greater in diploids (4.3±1.01 cm) compared to triploids (4.28±0.81 cm) and tetraploids (4.18±1.34 cm). Hence, PL was found to be decreased as the ploidy level increased.Mature fruit length (MFL) was greater in triploids (3.85±0.77 cm) than in diploids (2.42±0.65 cm).S10 Table).However, InD and MFW were significantly higher in triploids  than in diploids  and mature inflorescence length was significantly higher in triploids (4.05±1.02 cm) than in diploid (2.2±0.84 cm) and tetraploids (2.27±1.38 cm) as per the statistical analysis by Kruskal-Wallis H-test . It had a negative association with petiole length , leaf-lamina length , and leaf area  which is also depicted in .The correlation results revealed a significant positive correlation of GS with traits viz., mature inflorescence length , and fruit length  Fig 6C, 6D). Results indicate that 82 accessions were clustered into Cluster I and Cluster II, whereas, each cluster was divided into two sub-clusters (SCI and SCII). Primary groupings for entries were denoted by different colours (9 groups). Cluster I comprised one tetraploid, four triploids, and twenty-nine diploids. On the other hand, Cluster II have comprised of hexaploidy, one tetraploid, two triploids, and thirty-six diploids. Hence, out of six, four triploids were grouped in Cluster I (SCII) and higher MIL and MFL were observed in this cluster of genotypes. In general, the heat map demonstrated that Cluster I comprised triploids and comprised higher MIL and MFL than diploids. Although, the value of LLW, LLL, LA, PT, and PL were higher for the genotype belonging to diploids as compared to other ploidies. Additionally, PCA data  indicates that the expansion of traits was restricted for diploids as compared to triploids. Data also suggests that few accessions of diploids have a higher value for specific traits. For example, accessions MI-0540 and MI-0673 were outgroup 95% confidence circles because of a higher value of inter-nodal distance  and leaf area , respectively.Ploidy-species annotated phenotypic data was represented in hierarchical clustering with heat maps and principal component analysis  has been recognized as a source of evolutionary force and its role in species diversification has been well understood . Despite its importance, GS variation across the species and ploidy-level have not been considered ingermplasm material like tree plants mulberry, therefore itremains to be explored for a better understanding of fundamental aspects like (1) expansion of polyploidization/WGD occurred in mulberry; (2) preferred polyploid advantages present among the ploidy groups; (3) specific leaf/fruit traits associated with polyploid; and (4) how is population structure affected by polyploidization or WGD.Germplasm is an enduring resource management mission and asset for civilization and to fully explore the potential of germplasm material, particularly for the complex traits associated with yield in the face of the current threats of climate change, it is necessary to reconcile research advancement and innovation of practices M. indica, diploid) to 2.89 pg . Thus, the smallest GS: highest GS content ratio is about 4.01 (2.89/0.72) was observed within the studied population. Previous genome content studies revealed the 2C value of mulberry species ranged between 0.70 to 0.73 pg for diploid, 1.04 to 1.17 pg for triploid, 1.42 to 1.63 pg for tetraploid, and 2.02 to 2.48 pg for hexaploid, and 7.26 pg for decosaploid . Thus, the present results corroborate with a previous study that a higher level of variation is present both at the species level as well as within the accessions of the same genus. However, the estimated GS of diploid M. alba differed from the previous study by Ohri and Kumar , it was around 1.7pg for the n = 14. Whereas, diploid M. alba was estimated to be 1.7 pg/2C measured by micro densitometry using Feulgen stained which might have some technical shortcomings , 59.In the present section, we discuss the status of available polyploids of mulberry to understand polyploidy variation across the species level and try to address the first question. To address how and to what extent WGD occurred in mulberry, 157 mulberry accessions belonging to seven different species including popular varieties cultivated in India were selected and subjected to genome content analysis using FCM. GS data indicates that it ranged from 0.72 (M. indica (59 genotypes), M. alba (55 genotypes) and M. macroura (17 genotypes), respectively. Moreover, GS variations in terms of the fold change are about 2.79, 1.98 and 2.43 among diploids belonging to M. indica, M. alba and M. macroura. Intraspecific variation has been recorded in Morus and suggested the GS variation occurred by detection method, or by environmental factors, such as water stress, soil nutrition, or extreme climate condition, and with genetic factors, including duplications, or loss of some non-essential genes in an ongoing evolutionary process that increased or decreased the GS –63. Recently, Xuan et al. , illustrated that chromosome fissions/fusions were the key mechanisms underlying the evolution of differences in the basic chromosome number between M. notabilis and M. alba.In our study the intraspecific variations of diploid GS data indices that about 34%, 37%, and 18% variation among the genotypes belonging to M. rotundiloba, M. cathayana, M. australis, M. serrata, M. sinensis is non-significant because of their representation in small numbers (1–2 genotypes). Hence, for diploids, more variation in genome content values was observed in M. indicaand M. alba. Meanwhile, M. macroura showed a high level of variation in tetraploids as previously observed by Yamanouchi et al. . However, these estimates differ from previous FCM assessments, possibly because of differences in the DNA reference standard, dyeing time, and fluorochrome difference in the current study , 66. Propidium iodide (PI) is commonly referred to for GS estimation through FCM as used in our present study , 65.Variation in terms of genotypes from other species like . Breeding of mulberry takes about 12–16 years and to develop a variety, ploidy compatibility is an essential aspect of breeding. To date, the varieties  released by the researchers are not sufficient for the future sericulture industry, and an urgent need to develop climate-resilient mulberry varieties, including diploids and triploids. Ploidy and GS can influence reproductive compatibility, fertility, and heritability of traits hence our data can be useful for understanding the breeding potential between the different species of mulberry. In this context, we have provided a new platform for future ploidy breeding by focusing on GS and diversity analysis of mulberry accessions available in India and their relationship.That GS variability does occur among the various genotypes at an intraspecific level due to the involvement of different genomes during hybridization and selection as well as naturally. Further GS variations are also observed among the genotypes collected from different ecological conditions. Mulberry is reported to be a higher adaptability tree plant with a huge variety of species and ploidy. The use of wild species in breeding provides a lot of possibilities, to incorporate the traits such as drought, frost, and disease resistance into cultivated varieties , 57, 59. Attempts were made to count metaphase chromosome numbers using shoot-tips of original plants and present cytological evidence corroborates with previous studies –22, 68, which strengthening our FCM analysis.Besides the FCM analysis, classical cytological work to estimate the metaphase chromosome number is essential to validate the chromosomal number, which is lacking in previous studies Morus. However, the controversy over the ploidy of M. alba was clarified by Jiao et al.  that the chromosome number (2n) of M. alba, is 28 (diploid) which provides a basis for a better understanding of the developmental and regulatory mechanism of economic target traits. Moreover, present GS estimation for diploids, triploids, tetraploids, and hexaploids, across the species level, was found to be technically foolproof to discriminate the higher ploidy level in the mulberry, because, among the different methods for GS estimating, FCM is now considered as the most precise method , 71. GS varies significantly across plant life; however, variation of GS in vascular plants is associated with evolutionary consequences on plant development and ecological performance , 72. Moreover, the present research generated GS of 157 core collections, in turn; the information can be useful for effective genetic improvement and genetic diversity investigations. Additionally, the present result and discussion clarify that Morus spp. having a higher level of GS variation, not restricted at the species level but also within the accessions of the same genus.Till 2020, chromosome configuration, genome size, meiotic association, and chromosome assembly at the genetic level were restricted in . Despite the significant progress achieved through conventional breeding, even though to date it is distressingly slow, mainly because of the perennial growth habit and complex inheritance pattern . Additionally, the adoption of modern genomic approaches for crop improvement is severely constrained by the lack of sufficient molecular markers in mulberry . Finally, the paucity of genomic information for co-dominant marker systems has been a serious obstacle to using molecular breeding for the advancement of mulberry genetics .Understanding of genetic diversity of mulberry is another important aspect of the future breeding program. Genomics-based breeding was reported to be an effective and promising approach to reaching sustainable crop improvement and is more relevant specifically for perennial crops such as mulberry , 75. Because of their distinct qualities of co-dominant inheritance, multi-allelic nature, vast genome coverage, high abundance, and especially good reproducibility, SSRs are the most widely recognized genetic markers that are actively used in plant breeding . SSR markers are extensively employed in plant research for a wide range of studies including DNA fingerprinting, high-density mapping, genome comparative mapping, population diversity study, and assisted breeding . Although the genetic distances, evolution, and kinship of Morus have been studied by using molecular marker technology, the conclusions are still controversial , 78. In the present research, a set of stable and highly polymorphic SSR markers were employed as evidenced by greater PIC (0.556), PD (0.602), the observed heterozygosity (0.477), and a lower PI value of 0.398. Hence, markers’ inheritance clearly explains their potential as genetic resources in diversification studies. Moreover, the information generated in the present research based on SSR marker-based genotyping, higher PIC, PD, and observed heterozygosity can be efficiently used for future molecular breeding programs of the mulberry species/genotypes. Recently, Jain et al. , reported single-nucleotide polymorphism (SNPs) ranging from 241,897 (S1) to 657,137 (M. rotundiloba) in different mulberry accessions such as K2 reference genome with other accessions namely Punjab Local, M. indica, BR-8, M. multicaulis, and M. serrata showed higher genetic variations with ≥0.5 million SNPs which is conformity in our finding that phylogenetic analysis through SSRs revealed diverse relationship between selected species of Morus genome. SSR analysis of the mulberry germplasm confirmed a complicated genetic background and high genetic diversity. However, SSR markers were not able to differentiate among ploidy levels hence flow cytometry method is reliable for the determination of mulberry accession at the ploidy level. But, SSRs analysis is trustworthy for an understanding of the degree of genetic variation where the breeding of germplasm and mulberry germplasm conservation gets benefited. Moreover, the present research was focused to estimate genetic diversity using simple sequence repeats (SSRs), and coming to understand the phylogenetic relationship and population structure. Insight gained herein suggested the effect of genetic variation instead of ploidy, which might be the consequence of the high level of heterozygosity imposed by natural cross-pollination.SSRs are simple tandemly repeated DNA sequences comprising units of 1 to 6 nucleotide(s) and are ubiquitous in plant genomes Morus species. The combined clustering analysis revealed that only the majority of accessions of M. indica (CusterI) and M. macroura (ClusterII) were grouped in a specific cluster, while the accessions of M. alba, M. indica, and Morus spp. were distributed throughout the three major clusters suggesting a high level of genetic admixture in these species. Such results are attributed to the outbreeding and wind pollination reproductive system of Morus species . In turn, the present finding suggests genetic background of species is the account of admixture between the groups that occurred due to a high level of heterozygosity as the result of gene exchange between the species during the outbreeding reproductive system. In addition, accessions were collected from different countries and they are continuously used for mulberry breeding programmes and which resulted in a mixed population. For example, M. alba species originating from France and Indonesia are grouped separately from those originating from Japan and India. Understanding the molecular diversity and modifications in genome content triggered by WGD, not only helps in determining the evolution but also shapes the plant genome leading to adaption, response to stress, and diversification . In the era of high-throughput sequencing, the genomic resources in mulberry are enriched with the chromosome-level reference genome sequencing of M. alba and draft genome sequencing of M. indica, 79 will unravel the molecular composition and genome evolution .Additionally, population structure analyses are another important aspect of the present study because it reflects the relationship between GS and marker variation in the population structure analyses. The population structure analysis revealed the existence of three genetic clusters with a high level of intermixing, a high level of within-species genetic diversity, and a weak genetic structure among the studied Morus spp. However, female floral features, notably style length, were a well-recognized attribute used to classify mulberries in terms of female accessions , 84. Though, recently quantitative vegetative traits were considered and shown to be beneficial for mulberry plants . Moreover, in the present research, we have considered both vegetative and reproductive traits to recognize the effect of WGD on phenotypic plasticity. The correlation results indicate a significant positive correlation between GS with traits viz., mature inflorescence length , and Mature fruit length . Similarly, Wang et al.  observed a weak significant positive correlation between GS and fruit size in Chinese jujube. Whereas, the relationship between DNA content and the inflorescence characters was non-significant . Additionally, multiple trait-based PCA referred to GS linked with leaf area (LA), which is the account of the contribution of MIL, MFL, and InD (positive coordinate) and LLW, LLL, PT, and PL . Though, further research will require investigating how and to what extent GS is intimate to physiological and/or anatomical traits in mulberry.Polyploidy has been considered a source of evolutionary development, species expansion, as well as adaptation. Especially in plants, polyploids are most frequent events, which occurred as a consequence of WGD events and appear to be associated with environmental conditions. Hence, the present research tried to understand the phenotypic plasticity among the ploidy groups  in Morus spp.) research is to understand genetic complexity as their genetic makeup is complicated by cross-pollinate behavior with a huge number of ploidy variations across multiple wild species. In the present study, we have tried to highlight the intraspecific diversity of GS, phenotypic traits, and genetic variation of a worldwide collection of 157 mulberry accessions. Present investigation revealed that core collections bear a huge genome content variation, from diploid to hexaploids an about 4.01-fold difference of GS. Additionally, the phenotypic study suggests that a significant positive correlation between GS and mature fruit length (MFL) and mature inflorescence length (MIL). Furthermore, the population structure analysis revealed the presence of three genetic clusters with a high level of admixture, which implies the effect of genetic variation instead of ploidy on trait plasticity that could be a consequence of the high level of heterozygosity imposed by natural cross-pollination. Moreover, the information generated in the present study will complement further genetic diversity studies to identify unique/duplicate accessions, and GS/ploidy information as well as ploidy-associated traits will be useful for breeding and crop improvement. Besides mulberry breeding, the present study signifies downsize of traits with upscaling of GS, for example, petiole length . Therefore, future research to understand the consequences of polyploid mulberry need to be explored because biomechanical expenses of higher ploidy are more towards the maintenance of large cell size, large genome size, nucleic acid biosynthesis, and carrying out its physiological activities.An intriguing area of mulberry Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 TableThe reference column provides the source of the publication.(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 TablePhiPT values below diagonal and Nei’s genetic distance above diagonal.(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 Table(XLSX)Click here for additional data file.S11 Table(XLSX)Click here for additional data file.


Cities are at the forefront of European and international climate action. However, in many cities, the ever-growing urban population is putting pressure on settlement and infrastructure development, increasing attention to urban planning, infrastructure and buildings. This paper introduces a set of quantification approaches, capturing impacts of urban planning measures in three fields of action: sustainable building, transport and redensification. The quantification approaches have been developed to account for different levels of data availability, thus providing users with quantification approaches that are applicable across cities. The mitigation potentials of various measures such as a modal shift, the substitution of building materials with wood, and different redensification scenarios were calculated. The substitution of conventional building materials with wood was analyzed as having a high mitigation potential. Building construction, in combination with urban planning and design, are key drivers for mitigating climate change in cities. Given the data heterogeneity among cities, mixed quantification approaches could be defined and the measures and policy areas with the greatest climate mitigation potential identified. Himes and Busby showed that 9% global emissions can be reduced annually to meet the 2030 climate targets to stay below 1.5 degrees of warming if half of the expected new urban construction substitutes conventional materials with wood Concrete is expected to contribute 12% of global GHG emissions by 2060 Emissions in the transport sector are rising faster than in all other sectors One of the most referenced strategies to contain additional land consumption from increased urbanization within paradigms of climate-friendly urban development is the compact city approach Urban areas in the Global South face the double challenge of having to address the dramatic consequences of climate change, by which they are disproportionally more affected, while managing high population growth rates and linked rapid urbanisation This paper presents quantification approaches adapted to the heterogeneous data availability, capturing impacts of urban planning measures in the three fields of action: urban transport infrastructure, densification and building materials. The quantification approaches described in this paper have been developed and tested for different case - study cities across the global north & south to accommodate different levels of data availability. Taking into account this diversity, we provide a range of mixed bottom-up and top-down approaches for handling data heterogeneity. In doing so, cities will be provided with applicable ways of dealing with data heterogeneity to strengthen their mitigation pathways. Thereby contributing to the advancement of the Global Research and Action Agenda on Cities and Climate Change Science to be developed in the context of the IPCC's seventh assessment cycle, by providing insights into two of the outlined topical knowledge gaps on cities and climate change, namely urban planning and design as well as sustainable consumption and production. The following paper is organised as follows: first, in the methodological section, the case-study cities and the approaches used are presented. This is followed by a description of the different approaches adapted to the data heterogeneities. Following this, the results and a discussion with limitations are presented. At the end of the paper, a conclusion is given.22.1One of the key objectives underpinning this research was the validation of the selected measures, against the backdrop of data heterogeneity and socio-economic diversity. To explore the heterogeneity of dynamic urban development and data availability, therefore six cities with different characteristic phases of urban development and growth rates were selected. The cities were the regions where different approaches were used, and rather than with the focus on comparability. Among them cities with stable population growth and established administration, such as the cities of Leipzig and Essen (Germany). Over decades, a cohesive infrastructure has developed, with a focus on renovating old infrastructure and increasing resource efficiency. Nagpur and Rajkot (India) are examples of rapidly developing cities with infrastructural inequalities and a high proportion of informal settlements. The cities of Santa Rosa and Pasig City (Philippines), on the other hand, are examples of fast-growing cities that are redeveloping.2.2For the selection of mitigation measures, global datasets on reported measures to the CDP-ICLEI Track with expected GHG emission savings from cities were used We chose to focus on overall mitigation across modes because discussions are focused on multimodal measures An attempt was made to select measures that have a direct quantifable impact. For example, the impact of the measure of a 10% modal shift to non-motorised transport is direct quantifiable, in contrast to the measure of a general compact city approach . The aimModels were then developed for each field of action, to quantify the potential emission savings. The models were based on the availability of data in the cities and were designed flexibly accordingly. The GHG saving per measure is always given in absolute terms and in % compared to the actual situation. The approach presents a ‘what-if analysis’, assuming a thoroughly implementation of the separate selected measures.2.32.3.1The GHG emissions of the embodied building stock per city were calculated on the basis of the material stock estimates and by using suitable GHG emission factors (see SI). Subsequently, two scenarios were used to calculate the GHG savings through replacing abiotic parts of the building materials with an increased usage of timber. We chose replacement rates of abiotic material use is 5% and 15% respectively. For this, we used The key difference between the three approaches is grounded in the modelling of construction materials for the building stock, while emission factors, correction factors as well as assumptions (measures) for substitution were applied across approaches and sample cities. Thus, whilst approaches differed to some extent, we ensured that these still allowed for comparison and validation of GHG savings and the approaches themselves.Modelling of building stock via building typologies (Germany)The most local data were available for the German cities, so this approach follows a bottom-up approach. For the German cities of Essen and Leipzig, the residential buildings were modelled using building cadastre data comprising the number of buildings constructed by decade and the type of use (multi-family or single-family house) 2] and the material input [t/m2] to obtain the total material input of the building stock for each city. Following this, the selected scenarios were applied to calculate the GHG saving through the use of replacement of parts of the building materials with timber (5% and 15%). In order to estimate GHG emissions, the building material quantities were calculated with correction and emission factors for timber (see SI).The total quantity [t] of building materials was then calculated with the total gross floor area [mExtrapolating economic and material-flow data (India)con) with the associated emissions (Emissionscon) was used from the City of Mumbai con Rajkot/Nagpur) was calculated  data on construction for the two Indian sample cities Nagpur and Rajkot was lacking. Therefore, using a comprehensive material flow study for six Asian cities from the Asian Development Bank (2014), the Domestic Material Input (DMI) indicator was derived for construction materials  for the city of Bangalore for the year 2000. Dividing Bangalore's total construction DMI by local GDP, the construction material intensity of local GDP can be calculated. The carbon intensity of construction materials was derived using the same global emission factors as in the other sample cities of this study (SI). A reason we opted to use global emission factors was to facilitate the replication of the GHG mitigation calculation approach in other cities and to enable comparison of quantification approaches for the construction sector. Furthermore, in order to ensure robustness of the extrapolation, another MFA data set (DMIated Eq. .(3)DMIcoAfter that the correction factors were applied . In a fiDeriving material amounts embodied in the building stock via floor area and average materials used (the Philippines)2) estimates with the amount and type of construction materials used per m2 . In this approach the total floor is calculated by multiplying the number of existing households in a specific year (2015) 2 house unit. The amount of each material is provided for four construction typologies with different structural designs. The average of the four types was calculated for the whole unit (60 m2) and then expressed per m2 unit.As with the Indian cities, detailed data on the housing stock was also lacking for the Philippine cities. The approach taken and validated for the Philippine cities of Santa Rosa and Pasig extrapolated the total amounts of materials used in buildings by multiplying total floor area  and multi-family houses (MFH). Single-family houses in Essen, consist of an average of 309 t of building materials 2.3.2An approach for all 6 cities was adopted to quantify the impact of mitigation measures for transport, with differences in the methodology being the result of data quality and availability, especially in relation to average travel distances.General approach to calculating transport emissions (good data availability)For Leipzig and Essen, household surveys and statistical data from the traffic departments were used, it contains the necessary information on individual traffic activity Downscaling of transport data to calculate the baseline (low data availability)Detailed traffic indicators were lacking for the Philippine cities. Since Pasig is located in the metro Manila region and Santa Rosa is located close to it, values from the metro Manila region were used, which were then scaled down with local demographic data. For the Philippine cities of Pasig City and Santa Rosa daily GHG emissions were calculated by multiplying the emission factors of each transport mode with i) the share of each transportation mode Transport activity for all case-study citiesIn Leipzig, the car (driver and passenger only) has the highest modal share with 36.5% In Essen, the car has an even higher modal share (54%) than in Leipzig. Active transport has a lower share of 25% compared to Leipzig The car has only a small share of 5.7% in Nagpur's transport system In the less populous and smaller city of Rajkot, two-wheelers also have a high modal share (35%) As with Rajkot, walking is the most used form of mobility in the Philippine city of Pasig city (31%), followed by jeepneys (19%) and tricycles (16%) 2.3.3To estimate the impact of land use change and in particular redensification on emissions, we applied an urban scaling approach to redensification. Studies show that urban GHG emissions are roughly proportional to urban population or area To conduct the analysis for Essen and Leipzig, 2018 data from 47 German cities was taken from the Climate ActionPlanner, an internet-based software for balancing greenhouse gases After determining the parameters, two different scenarios were analysed with the Cobb-Douglas function, in which the area and the population were changed. Therefore Eq. Subsequently, the emissions (C) of the Cobb-Douglas function could be compared with each other and the additional GHG savings of the 3rd scenario compared to the 2nd scenario could be examined.3In the section following, the impacts of the selected mitigation measures are described according to the field of action.3.1The what-if analysis in sustainable construction involved calculating two scenarios to quantify the emission saving potential of timber in construction: substitution 5- and 15-% of construction material with timber.With a 5% increase in the use of wood as a building material, the potential savings range between 1.6 and 3.2% . The lowWith a 15% increase in the substitution of conventional building materials with sustainably managed wood, the GHG savings range between 5 and 9.5% . The two3.2The measure to reduce average travel length has the highest mitigation potential emissions savings compared to the baseline . Rajkot 3.3The parameters of Eq. If population increases (S1), the city increases GHG emissions . HoweverThis expectation is confirmed by our results: the emission savings from S2 to S1 differ between the German and Asian cities, but are all in a similar range of 0.39-0.68%, despite the growth rates of P and A are significantly higher in the Asian cities . Althoug4The emissions of the building stock materials are currently omitted from common GHG-inventory calculation protocols . We derive indicators that enable general recommendations for action in climate-oriented urban planning. Approaches and recommendations are often evaluated on the basis of homogeneous data, yet in reality, city-level data quality and availability varies substantially across the world. While several studies examine how spatial form and GHG emissions are related to individual emission sectors they do not take the data heterogeneity between cities into account 4.1With the expected expansion of the infrastructure, attention must be paid to the way in which new building infrastructure is constructed. The substitution of conventional building materials with wood was analyzed as having the highest mitigation potential. The mitigation potential depends on the material distribution in the base scenario. The share of steel and ferrous metals for Indian cities (18%) is higher compared to German cities (4%) or Philippine cities (5%). Deploying the correction factor (4 tons of wood substitute 1 ton of steel) cause an increase in material consumption for the Indian cities but a decrease for the other cities. Therefore, the mitigation potential for India is on average lower. As part of the 100 climate-neutral and smart cities by 2030, Leipzig must implement very ambitious measures. The very challenging substitution of 15% of the entire building stock would halve Leipzig's total emissions and come closer to the step of climate neutrality.The relevance of the building sector for climate change mitigation has also been identified by Guo et al. (2017), where they estimated that the energy consumption and carbon emissions of cross-laminated timber (CLT) buildings are 10% and 13% lower than those of reinforced concrete buildings, respectively, based on a full life cycle assessment The multimodal transport measures showed varying GHG impacts. If Leipzig as one of the 100 climate-neutral cities would adopt all the transport-specific measures considered in this study, they would halve their transport emissions. Both German Cities Leipzig and Essen, show a significant share of individual transport via cars, therefore a modal shift to fewer cars leads to higher mitigation potential, compared to the other cities. Creutzig et al. 2012 also showed significant GHG savings with a modal shift from car to less emission-intensive transport modes. Due to the correlation of polycentrism and traffic reductions, the modal shift effects can also be enhanced by considering polycentric structures in new districts The mitigation potential of public transport electrification depends strongly on the current share of electrified public transport and the share of renewable energy in the electricity grid The results for redensification in already densely populated cities indicate a low influence of the area on GHG emissions , indicating a high level of “noise” in the data sets used. However, with the Cobb-Douglas function, these have a considerable influence on the results and thus also on the emission savings.In some cases, older data was used, which is an issue that many cities face. Efforts have been made to use the most recently published data, which are similar for all cities. However, due to the nature of the scenarios, the timing of the data is less important. In addition, mitigation pathways on time scales of several decades represent a commitment and depend less on current data availability and more on future assumptions about change. An example is the case of the redensification approach, where the data is old but was not crucial for the success of the approach. The aim was primarily to use statistical approaches to determine the correlation and, based on this, to derive the relative results.All approaches were performed assuming a what - if - analysis, disregarding dynamic developments, such as demographic, economic or political changes. Further research is needed on the consideration of economies such dynamics over time, which would enable further detailed statements on the interrelationships between urban infrastructures and emissions.4.3In conclusion, the research reveals that successful urban interventions directed towards a climate-oriented development are place-specific, time-bound and highly dependent on the status quo with regard to the use of building materials, modes of transportation and the state of the urban form. Substitution of conventional building materials  with wood carries the highest emission savings potential among the fields of action studied across all sample cities. However, to avoid trade-offs, e.g. increased land-use competition, wood-based construction materials need to be enhanced in terms of material properties (e.g. CLT) and complemented with other biogenic materials.For the urban form, the research revealed a limited impact of population density (of the area) on emission reductions assuming an increasing population with maximized reduction potential achieved through a reduction in land area with steady population. Considering trade-offs with climate adaptation goals , the results also suggest that interconnected polycentric urban systems can achieve co-benefits and synergies between climate change mitigation and adaptation. Consequently, the focus of climate-oriented urban development needs to be on the urban form with the objective of facilitating a more polycentric form, exploiting efficiency gains from population density and reduced transport needs as well as enhancing urban resilience.This research also contributes to advancing mixed quantification approaches for assessing the carbon reduction potential with a focus on practicability and replicability for local practitioners and authorities. Despite different data availability, it was possible to derive applicable calculation approaches for all cities. This allows cities to incorporate mitigation potentials in different fields of action, which have not been considered so far, into their mitigation pathway. It demonstrates that with mixed bottom-up and top-down approaches for handling data heterogeneity, plausible results can be achieved that can inform policies on the ground for climate-oriented urban development. However, more work is needed to further advance mixed approaches and make them operational for local authorities.A considerable expansion of urban infrastructure can be expected in the future, especially in the Global South. However, the increase in new urban areas also offers an opportunity to design for an appropriate functional density, thus avoiding path dependencies or lock-ins with negative consequences such as urban sprawl, functional segregation and high traffic volumes Fabian Reitemeyer, David Fritz, Nikolai Jakobi, Juergen P. Kropp, León Díaz-Bone and Carla Mariño Viteri conceived and designed the experiments. Fabian Reitemeyer, David Fritz, Nikolai Jakobi and Carla Mariño Viteri performed the experiments. Fabian Reitemeyer, David Fritz, Nikolai Jakobi, Carla Mariño Viteri and Juergen P. Kropp analyzed and interpreted the data. Fabian Reitemeyer, David Fritz, Nikolai Jakobi, Carla Mariño Viteri and León Díaz-Bone contributed reagants, materials, analysis tools or data. Fabian Reitemeyer, Nikolai Jakobi, Juergen P. Kropp and León Díaz-Bone wrote the paper.Data will be made available on request.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Rana temporaria Linnaeus, 1758 as our target species and an improved eDNA protocol to: (i) investigate differences in species detection between three developmental stages in various freshwater environments; and (ii) study the diversity of mitochondrial DNA (mtDNA) haplotypes detected in eDNA (water) samples, by amplifying a specific fragment of the COI gene  commonly used as a barcode. Our protocol proved to be a reliable tool for monitoring population genetic diversity of this species, and could be a valuable addition to amphibian conservation and wetland management.In light of the dramatic decline in amphibian biodiversity, new cost-efficient tools to rapidly monitor species abundance and population genetic diversity in space and time are urgently needed. It has been amply demonstrated that the use of environmental DNA (eDNA) for single-species detection and characterization of community composition can increase the precision of amphibian monitoring compared to traditional  approaches. However, it has been suggested that the efficiency and accuracy of the eDNA approach could be further improved by more timely sampling; in addition, the quality of genetic diversity data derived from the same DNA has been confirmed in other vertebrate taxa, but not amphibians. Given the availability of previous tissue-based genetic data, here we use the common frog  Due to the general elusiveness of amphibian adults and limited detectability of early life stages, which often live in protected, difficult-to-access and/or season-dependent aquatic environments, accurate estimates of the presence and distribution of amphibian species are logistically difficult, costly and time-consuming to obtain3.Of all taxa affected by the current biodiversity crisis, amphibians are the most endangered group of vertebrates5, enabling them to adapt to environmental changes, and to the spread of new pathogens7. Moreover, the loss of genetic variability may have detrimental effects on individual health due to inbreeding depression, reducing fitness and ultimately increasing the risk of population and species extinction8. Due to their particular breeding strategy , low dispersal rates and high philopatry, all of which limit population connectivity, amphibians seem to be especially prone to genetic erosion9. Therefore, regular, cost-effective genetic monitoring should be considered a fundamental aspect of amphibian conservation strategies. Although the importance of protecting genetic diversity is widely recognized and has been addressed under the Aichi Biodiversity Targets10, the development of standardized monitoring frameworks for an accurate surveillance of genetic diversity trends in natural populations is still limited to few charismatic or economically relevant species11.In addition to species richness, within-species genetic diversity is of crucial importance for the persistence and evolution of natural populations13, including for amphibians . Specifically, single species eDNA surveys  have increased the speed and efficiency of amphibian detection, compared to traditional observational monitoring15, and have proven to be particularly useful for detecting rare or elusive amphibian species , improving the knowledge of habitat requirements and species distributions (e.g.21), and tracking invasive alien amphibians (e.g.24.). In addition, the eDNA metabarcoding approach is increasingly used as a cost-effective method for the simultaneous assessment of species composition in amphibian communities25. However, to our knowledge, only one study aiming at the development of a long-term eDNA-based monitoring program for an amphibian species has been published thus far26.For the last decade, environmental DNA  has been used for accurate, cost-efficient species detection in aquatic environments29; however, only a few pilot studies on amphibians have been published. For example, Gorički et al.30 used two short CytB fragments (about 100–150 bp) for the discrimination of two putative olm (Proteus anguinus) subspecies, but the protocol was not designed to estimate population-level genetic diversity. Wang et al.31 developed an eDNA metabarcoding protocol for the Chinese giant salamander (Andrias davidianus) to allow the detection of seven haplotypes corresponding to distinct evolutionary lineages (reporting the results of laboratory amplicon mixture), but again the study was not aimed at the assessment of within-population genetic variability.Very recently, the possibility of inferring intraspecific genetic diversity from eDNA has also been explored34. Therefore, developing and validating effective field sampling methods is essential to the application of eDNA-based survey methods37.Despite their great potential, eDNA methods are extremely sensitive to sampling design and field and laboratory protocols, as eDNA is not abundant and persists in aquatic environments for a limited time, from a few days to several weeksRana temporaria Linnaeus, 1758), a widespread European pond-breeding amphibian38, showing high genetic diversity40, to develop and validate an eDNA metabarcoding protocol allowing rapid and standardized assessments of within-population genetic variability from water samples. Specifically, by selecting 10 wetland sites, for which we had previous information on R. temporaria haplotypes identified using traditional tissue sampling41, we: (a) developed a metabarcoding protocol targeting a 331 bp long fragment of the COI region, which allowed discrimination of previously identified haplotypes; (b) optimized the sampling design, in terms of temporal and spatial replicates, for the collection of water samples in a variety of wetland habitats; (c) computed standard genetic diversity estimates for R. temporaria populations from eDNA metabarcoding, to compare eDNA-based results with previously available genetic data.In this study, we chose the common frog (40); however, despite being the most widespread amphibian species in Europe42, local population declines are frequent due to climate change and habitat degradation44. Our study area covers the Autonomous Province of Trento , a mountainous region located in the eastern Alps; mtDNA haplotypes data are available for R. temporaria across the study area from a recent genetic survey41.The common frog has been a model species for previous genetic diversity studies  noted in41.For the present study, 10 sites were selected from Marchesini et al.41 Fig.  that encWater samples were collected at each of the 10 sites during the reproductive season  from 2019 to 2021. In order to define a standard protocol, three temporal (T1-T3) and three spatial (S1-S3) replicates were collected, where T1 corresponds to a replicate collected when eggs or early-stage larvae were present at the site (early spring), T2, when late-stage larvae were present (mid-summer), and T3, when both adults and juveniles had abandoned the sites following reproduction (end of summer/ early autumn). Some of the wetland sites became partially desiccated during the study, thus collecting the T3 replicates was not always possible. When possible , for each temporal replicate, three spatial replicates were sampled (5/10 sites) close to observed life stages. For three of these sites, for one of the three spatial replicates , collected samples from the shore without disturbing the water body, and wiped all equipment with bleach and alcohol between each site. The water collected in the canister was stirred before being drawn up with a 100 ml syringe and filtered through a Sterivex-GP Filter unit ; this step was repeated until the filter clogged. A second filter was also used for the same spatial replicate. The quantity of water that could be filtered through two Sterivex-GP Filter units varied widely among sites, ranging from about 100 ml to more than one liter, depending on the suspended organic matter. Each filter was drained and capped at both ends with the inlet and outlet caps. Following the manufacturer’s instructions, all filters were kept at ambient temperature out of direct sunlight until arrival in the laboratory later the same day, and archived at − 20 °C until DNA extraction.46. All procedures were performed under BSL2 biological hoods. DNA extraction from filters was carried out using the DNeasy PowerWater Sterivex Kit (Qiagen), following the manufacturer’s instructions with these modifications: the heating step and bead beating tubes (PowerBead Tubes) were eliminated to reduce extraction of non-target genetic material from microorganisms. The two filters corresponding to the same spatial replicate S1, S2, or S3 were processed simultaneously and extracts were merged into a single tube at the 14th step of the protocol. DNA extraction was performed in batches of a maximum of 10 water replicate samples, including one negative control (extraction blank) for each extraction batch.All laboratory procedures were carried out at the Platform of Animal, Environmental and Antique DNA of the Conservation Genomics Research Unit, Fondazione Edmund Mach, following recommended guidelines for eDNA analyses, including separate pre- and post-PCR laboratoriesR. temporaria, Primer3Plus47 was used with the haplotype sequences available from Marchesini et al.41. The resulting primer pair, named Rt-aplo_COI F (GTAATAATTTTCTTTATGGT) and Rt-aplo_COI R (TCAAACAAAGAGGGGTGT), amplifies a fragment 331 bp long distinguishing 11 of the 12 mtDNA haplotypes listed in Marchesini et al.41 for the Province of Trento. Specifically, haplotype pairs CA2 and TN4, and SA1 and PR7 (the latter not being previously found in the study area) were not distinguishable, and therefore, were named CA2_TN4 and SA1_PR7, respectively, in the Results section. To distinguish all known haplotypes for the study area, a 569 bp sequence plus adapters  would have been required, but such a long fragment cannot be processed using the Illumina technology commonly adopted for metabarcoding (maximum length 600 bp). The primer pair was tested in vitro on DNA extracted from R. temporaria tissue samples available from previous studies, and amplification success of all samples was confirmed via screening on a Qiaxcel Advanced System (Qiagen). The amplification reaction took place in a final volume of 50µL, containing H2O (22.25µL), Promega Flexi Buffer 5X (10µL), MgCl2 25 mM (4µL), BSA 10 mg/mL (0.5µL), Rt-aplo_COI-F 10 ρmol/μL (1µL) and Rt-aplo_COI-R 10 ρmol/μL (1µL), dNTP’s 10 mM each , Promega GoTaq G2 5U/µL (0.25µL) and template DNA (10µL). The PCR mixture was denatured at 95 °C for 2 min, followed by 40 cycles of 30 s at 95 °C, 15 s at 48 °C and 40 s at 72 °C, and a final elongation step at 72 °C for 5 min. Only one PCR was run per replicate, and a second PCR was run only if the first one failed to produce results. One negative control (PCR blank) was included for each PCR reaction, along with all extraction negative controls.To design a primer pair for amplifying the diagnostic mtDNA region for Each amplification product was then purified with the MinElute PCR Purification Kit (Qiagen) following manufacturer’s instructions. 20 µl of each purified product were loaded into a single 96-well plate and sequenced at the FEM Sequencing and Genotyping Platform using paired-end sequencing (2 × 300 bp) on an Illumina Miseq  with a 30 000 bp coverage.48. Overlapping paired-end sequences were merged to obtain consensus sequences using the command ‘mergepairs’ with a minimum overlap length of 100 bp and maximum of one mismatch in the overlap region. Reads that did not contain the forward or reverse primers were discarded, and primers were trimmed from the remaining fragments with the command ‘trim’. Sequences were quality filtered using the command ‘filter’, assuming a maximum allowed expected error rate of 0.1% and a minimum length of 331 bp . The reference database for haplotype classification was assembled using published R. temporaria haplotype sequences found in Italy. The method ‘otu open_ref’ was used to cluster our sequences against this database with an identity threshold of 0.99, rejecting a sequence if the fraction of alignment to the reference sequence was lower than one, and discarding sequences with a read abundance value lower than 100 after dereplication. To avoid false positives, either from PCR or sequencing errors, or minor cross-contaminations between the replicate samples, OTUs represented by fewer reads than 5% of the total reads for a specific replicate sample were discarded27.All bioinformatic analyses were performed with the software MICCAR. temporaria, similar to other pond-breeding amphibians, is characterized by an ‘explosive’ reproduction, producing large numbers of egg masses sometimes crowded together. Furthermore, in our target species, generally one female lies one egg clutch, therefore the number of egg clutches roughly corresponds to the number of females in the population. Therefore, as eDNA samples were collected when larvae/eggs presence was visually confirmed, and by sampling multiple spatial replicates, the chances of obtaining a sufficiently representative sampling of the population’s gene pool were increased.To calculate the diversity indices, haplotype relative frequencies were obtained across spatial and temporal replicates through simulated datasets created with a Linux shell script. The datasets were built such as that, for each site and each recorded haplotype, the haplotypes frequencies were equal to the haplotype total number of reads (sequence counts) divided by 100. The simulated datasets thus represent hypothetical populations in which absolute abundance (in terms of sequence counts) of each haplotype ideally correspond to the number of individuals carrying the haplotype. As the abundances calculated from eDNA data could be influenced by several limitations , these estimates may not be as accurate as more invasive methods. For a more precise estimate of the population genetic diversity, a traditional sampling is still desirable. However, the reproductive behavior of 50, and Spearman rank correlations tests were performed using RStudio to compare these estimates with those reported in Marchesini et al.41. Since the estimates of haplotype frequencies and diversity indices from the two methods are not exactly the same , non-metric multidimensional scaling (NMDS), based on Bray–Curtis dissimilarity matrices and computed using the R package ‘vegan’ 2.6–4, was used to graphically represent differences between the eDNA metabarcoding and the reference datasets; these two datasets were then statistically compared with a Mantel test with 10000 permutations in RStudio. A correlation between the two measures was expected if eDNA was capable of detecting nearly all haplotypes .Standard diversity indices were calculated for each site  using the software DnaSP v6Rana temporaria DNA was successfully amplified from 32 of these. T1 replicates were the most successful, with 21/25 eDNA-positive replicate samples, while T2 replicates yielded results in 10/24 replicates . For eight out of 10 sites, in fact, the number of haplotypes detected by the two approaches was the same. The remaining two sites only differed for one haplotype. Similarly, nucleotide diversity for the eDNA data was strongly correlated with π of the reference dataset , as shown in Fig. p = 0.05). If only T1  was considered, the correlation between the number of haplotypes calculated with the two datasets remained unchanged , while both h and π estimated from eDNA data showed a slightly higher correlation with the same indices from the reference dataset .Ten COI haplotypes (including CA2_TN4 and SA1_Pr7) were detected, all belonging to the three Alpine lineages already known to be present in the province of Trentop = 0.005). In addition, the NMDS plot showed that three sites  cluster closely together with respect to the remaining seven, confirming the previously known distribution of R. temporaria haplotypes; that is, two clusters, one in western and one in eastern Trentino41.The NMDS plot , as this distribution of replicates allowed us to capture the full set of expected haplotypes from each sampling site. Regarding the temporal replicates, T1 alone allowed us to identify almost all haplotypes present per site in a given monitoring year. In fact, T1 yielded the same results, in terms of number of haplotypes identified, as those attained from the union of temporal replicates 1 and 2 (while no T3 replicates yielded target species DNA). Thus, if a multi-year monitoring program is being considered, we suggest implementing a simplified version of our protocol, with T1 as the optimal time point, as a strategy to maximize the cost/benefit ratio. However, as the combination of T1 and T2 replicates provided the most accurate data on haplotypes frequencies, for a more complete view of R. temporaria genetic diversity we propose that the protocol should be applied at least every three to five years. Replicate T3 did not provide any information on species presence and genetic diversity, and should not be considered for future eDNA protocol applications for amphibians that have a reproductive cycle similar to that of R. temporaria.Our experimental design allowed us to identify an optimal sampling regime, in terms of temporal and spatial replicates, for the collection of eDNA water samples in a variety of small wetland habitats. Our results are in agreement with the recommendation from previous studies41. In agreement with the NMDS plot  and the remaining seven (eastern Trentino).All the standard genetic diversity estimates computed here  proved to be statistically correlated with the same indices computed in the reference dataset from Marchesini et al.53. These habitats are especially prone to seasonal fluctuations in water availability; therefore, it is possible that the differences in the haplotypes detected with the two methods, as well as in their relative frequencies, might be due to the ephemeral nature and repeated recolonization of these three sampling areas. In fact, the influence of short-term climatic fluctuations on demographic and genetic characteristics of wild populations has been demonstrated recently54. Even so, the eDNA protocol was able to detect two haplotypes out of the three found at Lel in the reference dataset. In the case of PLa, the most abundant haplotype shows the same relative frequency according to both datasets, and our protocol was also able to detect the less common haplotype. In addition, our eDNA metabarcoding approach detected another haplotype, not previously reported for this site but known to be present in eastern Trentino (PR4, see Table Only three of the 10 sampling sites consistently showed different haplotype counts and frequencies between the two approaches, namely sites Lel, PLa and PMa (Laghestel, Passo Lavazé and Passo Manghen; Table Austropotamobius pallipes. The same approach could also be used to determine the presence of invasive species, such as the American bullfrog Lithobates catesbeianus, which occurs in the regions surrounding our study site, but has not been reported thus far in the Province of Trento. Finally, we advocate the integration of nucleotide diversity estimates in eDNA-based approaches, included in very few studies thus far (e.g.,29, in order to obtain a detailed reconstruction of the potential adaptability of populations living in human- and/or climate change-impacted areas.The protocol developed here, which is completely non-invasive and less time-consuming than traditional observational or tissue-based genetic surveys, could be implemented routinely in amphibian monitoring programs that integrate genetic diversity estimates with confirmation of the target species presence. We are currently improving and extending this holistic approach to all European amphibian species, but also to even rarer and more elusive invertebrates such as the European freshwater crayfish Supplementary Information.


This study aimed to assess the genetic diversity and population structure of 12 guinea fowl phenotypes from three climatic zones  of Benin. A total of 96 adult guinea fowl, aged at least 6 months, were selected for blood sampling. Fragmentanalysis was carried out using 17 polymorphic microsatellite markers. The informative marker combinations revealed a total of 83 allelesacross all loci, with an average of 5 alleles per locus and a mean polymorphic information content (PIC) of 0.793. This study showed an observedheterozygosity of 0.492. The inbreeding coefficient values ranged from  It contributes to food security, nutrition, poverty reduction and economic growth. Poultry farming, which is an integral part of the livestock subsector, is an important economic activity in many African countries. Itis characterized by rich and diversified poultry genetic resources. Indigenous guinea fowl phenotypes contribute to rural livelihoods despite theirlow productivity. Animal genetic resources make a large contribution to food production, but more and more, these resources are threatened by the lossof their genetic identity . Thus, local genetic resources have a great role to play in the context of climate change and where thecriterion of adaptation, an attribute of local resources, is a great difficulty for exotic breeds in tropical environments . Thereis evidence that new breeding practices contribute to the modification of the diversity of animal genetic resources through the creation of new breedsand the improvement of lower-performing breeds . Among these resources is guinea fowl, which contributes to the livelihoods of ruralpopulations. Indigenous guinea fowl ecotypes represent a reservoir of useful guinea fowl genomes and genes, which are desirable for their adaptabilityto production under a wide range of environmental conditions. Due to the harsh environmental conditions in which indigenous guinea fowl are raised, theymay contain genes and alleles important for their adaptation to particular environments . Despite all the advantages ofthis indigenous resource, its productivity is low and little is known about its genetic diversity. Indeed, their reproductive performance can be improvedby raising them in appropriate habitats under strict sanitary control and subjecting them to a balanced diet . However,considering only these environmental factors  would omit the genotypic factor, which is also very crucial in a geneticimprovement program. The study of the genetic diversity of indigenous populations does indeed appear to be interesting to distinguish the genes whichhave economic importance for poultry farmers . According to several authors , the characterization and conservation of various genetic attributes of indigenous species are imperative and long overdue in order tomaintain the genetic biodiversity of these animals, improve food security of the rural households and enhance economic empowerment in developingcountries. Thus, there is a need for a preservation policy for this important local breed. In order to effectively conserve and improve animal geneticresources, it is essential to provide a thorough and comprehensive description of the genetic diversity present within the current populations . The characterization of animal genetic resources provides appropriate data for their conservation and sustainable management for food securityand economic development . Information on genetic diversity is essential for decision-making in the implementation ofanimal genetic resources improvement programs .To this end, molecular markers offer the great advantage of allowing a direct assessment of genetic diversity, which is by definition independent ofenvironmental effects . Molecular markers, including simple sequence repeats (SSRs) or microsatellites, have become favoredmarkers in population genetics due to their genome-wide dispersion, high polymorphism and other advantageous characteristics .According to some authors, markers developed or identified for one avian species can be used in others in the same taxonomic family . Studies have highlightedthat polymorphisms of microsatellite marker loci developed in chicken and quail have been revealed in guinea fowl . An awareness of African states in the south of the Sahara has promptednumerous studies in recent years on the genetic diversity of indigenous animal resources, including indigenous guinea fowl populations. These studieshave focused on the genetic diversity and phylogenetic structure of these locally available guinea fowl in Ghana , the Republic of the Sudan , Burkina Faso  and Togo .However, information on the genetic characterization of indigenous guinea fowl in Benin is almost non-existent. Except for the summary phenotypiccharacterization conducted by Chrysostome (1995) and Houndonougbo et al. (2017), only the recent study conducted on morphobiometric characterizationand biodiversity has provided information on morphological and morphometric diversity within the indigenous guinea fowl population distributed in thethree climatic zones of Benin . Regarding molecular genetic diversity, no study to the best of our knowledge has beenconducted so far on the indigenous guinea fowl resources of Benin taking into account all phenotypes identified in all climatic zones. To contributeto filling this gap, this molecular characterization was undertaken in the three climatic zones of Benin in order to determine the molecular geneticdiversity and the structure of these local guinea fowl populations.22.1The study was carried out in the 12 districts of the 3 climatic zones of Benin: the Sudanian zone according to themanufacturer's recommendations. DNA samples were stored at 4 2.4Polymerase chain reactions (PCRs) were performed on gDNA from each guinea fowl, using specific primers for each microsatellite marker. A set of17 microsatellite markers were chosen for this study: 10 microsatellite markers  developed by Botchway et al. (2013) for guinea fowl; 6 microsatellite markers developed by Kayang et al. (2002) for guinea fowl, quails and hens; and 1 microsatellite marker (MCW0069) from the panel recommended by the FAO forthe study of diversity in hens. Each of them with forward primer was conjugated to one of the four fluorescent dyes FAM (blue), ATTO532 (green),ATTO550 (yellow) and ATTO565 (red). These markers were chosen based on their technical characteristics (good aptitude for amplification and easyinterpretation of typing) and their genetic characteristics . The characteristics of thesemicrosatellite markers are presented in Table 1.The amplifications were carried out in a thermal cycler using the following conditions: initial denaturation at 94 2.5The PCR was conducted in three multiplexes (Table 1), and each multiplex was formed according to specific criteria. For the alleles having almost thesame fragment size, different fluorochrome dyes were considered for the grouping, whereas different markers having a similar fluorochrome dye andalmost a similar fragment size were assigned to a different multiplex group.Taq® 2X Master Mix with Standard Buffer (NEB), using a PCRreaction volume of 20 Taq® 2X Master Mix and nuclease-free water.The microsatellite regions were first amplified using OneThe second round of PCR was conducted in the same way using 1 2.6Similar to above, the microsatellite regions were first amplified using Q5 High-Fidelity 2X Master Mix catalogue no. M0292 (NEB), using a PCRreaction volume of 20 The second round of PCR was conducted in a similar fashion as above, using 1 2.7The fluorescently labeled PCR amplicons were diluted at a ratio of 1 : 10. The diluted amplicons were then mixed with the LIZ500 sizing standard and highly deionized formamide . This mixture was denatured at 95 2.8The frequency of null alleles was checked first in the dataset using Micro-Checker 2.2.3 . This step was followed by anadjustment of the allele and genotype frequencies of the amplified alleles, for their use in further population genetic analysis.Observed heterozygosity  between subpopulations were estimated using GenAlEx 6.5 software . To investigaterelationships between subpopulations, neighbor-joining  dendrograms were constructed from Nei's genetic distances using theDendroUPGMA online platform . The factorial neighbor-joining trees based on inter-individual allele-sharing distancesamong the 12 phenotypes and 3 climatic zones were drawn using DARwin 6.0.021 software . Phenotypedifferentiation based on genetic information was performed using the test proposed by Raymond and Rousset (1995). After defining groups of phenotypesbased on their historical origin, a hierarchical analysis of the variance was carried out using the analysis of molecular variance. The factorialcomponent analysis was performed using DARwin 6.0.021 software  to visualize the differences between the studiedsubpopulations of indigenous guinea fowl.The genetic population structure analysis of the 12 guinea fowl subpopulation was assessed using the Bayesian admixture procedure implemented inSTRUCTURE 2.3.4 to infer the most likely number of clusters . The software had been programmed to run using the admixturemodel and correlated allele frequencies. The estimation of the number of assumed populations  has been observed for the white phenotype, whereas the highest  is confirmed forthe cinnamon phenotype (Table 3). Apart from the brown, pied pearl gray and white phenotypes, all other phenotypes showed a deficit inheterozygotes. The values of inbreeding coefficient (3.3The Guinean zone recorded the highest inbreeding index (0.194), while the Sudano-Guinean zone recorded the lowest inbreeding index (0.150). In theGuinean zone, 70.6 % of loci show a deficit of heterozygotes. A deficit of heterozygotes has been also obtained in the Sudano-Guinean and theSudanian zones, with the proportions of 76.5 % and 88.2 %, respectively (Table 4). Only five, four and two negative values of the inbreedingindex have been obtained in the Guinean, Sudano-Guinean and Sudanian zones, respectively. Each of the three subpopulations of climatic zones showedrelatively high positive values of 3.4The genetic distances between phenotypes are low, ranging from 0.001 (pied pearl gray and pearl gray) to 0.347 (cinnamon and white), unlike the strong geneticidentities (ranging from 0.707 to 0.998) that reflect a strong genetic similarity among the 12 phenotypes of indigenous guinea fowl studied(Table 5). The phylogenetic tree obtained from the pairwise genetic distance of Nei (1978) between subpopulations shows two separated groups, and thisclustering is supported by 23 % bootstrap values. Black, brown and white phenotypes formed a branch. In group two, there are two subgroupssupported with 18 % bootstrap values. Group 2 subgroup 1 includes multicolored, pearl gray, and pied pearl gray, and all the other six phenotypesformed a separate large group with branches . The subpopulations of the Sudano-Guinean and Sudanian zones tended to cluster together, whilethe subpopulation of the Guinean zone appeared to be relatively distinct from them .The neighbor-joining tree derived from pairwise inter-individual allele sharing distances revealed an admixture of individuals from all the 12phenotypes  and from the 3 climatic zones . The results of factorial correspondence analysis showed a low degree of geneticdiversity between the 12 indigenous guinea fowl phenotypes studied  and between the 3 subpopulations of the climatic zones studied. Among the three groups obtained at 4The results showed that all loci were polymorphic with an average polymorphism information content value of 0.793, considered to reflect informativeloci . Based on the classification suggested by these authors, 100 %  alsoreported low values of genetic distances. A genetic diversity study in Togo showed a very high and almost identical genetic similarity (0.98–0.99)between pearl gray, Bonaparte and multicolored guinea fowl phenotypes . In our study, only brown, white and pied white phenotypes,over the 12 phenotypes, stood out slightly from the others. These findings are evidence of a weak genetic variation between the phenotypes  and between the three climatic zones. The observed low genetic differentiation could be ascribed to the manyyears of non-selective breeding (based on feather color) and to the uncontrolled movements of birds between climatic zones and between bordercountries, which promotes gene flow . In addition, the SSR markers used in thepresent study are neutral markers and, therefore, are not involved in the determinism of the expression of feather coloring genes. The three climaticguinea fowl subpopulations are also genetically close (genetic identity ranging from 0.904 to 0.941). It would be interesting to include in futurestudies wild subpopulations from the forests of Benin as well as guinea fowl populations from neighboring countries such as Togo, Burkina Faso, Nigerand Nigeria. These findings could serve as baseline information for breeding strategies for the conservation and improvement of indigenous guinea fowlpopulations in Benin. It is necessary to improve the farming conditions such as the control of habitat, diet and diseases as suggested by ourprevious study . The 12 guinea fowl phenotypes identified in our studies on morphobiometric characterization and biodiversityof guinea fowl  are found to be from a single population.5The molecular genetic characterization that was carried out during the present study made it possible to determine the genetic diversity andstructuring of indigenous guinea fowl populations in Benin. The study showed that all the microsatellite markers used are polymorphic and have astrong informative capacity on the genetic diversity within the indigenous guinea fowl population studied. A deficit of heterozygotes was observed inthe overall population and in the subpopulations represented by the phenotypes, as well as weak genetic structuring in the local guinea fowl ofBenin. Due to the weak differentiation between guinea fowl populations in the three climatic zones, the future breeding strategy or program should nottake into account the different phenotypes. A breeding program could be developed and implemented for better management of the diversity existingwithin and between recorded guinea fowl subpopulations and for the sustainable production of this poultry species in Benin. Any program or strategyneeds to take into account not only the different uses of the species  butalso the local community preferences. An ecological niche modeling could be also carried out to understand to what extent they are tolerant toenvironmental factors and to predict their dynamics until 2085 based on the recommendations of the Intergovernmental Panel on Climate Change.