Glossina fuscipes (s.l.) in the Congo remains questionable. Previous claims of overlap between G. fuscipes (believed to be G. f. quanzensis) and G. palpalis palpalis around Brazzaville city further complicate the taxonomic status and population dynamics of the two taxa. This study aimed to determine the phylogenetic relationships between G. fuscipes (s.l.) and G. p. palpalis and to assess genetic variation among G. fuscipes (s.l.) populations in Congo Brazzaville.Despite the morphological characterization established in the 1950s and 1960s, the identity of extant taxa that make up G. fuscipes (s.l.) from northern and central regions, and 65 G. p. palpalis from southern part of the country. The mitochondrial cytochrome c oxidase subunit 1 (cox1) gene was amplified using taxa-specific primer pairs. Sequence data were analyzed in DnaSP and Arlequin to assess the genetic diversity, differentiation and demographic history of G. fuscipes (s.l.) populations.We collected 263 G. fuscipes (s.l.) and G. p. palpalis. BLASTn analysis for G. fuscipes (s.l.) showed > 98% identity with GenBank sequences for G. fuscipes (s.l.), with BEMB population showing 100% similarity with G. f. fuscipes. Glossina fuscipes (s.l.) populations showed high haplotype diversity , moderate nucleotide diversity ( = 0.012) and moderate (FST = 0.072) to high (FST = 0.152) genetic differentiation. Most of the genetic variation (89.73%) was maintained within populations. The mismatch analysis and neutrality tests indicated recent tsetse population expansions.The general BLAST analysis yielded a similarity of 99% for G. fuscipes (s.l.) and G. p. palpalis. Genetic diversity of G. fuscipes (s.l.) was high in the populations sampled except one. Genetic differentiation ranged from moderate to high among subpopulations. There was a restricted gene flow between G. fuscipes (s.l.) populations in the north and central part of the country. Genetic signatures based on cox1 showed recent expansion and recovery of G. fuscipes (s.l.) populations from previous bottlenecks. To fully understand the species distribution limits, we recommend further studies involving a wider sampling scheme including the swampy Mossaka focus for G. fuscipes (s.l.) and the entire range of G. p. palpalis in South Congo.Phylogenetic analysis revealed minor differences between  Trypanosoma brucei gambiense [Nemorhina  and can be broadly classified into two super-species i.e. Glossina fuscipes (s.l.) and G. palpalis (s.l.), which do coexist in certain situations. Apart from HAT, these vectors also transmit nagana, a debilitating disease that severely hinders livestock productivity in rural Africa [Riverine tsetse fly species are the most important vectors of human African trypanosomiasis (HAT)  in West ambiense . These fl Africa , 3.Glossina palpalis palpalis occupying the southern part of the country up to the south of Brazzaville city around the Djoué River [G. fuscipes quanzensis [Glossina fuscipes fuscipes [G. fuscipes (s.l.) (including G. f. quanzensis and G. f. fuscipes) and G. p. palpalis are reported to be important vectors of HAT in Congo Brazzaville [G. fuscipes (s.l.) (including its subspecies above) and G. palpalis is very difficult as the morphological differences between the two species and the subspecies are very minor, and field workers have had to use species distribution maps to help them decide which species they are dealing with [G. fuscipes  being considered synonymous taxa. Whereas morphological traits  have been useful in differentiating tsetse taxa, such traits are variable and require highly skilled and rare Glossina taxonomists. Unfortunately, in Congo Brazzaville, there have been no attempts to resolve the taxonomy of the closely related palpalis group tsetse by molecular characterization. Therefore, to date, there is no information on the molecular taxonomic status and distribution limits of the various subspecies of G. fuscipes in Congo Brazzaville and how they relate phylogenetically with G. p. palpalis which is reported to infest the southern part of the country.Based on morphological features, previous studies indicated that the two main tsetse vectors of HAT and nagana in Congo Brazzaville were ué River , 5, and anzensis  that extanzensis . The norfuscipes , with itfuscipes . Althougzzaville , there hing with . The ideing with , with diG. p. palpalis and G. f. quanzensis in south Congo up till the 1950s [G. p. palpalis had replaced G. f. quanzensis [G. f. quanzensis reappeared in the south of Brazzaville [G. f. quanzensis collected from three different localities were not identical, and interestingly, no clinal variation in size was observed between G. f. fuscipes and G. f. quanzensis subspecies [G. f. quanzensis found in the Ngabe corridor was slightly smaller compared to G. f. fuscipes found in Mossaka [G. f. quanzensis by G. p. palpalis, it was previously reported that there could be a hybrid zone of G. p. palpalis and G. f. quanzensis that existed around Brazzaville [palpalis group tsetse flies in Congo Brazzaville.Previous reports indicated the coexistence of he 1950s . Howeveranzensis  due to czzaville . In a sebspecies . However Mossaka . Apart fzzaville . The repc oxidase gene subunit 1 (cox1), is highly discriminatory and has been exploited to confirm monophyletic species across taxa [palpalis group of tsetse flies in Congo Brazzaville. According to Avise [Proper understanding of the taxonomic status and knowledge on population dynamics including population genetic diversity, gene flow and genetic differentiation is important for effective tsetse control, particularly by using area-wide genetic approaches such as the sterile insect technique (SIT) . Mitochooss taxa . Due to to Avise , analysipalpalis group flies from three populations (including nine subpopulations) in Congo Brazzaville by sequencing the cox1 gene. This gene is used in DNA barcoding as it has a high rate of nucleotide substitution that helps to discriminate cryptic species, reveal novel taxa and resolve relationships between genera [G. fuscipes (s.l.) in Congo Brazzaville, we compared mtDNA cox1 sequences generated in this study with sequences of G. fuscipes originating from Uganda deposited in the GenBank database [In this study, we investigated the genetic diversity and phylogeography of n genera . This gen genera  and histn genera , 17, incn genera , 18, 19.database , 18, 20.G. fuscipes (s.l.) and G. p. palpalis in the country as compared to past records based on morphological characterization reported in the literature. Additionally, we aimed to gain insight into the genetic diversity and breeding structure of G. fuscipes (s.l.) populations for the purpose of designing effective area-wide control strategies at the national level.Our overall aim was to validate and better understand the taxonomy and distribution of G. fuscipes (s.l.) were collected from three main regions in Congo Brazzaville with high tsetse density, namely, the Sangha region in the north at the Cameroonian border in Kadei River in BMSA village , the Plateau region in the HAT focus of BEMB  and TLG  in the Ngabe corridor  located in the Bouenza region of southern Congo. These tsetse infested regions can be grouped into two categories based on the nature of habitat occupied by the flies, i.e. River-Savannah  and River-Forest (BMSA)  and 65 G. p. palpalis tsetse flies were collected. The flies were caught from along the River Congo and its tributaries. Cross-sectional sampling was used to ensure wide coverage of tsetse infested areas. Four blocks were selected and purposively sampled based on areas reported to be infested with G. fuscipes (s.l.) and G. p. palpalis as per published information on tsetse occurrence and distribution [G. fuscipes (s.l.) and Bokosongo (BKS) for G. p. palpalis. Ten biconical traps were deployed in each block for seven consecutive days. Glossina species were identified morphologically using species identification keys [Two hundred and sixty-three ribution , 21, 22 ion keys . Fly appG. fuscipes (s.l.) before sequence analyses. In the context of this study, sampling sites were considered as putative subpopulations. Thus, there were nine (3 blocks × 3 sites/block = 9) subpopulations of G. fuscipes (s.l.). For G. p. palpalis, we collected 65 samples from two sites in Bokosongo. G. p. palpalis samples were only used for determining phylogenetic relationship between G. fuscipes (s.l.) and G. p. palpalis. Therefore, for phylogenetic studies and genetic diversity and differentiation analyses, a total of 328 flies (263 G. fuscipes (s.l.) and 65 G. p. palpalis) were successfully amplified and considered.The trapping sites were selected at intervals of at least 300 m and were georeferenced using a GPS handset  and included in Google Earth. Non-baited biconical traps  were useGlossina species [G. f. fuscipes colony at the International Centre of Insect Physiology and Ecology (ICIPE), Nairobi, Kenya, were used as reference for G. fuscipes. Similarly, G. p. palpalis samples from the Nigerian Institute of Trypanosomiasis Research (NITR), Kaduna, Nigeria, and served as reference for G. p. palpalis from the Central and West African clone and were kindly provided through Professor Soerge Kelm from the University of Bremen, Germany. Unfortunately, even after contacting several institutions, we were unable to obtain characterized reference samples of other G. fuscipes taxa (G. f. quanzensis or G. f. martini).Upon obtaining an import permit from the Department of Veterinary Services in Kenya, samples were transported from Congo Brazzaville to the Biotechnology Research Institute of the Kenya Livestock and Agricultural Research Organization (BioRI-KALRO) for further analysis. Samples were preserved in 95% ethanol in 1.5 ml Eppendorf tubes until DNA extraction, amplification and subsequent analyses. Species identification based on morphological characterization was confirmed in the laboratory at BioRI-KALRO using microscopy and based on identification keys for adult  species . SamplesGenomic DNA was extracted from individual tsetse flies by using a DNeasy extraction kit  following the manufacturer’s instructions. Briefly, two to three tsetse legs were removed from individual male or female flies that had been preserved in 95% ethanol and air-dried for 10 min. Air-dried legs were dipped into liquid nitrogen and ground using a micro-pestle. The resulting ground tissue was then lysed, precipitated, re-suspended and stored for further DNA extraction and purification as described in the manufacturer’s instructions. The quality and quantity of DNA was checked by using a NanoDrop spectrophotometer to measure the absorbance at 260/280 ratio range.G. fuscipes (s.l.) and G. p. palpalis using the COI/COII primers was carried out as previously described [G. fuscipes (s.l.) fly, the cox1 gene was amplified using 10 µM each of COIF1 (5′-CCT CAA CAC TTT TTA GGT TTA G-3′) and COIIR1 (5′-GGT TCT CTA ATT TCA TCA AGT A-3′) with a final concentration of 0.03 mM each, targeting a 570 bp region. Distilled water and PCR 2× Master Mix AccuPower®  containing Taq DNA polymerase solution were added to a total volume of 30 µl including 5 µl of DNA template added to individual tubes in a 96-well plate. The plates were vortexed shortly for 2 min to ensure total mixing. The amplification was performed using a 2720 Thermal Cycler  located at BioRI-KALRO, Muguga, Kenya. The cycling conditions were: an initial denaturation at 95 °C for 5 min followed by 37 cycles of 95 °C denaturation for 30 s, 54 °C annealing for 30 s, extension at 72 °C for 45 s. This was followed by a final extension step at 72 °C for 20 min and 4 °C hold.PCR analysis of mitochondrial DNA for the identification of escribed , 18, 25.Glossina p. palpalis DNA was amplified at 95 °C for 5 min followed by 35 cycles of 93 °C denaturation for 1 min, 55 °C annealing for 1 min, and extension at 72 °C for 2 min and final extension step at 72 °C for 7 min and 4 °C hold. The primers set used were: 10µM CI-J2195 (5′- TTG ATT TTT TGG TCA TCC AGA AGT-3′) and 10 µM CULR (5′-TGA AGC TTA AAT TCA TTG CAC TAA TC-3′) [A TC-3′) , 27 (priA TC-3′) . Three mPCR products with tracking dye were then directly submitted for sequencing since Bioneer Taq PCR master mix contains Taq DNA polymerase, with application for direct gene sequencing. One hundred microliters of the PCR product was sent for sequencing to Macrogen Corporation .cox1 gene sequences from the GenBank database. The analysis of sequences was performed using DnaSP v5.10.01 [Raw sequences were processed using the Qiagen CLC Main WorkBench version 7.7.3 software. Both forward and reverse sequences were trimmed, and a consensus obtained before being assembled. Conflicts were solved manually and contigs obtained. All consensus sequences and the output files were then exported as a FASTA format, followed by multiple sequence alignment (MSA) under “Alignment” and “Tree” on “Create Alignment” (CLC) and the implementation of a single file on MEGA7 using default settings. The BLASTn algorithm  in the Nv5.10.01  for deteCox1 sequences of both G. fuscipes (s.l.) and G. p. palpalis were aligned using MUSCLE [Glossina sequences was constructed in MEGA7 [cox1 trees and assess node support [G. fuscipes (s.l.) and also the tsetse tree based on individual sequences. Haplotype sequence analysis for G. p. palpalis was not conducted since only one population of G. p. palpalis (BKS) was sampled (vs three populations of G. fuscipes (s.l.)).g MUSCLE  and the in MEGA7  and confin MEGA7  using Ma support  using th support . The ML G. fuscipes (s.l.) cox1 sequences was generated from CLC Main Workbench version 7.7.3 (Qiagen) and processed in MEGA7 [Cox1 genetic heterogeneity among populations was quantified by assessing the total number of haplotypes (H), haplotype diversity (Hd), nucleotide diversity (π), and mean nucleotide differences (K) [An input file of in MEGA7 . Thereafin MEGA7  to estimnces (K) . Hd whicnces (K)  was evalnces (K)  (equationces (K)  equationnces (K) .ST) values as described in [G. fuscipes (s.l.) populations and nine subpopulations. Considering all the nine subpopulations ), groupings were made on the basis of the population of origin. Further population grouping for AMOVA was based on ecological settings (habitats) from where the flies were sampled. For example, we considered that BMSA is located in the rainforest adjacent to a national park whereas BEMB and TLG are located in savannah-river ecological zone.Haplotype distribution and frequencies were obtained and the total number of haplotypes per population was obtained including their sequences and positions. Thereafter, we constructed a median-joining haplotype network  using Neribed in . The staST)/2FST, with “m” being the female migration rate and “N”, the female effective population size.Rates of gene flow  within and among regions/blocks were expressed as the number of female migrants per generation by assuming the island model of population structure . For hapcox1 gene sequences conformed to the expectations of neutrality of evolution, we performed Tajima’s D test [r) and the sum of squared deviations (SSD). The nucleotide sequences of 46 haplotypes for G. fuscipes (s.l.) and representative sequences for G. p. palpalis from this study were deposited in the GenBank database under the accession numbers MN586284-MN586591 and MN750696-MN750714, respectively.We investigated the demographic history of the populations to assess spatial expansions by conducting the neutrality and mismatch distribution tests as implemented in Arlequin and DnaSP. A mismatch distribution is a tabulation of the number of pairwise differences among all DNA sequences in a sample. To test whether the s D test  and Fu’ss D test , based os D test . To testG. fuscipes (s.l.) flies per trap per day (FDT) was 16.54 in BMSA, 10.33 in BEMB, and 2.42 in TLG. FTD was 7.86 in BKS for G. p. palpalis  and G. p. palpalis (in Bokosongo in south Congo). These two species are closely related and have identical morphological and morphometric features (size and color). The discriminatory criteria used to tell apart the different taxa in the palpalis group are based on male genitalia  characters, particularly pertaining to the superior and inferior claspers. These criteria are described in [palpalis group taxa sampled in this study, we undertook molecular characterization by amplifying and sequencing the mitochondrial cox1 gene.Based on shape and size of male genitalia  and on size and shape of female dorsal plates , the speribed in  and provcox1 gene on the morphologically identified G. fuscipes (s.l.) from BMSA, BEMB and TLG, all northern Congo locations, yielded the expected PCR product (570 bp) as previously described [G. p. palpalis from Bokosongo in southern Congo yielded the expected product (850 bp) based on the primers designed by [G. fuscipes (s.l.) and G. p. palpalis cox1 gene also yielded sequences of 570 bp  and G. p. palpalis in Congo Brazzaville.PCR amplification of the escribed . Similarigned by . SequencG. fuscipes (s.l.) and 64 G. p. palpalis sequences after a quality check on CLC Main Workbench version 7.7.3 (Qiagen) as implemented in MEGA 7.0.26 [G. fuscipes (s.l.) sequences covered a length of up to 570 bp in BMSA, Bouemba and TLG against 850 bp for G. p. palpalis flies from Bokosongo. Comparison of the newly generated sequences using the BLASTn algorithm [cox1 sequences for Glossina spp. revealed a high similarity with G. fuscipes; BLASTn analysis showed high percentage identities  with specific G. fuscipes sequences in the database  [G. fuscipes (originating from G. f. fuscipes) and sequences generated from morphologically identified G. fuscipes (s.l.) samples obtained from TLG, BMSA and BEMB in Congo Brazzaville.We examined 263 A 7.0.26 . Sequenclgorithm  in the Nuscipes) , 45. TheGlossina species in this study, one representing G. fuscipes (s.l.) , represented by TLG103 and BMSA65, had a perfect match of 99.8% with G. f. fuscipes (GenBank: GU296784.1) from the neighboring DRC, a G. p. palpalis sample from Bokosongo (BKS256) had a common ancestry with a sequence of G. p. palpalis. Reference G. f. fuscipes samples from ICIPE (Kenya) clustered with the G. fuscipes (s.l.) samples collected from field sites in Congo Brazzaville. Similarly, G. p. palpalis reference samples from NITR (Nigeria) clustered with G. p. palpalis samples from Bokosongo, thus confirming the existence of both G. fuscipes (s.l.) and G. p. palpalis and their distinct habitats in Congo Brazzaville. Cluster I  from GenBank. Cluster II . Strangely, Cluster IIa also includes a published G. fuscipes sequence (GenBank: GU296784.1) [G. p. palpalis region, shares common ancestry with published sequences for G. f. quanzensis, G. f. martini and G. p. palpalis from neighboring DRC (GenBank: FJ767871.1) Fig. palpalis group of tsetse flies in Congo Brazzaville is much more complex and requires further studies to be fully resolved.We observed two main clusters of l.) Fig.  and the 96784.1) . Surpris). The number of haplotypes per population (h) ranged from 9 in BEMB to 25 in BMSA. In TLG h was 21. Haplotype diversity varied from 0.582 in BEMB to 0.860 in BMSA with TLG recording Hd of 0.812. Nucleotide diversity  was lowest in BEMB ( = 0.002) and marginally higher in TLG ( = 0.018) than in BMSA ( = 0.016). Out of the 263 sequences examined, the number of segregating sites (S) was 165, 9 and 186 in BMSA, BEMB and TLG, respectively. The average number of haplotype differences (K) for the three populations was, respectively, 7.09, 0.99 and 6.82. Overall, these results show that diversities were highest in BMSA and lowest in BEMB.Overall, we observed high haplotype diversity (Hd = 0.884) and moderate nucleotide diversity  = 0.012) at 204 segregating sites in the three sub-lineages. The total number of haplotypes observed was 46. Diversity indices varied among populations  population in BMSA. On the other hand, BEMB is constituted with trapped subnet with no free nodes of private haplotypes. All sub-networks are continuous and connected, indicating that BEMB is mainly populated by mixed haplotypes as well as the remaining private haplotypes from all localities. Colors for each pie slices represent locations of sample origin of collections and their size. Private haplotypes with a unique color and shared haplotypes with a mixed color (haplotype sequences) and dominant haplotypes with higher haplotype frequency, mixed colors (shared) and single color (private). The parallel lines represent mutations which have occurred in each haplotype of the sampled sequences at nucleotide positions . Levels of genetic differentiation were low between BMSA and TLG and between BEMB and TLG with FST values of 0.048 and 0.072, respectively. These indices indicate low to moderate genetic differentiation among the G. fuscipes (s.l.) populations sampled in Congo Brazzaville. These results thus indicate restricted exchange of genetic material between BEMB and BMSA and almost unlimited gene flow between TLG and BMSA or TLG and BEMB. Values of genetic differentiation were estimated by using Wright’s fixation index (FST) [Pairwise Fex (FST) .Pairwise genetic distances among subpopulations within population groups were low and insignificant. This was true for subpopulations within BEMB (group 1), TLG (group 2) and BMSA (group 3)  = 0.14, P = 0.003) and unlimited gene flow among subpopulations within populations  ~0.010, P = 0.046).Analysis of molecular variance (AMOVA) indicated that ~13% of the genetic variation was attributed to differences among the three populations studied . Less than 1% of the genetic variance was due to differences among subpopulations within populations, whereas most of the genetic variance (~86%) was explained by differences among individuals within subpopulations Table . The corWhen subpopulations were grouped according to ecological conditions, the variance due to ecological differences was ~8% whereas the differences attributed to subpopulations within ecological groups . Additionally, D values for TLG and BMSA were negative and statistically significant (P < 0.01), suggesting recent population expansions. In BEMB, however, Tajima’s D was negative but not significant (P > 0.1). Similarly, Fu’s Fs values in each of the three populations and overall, were negative but not significant.Neutrality test based on Fu’s Fs and Tajima’s D tests Table  conducteP-values  from Congo Brazzaville and GenBank accession numbers for sequences, sample codes and sample metadata are provided in provided in Additional file The sums of squared deviations (SSD) and Harpending’s raggedness index I-values and their associated es Table  suggestepalpalis group tsetse flies in Congo Brazzaville, particularly in the areas of Bomassa (BMSA) in the north, Bouemba (BEMB) and Talangai (TLG) in Plateau Batéké and in Bokosongo (BKS) in south Congo, has been largely unknown to date. Previous morphological characterization had classified existing Glossina taxa in the country into G. f. quanzensis, G. f. martini and G. f. fuscipes, and G. p. palpalis [G. fuscipes (s.l.) in BMSA, BEMB and TLG and G. p. palpalis in the only site of BKS (in the south) based on features of the male genitalia using identification keys  and G. p. palpalis [G. fuscipes (s.l.) [G. p. palpalis [G. fuscipes (s.l.) and G. p. palpalis in Congo Brazzaville [To the best of our knowledge, this is the first report on phylogenetic relationships of palpalis  as demons (s.l.)  and G. ppalpalis . Our reszzaville .G. fuscipes (s.l.) in BMSA along the Kadei River [. Glossina fuscipes is distributed in all hydrographical systems/networks in the Congo region and is hypothesized to have adapted to ecological conditions in south-east Cameroon, region at the Congo-Cameroon border [G. p. palpalis identified in BKS apparently stretches from Djoué River to the southern part of Congo [G. f. fuscipes found in the Ngabe Corridor (BEMB and TLG) [G. f. quanzensis [The finding of ei River , 48, 49,n border . The disof Congo , 22. It anzensis , 52 in tG. fuscipes (s.l.) and G. p. palpalis, with several sub-clusters within the main groups and a mixture of individuals from all localities  and NITR (Nigeria) clustered, respectively, with G. fuscipes (s.l.) and G. p. palpalis collected from areas of their expected natural distribution in Congo Brazzaville. Based on phylogenetic and BLASTn analysis, our results showed 100% homology of sequences in BEMB (sample BEMB402) with G. fuscipes haplotypes (GenBank: GU296784.1) found in Uganda [G. fuscipes, this sequence from Uganda originated from samples morphologically classified as G. f. fuscipes. In BMSA and TLG we observed 98.95% and 98.6% identities, respectively with GenBank sequences of G. fuscipes. These results indicate that whereas BEMB seems to be inhabited by G. f. fuscipes (100% similarity to East African G. f. fuscipes from Uganda), BMSA and TLG could be populated with a G. fuscipes complex composed of G. f. quanzensis, G. f. martini and G. f. fuscipes as previously reported in the Ngabe corridor and in the northern part of the country [G. p. palpalis) for instance, clustered with reference G. fuscipes sequences from GenBank  also clustered with BKS256 and curiously with other G. fuscipes (s.l.) sequences from the database as well. These findings send mixed signals. First, it is possible that both BKS256 and EU591840-2 are G. p. palpalis and not G. fuscipes (s.l.). Secondly, it can be hypothesized that BKS256 is indeed G. p. palpalis that could have recently dispersed into a predominantly G. fuscipes (s.l.) habitat, consistent with previous claims of an overlap in the distribution of the two taxa [G. fuscipes (s.l.) obtained from BMSA, BEMB and TLG (making up Cluster I) , or perhaps we are dealing with different subspecies of G. fuscipes such as G. f. quanzensis or G. f. martini. These two subspecies are known to share common ancestry [G. palpalis as demonstrated by the clustering together with G. p. palpalis from DRC with GenBank sequences of G. quanzensis and G. martini.The phylogenetic analysis showed two main clusters separating ies Fig. . This isn Uganda . Althoug country , 51, 52.two taxa . It is a I) Fig.  have cleancestry  and seemG. fuscipes (s.l.) and G. p. palpalis are very closely related subspecies but have differences at the molecular level which were not previously established and reported in Congo Brazzaville. We have also demonstrated that G. fuscipes (s.l.) in BEMB shares high identity (100%) to G. f. fuscipes in East Africa, and therefore it is likely that BEMB is colonized by G. f. fuscipes whereas BMSA and TLG are colonized by other taxa of G. fuscipes (s.l.) e.g. G. f. quanzensis and G. f. martini. However, this will need to be confirmed through further studies in future.Our study has provided prilimirary results that justify that indeed, G. fuscipes (s.l.) populations exhibited high haplotype diversity but low nucleotide diversity and moderate nucleotide differences across the three G. fuscipes sub-lineages in the Congo. The high haplotype diversity and low nucleotide diversity across all samples is an indication of a rapid demographic expansion [The present study shows that the sampled xpansion . The higG. fuscipes (s.l.) in the Congo seem to be undergoing expansion/repopulations after bottleneck pressures of the past, which could have occurred in BMSA and TLG, but not in BEMB perhaps as result of the long eradication efforts [The low nucleotide diversity in the three expanding populations is likely due to previous bottlenecks that affected nucleotide diversity. Populations of  efforts , 54, 56  efforts .morsitans group tsetse flies in southern Africa [G. palpalis group in central Africa.Nucleotide diversity is more likely to be affected by the number of segregating sites than haplotype diversity, and the number of segregating sites is more affected by the size of the current rather than historical populations . This con Africa . A transn Africa  to have ST values based on haplotype frequencies and genetic divergence among the nine subpopulations were not significant between BEMB and TLG and between TLG and BMSA subpopulations. Low FST values observed between these subpopulations indicate high rates of maternal gene flow among them, particularly through the network of rivers such as Kadei, Congo and Sangha Rivers. However, higher and significant FST values  were observed between BMSA and BEMB. This high genetic differentiation could be attributed to the large geographical distance (400 km) between the two locations and difference in tributaries of the rivers passing through the two regions. AMOVA results indicated considerable subdivision of G. fuscipes (s.l.) populations in Congo Brazzaville with ~13% and 8% of the genetic variation being attributed to differences among the three populations studied  when grouped according to region (north or south) or ecological conditions of habitats from where samples were collected. There was hardly any subdivision among the subpopulations and most of the genetic variance (~86%) was attributed to differences among individuals within subpopulations. The low structuring of G. fuscipes (s.l.) populations is indicative of ongoing maternal gene flow among the populations regardless of ecological settings.Pairwise Fcox1 haplotypes showed a composite structure of the two identified lineages of G. fuscipes (s.l.) in Congo Brazzaville. A high rate of private haplotypes was observed in BMSA and TLG subpopulations  populations in Talangai , Bouemba (BEMB) in plateau Batéké up to Bomassa (BMSA), in Sangha region in the northern Congo), based on the mtDNA cox1 marker. We have confirmed the presence of G. fuscipes at the species level (G. fuscipes (s.l.)) in northern Congo Brazzaville and the presence of G. p. palpalis in southern Congo Brazzaville, particularly in the Bokosongo area. A much more widespread sampling scheme is recommended to establish the full distribution of both G. fuscipes (s.l.) and G. p. palpalis in Congo Brazzaville. The phylogenetic analysis showed that the two taxa of palpalis group (G. fuscipes (s.l.) and G. p. palpalis) in Congo Brazzaville are closely related (> 99% similarity) and only have minor differences. All the three populations sampled in this study exhibited high haplotype and low nucleotide diversities, an indication that the populations are recovering from recent bottleneck events. Whereas BEMB and BMSA populations were significantly differentiated genetically due to the large geographical distance between them, subpopulations within BEMB, BMSA and TLG seem to exchange genetic material freely. Thus, any control efforts should consider the population and not the subpopulation as the smallest unit to be targeted for eradication by using area-wide approaches such as the sterile insect technique. The high proportion of private haplotypes in the populations from BMSA and TLG and the high rate of singletons in those from BMSA are indicative of haplotypes that are adapted to the local conditions. The rates observed in the mtDNA network structure showed that G. fuscipes (s.l.) populations were linked in the past and have undergone recent rapid demographic expansion. The results suggest a population bottleneck in BEMB and TLG that is likely associated with the low genetic diversity observed in the two localities.To the best of our knowledge, this study constitutes the first report on the genetic taxonomic status, diversity, and phylogenetic relationships of Additional file 1: Table S1. Summary of entomological data and the mean number of flies per trap per day (FTD), showing absolute number of flies, using the equation FTD = ΣF/T × D.Additional file 2: Text S1. Morphological characterization key for Glossina of the palpalis group.Additional file 3: Figure S1. a Amplification of the cox1 gene for G. fuscipes (s.l.) using the COIF1/COIR1 primer set. Expected amplicon size: 570 bp. b Amplification of cox1 gene for G. p. palpalis using the CI-J-2195/CULR primer set. Expected amplicon size: 850 bp.Additional file 4: Table S2. Fixation index (FST) based on haplotype frequencies between the three populations of BMSA and TLG and BEMB.Additional file 5: Table S3. Pairwise FST values among nine G. f. fuscipes subpopulations from three population groups in Congo Brazzaville. Values written in bold represent significant pairwise differentiation (p < 0.05). FSTP-values, matrix of significant FSTP-values, significance level = 0.05 are highlighted in bold.Additional file 6: Table S4. Hierarchical analysis of molecular variance (AMOVA) design and results based on distance method of genetic structure [tructure . PairwisAdditional file 7: Figure S2. Mismatch distribution of haplotype pairwise nucleotide differences for G. fuscipes (s.l.) in the three localities, BEMB (a), TLG (b) and BMSA (c), showing observed (red lines) and expected (green lines) frequencies obtained under a model following populations’ size change.Additional file 8: Table S5. Mitochondrial cox1 gene haplotype IDs, frequencies and order positions for G. fuscipes (s.l.) from Congo Brazzaville.Additional file 9: Table S6. Glossina fuscipes samples metadata files including Laboratory datasheet, voucher informations, taxonomy, specimen details and collection data.Additional File 10: Table S7. aGlossina fuscipes GenBank accession numbers and sequences, sample codes and ID. b Project/Code, sequences processed ID and sequence length.


Cyclura nubila nubila) as a model to highlight this challenge and examined patterns of its intraspecific genetic diversity across Cuba. We evaluated nuclear (microsatellite loci) and mitochondrial diversity across eight populations from the island and its off-shore cays, and applied the population genetics results for assignment of Management Unit (MU) status and Evolutionary Significant Units (ESUs) based on phylogeographic and time of divergence information. We identified at least six distinct Cuban Rock Iguana MUs, encompassing demographically isolated and genetically differentiated populations across Cuba, most with low effective population size, declining populations, and with high risk of inbreeding and genetic drift. Hence, each MU should be considered of urgent conservation priority. Given the key ecological seed dispersal role of C. n. nubila, the disappearance of any MU could trigger the loss of local ecological functional diversity and major negative impacts on their ecosystems. Two divergent ESUs were also identified, exhibiting an historical east–west geographic separation on Cuba. Based on a Caribbean phylogeographic assessment, our findings strengthen the conclusion that all geographically and evolutionarily differentiated Cyclura species and subspecies across the archipelago warrant ESU distinction.Defining conservation units is an important step in species management and requires interpretation of the genetic diversity and ecological function of the taxon being considered. We used the endemic Cuban Rock Iguanas ( To combat biodiversity loss, conservation efforts have focused on preserving diversity at several levels, including whole ecosystems, species and genetically distinct intraspecific populations2. Nonetheless, conservation of intraspecific genetic diversity has received less attention in policy and application3. In addition, conservation units (CUs) have been assigned and used in management planning, but variation in their definitions and applications is widespread.Conservation of biodiversity is considered a top global priority4 first defined Evolutionarily Significant Units (ESUs), with the purpose of identifying independently evolving segments within species5. The USA Endangered Species Act uses units similar to ESUs, termed Distinct Population Segments . Similarly, Green8 proposed the concept of Designatable Units (DUs), which are populations that have a different conservation status, genetic distinction, range disjunction, and/or biogeographic distinction. The DU concept has been adopted by the Committee on the Status of Endangered Wildlife in Canada for species assessment under the Canadian Species at Risk Act8. Funk et al.7 reviewed other units, including Management Units (MUs), explaining that multiple MUs may form a single ESU. Regardless of the type of “unit”, these are all variations of CUs and the overall goal of CUs is to conserve varying levels of intraspecific diversity. Coates et al.2 explained that two common goals to conserve population level diversity are: ‘historical’, namely independently evolving segments, or ‘adaptive’, focused on preserving functional ability. Which to prioritize (or both) is, however, situation dependent; the species, ecosystem, region, and politics at hand may dictate how to apply conservation efforts across whatever “units” are defined.Ryder9. Empirical data have shown that not all species serve equal roles in their respective ecosystems . Brodie et al.9 recommended focusing on primary functional roles that (1) prevent extinctions, (2) moderate biogeochemical processes, or (3) support ecosystem processes or stability. Pollination, dispersal, ecosystem engineering, and nutrient cycling are a few examples. Although specific suggestions have been provided on how and where to define conservation units, the ecological function of a species has not often been given enough attention11. If a species contributes what managers consider to be a disproportionately important role in its ecosystem, it may merit additional effort to conserve that species. For example, Leclerc et al.12 showed that anthropogenic pressures resulted in a loss of unique functions in a high percentage of endemic mammals and birds in insular ecosystems. In a recent study, Zipkin et al.13 showed that the decline in frog diversity due to the Chytrid fungus was probably indirectly responsible for the correlated decline in Neotropical snake diversity. The ecological ramifications regarding the disappearance or decline in abundance of a species is case specific, but the consequences can be immense. Thus, a variety of ecological and genetic issues may significantly complicate the recognition of CUs.A factor that has recently drawn more attention in wildlife management planning is maintenance of ecological function15, although those complexities make it a challenging system for conservation. West Indian Rock Iguanas (genus Cyclura) of the Caribbean exhibit a high degree of endemism, with only one case of sympatry among recognized taxa16. Consequently, they are among the most critically endangered lizards in the world, primarily as a result of habitat degradation, direct human-hunting practices, introduction of invasive species, the population dynamics of small populations, and impending sea level rise18. Rock Iguanas are the largest native herbivores on these islands, and their high biomass and role in seed dispersion have made them critical for ecosystem function19. Many Cyclura populations have already disappeared, with a consequent loss of species, genetic diversity, and ecological functions20. All extant species are considered critically endangered, endangered or vulnerable by the IUCN21 and are protected under CITES (Appendix I). Despite their immediate conservation urgency and critical keystone ecological roles, we lack intraspecific genetic data for many Rock Iguana species, while their CU assignment and consideration of their ecological contribution in management strategies is also currently needed22.The Caribbean island chain is an excellent model for examining complex ecological and evolutionary patterns and processesCyclura nubila nubila), was widely distributed across Cuba and the sub-archipelagos Sabana-Camaguey, Canarreos, and Jardínes de la Reina. This species has been recognized as having historically high abundance; however, its populations have been declining rapidly24. It is now classified as vulnerable by the IUCN25, although with deep information gaps and thus in urgent need of a new assessment. Moreover, although C. n. nubila has been included in a preliminary phylogenetic analysis20, and nuclear microsatellite characterization and diversity assessments have been done28, no comprehensive survey of genetic structure has been performed on wild Cuban populations. Given that the Caribbean archipelago and Cuba are considered to be of high conservation priority (Biodiversity Hotspot and Wildlife Conservation Society (WCS) priority29; Critical Ecosystem Partnership Fund (CEPF)30), it is imperative that threatened populations within this island system be managed properly, based on solid population and genetic data.One species in particular, the endemic Cuban Rock Iguana  and mitochondrial diversity across eight populations of Cyclura nubila nubila, at eight localities across the island of Cuba , the effective number of alleles per locus (Ne = 3.5–6.0), and the rarefied allelic richness (Ar = 4.53–6.54), showing the lowest value for Cayo Macho and the highest in Monte Cabaniguán. Observed and expected heterozygosity showed high values for all populations   per population, which exhibited low values, ranging from 1474.3 for Cayo Verde, 404.4 for Cayo Blanquizal to 82.1 and 24.6 for Cayo Alto and Cayo Macho, respectively , Cayo Obispo , and Cayo Blanquizal , while no M-ratio deficiencies were found.We estimated the effective population size  and Cayo Alto (0.251) and lowest for Monte Cabaniguán (0.115) and Cayo Verde (0.157) , Cabo Corrientes and Cayo Blanquizal , and the lowest between Cayo Obispo and Cayo Verde  and Cayo Blanquizal and Cayo Verde .We assessed the degree of genetic structure and differentiation among populations with different approaches. Population local Structure) , while that between genetic clusters and among individuals within populations was 11% and 18.0%, respectively (p < 0.001).Finally, the genetic structuring and individual admixture ancestry results (re) Fig. a supportre) Fig.  showed tAs another measure of differentiation and potential inbreeding, we estimated relatedness among individuals. Results showed a high proportion of unrelated individuals in all populations (78.3–100%), followed by half-siblings (7.9–12.8%), siblings (0.6–5.6%) and parent/offspring (0–3.3%). Notably, all 8 individuals from Cabo Corrientes were unrelated (Supplementary Table https://github.com/kshaney/Cyclura_Conservation_Units). The model of nucleotide substitution obtained for our dataset was GTR, with gamma shape = 1.015 and proportion of invariant sites I = 0.499. Phylogenetic inference (ML) results showed high bootstrap support values at the species level, while low within C. nubila nubila, which is common when evaluating relationships among populations with mostly small branches32  and the other (our Eastern group) included the eastern populations on the island. Samples of C. n. caymanensis were nested within the Eastern group, whereas those of C. lewisi were nested within the Western group. Four samples from GenBank had unconfirmed geographic origins .All of our man Fig. . Our C. Beast for the estimation of the times of divergence was concordant with the ML phylogenetic relationships. In accordance with our estimations, the divergence between the most basal Cyclura, C. pinguis  and the rest, and at 10.4 My (7.4–13.3) and 8.2 My (5.3–11.1) for the C. collei (C) and C. rileyi (D) divergences. The time to the most recent common ancestor (TMRCA) for the split between the Eastern and the Western C. nubila phylogroups (Node E) was dated at 6.8 My (4.2–9.7), where the former separated from C. cychlura (Node F) 5.6 My (3.1–8.4). Within the Eastern phylogroup, the Guantánamo and Marea del Portillo populations  showed a separation since 3.6 My (1.6–6.8), while within the Western group, the C. lewisi haplogroup (Node H) diverged from the Cuban populations 5.7 My (3.4–8.4). Finally, the westernmost populations, Guanahacabibes and Cabo Corrientes (Node I), exhibited a 2.8 My (1.3–4.7) separation from Cayo Macho , joined by the Grand Cayman haplotypes (C. lewisi). The Western group had few haplotypes, with one shared among Cayo Verde, Blanquizal and Obispo. The Eastern network showed many unique haplotypes belonging to different populations (including those on Little Cayman), although they were interconnected by several mutational steps. Guantánamo and Marea del Portillo were also separated, depicted as terminal haplotypes.Results from the minimum spanning haplotype network showed a pattern of general agreement with the phylogeographic topology Fig. b: the neBased on the population structuring obtained from microsatellite loci, we identified at least six different Cuban rock iguana Management Units (MUs) Fig. , while bCyclura nubila nubila within the framework of the West Indian Rock Iguanas (genus Cyclura) across the Greater Antilles. Our nuclear genetic data (microsatellite loci) showed significant structure in Cuba across six distinct genetic clusters, exhibiting likely introgression between some populations . This is a classic pattern where the species historically had a wide geographic distribution, which became more isolated contemporarily from a variety of potential factors, like habitat fragmentation, population decrease, or other isolating factors. Although genetic diversity measures are currently moderate to high, it is important to consider that recent bottleneck effects may cause a drop in alleles before a drop in levels of heterozygosity35. This means that some of these isolated populations may be suffering the initial stages of a bottleneck that are not yet fully manifested in genetic signatures in this long-lived species.Rock Iguanas are threatened with extinction mainly because they are harvested for meat and as pets, hence their management should be a high priority. We here present a comprehensive genetic and phylogeographic study for the endangered Cuban Rock Iguana, Ne) is sensitive to sample size and could be under-estimated30, it gives a good proxy of the low population numbers for this species36. In addition, Ne is directly related to the rate of loss of genetic diversity, the potential for bottlenecks, and increased chance of inbreeding38, as already exhibited by some populations of C. n. nubila.Based on these considerations, we determined that there should at least be six Cuban Rock Iguana MUs Fig. . Each MUNe, ranging from only 23–36). Population decline might be exacerbated by the nature of these archipelago systems, where limited habitat, small population size, and unique dispersal and colonization processes, plus potential sea level rise, increase island species vulnerability and susceptibility to extinction39. Furthermore, endemic insular species generally show proportionately lower genetic variation than island non-endemics40. We acknowledge that our sampling is limited and that we did not include other populations that should likely be considered for MU status as well. Undoubtedly, further sampling would aid in confirming the conservation units we suggest. Nonetheless, it is important to highlight that the distribution area of C. n. nubila has been reduced in the last 50 years to ca. 2000 km2, with many localities where it has completely disappeared23. Moreover, even back on 1999 Rodríguez-Schettino24 had already identified localities  where the species was extremely hard to find. Thus, our findings are timely and can set the basis for future strategic sampling.The proposed MUs for Cuban Rock Iguanas could persist if migration rates remain stable; nevertheless, our strong evidence of significant differentiation suggests that current migration rates are low. Dispersal may even be further limited by continuing anthropogenic pressures and growing habitat fragmentation. Several of these MUs are currently at risk of extirpation, especially those on Cayo Macho, Cayo Alto, Cabo Corrientes and Cayo Obispo , and with the subsequent events, where the easternmost  and the westernmost  populations diverged more recently  in island systems should be defined by neutral and adaptive loci. Funk et al.7 also suggested using robust genomic adaptive and neutral information for defining ESUs. However, the case of threatened rock iguanas from the remainder of the Caribbean archipelago presents an interesting example that may not fit their recommendations. First, given the scarcity of genomic data for this group, it is not currently an option. Second, even if we lack data from adaptive loci to test additional questions, we know that each Cyclura population may play a key functional role in seed dispersal and ecosystem functioning. Disappearance of any population would mean the loss of that role, regardless of what adaptive loci tell us about adaptive distinctiveness. Is this enough to qualify geographically isolated populations as ESUs?Robertson et al.Cyclura, we suggest it is better to move forward with hypothesized ESUs. Based on our mitochondrial phylogeographic results, we identified distinct Eastern and Western C. n. nubila phylogroups  across Cuba. In addition, our broader phylogeographic findings for all Cyclura species strengthen the conclusion that each of the taxa of Caribbean rock iguanas, based on their evolutionary and geographic uniqueness, warrant ESU distinction  emphasized that additional study is sorely needed. Hence, we believe our findings and proposed ESUs provide valuable additional evolutionary information for the unresolved taxonomy of these iguanas.In addition, although our study was not intended to evaluate the taxonomy of 29. Cuba is especially interesting due to its large size, habitat diversity, and unique array of species, many endemic. However, significant areas of the island’s forests and other natural ecosystems have been converted for agriculture, mining, and ranching47. Although all rock iguana MUs identified here overlap with protected areas, many of these areas appear to be quite small, which may limit them from successfully protecting the iguanas. In addition, fragmentation driven by land use changes has further divided natural habitats across the island. Therefore, based on the iguana’s habitat loss, and the genetic differentiation, small population sizes and inbreeding we documented, and considering IUCN guidelines (https://www.iucnredlist.org/assessment/process), we recommend that the IUCN status of C. n. nubila is reexamined.The Caribbean islands are collectively considered a biodiversity hotspot and have recently been included in the WCS top 14 global priorities strategy50. Recent methods have been developed that combine genetic, sampling, and statistical methods with that purpose; for instance, Wasser et al.48 developed a method that can estimate geographic-specific allele frequencies using microsatellite loci data, successfully identifying the origin of elephant tusks. Another wildlife forensic example used mitochondrial (haplotypes) DNA to identify geographic areas of origin across Southeast Asia of confiscated pangolin scales49. Our data could aid in testing the origin of traded Cuban Rock Iguana specimens for which our microsatellite data should allow mapping at a fine resolution. In addition, coarse-scale mapping with the mitochondrial data could at least narrow down identification to the species or subspecies level, as we demonstrated by identifying the locality of the GenBank sequences that had an unknown origin34.Being able to infer and/or verify the geographic origin of wildlife products  is crucial in conservation51. Beovides-Casas and Mancina52 found eight species of plants in C. n. nubila fecal samples, the two most common were fruits of Chrysobalanum icaco and Batis maritima. Hence, as iguana populations decline in Cuba, the loss of their ecological function is also of major concern. We identified at least six Cyclura nubila nubila MUs across Cuba, and potentially more could exist, which are nearly completely demographically isolated and with declining population trends. We also defined two ESUs in Cuba, which represent groups with potentially high levels of adaptive genetic variation. Importantly, these conservation units hold the genetic and evolutionary diversity needed to face the challenges of a shifting environment. Climate change is one major threat for species in the Caribbean islands system44. The likelihood that a species will persist amid climatic oscillations or sea level rise or disease is higher if several populations remain as intact and connected as possible44. Our approach shows the importance of considering the historical phylogeographic diversification and contemporary intraspecific genetic variation of species, in addition to their ecological function, for CU management planning.Caribbean ecosystems are rich in biodiversity and endemism, making the management of Cuban Rock iguanas a high priority. Rock Iguanas are an excellent example of an atypical plant-dispersing group whose loss from parts of the Caribbean could have major cascading effects53. Sampling was performed during 2004–2006, under the supervision of the Cuban flora and fauna protection authorities at all times; individuals were captured using a lasso (rope) or by excavating them from their shelters, and all animals were released at the site of capture. We obtained a total of 172 individuals (Supplementary Table 27. DNA was extracted using the AquaPure Genomic DNA kit (Biorad) following the manufacturer’s protocol.Fieldwork was performed at eight sampling localities (populations) across Cuba, two located at protected areas , two from the mainland inhabiting steep rocks, and the rest from northern offshore cays Fig. a. The inC. n. nubila28: HDZ35, HDZ66, HDZ148, HDZ151, HDZ152, HDZ154, HDZ181, HDZ373, HDZ419, HDZ494. Unfortunately, no samples from Grand Cayman or the Sister Isles were available for this analysis. PCR amplification was carried out in a 10 μl reaction with a final concentration of approximately 50 ng of genomic DNA, 0.2 μM each dNTP, 1.5–2.0 mM MgCl2, 10 × reaction PCR buffer , 0.5 units of Taq DNA polymerase, and 0.5 pmol/μl of unlabeled reverse primer and fluorescently labeled forward primer. PCR amplification  was done following the conditions described by28. Positive and negative controls were used throughout to ensure correct scoring. PCR products were sequenced at the Roy J. Carver Biotechnology Center-University of Illinois, and the individual genotypes were scored with GeneMapper 3.754.Microsatellite loci were amplified for all 172 individuals, testing 10 primers developed for 55), which amplify the ND4 and ND5 regions, for a subset of five individuals per sampling locality. PCR reaction was set for 50 μl volume containing: 2 mM MgCl2, 0.2 μM dNTPs, 0.6 μM of each primer, 0.8 µg/µl of BSA and 0.5 units of Taq DNA polymerase. Amplification was done in a PTC-100 Thermal Cycler (M.J. Research), with the following conditions: initial 5 min denaturation at 96 °C, 35 cycles consisting of 96 °C denaturing for 30 s, annealing for 30 s at 49 °C, a 4 min ramp to 72 °C, with a final extension at 72 °C for 1 min20. Sanger sequencing was performed by Macrogen-Korea. Sequences obtained were aligned with Geneious 9.0.5  with the Muscle algorithm.We amplified mitochondrial DNA with the primers ND4 and LEU . We conducted significance tests in GenePop using Fisher’s method, 10,000 dememorizations, 1000 batches, and 10,000 iterations per batch; where necessary, α value was adjusted for multiple comparisons applying Bonferroni correction58. The presence of null alleles and stuttering was estimated with the program Micro-Checker v.2.2.359, using a 95% confidence interval and 1000 repetitions. We estimated genetic variability indices, including the observed (no) and effective (ne) number of alleles, observed heterozygosity (Ho) and expected (He), and Nei’s unbiased expected heterozygosity , with GENALEX v.661 and adegenet in R62.Based on the microsatellite loci, we examined possible departures from Hardy–Weinberg equilibrium (HWE) with an exact test, and linkage disequilibrium (LD) by a log-likelihood ratio statistic (G-test) using GenePop v.4.0Ne) for populations with a sample size > 10, with NeEstimator v.2.163, based on linkage disequilibrium (LD), a random mating system, and Pcrit  of 0.02. We also calculated inbreeding coefficients in INEst v.2.164, which allows correcting for null alleles. We ran the individual inbreeding model (IMM), testing the ‘null’ model and all combinations of the parameters ‘n’ , ‘f’ (inbreeding), and ‘b’ (genotyping failure), using 200,000 Monte Carlo Markov chain (MCMC) iterations and 20,000 burnin; to assess the best model fit for the data, we estimated the Deviance Information Criteria (DIC) for each run. Finally, we evaluated evidence of recent genetic population bottleneck events with INEst, based on two tests: the heterozygosity excess in relation to allelic richness65, and the mean ratio of allelic richness to allelic size range deficiencies . We used the two-phase mutation model and default settings; significance was tested with both a Z-test based on the combined Z scores and a Wilcoxon signed-rank test, with 10,000 permutations.In order to explore demographic patterns, we estimated the effective population size . Higher local FST values mean greater differentiation of this particular population compared with the others. We used 100,000 iterations with a burn-in of 10,000 and a thinning interval of 100; Cabo Corrientes was not tested due to low sample size. Three independent runs with identical setting values were performed to check for consistency of the estimates. Next, we estimated pairwise differentiation based on FST estimated with FSTAT v.2.9.368), and Nei’s genetic distance  with GenAlex61.We used different approaches to assess the degree of genetic structure and differentiation among populations . First, we used a hierarchical Bayesian method , with values of K = 1–9, under an admixture ancestral model and the correlated allele frequencies model. We performed 20 runs for each value of K, 100,000 Markov chain Monte Carlo generations, after a burn-in of 50,000 iterations. We used the Evanno’s ΔK test to estimate the maximum number of clusters71; data were processed with Structure Harvester72. In addition, we examined the distribution of the genetic variance using a molecular analysis of variance (AMOVA), considering different hierarchical levels , based on FST with Arlequin v.3.0173; significance was calculated using a non-parametric test with 30,000 permutations of genotypes among populations.In order to estimate the genetic structuring and individual admixture ancestry, we used a Bayesian clustering method , we selected sequences from GenBank , and from Little Cayman (C. n. caymanensis) and Grand Cayman (C. lewisi), as well as sequences from C. pinguis, C. cornuta, C. ricordi, C. carinata, C. collei, C. rileyi, and C. cychlura. We selected sequences from Iguana iguana, I. delicatissima and Dipsosaurus dorsalis as outgroups. We estimated the best fitting model of sequence evolution for our dataset, which was subsequently used for maximum-likelihood (ML) phylogenetic analysis, with PhyML 3.075, using a Nearest-Neighbor Interchange and Subtree-Prune and Regraft moves (NNI + SPR) for branch length and topology optimization. We assessed clade support with 1000 non-parametric bootstrap replicates. In order to evaluate relationships among mitochondrial haplotypes, we constructed an unrooted network among unique haplotypes, using PopArt76, based on the minimum-spanning method77.In order to be able to define conservation units, we performed phylogenetic, phylogeographic and times of divergence analyses based on mitochondrial sequences, both from our Cyclura species across the Caribbean and for C. n. nubila within Cuba, we estimated divergence times  with Beast 1.10.478. Notably, although several studies regarding the systematics of iguanian lizards exist (see80), there are still taxonomic uncertainties, specially at the subspecies level (see ITWG16 and references therein), while divergence times for many genera are rather scarce or ambiguous. We were able to obtain two calibration points, the TMRCA for Dipsosaurus dorsalis at 20 million years  and for Cyclura spp , based on83. The analysis was done with the complete data set and the outgroup species as used for ML. Parameters included a Yule process tree prior, the GTR + I + G model of evolution across all gene and codon positions, an uncorrelated relaxed-clock dating, 500,000,000 generations sampled every 5000th and 20% of initial generations discarded as burnin. Convergence and stationarity were visualized with Tracer v.1.6 (http://tree.bio.ed.ac.uk/software/tracer/).In order to establish the historical time frame of the separation of 7 discussed grouping CUs using genomic datasets; however, the same principles apply to our microsatellite dataset and we follow their suggestions. They explain that ESUs (the largest intraspecific CU) should be defined using a combination of loci under selection and neutral loci because the goal is to delineate populations that have been shaped by neutral and adaptive processes. Conversely, Management Units (MUs), smaller intraspecific CUs, should be assessed with only neutral markers. Therefore, we base MU segment assignment on microsatellite data and ESU segment assignment on mitochondrial data. Additionally, they suggest Bayesian clustering programs like Structure as one potential option for evaluating population subdivision, which we used for evaluating MUs with microsatellite data.Funk et al.53.Fieldwork was performed in compliance with the Guidelines for use of live amphibians and reptiles in field and laboratory researchSupplementary Information.Supplementary Information 2.


To maintain the uniqueness of conserved chicken populations of local and imported breeds is of great importance. In this study, we genotyped small populations belonging to 14 breeds and 7 crossbreds using an Illumina Chicken 60K SNP (Single Nucleotide Polymorphisms) BeadChip and looked for appropriate methods to characterize their purity/variability. It was not straightforward to identify crossbred individuals, and the best approach was based on calculating the length and number of homozygous regions, or runs of homozygosity (ROH), in the populations studied. The latter enabled most accurate identification of crossbreds and can be served as an effective tool in testing genome-wide purity of chicken breeds.1 crossbred populations and identifying crossbreeding events via whole-genome evaluation of runs of homozygosity (ROH). The admixture models more efficiently reflected population structure, pinpointing crossbreeding events in the presence of ancestral populations but not in their absence. Multidimensional scaling and FST-based analyses did not discriminate properly between purebred populations and F1 crossbreds, especially when comparing related breeds. When applying the ROH-based approach, more and longer ROHs were revealed in purebred individuals/populations, suggesting this as an effective implement in genome-wide analysis of germplasm breed purity.Preserving breed uniqueness and purity is vitally important in developing conservation/breeding programs for a germplasm collection of rare and endangered chicken breeds. The present study was aimed at analyzing SNP genetic variability of 21 small local and imported purebred and F Assessment of the genome-wide diversity plays a significant role in conserving local and imported genetic resources and maintaining an effective program for breeding commercial populations e.g., ,2,3). Pr,3. Pr2,3A comparative genetic evaluation of livestock populations of different origin, population history, and size is an important source of information for genetic changes in their genome, including degree of homozygosity across genome-wide regions ,3,10. Th1 crossbreds, which could be served as a marker to detect potential occasional hybridization events.Since relevant ROH studies in small closed populations kept in a germplasm collection of local, rare, and endangered chicken breeds are missing, the goal of our study was a genome-wide evaluation of SNP genetic diversity in these populations, and a search for criteria to identify occasional hybridization events in population history in order to control breed purity. Herewith we investigated ROHs and linkage disequilibrium (LD) and hypothesized that, when monitoring intra-breed genetic structure, small purebred populations would have more and longer ROHs compared to Finter se), Frizzle (F), Hamburg Silver Spangled Dwarf (HSSD), Leghorn Light Brown  (LLB), Pushkin (P), Russian White , Sussex Light (SL), Tsarskoye Selo (or Tsarskoselkaya) (TC), Uzbek Game (UG); and 7 groups of F1 crossbred progeny: Brahma Light × Sussex Light (BL × SL), Sussex Light × Amroks Cuckoo (SL × A), Tsarskoye Selo × Sussex Light (TC × SL), Uzbek Game × Amroks Cuckoo (UG × A), White Cornish × (Brahma Light × Sussex) (C × BL × SL), White Cornish × (Sussex × Amroks Cuckoo) (C × SL × A), and Tsarskoye Selo × (Sussex Light × Amroks Cuckoo) (TC × SL × A) as shown in For this study, SNP genotypes of 673 birds were obtained for the following 21 small populations of local and imported breeds that represent a broad sample of the world’s chicken germplasm and are maintained at the RRIFAGB Collective Use Centre ‘Genetic Collection of Rare and Endangered Chicken Breeds’. These included 14 purebred populations: Amroks Cuckoo (A), Brahma Buff (BB), Brahma Light (BL), Bantam Mille Fleur (or Russian Korolyok) (BMF), White Cornish  as performed with the assistance of the Geneseek/Neogen Corporation. SNPs were filtered and removed from the further analysis if they met the following criteria: minor allele frequency ≤0.05, Hardy–Weinberg equilibrium probability ≤1 × 10r2) coefficients were calculated using the following formula in PLINK 1.9 software [1 and A2, and B1 and B2; f11, f12, f21 and f22 are the frequencies of haplotypes A1B1, A1B2, A2B1 and A2B2, respectively; and fA1, fA2, fB1 and fB2 are the frequencies of alleles A1, A2, B1 and B2, respectively.LD (or software :(1)r2= were found using the following formula [k is the number of ROHs, k)length FROH=1 crossbred populations was estimated in the admixture 1.3.0 program [Population variability of SNP markers in purebred and F program . To comp program .Multidimensional scaling (MDS) was performed in Plink 1.9 software  using a FST) values were computed using eigensoft 6.1.4 software [Additionally, to assess between population diversity, Wright’s  fixationsoftware . Phylogesoftware  and Intesoftware .FROH) than F1 inter-breed crossbreds produced by crossing two or three breeds , BMF (FROH = 0.291 ± 0.015), etc. (FROH = 0.023 ± 0.005 in the decorative F breed known in Europe since 1676 and characterized by a specific structure of feathers due to the F (frizzling) gene. A lower content of ROHs in this breed population can be associated with a targeted introgression of genes of the other breeds to reduce the effect of feather fragility as reflected by the admixture analysis , etc. . Among tanalysis .FROH in RW1 (0.307 ± 0.014) and a lower one in RW2  existed from 1953–2003, had one founder and underwent intensive selection for chick tolerance to cold ,26. In 21 crossbreds, we revealed a decrease in ROH metrics, while three-breed F1 crossbreds appeared to show a further lowering in their genomic content of ROH (In two-breed Ft of ROH .inter se had a greater (>20) number of ROHs , meaning a reduced genetic diversity similar to what was shown in small livestock populations kept by individual breeders or in local breeds [1 crossbred progenies contained a lesser (<6) number of ROHs . If data on the origin are not taken into account, F1 crossbred progenies are difficult to identify. In contrast, when conventional MDS and FST-based analyses (Characterization of genetic differentiation in conserved populations of local and imported breeds and monitoring of their purity/variability are an important component of breeding/conservation programs ,2. In throgenies . Howeveranalyses a,b are p1 crossbred animals, and this approach can be used as a tool in selecting purebred individuals for conserved propagation of local and imported breeds. ROH-based characterization in individuals and in the whole population can be used in adjusting germplasm breeding/conservation programs and identifying events of inter-breed gene transfer.Exploration of the length and number of ROHs can be helpful in discriminating between purebred and F


Linum marginale-Melampsora lini plant-pathogen interaction. Using pathogen isolates collected from three populations of wild flax  spanning 16 annual epidemics, we probe links between pathogen population dynamics, phenotypic variation for infectivity and genomic polymorphism. Pathogen genotyping was performed using 1567 genome-wide SNP loci and sequence data from two infectivity loci . Pathogen isolates were phenotyped for infectivity using a differential set. Patterns of epidemic development were assessed by conducting surveys of infection prevalence in one population (Kiandra) annually. Bayesian clustering analyses revealed host population and ecotype as key predictors of pathogen genetic structure. Despite strong fluctuations in pathogen population size and severe annual bottlenecks, analysis of molecular variance revealed that pathogen population differentiation was relatively stable over time. Annually, varying levels of clonal spread (0–44.8%) contributed to epidemics. However, within populations, temporal genetic composition was dynamic with rapid turnover of pathogen genotypes, despite the dominance of only four infectivity phenotypes across the entire study period. Furthermore, in the presence of strong fluctuations in population size and migration, spatial selection may maintain pathogen populations that, despite being phenotypically stable, are genetically highly dynamic.A priority for research on infectious disease is to understand how epidemiological and evolutionary processes interact to influence pathogen population dynamics and disease outcomes. However, little is understood about how population adaptation changes across time, how sexual vs. asexual reproduction contribute to the spread of pathogens in wild populations and how diversity measured with neutral and selectively important markers correlates across years. Here, we report results from a long-term study of epidemiological and genetic dynamics within several natural populations of the  Melampsora lini is a rust fungus that infects native flax, Linum marginale in south-eastern Australia where its epidemiology and evolution have been intensively studied since 1987. Over that time, substantial diversity in the pathotypic structure of M. lini has been demonstrated but an understanding of how genetic diversity in pathogen populations is maintained through space and time is lacking. Here we integrated phenotypic, genotypic and epidemiological datasets spanning 16 annual epidemics across three host populations to examine long-term pathogen genetic dynamics. The results show that host ecotype is the dominant selective force in the face of strong bottlenecks and annual patterns of genetic turnover. Results from previous studies indicate that in this geographic region, M. lini lacks the capacity to reproduce sexually–we thus expected to find limited genetic diversity and evidence for strong clonality influencing genetic dynamics within growing seasons. However, the breadth of genomic coverage provided by the SNP markers revealed high levels of genotypic variation within M. lini populations. This discovery contrasts with observed phenotypic dynamics as the epidemics of this pathogen were largely dominated by four pathotypes across the study period. Based on a detailed assessment and comparison of pathotypic and genotypic patterns, our study increases the understanding of how genetic diversity is generated and maintained through space and time within wild pathogen populations. The implications for the management of resistance to pathogens in agricultural or conservation contexts are significant: the appearance of clonality may be hiding high levels of pathogen diversity and recombination. Understanding how this diversity is generated could provide new and unique ways to mitigate or suppress the emergence of infectious strains, allowing to efficiently combat harmful diseases. Evolution within pathogen populations poses ongoing risks for human health, agricultural productivity, and ecosystem structure and function. The response of pathogen populations to novel resistance genes and changing environmental conditions contribute to evolutionary dynamics that drive the emergence and spread of new infective pathogen strains –4. EvoluWith respect to antagonistic coevolution between a host and pathogen, there are two theoretically well-grounded scenarios: directional selection and balancing/fluctuating selection , 20. DirIn addition to genetic change in response to selection imposed by the host, pathogen life-history traits can be important determinants of the frequency and predictability of interactions, influencing pathogen demography and hence patterns of genetic variation , 30. ForTogether with pathogen life history traits and spatial structure, varying environmental conditions, host phenotype (e.g. resistance) and life history  shape the evolutionary potential of pathogen populations , 34 and Melampsora lini and present a detailed analysis of the links between genetic and demographic dynamics in one representative population over the course of 16 annual epidemics through 24 years. Rust epidemics on wild flax, Linum marginale, are characterized by strong seasonality and occur within a fragmented structure of partially isolated local populations. This situation results in strongly defined boom and bust dynamics [Here we investigate long-term patterns of genetic change in several local populations of the flax rust pathogen dynamics  where epdynamics , 37. Newdynamics  but it iLinum-Melampsora system is well established using a host differential set–a standard set of plant lines with differing resistance backgrounds. [AvrP123; AvrP4) that have relatively high levels of sequence variation and are under strong diversifying selection [M. lini isolates collected between the years 1987 and 2010 in three sub-alpine populations. The host populations that carry them are geographically close to each other, but experience different ecological conditions, and are morphologically and genetically differentiated, such that the ‘hill’ (Kiandra) and ‘bog’ (P1 and P2) host populations represent different ecotypes [M. lini is represented by two distinct lineages that differ in their heterozygosity, an AB lineage being common in the sub-alpine region and an AA lineage in areas with hot summer conditions [To investigate pathogen evolution, we characterized pathogen variation with a suite of markers–SNPs, infectivity genes and infectivity phenotypes. Characterization of phenotypic infectivity in the grounds. , 40. In grounds. , 41, andecotypes . At a brnditions . We testM. lini isolates collected between 1987 and 2010 from three populations . Pathotypic diversity was highest in P1 (Shannon H = 2.02) vs Kiandra or P2  of infecM. lini isolates from all three populations using Nextrad sequencing and performed hierarchical AMOVA to assess how SNP variation was distributed among populations, years, and pathotypes (P = 0.07) and variation within year again explained the largest proportion of the variation  of the SNP data based on Prevosti’s distances. The PCA revealed clear differentiation among populations . The firR = 0.45; P = 0.001; R = 0.59; P = 0.001; H = 1.34; vs in P1 H = 1.31 and P2 H = 0.62). With regards to infectivity phenotypes, the clusters consisted of several different pathotypes  revealed six clusters with assignment probability of 99.2%, including one cluster (Cluster 3) that was strongly differentiated from all the others along the first principal component axis and had a distant placement in a neighbour-joining tree ; S1 Fig.thotypes .AvrP4 and AvrP123  = 0.70, vs P1 H = 0.55, and P2 H = 0.20; In addition to SNP markers, we also characterized nucleotide variation at loci underlying pathogen infectivity. Specifically, we PCR amplified and sequenced alleles of the infectivity genes  (as per ). We conplotypes , comprisclusters . Analysiariation . In contariation . With reTo understand whether clonal increase drives annual epidemics, we first assigned isolates to multi-locus genotypes (MLGs). To avoid potential underestimation of clonality due to sequencing error, we used a 5% error rate (the maximum error rate observed from the replicate sequencing of three control isolates). After filtering the SNP data using this 5% error rate, the data indicated most isolates were genetically unique– 83.9% of the MLGs were sampled only once. Consequently, a relatively high number of MLGs were found in each of the sampled populations . The 67 MLGs sampled more than once constituted 44.1% of the sampled isolates. When we analysed the prevalence of non-unique MLGs we found that each appeared between 2–13 times (mean 4.05) during the study period. Within annual epidemics, clonal spread comprised 0–44.8% of the isolates collected per season .Cloncase analysis [Cloncase is a program that implements algorithms to estimate the expected sexual and clonal reproduction using MLG frequencies and the estimates of clonal cycles before and after the winter season. However, it does not use allele information obtained by SNP genotyping, thus does not produce any estimate of linkage between loci or similarity of MLGs. The analysis estimated variable rates of potentially sexual reproduction across years (0.06–98% of the population), and high effective population sizes from 481 to 10580  and pathotype prevalence in the study populations. The expectation is that pathotypes with greater host range will be more frequent than their counterparts with a narrower host range in highly resistant populations whereas in susceptible populations the opposite is expected. Our results showed that infectivity was negatively correlated with prevalence . While there was no significant effect of pathogen population  and allopatric (from a more distant population) hosts used in a different study [2 = 0.02; d.f. = 1,55; F = 0.01; P = 0.9106; The differential set of nt study . While iLinum marginale—Melampsora lini association has been extensively described [The escribed , 36–38 bescribed . The potescribed , 42, 46 The host and pathogen populations surveyed and sampled for this study are located within the Kiandra Plain, in the northern part of Kosciuzko National Park, New South Wales, Australia. The main site (Kiandra) is located on a hillside; at this site epidemic development was assessed in addition to the collection of individual pathogen samples. At the other two sites , located on flat areas adjacent to stream courses, only pathogen samples were collected. The host plant occurs in these sites as two ecotypes: hill (Kiandra), and bog (P1 and P2) which differ both morphologically  and genetically (isozyme patterns) . PopulatThe number of sampling times changed across the course of the study; in the early years samples were collected at both the start of epidemics (just a few samples) as well as at epidemic peaks (many samples). In later years most sampling was done during the epidemic peak. The sampling intensity was scaled to the epidemic size. Thus, in years in which conditions were unfavourable to the pathogen (i.e. no epidemic occurred), the number of samples collected was small (e.g. Kiandra in the years 1994–1995). On each sampling occasion in each year, pathogen sampling was conducted by examining plants chosen haphazardly across the entire site; this was done deliberately to avoid sampling the same local infection focus multiple times. No attempt was made to try to revisit the same host individuals across years. The infectivity of individual isolates from the three sites was assessed over multiple years  hosts, inoculations were performed on hosts and pathogens collected from six other populations, G1, G3, SH1, SH2, WHP1 and WHP2 by inoculating the pathogens on hosts from their own population and on hosts from their five allopatric populations as described elsewhere .For each isolate, total genomic DNA was extracted from approximately 100 mg of urediospores using a DNeasy plant mini kit  and quantified using a Qubit 3.0 fluorometer. In addition, we genotyped three samples as duplicates in separate plates to estimate sequencing errors. Genotyping was conducted using a genome-wide single nucleotide polymorphism (SNP) method based on a Nextera-tagmented, reductively amplified DNA protocol (nextRAD ). NextRAIn brief, genomic DNA was first fragmented with Nextera reagent , which also ligates short adapter sequences to the ends of the fragments. The Nextera reaction was scaled for fragmenting 4 ng of genomic DNA, although 6 ng of genomic DNA was used for input to compensate for degraded DNA in the samples. Fragmented DNA was then amplified, with one of the primers matching the adapter and extending 8 nucleotides into the genomic DNA with the selective sequence GTATAAGG. Thus, only fragments starting with a sequence compatible with the selective sequence of the primer were efficiently amplified. The nextRAD libraries were sequenced on a HiSeq 2500 . The genotyping analysis used custom scripts  that created a de novo reference from abundant reads, and then mapped all reads to the reference with an alignment identity threshold of 95%. Read mapping and variant calling were done using samtools  and bcftM. lini infectivity loci we additionally PCR amplified and sequenced the ‘A’ genome alleles of the AvrP4 and AvrP123 infectivity genes using previously described methods [AvrP4 gene we found 14 allelic variants. Two of these (Lm4 and Lm8) were present at high frequencies whereas other variants were relatively uncommon. At the Avr123 gene we found two alleles (Lm1 and Lm2) that were present at high frequency, with an additional 29 singletons sampled. The singletons were treated as sequencing artefacts and removed from the analysis. We constructed multi-locus AvrP4/AvrP123 haplotypes by combining the sequenced infectivity genes for each isolate. A total of five commonly occurring haplotypes were found.To characterize variation at  methods . AltogetPrior to analysis, we removed uninformative loci  from the VCF file and excluded pathogen samples with >30% loci missing, leaving a total of 629 samples genotyped across 1567 polymorphic loci  that differ in their heterozygosity [Population subdivision into genetic clusters was performed using discriminant analysis of principal components (DAPC) . DAPC wa package  in R sof package , 52. To  package . All 629zygosity  were asspoppr package [To estimate clonality in the populations, multi-locus genotypes (MLGs) were assigned using the  package  in R asscloncase R package [Cloncase is a program that implements algorithms to estimate the expected clonal and potentially sexual reproduction using MLG frequencies and the estimates of clonal cycles before and after the winter season. However, it does not use allele information obtained by SNP genotyping, and thus, does not produce any estimate of linkage between loci or similarity of MLGs.To estimate the rate of potentially sexual reproduction and effective population size across years we performed analyses using  package  using thPhi) test for global recombination in Splitstree software [Phi test assesses the probability that recombination has occurred by comparing observed and permuted phi statistic (Φ), with significance set at 0.05. Observed linkage among loci was evaluated against permuted expected distributions using the index of association (IA) and rD [A and rD were calculated using the poppr package [IA values between loci was tested for each population with 999 permutations. Significant P values in Ia and r¯d therefore indicate linkage among loci.We performed a pairwise homoplasy index (software  separate) and rD –55. IA a package  in R seppoppr package [randtest in the R package ade4 [To test whether isolates from different populations clustered separately, we performed principal component analysis (PCA) based on Prevosti’s distances of the SNP data. To understand how genetic variation was partitioned among populations, years, pathotypes and DAPC clusters, analyses of molecular variance (AMOVA) were performed using the  package  in R. Tw package  were conage ade4 .poppr. Rare allelic variants (7.5% of the samples) that occurred only once in the dataset were excluded from this analysis.To understand how variation in infectivity genes was structured among populations, as well as within and between years, AMOVA was performed on infectivity gene alleles using the R software package poppr. To analyse diversity measured with SNPs (MLGs and clusters), infectivity gene haplotypes, and infectivity phenotypes within populations and across years, the Shannon index (H) was calculated [poppr [Overall population genetic richness and diversity was examined for the three populations as well as among years using the assigned MLGs. The expected number of genotypes was calculated using rarefaction in the R package lculated –59 with d [poppr .vegan package [In order to understand whether measures of population diversity among years using different markers were correlated, we performed sets of Mantel tests for each population and for the whole data set. An ANOSIM analysis was performed using the R  package  to disenL. marginale differential set and infectivity measured on local or more distant host populations. We ran these analyses as Generalized Linear Mixed Models in SAS 9.4 PROC GLIMMIX . We performed two analyses using data collected from the pathogen populations. To assess the relationship between pathotype infectivity and the prevalence of the pathotype each year we used infectivity as the response variable and prevalence as a covariate. Pathogen population was used as an explanatory class variable and year as a random variable. Infectivity was arcsine transformed to meet model requirements of normality. In the second model, in order to examine the extent to which strain prevalence in a population correlated with length of time (years) it was resident, pathogen population was analysed as an explanatory class variable and mean prevalence of each pathotype was used as a covariate. A Poisson distribution of error was assumed for years of pathogen prevalence. To test whether infectivity measured on differential hosts correlated with infectivity on sympatric and allopatric hosts, data collected from six populations, G1, G3, SH1, SH2, WHP1 and WHP2 was used. The mean infectivity of pathogen strains as measured on differential hosts was regressed against its mean infectivity on sympatric hosts and mean infectivity on allopatric hosts. To increase normality, infectivity data were arcsine transformed.We performed another set of statistical analyses to understand: 1) the extent to which pathotype infectivity  and frequency in the pathogen population were correlated; 2) the relationship between the overall prevalence of pathotypes and the consistency of their presence across years; and 3) the relationship between infectivity as measured on the M. lini pathogen populations across 16 annual epidemics identified the presence of six clusters of genetically related individuals. Using this framework, we were able to evaluate the relative importance of spatial and temporal aspects in maintaining pathogen variation. We found that genetic variation was maintained through time via among-population differentiation, and that pathogen populations are genetically and phenotypically dynamic . We alsUsing the clusters identified through analysis of extensive genotype and phenotype data, we were able to evaluate the relative importance of spatial and temporal scale in maintaining variation in pathogen populations. While pathogen populations were genetically and phenotypically dynamic through time, we found that strong spatial structuring among populations was the predominant factor maintaining the different genetic clusters within the metapopulation over time in the Kiandra and P1 populations. Despite their close physical proximity, strong fluctuations in population size and severe annual bottlenecks, these populations are clearly differentiated from each other in terms of genotypic composition (i.e clusters) and diversity. Similarly, Avr haplotypes and pathotype composition were strongly differentiated among populations but showed only small temporal changes. These results are consistent with data on host genetic structure, whereby populations of genetically discrete ecotypes (hill and bog) are maintained over time .Both SNP and infectivity gene diversity were higher in the Kiandra and P1 populations than in P2. This may reflect in part the higher resistance and infectivity diversity observed previously in Kiandra . GeneticL. marginale differential set showed that L. marginale populations hosted a diversity of pathotypes in each year, the majority of which went extinct during the following overwintering period. Only a minority of pathotypes persisted over the entire study period and were present in all three populations. This result is consistent with the high MLG diversity revealed by SNP genotyping. While the among-year turnover of pathotypes was high, population pathotype structure was strongly influenced by ecotype (bog vs. hill). Pathotype A generally dominated in Kiandra, while four different pathotypes (including A) formed a major component of the other populations. This is consistent with previous studies on this system [Pathotyping using the s system , 65. HowM. lini populations [L. marginale plants die back to rootstocks, meaning that the foliar tissue required to sustain this biotrophic pathogen is largely absent. The infection cycle begins in spring from infections thought to have survived on scarce foliar tissue or from spores migrating from other populations. Our results indicate that both life history and environmental conditions strongly influence pathogen genetic dynamics and population structure. For example, in the Kiandra population in 1994 and 1995, this bottleneck was followed by a poor epidemic, which resulted in only a small number of isolates from a single cluster being sampled. Thus, genetic diversity can be greatly limited by demographic processes in some years. Conversely, despite the sample being dominated by strains resulting from asexual production in most years, assigned genotypes (MLGs) were largely ephemeral and unlikely to persist between growing seasons, with over 80% of genotypes sampled only once. Overall, while we cannot rule out cryptic local infections as a source of epidemics, our results suggest that the broader metapopulation is a crucial genetic reservoir, in that high levels of genotypic diversity are maintained at larger spatial scales, and migration from other populations appears to be important in re-establishing local pathogen populations at the start of epidemics.As for many plant pathogens, annual variation in the intensity of epidemics and seasonal bottlenecks are expected to strongly shape the genetic dynamics and evolutionary potential of ulations . At the Our findings also support a role for selection in driving temporal change in population genetic and phenotypic structure. For example, a clear genetic turnover event was observed in Kiandra in 2004, when the composition of the entire sampled population shifted to be dominated by a single genetic cluster . Indeeds region , 66, ands region , our obss region , 69, andMelampsora larici-populina showed clonal spread only at very fine spatial scales within leaf and twig [Sclerotinia sclerotinium [Plasmopora viticola [Several recent studies of clonal or partially clonal pathogens have reported similarly low levels of clonality. A recent study of the closely related pathogen and twig . Similarrotinium , while hviticola . These sPhakopsora pachyrhizi)[Puccinia striiformis [P. graminis f. sp. tritici [Cloncase analysis does not account for migration from other populations. Migration may also bias the Cloncase estimated Ne.Somatic hybridization is one mechanism by which genetic exchange among asexual fungi can be facilitated , 73, andchyrhizi), Pucciniiiformis  and P. g tritici , or alteOne caveat is that although local population structure appeared to be highly dynamic in space and time, we assigned MLGs by estimating the error rate based on a set of duplicate samples and cannot exclude the possibility that some of the MLG dynamics observed are due to the method of assigning MLGs. The same MLGs were recaptured over successive years which is a pattern that could arise either because a given MLG is persisting stably in a population  or becauS. sclerotinium and found no correlation between mycelial compatibility groups and genotypes even though in agricultural populations such associations occurred [Verticillium dahlia [M. lini we observed genetic diversity within pathotypes similar to that found in M. larici-populina, where the same pathotype could be represented by several genotypes [P. graminis, high within population diversity has been reported on both wheat landraces [We found no direct correlation between pathotypes and genotypes although pathotypes explained 25% of the variation in the AMOVA. Varying correlations between different marker types have been reported in other studies. Kohn  investigoccurred . Similaroccurred , 80, othoccurred  and nemaoccurred . In contm dahlia ). Other m dahlia , 85 suggenotypes . In otheandraces  and suscL. marginale population in which high levels of resistance occur [In modern agricultural systems, selection often leads to the dominance of infectious pathotypes that are genetically uniform , 87. Ource occur , one locce occur  prevaileThis is one of the first studies to explicitly link long-term epidemiological, genetic and pathotypic dynamics in the wild. Analyses of pathotypes clearly showed that selection at the level of host ecotype (bog vs. hill) leads to the maintenance of specialist pathogens within populations over time. Interestingly, the diversity obtained by SNP genotyping and the lack of direct links between MLGs, pathotypes and infectivity gene variation suggests that polymorphism arises from multiple origins rather than diversification of one or few clonal lineages. High levels of among-population migration are also supported by evidence for multiple colonisations by AA lineage isolates. Together with varying frequency of clonal patterns, these findings give new insights into the evolutionary potential of pathogen populations in the wild and emphasize the need for long-term studies as well as directions for future avenues of research. Thus, to fully understand the contribution of clonal dynamics to natural host-pathogen interactions, studies spanning multiple hierarchical levels are needed. We propose that characterising the sources of such hidden genetic diversity and its impact on disease evolution and spread is of crucial importance to engineering strategies to deploy durable resistance and mitigate the harmful effects of epidemics in managed systems.S1 FigThe colours indicate genetic clusters purple = 1, blue = 2, orange = 3, brown = 4, red = 5, and green = 6.(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 TableMelampsora lini isolates that were pathotyped (N = 451), and infectivity gene genotyped (N = 539).Results are presented from the whole dataset (N = 629) and the dataset on the (DOCX)Click here for additional data file.S2 TableIa) and rD. Significant Ia and rD values indicate linkage among loci. Phi test results as mean and observed linkage. Significant P values in Phi test indicate recombination.Linkage disequilibrium as index of association ((DOCX)Click here for additional data file.S3 TablePhi test results on mean and observed linkage, and P value for probability of recombination.(DOCX)Click here for additional data file.S4 TableIa), rD, and P values indicating probability of significant levels of LD.Linkage disequilibrium as index of association ((DOCX)Click here for additional data file.