Traits recorded on animals that are raised in groups can be analysed with the social effects animal model (SAM). For multiple traits, this model specifies the genetic correlation structure more completely than the animal model (AM). Our hypothesis was that by using the SAM for genetic evaluation of average daily gain (ADG) and backfat thickness (BF), a high rate of improvement in feed conversion ratio (FCR) might be achieved, since unfavourable genetic correlations between ADG and BF reported in a Duroc pig line could be partially avoided. We estimated genetic and non-genetic correlations between BF, ADG and FCR on 1144 pigs using Bayesian methods considering the SAM; and responses to selection indexes that combine estimates of indirect (IGE) and direct (DGE) genetic effects for ADG and BF by stochastic simulation.Estimates of the ratio of the variance of DGE to the phenotypic variance were 0.31, 0.39 and 0.25 and those of the total genetic variance to the phenotypic variance were 0.63, 0.74 and 0.93 for ADG, BF and FCR, respectively. In spite of this, when the SAM was used to generate data and for the genetic evaluations, the average economic response was worse than that obtained when BV predictions from the AM were considered. The achieved economic response was due to a direct reduction in BF and not to an improvement in FCR.Our results show that although social genetic effects play an important role in the traits studied, their proper consideration in pig breeding programs to improve FCR indirectly is still difficult. The correlations between IGE and DGE that could help to overcome the unfavourable genetic correlations between DGE did not reach sufficiently high magnitudes; also, the genetic parameters estimates from the SAM have large errors. These two factors penalize the average response under the SAM compared to the AM. Since feed represents the largest cost of pig production, improving feed efficiency would result in a high economic benefit . For thiIn this study, our aim was to explore whether the expected response in feed conversion ratio (FCR), as a result of indirect selection for average daily gain (ADG) and BF when using the SAM for genetic evaluation, is improved compared to that achieved when using the traditional animal model (AM). In this analysis, we accounted for the uncertainty regarding the genetic and non-genetic parameters that are required to conduct the evaluation of the selection candidates. For the Duroc population considered in this study, we have already reported genetic correlation estimates between performances (ADG and BF) and feed efficiency traits (FCR) using the AM and SAM . Based oPerformance during the growing period was recorded on 1144 Duroc pigs in 10 batches from 2007 to 2017 at the Center of Porcine Evaluation  using IVOG feeding stations  with one single-space electronic feeder per pen. Herrera-Cáceres et al.  describeFor this study, average daily gain , feed conversion ratio , and backfat thickness  were computed at 180 days of age, as described by Herrera-Cáceres et al. . Basic sA multi-trait linear model was fitted to ADG, BF, and FCR. The same systematic effects were considered for the three traits: batch, age at the end of the fattening period (covariate), and number of pigs per pen (covariate). In addition, pen, litter, and genetic effects were included. The genetic component of the model was accounted for by fitting direct and indirect genetic effects separately , as in tpThe prior distributions of pen, litter and residual effects were: Variance components were estimated by using Bayesian procedures. Flat priors were used for the systematic effects  stochastic simulation was used. Each replicate of the stochastic simulation was defined by three arguments: (a) the variance components used for generating the data, which was based on a saved sample from the posterior distibutions, (b) the genetic evaluation procedure, i.e. SAM or AM, and (c) the selection index used to rank candidates. The last two arguments define the relevant scenarios in our study, with the uncertainty of RS due to the uncertainty about the variance components being evaluated based on changes in the first argument across the different replicates.Due to computational time constraints, we did not use all saved samples from the joint posterior distribution of the variance components. Instead, we used a random subset of 1000 vectors from the joint distribution to characterize the marginal posterior distributions of the expected genetic responses under different scenarios. For a particular replicate within a given scenario, the simulation to evaluate RS started by taking one sample from the posterior distribution of the variance components. Based on these variance components, new ADG, BF and FCR records were generated using a SAM that was similar to the model used for the analysis of the Duroc data, i.e. with random litter, pen, DGE and IGE effects, along with litter size, parity order, and batch effects as systematic factors. The simulations were conducted assuming an infinitesimal model , i.e., bFor the genetic evaluation, only BF and ADG records were considered, using either the SAM or the AM. For the SAM, predictions of breeding values were obtained by solving the mixed model equations with variance components equal to those used in the simulation (best linear unbiased prediction (BLUP)). For the AM, predictions of breeding values were obtained by solving the mixed model equations associated with the AM, with variance components equal to those obtained in a previous residual maximum likelihood (REML) step (REML-BLUP) on the data generated in that replicate. The programs used to obtain the predictions of breeding values were blupf90 and remlf90 .Within each scenario, the complete procedure was repeated for each one of the 1000 samples of the posterior variance components retained. The simulated datasets mimicked the management of a pig selection nucleus of 400 sows and 20 boars, where mating between close relatives, i.e. with common grand-parents, was avoided. Reproduction was organized in four batches per generation, but the genetic evaluations and selection process were performed using the first two batches of each generation. The other two batches generated information for genetic evaluation in the next generation. Each batch comprised approximately 2500 selection candidates  in pens of eight animals, litters were processed by order, thus, in general, each pen included animals from more than one litter; in general, less than 25% of the pens were formed by animals from a single litter. The best 200 females from the batch were selected and the males were selected within sire families, thus the best offspring from each sire family was selected. Given that candidates were generated in two batches the average ratio offspring/sow was 12.5The alternative scenarios to evaluate RS were defined by the different indexes used to rank the animals. The objective was to reduce BF while increasing ADG, but with different assigned weights. When the genetic evaluation relied on REML-BLUP predictions from the AM, selection indexes were defined by assigning alternative weights to predictions of breeding values for ADG and BF Table . When thAs stated above, during data generation, ADG, BF and FCR records were obtained in the first two batches in each generation; ADG and BF information was used to take selection decisions while FCR was used just to assess the correlated response after selection for the proposed indexes. In addition, three profit traits (€), B1, B2 and B3, were derived based on three profit functions that represent three Spanish or European markets: B1 was based on a market without any constraint on BF; B2 represents a lean meat production market in which a penalty is applied if BF is outside the 6 to 10 mm range; and B3 represents a fat meat production market, in which a penalty is applied if BF is outside the 10 to 20 mm range. The general equation used to compute these profit traits was:mentclasspt{minimaThe simulation was run for five generations and responses for the three biological traits and the three profit traits were defined as linear regression coefficients of the average phenotype of traits across selection candidates in a given generation on generation number. One thousand regression coefficients were obtained for each trait and scenario, one for each sample from the joint posterior distribution of the variance components in the SAM. Thus, each regression coefficient can be interpreted as a sample from the marginal posterior distribution of the RS for that trait for a given scenario. Based on this procedure, we can characterize the expected RS as well as the uncertainty of this expectation, the latter being a consequence of errors in the estimates of variance components.The simulation process was implemented in a software pipeline that combined own Fortran90 code for data generation and R code to edit the data and to compose parameter files needed to run the genetic evaluation programs (remlf90 or blupf90) and to create the list of the selected males and females, which was then read by the Fortran90 program for us as parents of the next generation. This complete pipeline package is available upon request.The complete procedure, i.e. based on simulations using samples of the posterior distribution of the variance components from the SAM, was repeated for samples from the posterior distribution of variance components obtained from the AM. The AM had the same factors and prior structure as the SAM, except that a single additive genetic effect was fitted for each trait instead of both DGE and IGE. Thus, the genetic covariance matrix under this model was a 3 × 3 matrix. The simulated data for assessment of RS was based on an AM that fitted litter, pen, and additive genetic effects as random factors, and litter size, parity order, and batch as systematic effects. The genetic evaluation conducted for each generation was based on the same AM that generated the data by setting the variance components to those used for generating the data (BLUP). In this assessment, which did not fit social genetic effects, the same scenarios as those considered when data were generated with the SAM but the evaluation was conducted with the AM were assessed Table .For the three biological traits, we estimated genetic parameters using the SAM (see Table C) value  associatentclass1pt{minimaTable When the data were generated using the SAM and genetic evaluations were based on the AM, the overall pattern of correlated responses in FCR was similar to that described for genetic evaluations based on the SAM Table . EstimatAM Table  was usedAM Table . HoweverAs a baseline situation, we explored the genetic responses when IGE were ignored during the process of data generation (i.e. AM) and also during the genetic evaluation of the candidates, which was conducted by applying an AM with the variance components set to values that were used for the simulation in each replicate Table . The mosIn general, genetic parameters estimated with the SAM are subject to large estimation errors. In our case, these errors were particularly large because of the small size of our dataset, but other studies that used much larger datasets reported a similar magnitude of errors , 14–17. As expected, the uncertainty of the expected responses was much larger when the SAM was used for generating the data and for evaluation than when the AM was used. Direct responses in ADG and BF were only slightly smaller when the AM was used to rank the candidates Table  than wheOur initial hypothesis was that using the SAM for the genetic evaluation would allow us to take advantage of genetic correlations among DGE and IGE of traits, which might help to alleviate the unfavorable genetic correlations that exist between BF and ADG, or between ADG and FCR: for example, a negative genetic correlation between IGE of FCR and DGE of ADG. Based on our results, this hypothesis does not hold for the population under study because the highest economic responses were obtained when the AM was used for genetic evaluation Table . As mentIn spite of the overall rejection of our hypothesis, it should be noted that when the genetic evaluation was conducted using the SAM Table , the besAnother point that could explain the failure of our initial hypothesis is that the correlations that could eliminate the antagonistic relationship between traits did not reach a relevant magnitude in the population under study. This could happen, for example, if the correlation of IGE for ADG with DGE for BF was low. Thus, one trait could be modified by selecting on the IGE and the other by selecting on DGE. Then, a correlated response in FCR could be expected if the correlation between IGE for ADG and IGE for FCR was negative and that between DGE of BF and DGE of FCR was positive. Some of these requirements necessary for our hypothesis to hold were fulfilled in our case, i.e., the posterior mean of the correlation between IGE for ADG and DGE for BF was lower than that of the correlation between DGE for the two traits. However, other requirements were not satisfied. The correlations mentioned just above are only a part of the whole correlation structure, for example those between IGE and DGE across traits should be also considered. Accounting for the whole structure is the reason why we conducted an assessment of responses based on simulation to explore the consequences of different selection indexes combining IGE and DGE.Regarding the applied problem addressed in our study, i.e. indirect improvement of FCR by joint selection on ADG and BF, our results indicate that, in this Duroc pig population, response in FCR is highly driven by a reduction in BF, and selecting only on ADG by ignoring BF, would lead to economic losses. When the data were generated with the AM, the economic responses resulted from both a direct reduction in BF and a correlated response in FCR. However, when the SAM was used to generate the data, the achieved economic benefit did not result from improved FCR but only from a reduction in BF, which was of value in some of the markets assessed.The magnitude of genetic parameters is known to depend on the population under study, and for the traits considered here, a wide range of estimates have been reported in the literature for different lines and breeds. Although the genetic correlation between ADG and BF tends to be positive , 20, somUsing the SAM in genetic evaluation to indirectly improve FCR by selection on ADG and BF does not overcome the unfavourable genetic correlations that exist between the traits when they are evaluated with the traditional AM. On the one hand, this is due to the large magnitude of the estimation errors of the genetic parameters estimated in more complex models such as the SAM. On the other hand, the correlations between IGE and DGE that could help overcome the unfavourable genetic correlations between DGE did not reach sufficiently high magnitudes.Additional file 1: Figure S1. Trace plots of Markov chains of the genetic parameters for the social animal model. This file contains the trace plots of the Markov chains for the genetic correlations and heritabilities of ADG, BF and FCR. This can be used to assess that our Markov chains have an acceptable rate of mixing.Additional file 2: Table S1. Posterior mean (posterior SD) of genetic  and phenotypic  correlations, and heritabilities . This table contains the estimated genetic parameters for ADG, BF and FCR using the AM. These estimates can be compared to those obtained when the SAM  of the correlation between true and predicted breeding values using the SAM. This table contains the correlations between true and predicted values of DGE and IGE of traits included in the selection index (ADG and BF) for the 25 studied scenarios using the SAM. This table also contains the correlations between true and predicted values of an index calculated based on the total breeding value definition of the involved traits. Table S3. Posterior mean (posterior SD) of the correlations between true figures for total breeding value, direct and indirect genetic effects, and breeding values predictions obtained with the AM. This table contains the correlations between predicted values of EBV using the AM and the true values of DGE, IGE, and TBV used in the simulation, using the SAM, for the traits included in the selection index (ADG and BF). Table S4. Posterior mean (posterior SD) of the correlations between true and predicted breeding values using the AM for data generation and evaluation. This table contains the correlations between true and predicted values of EBV for traits included in the selection index (ADG and BF) when both simulation and genetic evaluations are conducted using the AM.


Plasmodium falciparum transmission dynamics in the area, this study characterized the genetic diversity of parasites circulating within the Vhembe District.South Africa aims to eliminate malaria transmission by 2023. However, despite sustained vector control efforts and case management interventions, the Vhembe District remains a malaria transmission hotspot. To better understand He), multilocus linkage disequilibrium (LD) and genetic differentiation, and were associated with temporal and geospatial variances.A total of 1153 falciparum-positive rapid diagnostic tests (RDTs) were randomly collected from seven clinics within the district, over three consecutive years  during the wet and dry malaria transmission seasons. Using 26 neutral microsatellite markers, differences in genetic diversity were described using a multiparameter scale of multiplicity of infection (MOI), inbreeding metric (Fws), number of unique alleles (A), expected heterozygosity (He = 0.74 ± 0.03) was observed in the parasite population. This was ascribed to high allelic richness (mean A = 12.2 ± 1.2). The majority of samples (99%) had unique multi-locus genotypes, indicating high genetic diversity in the sample set. Complex infections were observed in 66% of samples (mean MOI = 2.13 ± 0.04), with 33% of infections showing high within-host diversity as described by the Fws metric. Low, but significant LD  was observed that indicates recombination of distinct clones. Limited impact of temporal (FST range − 0.00005 to 0.0003) and spatial (FST = − 0.028 to 0.023) variation on genetic diversity existed during the sampling timeframe and study sites respectively.A total of 747 (65%) samples were successfully genotyped. Moderate to high genetic diversity (mean P. falciparum diversity in the area was moderate to high and complex. This study showed that genetic diversity within the parasite population reflects the continued residual transmission observed in the Vhembe District. This data can be used as a reference point for the assessment of the effectiveness of on-going interventions over time, the identification of imported cases and/or outbreaks, as well as monitoring for the potential spread of anti-malarial drug resistance.Consistent with the Vhembe District’s classification as a ‘high’ transmission setting within South Africa,  Malaria remains a global health problem, with about 228 million cases reported worldwide in 2018, 93% of which occurred in the World Health Organization (WHO) African Region. As the southern Africa region, excluding high-transmission countries like Mozambique, accounted for < 10% of the 213 million cases reported in the WHO African region, several southern African countries have been earmarked for malaria elimination by 2023 guided by the WHO Global Technical Strategy for Malaria .Plasmodium falciparum is the predominant species which accounts for the majority of cases and fatalities in the South Africa [Anopheles funestus and the Anopheles gambiae complex the main vector species associated with transmission, which mainly occurs in the hot and rainy season between September and May [An. gambiae and An. funestus [h Africa , with An and May . While S and May –9. The c and May , 5. The  and May . While i and May , 10. Othfunestus , 11–15.The addition of malaria parasite population genetic data to the standard surveillance data collected by malaria control programmes has assisted in understanding malaria transmission dynamics and also allowed for spatio-temporal inferences to be made from e.g. local vs. imported malaria cases –24. ThesP. falciparum parasites in South Africa at different spatial and temporal scales. This work identifies the contributing factors associated with sustained residual transmission in the Vhembe District in South Africa as the country works towards malaria elimination.To better understand the malaria parasite-associated factors that contribute to residual malaria transmission in the Vhembe District, microsatellite genotyping was applied to describe the population of P. falciparum-specific RDTs  were randomly collected from the seven different clinics in the wet and dry seasons from January to December of the years 2016, 2017 and 2018. Samples were transported and stored at room temperature in sealed bags with desiccant. Demographic and travel history information on each of the individual patients was obtained from the LMC database. After linking genotyped data with patient information from the LCM database, samples were stratified based on the source of infection health district  within the Vhembe District, Limpopo Province, South Africa Fig. . Out of P. falciparum using ultra-sensitive varATS [DNA was extracted from the nitrocellulose strip of First Response™ RDTs in accordance with the Worldwide Antimalarial Resistance Network Guidelines . The Sape varATS  and TAREe varATS , 30 quanhttps://github.com/Greenhouse-Lab/MicroSPAT/releases/tag/v2.0.3) was then used to automate identification of true alleles and differentiate real peaks from artefacts of the resulting electropherograms using a classifier algorithm based on the location and size of locus-specific patterns relative to a primary peak as done in studies conducted in Eswatini [A total of 1153 samples were genotyped using a previously described 26 microsatellite marker panel protocol , 26, 31.Eswatini , NamibiaEswatini  and ChinEswatini  to avoidP. falciparum clones) and the within-host fixation index (FWS). To reduce the probability false positive alleles influencing MOI, the MOI in an individual sample was defined as the second highest number of alleles detected at any of the 26 microsatellite loci genotyped. Since the P. falciparum parasite is haploid in the human host, multiple peaks or alleles correspond to an infection with multiple genotypes or strains .The within-host diversity of infections was determined using multiplicity of infection  were used to identify samples containing a single genotype  in spite of additional genotypes that may be present at relatively low proportions; and Fws ≤ 0.70 (1-Fws ≥ 0.30) to describe samples with highly diverse infections respectively [The other measure of the within-host diversity, the Fescribed , 26, 32.ectively , 32–34.He) which is defined as the probability of randomly drawing a pair of different alleles from the allele pool. Heterozygosity was, therefore, calculated on each locus using the equation: i is the frequency of the ith allele in the population [He range from 0 indicating no diversity, to 1 indicating that all alleles are different and therefore there is maximum diversity. The mean He was calculated by taking the average He across all loci. The number of haplotypes (unique multilocus genotypes) as well as the unique alleles detected per locus , were also determined.Population level genetic diversity was estimated using expected heterozygosity  was determined as previously described  using thsoftware . LD was software . The MonWS and He between the parasite populations stratified by source health district. Population substructure between the geographic areas was investigated by measuring Wright’s F-statistics (FST), using the adegenet package [ST and Jost’s D, were calculated using the mmod package [ST between source health districts. In this study, 999 permutations were completed. Additionally, discriminant analysis of principal components (DAPC) using the adegenet package in R software was used to confirm signatures of population structure [To determine the influence of geographic origin of infections on genetic diversity, the ANOVA pairwise t-test was used to compare MOI, 1-F package  in R. He package  in R. Getructure , 39. DAPtructure . K-meansn is the number of genotyped loci; Si is the total number of shared alleles at locus i between samples X; and YiXi is the number of alleles in sample X at locus I; and Yi is the number of alleles in sample Y at locus i. A total of 85,078 pairs of infection within the Vhembe District dataset were analysed and highly related infection pairs above the cut-off of IBS ≥ 0.5 [To determine the genetic connectivity/relatedness of pairs of infections including all alleles detected in both monoclonal and polyclonal infections of successfully genotyped samples, a modified identity by state (IBS) metric was used . OverallBS ≥ 0.5  identifiWS and He between the parasite populations stratified by year of transmission. Finer scale stratification by month of infection was also assessed to determine how MOI changes in the dry and wet transmission seasons and pairwise t-tests were used to compare MOI between the different seasons.To determine the influence of temporal changes in transmission on genetic diversity, the ANOVA pairwise t-test was used to compare MOI, 1-FP. falciparum was also assessed based on the number of unique genotypes (MOI) identified in sprayed vs. unsprayed households. IRS in the Limpopo Province typically takes place at the beginning of each malaria season (wet season between September and May), with the number of households to be sprayed determined by the provincial malaria control programme based on factors such as the number of structures within the malaria endemic area, insecticide availability and resistance data [The impact of the main strategy for control, indoor residual spraying (IRS) on the within-host diversity of nce data .P. falciparum RDT positive samples were collected from the Vhembe district, the highest proportion (56%) of which were collected in 2017 and, for those with known source health district data, the majority came from within the Mutale district  between male (mean MOI = 2.11) and female participants (mean MOI = 2.16). The different age groups also exhibited similar mean MOIs (from 2 to 2.17) which were not significantly different .Of the 1153 genotyped samples, 65% of the samples (747/1153) had sufficient coverage at a minimum of 15 of the 26 microsatellite loci evaluated and were included in the final sample set for population genetics analysis. Overall, a high proportion (66%) of polyclonal infections was observed with a mean MOI = 2.13 in the genotyped samples  was seen between outbreeding (1-FWS) and MOI, which suggests that both metrics support the presence of within-host diversity. Similarly to the one third of samples appearing monoclonal observed, only 40% of samples had 1-FWS < 0.05  was low at 0.22, with only 33% of samples with a stringent 1-Fws value of ≥ 0.30 (Fws ≤ 0.70), suggesting that these were the most highly diverse infections  and contributed to the high genotypic diversity whereas locus Ara2 had the most evenly distributed alleles (0.86) and, therefore, contributed to the most genotypic evenness. Overall, the sample set had a moderate to high genotypic richness, evenness and diversity.On a population level, the majority of the samples 99%, 742/747) had unique haplotypes thus indicating high levels of outcrossing and, therefore, high genotypic diversity in the parasite population. The high number of unique haplotypes implies underlying allelic richness which is indeed reflected in the high mean number of unique alleles (mean A of 12.2) with anywhere from 3 to 26 unique alleles detected across all 26 loci . This was emphasized by a small proportion  of pairwise infections in the sample set being highly related (IBS ≥ 0.5). The significantly low LD therefore indicates high recombination of distinct parasite clones which supports the moderate to high within-host diversity observed in Vhembe, and is consistent with the presence of some degree of local transmission.Additionally, low LD  was observed between alleles of the es Table  and thisTo further evaluate the genetic relatedness between the parasite genotypes, k-means clustering was employed based on individual multi-locus genotype discrimination. Eight genetic clusters were identified in the parasite population with the parasite populations in genetic clusters 1, 2, 4, 7 and 8 observed using DAPC Fig. a separatP = 0.36, n = 747, Fig. P = 0.27, n = 747, Fig. P = 0.23, n = 747, Fig. P ≤ 0.01 and P ≤ 0.05, respectively), which may be due to small sample size from these sites. The different source districts also did not contribute to the genetic clusters observed from the DAPC analysis, as infections from the different areas were represented/distributed in the eight different inferred genetic clusters, implying free gene flow and possible parasite mixing between these sites. This was confirmed by the very low FST values  within source areas (mean 1-IBS = 0.76 ± 0.022) compared to between source areas (mean 1-IBS = 0.56 ± 0.007) which confirms some level of local transmission.Overall, the levels of within-host and population level diversity were not influenced by where infections were reported since the mean MOI  for MOI  between parasite populations from the different years was observed, which suggests that the malaria outbreak experienced in 2017 was not due to an introduction of a completely distinct/new parasite population to Vhembe.The level of genetic diversity did not differ between the years of transmission as demonstrated by global ANOVA MOI Fig. a, outbreMOI Fig. b and hetMOI Fig. c of 0.90P = 0.42, Bonferroni P adjustment method) between the two seasons . Infections were similarly complex  between infections from sprayed or unsprayed households suggesting the maintenance of complex infections in spite of vector control implementation during wet seasons, suggesting possible co-transmission of strains. These data may indicate that the mean MOI may be seasonally stable thus reflecting the impact of continued residual transmission on the complexity of infections in the Vhembe District. Out of the 220 patients who knew which insecticides were sprayed in their households, 53% (116/220), 44% (97/220) and 3% (7/220) reported use of DDT, Fendona® and K-Othrine®, respectively.Stratification of MOI distribution by transmission season (month of infection) Fig. d showed P. falciparum parasite genetic diversity is only now being clarified in South Africa. By using multilocus genotyping, this is the first study that shows that continued residual malaria transmission in a malaria hotspot within South Africa is associated with parasite diversity.The impact of malaria transmission intensity on P. falciparum genetic data in the southern African E8 region which will facilitate more detailed evaluations of cross-border parasite spread in future studies. The genetic data may also be useful to control programmes with decision making for malaria elimination as it can be used as a reference point for the assessment of the effectiveness of on-going interventions over time, selection and/or targeting of interventions. Furthermore, the genetic data may be used for the identification of imported cases and/or outbreaks, as well as monitoring for the potential spread of anti-malarial drug resistance and potentially revealing infection patterns.The results generated in this study are a useful addition to the growing resource of P. falciparum parasite population in the Vhembe District is complex and diverse. This is typically associated with high levels of gene flow between areas of different transmission intensities that serve to compound allelic richness by introducing new alleles into the population, thereby increasing the level of heterozygosity in the parasite population [Although South Africa is overall a low transmission setting, the Vhembe District is a transmission hotspot and here it is shown that the parasite population within this hotspot behaves genetically like those typically seen in high transmission settings (based on incidence data) such as Guinea, Mali and The Gambia , 40–42 -pulation , 25. Altpulation . GeospatA major limitation of this study was that a large number of samples were classified as ‘unknown’ and could not be linked to their district of origin, thereby leaving the geospatial analyses to a certain extent underpowered in most districts. Additionally, the majority of the samples coming from the Mutale district may have been influenced by a biased sampling plan from the onset based on the fact that samples were collected from a few selected clinics in a known hotspot area based on case incidence data. It was, therefore, not possible to get genetic representation of parasites from the other source districts and those seeded through importation from neighbouring high transmission settings reported in the province if any and what their contribution to the local transmission dynamics is.Hotspot areas in other low transmission settings, such as Malaysia have demonstrated opposite trends where parasite complexity and heterozygosity are low, and LD is high using both microsatellite markers and merozoite surface protein variants for genotyping , 44. In Plasmodium chabaudi, minor clones in mixed/multiple infections may produce as many, or more, oocysts than they would have as a single clone infection [Some clones in an infection may exist at lower proportions than others due to either competitive suppression by other genotypes or possibly host specific selection due to immunity or receptor polymorphisms. In nfection , highlignfection –50. WhilP. falciparum genetic diversity in the Vhembe District is demonstrated. The P. falciparum population is moderate to highly diverse and genetically complex, which is key and advantageous to the parasite’s evolution and survival. This data could be informative as a reference point in evaluating the efficacy of strategic control interventions over time, aimed at eliminating residual malaria transmission in malaria transmission ‘hotspots’ in South Africa. Furthermore, this data can be used to identify imported cases and/or outbreaks, as well as monitor for the potential spread of antimalarial drug resistance. Linking data on P. falciparum genetic diversity from Vhembe/South Africa to that from neighbouring sub-Saharan countries in the E8 regional initiative would need to be investigated as the transmission dynamics in the region is not fully understood.In this study, the impact of continued malaria transmission intensity on


Porites lobata species complex’ consists of branching and mounding corals that form reefs across the Pacific. We used reduced representation meta-genomic sequencing to examine genetic relationships within this species complex and to identify candidate loci associated with colony morphology, cryptic genetic structure, and apparent bleaching susceptibility. We compared existing Porites data with bleached and unbleached colonies of the branching coral P. compressa collected in Kāneʻohe Bay Hawaiʻi during the 2015 coral bleaching event. Loci that mapped to coral, symbiont, and microbial references revealed genetic structure consistent with recent host-symbiont co-evolution. Cryptic genetic clades were resolved that previous work has associated with distance from shore, but no genetic structure was associated with bleaching. We identified many candidate loci associated with morphospecies, including candidate host and symbiont loci with fixed differences between branching and mounding corals. We also found many loci associated with cryptic genetic structure, yet relatively few loci associated with bleaching. Recent host-symbiont co-evolution and rapid diversification suggests that variation and therefore the capacity of these corals to adapt may be underappreciated.The ‘species’ is a key concept for conservation and evolutionary biology, yet the lines between population and species-level variation are often blurred, especially for corals. The ‘ In the absence of geographic boundaries to dispersal, marine species often diverge across ecological boundaries3, and the examination of genetic structure across ecological gradients often provides clues to how organisms adapt, e.g.8. Coral reefs are hotspots of biodiversity, yet diversification across these heterogeneous environments is poorly understood. In the case of reef building coral, most species are members of a species complex-closely related lineages that could be the result of phenotypic plasticity, phenotypic polymorphism, or hybridization between species15. Speciation and hybridization are both processes on an evolutionary continuum17 where populations drift apart or merge together driven by factors such as isolation19, fertility barriers19, adaptive genetic polymorphisms20, and relationships between hosts and their obligate symbionts or parasites23. Examination of genomic variation across environmental gradients for corals and their symbionts may provide key insights into the underlying mechanisms and pace of adaptation and speciation.The majority of closely related species may be associated with adaptation and divergence across heterogeneous environments, and a considerable fraction of biodiversity is of recent origin25, and correlation with fine scale environmental differences such as temperature variation associated with proximity to shore27. In the genus Porites, which is one of the most abundant corals in the tropics, there are several examples of morphologically distinct ‘species’ that genetic studies have been unable to resolve, consistent with phenotypic polymorphism, incipient speciation, hybridization between species28, or epigenetic differences29. The ‘P. lobata species complex’ contains as many as seven nominal species ranging from massive to branching morphology in a variety of colors, with a geographic range spanning the entire tropical Pacific Ocean and the Red Sea28. Several studies have found unexpected genetic structure within this species complex. For example strong genetic structure was found between P. lobata populations from lagoons and forereef locations in Samoa30. In Hawaiʻi, similar strong genetic structure was found between different sampling locations on the island of Oʻahu10, and between nearshore and offshore populations of P. lobata exposed to contrasting anthropogenic stress across the main Hawaiian Islands32. In addition, recent work found cryptic genetic structure within P. compressa in Kāneʻohe Bay that was correlated to annual average sea surface height25. The branching (P. compressa) and mounding (P. lobata) corals in this species complex, have a fairly continuous range of intermediate morphologies that can occur in the same habitat, ruling out phenotypic plasticity , subclade C15. During the 2014–2015 coral bleaching event in Hawai‘i, we observed that the bleaching response was highly variable in P. compressa, with stark white coral colonies often occurring side by side with colonies that appeared unaffected  and mounding (P. lobata) morphospeices. We collected paired bleached (n = 19) and apparently unaffected (n = 18) P. compressa colonies that were located side by side on the same reefs in Kāne‘ohe Bay O‘ahu during the 2015 thermal stress event. We used a reduced representation genomic approach36 to extract metagenomic libraries from these samples to compare to additional P. lobata and outgroup (P. evermanni) libraries from a previous study (n = 17) using the same method (ezRAD) developed in our laboratory10. In addition, we included a single sample from an unidentified and unusual Porites coral (P. sp1) recently discovered to be rapidly proliferating in Honolulu Harbor, seemingly unaffected by multiple stressors and bleaching events37. This coral was placed well within the gonochoric broadcast spawning P. lobata species complex, by mitochondrial and nuclear markers, despite observations of unusual color, colony morphology, and asexual brooding mode of reproduction37.The dinoflagellate endosymbionts of P. lobata mitochondrial genome38, as well as a newly available genomic reference data set consisting of annotated Porites coral-host, symbiont, and bacterial subsets39. We also mapped all libraries to a P. lobata annotated transcriptome40 in order to conduct Gene Ontology (GO) enrichment analysis of outlier loci to infer potential functional differences between groups. We sought to determine if GO terms of genes most often associated with bleaching, cryptic genetic groups, or morphological variation significantly deviated from a random distribution to gain insights into the possible mechanisms driving variation within this species complex.We mapped all of these metagenomic libraries to a previously published 38, the mitochondrial genome had very low levels of polymorphism; only 130 bp were variable with 89 parsimony informative characters across all species. There were no fixed mitochondrial differences between the P. lobata and P. compressa morphotypes, which shared 100% identical whole mitochondrial genome haplotypes for the majority of individuals , with the exception of a single P. lobata colony . The proportion of bleached P. compressa colonies was similar in both clade B and C, indicating a lack of correspondence between bleaching susceptibility and the genetic structure within the species complex  coverage per library Table , resultials Fig. A,B. The P. lutea, and the annotated transcriptome of P. lobata. The P. lutea reference metagenome consists of three components; (1) the host genome; P. lutea genome v.1.1, (2) the symbiont genome; Cladocopium C15, version 2.1, and (3) the microbiome consisting of 52 bacterial and archaeal assemblies that we concatenated as a reference for this analysis. Mapping reads to the coral host genome with ‘relaxed’ filtering settings resulted in 3,091,663 SNPs. Filtering by missing data, depth of coverage, or allele frequency did not affect the patterns of genetic structure according to PCA plots  was consistently distinct. Several unidentified corals with atypical colony morphology  also resulted in similar patterns that closely resembled the results from the metagenomic data, regardless of the filtering settings, such as by missing data, depth of coverage, or allele frequency. Minimal filtering of the transcriptomic data yielded 2,578,830 SNPs, and the strictest filtering (loci shared by at least 90% of the samples) resulted in 152,690 SNPs. Mapping reads to the Cladocopium C15 reference resulted in 174,356 SNPs, and mapping to the concatenated metagenome-assembled microbial genomes resulted in 26,721 SNPs. PCA plots of each of these datasets revealed highly similar patterns regardless of reference dataset  yielded 167 ‘highly significant’ candidate loci, whereas comparisons between the cryptic clades within P. compressa (Clade B and C) yielded 74 ‘highly significant’ candidate loci, while there were only ten ‘significant’ candidate loci associated with coral bleaching status :  cutoff of 0.01. An FDR threshold of 0.05 revealed additional significant enrichment of ‘single-organism processes’ and ‘metabolic pathways’  as might be expected, but rather these loci were involved in dephosphorylation, phosphorus metabolic processes, or phosphate containing compound metabolic processes. GO enrichment analysis of these nearly alternatively fixed loci also indicated significant enrichment for processes involving phosphorus. GO analysis of candidate loci at a P-value FDR cutoff of 0.01 revealed significant enrichment for (1) oxidative phosphorylation, (2) mitochondrial electron transport, and (3) chemosensory and olfactory behavior. A threshold value of 0.05 also resulted in significant enrichment for the three similar categories: (1) mitochondrial processes; (2) phosphorylation, catalytic activity, and binding; (3) neural, olfactory, or chemosensory processes  and mounding (P. lobata) morphospeices; there are at least two genetic groups within each morphospecies, and this genetic structure has no association with susceptibility to coral bleaching. From an evolutionary perspective, the cryptic genetic structure in this complex is very recent, if not contemporary. With only 0.04 ± 0.02% (mean ± S.D.) divergence between mitochondrial clades B and C if we assume a synonymous substitution rate of 0.05%/site/106 years for mitochondrial genes41, then these clades are at most 400 k years old. Since both morphospecies share identical mitochondrial genomes, they have likely interbred over very recent evolutionary or even ecological time scales. The results of PCA analysis of SNPs that map to the coral host genome, the transcriptome, the algal symbiont, or microbial community were surprisingly consistent, indicating some correspondence with morphologically defined species, likely indicating co-evolution between host, symbiont and microbial community. For each method of analysis, both branching and mounding morphospecies overlapped to some degree and there was no correspondence with bleaching susceptibility.Adaptation depends on population-level variation, yet for corals the lines between population and species-level variation may be obscured by plasticity, polymorphism, recent speciation and hybridization between species. To place bleaching susceptibility and cryptic genetic structure within this broader context, we examined genetic structure from the whole mitochondrial genome, as well as loci that map to the coral host, the transcriptome, the algal symbiont, and microbial community. The coral mitochondrial genome tree was generally consistent with the coral host genomic tree, revealing that there is cryptic genetic structure within this complex nested within the branching , and host (Ras-related protein RABD2a)  and mounding (P. lobata) phenotypes are consistent with very recent or ongoing differentiation, or strong disruptive selection operating on both the coral host and algal symbiont.Examination of candidate outlier loci associated with branching or mounding phenotypes provided additional evidence of co-evolution between host and symbiont. Many coral host genomic and symbiont outlier loci were highly significant Figs.  and S5. 2a) Fig. . Fixed d30, and in Hawaiʻi32. Gene ontology enrichment analysis of the most highly differentiated  loci between the mitochondrial clades (B and C) were significantly enriched for processes involving phosphorus as well as mitochondrial electron transport, and chemosensory and olfactory behavior. Phosphorus is an essential element to life, integral to processes such as skeletal deposition, metabolism, growth, and reproduction. Phosphorus availability limits primary productivity in the ocean and this limiting reagent is known to have a strong effect on species distributions and ecosystem structure along a gradient away from the shoreline43. This observation could be a coincidence, however; the hypothesis that phosphorus availability is driving selection across a nearshore-offshore environmental gradient that coincides with this cryptic genetic structure could be readily tested by future studies.Mitochondrial and host genomic SNP trees revealed cryptic genetic structure within this species complex that had no relationship to morphology or bleaching susceptibility, which begs the question; what might be responsible for multiple clades within each morphospecies? Hybridization and introgression between species may be a tempting explanation for three clades; two parental species clades (A and B) and a clade of mixed individuals with minimal backcrossing  are significantly enriched for processes involving phosphorus. Previous work has shown that these cryptic genetic groups are likely related to a nearshore/ offshore gradientPorites’ (P.sp1) that was described previously37. These metagenomic libraries were compared with other Porites collected on O‘ahu from a previous study in our laboratory generated using the same method10. All colonies identified as ‘P. lobata’ or ‘P. compressa’ represented the most typical colonies and intermediate morphologies were avoided, although two unidentified samples (P.sp1 and P.sp2) were included as well as the nearest known congeneric species (P. evermanni) to provide outgroups and additional context on variability within the species complex.Coral samples were collected under the State of Hawaiʻi Special Activity Permit (SAP2015). We collected paired bleached (n = 19) and apparently unaffected colonies (n = 18) during the 2015 that were located side by side on the same reefs in Kāne‘ohe Bay O‘ahu during the 2015 thermal stress event (see Table 2) were removed from colonies of Porites compressa from two patch reefs (#25 and 44) in Kāneʻohe Bay Oʻahu and immediately placed in 500 μl of DNA buffer  with 1% SDS. The coral biopsies in DNA buffer were heated to 65 °C for 60–90 min. After this the tubes of DNA buffer were stored at 4 °C. 25 μl of Proteinase K (at 10 mg/ml) was added to 500 μl of DNA buffer in each sample. The tubes were vortexed and then incubated at 55 °C for 2–3 h. 100 μl of buffer was removed from each tube and used for subsequent DNA extraction. DNA extraction followed the phenol chloroform protocol as described by (10.17504/protocols.io.dyq7vv). Briefly 200 μl of CTAB was added to each tube and incubated at 65 °C for 30 min. 300 μl of Chloroform was added to each tube, and then the tubes were rotated for 2–3 h. Tubes were centrifuged at 10,000g for 10 min and 250 μl of the top layer was removed and placed in a new tube. 500 μl of 100% ethanol was added to each tube, vortexed and then placed in the freezer for > 2 h. Tubes were centrifuged at 10,000g for 10 min and the ethanol was removed. Tubes were dried in a SpeedVac vacuum concentrator at 45 °C for 45 min. 100 μl of 0.3 M NaOA was added to each tube and then vortexed. 200 μl of 100% ethanol was added to each tube and placed in the freezer for > 2 h. The tubes were centrifuged at 10,000g for 10 min the supernatant was removed. 100 μl of 70% ethanol was added to each tube and then the samples were vortexed and centrifuged at 10,000g for 10 min and then the ethanol was removed. The tubes were dried in the SpeedVac for 45–60 min at 45 °C. The DNA was then re-suspended in 30 μl of TE buffer  and stored at − 20 °C.Coral biopsies  while also generating ‘stacks’ of reads for a reduced portion of the genome suitable for SNP genotyping. Briefly, DNA was run on a 0.7% agarose gel to ensure that each sample had high molecular weight DNA. DNA was quantified in each extract using the Biotium Accuclear ultra high sensitivity dsDNA quantification kit with standards following the manufacturer’s instructions. The volume was adjusted for each sample to ensure that library preparation started with 1.3 μg of DNA in 25 μl of TE buffer. Each DNA sample was digested with 25 μl of DPNII master mix  at 37 °C for 3 h, 20 min at 65 °C and then held at 15 °C. After digestion samples were cleaned using Agencourt AMPure XP beads at a DNA:bead ratio of 1:18 (50 μl of DNA to 90 μl of beads). After the bead cleaning, the DNA was resuspended in 28 μl of water. The samples were checked to ensure digestion on a 1.4% agarose gel run at 100 V for 45 min.The libraries of genomic DNA were prepared following the ezRAD protocolPorites RAD libraries generated in the same laboratory10: NCBI BioProject PRJNA380807). Quality statistics were assessed with FastQC53 and compiled with MultiQC54  were split in half and barcoded with different adapters as technical replicates. The libraries were size selected for a target of 350–700 bases using SPRI beads. Every sample was run on PCR for 12 cycles (as is recommended in the KAPA kit) to ensure adequate concentration of DNA for sequencing. After PCR the DNA was cleaned using a 1:1 ratio of DNA to AMPure XP beads. The DNA was submitted to the Genetics Core Facility (GCF) at HIMB and all 32 libraries were sequenced on an Illumina Miseq with a 300 bp paired end reads, the raw reads were uploaded to NCBI as BioProject ID PRJNA665500. These libraries were compared with previously published 54 Table .P. lutea) and genetic structure was examined with respect to a variety of SNP filtering settings for the coral host, the symbiont, and microbial components; (3) reads were mapped to a set of P. lobata annotated transcriptomic loci from a previous study40 that are orthologous to coral loci with contamination by Symbiodiniaceae and bacterial components filtered by amino acid homology, which may leave behind non-protein coding loci such as ribosomal genes. The patterns of genetic structure revealed by all three approaches were compared for consistency and used to guide the exploration of outlier loci that were associated with morphological species (branching vs. mounding morphology), genetic structure within morphological species, as well as association with bleaching susceptibility.The sections below describe three methods that were used to examine genetic structure; (1) consensus sequences of complete or nearly complete mitochondrial genomes from each library were assembled and aligned; (2) reads were mapped to the closest available reference; a metagenomic assembly of a closely related congeneric coral . Forward and reverse reads were grouped into a paired list and quality trimmed to allow no more than a 0.1% chance of error and adaptor trimmed from both ends of reads allowing no mismatch and a minimum overlap of 8 bp. Each library was then mapped to the whole mitochondrial reference genome for Porites lobata38, using default parameters. The assemblies were visually inspected and appeared to be very high quality with very low levels of polymorphism with the exception of several ends of reads with consecutive polymorphisms that differed from other reads, which were manually trimmed. These few instances were manually removed before consensus sequences were called for each library (not including the reference sequence) using the 0% majority option. The resulting consensus sequences were aligned and maximum likelihood trees were generated using RAxml v.855, with 1000 bootstrap pseudoreplicates, a haplotype network was drawn in R56 using the haploNet function in the Pegas package57. Pairwise genetic distance between groups was calculated with MEGA v10.0.458 with 500 bootstrap pseudoreplicates.Whole (or major portions of) mitochondrial genomes were extracted from each library using a method described previouslyP. lutea coral-host, symbiont, and bacterial assemblies were downloaded from https://plut.reefgenomics.org/ on 3/27/201839. Reads from all libraries were mapped to the references using the default read trimming and mapping parameters in the dDocent v2.2.2559 bash wrapper pipeline for programs including cutadapt60, BWA-MEM61, and Freebayes62. The BamQC option in Qualimap63 was used to summarize mapping quality statistics  and imported into TASSEL v5.2.5265 using the SortGenotypeFilePlugin. Samples with less than 20% of shared loci were removed from further analysis. PCA plots were generated in TASSEL v5.2.52 for a range of filtering settings to visualize possible effects of filtering assumptions on genetic structure. These PCA plots included a variety of data subsets filtered by VCFtools64 to examine the effects of missing data (–max-missing-count 3–15), quality (minQ 20 to 45), depth (–minDP 3–100), and allele frequency . The total evidence dataset and several filtered data subsets were further examined with Maximum Likelihood trees. The VCF files were converted to fasta format with VCFkit (https://github.com/AndersenLab/VCF-kit) using the vk phylo command, which minimizes ambiguities by sampling the most common allele from each genotype. The resulting fasta file consisted of a 16.4 million bp alignment with 62% missing data, 13.6 million variable positions (83%), of which 8.9 million (55%) were parsimony informative sites as determined by the program AMAS66 that was used to generate summary statistics. Phylogenetic trees were then generated with RAxml v.855, with the GTRGAMMA model and -f a options and 100 bootstrap pseudoreplicates.The cs Table . The resP. lobata coral transcriptome was downloaded from https://comparative.reefgenomics.org40. Reads were mapped to the P. lobata transcriptome with dDocent v2.2.2559 using the same read trimming settings as described above for the metagenome, and mapping quality statistics were generated in Qualimap63. The data was imported into TASSEL as outlined above for visualization and comparison of filtering options with PCA plots and trees were generated as described for the coral metagenome above. The transcriptomic dataset was filtered to remove libraries with more than 20% missing data with loci shared by at least 50% of libraries in TASSEL v5.2.5265.A previously published and annotated P. compressa mitochondrial clade C (n = 18) vs. P. compressa mitochondrial clade B (n = 19), and bleached P. compressa (n = 19) vs. unbleached P. compressa (n = 18). These trait files were merged with the genotype file with the-intersect command. These comparisons were made to examine the relative number of loci that differ between morphotypes, mitochondrial clades, and bleaching status. To identify candidate outlier loci, we ran the GLM analysis, which uses a least-squares fixed-effects linear model and F tests to examine the association between a locus (SNP) and a trait as defined in the phenotype definition files. We used the R package qqman67 to visualize Manhattan plots and the Bonferroni type I error correction68 for multiple comparisons. Using this approach, we considered SNPs with alpha values below the threshold of 4.002 × 10−08 as candidate outlier loci. In addition, we estimated thresholds using the R package Qvalue , which converts p-values into q-values69 and can estimate significance thresholds for a particular FDR. We chose an FDR rate of 0.01, which yielded a threshold value of 3.4685 × 10–06. We considered SNPs with p-values below the Bonferroni threshold as ‘highly significant’ and below the the q-value threshold as a ‘significant’ outlier loci.Outlier loci were identified with a generalized linear model (GLM) implemented in TASSEL v5.2.52 based on a priori groups of phenotype definition files: branching morphology (n = 41), massive morphology (n = 18), 70. We then used ShinyGO v0.60 Gene Ontology Enrichment Analysis (https://bioinformatics.sdstate.edu/go/), in order to determine if GO terms were statistically overrepresented and enriched relative to over 315 plant and animal species71. In other words, we tested the hypothesis that GO terms (i.e. broad categories of gene function) for the candidate outlier loci that differed the most between groups  were significantly different from randomly selected GO terms from a variety of organisms. The option ‘Best matching species’ was selected in the organism field for ‘all available gene sets’. P-value (FDR) cutoffs values were examined for a threshold of 0.01 and 0.05 for outlier loci from species, mitochondrial clade, and bleaching susceptibility comparisons.Although the coral transcriptomic data was filtered to include primarily coral orthologous genes, we confirmed if candidate outlier loci belonged to the coral host, algal symbionts, or other associated organisms, with a query of the NCBI database for each list of likely candidate outlier loci on October 8, 2019 using the megablast tool against the nr/nt database Table . In ordeSupplementary Figures.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.


Imperata cylindrica). Cogongrass is a widespread invasive species throughout the southern United States (US). Patterns of infestation vary among US states. Cogongrass is pyrogenic, and its invasion threatens softwood (Pinus spp.) plantations, a substantial economic market for this US region. Over 600 individuals were sampled from seven invaded US states, using amplified fragment length polymorphisms (AFLPs) to assess genetic diversity and population structure. We suspected that differences in historical management efforts among US states influenced differences in genetic diversity and structure. We detected two genetic lineages at the highest level of analysis. One genetic lineage was locally restricted, whereas the other was found throughout the study region. Admixed individuals were found in all US states and consistently co-occurred with the dominant lineage, suggesting that secondary contact and hybridization may have facilitated expansion. The widespread prevalence of only one of the two detected genetic lineages suggests a primary genetic lineage responsible for on-going population expansion in the US.The spatial expansions of invasive organisms in the novel range are generally expected to follow an isolation-by-distance relationship (IBD) if the invasion is biologically driven; however, many invasions are facilitated anthropogenically. This research focused on the extant expansion patterns of cogongrass ( Biological invasions continue to increase during this era of increased global connectivity , while rImperata cylindrica), are of great concern to the agriculture of softwood timber plantations, primarily pines (Pinus spp.). Previous work on this species has shown that anthropogenic land use  is an important driver of this species’ distribution, and therefore of range expansion [Imperata cylindrica) has invaded seven southern US states and has negatively impacted both the economy and the ecology of these states and the agronomy of the region [Pinus spp. are especially susceptible to cogongrass fire events; after a fire, cogongrass rhizomes are generally the first to re-sprout and then dominate the landscape, excluding other plants and animals from recolonizing [Invasions, such as cogongrass  markers is considered adequate for this analysis. The same AFLP markers have been previously utilized to assess genetic diversity and population structure in invasive US cogongrass populations ,38,39,40e and UHe) was 0.016 (SE ± 0.001). Spearman’s correlation coefficient (ρ) values between the sample size and genetic diversity were calculated for (1) all states, and (2) for states with more than 50 sampled individuals . All relationships between the sample size and genetic diversity were not significant (P > 0.05), suggesting that unevenly sampling states did not unnecessarily bias results or interpretation.The AFLP analysis resulted in 2057 polymorphic loci from 676 cogongrass individuals. We observed a good reproducibility for this method  among the samples analyzed. The state-level average percentage of polymorphic loci was 23% (SE ± 7%), ranging from 2% (GA) to 56% (SC). The mean Shannon’s Information Index (I) was 0.030 (SE ± 0.001). Nei’s gene diversity for all states analyzed ranged from 0.006 (GA) to 0.042 (SC) with a mean of 0.023 . HeterozAn overall reduction was observed in the clonal diversity analysis from 676 sampled individuals to 321 unique multi-locus genotypes . The meaK = 2; mean LnP(D) = −107,561; Of the 676 individuals sampled, 485 were assigned with >90% posterior probability to a single lineage; 73 to the MS-type lineage red,  and 412 ST values were observed between the two genetic groups within MS patches ; therefore, a pairwise FST analysis separated MS populations into two groups: 1) coastal Mississippi (MS-Coast) populations from the other 2) Mississippi lineage  (ST = 0.060), while the AL and SC populations were the least genetically differentiated from the TX population , excepting MS-central (FST = 0.314). Pairwise FST between TX and AL (FST = 0.090) was very similar to pairwise FST values between TX and SC, and between TX and FL (both FST = 0.083). The greatest genetic dissimilarity existed between TX and GA (FST = 0.553).Significant population pairwise FCentral) . The SC A principal coordinate analysis (PCoA) was consistent with the population structure as inferred by the Bayesian cluster analysis in STRUCTURE. The first two axes explained 61% of individual genetic variance . Two cluST = 0.363, P < 0.001) between the groups as defined above.The genetic differentiation was evaluated between the MS-type cluster and all other individuals based on inferences from STRUCTURE and the PCoA. Ambiguous individuals were lumped with the AL-type in an analysis of molecular variation AMOVA; ; this grST = 0.363, P < 0.001) from the MS-type lineage, even when inclusive of ambiguously assigned individuals. A two-lineage scenario provided the only evidence of strong population structuring throughout the invaded US range. The majority of individuals sampled for this work were assigned with a 90% or greater probability to the AL-type lineage , and demonstrated a relationship between fitness benefits and increased heterozygosity in invasive populations [The present analyses further demonstrated that, although the two genetic groups remain significantly differentiated at the scale of this study, admixture is present within individuals at the local, patch scale (mean α = 0.165). Admixture is also suggested to have increased upon regional evaluation as compared to previous research constrained to MS and AL (where mean α = 0.08) . Similarulations . Such reulations ,43. Typiulations ,46,47,48ST = 0.363, P < 0.001) is similar to that of early successional plant species (mean FST = 0.37) [e and UHe = 0.016 ± 0.001) is still lower than such values in long-lived perennial plant species (He = 0.68) or those capable of selfing (He = 0.41) [Although we found a significant genetic distance to exist among the states , the ove = 0.37) . Even co = 0.41) .retired) with chemical herbicide and mechanical removal decades before all other southern US states. Furthermore, the Georgia Forestry Commission currently utilizes a chemical-application strategy that has been most effective [We expected that differential funding and historical management efforts among the seven US states would contribute to contemporary state-level genetic diversity differences across the region. Differing state-level management practices, efforts, and starting-acreages of infestation affected the relative genetic diversity . The staffective , along wffective . This paffective ,51.Cogongrass possesses a broad global distribution ,24, indiThe regional expansion of cogongrass since its initial introduction in 1919 has benefitted from the direct and purposeful anthropogenic transport of propagules, multiple introductions and secondary contact ,30,31. TLive cogongrass leaf tissues were collected during 2008–2009 from seven states in the southern region of the US: Alabama , Florida , Georgia , Louisiana , Mississippi , South Carolina  and Texas  is a listed Federal Noxious Weed; all sampling was conducted with approval by the U.S. Dept. of Agriculture, Animal and Plant Health Inspection Service, Plant Pest Quarantine . Additional permissions were required for access to specific areas, including a collection agreement with The Nature Conservancy (TNC) and Miami-Dade County Parks and Recreation (Permit #145). Most sampling was conducted on public land including National Forests (USDA Forest Service), interstate/highway rights-of-way and on private land with permission from state forestry agencies and associated landowners, as appropriate.Cogongrass  and exported into a general text format. Detected fragments were sorted on migration size (basepairs) and objectively scored utilizing an independently developed procedure  using both Excel 2007  and PASW v.18.0  as specified in Lucardi et al. [Fragment data were visualized in GeneMarkeri et al. ,36.e) and unbiased (UHe)) and Shannon’s Information/Diversity Index (I), serving as a coefficient of similarity, for each state for state-level diversity estimates (GenAlEx 6.3) [Data conversions of presence-absence matrices were conducted in AFLPdat source script  in R v2.lEx 6.3) . PopulatlEx 6.3) ,56,57.via rhizomes, we conducted a clonal analysis using the “Clones” function within the AFLPdat package [Because of cogongrass’ capacity to clonally reproduce  package . The staST (Arlequin v.3.5 [K) based on posterior log-likelihood probability values from each simulation. We utilized the Evanno et al. [K ≥ 2. Multiple simulations were conducted for K = 1 through 7, based on the seven sampled US states, with the following parameters: admixture ancestry model (to infer α), burn-in 10,000 and 50,000 Markov Chain Monte Carlo (MCMC) [K, was plotted to determine the mode. Individuals were assigned to a single lineage if they contained a probability of membership of at least 0.90 (90% threshold) to one of the clusters; all other individuals that were not strongly assigned to one of the lineages were assigned as ambiguous. We further assessed the population structure with a principal coordinates analysis  and analysis of molecular variation  and thein v.3.5 , to infeo et al.  method to (MCMC) . The ad   perform (AMOVA,  using ge


Advanced Genetic Counseling: Theory and Practice from Oxford University Press. Understanding the social as well as biological factors that underlie how each individual responds to personal genetic information is key work of genetic counselling. This book sets out to demystify the work of Genetic Counsellors and, in doing so, has provided an up-to-date and practical guide for those new and experienced in the field.“Families are messy in their structure and their interaction.” This short statement, nested halfway through Chapter 4, is at the core of the newly published The book starts with a history of the intertwined origins of genetic counselling as an activity and Genetic Counsellors as a profession. Later on, counselling and ethical theories that can be successfully used to guide genetic counselling are described. Each counselling theory, its strengths and relevance to genetic counselling are separated out with illustrative effect. Widely familiar approaches, such as Cognitive Behavioural Therapy, and contemporary methods, such as strength-based counselling, are given equal footing, with Genetic Counsellors encouraged to find out in the application what best serves their style and their patients’ needs. Ethical theories are also laid out in accessible ways and applied to well-chosen case scenarios. For clinical practice, there is a consistent message weaved through the theory and evidence: respond to individuals, be wary of assumptions and patterns of response based on the practitioner's experience and preconceptions. Supervision is strongly encouraged.The case for research evidence-based practice is convincingly made, with helpful ideas for ways in which Genetic Counsellors can be involved or lead this. As the reference lists show, the authors speak with experience and authority on quality research in genetic counselling. This endeavour demands time and collaborative efforts. Often, there can be a tension for clinical Genetic Counsellors between delivering a full-time caseload (and being well placed to pose the pertinent research questions) and a desire to generate patient-centred research data to inform practice. This division of time and commitment is not addressed in the otherwise compelling Chapter 7, ‘Conflict of Interest and the Code of Ethics’.The book concludes with thoughts from the authors on future directions in Chapter 11, ‘Genetic Counseling in the Genomic Era’. We have moved from a time of few tests and large informational patient needs to vastly expanded tests and a patient population informed by the Internet. Increasingly, pre-test consent is obtained by other health professionals, as Genetic Counsellors move towards a post-test working environment, in which patients are referred with pertinent results. Considerations on the transition, bringing gains and losses and new technology, are given insight in this final chapter.The information content of genetic counselling changes with the times. The authors are clear on what does not – the ineffectiveness of an educational model, compared to a counselling focus. The latter, advanced by Seymour Kessler's writing in the 1990s, is given space to breathe and bolstered with new evidence from adult education research. Rather than loosely advocate a ‘less is more’ approach to giving information, readers are reminded why relating new information to a person's understanding and beliefs is the most promising way to facilitate learning. The adult education research selected here suggests that people are unlikely to recall more than three to five pieces of new information in one session, however motivated and capable they are. Provision of new genetic information is related in terms of the degree of psychological threat. There is a solid and important message here: to inundate patients with relevant information does not make for competent genetic counselling.Regarding a psychological and social focus in genetic counselling, the authors disabuse any “vague notion of offering support.” The various counselling approaches tell of concrete techniques that Genetic Counsellors use to convey risk, grasp opportunities for intervention and ultimately help patients make the best decision they can at a particular time point. For our patients, the quality of uncertainty may change, with new technologies shortening the time to a diagnosis, but overall quantity may not, as families move on to the next phase of living with a hereditary condition. This creates new issues, with a role for ongoing care. The book ends with an entreaty to Genetic Counsellors “to help guide patients and their families on their lifelong journeys.”It is a US-centric narrative, which the authors acknowledge. From a UK perspective, this shows in some terminology, the historical lens and the descriptions around provision of healthcare. In other regions of the world, particularly with emerging genetic counselling practices, it is unclear how applicable the assertion is that “any attempts to measure the success, conduct, and outcomes of genetic counseling must refer back to [the United States National Society of Genetic Counselors] definition.”Advanced Genetic Counseling does not assume to answer all of the questions on current practice, but sets them within a history and vision that provoke fresh thinking on recurrent themes: Do original definitions of genetic counselling stand up? How best to parse uncertainty with our patients? What is the role of genetic counsellors in a technologically new world order? This is a book that deserves to be read and referred to as the conversation and the practice evolve.Taken as a whole, this is a passionate account of the state of play with a broad view on the many changes shaped, weathered and predicted.


Fhb1, confirming that the gene is not widely used in Chinese wheat breeding programs. The resistant lines and molecular markers developed in this study are resources and information for enhancing FHB resistance in breeding populations by marker-assisted recurrent selection and gene stacking.Fusarium head blight (FHB) is a devastating wheat disease worldwide. To decipher the genetic architecture of FHB resistance in Chinese germplasm, a Wheat Association Panel for Scab Research (WAPS) consisting of 240 leading Chinese wheat cultivars and elite lines was genotyped using the 90K single nucleotide polymorphism (SNP) arrays. The FHB response was evaluated in the field nurseries in Wuhan in Hubei Province over four consecutive years from 2014 to 2017. Five quantitative trait loci (QTL) were consistently identified on chromosome arms 1AS, 2DL, 5AS, 5AL, and 7DS using a mixed linear model (MLM), explaining 5.6, 10.3, 5.7, 5.4, and 5.6% of phenotypic variation, respectively. The QTL on 5AS, 5AL, and 7DS QTL are probably novel. The allelic frequency analysis indicated that cultivars from the Middle and Lower Yangtze River Valleys harbored more favorable alleles and were therefore more resistant than those from other regions. To facilitate in-house germplasm screening and marker-assisted selection (MAS), SNP-derived PCR markers were developed for the QTL regions on 1AS, 5AS, and 5AL QTL. In addition to the above five QTL, the WAPS population had a very low frequency of  Fusarium graminearum Schwabe, is a devastating disease of wheat worldwide, particularly in warm and humid regions , 15-O-acetyl-DON (15-AcDON), DON-3-G, and NIV, making grain unsuitable for food and feed  have been formally cataloged. Fhb2 on the short arm of 6B, Fhb4 on the long arm of 4B, and Fhb5 near the centromere of 5A were finely mapped  have been used to dissect the genetic basis of complex economic traits in wheat, such as pre-harvest sprouting resistance  and graiIn the current study, we analyzed a panel of Chinese wheat cultivars and elite breeding lines including more than 50 widely grown cultivars using the wheat 90K arrays to: (1) study the phenotypic variance of FHB response in Chinese wheat germplasm, and (2) determine the genetic architecture of FHB resistance in the panel. The results should provide a better understanding of the genetic basis and diversity in FHB response of the Chinese cultivars, and facilitate the improvement of FHB resistance levels by stacking FHB resistance QTL using MAS.Triticum aestivum) cultivars and elite lines from China  medium at −20°C until use. The mycelium was inoculated into a 5% mung bean broth medium to grow F. graminearum conidia with shaking at 180 rpm under 28°C for 5–7 days. The flasks content was filtered and the concentration of conidia was examined with blood counting chamber under a microscope.The A combination of type I and type II resistance was assessed in the field nurseries at Nanhu Experimental Station , Food Crops Institute, Hubei Academy of Agricultural Sciences, during the 2013–2014, 2014–2015, 2015–2016, and 2016–2017 cropping seasons, hereafter referred to as 2014, 2015, 2016, and 2017, respectively. The average annual rainfall is about 1,270 mm at this location and the growing season extends from early November to May. The trials were carried out in randomized complete blocks with two replications. Each plot comprised 2 × 1 m rows with 25 cm space between rows.F. graminearum conidia at a concentration of 50,000 spores/mL were sprayed on the labeled spikes, which were then assessed 21 days post-inoculation for FHB response by counting the number of diseased spikelets and total spikelets per spike. The FHB index (%) was calculated as (Severity × Incidence)/100 /100 , where ince)/100 .2 on the soil surface about 1 month before anthesis, and FHB was visually scored 28 days post-anthesis.In 2017, the population was planted in a rice field with the same experimental design. Grain-spawn inoculation was conducted by scattering 600 g scabby wheat grains per 100 mThe plant heights for all the accessions used in this study were recorded about 2 weeks after anthesis. For each plot, the plant heights were measured two times to calculate the average.1) database. The marker density was drawn using the CMplot package in R2. The flanking markers of 11 known FHB QTL (Fhb1 (UMN 10), Fhb2 (Xwmc179), Qfhs.ifa-5A (Xbarc186 and Xbarc180), QTL on 3A from Frontana (Xdupw227), QTL on 5A from Frontana (Xbarc197 and Xgwm129), QTL on 2DL from Wuhan 1 (Xwmc245), QTL on 4BS from Wuhan 1 (Xwmc238 and Xgwm149), QFhs.nau-2DL (Xgwm157 and Xgwm539), Qfhs.ndsu-3AS (Xgwm2), and Qfhs.fcu-7AL (Xbarc121 and Xwmc488) were successfully applied. Diagnostic markers PFT-CAPS for Fhb1 candidate gene PFT and His-InDel for Fhb1 casual gene His  were calculated across the environments using the ANOVA function in IciMapping V4.0 software (Comparisons of the means of different groups were done using software .3. The Genomic Association and Prediction Integrated Tool (GAPIT) was used to generate distance-based clustering distribution and scree plots4.Population structure analysis was performed using ADMIXTURE5. The kinship (K)+PCA model was chosen to perform MLM analysis to control background variation and eliminate spurious MTAs. Markers with adjusted –log10  ≥ 3.0 were considered significant markers for FHB resistance  were shown in Q-Q plots. Manhattan and Q-Q plots were drawn using CMplot. To compare the physical positions of the QTL identified in the current study and the known genes/QTL, relevant markers and gene sequences were used to blast Chinese Spring reference genome sequences (IWGSC RefSeq v1.06).Associations between genotypic and phenotypic data were analyzed using Tassel v5.0sistance . The P-v7 and fluorescence signals were detected using the multifunctional microplate reader PHERAstarPlus  and analyzed using KlusterCaller software . The primers for other types of markers were designed based on chromosome-specific polymorphisms among homologous sequences in the target regions obtained from the EnsemblPlants database8. The PCR products were visually detected by 1.5% agarose gel electrophoresis.The SNPs significantly associated with FHB resistance were converted to KASP, CAPS or conventional PCR-based markers to facilitate their application in MAS. Primers for KASP markers were designed by PolyMarkerAnalysis of variance of FHB indices showed significant variation for years, genotypes, and genotype-by-year interaction . HeritabDisease symptoms were moderate to high in the first 3 years under spray inoculation. The mean FHB indices of the WAPS population were 58.3, 49.2, and 69.3% in 2014, 2015, and 2016, respectively. The highest FHB index was recorded in 2016, probably due to warmer weather during inoculation and disease development. FHB symptom in 2017 was lighter than those in the earlier years, with a mean FHB index of 29.1%. This might be attributed to the grain-spawn inoculation method providing less pathogen pressure than spray inoculation. The variation in symptom levels might cause differences in the frequency distribution of the FHB index. The frequency distribution in the years 2016 and 2017 did not fit a normal curve .The correlation coefficients of FHB indices among the first 3 years ranged from 0.41 to 0.66, while those between 2017 and the earlier years were relatively lower with a range of 0.31–0.52 .The BLUE values across 4 years for FHB indices exhibited wide variation ranging from 5.3 to 88.6% . Sumai 3Of 81,587 SNPs called in the assay, 22,922 were polymorphic in the population and selected for subsequent analysis. After removing SNPs with MAF <5 and >20% missing data, 19,803 were employed for GWAS. These SNP loci spanned a physical distance of 14,036.0 Mb, with an average density of 0.963 Mb per locus. SNP density for the D genome (1.781 Mb per SNP) was lower than those for the A (0.852 Mb per SNP) and B (0.785 Mb per SNP) genomes .The WAPS population was divided into three subgroups, essentially consistent with geographic origin and pedigree . For exaQFhb.hbaas-1AS, QFhb.hbaas-2DL, QFhb.hbaas-5AS, QFhb.hbaas-5AL, and QFhb.hbaas-7DS, respectively, were significant in at least two environments, explaining phenotypic variation ranging from 4.9 to 10.3%   . The oth in 2015 .QFhb.hbaas-1AS, QFhb.hbaas-2DL, QFhb.hbaas-5AS, QFhb.hbaas-5AL, and QFhb.hbaas-7DS, respectively, based on marker analysis  associated with QFhb.hbaas-1AS, QFhb.hbaas-5AS, and QFhb.hbaas-5AL were converted to PCR-based markers and designated FHB-1AS-PCR, FHB-5AS-KASP, and FHB-5AL-CAPS, respectively. FHB-1AS-PCR produced no PCR products for the resistance allele, and a 389 bp fragment for the susceptibility allele , the products of cleaved fragments of 213 and 121 bp were obtained for the susceptibility allele, whereas the resistance allele was represented by the intact 334 bp fragment  less than 2 Mb away from the representative SNPs of baas-1AS . Two genThe WAPS population with the wheat accessions collected from different agro-ecological regions represents a large proportion of the genetic diversity in modern Chinese wheat cultivars. Thus, we can use mean FHB indices in the different regions to assess relative levels of FHB resistance across the regions. Cultivars from Jiangsu and Hubei, in the Middle and Lower Yangtze River Valleys, had the highest levels of FHB resistance, whereas cultivars from other provinces tended to be more susceptible, particularly those from Shandong, Henan, and Hebei in the Yellow and Huai River Valleys (Zone II) where almost one-half of the total Chinese wheat crop is produced.This geographical differentiation for FHB resistance agrees with the prevalence of FHB. The Middle and Lower Yangtze River Valleys are the regularly hard-hit areas of FHB in China . The higTh. ponticum as sources of FHB resistance in breeding  for QFhb.hbaas-1AS identified in this study. Both QTL probably represent the same resistance locus. Minor QTL on 1AS were also reported in susceptible cultivars Wheaton  and Qfhs.ifa-5AS (70.7–119.9 Mb). Both distinguish from QFhb.hbaas-5AS mapped in the present study based on their physical positions.A QTL on 5A designated nd DH181 . The genosome 5A . The phyVrn-A1 and Q on chromosome 5AL were associated with FHB response . For a long term, other important genes/QTL can be gradually introduced into new cultivars with Fhb1 through back-cross and MAS. Investigations of FHB resistance in indigenous accessions could also lead to the discovery of more sources of FHB resistance.Sumai 3 was identified as the most FHB resistant cultivar in the present study. It has been used as a resistant parent in many crosses in China, but very few cultivars carry Fhb1 , which iPRJEB36125.The datasets generated for this study can be found in EVA accession ZZ performed the experiment and wrote the manuscript. LC and WZha contributed to genotyping. LY, WZhu, JuL, YL, and HT participated in the field trials. LF, JiL, and AR participated in marker development and the data analysis. CG, YH, XX, and ZH designed the experiment and assisted in writing the manuscript. All authors have reviewed the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Plecotus balensis, an understudied bat that is restricted to the remnant natural high‐altitude Afroalpine and Afromontane habitats. We integrated ecological niche modelling, landscape genetics and model‐based inference to assess the genetic, geographic and demographic impacts of past and recent environmental changes. We show that mountain range isolation and historic climates shaped population structure and patterns of genetic variation, but recent anthropogenic land‐use change and habitat degradation are associated with a severe population decline and loss of genetic diversity. Models predict that the suitable niche of this bat has been progressively shrinking since the last glaciation period. This study highlights threats to Afroalpine and Afromontane biodiversity, squeezed to higher altitudes under climate change while losing genetic diversity and suffering population declines due to anthropogenic land‐use change. We conclude that the conservation of tropical montane biodiversity requires a holistic approach, using genetic, ecological and geographic information to understand the effects of environmental changes across temporal scales and simultaneously addressing the impacts of multiple threats.While climate change is recognized as a major future threat to biodiversity, most species are currently threatened by extensive human‐induced habitat loss, fragmentation and degradation. Tropical high‐altitude alpine and montane forest ecosystems and their biodiversity are particularly sensitive to temperature increases under climate change, but they are also subject to accelerated pressures from land conversion and degradation due to a growing human population. We studied the combined effects of anthropogenic land‐use change, past and future climate changes and mountain range isolation on the endemic Ethiopian Highlands long‐eared bat,  These sky islands contain unique diversity and provide natural laboratories to study evolution, speciation and the effects of habitat fragmentation Hedberg, . Excepti2 in the otherwise arid zone of the Horn of Africa and include 73% of the Afroalpine biome  in Sub‐Saharan Africa  or Afrotropical origin   across temporal scales to track the effect of past climate changes on range suitability for 22.1Juniperus procera) and African redwood (Hagenia abyssinica) in the drier northern slopes and Erica (Erica arborea) and yellow wood (Podocarpus sp.) forests in the warmer and more humid southern slopes. We obtained 3 mm wing biopsies from 50 Plecotus balensis bats from 19 sites (Table Bats were sampled during 2014–2015 in five isolated mountain ranges across the Ethiopian Highlands: (a) Bale Mountains National Park, the largest and southernmost massif that covers partially the most extensive Afroalpine area in the continent Hillman, , (b) Arses Table . Bats we2.2P. balensis across the Horn of Africa. The Horn of Africa was selected as the study area because it represents a biotic region that was likely accessible for the species over the relevant time periods covered by the modelling. As only 14 unclustered location records are available for this understudied species, we used a bias layer to account for potential uneven sampling efforts and low sample sizes. We assigned a value of 10 for mountain ranges surveyed during our expeditions and a value of 1 to the remaining study area. These values were used by Maxent to give weights to random background data during the modelling process so that pseudo‐absences better reflect potential geographic bias in sampling efforts  were generated with the programme MaxEnt v3.4.1  and a static topographic variable, topographic ruggedness .Models were hind‐casted to the Last Glacial Maximum (LGM) and mid‐Holocene, and projected to the future (2070) using three General Circulation Models  for each time period, and two Representative Concentration Pathways (RCP) scenarios for future projections only, the “worst case” scenario, RCP + 8.5 W/mio IPCC, . In our 2.3Genomic DNA was extracted from all wing biopsy samples. The extraction protocol consisted of DNA precipitation with isopropanol after purification using saline precipitation. We selected three markers with different mutation rates, covering events that occurred during the Pleistocene  versus recent‐contemporary impacts from the last few decades‐centuries (microsatellites). Two mitochondrial DNA (mtDNA) fragments were amplified as follows: a 650 bp fragment of the gene cytochrome b (cytb) using the primers MOLCIT‐F  model of DNA substitution with proportion of invariable sites model of rate variation for the cytb sequences and the HKY model with gamma‐distributed rate variation for the HVI sequences  and the remaining haplotypes, followed by the divergence of Simien haplotypes. Only later haplotypes were split between north‐west and south‐east of the Rift Valley Figure . Populatey Table .Based on the mtDNA dataset, the Bale‐S population had highest nucleotide diversity, substantially higher than the rest of the populations. The Simien population had lowest nucleotide diversity, but the highest haplotype diversity, though differences in haplotype diversity between populations were negligible Table . Based oF = 10.41, df = 1,3, p = .048, R2 = 0.717; Figure Allelic richness corrected for sample size decreased as the proportion of arable land in 5 km radius around the population capture location increased  had the strongest support , followed by the ecoregions hypothesis . Confidence intervals of both variables did not overlap zero, supporting their effect on gene flow Table . Projecty Figure . The rem3.4The best‐supported scenario was of a recent approximately six‐fold decline of the south‐eastern population but no change in the north‐western population  = 0.046 ± 0.04; Figure 4Assessing threats to biodiversity under global change requires an understanding of the effects of past, current and future environmental changes on species distribution, abundance and genetic composition. Through combining genetic, ecological and geographical information with ecological niche and evolutionary history modelling approaches we show how historic climatic changes during the Quaternary have shaped the population structure and patterns of genetic variation of the Ethiopian Highlands long‐eared bat. We further show the impact of recent anthropogenic land‐use change and habitat degradation on population declines and loss of genetic diversity, and warn of threats due to disappearing suitable niche under future climate change. Our findings have important implications for biodiversity conservation in Afromontane and Afroalpine ecoregion more generally, which represent some of the most endemism rich and endangered ecosystem in the world  may have reduced the power of the landscape genetics statistical analysis, in particular when considering the combined impact of more than one variable, and therefore this limitation may have obscured the importance of some drivers of landscape resistance. Although native church forest patches and small community‐managed forest reserves offer some level of landscape connectivity across the agricultural and human settlement landscape, per cent tree cover did not affect genetic connectivity. However, this may be attributed to the tree cover map used, which does not distinguish between native forests and eucalyptus plantations (Hansen et al., P. balensis.Despite the strong effect of anthropogenic land‐use change on genetic diversity and population decline in P. balensis, as is the case with other cold‐adapted high‐altitude mammals, like the American pika, Ochotona princeps (Castillo et al., The landscape genetics analysis highlights the importance of altitude and Afroalpine and Afromontane habitats. The best‐supported model gave lowest resistance costs to the high‐altitude alpine moorland ecoregion, rather than the montane forest ecoregion, perhaps due to the more restricted distribution of native montane forest than the ecoregion classification suggests because of extensive deforestation in the past century. Alternatively, this may reflect temperature or other climatic limitations on the movement of 5P. balensis, we found that despite strong associations with high‐altitude environments and mtDNA pattern associated with sky island structure, some level of genetic connectivity is maintained among sky islands, and only substantially lower altitudes, like the Rift Valley, form a true barrier to gene flow. Of particular concern are evidence of recent population decline, likely in response to deforestation and land conversion, the decline in genetic diversity with increasing arable land cover, and the importance of Afroalpine and Afromontane ecoregions for both range suitability and genetic connectivity between sky islands. Given that similar patterns of genetic population structure have been recorded in other high‐altitude mammals and amphibians, such losses of genetic diversity and population declines are likely a widespread ongoing process in tropical montane ecosystems. The situation described for this bat epitomizes a wider trend in high‐altitude Afromontane and Afroalpine biodiversity, which is under threat from extensive deforestation, agricultural conversion and overgrazing following rapid human population expansion (Yalden et al., Using a combination of ecological niche models, landscape genetics and ABC model‐based inference of demographic history, we show how historic climate change and geographic barriers interact with recent anthropogenic habitat loss and degradation to shape the population size, structure, diversity and connectivity of tropical montane biodiversity. Focusing on the endemic Ethiopian Highlands bat, None declared.OR and JJ conceived and designed the study. OR, MK, HS and JJ collected the samples. OR and JJ carried out the laboratory work. OR performed the data analysis and modelling. OR and JJ wrote the manuscript, and all authors contributed to revisions.AppendixAClick here for additional data file.AppendixBClick here for additional data file.