Fusarium species were identified. The predominant species were F. graminearum s.s. (81%) and, to a lesser extent, F. avenaceum (15%). The emergence of the 15ADON genotype was found ranging from 83% to 87% of the total trichothecene genotypes isolated in 2016 and 2017, respectively. Our results indicate two dramatic shifts within fungal field populations in Poland. The first shift is associated with the displacement of F. culmorum by F. graminearum s.s. The second shift resulted from a loss of nivalenol genotypes. We suggest that an emerging prevalence of F. graminearum s.s. may be linked to boosted maize production, which has increased substantially over the last decade in Poland. To detect variation within Tri core clusters, we compared sequence data from randomly selected field isolates with a panel of strains from geographically diverse origins. We found that the newly emerged 15ADON genotypes do not exhibit a specific pattern of polymorphism enabling their clear differentiation from the other European strains.Fusarium head blight (FHB) of cereals is the major head disease negatively affecting grain production worldwide. In 2016 and 2017, serious outbreaks of FHB occurred in wheat crops in Poland. In this study, we characterized the diversity of Fusaria responsible for these epidemics using TaqMan assays. From a panel of 463 field isolates collected from wheat, four  Fusarium-damaged kernels (FDK)  is one of the most devastating small-grain cereal diseases worldwide. Although globally FHB mostly affects barley, rye, oats, and triticale, it is considered the most precarious head disease of wheat . Warm anls (FDK) . AccumulF. graminearum field populations [Fusarium species associated with FHB.Control of the disease is focused on agricultural practices, fungicides, and cultivar resistance . Among fulations ,8,9. Theulations , and a bF. graminearum sensu lato (s.l.) is nowadays the most frequently isolated causal agent of FHB worldwide. Phylogenetic species recognition, with genealogical concordance [F. graminearum sensu lato comprises at least 16 biogeographically structured and phylogenetically distinct species, also known as the Fusarium graminearum species complex (FGSC) [F. graminearum sensu stricto (s.s.) is the dominant FGSC species associated with FHB in North and South America [F. asiaticum appears to be the major species in temperate regions of Asia [F. graminearum s.s. displaced F. culmorum, which had been the major FHB agent of wheat since the 1840s [F. culmorum in some European localities identified in several surveys may be explained by the influence of climatic conditions in increasing the prevalence of each species [cordance , has prox (FGSC) ,13,14,15 America ,18,19, w of Asia ,21,22. Ihe 1840s . However species .F. culmorum; F. graminearum s.l.; and, to a lesser extent, F. avenaceum [F. graminearum is associated with displacement of F. culmorum by F. graminearum, which corresponds to previous surveys from the Netherlands [F. graminearum s.l. and F. culmorum were responsible for the last outbreak, with emphasis on the former.FHB epidemics occurred in Poland in 1974 and 1999, and over the next decade, the incidence of the disease in small grain cereals was rather limited ,26,27,28venaceum ,30. Stępherlands  and Luxeherlands . Howeverherlands  by incorherlands ,33. A laF. culmorum and F. graminearum s.s. can belong to different chemotypes: 3ADON (producing DON and 3ADON), 15ADON (producing DON and 15ADON) (absent in F. culmorum), and NIV (producing NIV and 4ANIV) [Fusarium field populations has been found to be important in determining population structure, monitoring chemotype shifts, and predicting the presence of trichothecene compounds in grains [Both d 4ANIV) . Knowledn grains ,35,36. Tn grains ,37,38, sn grains , and mayn grains ,40,41.Tri genotypes/chemotypes have been observed in geographically diverse locations such as North America, China, Denmark, and Luxembourg [Field populations of trichothecene producers are often characterized by incorporation of mycotoxin genotyping tools allowing rapid detection of 3, 15ADON, and NIV genotypes ,43,44,45xembourg ,44,46,47Fusarium species are the natural boundaries and climates of agricultural regions, which may define pathogen potential [Tri genotype differences. There is an urgent need to update results, mainly from the most recent FHB outbreaks, which occurred during the 2016 and 2017 growing seasons. In 2016, the disease occurred over the entire country, while in 2017, FHB occurrence was restricted to northern Poland. We have characterized the collection of field isolates responsible for FHB in Poland in both growing seasons. The isolates were recovered mainly from FDK of wheat from geographically diverse fields. To determine Fusarium species and trichothecene genotypes, we used different TaqMan-based assays. To detect variation within Tri core clusters, we compared sequence data from randomly selected field isolates with a panel of strains from geographically diverse origins. The updated results of this study could be used to develop a reliable plant protection strategy against currently emerged fungal species.The important areas in which to focus research on otential . The cliotential ,29,33. IFusarium species were identified (F. graminearum s.s. (81.2%) and, to a lesser extent, F. avenaceum (14.7%). Both F. culmorum and F. poae were occasionally isolated. Our results indicated high regional and temporal differences in species composition. For example, predomination of F. graminearum s.s. appeared to be complete in wheat samples from northern Poland , where F. graminearum s.s. was the solely isolated species. In contrast, other sampling locations  exhibited a slightly decreased incidence of F. graminearum s.s., ranging from 46.1% to 54.6% of the total FDKs analyzed. Barley samples analyzed in this study showed the lowest (<17%) incidence of F. graminearum s.s. , F. avenaceum accounted for over 44% of the diseased kernels. Moreover, an unexpectedly high incidence of F. avenaceum was found on barley kernels from Modzurów, accounting for 80.4% of FDKs. F. avenaceum was not recovered from 5 out of 17 wheat sampling locations.In 2016, where FHB affected an entire country, F. culmorum was found in our study (3.9%). Only one isolate was recognized as F. poae (0.2%). F. culmorum was identified in 4 out of 17 locations. None of the F. poae isolates were found in 2017.In contrast to previous surveys by Wiśniewska et al.  and ChełF. culmorum/F. graminearum s.s. isolates from wheat kernels showed a high prevalence of the 15ADON genotype in Poland. In the 2016 and 2017 growing seasons, the 15ADON genotype accounted for 82.9% and 87.4% of F. culmorum/F. graminearum s.s. isolates, respectively. All 333 isolates identified as 15ADON genotype belonged to F. graminearum s.s. Among the total of 61 3ADON genotypes, 70.5% and 29.5% belonged to F. graminearum and F. culmorum, respectively. The 3ADON genotype was more prevalent in 2016, accounting for 17.1% of the total Tri genotypes. In 2017, a shift towards the 15ADON genotype was observed, leading to a decreased incidence of 3ADON genotypes (12.6%). None of the NIV genotype was determined in our collection of field isolates.Genotyping of a collection of 394 Tri clusters enables specific characterization of the emerged 15ADON genotypes. To achieve it, the complete Tri core clusters from four emerging (isolated in 2016) 15ADON field isolates were compared with the panel of 31 strains from geographically diverse origins. Our collection also included four historical Polish isolates recovered between 2003 and 2004  were the most common type of DNA polymorphism determining genetic variability of the strains within this cluster. All identified SNPs were synonymous.Tri core cluster totaling 55 SNPs (Tri14 gene (1170 bp), while the remaining 24 SNPs were found within the short (163 bp) upstream region of the Tri14 gene.The emerging 15ADON isolates did not show a specific pattern of polymorphism enabling their clear differentiation from the other European strains. However, we showed that the studied strains displayed polymorphism related to their European and American origins. The most remarkable differences between these two groups were found within the 1333 bp end of the  55 SNPs . Thirty-F. graminearum sensu lato in different European countries, for example, The Netherlands [FHB surveys performed in Europe over the last few decades have verified an increased predominance of herlands , France herlands ,49, Germherlands , and Itaherlands ,52.F. graminearum s.l. in wheat in Poland during the FHB epidemics in 2009. At the same time, however, considerable incidence of F. culmorum was found in some Polish localities [F. graminearum s.s. dramatically predominated in the growing seasons of both 2016 and 2017. Our results highlighted, for the first time, a huge disproportion between F. graminearum s.s. and F. culmorum. Although some regional and temporal differences were found, it is worth mentioning that these changes were mainly attributed to a noticeable shift towards F. avenaceum in 2017.A previous large-scale survey by Chełkowski et al.  highlighcalities ,33. In oF. culmorum or F. graminearum s.s.Our results showed that the 15ADON genotype predominated in diseased kernels, ranging from 83% to 87% of the total trichothecene genotypes isolated in 2016 and 2017, respectively. The revealed predomination of the 15ADON genotype in Poland is in line with other European surveys ,51,52,53F. graminearum in Europe has been mainly linked to boosted production of maize as the primary host of F. graminearum [F. graminearum s.s. in Poland may also be linked to the rise in maize production. Since the last reports on FHB incidence in 2009 [http://www.fao.org/faostat/en/#data/QC), which is close to the maize cultivation area in Germany  and Italy . In both countries, as well as in France (the largest maize producer in Europe), predomination of the 15ADON genotype of F. graminearum has been highlighted [The emergence of minearum ,31,56. Mminearum , which aminearum  and mighminearum . Recentlminearum . The obs in 2009 ,33, the hlighted ,50,51.F. graminearum s.s. [Besides cropping systems, changes in climate patterns, such as the increase in annual temperature, have also been suggested as key factors affecting recent emergence of rum s.s. ,61,62,63rum s.s. . The averum s.s. .Tri genotype frequency may change over time and space. In addition, a chemotype occupying a certain geographic location can be replaced by another. Most recent chemotype shifts appear to result from the introduction of new genotypes into new areas [F. graminearum s.s. in Canada [F. asiaticum in Southern China [F. asiaticum in the southern United States [Production of different analogs of trichothecene compounds by fungi affect their virulence ,37,41 anew areas . Such exn Canada , 3ADON grn China , and thed States  and Brazd States .Tri genes exhibit the strongest signals of selection contributing to population divergence. In this study, we found that the newly emerged Polish 15ADON genotypes do not exhibit specific patterns of polymorphism enabling their clear differentiation from the other European strains. This may suggest previously uncovered evidence of frequent gene flow between different European populations of F. graminearum s.s. [Tri core cluster regarding their European and American origins. The highest number of SNPs was reported in the Tri14 gene  were assigned with unique isolate codes and were stored at −25 °C in the fungal collection of the Department of Botany and Nature Protection, University of Warmia and Mazury in Olsztyn, Poland.For storage purposes, colonies were transferred to new PDA plates, and after six days of culturing, mycelia were covered with 1.5 g of sterile soil . All plaA patch of mycelium (approximately 0.1–0.2 mg) was scraped from the surface of the PDA plate and transferred to tubes with 1 mm silica spheres . Samples were homogenized at 40 s at the speed 6.0 m/s in 1 mL of deionized water on a FastPrep-24 instrument . After homogenization, 40 µL of homogenate was subsequently diluted with 160 µL of water and incubated for 2 min at 90 °C. 2 µL of aqueous phase was used for qPCR.Tri12 gene [Four species-specific TaqMan assays were used to identify all field isolates to the species level . Trichoti12 gene . Each reFor next generation sequencing (NGS), DNA from 35 isolates/strains was extracted from 0.1 g of mycelium as previously described in Kulik et al. . A patchTri clusters deposited in the GenBank database under accession numbers: KU572424 (3ADON genotype) and KU572428 (15ADON genotype) [http://www.geneious.com), as previously described in Kulik et al. [Tri clusters have been deposited in the NCBI database under the GenBank accession numbers MH514924-MH514957. The identity of all 35 analyzed strains were confirmed using TEF1 gene (translation elongation factor). The sequences have been deposited in GeneBank under accession numbers MH572233-MH572267.Whole-genome sequencing was performed at Macrogen . Genome libraries were constructed using a TruSeq DNA PCR-free library preparation kit . An Illumina HiSeq X platform was used to sequence the genomes using a paired-end read length of 2 × 150 bp with an insert size of 350 bp. For assembling, de-multiplexed and trimmed reads were aligned to the complete sequence of the core enotype) . Sequenck et al. . Annotat


Coix lacryma-jobi L. is an important minor cereal with a high nutritional and medicinal value in Asian countries. The hilly region of South China is the secondary center of diversity of Coix lacryma-jobi L. In the present study, we took a sample of 139 Coix lacryma-jobi L. genotypes from four geographical regions in Southwest China and analyzed the genetic diversity and population structure using AFLP markers. Six primer combinations detected a total of 743 (89.52%) polymorphic loci. The percentage of polymorphic bands within the four geographical populations ranged from 56.02% (Guangxi) to 86.75% (Guizhou). The overall genetic diversity of 139 Coix lacryma-jobi L. was relatively low (h ranged from 0.1854 to 0.2564). The neighbor-joining method grouped all Coix lacryma-jobi L. genotypes into two clusters with no geographical affinity observed among genotypes within the same group. The Fst indicated the two clusters existed great genetic differentiation. AMOVA analysis showed the molecular variation within populations was much higher than that among populations of geographical regions and subpopulations derived from STRUCTURE. Human activities and the natural outcrossing system of Coix lacryma-jobi L. may have a great influence on its distribution, genetic diversity, and population structure. Our study provides useful information for local breeding programs of Coix lacryma-jobi L. as well. Coix lacryma-jobi L., also commonly known as adlay or Job's tear, is a tall, robust, and strongly tillering grass belonging to the family Poaceae. It is native to Asia and now has been naturalized throughout the tropics and subtropics about 22°N and S [Coix lacryma-jobi L. has higher protein content than other cereals that makes it a good source of nutrition for humans and animals [Coix lacryma-jobi L., as a remedy in traditional medicine [Coix lacryma-jobi L., such as antioxidant activity [°N and S , 2. Coix animals , 3, 4. B animals . People medicine . Recent activity , 7, antiactivity , 9, antiactivity , antiallactivity , antiobeactivity , 13, andactivity –16.Coix lacryma-jobi L. is a cultivated species in the genus Coix which has 9 to 11 species. Southwest China is the secondary center of diversity of Coix lacryma-jobi L., and Guangxi (GX) might be one of its origin areas [Coix lacryma-jobi seed was found at the Hemudu site, indicating that Coix lacryma-jobi has been cultivated in China for more than 6000 years [Coix lacryma-jobi L. in China is estimated around 73,000 ha with a grain yield of 0.22 million tons [Coix lacryma-jobi L. both in China and in Southeast Asia [in areas , 18. It 00 years . The curion tons . Guizhouast Asia , 21.Coix germplasm using RAPD (random amplified polymorphic DNA) [Coix lacryma-jobi accessions and found accessions from GX, China possessed greater genetic diversity than the accessions originating from Korea [Coix lacryma-jobi L. accessions using SSR (simple sequence repeats) developed from maize and rice [Coix lacryma-jobi L. using SRAP (sequence-related amplified polymorphism) markers [Diversity analysis of a particular species or population is one of the key aspects of research for germplasm evaluation and crop genetic improvement, conducive to protection and effective utilization of germplasm resources. DNA molecular markers are a powerful tool for diversity analysis by detecting the genetic variations between individuals. Li et al. first performed genetic evaluation on hic DNA) . Ma et aom Korea . Guo et and rice . Wang et markers . Compare markers , 27. It  markers , linseed markers , and wil markers .Coix lacryma-jobi L. from China using AFLP markers. To our knowledge, this is the first attempt to characterize the genetic variability amongst Coix lacryma-jobi L. genotypes from four regions of southwest China using molecular markers. The results obtained from this study will provide useful information for germplasm utilization and other related studies in the future.In this study, we aim to assess the genetic diversity and population structure of 139 genotypes of Coix lacryma-jobi L. collected from four regions of southwest China  for 3 h at 37°C and then at 65°C for 3 h. Restricted DNA fragments were ligated to the EcoRI adaptor (50 μM) and MseI adaptor (50 μM) using T4 ligase  at 16°C overnight. The ligation product was diluted five times with ddH2O and then used as a template for preamplification. 2 μL of adaptor-ligated DNA was preamplified with primers E00 and M00 using the thermocycle carried out as follows: 94°C for 3 min, followed by 26 cycles of 94°C for 45 s, 50°C for 45 s, 72°C for 1 min, followed by 72°C for 10 min. The selective amplification was subsequently conducted using different combinations of FAM-labeled EcoRI primers and unlabeled MseI primers with three selective nucleotides. The preamplified product (20-fold diluted) was used as the template for the selective amplification in a total volume of 20 μL. The selective amplification was carried out using touchdown PCR programs as follows: 95°C for 5 min, 12 cycles of 95°C for 35 s, 65°C (−0.7°C per cycle) for 35 s, 72°C for 1 min, 23 cycles of 94°C for 30 s, 56°C for 30 s, 72°C for 1 min, and final extension at 72°C for 10 min. 1 μL of the diluted (10-fold) labeled PCR product combined with 15 μL solution of Hi-Di formamide and internal size standard LIZ-500 (volume ratio was 100 : 1) was analyzed via capillary electrophoresis on a DNA analyzer ABI3730XL (Applied Biosystems). The fragment analysis was carried out by the GeneMarker software.AFLP analysis was carried out using the protocol by Vos et al.  with somh), and Shannon's index (I) using POPGENE32 [http://taylor0.biology.ucla.edu/structureHarvester/). The optimum number of populations (K) was set in the range of 2 to 15. A burn-in period length of 10,000 followed by 100,000 MCMC (Markov chain Monte Carlo) repetitions was run ten times for each value of K number. The optimum K value was determined according to the method by Evanno et al. [The AFLP fragments in the range of 35–500 bp were obtained and scored as present (1) or absent (0) to generate a binary data matrix. The binary matrix was used to analyze the polymorphic features of each primer combination such as percentage polymorphism, polymorphism information content (PIC), Nei's genetic diversity index  were polymorphic. The details of marker features for each primer combination are given in Six primer combinations generated a total of 830 AFLP bands from 139 Coix lacryma-jobi L. genotypes into two major clusters, namely, I and II , Shannon's information index (I), and percentage polymorphism (PP). Nei's gene diversity values ranged from a minimum of 0.1854 to a maximum of 0.2564, Shannon's information index values varied from 0.2809 to 0.3906, and the highest percentage polymorphism were observed for the population of GZ (86.75%) followed by YN (77.59%), SC (70.60%), and GX (56.02%). The values of Nei's gene diversity, Shannon's information index, and percentage of polymorphic bands were all highest in GZ and lowest in GX.At the geographical level, results of genetic analysis of the four The pair-wise genetic distance values were also calculated for all four populations . Nei's gK based on the Bayesian clustering method was scored when K equaled 2  in comparison with previous studies in other crops, e.g., 72% polymorphism in maize [Coix lacryma-jobi L. Three parameters, PIC, EMR, and MI, were also calculated to evaluate the AFLP system applied in Coix lacryma-jobi L. where MI is considered a measure of the overall efficiency of a marker system [Coix lacryma-jobi L. genotypes growing under other climates.This is the first report of genetic characterization of in maize  and 85% in maize . This inr system . MarkersCoix lacryma-jobi L. from GZ, GX, YN, and SC is relatively low, h ranging from 0.1854 to 0.2564 and I ranging from 0.2809 to 0.3906, consistent with Xi et al.'s study that Coix lacryma-jobi L. had low genetic diversity at both the species level and the accession level [Coix lacryma-jobi L. also showed relatively high genetic similarity among Coix lacryma-jobi L. genotypes, such as 0.30~0.92 with an average of 0.78 in Guo et al.'s study [Coix lacryma-jobi L. was not region specific. In other words, genotypes collected from the same geographical region differed genetically. Genotypes from GZ, SC, and YN were scattered all over the dendrogram revealing higher levels of polymorphism while genotypes from GX, 6 out of 9 were grouped closely in one subcluster. This was in agreement with the level of polymorphism in Coix lacryma-jobi L. from the four different regions. GX had the lowest polymorphism (56.02%) while GZ had the highest polymorphism (86.75%). However, it was discordant with our expectation that Coix lacryma-jobi L. from GX should be more genetically diverse since GX was considered to be one of origin areas for Coix lacryma-jobi L. [Coix lacryma-jobi L. from GX were used for this study, and they were all from places adjacent to GZ. Therefore, the 9 genotypes did not represent the extent of genetic variations of Coix lacryma-jobi L. in GX. And this is the case with YN as well. Thus, a further study with a larger collection of Coix lacryma-jobi L. from different districts in GX, YN, and SC should be carried out. Another reason might be due to anthropological activities such as people who brought the seed to a new place to grow when they migrated, that would cause the fact that the true origin of the genotypes was not where they were collected. Economic development and government policy also have a great influence on the local distribution of Coix lacryma-jobi L. It is reported the habitat of wild Coix lacryma-jobi L. and Coix aquatica Roxb. in China has been damaged increasingly, compared with that in 80s of the last century, 70% wild Coix habitat in GX was destroyed [Coix lacryma-jobi L. from outside GZ or even China could likely be introduced into GZ, stored and planted by local farmers, resulting in increasing the Coix lacryma-jobi L. diversity in GZ. This may partially explain the reason that GZ had higher polymorphism than GX. Moreover, the result of the clustering of the four populations showed three groups were formed: GZ and SC populations presented in one group, and YN and GX populations were the other two groups. This may indicate the four Coix lacryma-jobi L. populations were originally from some areas in GX and then differentiated highly due to human activities like moving across regions or selective breeding over the course of time.Based on our genotyping data, the overall genetic diversity of on level . Moreove's study , 0.48~0.'s study . The den-jobi L. , 18. Oneestroyed , while Gestroyed , 21. TheCoix lacryma-jobi L. into two groups, but one genotype GZ-136 was not consistent. GZ-136 was in the bigger subpopulation by STRUCTURE while it was in subcluster IIC of the smaller group in the dendrogram. Note that GZ-136 was the only genotype in subcluster IIC indicating it, to a certain degree, was not genetically close to the other genotypes in the smaller cluster. The Fst among the two clusters was 0.168 indicating the two clusters existed great genetic differentiation according to Wright [Coix lacryma-jobi L., it may be due to the breeding system of Coix lacryma-jobi L. Mello et al. [Coix lacryma-jobi populations, the rate of natural outcrossing of C. lacryma-jobi ranged from 35.9% to 72.8%. Thus, the mixed breeding system may account for it having higher genetic variation within populations but not among them [Population structure analysis by STRUCTURE was generally in line with clustering based on genetic distance as they both divided 139 o Wright . Moreoveo et al.  investigong them .Coix lacryma-jobi L. from four regions in southwest China, the world secondary diversity center, were analyzed genetic diversity and population structure using AFLP markers. Six primer combinations identified a total of 743 polymorphic bands. The neighbor-joining method grouped all Coix lacryma-jobi L. genotypes into two clusters with no geographical affinity observed among genotypes within the same group. The Fst indicated the two clusters existed great genetic differentiation, but the overall genetic diversity of 139 Coix lacryma-jobi L. was relatively low; h ranged from 0.1854 (GX) to 0.2564 (GZ). Coix lacryma-jobi L. from GZ showed a higher level of genetic diversity compared to genotypes from other three regions. That may be due to the sample size difference among the four regions and human activities that affected the habitat and distribution of Coix lacryma-jobi L. AMOVA analysis showed that the molecular variation within populations was much higher than that among populations of geographical regions and subpopulations, which may be explained by the natural outcrossing system of Coix lacryma-jobi L.139


We investigated the numerical behaviour of our approach with simulations and showed that it can accurately describe complex spatial hierarchical structures. To demonstrate the intuitive and straightforward interpretation of our diversity measures in terms of effective number of components , we applied the framework to a real data set on coral reef biodiversity. We expect our framework will have multiple applications covering the fields of conservation biology, community genetics and eco‐evolutionary dynamics.Biological diversity is a key concept in the life sciences and plays a fundamental role in many ecological and evolutionary processes. Although biodiversity is inherently a hierarchical concept covering different levels of organization , a diversity index that behaves consistently across these different levels has so far been lacking, hindering the development of truly integrative biodiversity studies. To fill this important knowledge gap, we present a unifying framework for the measurement of biodiversity across hierarchical levels of organization. Our weighted, information‐based decomposition framework is based on a Hill number of order  However, for many decades, biodiversity has been treated in a piecemeal manner with ecologists focusing on species diversity  and population geneticists focusing on genetic diversity. This dichotomy has led to large differences in the type of diversity indices that have been used to measure species, trait, phylogenetic and genetic diversity. Ecologists were initially focused on empirical developments and generated a very large number of species diversity indices that strongly differ in their numerical behaviour Jost,  and estiRecently, motivated by this lack of common measures for biodiversity at different levels of biological organization, population geneticists have carried out methodological developments that extend the use of popular species diversity indices to the measurement genetic diversity at different levels of spatial subdivision [e.g., Shannon's and Simpson's indices  needed to compare different communities. This relatively easy access to real data and an initially limited interest in mechanistic models fostered the development of a large number of diversity measures to explore species distributional data  are structured geographically. These problems have been described in great detail in the literature . Although there has been some confusion =(1−FIS)(1−FST), where all terms are entropy measures describing the uncertainty in the identity by descent of pairs of alleles, when they are sampled from the whole set of populations (metapopulation) (1−FIT), from within the same population (1−FIS), or from two different populations (1−FST).Partitioning components of diversity is central to progress on these problems. Ecologists have related the traditional alpha, beta and gamma diversity using both additive and multiplicative schemes of partitioning. On the other hand, population geneticists have always used the multiplicative scheme based on the partitioning of the probability of identity by descent of pairs of alleles  population that has the same magnitude of random genetic drift as the real population being studied. There are different ways in which we can measure the strength of genetic drift, the most common being change in average inbreeding coefficient, change in allele frequency variance and rate of loss of heterozygosity, and each lead to a different type of effective size. Thus, the ideal and the real populations are equivalent in terms of the rate of loss of genetic diversity and not in terms of equal representation of distinct individuals. Probably the only similarity between Ne and the rationale underlying Hill numbers is in the sense that all the individuals in the ideal population contribute equally (on average) to the gene pool of the next generation.Note that the concept of effective population size, S denotes the number of species or alleles, pi denotes the relative abundance or frequency of species or allele i, and the exponent and superscript q is the order of the diversity and indicates the sensitivity of qD, the numbers equivalent of the diversity measure being used, to common and rare elements  is completely insensitive to species or allele frequencies and is known, respectively, as species or allelic richness depending on whether it is applied to species or allele frequency data. The diversity of order one (q = 1) weights the contribution of each species or allele by their frequency without favouring either common or rare species/alleles. Although Equation q = 1, its limit exists 1D=exIt is worth emphasizing that among all these different number equivalents or true diversity measures, the diversity of order 1 is key because of its ability to weigh elements precisely by their frequency without favouring either rare of common elements Jost, . Therefo4q = 1). In what follows, we first describe the framework in terms of abundance (species/genetic) diversities and then we provide an equivalent formulation in terms of phylogenetic diversity. For simplicity, we will use the notation D to refer to abundance diversities and PD to refer to phylogenetic diversities both of order q = 1. Appendix Our decomposition framework is focused on the information‐based diversity measure  and genetic data, to estimate alpha, beta and gamma diversities  across different levels of a hierarchical spatial structure. In this section, we consider a very simple example of an ecosystem subdivided into multiple regions, each of which in turn are subdivided into a number of communities when considering species data or a number of populations when considering genetic data. However, our formulation is applicable to any number of levels within a spatially hierarchical partitioning scheme and their associated number of communities and populations at each level , such as the example considered in our simulation study below  diversity into its regional and community/population‐level components. We do this using the well‐known additive property of Shannon entropy across hierarchical levels (and thus multiplicative partitioning of diversity) Batty, . Table 1Dα(1),Dα(2)andDγ. These diversity measures are defined in terms of relative abundances of the distinct elements  at the respective levels of the hierarchy. In what follows, we first present the framework as applied to allele count data and then explain how a simple change in the definition of a single parameter allows the application of the same framework to species abundance data. We assume that we are considering a diploid species  and focus on the diversity of order q = 1, which is based on the Shannon entropy  copies of allele i in population j and region k. Then, the total number of copies of allele i in population j and region k is Nijk=∑n=02nNinjk, and from this, we can derive the total number of alleles in population j and region k as N+jk=∑i=1SNijk, the total number of alleles in region k as N++k=∑j=1JkN+jk, and the total number of alleles in the ecosystem as N+++=∑k=1KN++k. All allele frequencies can be derived from these allele counts. For example, the relative frequency of allele i in any given population j within region k is pi|jk = Nijk/Njk+. In the case of region‐ and ecosystem‐level allele frequencies, we pool over populations within regions and over all regions and populations within an ecosystem, respectively. We define the weight for population j and region k as wjk = Njk+/N+++; the weight for region k thus becomes w+k=∑j=1Jkwjk=N++k/N+++. Table Genetic diversity indices are calculated separately for each locus, so we focus here on a locus with  = 0, 1,  copies oDα(1),Dα(2)andDγ; all other diversity measures can be derived from them , for each individual region k and then obtain the weighted average over all regions, Hα(2). Finally, we calculate the exponent of the region‐level entropy to obtain Dα(2), the alpha diversity at the regional level. We proceed in a similar fashion to obtain Dα(1), the diversity at the population level but in this case, we need to average over regions and populations within regions.Using these frequencies, we can calculate the genetic diversities at each level of spatial organization. Table Dα,Dα2)andandDα(1),Nijk represents the number of individuals of species i in population j and region k. All formulas for gamma, alpha and beta, along with the differentiation measures, at each level are given in Table The calculation of the equivalent diversities based on species count data can be carried out using the exact same procedure described above but in this case, 4.2We first present an overview of phylogenetic diversity measures applied to a single nonhierarchical case, henceforth referred to as single aggregate for brevity, and then extend it to consider a hierarchically structured system.4.2.1B branch segments in the tree, and thus, there are B corresponding nodes, B ≥ S. The set of species/alleles is expanded to include also the internal nodes as well as the terminal nodes representing species/alleles, which will then be the first S elements  .Let q, qPD, derived asChao et al. , 2014 geT years ago to the present. If q = 0, then 0PD=∑i=1BLi, which is the well‐known Faith's PD, the sum of the branch lengths of a phylogenetic tree connecting all species. However, this measure does not consider species abundances. Rao's quadratic entropy Q . Again, here we focus on qPD measure of order q = 1, which can be expressed as a function of the phylogenetic entropy , number of nodes/branches B and branch length Li in a similar manner as those in a single aggregate.The single‐aggregate formulation can be extended to consider a hierarchical spatially structured system. For the sake of simplicity, we consider three levels  as we did for the species/allelic diversity decomposition. Assume that there are pijk, pi+k and pi++, i = 1, 2, …, S as the ith species or allele relative frequencies at the population, regional and ecosystem level, respectively. To expand these relative frequencies to the branch set, we define aijk, i = 1, 2, …, B, as the summed relative abundance of the species/alleles descended from the ith node/branch in population j and region k, with similar definitions for ai+k and ai++, i = 1, 2, …, B; see Figure For the tip nodes, as in the framework of species and allelic diversity in Table , define,5The framework described above has been implemented in the R function iDIP (information‐based Diversity Partitioning), which is provided as Data The R function iDIP requires two input matrices:Abundance data: specifying species/alleles (rows) raw or relative abundances for each population/community (columns).Structure matrix: describing the hierarchical structure of spatial subdivision; see a simple example given in Data The output includes (i) gamma  diversity, alpha and beta diversity for each level, (ii) proportion of total beta information (among aggregates) found at each level and (iii) mean differentiation (dissimilarity) at each level.https://chao.shinyapps.io/iDIP/. This interactive web application was developed using Shiny (https://shiny.rstudio.com). The webpage contains tabs providing a short introduction describing how to use the tool, along with a detailed User's Guide, which provides proper interpretations of the output through numerical examples.We also provide the R function iDIP.phylo, which implements an information‐based decomposition of phylogenetic diversity and, therefore, can take into account the evolutionary history of the species being studied. This function requires the two matrices mentioned above plus a phylogenetic tree in Newick format. For interested users without knowledge of R, we also provide an online version available from 6Here, we describe a simple simulation study to demonstrate the utility and numerical behaviour of the proposed framework. We considered an ecosystem composed of 32 populations divided into four hierarchical levels  deviates ɛil for each population i and locus l. The random deviates were transformed into allele frequencies constrained between 0 and 1, using the simple transform:pil is the relative frequency of allele A1 at the lth locus in population i and, therefore, qil=(1−pil) is the relative frequency of allele A2. Each bi‐allelic locus was analysed separately by our framework, and estimated values of Dγ,DαandDβ for each spatial level .To simulate a realistic distribution of number of individuals across populations, we generated random values from a log‐normal distribution with mean 0 and log of standard deviation 1; these values were then multiplied by randomly generated deviates from a Poisson distribution with λ = 30, to obtain a wide range of population/community sizes. Rounded values  were then multiplied by Dγ,Dα,DβandΔD. Figure For each spatial structure, we generated 100 matrices of allele frequencies and each matrix was analysed separately to obtain distributions for Dα,DβandΔDvalues for the three levels of geographic variation below the ecosystem level . The results clearly show that our framework detects differences in genetic diversity across different levels of spatial genetic structure. As expected, the effective number of alleles  increases per region and subregion as the spatial structure becomes weaker  but remains constant at the population level, as there is no spatial structure at this level  so diversity is independent of δ.Figure Dβ component (middle row) quantifies the effective number of aggregates  at each hierarchical level of spatial subdivision. The larger the number of aggregates at a given level, the more heterogeneous that level is. Thus, it is also a measure of compositional dissimilarity at each level. We use this interpretation to describe the results in a more intuitive manner. As expected, as δ increases dissimilarity between regions (middle left panel) decreases because spatial genetic structure becomes weaker and the compositional dissimilarity among populations within subregions (middle right panel) increases because the strong spatial correlation among populations within subregions breaks down  measures the mean proportion of nonshared alleles in each aggregate and follows the same trends across the strength of the spatial structure  as the compositional dissimilarity Dβ. This is expected as we kept the genetic variation equal across regions, subregions and populations. If we had used a nonstationary spatial covariance matrix in which different δ values would be used among populations, subregions and regions, then the beta and differentiation components would follow different trends in relation to the strength in spatial genetic variation.The differentiation components Dγ = 1.6 on average across simulations for δ=0.1 up to Dγ=1.9 for δ = 6. In other words, the effective total number of alleles increases as genetic structure decreases. In terms of an equilibrium island model, this means that migration helps increase total genetic variability. In terms of a fission model without migration, this could be interpreted as a reduced effect of genetic drift as the gene tree approaches a star phylogeny  changes as a function of the strength of the spatial genetic structure, but values increase monotonically with δ −7Etelis coruscans =37.77,Island:Dα(1)=27.75). Given that there are eight and nine islands, respectively, in MHI and NWHI, one can interpret this by saying that, on average, each island contains a bit more than one endemic species equivalent. The beta diversity Dβ(2)=1.29 represents the number of region equivalents in the Hawaiian archipelago while Dβ(1)=1.361 is the average number of island equivalents within a region. However, these beta diversities depend on the actual numbers of regions/populations as well as on sizes (weights) of each region/population. Thus, they need to be normalized so as to obtain ΔD  at the individual island level  is much larger than those of order q = 1, 2, which indicates that all islands contain several rare species. Conversely, diversities of order q = 1 and 2 for Nihoa (and to a lesser extent Necker) are very close, indicating that the local community is dominated by few species. Indeed, in Nihoa, the relative density of one species, Chromis vanderbilti, is 55.1%.We can gain more insight about dominance and other assemblage characteristics by comparing diversity measures of different orders , a pattern that contrast with what is observed for species diversity (see above). Finally, both species exhibit similar patterns of genetic structuring, with differentiation between regions being less than half that observed among populations within regions. Note that this pattern contrasts with that observed for species diversity, in which differentiation was greater between regions than between islands within regions. Note also that, despite the similarities in the partitioning of genetic diversity across spatial scales, genetic differentiation is much stronger in E. coruscans than Z. flavescens, a difference that may be explained by the fact that the deep‐water habitat occupied by the former may have lower water movement than the shallow waters inhabited by the latter and, therefore, may lead to large differences in larval dispersal potential between the two species.Tables q = 0, 1, 2) is much less spatially variable than species diversity  to decompose global genetic and species diversity into their various regional‐ and community/population‐level components. The framework is applicable to hierarchical spatially structured scenarios with any number of levels . We also developed a similar framework for the decomposition of phylogenetic diversity across multiple‐level hierarchically structured systems. To illustrate the usefulness of our framework, we used both simulated data with known diversity structure and a real data set stressing the importance of the decomposition for various applications including biological conservation. In what follows, we first discuss several aspects of our formulation in terms of species and genetic diversity and then briefly address the formulation in terms of phylogenetic diversity.Biodiversity is an inherently hierarchical concept covering several levels of organization and spatial scales. However, until now, we did not have a framework for measuring all spatial components of biodiversity applicable to both genetic and species diversities. Here, we use an information‐based measure . Our framework is based on a Hill number of order q = 1, which weights all elements in proportion to their frequency and leads to diversity measures based on Shannon's entropy. This is a fundamentally important property from a population genetics point of view because it contrasts with measures based on heterozygosity, which are of order q = 2 and, therefore, give a disproportionate weight to common alleles. Indeed, it is well known that heterozygosity and related measures are insensitive to changes in the allele frequencies of rare alleles  and provide measures that could be applied to species abundance and allele count data, as well as species distance matrices and functional data  exhibits negative bias to some extent. Nevertheless, the undersampling bias can be largely reduced by novel statistical methods proposed by Chao and Jost  and differentiation measures are derived conditional on knowing true species richness and species abundances. In practice, species richness and abundances are unknown; all measures need to be estimated from sampling data. When there are undetected species or alleles in a sample, the undersampling bias for the measures of order and Jost . In our Dβ and differentiation ΔD measures can uncover the increase in differentiation between marginal and well‐connected subregions within a region as spatial correlation across populations (controlled by the parameter δ in our simulations) diminishes . Moreover, in the case of species diversity, differentiation among regions is much stronger than among populations within regions, but we observed the exact opposite pattern in the case of genetic diversity, genetic differentiation is weaker among regions than among islands within regions. This clearly indicates that species and genetic diversity spatial patterns are driven by different processes.We expect our framework to have important applications in the domain of community genetics. This field is aimed at understanding the interactions between genetic and species diversity Agrawal, . A frequq = 1, all species  are considered to be equally distinct from each other such that species  relatedness is not taken into account: only species abundances are considered. To incorporate evolutionary information among species, we have also extended Chao et al. , which implies that we cannot use the information provided by the association of alleles at different loci. This situation is similar to that of functional diversity (see preceding paragraph) and requires the consideration of a distance matrix. More precisely, instead of considering allele frequencies, we need to focus on genotypic distances using measures such as those proposed by Kosman  and GregAn essential requirement in biodiversity research is to be able to characterize complex spatial patterns using informative diversity measures applicable to all levels of organization (from genes to ecosystems); the framework we propose fills this knowledge gap and in doing so provides new tools to make inferences about biodiversity processes from observed spatial patterns.https://doi.org/dx.doi.org/10.5061/dryad.qm288) and BCO‐DMO (http://www.bco-dmo.org/project/552879).All data used in this manuscript are available in DRYAD ( Click here for additional data file. Click here for additional data file.


Gyps coprotheres) has undergone severe population collapse which has led to a listing of Endangered by the IUCN. Here, a comprehensive genetic survey of G. coprotheres is conducted using microsatellite markers. Analyses revealed an overall reduction in heterozygosity compared to other vulture species that occur in South Africa . Bayesian clustering analysis and principal coordinate analysis identified shallow, subtle population structuring across South Africa. This provides some support for regional natal philopatry in this species. Despite recent reductions in population size, a genetic bottleneck was not detected by the genetic data. The G. coprotheres, however, did show a significant deficiency of overall heterozygosity. This, coupled with the elevated levels of inbreeding and reduced effective population size, suggests that G. coprotheres is genetically depauperate. Given that genetic variation is considered a prerequisite for adaptation and population health, the low genetic diversity within G. coprotheres populations is of concern and has implications for the future management and conservation of this species.Globally, vulture species are experiencing major population declines. The southern African Cape vulture ( Habitat fragmentation and reduced effective population size can affect the genetic structure of endangered species3, by increasing levels of inbreeding and genetic drift within populations which in turn reduces the amount of genetic diversity within the gene pool5. Reduced genetic diversity in turn can have detrimental effects on species ability to recover from demographic, environmental and genetic stochasticity6, and can be a contributing factor in reduced long- and short-term survival of endangered species7.Examining the spatial distribution of genetic diversity, within and among populations, is important in the management and conservation of threatened taxa10. During the last decade, Africa and Asia have experienced the highest decrease in vulture populations12, as such these species are conservation priorities. There are 23 extant vulture species worldwide, of which 16 species are currently classified as threatened to critically endangered10. As a result, the majority of species are now endangered or critically endangered10. African vultures are particularly vulnerable, with a recent study suggesting that populations of six of the eleven African species have declined by an average of 62% over the past 30 years9. Despite this alarming trend, little is known about the population genetics of African vultures and what impact the reduction in population size has had on the genetic diversity of these birds. Conducting thorough genetic assessments of wide-ranging, highly mobile species, such as vultures, which move between countries or even between continents, poses a considerable challenge. For vultures, and many other raptors, direct estimates of abundance, natal philopatry and dispersal are particularly challenging because individuals are not only difficult to capture and mark, but re-capture rates are also very low15. Genetic methods can be valuable tools to evaluate genetic diversity, effective population size and gene flow among fragmented populations and have shown to be useful in highly mobile species17.Global vulture populations have plummeted in recent yearsGyps coprotheres) is one of the most well studied vulture species in southern Africa, after decades of conservation efforts the population continues to decline19. The species is currently listed as Endangered on the International Union of Conservation of Nature (IUCN) red data list19. The decline of this species is largely attributed to anthropogenic factors including habitat loss, reduced food supply and poisoning20. Current estimates suggest a wild population of approximately 9400 individuals21.Although the Cape vulture ; the second in the high lying regions of the KwaZulu-Natal and Eastern Cape provinces of South Africa and Lesotho29; and an small isolated breeding colony in the Western Cape province of South Africa18. The connectivity of these regional populations is unknown. Strong natal philopatry should result in pronounced populations substructure even in species such as vultures that travel extensive distances.The distribution of the species has also undergone a significant reduction with birds no longer breeding in much of their historical range Fig. . This spG. coprotheres. The genetic diversity of South African G. coprotheres populations was evaluated using microsatellite loci. The genetic diversity was compared to three ecologically similar vulture species also found in South Africa, but which have wider continental-scale distributions, namely the near-threatened bearded vulture (Gypaetus barbatus); the critically endangered hooded vulture (Necrosyrtes monachus); and the critically endangered white-backed vulture (Gyps africanus)10. In addition, this study aims to examine the regional connectivity among six breeding colonies and provide insight into natal philopatry in G. coprotheres.This study aims to determine how the recent dramatic reduction in population size has shaped the genetic diversity of G. coprotheres individuals collected from across the southern African distribution of the species. The missing data included in the final data set varied across loci, but was minimal (mean = 11%). Identity analysis estimates showed that all specimens were unique  and all 605 G. coprotheres individuals were used in subsequent analyses. The mean null allele (No) frequency across the 13 microsatellite loci for G. coprotheres data was 5.4% , N. monachus (n = 54), and G. africanus (n = 68) were also included and genotyped using the same microsatellite loci, to allow for direct comparison of genetic diversity values. The mean observed heterozygosity in G. coprotheres (Ho = 0.38) was much lower than that observed in G. barbatus (Ho = 0.50), N. monachus (Ho = 0.71), and G. africanus (Ho = 0.65). Gyps coprotheres and the much smaller, isolated South African G. barbatus population showed elevated levels of inbreeding (mean F = 0.17). Inbreeding was not detected in the other two vulture species N. monachus F = −0.07 and G. africanus F = 0.07  were recovered between the Potberg colony and all colony pairs and the Skeerpoort colony and all colony pairs as shown by pairwise FST estimates reported in Supplementary Table ST analysis showed no clear genetic partitioning when individuals were assigned to localities . Figure G. coprotheres sampled. When all 605 G. coprotheres individuals were analysed  was performed on sed Fig.  graph a sed Fig.  graph b.sed Fig.  graph c,G. coprotheres individuals collected at the six breeding colonies . The majority of genetic variation, however, occurred within individuals  for G. coprotheses grouped by geographic region showed that the highest gene flow occurred from the Middle region to the Northern region  = 0.30 to 0.32; Table 14 and the majority of these samples were collected from feeding sites. Interestingly, the rates of migration between the Western and Northern regions are low (m < 0.04), indicating that these two regions are demographically independent from each other. The results suggest that the Middle region may act as a source population for both the Western and Northern regions.Migration rates below 0.10 were used to indicate demographic independenceG. coprotheses collected at the six breeding colonies showed that the highest migration rates were observed out of the Collywobbles colony to all other colonies .The migration rates between G. coprotheses grouped by geographic region. The heterozygote excess method (Hx) for both mutation models showed no signs of recent bottleneck . No significant deviation from the normal L-shaped distribution (Mode-shift) was observed in any of the regions. A similar result was observed when G. coprotheses from the six breeding colonies were analyzed  for the Wilcoxon test under both mutation models. G. coprotheres population deviated from the expected ratio (1:1 for heterozygous deficiency to heterozygous excess) for non-bottlenecked, mutation equilibrium populations. None of the colonies deviated from the expected ratio for any of the tests. However, when all individuals collected from the six breeding colonies were observed as a single colony unit, significant deviation from the expected Wilcoxon test for heterozygous deficiency and sign test under SMM was observed. The heterozygous deficiency indicates that when the breeding colonies are considered as a single colony unit, they do not behave in mutation-drift equilibrium.Interestingly, two regions (Middle and Northern) showed significant heterozygosity deficiency  for the Wilcoxon test under both mutation models. Additionally, the sign tests for the Middle region and Northern region indicated significant heterozygosity deficiency  under both mutation models. Both regions significantly deviate from the expected ratio (1:1 for heterozygous deficiency to heterozygous excess) for non-bottlenecked, mutation equilibrium populations. When G. coprotheses. The monogamous mating model estimated Ne = 409 individuals : 318.5; 537.2) the ratio of effective population size to census population size (Ne/N)31 was 0.044. When the random mating model was selected Ne = 208 individuals  Ne/N = 0.022. This is much lower than that reported number of G. coprotheses in recent census data 19.Effective population size (Ne) was estimated assuming both monogamous and random mating for G. coprotheres has a relatively narrow distribution in comparison to other African vultures, which have continental scale distributions. As such this species provides a unique opportunity for us to intensively sample across the distribution of the species and thoroughly assess the genetic diversity of the species. This study represents the first comprehensive genetic assessment of the endangered G. coprotheres and provides important genetic baseline data for the conservation and management of the species.G. coprotheres were recorded in this study. Genetic diversity estimates of G. coprotheres were also lower than that observed in the three other ecologically similar vulture species . Although the sample sizes of the other three species were much smaller than that used for G. coprotheres the same genetic markers were amplified across all vulture species examined, allowing for direct comparison of levels of genetic diversity. This study found higher allelic diversity  in G. coprotheres probably as a result of differences in sample size, but, alarmingly N. monachus and G. africanus showed higher mean observed heterozygosity estimates. N. monachus revealed significantly higher levels of heterozygosity and low levels of inbreeding (F = −0.07). The genetic diversity values for G. africanus and G. barbatus estimated in this study are similar to the values reported in the literature for the Namibian G. africanus population7 and European G. barbatus populations32. This provides support for the results recovered in the present study not being biased by sample size or marker choice, but rather reflect the true levels of genetic variation present in populations of these African vultures. The lower than expected genetic diversity in G. coprotheres is of concern, given that both N. monachus and G. africanus are currently at higher extinction risk than G. coprotheres. N. monachus and G. africanus are currently listed as Critically Endangered by the IUCN10. The genetic diversity observed in G. coprotheres was lower than that observed in other threatened raptors such as the Spanish imperial eagle 3; and the Eurasian black vulture (Aegypius monachus)33.Low levels of observed heterozygosity (mean Ho = 0.38) in ST values suggested genetic partitioning at a regional scale. Vultures from the two northern colonies (Skeerpoort and Kransberg) were genetically distinct from the other colonies. Given the geographic isolation and small size of the Potberg colony, we expected this colony to be the most effected by genetic drift. Although no marked allele frequency differences were observed in the STRUCTURE analysis, this colony did show the highest private allelic richness which is indicative of restricted gene flow. This is supported by the pairwise FST values in Supplementary Table The Bayesian assignment tests indicated very shallow genetic structure across the range of the species. However, the pairwise F37. The lack of genetic structuring in avian populations is attributed to high dispersal capabilities40. A similar pattern of low genetic structure was observed in Egyptian vulture (Neophron percnopterus)41 and griffon vulture (G. fulvus)42. In contrast, other wide-ranging vulture and raptor species such as the bearded vulture G. barbatus43, the Eurasian black vulture (Aegypius monachus)33; and the white-tailed eagle 44 have shown high levels of population differentiation.The shallow genetic structuring seen in this species was not unexpected; given that birds often show shallower genetic structuring than that of other vertebrate speciesG. coprotheres exhibit natal philopatry28. Analysis of individuals collected from the six breeding colonies across South Africa did not support strong natal philopatry in this species, rather the genetic data suggests regional philopatry. The migration and pairwise FST results showed that the Potberg colony was demographically independent from all other colony pairs. The Skeerpoort and Kransberg colonies were also distinct, while the Collywobbles, Msikaba and Umzimkulu colonies seems to be operating as a single large regional unit.The degree of natal philopatry  can influence the extent of genetic structuring in animal populations. Data from radio tracking and ringing studies have suggested that G. coprotheres are listed as endangered by IUCN due to declines in overall population numbers19. Some recent surveys have however, reported increasing population number in some areas45. Populations that have experienced a recent bottleneck event are usually characterized by an excess of heterozygotes, as opposed to a population at mutation-drift equilibrium47. Over time however, inbreeding and genetic drift following a severe bottleneck event would lead to populations containing more homozygotes (heterozygote deficient). In the data generated in the present study, no signs of heterozygosity excess were observed in South African G. coprotheres populations. This could be because G. coprotheres may be currently experiencing a bottleneck and not enough generations have passed for the genetic signal of this event to be detected. In contrast, population genetic theory also suggests that G. coprotheres populations may have reduced genetic variation due to older bottleneck events48. This latter hypothesis was supported by results of Wilcoxon signed rank tests which found significant heterozygote deficiency in vultures from all three geographic regions and four colonies .G. coprotheres populations, which have higher genetic variability, is an important step forward in conserving these birds. Genetically diverse populations could be valuable source populations for future translocation programmes. Six of the 24 localities showed elevated heterozygosity , whereas all other localities showed reduced heterozygosity. The isolated Potberg colony in the Western Cape had the lowest genetic variation of all the breeding colonies. This is not unexpected given the isolated nature of this colony. Careful management and monitoring of this colony is needed to avoid the detrimental effects of inbreeding and genetic drift. Given the shallow genetic structuring observed across the range of the species and the high levels of gene flow among breeding colonies, the present study suggests that the entire South African G. coprotheres population be managed as a single management unit.Identifying and prioritizing G. coprotheres populations. This, together with the rapid decline of this species21, highlights the need for conservation strategies to include the maintenance of genetic diversity as a population management tool. Reductions in genetic diversity can have serious implications on the evolutionary potential of a species49. The present study highlights the utility of microsatellite markers for the assessment and monitoring of genetic diversity in an African vulture species. The data produced in this study can be used as a baseline for future genetic monitoring and species recovery programmes.The results from this study showed low levels of genetic diversity and variability in South African G. coprotheres from 24 localities, across the South African distribution of the species, were sampled for this study (Supplementary Table G. barbatus (n = 54), N. monachus (n = 54), and G. africanus (n = 68) were also included and genotyped using the same microsatellite loci, to allow for direct comparison of genetic diversity values.A total of 605 18. Blood samples were also collected when vultures were captured and fitted with global positioning system/global system for mobile transmitters14. Bloods were stored on Whatman FTA® Elute cards . Archival museum samples (dried skin snips) were sourced from local South African museums  was used for all DNA extractions. Extractions protocols were modified slightly for feather and archival samples to improve DNA yield. Modifications included: incubation of the sample and proteinase K for 48 hours in a shaking water bath (56 °C), the lysate was incubated in B3 buffer for 45 minutes (70 °C), the final volume of pre-warmed Buffer BE was decreased to 80 μl and the samples were incubated at 70 °C for 20 minutes followed by centrifuging and then reapplication of the solution onto the membrane and a final incubation at 70 °C for an additional five minutes followed by a final centrifuging step.G. fulvus50 and G. barbatus51 (Supplementary Table TM Fast Multiplex PCR kit (KAPA Biosystems). The 10 μl reactions consisted of ~2–30 ng template DNA, 5 μl KAPA2G Fast Multiplex mix, 0.2 μM of each primer, 0.1 μl of 1 mg/ml bovine serum albumin (BSA) and purified water. In reactions performed using DNA extracted from feather and archival samples, template DNA was increased to ~20–200 ng to improve amplification success. The cycling parameters for all loci followed the standard KAPA2GTM Fast Multiplex PCR kit protocol except for multiplex 1, where the annealing temperature was reduced to 58 °C. All amplified products were sent to the Central Analytical Facility at Stellenbosch University, South Africa for fragment analysis. The software GeneMarker v2.4.0 (Soft Genetics) was used for genotype scoring. To ensure genotyping consistency, all archival samples were re-amplified, and each locus was genotyped multiple times (up to five times). In addition, 20% of all feather, muscle and blood samples were re-amplified multiple times (up to five times) to verify the reliability of the data.Thirteen microsatellite loci were chosen from a suite of markers used in previous studies of 52. Cervus was also used to estimate polymorphic information content (PIC) for each locus. Null allele frequencies for each locus were estimated in FreeNA53 using the expectation maximization (EM) algorithm54. Because null alleles can bias population structure analysis55, uncorrected global FST were compared to FST values corrected using the excluding null alleles (ENA) method53 using a paired t-test. Linkage disequilibrium was tested using Genepop v4.256. Deviations from Hardy-Weinberg equilibrium were estimated in GenAlEx v6.50257. The effective population size (Ne) was estimated in NeEstimator v2.0158 implementing both random and monogamous mating.To ensure that duplicated genotypes were not included when feathers were collected, identity analysis was performed in Cervus v3.0.759. The observed number of alleles in a population is dependent on sample size and given the opportunistic nature of sampling used in this study sample size varied across localities. Consequently, allelic richness was calculated using a rarefaction index estimated in FSTAT and the number of private alleles within populations were estimated using the rarefaction method implemented in HP-RARE v1.0 software60. All multiple comparisons were adjusted using the Bonferroni correction.Genetic diversity estimates  were calculated in GenAlEx. Tests for deviation from Hardy-Weinberg equilibrium were performed in Genepop. Allelic richness was estimated in FSTAT v2.9.3.261. For each analysis ten independent runs were performed consisting of 100000 Markov chain Monte Carlo (MCMC) replicates with a burn-in of 10000 and the proposed number of genetic clusters (K) ranging from one to ten. The admixture ancestry model with correlated allele frequencies was selected for all runs. Sampling locality information was incorporated using the LOCPRIOR model. STRUCTURE Harvester62 was used to estimate the most probable number of genetic clusters63. Membership probabilities  for each individual and for each genetic cluster were estimated using ClumpAK64. Bar plots from STRUCTURE runs were created in Pophelper65.Bayesian assignment tests were performed in STRUCTURE v2.3.4.66. For this reason, Principle Coordinate Analysis (PCoA) was also performed in GenAlEx using pairwise codominant genotypic genetic distance. PCoA does not rely on a model but rather multidimensional scaling to visualize similarity in a dataset.The correct identification of the number of genetic clusters can be challenging if there is unbalanced sampling or complex phylogeographic structure i.e. when there are unequal genetic distances among subpopulations or unbalanced sample sizesST values were estimated in FSTAT. Mantel tests67 were performed in GenAlEx, to test for correlation between geographic distance and genetic distance.Analysis of molecular variance (AMOVA) was performed in GenAlEx. Three AMOVA’s were conducted, one grouping individuals by sampling locality, a second grouping individuals by geographic region. A third AMOVA was conducted only on individuals collected from the six breeding colonies. Pairwise F68. Two analyses were performed consisting of 10000000 iterations, with a burn-in of 1000000 iterations and a sampling frequency of 90000 iterations. Multiple runs were performed with varying run lengths to ensure MCMC chain convergence. The delta values for each parameter were adjusted to achieve a 20–60% acceptance rate68. In analysis to estimate gene flow among the three geographic regions, the final delta values were delta allele frequency = 0.30, delta migration rate = 0.10 and delta inbreeding coefficient = 0.50. In analysis to estimate gene flow among the six breeding colonies, the final delta values were delta allele frequency = 0.50, delta migration rate = 0.10 and delta inbreeding coefficient = 0.70. For both analyses, migration rates below 0.10 were used to indicate demographic independent populations30.Migration rates (gene flow) between the three geographic regions and the six breeding colonies were estimated in BayesAss v1.369 was used to test for evidence of heterozygosity excess (Hx)47 in G. coprotheres populations. Two analyses were performed, one grouping individuals by geographic region, and a second only including individuals collected at the six breeding colonies. Each analysis ran two mutation models. The conservative stepwise mutation model (SMM) and the two-phase model (TPM) with 90% stepwise mutation71 and a variance of 1269 to include the observed range of multistep mutations in natural populations72. Two statistical tests (Wilcoxon sign-rank test and the sign test) as well as a qualitative test (Mode-shift test), were performed. The Wilcoxon sign-rank test is effective in detecting recent declines in effective population size (Ne) and assumes that populations that have recently undergone a decline in effective population size will have a higher level of heterozygosity compared to a population at mutation-drift equilibrium5. The sign test was used to identify the number of microsatellite loci that have either heterozygosity excess (Hx) or heterozygosity deficiency (Hd). Changes in heterozygosity (excess or deficit) can occur after a recent change in the effective population size or if heterozygotes have a selective advantage or disadvantage69. In populations experiencing a bottleneck event, alleles are usually lost faster than heterozygosity, thus producing a heterozygote excess47. The Mode-shift is a qualitative indicator that can differentiate between bottlenecked and stable (non-bottlenecked) populations. This test can detect the genetic changes caused by a population decline within a few dozen generations73. Therefore, only recent bottleneck events will be identified using this method.BOTTLENECK v1.2.02Supplementary Table


Plasmodium falciparum migration may inform strategies for malaria elimination. Here we elucidate fine-scale parasite population structure and infer recent migration across Southeast Asia using identity-by-descent (IBD) approaches based on genome-wide single nucleotide polymorphisms called in 1722 samples from 54 districts. IBD estimates are consistent with isolation-by-distance. We observe greater sharing of larger IBD segments between artemisinin-resistant parasites versus sensitive parasites, which is consistent with the recent spread of drug resistance. Our IBD analyses reveal actionable patterns, including isolated parasite populations, which may be prioritized for malaria elimination, as well as asymmetrical migration identifying potential sources and sinks of migrating parasites.Estimates of  Plasmodium falciparum parasites and inferring migration patterns can guide malaria elimination strategies. Using genome-wide data for 1722 parasites collected from 54 districts, the authors use identity-by-descent approaches to estimate regional parasite migration and spread of artemisinin drug resistance.Understanding genomic variation in  Plasmodium falciparum resistance to both artemisinins and key partner drugs has compromised the efficacy of current first-line artemisinin-based combination therapies in the Greater Mekong Subregion, and poses a serious threat to renewed hopes for global malaria eradication1. To mitigate this threat, the World Health Organization has recommended elimination of P. falciparum malaria from the Greater Mekong Subregion by 20302. As elimination efforts proceed, increasingly local information about malaria risk will be important for prioritizing resources and optimizing strategies for malaria elimination5. Estimates of parasite migration may be important in stratifying malaria risk; however, to be informative for elimination efforts, such estimates need to reflect recent patterns of parasite movement in time and space.The emergence of 8, with the length of haplotypes shared between individuals being inversely proportional to the time since divergence from that common ancestor10. Shorter haplotypes, which have been broken down by recombination over time, indicate more historic demographic events, while longer haplotypes, which have undergone less recombination, are indicative of more recent events12. Identity-by-descent (IBD) approaches are increasingly used in human genomics for inference of recent demographic events13. Such approaches are now being used to document changes in malaria parasite population demography as a result of reduced malaria transmission14 or sweeping drug resistance mutations15 and to examine connectivity between populations16.Genomic regions that have been inherited from a common ancestor are said to be identical-by-descent19. In the past, inferences about the relationship between genetic population structure and geographic distance often involved post hoc geographic interpretation of genetic subpopulations determined without consideration of their spatial coordinates21. However, more recently, approaches have been developed that explicitly model the spatial structure in the data23. One such approach is estimated effective migration surfaces (EEMS)23. The EEMS toolkit is a promising ecosystem of computational tools that couples geospatial mapping capabilities with gene-flow modeling to allow researchers to visualize geographic variation associated with genetic population structure. By finding contrasts between genetic distance and geographic distance, EEMS23 makes use of geo-referenced genomic data to visualize regions of relatively high- and low-effective migration within a geographic region, resulting in a visually intuitive representation of population structure within a given geographic area. In other words, for a given geographic distance between different sampling locations, EEMS estimates whether there is more or less migration compared to other locations of equal distance. In this study, we use estimates of IBD and EEMS to identify fine-scale population structure as well as migratory patterns of P. falciparum driven by contemporary and relatively recent demographic events.Patterns consistent with isolation-by-distance, a model predicting that populations that are closer in geographic distance are more genetically similar, have been shown using a variety of methods for many different organismsP. falciparum isolates collected from 54 districts within the Greater Mekong Subregion and Bangladesh during 2008–2013 25 for each pair of isolates, the cumulative IBD sharing between sample pairs ranged from 0 to 1650 cM  genotyped from 1722 clinical 013 Fig. 24. When When examining pairs of isolates both within and between districts Fig. , IBD wasWe next examined IBD sharing between district pairs, or regional relatedness. Regional relatedness was estimated as the average pairwise IBD sharing between isolates from two districts, stratified by IBD segment length Fig. . RegionaThe presence of a greater number of genetically related parasites within a region is analogous to the presence of family structures in human populations. As is commonly done in population studies in humans, where genetically related individuals are excluded to avoid confounding, we performed analyses both including and excluding highly genetically related parasites (see Methods). The inclusion of genetically similar isolates increased the magnitude of regional relatedness estimates within some districts  between regional relatedness and geographic distance  and the admixed isolates of a second district (D2) provide insights into the possibility of migration from D1 to D2. As seen in Supplementary Table p value < 5 × 10−4) from Bago  to Ranong (southern Thailand) and from Kawthaung (southern Myanmar) to Ranong (southern Thailand). We also detected several asymmetrical migrations between districts in central, western, northern, and southern Cambodia  (N = 475) and those isolates possessing the wild-type allele (N = 437). Average IBD sharing estimates were higher within districts than between districts for both K13 mutant and K13 wild-type isolates (Table within = 17.31 cM) than between K13 wild-type isolates (average IBDwithin = 7.07 cM). Between districts, there was 5.88-fold greater IBD sharing between K13 mutant isolates (average IBDbetween = 5.59 cM) than between K13 wild-type isolates (average IBDbetween = 0.95 cM). These differences were statistically significant, based on permutation tests . When stratified by IBD segment length, the significant difference in regional relatedness of K13 mutant and K13 wild-type isolates was only recapitulated at segment lengths greater than 30 cM  contoursion Fig.  and withion Fig. . Using tion Fig.  in the Bion Fig. .Fig. 5EETo test the robustness of the EEMS model, we undertook a population-level jackknife sampling approach to iteratively exclude isolates from a single district and estimate migratory patterns. We observed similar patterns of migratory surfaces with a few exceptions , as these areas are less likely to experience reintroduction of parasites from other areas. Likewise, elimination programs may want to target sources of transmission prior to sinks, again, to avoid reintroduction of P. falciparum from the former to the latter. Based on EEMS results, the landscape may be divided into areas of relatively higher or lower parasite migration, which may allow identification of discrete geographic regions separated by barriers to migration that could be exploited when planning intervention strategies. These approaches thus hold promise for accelerating the pace of malaria elimination in this region and preventing the global spread of multidrug resistance.Based on IBD, we are able to observe population structure at the district level. Microstratification of malaria risk to more local levels  would be desirable for public health officials who are planning and carrying out interventions. As a next step, we will apply these approaches to determine whether population structure can be detected at the village level. A recent study by Taylor et al.P. falciparum infections confirmed by microscopy or polymerase chain reaction56. The study sites spanned the Greater Mekong Subregion , bordering regions of Bangladesh, and two sites in Africa. All samples were collected from 2008 to 2013 with informed consent from symptomatic individuals that met the inclusion criteria of the initial study protocol with prior approval from the local ethical review boards56. Parasite genotyping and genomic analyses were undertaken after prior approval of the University of Maryland School of Medicine Institutional Review Board.Samples were collected from completed studies involving passive surveillance for clinical malaria and clinical trials of Plasmodium falciparum Community Project (n = 1468)57, or, for samples that did not meet quality control criteria for whole-genome sequencing or were not part of the Community Project (n = 717), were genotyped using a P. falciparum-specific NimbleGen 4.2M probe custom DNA microarray58. The same nucleotide positions typed on the microarray were extracted from whole-genome data for analysis. Genotyping data are publicly available through the MalariaGEN website  or through the NIH Gene Expression Omnibus (www.ncbi.nlm.gov/geo/) (Accession number: GSE100704) and European Variation Archive (Accession number: PRJEB28530). Based on previous analyses, 28,496 SNPs could be reliably typed58. Beagle software59 was used to impute missing SNP calls for genotyped isolates. ADMIXTURE60 was used to cluster parasites prior to imputation. Parasites with 100% ancestry values in each cluster were imputed within that cluster, and these imputed pures were used as a reference set to impute admixed parasites. A genotype probability of 90% was used to call an imputed SNP. Only biallelic SNPs were included in the dataset. Heterozygous SNP calls indicative of the presence of multiple parasite clones were coded as missing and thus not included in the analysis of haplotypes. Samples and SNPs were excluded after applying missingness cut-offs of 10% for samples and 15% for SNPs using the PLINK genomic analysis toolkit (version 1.9)61. District-level geographic coordinates were available for 1722 samples that were used for downstream analysis.A total of 2185 samples were genotyped. Single-nucleotide polymorphisms (SNPs) were either called from whole-genome sequences generated at the Wellcome Sanger Institute as part of the MalariaGEN 62. Genetic map files utilized to identify IBD segments and convert chromosomal positions (bp) to genetic positions (cM) were determined from supplementary information provided in Jiang et al.25. IBD tracts of lengths 2 cM or longer were inferred from the BEAGLE results. Since BEAGLE assesses IBD tracts for each haplotype of a diploid genome, we additionally merged duplicate and overlapping IBD tracts within each pair of samples generated due to the haploid nature of the P. falciparum genome to generate a final set of nonduplicated, nonoverlapping IBD segments of length 2 cM or longer shared between two samples. Furthermore, cumulative IBD sharing between two samples was utilized to identify genetically related samples. Two samples were considered highly genetically similar if greater than 625 cM (25%) of the genome was identical-by-descent . Samples were then identified iteratively until no two samples shared greater than 625 cM in cumulative IBD tract lengths.The SNPs genotyped and identified across all samples were separated into individual chromosomes. Only bi-allelic SNPs were retained for estimation of IBD measures after excluding all singletons. The final set of bi-allelic SNPs were utilized to compare all samples to identify genomic segments that are IBD using the BEAGLE software tool (version 4.1)D1 and D2  were computed from the mean length of total IBD sharing for all pairs of individuals where one individual is from D1 and the other individual is from D2. Total number of pairs, NPairs = ND1 × ND2 if D1 ≠ D2 and NPairs = N × (N − 1)/2 if D1 = D2. Regional relatedness estimates can be noisy for regions with small sample sizes, hence districts with fewer than three isolates were excluded. Regional relatedness was estimated including and excluding genetically similar isolates to illustrate regional relatedness driven by genetically similar isolates. Regional relatedness was also estimated based on stratification of IBD segments based on segment length. In addition, these estimates were further stratified by nonadmixed and admixed samples within a population and alternatively were estimated specifically for drug-sensitive and drug-resistant parasites in a population. To test for significance in the latter, we compared the average total IBD sharing between K13 mutant and K13 wild-type isolates within and between districts and computed significance by permuting across the mutation status. These measures of genetic similarity between geographical regions have been used for visualization and downstream computational analyses. Visualizations were generated using R packages ggplot263, ggmap64, and geographical coordinates for each district.We computed the cumulative shared IBD estimates, the cumulative number of shared IBD tracts, and the average IBD tract length for every pair of individuals. We also stratified the IBD tracts by length into three bins of IBD lengths between 2 and 15 cM, 15 and 30 cM, and greater than 30 cM based on the distribution of lengths observed in Supplementary Fig. p value estimate. In addition, to model the expected decay of IBD sharing with increasing geographical distance, we correlated the genetic similarity and geographical distance vectors of a single population with every other population using the Spearman method to compute correlation R values and significance p values.Using longitude and latitude coordinates for each of the districts, we calculated the geographical distance between two populations. The geographical distance matrices of distance measures between each population pair were compared to the genetic similarity matrices computed from IBD sharing estimates. To quantify the correlation between the geographical distance and genetic distance matrices, we use the Mantel test (provided in the R package “ecodist”). The Mantel test was computed using 10,000 permutations to generate a significance 23. The EEMS analysis involves covering the study area with a dense equidistant triangular grid (Voronoi network), and each sample location is adjusted to the closest deme (vertex) on the grid. A Bayesian approach is then used to estimate migration parameters based on a stepping-stone model, which assumes individuals migrate locally between demes and each deme exchanges migrants only with its neighbors. We computed genetic dissimilarity matrices using EEMS and assigned geographical coordinates to each sample from each district to contrast geographic and genetic distances between demes. Migration surface contours were estimated using 400 demes for all Greater Mekong Subregion districts as well as for a subset of isolates from Cambodia and neighboring districts. The MCMC analysis was run for 15,000,000 MCMC iterations including 14,000,000 burn-in iterations and repeated using 10 different seeds to ensure the convergence of the MCMC chains as well as the accepted distributions of parameters as specified by the developers. Final spatial visualizations illustrating migratory surfaces were generated using R scripts provided by EEMS. In addition, to test the robustness of the models, we applied a jack-knife sampling approach and repeated the EEMS runs after iteratively excluding isolates from a single district.Estimated effective migration surfaces (EEMS) is a new approach to estimate genetic migration patterns over a given geographic regionP. falciparum within the Greater Mekong Subregion, we introduced a relatively simple method for detection of asymmetric migration using IBD estimates. We stratified the samples from a single geographical location into nonadmixed and admixed samples using admixture estimates from a prior analysis. We computed average estimates of IBD sharing  between the nonadmixed samples of D1 and the nonadmixed samples of D2 and compared them to the IBD estimates between the nonadmixed samples from one population and the admixed samples from the other population  and the converse . Hence, we computed the regional relatedness between nonadmixed isolates (N) from one district to the admixed isolates (A) of another district  and vice versa  for all district pairs. We also computed the regional relatedness between the nonadmixed isolates from both districts , which indicates the potential of the isolates of the two districts to share a recent common ancestor. Only district pairs where the regional relatedness estimate RRXN-YA or RRYN-XA was greater than RRD1,N-D2,N were considered. These genetic similarity measures between the nonadmixed and admixed samples across populations were used to estimate the relative levels of migration between the two populations. The significance of the difference in relative migration levels was estimated using permutation tests.To provide insights into the migratory patterns of P.R. is a staff member of the World Health Organization. P.R. alone is responsible for the views expressed in this publication and they do not necessarily represent the decisions, policy or views of the World Health Organization. The views expressed are those of the authors and do not reflect those of the U.S. Department of Defense or the U.S. Government. Further information on research design is available in the Supplementary InformationReporting Summary


Strix occidentalis caurina) using genotypes of 339 Northern Spotted Owls obtained for 10 microsatellite loci. Spatial clustering analyses identified a distinct genetic cluster at the southern extent of the region examined. This cluster could not be linked to landscape connectivity patterns and suggested that post-Pleistocene processes were involved with its development rather than contemporary landscape configuration. We also compared matrices of pairwise inter-individual genetic distances with resistance distances derived from a circuit-theory based framework. Resistance distances were obtained for an idealized raster map that reflected continuous unimpeded dispersal habitat across the landscape along with five empirically-derived raster maps reflecting the 1870’s, 1940’s, 1986, 1994, and 2012. Resistance distances from the idealized map served as surrogates for linear geographic distances. Relative to idealized conditions, resistance distances were ~250% higher in the 1940’s and ~200% higher from 1986 onward. Resistance distances from the 1870’s were ~40% higher than idealized conditions. Inter-individual genetic distances were most highly correlated with resistance distances from the idealized map rather than any of the empirical maps. Two hypotheses explain our results. First, our results may reflect temporal lags between the onset of large-scale habitat alterations and their novel effects on genetic structure in long-lived species such as Northern Spotted Owls. Second, because Northern Spotted Owls disperse over long distances, our results may indicate that forest habitat has never been sufficiently fragmented to the point where connectivity was disrupted. The second hypothesis could indicate that forest management practices mandated by the Northwest Forest Plan succeeded with one of its primary goals. However, our results do not represent a complete portrayal of the status of Northern Spotted Owls given detection of significant population declines and bottlenecks in other studies. Future investigations based on computer simulations may help distinguish between hypotheses.Landscape genetics investigations examine how the availability and configuration of habitat influence genetic structure of plants and animals. We used landscape genetics to evaluate the role that forest connectivity plays in determining genetic structure of the federally-threatened Northern Spotted Owl ( Landscape genetics, a fusion of population genetics and landscape ecology , aims toStrix occidentalis caurina). Northern Spotted Owls occur along the northwest coast of North America from southwestern British Columbia to central California [S. varia) has more recently become a major contributing factor [In this study, we applied landscape genetic techniques to investigate how forest habitat and configuration are associated with genetic differentiation patterns across the range of Northern Spotted Owls ..Strix oclifornia , but comg factor –8. Sinceg factor , Northerg factor . Likewisg factor .Previous assessments of landscape genetics patterns in Northern Spotted Owls  primarilWe analyzed a data set comprised of 10 nuclear microsatellite loci , 15 thatS. o. occidentalis) [5 Markov-Chain Monte-Carlo replicates and a thinning interval of 500 (1000 saved replicates). The first 200 saved replicates were discarded as burnin in post-processing steps. Geneland was also used to quantify the extent of differentiation of any detected clusters using the standard measure FST to reflect the magnitude of differentiation.Previous analyses  based onentalis) . Given ohttps://doi.org/10.5066/F7J67FVW).Matrices of inter-individual genetic and geographic distances were calculated for all pairwise combinations of individuals with the program Alleles in Space . GeneticAll conductance surfaces were scaled to range between 0  and 100  and were produced at a 5-km spatial resolution (5 km raster cells). This distance is well below the median juvenile dispersal distances described in Forsman et al.  (Median zt [r) and associated P-value for each analysis. To help clarify our results, we also used zt to evaluate correlations between each of the 6 resistance distance matrices derived for our dispersal habitat maps /2 is the number of pairwise resistance distances. Δr essentially reflects the average percent difference of elementwise resistance distances between the two matrices being compared.We compared the matrix of resistance distances from each time point to the congruent matrix of resistance distances produced from analyses of the idealized habitat map portrayed in ST = 0.036 between clusters).Spatial genetic clustering analyses consistently identified 2 distinct genetic clusters among each of the five analysis replicates performed. Similar to that observed in prior studies , the clur = 0.103), whereas distances associated with forest condition from the 1940’s produced the lowest (r = 0.053). The correlation between genetic distances and simple geographic distances was on par with that observed for the idealized habitat (r = 0.098). Each resistance distance matrix was also highly correlated with geographic distances (r = 0.981) and the 1940’s resistance distances producing the lowest correlation , correlations between pairwise inter-individual genetic distances and all measures of landscape connectivity . Of the time points examined, data from 1986 onward should be considered most accurate and robust due to its reliance on contemporary, high-resolution approaches , 30. Data priori, we would not necessarily expect to identify the strongest correlation between genetic distances and resistance distances from the 2012 conductance surface, or possibly even the 1986 or 1994 conductance surface, given that samples were collected in the mid 1990’s or later and that Northern Spotted Owls have a generation time of ~7–12 years [Pyrrhura pfrimeri) from the Paranã River Basin in Brazil, where genetic structure patterns were most highly correlated with landscape connectivity patterns from the 1970’s (the earliest time points examined)[We suggest that there are two possible non-exclusive hypotheses that explain our results. First, our findings may indicate the presence of a temporal lag between landscape condition, as reflected by the empirical raster maps, and genetic structure patterns in Northern Spotted Owls. Temporal lags in genetic structure patterns are expected following changes in landscape connectivity, as many generations may be necessary for genetic drift to alter allele frequencies in populations . Thus, a12 years , 45. Undexamined) prior toexamined). In contexamined) and contSecond, our analyses may indicate that forest habitat suitable for dispersal in the Pacific Northwest has not yet been sufficiently fragmented to the point where genetic structure patterns of Northern Spotted Owls have been influenced. This hypothesis is particularly tenable for long-lived species with high dispersal abilities such as Northern Spotted Owls. Juvenile Northern Spotted Owls can disperse in excess of 100 km , 48, witIf this second hypothesis is correct, one additional interpretation of our findings is that the federal forest management practices mandated by the Northwest Forest Plan have, to date, been successful in fulfilling one of its many goals: maintaining genetic connectivity for Northern Spotted Owl throughout their range. Despite the taxon’s potential for long distance dispersal, there is likely to be a lower limit threshold for habitat connectivity that, if exceeded, would have negative consequences for population connectivity. Under the second hypothesis, our results may indicate that this threshold has not been exceeded, although we do not know the upper limit of habitat fragmentation that can be tolerated in this system before connectivity is affected or if that threshold would have been approached if the Northwest Forest Plan had not been in effect. Simulation models for Northern Spotted Owls could be developed to help further explore this system and distinguish between hypotheses , perhapsOther factors aside from habitat connectivity and forest condition likely influence genetic structure in Northern Spotted Owls. Recent analyses of long-term dispersal data sets suggest that juvenile spotted owl dispersal distances decreased on the order of ~1 km per year between 1983 and 2012 and that geographic variation in dispersal distances exists, with owls from Oregon demonstrating larger dispersal distances and more long distance dispersal than birds from Washington . The speS1 Fig(PDF)Click here for additional data file.