Ophiodon elongatus), a commercially and recreationally important fishery species along the west coast of North America. We used 16,749 restriction site‐associated DNA sequencing (RADseq) markers, in 611 individuals collected from across the bulk of the species range from Southeast Alaska to Baja California, Mexico. In contrast to previous population genetic work on this species, we found strong evidence for two distinct genetic clusters. These groups separated latitudinally with a break near Point Reyes off Northern California, and there was a high frequency of admixed individuals in close proximity to the break. F‐statistics corroborate this genetic break between northern and southern sampling sites, although most loci are characterized by low FST values, suggesting high gene flow throughout most of the genome. Outlier analyses identified 182 loci putatively under divergent selection, most of which mapped to a single genomic region. When individuals were grouped by cluster assignment , 71 loci were fixed between the northern and southern cluster, all of which were identified in the outlier scans. All individuals identified as admixed exhibited near 50:50 assignment to northern and southern clusters and were heterozygous for most fixed loci. Alignments of RADseq loci to a draft lingcod genome assembly and three other teleost genomes with chromosome‐level assemblies suggest that outlier and fixed loci are concentrated on a single chromosome. Similar genomic patterns have been attributed to chromosomal inversions in diverse taxonomic groups. Regardless of the evolutionary mechanism, these results represent novel observations of genetic structure in lingcod and designate clear evolutionary units that could be used to inform fisheries management.Delimiting intraspecific genetic variation in harvested species is crucial to the assessment of population status for natural resource management and conservation purposes. Here, we evaluated genetic population structure in lingcod ( To delineate population boundaries, species assessments have typically relied on life history and demographic data  are a commercially and recreationally valuable species of groundfish, with landings and revenues within U.S. waters totaling 1,628 metric tons in 2016, and an average growth of over 200 metric tons per year since 2010  reproduction is characterized by migrations to relatively shallow, coastal areas; (b) early life‐history stages inhabit nearshore and estuarine habitats; and (c) adult home ranges are relatively small with individuals generally exhibiting strong site fidelity Hart, . PreviouThe objective of this study was to evaluate population structure more comprehensively using modern genomic techniques combined with high‐resolution spatial sampling throughout the bulk of the species range in order to provide updated information on population structure to aid fisheries management. To address this objective, we used restriction site‐associated DNA sequencing (RADseq) to generate thousands of polymorphic loci that sample widely throughout the genome, increasing our power to detect population structure and to identify signatures of adaptive loci. We present these results in the context of a draft lingcod genome assembly to better understand where highly differentiated RADseq loci occur in the genome.22.1Lingcod are benthic predators endemic to the North Pacific, ranging from the Gulf of Alaska to central Baja California, Mexico Hart, . Lingcod2.2We chartered recreational fishing boats to collect lingcod using hook‐and‐line fishing gear on high relief rocky habitat at 25 ports from Southeast Alaska to Southern California. Sample collections out of each port typically occurred across 2–5 days. To ensure a thorough collection of lingcod across a wide range of age and size classes, both shallow (<60 m) and deep 60–170 m) nearshore and offshore reefs were targeted by our fishing efforts. Most of the individual samples analyzed in this study came from fin clips or gill tissues obtained from these collections. Supplemental tissue samples were provided by bottom fish trawl surveys conducted in lower‐relief habitat by the Alaska Fisheries Science Center's Resource Assessment and Conservation Engineering (RACE) Division and the Northwest Fisheries Science Center's Fishery Resource Analysis and Monitoring (FRAM) Division, opportunistic port sampling, and via collaborators. In total, this study included samples from 42 sites encompassing most of the species range , and 100 bp paired‐end sequencing was conducted in 24 lanes using the HiSeq 4000  at the University of Oregon Genomics and Cell Characterization Core Facility (GC3F).We extracted genomic DNA from 940 samples using the Qiagen DNAeasy Blood & Tissue 96 extraction kit  and then quantified extractions using a BioTek FLX800 Microplate Fluorescence Reader. Samples with extractions yielding <12.5 ng/μl were not sequenced. Individual DNA concentrations for the 841 remaining samples were normalized to 12.5 ng/μl and 125 ng of starting material was used for RADseq library prep. We used the RADseq protocol of Ali et al.  to constStacks  and the maximum number of bp mismatches between sample loci (‐n). After initial trimming and filtering for low‐quality reads and PCR clones, loci were identified in each individual using ustacks with a minimum allele depth (‐m) of 3. Individual samples with fewer than 3 million reads were excluded from downstream analyses based on an exploratory examination of genotyping rates versus read depth (data not shown). A catalog of consensus loci was constructed with cstacks using 84 individuals from throughout the range with high coverage and sstacks was used to match individual sample loci to the catalog. We then used populations to remove loci that failed to meet the following criteria: present in ≥80% of individuals, minor allele frequency ≥1%, and maximum observed heterozygosity of 70%. We exported the resulting SNP dataset from Stacks and further filtered using VCFtools v.0.1.13 , removed loci in individuals that were below 10x depth of coverage (‐‐minDP 10), refiltered for loci found in ≥80% of individuals (‐‐max‐missing 0.8), and then removed individuals with >30% missing loci from the final dataset (‐‐remove), which was exported for downstream analyses. Finally, we identified and removed duplicate individuals using a custom R script written by Garrett McKinney (https://github.com/gjmckinney/IDduplicateSamples).We used  Catchen  and Roch Catchen  and foun2.4Our analyses focused on evaluating range‐wide genetic differentiation in lingcod. It proceeded in three steps. First, we evaluated geographic patterns in genotypic differentiation using population genetic structure analyses to identify population clusters, with no a priori assumptions about population boundaries. Second, we calculated population genetic statistics for these clusters and for individual sampling site locations to quantify differentiation. Third, to better understand the underlying molecular basis and potential evolutionary mechanisms of the observed genetic differentiation, we conducted an outlier analysis, determined the genomic distribution of outlier and fixed loci using a draft lingcod genome assembly together with three other teleost genomes with chromosome‐level assemblies, and assessed mtDNA sequence diversity in a subset of individuals showing strong differentiation. These approaches are described in further detail below.2.5STRUCTURE v2.3.4  and discriminant analysis of principal components (DAPC), which do not rely on population genetic models, to summarize the diversity and variation across RADseq loci using the R package adegenet v2.1.1  was identified with find.clusters, which employs a Bayesian information criterion method. Preliminary results suggested strong clustering in lingcod, which appeared to be driven by outlier loci . We re‐evaluated structure patterns within initially identified clusters as well as with datasets excluding either neutral or outlier loci using STRUCTURE and PCAs. We also evaluated structure patterns based on sex and age when known.To identify distinct genetic clusters, we first ran population structure analyses without any a priori assumptions about population boundaries. We used a model‐based Bayesian clustering analysis implemented in Jombart, . In the 2.6STRUCTURE analyses for a subset of analyses including cluster pairwise FST comparisons and locus‐specific F‐statistics . We tested each locus over the entire dataset for being in Hardy–Weinberg proportions using an exact test based on 1,000 Monte Carlo permutations of alleles as implemented in pegas v0.12  for lingcod with individuals grouped by STRUCTURE identified genetic clusters using NeEstimator v2.1  and then by the distinct cluster assignments from Paradis,  and exac Goudet, . Using b2.7BayeScan v2.1 , mitochondrial sequence divergence was evaluated a posteriori to assess the possibility of two cryptic species. Mitochondrial DNA is expected to accumulate differences relatively quickly due to a high mutation rate and smaller Ne than nuclear DNA, making mitochondrial markers excellent for differentiating divergent species or populations. We sequenced a 440‐bp segment of mitochondrial D‐loop using the primers H16498 5' (CCTGAAGTAGGAACCAGATG)  running on minIT . Genome assembly was first conducted only with the HiSeqX10 data. FastQC was used to evaluate the quality of the HiSeqX10 data, and data were then used for assembly using MaSuRCA v3.3.1 . Assemblies were evaluated for completeness of conserved eukaryotic genes using BUSCO v3.1.0 with the odb9 databases  of a coastal Washington male individual using the Qiagen midi‐Prep kit ; DNA from this individual was used for shotgun genome sequencing library prep and HiSeqX10 (150 base paired end) sequencing at the University of Washington Genome Sciences lab. High molecular weight DNA from a second individual  was used for nanopore sequencing, as tissues from the first individual (stored in ethanol) did not yield the high molecular weight DNA ideal for long contiguous sequencing protocols, like that for nanopore . Both of these individuals were included in RADseq analyses and identified as northern cluster individuals. The Qiagen Midi‐Prep  and the Circulomics Nanobind Tissue Big DNA  kits were both used to extract high molecular weight DNA from the second individual. Several nanopore libraries were prepared from the same individual for multiple instances of sequencing, and they included (a) three libraries prepared using the LSK‐108 library prep protocol  for DNA extracted with the Qiagen midi‐prep kit; two of these used the Covaris G‐tube  shearing following manufacturers protocol, and one library was prepared without shearing, (b) two libraries prepared using the Circulomics extracted DNA; one with the LSK‐109 library prep kit (Oxford Nanopore Technologies), and one using the field sequencing kit (Oxford Nanopore Technologies). All libraries were sequenced on the minION using R9.4 flow cells, and raw data were basecalled using either raGOO . In consideration of potential reference bias on scaffolding results, we generated three sets of lingcod CSP assemblies using the following teleost reference genomes: Sebastes schlegelii, Korean rockfish  with the minimum coverage for a marker to be considered present in an individual (–min_cov) set to one. We chose to use this CSP assembly because of all the reference genomes used, it yielded the highest alignment rates of lingcod RADseq markers. We visualized RADsex results with the R package radsex‐vis (https://github.com/RomainFeron/RADSex‐vis).The RADseq contigs from the  Turner, . For the3Stacks filtering parameters resulted in 443,489 initial SNPs. Using VCFtools, we retained only the first SNP on each marker, which reduced the dataset to 228,426 SNPs. Subsequent filtering for ≥10× coverage in each locus of every individual followed by filtering for loci found ≥80% of individuals resulted in 16,749 loci. After individuals with more than 30% missing loci and duplicate individuals were removed, 611 individuals remained  and [L(K)] both found a = 2 was the most likely scenario with results showing two distinct genetic clusters. Individuals either definitively assigned to one of the two clusters (Q > 0.99), which segregated strongly latitudinally, or showed near equal assignment to both clusters ] being K = 1—no structure—while (ΔK), which cannot detect a scenario of K = 1, suggested K = 3 was most likely ]. For the northern cluster, the highest likelihood (ΔK) was K = 3; however, Salish Sea individuals showed signs of slight differentiation for all K values  showed Salish Sea individuals differentiated from other regions in most K > 2 plots  = 2 yielded the lowest BIC score , suggesting it was the most likely number of clusters. For this DAPC analysis, we retained 50 PCA axes and one discriminant function. Although the analysis groups admixed individuals with southern cluster individuals as opposed to northern cluster individuals, admixed individuals fall out between the northern and southern cluster groups along discriminant function 1 . In order to estimate the strength of population differentiation, we calculated F‐statistics for different groupings of samples with and without outlier loci. F‐statistics analyses with individuals grouped by the 42 sampling sites yielded global FST, FIS, and FIT values  of 0.0115 (0.0098–0.0132), 0.0024 (0.0006–0.0043), and 0.0139 (0.0112–0.0165), respectively. Locus‐specific FST varied from −0.0575 to 0.8896, but most loci exhibited low values with a small subset exhibiting higher values  and relatively high values when comparing between sites on opposite sides of the break. When candidate outlier loci were excluded, global FST, FIS, and FIT values  were 0.0009 (0.0007–0.0011), 0.0021 (0.0002–0.0043), and 0.003 (0.0012–0.0052), respectively. Notably, global FST was over an order of magnitude lower, implying that most of the genome is characterized by low differentiation. Similarly, pairwise FST comparisons excluding outliers frequently showed values an order of magnitude lower between sites on the opposite side of the latitudinal break, while comparisons on the same side of the break decreased slightly, but generally remained similar to values calculated with outlier loci included  with admixed individuals being heterozygous at 69 of the 71 fixed sites , northern cluster and admixed individuals (0.0119 & 0.0002), and southern cluster and admixed individuals (0.0111 & 0/nonsignificant).When F‐statistics were estimated with individuals grouped by ps Table . Indeed,es Table . All 71 e estimates were infinite or undefined for the southern cluster  and 29,237  for the northern cluster. Although high, these point estimates presumably are downwardly biased to some extent by physical linkage, which depends on the taxa‐specific number of chromosomes .NST fixation indices were low between clusters , corroborating the low differentiation among cluster haplotypes visualized in the haplotype networks.Mitochondrial D‐loop haplotypes were shared across clusters and sampling locations Figure  with no 3.4MaSuRCA. This assembly estimated the genome size of lingcod to be ~900 Mb assembled into 345,190 contigs, with a resulting contig N50 of 4.1 kbp. Using BUSCO to evaluate the completeness of the initial assembly against the Actinopterygii core gene set, 75% of the gene set was represented in either complete or fragmented form, and 25% of the gene set was missing. From the Nanopore data, a total of ~5.4 million reads were obtained from multiple runs . The hybrid assembly had a contig N50 of 1,747 kbp. This final genome assembly, which was subsequently scaffolded with three related teleost genomes (using raGOO), contained 98% of the Actinopterygii conserved genes.From the HiSeqX10 data, ~979 million reads were obtained and assembled with 3.5Sebastes schlegelii, 93.3% and 50.4% for the three‐spined stickleback, Gasterosteus aculeatus, and 91.7% and 50.4% for the large yellow croaker, Larimichthys crocea. In all three lingcod CSP assemblies, 176/182 (96.7%) outlier and 71/71 (100%) fixed loci aligned to a single CSP , respectively, were 97.4% and 66.9% for the Korean rockfish, P Figure . AlthougP Figure ,S12.Linkage disequilibrium (LD) heat maps for lingcod loci on each CSP harboring the majority of outlier loci showed strong patterns of linkage in concentrated blocks corroborating outlier patterns seen in the Manhattan plots Figure . Visual Sebastes‐guided assembly, that exhibits a statistically significant association with sex . However, additional genome sequencing data from a southern haplotype individual is needed to confirm that this structural variation exists.In this study, we identify two distinct genetic clusters of lingcod showing a latitudinal cline in the Eastern Pacific, with a genetic break centered off Northern California between Stewarts Point and Point Reyes. This novel delineation of population boundaries differs from previous lingcod population genetic studies, which have generally suggested coastwide panmixia  species, a high proportion of haplotypes were found to segregate in these cases ; however, the lack of divergence in mtDNA and the heterogeneous genomic patterns do not support this alternate hypothesis. If pre‐ or postzygotic barriers prevented backcrossing of admixed individuals with pure cluster individuals, we would expect no gene flow between clusters and therefore significant differentiation of mitochondrial haplotypes as well as strong patterns of differentiation throughout the entirety of the nuclear genome. Yet, we saw no evidence of D‐loop haplotype segregation in northern or southern clusters Figure  corroborAnother alternative hypothesis for the differentiation pattern we observed in lingcod is divergent selection in the absence of a chromosomal rearrangement, which is expected to produce a distinctive region of elevated divergence or “genomic islands of divergence.” However, islands derived from divergent selection are expected to be dispersed throughout the genome and on the order of kilobases not megabases  and found evidence for two distinct genetic clusters with the break centered between Stewarts Point and Point Reyes, California. These results emphasize the need to consider transboundary stock assessments and to reconsider the location of the current stock assessment and management boundary located at the U.S. Oregon–California border  are large for both clusters, indicating population sizes are large or were in the recent past. Although replenishment via dispersal of northern cluster larvae to Southern California seems highly plausible based on a 90‐day PLD, the latitudinal cline suggests that the realized dispersal may be less extensive than potential dispersal , as it is currently assessed, has experienced slower recovery and greater fishing pressure than the northern stock . This genetic break is driven entirely by a small number of loci in linkage disequilibrium that are likely colocalized on a single chromosome that may be characterized by an inversion. Future work is necessary to confirm the presence and nature of the putative inversion; if confirmed, additional work will need to evaluate the ecological life‐history significance and fitness implications of such an inversion. We also found evidence for substructure within the northern cluster based on weak but significant differentiation between the inland waters of the Salish Sea and the outer coast that is likely driven by reduced connectivity between these areas. Current stock boundaries for management of the species differ somewhat from the patterns we observed. Our results imply that for lingcod, as with many species, an evaluation of how genetic population structure, jurisdictional boundaries, and life‐history considerations come together to influence assessment accuracy is likely to provide important insights for management of the species.None declared.Fig S1Click here for additional data file.Fig S2Click here for additional data file.Fig S3Click here for additional data file.Fig S4Click here for additional data file.Fig S5Click here for additional data file.Fig S6Click here for additional data file.Fig S7Click here for additional data file.Fig S8Click here for additional data file.Fig S9Click here for additional data file.Fig S10Click here for additional data file.Fig S11Click here for additional data file.Fig S12Click here for additional data file.Fig S13Click here for additional data file.Fig S14Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.Table S14Click here for additional data file.Table S15Click here for additional data file.Table S16Click here for additional data file.Table S17Click here for additional data file.Table S18Click here for additional data file.


Brachiaria species, thus a high genetic variation in natural populations of Brachiaria is expected. Overgrazing and clearing of natural vegetation for crop production and nonagricultural uses and climate change continue to threaten the natural biodiversity. In this study, we collected 79 Brachiaria ecotypes from different parts of Kenya and examined them for genetic variations and their relatedness with 8 commercial varieties. A total of 120 different alleles were detected by 22 markers in the 79 ecotypes. Markers were highly informative in differentiating ecotypes with average diversity and polymorphic information content of 0.623 and 0.583, respectively. Five subpopulations: International Livestock Research Institute (ILRI), Kitui, Kisii, Alupe, and Kiminini differed in sample size, number of alleles, number of private alleles, diversity index, and percentage polymorphic loci. The contribution of within-the-individual difference to total genetic variation of Kenyan ecotype population was 81%, and the fixation index (FST = 0.021) and number of migrant per generation (Nm = 11.58) showed low genetic differentiation among the populations. The genetic distance was highest between Alupe and Kisii populations (0.510) and the lowest between ILRI and Kiminini populations (0.307). The unweighted neighbor-joining (NJ) tree showed test ecotypes grouped into three major clusters: ILRI ecotypes were present in all clusters; Kisii and Alupe ecotypes and improved varieties grouped in clusters I and II; and ecotypes from Kitui and Kiminini grouped in cluster I. This study confirms higher genetic diversity in Kenyan ecotypes than eight commercial varieties  that represent three species and one three-way cross-hybrid Mulato II. There is a need for further collection of local ecotypes and their morphological, agronomical, and genetic characterizations to support Brachiaria grass breeding and conservation programs.Brachiaria grass is an emerging forage option for livestock production in Kenya. Kenya lies within the center of diversity for  Brachiaria consists of about 100 documented species of which 7 perennial species of African origin have been used for pasture production in South America, Asia, South Pacific, and Australia [Brachiaria plays important roles in soil erosion control and ecological restoration. Brachiaria species have been an important component of sown pastures in humid lowlands and savannas of tropical America, with current estimated acreage of 99 million hectares in Brazil alone [Brachiaria grass is one of the most important tropical grasses distributed throughout the tropics, especially in Africa . The genustralia . It has ustralia ,4. Brachustralia –8. BrachBrachiaria species for pasture improvement started during the 1950s. These researches focused on B. brizantha, B. decumbens, B. mutica, and B. ruziziensis for forage production, agronomy , compatibility with herbaceous and tree legumes, nutritive values, and their benefit to ruminant production. These studies concluded the suitability and broader adaptation of several Brachiaria species to different agroecological zones in Africa [Brachiaria as an ideal forage option for the livestock farmers in East Africa.In Africa, the evaluations of n Africa . Howevern Africa ,12. ThesBrachiaria improvement program to develop varieties that are tolerant to biotic and abiotic stress for different environmental conditions. Germplasms of broad genetic base is the prerequisite for any crop improvement. The best approach to increase genetic variations in apomictic species such as Brachiaria is tapping natural variations from the center of diversity. Since the 1950s, multiple missions were undertaken in Africa to collect Brachiaria germplasms, with a current inventory of 987 accessions of 33 known Brachiaria species [Brachiaria in Africa and size of the continent, the number of samples available in collection is definitely non-exhaustive and warrants further collection efforts. However, the existence of these genetic resources in Africa is continuously threatened by overgrazing and clearing of vegetation for crop production and nonagricultural uses and adverse effects of climate change.Despite high popularity, the Brachiaria acreage in Africa is low and relies on a few varieties that were developed for tropical Americas and Australia. Within a short period of introduction, some of these varieties have shown susceptibility to pests and diseases, raising question on the expansion of Brachiaria acreage in Africa with these varieties. There is therefore a need for an Africa-based  species . ConsideBrachiaria. Therefore, high natural variation is expected among Brachiaria populations in Kenya. This study aimed to create a collection of local Brachiaria ecotypes in Kenya, assess their genetic diversity using microsatellite markers, and examine their genetic relationships with eight commercial cultivars. The study will broaden geographical coverage and/or genetic base of the global Brachiaria collection and provide invaluable information for Brachiaria improvement and conservation programs.Kenya is located within a region that represents a center of diversity for genus Descriptive statistics for all marker sets were computed . The majThe population diversity indices for five ecotype populations from Kenya were summarized . The IntThe pairwise genetic distance and population matrix of Nie genetic identity were calculated . The genFST) and number of immigration per generation (Nm) for study populations were 0.021 and 11.585, respectively.The partitioning of the total variation in population at different levels was estimated with AMOVA . Within-Brachiaria ecotypes in Kenya and documented their genetic variations using microsatellite markers.The genetic complexity, primarily apomictic mode of reproduction, and abundant natural variations in Africa urge for a two-pronged approach (selection and breeding) for improving Brachiaria grass in Africa. All-inclusive germplasm base with documented variations are prerequisite for the effective breeding programs. This study collected 79 Brachiaria ecotypes. The PIC value in this study is within the range reported by Silva et al. [The PIC values for 22 SSR markers averaged to 0.5825, suggesting markers were capable of differentiating 79 Kenyan a et al. , Jungmana et al. , and Viga et al. , but wasa et al.  and Pessa et al. . Similara et al. , Jungmana et al. , and Viga et al. , but wasa et al.  and Pessa et al. , respectHO was higher than HE, indicating mixing of previously isolated populations. This is consistent with the human involvement in moving planting materials and outcrossing nature of some Brachiaria species, for example, B. ruziziensis.The analysis of the distributions of alleles across populations is important for explaining genetic diversity and population relationships . PrivateB. ruziziensis in Kitale, Kenya [B. ruziziensis seeds [Brachiaria seeds might have reached farmers’ fields and other research stations in Kenya, including the ILRI, and afterwards naturalized in the wild. If this hold true, a low genetic distance (0.307) between the ILRI and Kiminini  populations could be because of shared genetic materials in early days.The study population varied in genetic distance and genetic identity coefficients. The highest genetic distance between Alupe and Kitui populations can be explained by the wider geographical distance between these two locations (675 km), but the genetic distance between other populations could not be associated to geographical proximity. Reports are available on forage research, including seed production of e, Kenya ,22, and is seeds . It is lB. brizantha (84%) and B. ruziziensis (88%) populations. Similarly, Garcia et al. [B. humidicola accessions as being due to the subdivision of the germplasms into five groups. The FST and effective number of migrants per generation (Nm) values of 0.021 and 11.580 indicated a relatively low genetic differentiation among populations [The contribution of within-individual difference to total variation was 81%, whereas among-theindividual and among-populations differences contributed 17% and 2%, respectively . These oa et al.  and Azeva et al.  reporteda et al.  reportedulations  and relaulations , respectulations ,26,29–32ulations ,26,30.Brachiaria ecotypes in Kenya and identify the potential values of these genetic resources in the Brachiaria breeding program. It is important to acknowledge some methodological limitations of this study while inferring population genetic parameters such as unequal and/or small sample sizes , unknown species and ploidy status of ecotypes, and dominant scoring scheme used in recording SSR fragments. The current Brachiaria taxonomy is far from satisfactory and the problem of generic identity, and species composition across entire taxa needs to be undertaken [Brachiaria spp. have been constrained by a limited understanding of Brachiaria genetics, cytogenetic and reproductive biology, and unavailability of reference genome. The agricultural and environmental importance of Brachiaria has recently spurred several studies on Brachiaria, including sequencing of B. ruziziensis genome. Therefore, Kenyan ecotypes collected in this study should be conserved and characterized further with the advent of new genomics and bioinformatics tools developed for species with complex genome.This study is an effort to build a collection of dertaken ,22. ApplBrachiaria ecotypes from different parts of Kenya and examination of their genetic differences using microsatellite markers. The genetic diversity data revealed that ecotypes, though representing a few locations of Kenya, contained much more diversity than currently available 8 improved Brachiaria varieties, which represent three species  and three-way cross-hybrid Mulato II (B. brizantha × B. decumbens × B. ruziziensis). These results clearly indicate a need for (I) further collection of local ecotypes in Kenya and other east and central African countries that represent center of diversity of Brachiaria species to enrich the Brachiaria genepool in the gene bank collections; (II) genetic characterization of local ecotypes and currently available gene bank materials to understand diversity and ascertain the need for further collection; and (III) morphological characterization of available genetic resource to identify/develop varieties suitable for different production environments.This is among the very first studies of this century in sub-Saharan Africa that involved collection of local Brachiaria ecotypes were collected from five different parts of Kenya: Alupe (n = 4), ILRI Farm (n = 60), Kiminini (n = 7) Kisii (n = 5), and Kitui (n = 3) in 2013 and 2014, and maintained in field at forage research plots of International Livestock Research Institute (ILRI), Headquarters, Nairobi, Kenya. Seeds of eight improved varieties—B. decumbens cv. Basilisk, B. brizantha cvs. Marandú, Xaraés, Piatá, and MG4, B. humidicola cvs. Humidicola and Llanero , and Mulato II —were planted in a variety demonstration plot at the ILRI Campus. About 4-week-old leaves were harvested from all 79 ecotypes and 8 varieties (one sample/variety), freeze-dried, and stored at −80◦ prior to DNA extraction. Ecotypes from all location but the ILRI Campus were collected jointly by Biosciences eastern and central and Africa-International Livestock Research Institute (BecA-ILRI) Hub and Kenya Agricultural and Livestock Research Organization (KALRO). The collection details are summarized in Whole plant sample of 79 ◦C for 30 min, followed by incubation at room temperature for 5 min and centrifuged at 3500 rpm for 10 min. After centrifugation, 400 µL of supernatant was transferred into new microfuge tubes and 400 µL of chloroform iso-amyl alcohol (24:1) was added to each tube and mixed by inversion for 10 min. Tubes were spun at 3500 rpm for 10 min, aqueous phase was transferred to clean microfuge tubes, and 400 µL of chloroform iso-amyl alcohol (24:1) was added again to each tube and spun for 10 min at 1100 rpm; this process was repeated twice. After the final centrifugation, the DNA was precipitated in 300 µL of cold isopropanol (100%) and inverted about 50 times to facilitate the mixing and precipitation, and incubated overnight at −20 ◦C. The following day, the microfuge tubes were removed from the freezer, thawed and spun at 3500 rpm at 4 ◦C for 20 min. The isopropanol was decanted and the genomic DNA pellet was air-dried. The DNA pellet was rinsed with 300 µL of 70% (w/v) ethanol and dissolved in 100 µL of low-salt TE buffer containing 3 µL of 10 mg/mL of 1% RNase solution and incubated in a water bath at 45 ◦C for 90 min. DNA quality and quantity were checked by 0.8% agarose gel (w/v) and NanoDrop Spectrophotometer. The genomic DNA was adjusted to the final concentration of 20 ng/µL and stored at 4 ◦C for PCR amplification.The DNA was extracted using the cetyl-trimethyl ammonium bromide (CTAB)  method w®PCRPreMix with Bioneer negative dye . A reaction volume of 10 µL containing 0.4 µL MgCl2 , 0.4 µL each of forward and reverse primers labeled with different fluorescent dyes (6-FAM (blue), VIC (green), NED (black), and PET(red)), 2 µL template DNA (20 ng/µL), and 6.8 µL of sterile distilled water was used for PCR amplification. A total of 22 SSR markers  stained with 2.5 µL of GelRed solution. The agarose gel images were visualized under Ultra-Violet and the digital image was captured. The size of amplified fragments was estimated comparing with 1 kb DNA ladder . The SSR fragment sizes and allele variations in the repeats were assessed by capillary electrophoresis of amplicons and sequencing of the amplified loci. The multiplexed PCR products were mixed with 8.87 µL Hi-Di-formamide and 0.135 µL fluorescent-labeled GeneScan™ LIZ size standard  in a 96-well microtiter plate. The mixed products were denatured at 95 ◦C for 3 min and snap-chilled on ice for 5 min to avoid the formation of double-strand DNA. The products were loaded to Applied Biosystems 3730xl DNA Analyzer .The genomic DNA was amplified using AccuPower markers  initiallis study . The PCRThe allele sizes generated by all 22 SSR markers on 79 ecotypes and 8 commercial varieties were scored using GeneMapper v4.1 software . Since the information on ploidy levels of test ecotypes was not available, SSR fragments were analyzed following a dominant scoring scheme, as used for other polyploidy species –37. ALS-Brachiaria improvement program. This study with collection of 79 Brachiaria ecotypes from a few locations of Kenya and their genetic diversity analyses revealed the presence of substantial genetic variations among Kenyan ecotypes, and close genetic relationships among improved landraces and Hybrid Mulato II. This study suggests need for collecting more ecotypes from different agroecological regions of Kenya to broaden genetic bases of existing genebank collections, and their morphological, agronomical, and genetic characterizations to support Brachiaria grass breeding and conservation programs.Brachiaria is a native African grass which is widely distributed in Kenya. It is one of the most extensively cultivated forages in tropical Americas, Australia and East Asia. However the cultivation of Brachiaria for pasture production in Kenya and Africa in general has been recently initiated through the repatriation of Brachiaria in the form of hybrids and improved landraces from South America. Despite excellent herbage production performance and benefits to livestock productivity, some of these introduced materials have shown susceptibility to pests and diseases within a short period of establishment. It has raised serious concern on the expansion of Brachiaria acreage in Kenya urging the needs for the Africa based


Petromyzon marinus). Our objective was to discover single nucleotide polymorphism (SNP) loci to conduct pedigree analyses to quantify reproductive contributions of adult sea lampreys and dispersion of sibling larval sea lampreys of different ages in Great Lakes tributaries. Additional applications of data were explored using additional geographically expansive samples. We used restriction site‐associated DNA sequencing (RAD‐Seq) to discover genetic variation in Duffins Creek (DC), Ontario, Canada, and the St. Clair River (SCR), Michigan, USA. We subsequently developed RAD capture baits to genotype 3,446 RAD loci that contained 11,970 SNPs. Based on RAD capture assays, estimates of variance in SNP allele frequency among five Great Lakes tributary populations  were concordant with previous microsatellite‐based studies; however, outlier loci were identified that contributed substantially to spatial population genetic structure. At finer scales within streams, simulations indicated that accuracy in genetic pedigree reconstruction was high when 200 or 500 independent loci were used, even in situations of high spawner abundance . Based on empirical collections of larval sea lamprey genotypes, we found that age‐1 and age‐2 families of full and half‐siblings were widely but nonrandomly distributed within stream reaches sampled. Using the genomic scale set of SNP loci developed in this study, biologists can rapidly genotype sea lamprey in non‐native and native ranges to investigate questions pertaining to population structuring and reproductive ecology at previously unattainable scales.Genomic tools are lacking for invasive and native populations of sea lamprey ( FST range 0.001–0.020). Genome‐wide annotation based on the sea lamprey genome facilitated the identification of putative functions of outlier loci (range of interpopulation FST 0.205–0.374). Simulation and empirical analyses demonstrated accuracy in pedigree assignment, and facilitated estimation of effective breeding population size, aspects of the species reproductive ecology, and extent of larval dispersal.We developed a single nucleotide polymorphism (SNP) panel for pedigree analyses for larval sea lamprey to characterize levels of diversity within and among populations (mean interpopulation  The additional information gained has substantially increased the precision and accuracy of population genetic inference  are an anadromous fish native to the Atlantic Ocean and can be found in North American and European waters  populations  and Duffins Creek (DC) Figure  by U.S. asin n = 5, St. Maasin n = 5, St. Ma2.2Ichthyomyzon spp., Lampetra appendix) using morphometric features including trunk myomere counts and pigment location and density. We also used a genetic method to verify species identity. DNA was extracted from 288 larval sea lampreys collected in the SCR  single‐species DNA barcoding PCR primers  and reverse (5′‐ GGCTAAGTGTAAGGAAAAGATTGTTAGGTCGAC‐3′) primers at 10 pmol/µl, 15.37 µl of diH2O and 0.13 µl of Amplitaq Gold (Applied Biosystems) at 5 U/µl) for a total volume of 25 µl. Cycling conditions were as follows: an initial denaturation step of 5 min at 95°C, followed by 35 cycles of 30 s at 95°C, 30 s at 55°C and 30 s at 72°C, and a final extension step of 5 min at 72°C. PCR products (expected size of 225 bp) were run on 1.5% agarose gels along with a 100 bp DNA ladder (Invitrogen) and stained with ethidium bromide for visualization. All PCRs were conducted included negative (no DNA) and positive (known DNA from adult sea lamprey) controls.Field crews distinguished larval sea lampreys from larvae belonging to native lamprey species , and samples were normalized to a concentration of 10 ng/µl before proceeding with RAD library preparation and sequencing.SbfI (New England Biolabs). After 60 min of digestion at 37°C and a 20‐min inactivation step at 65°C, BestRAD adapters were ligated to SbfI cut site overhangs using T4 Ligase (New England Biolabs). For each 96‐well plate of samples, equal volumes of adapter ligated DNA were pooled for each sample and adapter dimers were removed using Ampure XP beads . The multiplexed libraries were then eluted in 130 µl Tris‐EDTA buffer and sheared using a Covaris M220 Ultrasonicator (Covaris) to an average fragment length of approximately 550 bp. Sheared and barcoded RAD loci were then isolated using the streptavidin bead‐binding assay described in Ali et al. (Restriction site‐associated DNA (RAD) libraries were prepared following the “BestRAD” protocol described in Ali et al. . After ni et al. . Illuminz‐scores less than or greater than 7 were done using the program HDplot .Using the SNP discovery dataset described above, we designed RNA baits needed to apply the targeted RAD capture (RAPTURE) sequencing approach described by Ali et al. . Consenshttps://arborbiosci.com/wp-content/uploads/2018/04/myBaits-Manual-v4.pdf; April 2018). RNA baits were allowed to hybridize to sea lamprey genomic target sites for 16 hr at 65°C and then washed with Wash Buffer X at between 65 and 67°C. Washed capture reactions were amplified for 11 cycles using KAPA library amplification kit for Illumina (KAPA Biosystems) and quantified using three replicate PicoGreen assays. We determined that the library was free of adapter dimers and was of the correct insert size by evaluating the electropherogram produced by a Bioanalyzer High Sensitivity DNA Assay (Agilent Technologies). The library was again quantified and checked for adapter dimers using qPCR and a Bioanalyzer assays, respectively, before being sequenced in a single HiSeq X lane (Illumina) using a 5% phiX spike‐in (Novogene Corporation).RAD capture libraries were prepared using the library preparation protocol described above for the discovery libraries; however, libraries were sheared using the default protocol for 300 base pair fragments using a Covaris M220 Ultrasonicator (Covaris), and following PCR, equal amounts of each library (50 ng) were pooled before proceeding with a MyBaits hybridization capture reaction. Adult samples from Brule River, Carp River, and St. Mary's River were also included in these libraries. The capture reaction was carried out using the MyBaits Version 4.01 protocol (2.4http://broadinstitute.github.io/picard). Read mapping was performed using BWA‐mem with default settings , expected heterozygosity (HE), Wright's inbreeding coefficient (FIS), and minor allele frequencies (MAFs) were calculated for each locus–population combination. MAFs were calculated using the “minorAllele” function of the R package adegenet  was assumed to be 10, 100, or 1,000. These numbers were chosen to assess assignment power over a broad range of possible breeding scenarios and spawning adult abundance. For each simulation, the size of the breeding matrix  was allowed to vary such that the Nmales:Nfemales sex ratio was sampled from a uniform distribution  that mated with the ith female was randomly drawn from a Poisson distribution (λ = 3), given that evidence for polygyny and polyandry was established by Gilmore (Noffspring) for the ith female was sampled from a uniform distribution  and the size of the breeding matrix . In the simulations, the average number of mates for a female was ~3 (range 2.7–3.2 across breeding matrices simulated) and for a male, it was ~2 (range: 1.5–2.2 across breeding matrices simulated).Nparents simulations was evaluated at 100, 200, or 500 independent loci (Nloci), where the allele frequencies for Nloci were randomly drawn from the observed allele frequencies based on the original RAD discovery dataset. The above information was provided to the simulation module within COLONY  to infer full‐ and half‐sibling relationships for each independent simulation. Analyses were conducted in parallel on MSU iCER HPCC. For each simulation, the “Best Configuration” genetic pedigree was analyzed to assess power. Importantly, known parental genotypes were not provided to COLONY when the program was reconstructing the pedigrees, but that the parents that actually produced any given offspring were known, as their names were embedded in the names of their respective offspring. In addition, these data require comparisons between inferred and known dyadic (pairwise) relationships to assess error and accuracy (below). That is, the inferred (via COLONY) and known (based on simulation information embedded in offspring names) familial relationship was determined by counting how many parents the two individuals shared: Full siblings share both parents, half‐siblings share one parent, and unrelated dyads share no parents. Using this information, accuracy, the false‐positive rate and the false‐negative rate can be calculated for each simulation. Here, accuracy is defined as the proportion of inferred dyads  that were correctly assigned. The false‐positive rate is 1‐accuracy  that were incorrectly assigned). Given the direct relationship between accuracy and the false‐positive rate, we report only accuracy. The false‐negative rate is defined as the proportion of known dyads  that were incorrectly inferred. The expected distributions of accuracy and false‐negative rates for full‐sibling, half‐sibling, and unrelated dyads across 10, 100, or 1,000 parents genotyped at 100, 200, or 500 loci are presented below. Note that COLONY does provide probabilities that any given dyad in the genetic pedigree reconstructed is true, but no user‐defined cutoff was chosen because we implemented the full‐likelihood method of COLONY, which effectively balances the false‐positive and false‐negative rates throughout the dataset  and had high minor allele frequencies . The number of loci per chromosome varied based on chromosome size . The above filtering identified 454 independent loci to be used in genetic pedigree reconstruction, which is comparable to the simulations based on 500 loci (see above). All other COLONY input parameters were the same as described for the simulations above.We inferred full‐ and half‐sibling relationships for each age cohort of larval sea lampreys sampled within DC and the SCR separately in COLONY. Larval age was estimated using length–frequency distributions of larvae sampled in each stream (as described above). The above simulations assumed that loci were independent of each other. Thus, we selected loci for genetic pedigree reconstruction that were two megabases apart on the same chromosome  that produced each larval sample using the sibship method based on pedigree membership to full‐ and half‐sibling groups, as implemented in program COLONY  using the LDNe method  was due to chance alone. The randomization procedure first enumerated all possible pairwise relationships among the number of genotyped (Ngt) offspring !)), with individual sampling locations recorded, as well. NRW dyads were randomly assigned as related, and the proportion of NRW observed within the same sampling locations, , was recorded. The above procedure was repeated 1,000 times to generate a null distribution, representing the expected proportion of dyads observed within sampling locations due to chance. We calculated statistical significance as the proportion of the null distribution greater than the observed PNW per cohort. In addition, Fisher's exact tests were used to test whether the PNW per cohort differed significantly and thus whether spatial dispersion of larvae was concordant among age cohorts. Finally, a measure of coancestry was calculated per pedigree using an R function created by Bartron, Sard, and Scribner  dispersal of offspring and assuming the farthest upstream locale where a full or half‐sibling was collected were a proxy for the spawning location, we were able to coarsely characterize larval dispersal. Randomization procedures were used to test whether the observed number of within sampling location dyads that were related , with an average of 688,208 reads per individual. The mean PCR duplication rate was 50.7%, and we retained a total of 133,265,326 reads after removing duplicates. On average, 93.1% of read pairs were maintained after quality trimming and removing adapter contamination. We retained a total of 123,325,992 read pairs after trimming. A total of 110,472,816 read pairs were mapped to the sea lamprey reference genome in proper pairs with mapping qualities (MQ) greater than 30.http://github.com/ScribnerLab/SeaLampreyRapture).FreeBayes detected a total of 33,341 variable SNPs and indels overlapping the coordinates of targeted RAD loci. Of these loci, 8,540 were indels and 24,801 were SNPs. Of these SNPs, 23,950 had two alleles and 851 had more than 2 alleles . Gstacks detected and called 71,705 biallelic SNPs that overlapped the coordinates of targeted RAD loci. A total of 19,510 biallelic SNPs were identified by both genotyping algorithms. Only SNPs occurring in both methods were retained for further analysis. We retained 12,435 SNPs after filtering the VCF generated by FreeBayes. After downsampling individuals from DC to include only one individual from each full‐sibling family and removing individuals with >50% missing data, we were left with 11,818 biallelic SNPs and 140 individuals. Sequences for baits used for genotyping the analyzed SNPs are available on GitHub  per megabase. Targeted loci aligned to 188 out of 12,061 scaffolds from the Smith et al. . Previous studies have found that SNP loci with AB values between 0.3 and 0.7 can often be validated using Sanger sequencing  in length was 4.02 RAD loci , expected heterozygosity (He), degree of deviation of observed from expected heterozygosity (FIS), and minor allele frequency (MAF) are provided in the GitHub repository. Distributions of FIS were slightly positive in all populations examined, with Duffins Creek having the largest observed deficit of heterozygotes . Estimates were obtained for all 11,818 SNPs that were retained after sample sizes were reduced for the DC population. Global interpopulation FST across all loci and populations was 0.01, and mean pairwise FST values between populations ranged from −0.001 to 0.0018 , of which we retained 100 based on Jombart et al. . RetaineFST distribution for neutral loci. Manhattan plots of distributions of –log10‐transformed p‐values  and the remainder of the sampling locations (discriminant function 1 axis) and among samples from other Great Lakes tributary locations (discriminant function 2 axis). Because of the species' semelparous life history, individuals from different cohorts are unlikely to be related , and the multivariate ordination of individuals from the age‐2 and age‐3 larval cohorts from the SCR cluster is interesting. Cohorts cluster separately and with adults from different populations, suggesting different origins of individuals spawning in the St. Clair River in different years  resulted in a great improvement in assignment accuracy.Given that interindividual dyadic relationships will not be known in datasets generated by field sampling and subsequent genotyping, it was important to assess accuracy among inferred dyads (the proportion of inferred dyads that were correctly inferred). Most inferred full‐sibling and unrelated dyads were correctly inferred across most s Figure . Accurac3.6n = 217) and the SCR (n = 33). Parameters estimated for cohorts from DC are likely tied to larval sample sizes that varied across age classes (Table Ns range: 16–42). The mean and variance in adult reproductive success varied across cohorts (Table Nb) in age‐1 and age‐2 age cohorts relative to the age‐3 cohort . The most upstream sampling location in DC  differed significantly (p < .05) among age‐1, age‐2, and age‐3 and older cohorts sampled in DC.Clusters of related  individuals were observed in DC for each cohort (details in GitHub repository). Randomization tests sought to determine whether the pattern of related individuals within sampling locales was the result of sampling locations for specific larvae. Results indicated that related larval lamprey were not randomly dispersed in DC . Based on the broad spatial scale over which larval samples were collected Figure , adult l4N = 11,818), and a rapid and a cost‐effective assay to facilitate future sea lamprey genotyping studies. We demonstrated applications of SNP loci associated with objectives to quantify aspects of sea lamprey reproductive ecology including estimation of the number of adults associated with larvae based on pedigree analysis. Simulations accompanying pedigree analyses of larval sea lampreys collected demonstrated high accuracy of pedigree assignment even in situations of high adult abundance. Results from sampling just over 200 larvae from DC (a) are consistent with previous studies that indicate sea lamprey display a polygamous mating behavior Gilmore, . PopulatGilmore,  and micrGilmore, .IdentifiGilmore, , for exaGilmore, .4.1Our genetic structure results are consistent with previous analyses of spatial genetic structure of sea lampreys across the Great Lakes  were documented based on differences in SNP allele frequency across populations. Interestingly, outlier loci appear to contribute disproportionately to loadings on the DAPC axis that separates DC from all other populations evaluated  recapturing marked individuals after release and (b) assumption that capture, marking, releasing does not affect postrelease dispersal. The panel of loci developed as part of this study will enable greater efficiency to capture related individuals and thus study dispersal through the life cycle of sea lamprey. Results from our study show that larval lamprey families dispersed downstream across multiple cohorts. Given this information, it may be necessary to treat larger stream areas. Finally, our ability to detect large larval families in DC relative to our inability to detect any families in STC highlights the need to better understanding the sampling efforts necessary for streams of varying sizes and sea lamprey spawning populations. Such information will inform potential large‐scale close‐kin mark–recapture studies.4.44.4.1Nb) estimated using either LD , which would require additional analyses. One benefit to sea lamprey control of using pedigree analysis of larvae rather than mark–recapture of adults to estimate stream‐specific annual adult abundance is that any captured adults could be immediately removed from the system rather than marked and released . Pedigree analysis of larvae could also provide estimates of adult abundance in rivers where traps are not operated . One drawback to larval‐based adult abundance estimates, however, is that it requires a time lag , whereas mark–recapture of adults provides information to managers during the year of spawning.Identification of sibling sea lamprey across life stages  could address one of the highest priorities of the sea lamprey control program: the ability to identify the stream of origin of the parasitic and adult lampreys in each lake. One benefit of genetic mark–recapture for stream origin questions is that marked individuals do not need to be handled; only their siblings. Thus, a sample of larvae collected during treatments or other outmigrant/larvae collections could be used to recapture (as adults or parasitic juveniles) their siblings. This seems to have at least the same power as coded wire tagging studies but does not rely on assumptions of handling effects and postrelease mortality  Families occupy different habitats, and treatment effectiveness varies among habitats; and (b) families have different physiological tolerances to lampricides .The RAPTURE panel described here may be used for similar efforts in the native range for sea lamprey. Large‐scale genetic parentage studies have provided several valuable insights into sea lamprey conservation efforts in native marine systems including estimating population abundance, evaluating environmental correlates of fitness, as well as estimating effective population size (e.g., Duong et al., Results from this project highlight several biological attributes of sea lamprey that can be used for future control and conservation efforts. The following represent a nonexhaustive set of examples of additional research and management questions that can be addressed with data from this RAD capture SNP panel.5None declared.GB, JA. CH, PH, KT, and KS designed the research; GB, CH, PH, and KT were involved in field collection of samples; NS, SS, JH, and JK were responsible for laboratory analyses; and NS, SS, JH, and JA conducted bioinformatic and statistical analysis of molecular data and field data. All authors contributed to writing of the manuscript.


Genetic diversity is estimated to be declining faster than species diversity under escalating threats, but its spatial distribution remains poorly documented at the global scale. Theory predicts that similar processes should foster congruent spatial patterns of genetic and species diversity, but empirical studies are scarce. Using a mined database of 50,588 georeferenced mitochondrial DNA barcode sequences (COI) for 3,815 marine and 1,611 freshwater fish species respectively, we examined the correlation between genetic diversity and species diversity and their global distributions in relation to climate and geography. Genetic diversity showed a clear spatial organisation, but a weak association with species diversity for both marine and freshwater species. We found a predominantly positive relationship between genetic diversity and sea surface temperature for marine species. Genetic diversity of freshwater species varied primarily across the regional basins and was negatively correlated with average river slope. The detection of genetic diversity patterns suggests that conservation measures should consider mismatching spatial signals across multiple facets of biodiversity. Biogeographic patterns of genetic diversity are poorly documented, especially for fish species. Here the authors show that  genetic diversity has global spatial organization patterns with different environmental drivers for marine and freshwater fishes, where genetic diversity is only partly congruent with species richness. Indeed, the cost of sampling and genotyping a sufficient number of individuals within species has limited our understanding of the determinants of intraspecific genetic diversity, particularly at large scale. Spatial patterns of genetic diversity are mainly documented locally or regionally, mostly for a single species or a few species in phylogeographic6 or landscape genetics studies8.The ongoing sixth mass extinction crisis, under ever increasing human pressures, urgently calls for a better understanding of the main processes shaping the distribution of biological diversity on Earth. However, most global studies are investigating biodiversity at the species level9. Genetically distinct local populations may go extinct before the whole species does12, resulting in the erosion of genetic diversity and adaptive potential for many species13. In this context, investigating the key determinants of genetic diversity patterns and their underlying biological processes would help to design comprehensive conservation schemes, i.e. protected areas, for this neglected component of biodiversity16. Surprisingly, there is currently only a limited description and comprehension of the large-scale organisation of genetic diversity18.Yet, determining the global distribution of intraspecific genetic variation and its main drivers is urgent, given that genetic diversity might be undergoing silent and poorly documented erosion under global changes22. Among the hypotheses explaining spatial congruence between intra- and inter-specific levels of diversity, the evolutionary speed hypothesis posits that higher temperatures foster higher metabolic and mutation rates, as well as faster generation times, which should in turn increase genetic divergence, speciation rate and, ultimately, species diversity23. Under this hypothesis, species and genetic diversity are both expected to be higher in warmer regions. A positive association between species, genetic diversity, and temperature is also expected under the “colonisation hypothesis” (or “stability hypothesis”) where demographic fluctuations are associated with environmental instability which in turn limits diversity. These events are generally followed by stochastic recolonisation generating bottlenecks, which may lower both species and genetic local diversity24. Typically, warmer areas in the tropics have experienced less historical variability, whereas cold areas were highly unstable, generating species diversity clines along temperature gradients4. The energy hypothesis assumes that more productive areas sustain larger population sizes, which should favour higher genetic diversity and allow the persistence of more species along with, eventually, a higher speciation rate26. Finally, the physical complexity hypothesis states that areas with higher habitat complexity should provide more ecological niches and hence support higher species diversity28, but also more spatially structured populations in a given area so a higher genetic diversity29. This physical complexity hypothesis strongly depends upon the spatial grain and extent of the study area. For instance, at a large scale, a complex network of watercourses, typically characterising freshwater habitats, should promote higher genetic (and species) diversity than more homogenous and continuous marine waters.Intraspecific genetic diversity might show biogeographic patterns congruent with those of species diversity as a result of processes acting along a micro- to macroevolution continuum18 were the first to take advantage of the vast and ongoing accumulation of georeferenced genetic information on DNA sequences. From a compilation of thousands of short genetic sequences for terrestrial vertebrates (<600 bp), they revealed higher genetic diversity in tropical than in temperate regions. Although this finding seems coherent with known patterns of species diversity in vertebrates30, the extent to which intraspecific genetic and species diversity show similar distributions across regions or ecosystems remains to be explored31.Miraldo et al.Actinopteyigii) are an old clade of vertebrates that radiated into diverse habitats including marine and freshwater environments, from the tropics to the poles33. They represent a fascinating case study to investigate the association between genetic diversity, species diversity and the environment in different regions and ecosystems. Freshwater fish diversity  is higher than marine fish diversity  while marine environments cover ~70% of Earth and 97% of all waters35. In marine ecosystems, fish species diversity is concentrated in coastal waters (depths of <200 m) that represent <1% of the world’s sea surface36. Marine and freshwater fish diversity also declines with decreasing temperature at large spatial scale37. These global patterns suggest that differences in ecosystem productivity, environmental conditions and habitat connectivity or complexity likely shape fish species diversity27. Whether these patterns hold at the intraspecific level, i.e. genetic diversity, has not yet been investigated.Ray-finned fishes . We then estimate nucleotide diversity for each species in each grid cell at a spatial resolution of 200 km. This nucleotide diversity is averaged across the species in each cell. We first investigate the correlation between genetic and species diversity separately for marine and freshwater fishes. Next, we explore the global environmental and geographic determinants of the mean nucleotide diversity across species per cell, hereafter called genetic diversity. We interpret our results according to the micro-macro continuum concept and in the light of the evolutionary speed, colonisation, energy and habitat complexity hypotheses.In total 34,782 and 15,806 sequences were retrieved for marine and freshwater fish species, respectively, and were used to estimate the mean nucleotide diversity across species within each cell on a worldwide grid with a 200-km spatial resolution . Specifically, the median value of genetic diversity per cell is two times higher in freshwater  than in marine fishes , this difference being significant when accounting for latitude . For freshwater, river slope range was removed from the analysis because it was identified as highly collinear with average river slope (vif > 5).We used linear models to explore the relationship between fish genetic diversity per cell and three types of factors  .The most parsimonious models were selected based on the lowest Akaike Information Criterion (AIC) to obtain parameter coefficients and partial plots. For marine species, sea surface temperature, regions, the spatial autocovariate and the number of species were retained in the final model (Supplementary Table p < 0.0001)  Fig. . The geo01) Fig. .Fig. 3Det-test for spatially dependent variables applied on genetic and species diversity, and the models while selecting cells with more stringent filters   of the models always increased with more stringent filters except when only <34% of cells were retained and filtering for cells with at least two species and with more than four sequences or with a taxonomic coverage per cell of 5% in each cell. For freshwater species, the effect of average river slope always decreased with more stringent filters . The effect is less clear on the region coefficient but this main factor (region) was significant in all cases (Supplementary Table r2) increased in all cases.In order to test the influence of the number of sequences and species in each cell as well as cell taxonomic coverage on the estimate of genetic diversity and model outputs, we re-ran both the modified Here we show that the genetic diversity of marine and freshwater fishes is not distributed uniformly across the globe but displays clear biogeographic patterns Figs.  and 2. T39. The higher fish genetic diversity in warmer cells could result from the positive effect of energy on population sizes26. However chlorophyll a, as a surrogate for productivity40, was not retained in the final model and was weakly and negatively correlated to temperature . This result suggests that its effect on genetic diversity through population size, if any, is masked by other factors, such as temperature stability. In contrast, our results are consistent with the evolutionary speed hypothesis, which posits that warm temperatures shorten generation times and speed-up mutation rates thus, potentially increasing genetic diversity41. Our results are also consistent with the colonisation hypothesis and with the idea that past demographic events can shape the current global patterns of marine genetic diversity. For example, in the Indo-pacific, climatic stability buffered the effect of Quaternary climatic fluctuations on species extinction4 and might have limited genetic diversity erosion. Our study cannot disentangle the predictions of the speed and colonisation hypotheses to explain the global pattern of genetic diversity in marine fish. However, our findings represent one of the first study suggesting that temperature might be causally linked to global patterns of intraspecific diversity. All these patterns and potential effects are conserved in marine species when selecting only one-third of cells with more reliable estimations of genetic diversity (higher number of sequences or species), suggesting that our conclusions are robust to sampling bias.For marine species, the energy, evolutionary speed and colonisation hypotheses are all candidate mechanisms to explain the positive relation between genetic diversity and sea temperature42. Our analyses do not show a marked difference in median species diversity per cell  explains 67% of partial variation in genetic diversity 31. Moreover, we only focus on a small barcode representing mostly neutral genetic variation, while genetic diversity should be best studied across the whole genome55. The explanatory power of statistical models is relatively low. A large part of the unexplained variation can be due to noise in the data at different levels . The remaining variation might be due to a lack of factors that may contribute to better explain variations in genetic diversity . In addition, past demography history can also have a strong effect on current genetic patterns and is probably not fully integrated into the models, or absorbed by the regional structure. To partially account for missing underlying factors, we used a spatial autocovariate at a resolution of ~200 km, which is reasonable given the large scale of our analyses56. We used sea-surface temperature to explain patterns of genetic diversity for fishes while some inhabit deep waters. They represent ~40% of fish species  potentially not living close to the surface among the 3815 marine species considered in our study. Yet, sea surface temperature is strongly correlated with sea bottom temperature at the scale of continental shelf (0–200 m)57. In addition, in our case, if sea surface temperature was a poor predictor for those species living in deeper seawaters, we should have detected an effect of the distance to the shore in the model. However, this factor was not shown to significantly influence mean genetic diversity per cell.Although spatially extensive, our study has some limitations associated with the data mining and the analyses performed. The samples used in Fig. 55, but see18. The positive, albeit weak, association between genetic and species diversity can facilitate the conservation of both biodiversity components where those metrics are congruent58. Conversely, more conservation challenges arise where those metrics are not congruent60. As fish genetic diversity shows a spatial signature, this information can be used to frame conservation actions at global scale to maintain multi-layered biodiversity in the Anthropocene15 and to better guide future attempts to mitigate the impacts of global changes on vulnerable aquatic vertebrates61. Therefore, genetic diversity, as well as functional and phylogenetic diversity, should be taken into account when protecting biodiversity in its broadest sense, and this complexity requires a multifaceted framework in conservation62. Future studies using recent population genomics approaches (such as pool-sequencing63), which allow for the sequencing and/or genotyping of thousands of molecular markers (such as SNPs) evenly spread over the entire genome  in both neutral and adaptive regions should greatly improve our understanding of large-scale patterns of genetic diversity over space, time and taxa. Such studies would tell us how past demographic events  as well as genetic processes such as mutation, selection, gene flow or drift have shaped genomes in space and time64, in order to ultimately better inform conservation strategies under ever more fluctuating and uncertain conditions.In conclusion, by adding a spatial perspective, our study provides one missing piece of the current debate on the global determinants of genetic diversity that was lacking in recent works18, we collected mitochondrial “actinopterygii” gene sequences BOLD (http://www.boldsystems.org at 09/17/2018) using customised scripts. We only kept sequences with species, coordinates  and region name information. Sequences with the region information were georeferenced using GeoNames.org (http://api.geonames.org). This tool assigns GPS coordinates to locality names. We removed sequences with IUPAC ambiguity. We kept sequences longer than 500 bp and annotated as Cytochrome Oxidase Subunit 1–5′ Region (CO1). Species with only one sequence were also removed. The number of sequences and species retrieved at each filtering step is reported in Supplementary Table Following on from Miraldo et al.65. The alignments were checked manually using the software ugene66. In addition, only pairwise alignments with overlap >50% were kept to calculate genetic diversity. Aligned sequences were separated out for the freshwater (1781) and marine species (4131). The list of marine species was extracted from fishbase67. We assigned each of these aligned sequences to its cell in the grid based on its coordinates. We calculated the genetic diversity per cell following Miraldo et al.18. Based on its geographic coordinates, each sequence is assigned to a cell on the grid. For each cell, we estimated the nucleotide diversity (Π) of each species as the average number of variable sites in each pairwise sequence comparison following Eq. (68.kij the number of nucleotides that are different between sequence i and sequence j. n is the number of sequences and mij is the number of shared base pairs between sequence i and j. We estimated the genetic diversity (GD) in each cell of the grid as the mean of all species nucleotide diversities averaged across species.Mean fish genetic diversity per cell was estimated across all species and mapped on a grid cell covering the study area for marine Fig.  and freseshwater 781 and mhttp://www.iobis.org). We inventoried 16,238,200 occurrence records from 34,883 entries. We cleaned the data by identifying synonyms, misspellings and rare species (only one occurrence) and restricted them to species present in the marine environment according to FishBase67. We reconstructed distribution maps for each species, defined as the convex polygon surrounding the area where each species was observed. The resulting polygon was divided into four parts across the world to integrate possible discontinuity between the two hemispheres and the Atlantic and Pacific Oceans. For example, antitropical species are distributed in northern and southern hemisphere, but show range discontinuity near the tropics69 and a polygon division can account for this singularity. We refined each species distribution map by removing areas where maximum depths fell outside the minimum or maximum known depth range of the species70. As the OBIS database does not properly represent tropical fish assemblages, we merged this database with the Gaspar database at 1° resolution that encompass 6316 coral reef species4. We obtained a world database containing most marine fish species that we aggregated on a 1° resolution grid covering all oceans, as this resolution is useful for other projects. A freshwater dataset was obtained from the occurrence data provided by Tedesco et al.71. We first compiled the polygon of each available species from the occurrence table, and then aggregated this information into a presence-absence matrix. We obtained a world database containing freshwater fish species on a 1° resolution grid covering all terrestrial parts of the Earth. Considering the scale and resolution of the current study (200 km), our polygons represent the distribution of species with sufficient accuracy compared to those commonly used in macroecological studies73.Marine fish species data were obtained from the Ocean Biogeographic Information System  was reported in the plot representing the variability of genetic diversity at latitudinal bands amongst cells Fig. . We alsot-test of spatial association 74. To test for the difference between marine and freshwater diversity (both at the intra- and interspecific levels respectively) while accounting for latitudinal variation, we applied a linear model between genetic (or species) diversity and two factors: latitude and a binary variable indicating whether the sequences are from marine (1) or freshwater (0) fishes. Diversity metrics were log-transformed and standardised to produce variables following a normal distribution before the linear regressions. Genetic Diversity (GD) is, in theory, a proportion. However, in practice it takes only small values , environmental (Y) and sampling (S) factors , and, for marine species oxygen concentration and chlorophyll-a (Supplementary Table To explore the determinants of fish genetic diversity (GD), we used linear models to test the influence of geographic , we regressed each autocovariate variable against the whole set of environmental factors using a linear model. We extracted the model residuals and then used them as a spatial variable independent of the environmental factors considered in the study to predict fish genetic diversity. This new spatial variable  was called autocor in our analyses.Both the environmental factors and the diversity metrics inevitably show some spatial autocorrelation. We investigated the spatial autocorrelation in genetic diversity with a Moran spatial autocorelogramme using the R function pgi.cor (package pgirmess). We built an autocovariate variable for the fish genetic diversity metrics (function autocov_dist in spdep R package) to account for spatial dependency and estimate how any cell reflects the values of the neighbouring cellsS) were forced in all models, even those which were not significant since they account for data structure. Relative variance of genetic diversity explained by the various factors was estimated and represented as partial plots with the package hier.part. We visually checked the independence and normality of the residuals of all models. In addition, we performed a spatial analysis of the residuals ) to test whether they are not spatially autocorrelated (H0) indicating that the model is not missing a major variable with a spatial structure and that the coefficients of explanatory variables are not biased. It represents an a posteriori analysis evaluating whether the model outputs are reliable.We then applied a stepAIC regression procedure to each linear model and considered the model with the smallest AIC as the best, i.e. most parsimonious, model. The sampling factors . We choose thresholds that allowed to select around one-third and two-third of the “best” grid cells when possible (Supplementary Tables Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Fagus sylvatica under natural conditions.The growing pace of environmental change has increased the need for large‐scale monitoring of biodiversity. Declining intraspecific genetic variation is likely a critical factor in biodiversity loss, but is especially difficult to monitor: assessments of genetic variation are commonly based on measuring allele pools, which requires sampling of individuals and extensive sample processing, limiting spatial coverage. Alternatively, imaging spectroscopy data from remote platforms may hold the potential to reveal genetic structure of populations. In this study, we investigated how differences detected in an airborne imaging spectroscopy time series correspond to genetic variation within a population of F. sylvatica individuals collected at the site. We identified variation in foliar reflectance independent of annual and seasonal changes which we hypothesize is more likely to correspond to stable genetic differences. We established a direct connection between the spectroscopy and genetics data by using partial least squares (PLS) regression to predict the probability of belonging to a genetic cluster from spectral data.We used multi‐annual APEX (Airborne Prism Experiment) imaging spectrometer data from a temperate forest located in the Swiss midlands , along with microsatellite data from F. sylvatica population in natural conditions.We achieved the best genetic structure prediction by using derivatives of reflectance and a subset of wavebands rather than full‐analyzed spectra. Our model indicates that spectral regions related to leaf water content, phenols, pigments, and wax composition contribute most to the ability of this approach to predict genetic structure of This study advances the use of airborne imaging spectroscopy to assess tree genetic diversity at canopy level under natural conditions, which could overcome current spatiotemporal limitations on monitoring, understanding, and preventing genetic biodiversity loss imposed by requirements for extensive in situ sampling. Declining intraspecific genetic variation is likely a critical factor in biodiversity loss. In this study, we demonstrated the high potential of airborne imaging spectroscopy to access information about intraspecific genetic variation in a systematic and spatially continuous manner. Recent reviews by Bush et al.  can be detected using the spatial resolution of the imaging spectrometer, and that a minimum of 2–3 sunlit crown pixels for each individual can be identified in the airborne data. We established the link between spectral and genetic data by using partial least square (PLS) regression to assess the explanatory power of distinct wavelength regions  within the solar radiation reflected from the tree canopy. By combining interdisciplinary approaches, we take a step toward using the potential of imaging spectrometry for spatiotemporal biodiversity mapping of genetic variation within species.We base our study on the hypothesis that the foliar reflectance changes on an annual and seasonal basis, which contrasts the expression of stable, genotype‐specific phenotypic features of individual trees that are maintained over years. Based on that, we expect to identify links between remotely sensed predictors  and genetic structure (membership probability to the genetic clusters) which are maintained over multiple years. To test this hypothesis, we use multi‐annual airborne imaging spectroscopy data from a temperate forest in Switzerland along with genetic information derived from microsatellite analyses of individual trees at the study site. Our approach is based on the conditions that high‐fidelity spectral measurements are available  is the dominant species. F. sylvatica trees are wind‐pollinated, monoecious plants. Tree age spans between 53 and 185 years with a mean height of 30.6 m and a diameter at breast height of up to 150 cm  Figure . Our stu2.2F. sylvatica individuals located in the study area. The individuals were georeferenced using a tachymeter in April 2013. The tachymeter measurements were tied to reference points of the Swiss national grid (LV95). We used a polygon traverse and additional fixed points to measure all tree locations in the challenging terrain  Airborne Imaging Spectrometer (AIS)  and high‐resolution drone measurements resulting in a crown map of the site  were averaged per crown.We geometrically coregistered the multi‐temporal APEX datasets based on a spectrally distinct landmark . As multitemporal pixels never align perfectly, we used the spectral angle mapper (SAM) method  the 1st derivative of “b.” The z‐score of the mean tree crown reflectance was calculated separately for each spectral band and independently for each year with the formula: (xl − µl)/σ1, where x is a value of a single spectral band from each F. sylvatica individual, µ and σ are respectively mean and standard deviation values of a single spectral band from the whole analyzed F. sylvatica population and l stands for spectral band. Z‐score of reflectance (dataset b) and their 1st derivative (dataset c) were included in the analysis to reduce the impact of multi‐temporal variation in the reflectance magnitudes and simultaneously emphasize the relative differences between reflectance and absorption/transmittance influenced by structure, water content, and organic compounds  yearly averaged reflectance data per tree crown, (b) 2.4F. sylvatica individual, we predicted the probability of membership in one of five recognized genetic clusters rather than assigning membership, because the total genetic distance among trees in this population is relatively small. Thus, we conducted a PLS regression rather than PLS discriminant analysis (PLS‐DA).We investigated the relationship between imaging spectroscopy and genetic information by using partial least squares (PLS) regression. This method is commonly used in chemometrics Wold,  and moreF. sylvatica individual, we predicted membership probability to five identified genetic clusters by using the spectral fingerprint  as predictors. Due to the relationship between genetic clusters of the analyzed population, we used one model to predict membership probability in all five genetic clusters, rather than constructing separate models for each of the genetic clusters. We predicted genetic information of each analyzed tree with leave‐one‐out cross‐validation and adjusted the number of components based on the PRESS statistic , by waveband. The VIP score corresponds to the contribution of each waveband in the model prediction and is dependent on variance of wavebands and the variance of genetic structure of the trees, as well as the variances between the two Wold, . We aver–5 minimum vertical distance. We estimated the prominences using the findpeaks function in MATLAB (MATLAB ver. R2017b). The VIP scores that were not identified as local maxima were assumed to have lower importance than any local maxima and were assigned a ranking based on their absolute magnitude.Subsequently, we segregated all the wavebands by their importance in genetic cluster prediction. To do so, we identified local maxima of the averaged VIP scores over years and ranked them in descending order based on their prominences. We refer to prominences as the measure of how much the local maxima stands out due to its intrinsic magnitude and its location relative to other local maxima. The threshold for local maxima identification was set to 10N models out of n spectral predictors selected based on the nth most prominent VIP scores, where n is the number of spectral variables included for each model, increasing from 1 to the total number of predictors (N). We calculated the root‐mean‐square error (RMSE) for each model. We developed individual models for each year separately in order to retain intra‐annual variation. We then averaged the RMSE over all seven years, which we expect should reduce the influence of phenotypic plasticity, and thus increase the influence of stable genetic variation on differences calculated from spectra.Afterward, we generated plsregress function in MATLAB (MATLAB ver. R2017b) for model development.We conducted the procedure for each of the signal transformations  separately and used the We expect that the model with the lowest RMSE is constructed based on spectral information that is the most relevant to the genotype‐specific phenotypic features maintained throughout the seven years.33.1F. sylvatica individuals over the full‐analyzed solar spectrum . Wavelengths with high VIP scores in this dataset are also characterized by higher standard deviation of interannual data than those with lower reflectance .In the reflectance dataset , the near‐infrared (0.75–1.4 μm) region of the spectrum is the most influential on predicting genetic structure. Over the full‐analyzed reflectance spectrum, the VIP score is positively correlated to the absolute reflectance .In the normalized reflectance dataset (dataset b), the highest VIP score in predicting the genetic structure was identified for spectral features around 0.48, 0.70, and 2.40 μm. In contrast to the reflectance‐ and derivative‐based analyses, the VIP scores do not vary significantly between 0.70 and 2.30 μm and are not strongly correlated to the absolute reflectance .In the derivative dataset (dataset c), the spectral regions that show a closer relation to the genetic structure of analyzed trees are located at 0.55, 0.68, 1.45, 2.00, and 2.27 μm. In comparison with the VIP scores derived from reflectance and normalized reflectance datasets, the derivative dataset shows significant variation in VIP scores throughout the solar spectrum and exhibits a moderate negative correlation to the absolute reflectance  resulted in RMSE of genetic structure prediction of 0.3013. Compared with the full spectrum‐based analyses, the prediction improvement is of 0.4%. Likewise, the use of 37 spectral bands in the normalized reflectance dataset (RMSE: 0.2978) reduced RMSE by 0.7% in comparison with the model prediction based on all spectral bands. The best membership prediction was achieved for a subset of nine predictors derived from the derivative‐based signal (RMSE: 0.2897). The prediction of the model constructed from those nine variables resulted in an improvement of 4.2% in comparison with the model based on the full spectrum. In the reflectance and normalized reflectance datasets, the use of the single most prominent band did not improve the prediction. However, in the derivative dataset, the use of the single most informative predictor selected based on the VIP score, rather than the full spectrum, resulted in a 1.5% improvement in genetic structure prediction (RMSE: 0.2979). The spectral indicator subsets of the datasets that result in the best‐performing models are presented in Figure Prediction of genetic structure does not change significantly with the use of more than 120 and 90 spectral bands in reflectance and normalized reflectance datasets, respectively. In contrast, the addition of further information  in the derivative dataset has an influence on the PLS model performance. In addition to the spectral predictors that construct the models resulting in lowest RMSE, incorporation of predictors derived from 1.74, 2.24, and 2.33 μm wavelengths in the derivative and 1.55 μm wavelength in the reflectance dataset substantially reduce the RMSE of the genetic structure prediction (not shown).44.1Our results show that the use of spectral subsets rather than the full‐analyzed spectrum results in a lower error in the prediction of genetic structure.In contrast to spectroscopic measurements made in fully controlled environments, aerial measurements under natural conditions are influenced by many factors other than the genotype‐specific features. The atmospheric influence  and specifically those influenced by tissue water content , the outcomes related to those specific wavelengths should be interpreted with caution.4.2The difference in outcome from analyses made on various forms of the electromagnetic signal indicate the importance of signal transformation in multi‐temporal data analyses. Our analyses indicate that the transformation of the spectrum may be particularly important in analyzing fine‐scale characteristics like intraspecific genetic variation.The strong correlation of wavelength importance and the magnitude of the nontransformed signal could be caused by statistical limitations of the PLS regression method. The importance of specific wavelength regions might be assigned by overbalance of variation within one wavelength over fine genetically relevant information recorded at that particular wavelength. This could be supported by the observed reduction of importance of wavelengths with high magnitude in the normalized reflectance dataset, so in the signal where interannual variation caused by system instability is reduced. However, normalization may remove many sources of spectral variation, including the variation among spectral features of genetic clusters. This problem is remedied in the derivative dataset, where differences among individual measurements are emphasized and the intensity is not a major source of variation across acquisitions. Additionally, each predicting variable in the derivative dataset is an outcome of two neighboring spectral bands and thus represents more information, but also information that is averaged over a wider spectral window. The reduction of spectral resolution from high spectral resolution data results in higher signal‐to‐noise ratios ; Data curation (lead); Formal analysis (lead); Investigation (lead); Methodology (lead); Visualization (lead); Writing‐original draft (lead); Writing‐review & editing (lead). Carla Guillén Escribà: Conceptualization ; Data curation (lead); Investigation ; Methodology ; Supervision ; Writing‐original draft . Hendrik Wulf: Conceptualization ; Data curation (lead); Investigation ; Methodology ; Supervision ; Writing‐original draft ; Writing‐review & editing . Andrew Tedder: Data curation ; Formal analysis (supporting); Investigation (supporting); Methodology (supporting); Writing‐review & editing (supporting). Meredith C. Schuman: Writing‐original draft ; Writing‐review & editing . Fabian D. Schneider: Data curation ; Writing‐review & editing . Michael E. Schaepman: Conceptualization (lead); Project administration (lead); Resources (lead); Supervision ; Writing‐original draft .Supplementary MaterialClick here for additional data file.