Balaenoptera physalus. Past studies reported very low levels of genetic diversity among Gulf of California fin whales and a significant level of genetic differentiation from con-specifics in the eastern North Pacific. The aim of the present study was to assess the degree and timing of the isolation of Gulf of California fin whales in a population genetic analysis of 18 nuclear microsatellite genotypes from 402 samples and 565 mitochondrial control region DNA sequences . The analyses revealed that the Gulf of California fin whale population was founded ~2.3 thousand years ago and has since remained at a low effective population size (~360) and isolated from the eastern North Pacific (Nem between 0.89–1.4). The low effective population size and high degree of isolation implied that Gulf of California fin whales are vulnerable to the negative effects of genetic drift, human-caused mortality and habitat change.The Gulf of California, Mexico is home to many cetacean species, including a presumed resident population of fin whales,  A population acquires genetic variation by mutation and immigration. Genetic variation is lost by random genetic drift resulting in higher rates of loss in small and isolated populations compared to larger, connected populations2. Possible consequences of low genetic variation include increased genetic load and loss of adaptive traits, which in turn can elevate short- and long-term extinction rates6. Consequently, understanding the processes that shaped contemporary levels of genetic variation may add vital insights to inform the conservation and management of local populations.Population genetic data hold the potential to provide insights into the basis of genetic variation in natural populations10. Baleen whales typically undertake long-range seasonal migrations to forage in cold nutrient-rich waters at high latitudes during the summer and winter in warm tropical waters during the breeding season11. Exceptions to this pattern are Omura’s 12 and Bryde’s whales 13, which remain at low latitudes all-year as well as bowhead whales  that are confined to polar waters14. A few populations among those baleen whale species that undertake seasonally migrations, do not migrate but remain in the same area year-round, such as blue whales 16 and humpback whales (Megaptera novaeangliae) in the Arabian Sea20.Population genetic assessments have proven particularly useful to study elusive species, such as baleen whales23. These whales were probably first noted by, and the canal named by, the Croatian Jesuit cartographer Fernando Consag during his 1746 expedition to the Upper Gulf of California undertaken to prove that Baja California was not an island. The whales in the Canal de Ballenas were identified as fin whales as early as the late 18th Century24 and were later “rediscovered” during the 1940 expedition on the Western Flyer in the Sea of Cortez25. Gilmore26 was the first to suggest that the fin whales in Gulf of California were all-year residents, a notion later repeated by Norris27. Since the 1980s, regular surveys aimed at baleen whales in the Gulf of California have been conducted in the waters off the Midriff Islands, in particular Canal de Ballenas30. Fin whales, and their vocalizations have been recorded throughout the year in the Gulf of California and appear most abundant in the Upper Gulf32.Baleen whales are also observed year-round in the Gulf of California, Mexico. Baleen whales are commonly observed around the Midriff Islands and in the Canal de Ballenas between the east coast of the Baja California Peninsula and Isla Angel de la Guarda35, although some fin whales were sighted south of 24°N in February during the annual surveys between 1965 and 196736. The presumed residency of this fin whale population was also supported by movement data from 11 satellite-radio tagged fin whales, which remained in the Gulf of California while transmitting location data37. Further supporting the notion of isolation were the 20 Hz fin whale calls recorded in the Gulf of California that were unique and distinct from fin whale calls recorded in the North Pacific and North Atlantic38. However, some occasional tempo-spatial overlap was noted in fin whale calls recorded in the Gulf of California and off Southern California, indicating the possibility of some connectivity between these two areas39.The Gulf of California fin whales appear isolated from con-specifics in the eastern North Pacific. Fin whales have not been observed in the entrance of the Gulf of California and sightings off the southern tip (south of 24°N) of the Baja California Peninsula have been scarceet al.41 confirmed the presumed reproductive isolation of the Gulf of California fin whales. Bérubé et al.40 compared mitochondrial control region (mtCR) DNA sequences and genotypes at 16 nuclear microsatellite loci in 12 samples from the eastern North Pacific with 77 samples from the Gulf of California. Bérubé et al.40 detected a high degree of genetic differentiation between the two areas; Weir’s θ42 was estimated at 0.24 and 0.22 for mtCR DNA sequences and microsatellite genotypes, respectively. The high degree of genetic differentiation estimated by Bérubé and co-workers41, in combination with the very low levels of genetic diversity observed among the Gulf of California fin whales, strongly suggested that this population was reproductively isolated from con-specifics in the eastern North Pacific. However, to date no study has attempted to estimate migration rates between the Gulf of California and the eastern North Pacific or when the Gulf of California population was founded and presumably became isolated. Nigenda-Morales et al.43 also detected low levels of genetic diversity in the Gulf of California fin whales at the usually highly diverse major histocompatibility complex (MHC) DQB-1 locus. Nigenda-Morales and colleagues43 detected only three alleles in a sample of 36 fin whales, a level of MHC diversity comparable to that observed in the severely bottlenecked northern elephant seal, Mirounga angustirostris44. The low genetic diversity at the mtCR DNA as well as at nuclear microsatellite and MHC loci suggested that effective population size (Ne) of the Gulf of California was small, but so far, no actual estimates of Ne have been reported.Genetic analyses conducted by Bérubé Ne as well as the duration and degree of isolation. Extended periods at a low Ne increase random genetic drift possibly leading to inbreeding depression45 as well as loss of adaptive traits and potentially increasing the overall risk of extinction46. The aims of this study were to estimate when the Gulf of California fin whale population was founded, the census (Nc) and Ne, as well as the degree of connectivity with con-specific populations in the eastern North Pacific. Towards this end, a population genetic assessment of the Gulf of California and eastern North Pacific fin whale specimens was undertaken based upon genotypes at 18 nuclear microsatellite loci in 402 samples and 565 mtCR DNA sequences . The estimates suggested that the Gulf of California fin whale population was founded approximately 2.3 thousand years ago (kya), and has since remained at a low Ne. The estimates of historic and contemporary connectivity with the eastern North Pacific fin whales were low as well.The low level of genetic diversity among the Gulf of California fin whales is a function of A total of 402 tissue samples were collected from individuals off Kodiak Island, off the Californian coast and in the Gulf of California. The sample sizes per year were: Kodiak Island, 14 samples in 2002; coastal California, 16 samples in 1996 and Gulf of California, 372 samples collected in 1993–1995 and 1997–2004 (Table PI)47 in the three samples were sufficiently low to discern among unrelated individuals. PI was estimated at 2.15 × 10−22 in the Kodiak Island sample, 1.75 × 10−22 in the coastal California sample and 9.08 × 10−12 in the Gulf of California sample. Accordingly, the expected numbers of pairs of unrelated individuals with identical genotypes by chance in the sample were estimated at 9.24 × 10−21, 8.57 × 10−21 and 3.04 × 10−7, respectively in the above mentioned samples. Accordingly, all specimens with identical multi-locus microsatellite genotypes, sex and mtCR DNA sequences were inferred as duplicate specimens collected from the same individual, and only data from a single tissue sample were retained in each sample partition during data analysis. No statistically significant deviation from parity was observed for the sex ratio among samples from Kodiak Island, coastal California and the Gulf of California.The estimates of the probability of identity  from the expected Hardy-Weinberg genotype proportions was detected, whereas significant deviations were detected at eight loci in the Gulf of California sample after applying a false discovery rate (FDR)05 Table . After r05 Table . UnsurprϕST = 0.027 for the mtCR DNA sequences and FST = 0.016, 95% confidence interval (CI): 0.004–0.03 for the microsatellite genotypes; Table per se, given the modest sample sizes available from the eastern North Pacific region.Estimates of genetic diversity at nuclear loci and mtCR DNA sequences in sampling areas outside the Gulf of California yielded values approximately twice the levels observed in the Gulf of California Table . The levNem) from the eastern North Pacific into the Gulf of California (Table Ne at 360 (see below). Excluding loci with possible null alleles from the assessment did not alter the estimates (Table Contemporary immigration rates were estimated using the approach implemented in BAYESASS. The Markov chain Monte Carlo (MCMC) mixing parameters for the migration rates was at 0.07, the allele frequencies at 0.2 and inbreeding coefficients at 0.15  obtained from the Gulf of California samples yielded an estimate of an ancestral Ne at 12,000 , which was reduced to an Ne at ~250  approximately 19 kya   was preferred with a posterior probability at 0.89 [95% CI: 0.88–0.9] using the logistic approach ch Table  and S11.Only four mtCR DNA sequence haplotypes were detected in the Gulf of California sample, of which three were also detected in the eastern North Pacific sample Fig. . One of Ne and Nc estimates for the Gulf of California population  were low as was the IMAP2 estimate of the “long-term” Ne (~40) suggesting that this fin whale population has remained small since it was founded, which in turn explains the very low levels of contemporary genetic diversity in this population. The contemporary, highly productive, oceanographic conditions in the Gulf of California were established ~2.8 kya55, which correspond remarkably well with the IMAP2 estimate of the divergence time between the Gulf of California and eastern North Pacific fin whales. The primary productivity in the Upper Gulf of California can reach very high levels (364 mgC m−3 D−1)56 seemingly resulting in conditions that can sustain a resident population.The analyses in this study revealed that the Gulf of California fin whale population was founded approximately 2.3 kya and subject to very limited gene flow from the neighbouring populations in the eastern North Pacific. The contemporary Ne at ~7,000 to ~40 approximately 2.3 kya. The MSVAR assessment also suggested a similar reduction, although the timing of the change in Ne inferred from the MSVAR assessment was much older than the IMA2P estimate, but included in the 95% HPD of the IMA2P estimate. The IMAP2 estimates accounted for migration, which was not the case for the estimates obtained using DIYABC or MSVAR. The MSVAR estimate could also reflect another, older change in Ne. Despite the quantitative differences, the qualitative conclusion among these slightly different analytical approaches was similar; i.e., the Gulf of California fin whale population was founded more than one kya, and most likely around 2.3 kya.Our results supported the notion that the establishment of the Gulf of California fin whale population was not a recent phenomenon. The DIYABC-based assessment yielded strong support for a divergence time between the Gulf of California population and the eastern North Pacific more than 40 generations ago (>1 kya). The IMA2P divergence time estimate agreed with the DIYABC-based assessment, suggesting a reduction from an ancestral 59. The contemporary and “long-term” Nem were estimated at ~1.4 and 0.9 respectively. All estimates of migration rates (and effective population sizes) are sensitive to ghost populations 60. However, the distinct and very low levels of genetic diversity in the Gulf of California fin whales compared to the adjacent eastern North Pacific fin whale populations  suggest that immigration from these adjacent populations has been minimal, and hence likely not of major concern in this case. Moreover, the Gulf of California is a semi-enclosed sea, hence immigration from other populations besides the eastern North Pacific was unlikely.The estimates of migration rates between the Gulf of California and the eastern North Pacific were well below the values viewed as evidence for evolutionary independence40. Overall the results obtained during this study were in agreement with the inferences drawn in previous genetic41 and acoustic assessments60, as well as the restricted range observed in satellite-tagged fin whales in the Gulf of California37 and the scarcity of fin whales sightings at the southern tip (south of 24°N) of the Baja California Peninsula35.The estimated contemporary immigration rates were roughly consistent with the identification of only two individuals with an immigrant ancestry among the 259 individuals sampled in the Gulf of California during the 12-year sampling period. One of the two individuals with an immigrant ancestry, was a male inferred as a first-generation immigrant, whereas the second “immigrant” individual was inferred as a F1, i.e., the offspring of an immigrant individual. The latter individual was previously reported by Bérubé and colleaguesFST ~ 0.2 for microsatellite genotypes and ΦST ~ 0.2 for mtCR DNA sequence data), compared to levels observed among other con-specific populations in other baleen whale species in the same oceanic basin40. Fin whales in the Mediterranean Sea also appeared to be (at least in part) a resident population41. The degree of differentiation using mtCR DNA sequences between the Mediterranean Sea fin whales and their eastern North Atlantic con-specifics was slightly lower (ranging from 0.09 to 0.15)41 compared to the Gulf of California and eastern North Pacific in this study. Nem between the North Atlantic and the Mediterranean Sea fin whales was estimated at two  migrants per generation61. However, the degree of genetic differentiation between the North Atlantic and the Mediterranean fin whale populations at nuclear microsatellite genotypes was much lower (FST ~ 0.007), perhaps due to occasional male-mediated gene flow62.The degree of population genetic differentiation between the Gulf of California and the eastern North Pacific was relatively high  than the genetic diversity observed among Gulf of California fin whales. An illustration of the very low genetic diversity among the Gulf of California fin whales was the observation that a single mtCR DNA sequence haplotype accounted for ~92% of all the sampled individuals. The mtCR DNA sequence haplotype (h) and nucleotide diversity (π) was estimated at 0.14 and 0.0006, respectively, which were among the lowest values reported for cetaceans69. For instance, reported estimates of mtCR DNA sequence diversity in the highly endangered western North Atlantic right whale  were substantially higher (ℎ = 0.69 and 𝜋 = 0.006)66.The results presented here suggested that the low genetic diversity in the Gulf of California fin whales was a product of a low, long-term 72. A few recent genomic-based studies have detected an increase in genetic load at a low Ne. Pedersen et al.73 identified elevated levels of detrimental alleles among Greenlandic Inuit (Homo sapiens), which underwent a bottleneck for 15–20 kya at a Ne at ~1,000. Rogers and Slatkin74 identified an apparent increase in the number of detrimental mutations in a small isolated population of woolly mammoth (Mammuthus primigenius) on Wrangel Island, which underwent a bottleneck of a similar extent and duration to that inferred for the Greenlandic Inuit. Both examples confirmed the expected increase in detrimental traits following a protracted population bottleneck and hence a reduction in population fitness. In both examples, the negative effects were observed in populations with a Ne larger than the estimated in the Gulf of California fin whale population. However, the duration of the bottleneck in the two mentioned examples was much longer.The very low level of genetic diversity among the Gulf of California fin whales is of potential concern in terms of loss of adaptive traits and a possible increase in genetic loadLynx pardinus), which appeared to have persisted at a Ne at 200–300 for 8.1–50 kya75. Among marine mammals very low levels of mtCR DNA sequence diversity has been reported in the narwhal (Monodon monoceros)69 with no apparent adverse effects and a large contemporary populations size . In contrast to the narwhal, Nc of the Gulf of California fin whale population was estimated at only 730 individuals. The genetic tagging8 estimate of Nc for the Gulf of California fin whale population was concordant with previous Nc estimates based upon line transect surveys (ranging from 297 to 820)34 and a single mark-recapture estimate (600) based upon individual identification from natural markings77.On the other hand, there have also been examples of mammal populations that persisted for thousands of years despite low levels of genetic diversity, such as the endangered Iberian lynx , entanglement in fishing gear, ship strikes, possible disturbance by whale-watching boats, anthropogenic sound79 and habitat degradation due to chemical pollution, microplastics80 and urban development35.The results presented here implies that Gulf of California fin whales could be subject to elevated rates of genetic load and loss of adaptive variation, although the population appeared to have persisted at a low Ne through several thousand years. The low Ne and Nc of the Gulf of California fin whale population, coupled with the very low connectivity with the eastern North Pacific fin whales documented here, indicates that a cautionary management scheme should be considered in order to avoid this population joining the other small baleen whale populations currently declining from anthropogenic threats83, especially bycatch84, entanglements and ship strikes.Such anthropogenic effects on mortality and reproductive rates may add to the genetic effects stemming from a low 85 and sloughed skin86 from free-ranging fin whales, or during necropsies of beached fin whales. Tissue samples were collected by Universidad Autónoma de Baja California Sur with the approval and permits issued by the Mexican Wildlife Agency . Samples from US waters were collected by the Southwest Fisheries Science Center and by the National Marine Mammal Lab in accordance with national guidelines and regulations. The research permits also included the necessary ethical approval in terms of sample collection. All sample collection was undertaken in accordance with relevant national guidelines and regulations. Tissue samples were preserved in a saturated sodium chloride solution with 20% dimethyl sulfoxide87 and stored at −20 degrees Celsius (°C).Tissue samples were collected as skin biopsies88 or using QIAGEN DNEasyTM extraction columns for animal tissue, following the manufacturer’s instruction (QIAGEN Inc.). The quality of the extracted DNA was visually checked by electrophoresis through a 0.7% agarose gel, and quantified with a QubitTM following the manufacturer’s instructions (Thermo Fisher Scientific Inc.). DNA extractions were normalized to a concentration of 20 ng DNA/μL. Sex was determined by the differential amplification of the zinc finger coding regions, ZFX and ZFY89 as well as by co-amplification of a SRY-specific region with one or more autosomal microsatellite loci .Genomic DNA was extracted by either standard phenol-chloroform extractions90, GATA05390, GATA09890, GATA41790, GGAA52090, TAA02390, GT01141, EV00191, EV03791, EV09491, GT02392, GT57592, GT19592, GT21192, GT27192 GATA25072, GATA43950 and GATA6063862 . Negative and positive controls were included with each set of polymerase chain reaction93 (PCR) amplifications. Individual PCR amplifications were carried out as described by Palsbøll, et al.90 and Bérubé, et al.92. PCR fragments were separated by electrophoresis on an ABI 3730 DNA Analyzer™ (Applied Biosystems Inc.) with GeneScanTM-500 ROX (Applied Biosystems Inc.) as size standard. The length of each PCR product was determined with GENEMAPPER™ . The consistency rate per multi-locus genotype was estimated by re-amplifying ten loci  in 47 randomly selected DNA extractions.The genotypes were determined at 18 microsatellite loci: GATA02894 and BP16071R95. The PCR amplifications were performed in 20 μL reaction volumes with 0.2 μM of each dNTP, 67 mM Tris-HCl (pH 8.8), 2 mM MgCl2, 17 mM NH3SO4, 10 mM β-mercaptoethanol, 0.1 μM of each oligo-nucleotide, 0.4 units of Taq DNA polymerase (New England BioLabs® Inc) and 10–20 ng of DNA extraction. The thermo-cycling profile was 2 min at 94 °C, followed by 25 cycles each with 15 seconds at 94 °C, 30 seconds at 54 °C and 120 seconds at 72 °C. Unincorporated nucleotides and excess oligo-nucleotides were enzymatically removed using shrimp alkaline phosphatase and exonuclease I as described by Werle, et al.96. Cycle-sequencing was conducted according to the manufacturer’s instructions using Big Dye™  with the oligo-nucleotides employed in the initial PCR amplification. The cycle-sequencing products were precipitated with 96% ethanol88 and re-suspended in 10 μL deionized formamide . The order of cycle-sequencing products was resolved by electrophoresis on an ABI 3730 DNA Analyzer™ (Applied Biosystems Inc.). The reverse strand was sequenced in DNA extractions with unique mtCR DNA sequence haplotypes. Additional mtCR DNA sequences from Bérubé, et al.41 and Archer, et al.97 were retrieved from NCBI (Table The first 285 base pairs of the 5′-end of the mtCR were amplified using the oligo-nucleotides MT4FBI Table .PI47 was estimated using CERVUS (ver. 3.0.7)98 assuming that all individuals were unrelated, in order to determine the probability that two unrelated individuals had identical multi-locus genotypes.PI. The PI was also used to estimate the number of loci needed to identify an individual99, and this threshold was used to check the samples with few mismatches. The probability of an observed deviation from parity of the sex ratio was estimated as the fraction of 2,000 random permutations of the data in which χ2 was equal to or larger than the observed value of χ2 by means of a χ2 test in R (ver. 3.0.1)100.The expected number of pairs of unrelated individuals with identical genotypes by chance in the sample was estimated by multiplying the number of pairwise comparisons by 101. Departures from Hardy-Weinberg equilibrium were assessed for each microsatellite locus in each sample from 10,000 dememorization MCMC steps followed by 100,000 steps. The probability of linkage disequilibrium between all pairs of loci per population were estimated from 10,000 permutations. The significance of the departures from Hardy-Weinberg equilibrium and linkage equilibrium was corrected using FDR48 at 0.05. The possible presence of null alleles, large allele dropout and scoring errors due to stuttering was assessed using MICRO-CHECKER (ver. 2.2.3)102.Departures from Hardy-Weinberg equilibrium and linkage disequilibrium were estimated using ARLEQUIN (ver. 3.5.2.2)Na) as well as the observed (Ho) and expected heterozygosity (He) were estimated using ARLEQUIN. Allelic richness (AR) was estimated with the package ADZE (ver.1.0)103 for all 18 microsatellite loci in sample sizes of 18 . The inbreeding coefficient (FIS)104 was estimated as implemented in FSTAT (ver. 2.9.3.2)105. The mtCR DNA sequences were aligned using the Clustal W algorithm106 implemented in MEGA (ver. 6.06)107 with the default settings. DNASP (ver. 5.10.1)108 was employed to estimate the number of segregating sites (S) and haplotypes (hn) as well as π, h and θ (per site) from S109.The number of alleles 105. The 95% CI was estimated from 15,000 permutated datasets by bootstrapping loci.The degree of population genetic differentiation was estimated as 110. Preliminary estimations were performed to adjust the MCMC mixing parameters of migration rates, allele frequencies, and inbreeding coefficients to acceptance rates at 20–60%. A total of 11 independent MCMCs were performed, each with different random starting seeds and a burn-in of 1,000,000 steps followed by 10,000,000 steps sampling every 1,000th step. The final parameter estimates were inferred from the estimation with the lowest Bayesian deviance using the R script authored by Meirmans111.“Contemporary” immigration rates were estimated from the microsatellite genotypes employing the Bayesian approach implemented in the software BAYESASS (ver. 3.0.4)Ne was estimated from the degree of linkage disequilibrium among the microsatellite loci using the bias-corrected approach by Waples and Do112 as implemented in NEESTIMATOR (ver. 2.1)113. The estimation was conducted excluding the first-generation immigrant detected during the BAYESASS assessment because the linkage disequilibrium approach in NEESTIMATOR assumes that all genotypes originate from a single panmictic population. The second-generation migrant was included in the dataset because the individual was part of the Gulf of California population. The critical minimum allele frequency was set to 0.02. The 95% CI of each point estimate was estimated by jack-knifing among samples114.Contemporary Nc) was estimated using the POPAN derivation of the Jolly-Seber model as implemented in MARK (ver. 6.1)115 via the RMark package116. Each year was employed as a single capture event . Two duplicate runs were conducted, each with different random seeds using the exponential population size change model. Starting values of each duplicate run were initiated from the upper and lower range of the prior distribution of each parameter . Each MCMC comprised of 10,000,000 steps with sampling at each 1,000th step. The first 5,000 of the 10,000 recorded steps were discarded before assessing convergence and parameter estimation.A possible change in effective population size in the Gulf of California sample was estimated using MSVAR (ver. 1.3)Ne, and immigration rates were estimated using the parallelised version of the isolation-with-migration model50 implemented in IMA2P51. Two different datasets were analysed; a dataset comprised solely of mtCR DNA sequences and another dataset including both mtCR DNA sequences and microsatellite genotypes. Microsatellite loci with “micro-variant” alleles were excluded from this estimation as well. The Hasegawa-Kishino-Yano, HKY, substitution model118 was employed with a mutation rate at 5.2 × 10−8 per site per year119 for the mtCR DNA sequences. The mutation rate per locus was based upon 280 base pairs. A stepwise mutation model was employed for the microsatellite genotypes using a mutation rate at 7 × 10−4 per generation . A generation time at 25.9 years120 was used for converting the generational mutation rate into an annual mutation rate. Preliminary estimations were conducted to identify the optimal parameter prior ranges. The final estimates for the mtCR DNA sequences were based upon three replicate estimations: each with ten MCMCs consisting of 400,000 steps, of which the first 200,000 were discarded, and sampling each 100th step. In addition, one run was started using the Markov chain state generated in a previous run.The population divergence time, long-term For the dataset including both mtCR DNA sequences and microsatellite genotypes a random sample of 30 individuals from the Gulf of California was employed in the analysis, in order to avoid effects stemming from unequal sample sizes between the eastern North Pacific and the Gulf of California. The estimations for the combined data were based upon four replicates: each with ten MCMCs consisting of 250,000 steps, of which the first 150,000 were discarded, and a sampling interval at every 100th step. In addition, two runs were started using the Markov chain state generated in previous runs. Reported estimates were those obtained from the run with the highest ESS.53 was used to test between a recent (scenario 1: 0–20 generations ago) versus an older (scenario 2: 40–900 generations ago) population divergence time. The point estimate of the population divergence time given by IMA2P analysis using only mtCR DNA sequences was used as the starting point for the divergence time distribution of scenario 2 . The divergence time distribution of scenario 1 starts at zero and ends at 20 generations in order to not overlap with scenario 2. DIYABC assessments were based upon two million simulated datasets for each scenario using mtCR DNA sequences and microsatellite genotypes. Uniform prior distributions were used for the demographic parameters: North Pacific effective population size, N1 ; Gulf of California effective population size, N2 , scenario 1 divergence time, T1 [0–20] and scenario 2 divergence time, T2 [40–900]. A HKY mutation model was used for the mtCR DNA sequences with a uniform distribution from 1 × 10−7 to 1 × 10−5 and 96% of invariable sites. Default values were used for the other mutation model parameters. The posterior probability of each scenario was estimated from a logistic regression performed on one percent of the simulated datasets closest to the observed values after a linear discriminant analysis121. The confidence in the scenario choice was evaluated by estimating the type-I and type-II error rates for 1,000 simulated datasets in a linear discriminant analysis. The model checking ng Table  was perfcoda122.MCMC-based estimations were deemed to have converged when estimates were consistent among independent replicate MCMC runs  and when effective sample sizes (ESS) were above 200. The ESS, trace plots, and Gelman-Rubin diagnostic were estimated for MSVAR and BAYESASS using the R package 123 was inferred using POPART (ver. 1.7)124 with ε set at zero.A median-joining mtCR DNA sequence haplotype networkSupplementary_material


Fragaria vesca) in a common garden. A quantitative genetic approach was then used to test the ability of two such proxies—mesoclimatic variation  and landscape isolation and geographic distance between populations —to predict spatial genetic variation in three quantitative traits . Our results indicated a significant but variable effect of mesoclimatic conditions in structuring genetic variation in the wild, in addition to other undetermined regional scale processes. As a proxy of gene flow potential, landscape isolation was also a likely determinant of observed patterns—as opposed to, and regardless of, geographic distance between populations. We conclude that harnessing proxies of adaptive and nonadaptive evolutionary processes could provide a robust and valuable means to identify genetic variation in CWRs. We thus advocate wider use and development of this approach amongst researchers, breeders and practitioners, to expedite the capture and in situ conservation of genetic resources provided by crop wild relatives.Crop wild relatives (CWRs) offer novel genetic resources for crop improvement. To assist in the urgent need to collect and conserve CWR germplasm, we advance here the concept of an “evolutionary” approach. Central to this approach is the predictive use of spatial proxies of evolutionary processes  to locate and capture genetic variation. As a means to help validate this concept, we screened wild‐collected genotypes of woodland strawberry ( The traits studied were plant size, early season flower number and flower frost tolerance. This information was then spatially related back to the original source populations, to test the ability of two proxies of evolutionary processes to predict genetic variation. Specifically, we tested whether genetic variation could be predicted (i) by local mesoclimatic variation —as defined by common a priori‐selected climatic variables; (ii) by functional landscape isolation and geographic distance ; and (iii) by other undetermined spatial drivers . Based on these findings, we further elaborate on the concept of an evolutionary approach—including means of practical implementation, and the potential for integrated use with complementary strategies.As a means to help validate the practical use of this approach, we collected and screened wild genotypes of woodland strawberry ) immediately after planting, to increase winter survival. Plants were then allowed to establish for two growing seasons prior to trait measurements. A small amount of mortality randomly occurred during this time. We therefore limited the use of genotypes to those with full replication only .In contrast to a strong emphasis on “within‐population” sampling in population genetic studies, ecological genetic (or genecology) studies typically seek to maximize sampling efforts across many diverse populations . Plant size was quantified as the volume of a partial sphere (in dm3), derived from measurements of plant height and diameter. Total early season flower number per plant was recorded as the sum of flowers that had reached anthesis or postanthesis stage by 15 June 2015. We recorded flower frost tolerance as the inverse of the proportion of frost damaged flowers per plant. Frost damage is evident by a total blackening of the immature ovary, which subsequently fails to develop into a fruit. Trait values for plant size and early flower production were approximately normally distributed. Flower frost tolerance data were arcsine‐transformed to improve normality after first offsetting 0 and 1 values—which are problematic in angular transformations. This was achieved by replacing 0 values with (1/4n) and 1 with [1−(1/4n)], where n = the number of observations.Horticulturally important traits considered in this study included plant size, early flower number (as an indicator of early season yield) and flower tolerance to frost damage. Plant size appears to largely influence or covary with several other quantitative traits in woodland strawberry . We therefore confined our selection to the above three independent traits, which did not share more than moderate levels of phenotypic correlation  models to analyse trait phenotypic variation across genotypes. As the error term of this model provided a measure of environmental  variation, this model structure could hence be used to partition phenotypic variation into its genetic and environmental components Hill, . Accordi2.42 spatial resolution by bilinear interpolation (in ArcGIS 10.2.2), to match the resolution of Worldclim data. From a multitude of initial possibilities, we made an a priori selection of six commonly used climatic variables for use in this study. These included variables describing extremes in temperature (max. and min. T of the warmest and coldest months—in °C), precipitation (for the driest and wettest months—in mm) and two derived variables: length of the growing period (no. of days per year that T and soil moisture are conducive to plant growth) and the number of frost‐free days. As there was a strong negative correlation between these variables (r2 = .55), we opted for use of the former, due to its greater predictive power in models.Climatic data were sourced from the Worldclim (v1.4) and Global Agro‐ecological Zones (GAEZ v3.0) bioclimatic datasets  raster land cover map for Sweden (“Svenska Marktäckedata” v1.1). According to this map, the focal region in which plant material was collected constituted a total of 56 unique land cover classes. To produce fewer more functionally relevant categories, we consolidated these into eight broad land class types  for quantification of two landscape metrics: “similarity index” (SIMI) and “contrast‐weighted edge density” (CWED). Although strictly categorized as measures of landscape aggregation and contrast  length between forests and other land class patches in the landscape, weighed by their relative structural contrast , to test for climatically structured genetic variation. Models were fitted by REML estimation in the “lme4” package—used in conjunction with the “lmerTest” package  function in the R package “vegan”  of trait genetic values as a multivariate response; F, a multivariate multiple regression  of climatic and landscape explanatory variables on Y; and R, a partial residual analysis (a modified form of PCA) of the remaining variation, after partialling out the effects of climatic and landscape variables.Spatial variation in trait genetic values was evaluated using a Moran's eigenvector approach, to test for patterns in genetic variation at multiple spatial scales. The analysis was applied in three steps, following the approach and notation outlined in Dray et al. . In brieY, F and R were visually mapped and statistically analysed in a scalogram. Scalograms hence tested at which scale(s), if any, spatial patterns in genetic variation—and the effects of climatic, landscape and other undetermined processes—were evident. As inputs to produce the scalograms, the packages “spdep” and “adespatial” were first used to generate both a Gabriel graph  explained by each spatial scale in the scalogram was then tested by means of a permutation procedure based on 999 repetitions.The resulting multivariate scores from 2.6.4Y), to decompose the relative influences of climate, landscape isolation and spatial variation . Prior to the analysis, we used a forward selection procedure to limit the number of MEM variables included to those which described significant variation only. Forward selection was implemented using “vegan” and employed the double stopping criterion of Blanchet, Legendre, and Borcard , to decompose the relative influences of climate, landscape isolation and spatial variation . Prior to the analysis, we used a forward selection procedure to limit the number of MEM variables included to those which described significant variation only. Forward selection was implemented using “vegan” and employed the double stopping criterion of Blanchet, Legendre, and Borcard . Of the five bioclimatic variables examined, three of these  successfully predicted trait genetic variation. For plant size, a significant negative interaction between LGP and Tmax was observed, indicating that LGP more strongly influenced plant size in areas where low values of Tmax occurred, and vice versa. High precipitation in the driest month positively impacted the number of early flowers produced. Outside of climatic influences, habitat of provenance (forest or open land) held very little influence on genetic variation , but not for early flower number  or flower frost tolerance . The partial‐regression coefficients of this model thus indicated a significant effect of landscape similarity index (SIMI) on plant size , in addition to an important but nonsignificant fine‐scale component . The RDA exhibited a single prominent axis , minimum temperature of the coldest month (r = −.66) and landscape edge density (r = −.64). The associated scalogram —consistent with the fact that most of the above explanatory variables varied at this scale. Partial residual analysis of the remaining unexplained variation  indicated notable fine‐scale and broadscale variation . Thus, other undetermined spatial drivers also appeared important in structuring genetic variation across the region of analysis.A significant broadscale pattern of genetic variation was observed in multivariate analysis of quantitative traits (3.4R2 = .42) was as a whole explained by the model , of which variation partitioning  in general exhibited good predictive association with genetic variation in the studied F. vesca and thus serve as an effective proxy of this evolutionary process.In partitioning the relative predictive power of the employed evolutionary proxies, climate was deemed to be of marginally greater importance in structuring trait genetic variation. In univariate analyses of traits, the largest effect was observed for plant size. Aside from its positive association with yield in strawberry Lacey, , plant sFor early flower number, a positive association was observed with precipitation of the driest month, suggesting in addition the occurrence of climatically structured genetic variation. Early flower number is a good indicator of early season yield in strawberry. However, in many growing climates, flower frost damage presents a significant barrier to prolongation of the growing season  the extent of occurrence and relative importance of evolutionary processes in shaping spatial genetic variation in a given CWR; (ii) whether species‐specific proxies of these processes can be parameterized; and (iii) the most appropriate spatial scale(s) of focus. Table Matching the search for adaptive trait variation to an appropriate spatial scale of focus is an issue of critical importance and has also been raised by previous authors  could nonetheless supplement the level of total genetic diversity available to breeders. Indeed, while this scenario can prove deleterious in wild populations, this need not necessarily be the case for purpose of plant breeding and cultivation.In designing strategies for the collection and conservation of genetic variation explicitly guided by evolutionary principles, other targets of priority may include the following: 1.) areas of recent migration or contact between historically isolated populations or disjunct/minor subranges , in which genetic admixture can promote the rapid evolution of unique genotypes  for many CWRs, good availability of spatial records and metadata  housed in online databases and herbaria may otherwise prove valuable in implementing an evolutionary approach. For example, prioritized efforts have been made to systematically curate occurrence data on CWRs—an example of which is the Crop Wild Relative Global Occurrence Database , including amongst CWR researchers, field collection practitioners, conservationists and plant breeders. Placing new and improved tools into the hands of this community is vital towards meeting the urgent need to adapt crops and safeguard wild germplasm into the future.Moving forward, exploring ways to further develop and integrate this expanded evolutionary approach with other established techniques  will prove especially valuable. While each shares some degree of conceptual overlap, such integration still undoubtedly offers potential for synergy. Spatial proxies developed for evolutionary process (e.g., as here for gene flow potential, Figure None declared.https://doi.org/10.5061/dryad.g51kg15.Data for this study are available at the Dryad Digital Repository:  Click here for additional data file.


Local climate zone (LCZ) maps that describe the urban surface structure and cover with consistency and comparability across cities are gaining applications in studies of urban heat waves, sustainable urbanization and urban energy balance. Following the standard World Urban Database and Access Portal Tools (WUDAPT) method, we generated LCZ maps for over 20 individual cities and 3 major economic regions in China. Based on the confusion matrices constructed by manual comparison between the predicted classes and ground truths, we highlight the following: (1) notable variation in overall accuracies  among cities were observed, which was mainly due to class incompleteness and distinct proportions of natural landscapes; (2) building classes in selected cities were poorly classified in general, with a mean accuracy of 48%; (3) the sparsely built class , which is rare in the selected Chinese cities, had the lowest classification accuracy (32% on average), and the class of low plants had the widest accuracy range. The findings indicate that the standard WUDAPT method alone is insufficient for generating LCZ products that demonstrate practical value, especially for built-up areas in China, and the misclassification is largely caused by the lack of building height data. This result is confirmed by a refinement test, in which the urban DEM retrieved from Sentinel-1 data with radar interferometry technique was used. The study shows a detailed and comprehensive assessment of applying the WUDAPT method in China and a feasible refinement strategy to improve the classification accuracy, especially for the built-up types of LCZ. The study could serve as a useful reference for generating quality-ensured LCZ maps. This study also examines and explores the relationship between socio-economic status and LCZ products, which is essential for further implementations. These challenges are being addressed with advances in urban climatology, where numerical and physical models in combination with urban canopy parameters (UCPs) have been used to study the interaction between city structure and local climate9. To acquire UCPs, the existing land use and land cover (LULC) data are usually compulsory. However, although several global LULC datasets are available12, they are inadequate for calculating UCPs because the description of urban morphology is deficient. To fill the data gap globally, the World Urban Database and Access Portal Tools (WUDAPT) project was initiated14. As a level 0 product, the local climate zone (LCZ) data have been developed to date. The LCZ scheme categorizes the urban surface into 10 built types and 7 natural types16, from which UCPs can be determined and thereby improved climate model results can be achieved. Due to its detailed description of urban areas, the LCZ scheme is gaining popularity in studies of local air/surface temperature features21, urban heat islands24, urban energy budgets26, outdoor thermal comfort28, machine learning29 and urban ventilation simulations30. When historical LCZ datasets are available, there are useful for examining the impacts of local cover change on the spatial pattern of land surface temperature31, evolution of the climatological effects of urbanization32, and urban growth monitoring33.Urbanization, as a process that gradually changes the physical landscape, is often accompanied by environmental problems and health challenges, such as urban heat islands, air quality degradation, heat stroke, overweight, hypertension, infection diseases and increasing deaths caused by injuries20. In this work, following the standard WUDAPT processing method, we generated LCZ maps for over 20 individual cities and 3 major economic regions in China from Landsat images acquired from 2014 to 2015. The cities vary in size and have distinct geographic locations; additionally, the cities have different levels of economic development. The quality assessment and cross-comparison among these LCZ maps are then rigorously conducted to reveal the factors that limit the accuracy of the WUDAPT method in Chinese cities. To explore the strategies that can improve the accuracy of current LCZ products, we evaluated the role of the urban digital elevation model (DEM) generated from Sentinel-1 data by the synthetic aperture radar interferometry (InSAR) technique. Although SAR data have been used for LCZ classification, only SAR intensity information has been adopted36, and the role of an InSAR-derived urban DEM for the refinement of LCZ products has not yet been investigated.Because the WUDAPT community provides a standard processing workflow for the generation of LCZ data from freely available satellite images  and training samples that have been identified according to Google Earth images with open source SAGA software, there is currently abundant LCZ data for more than 120 cities in different continents; these data are being produced and uploaded to the WUDAPT portal after quality assessments39. Second, by mining the confusion matrices of such a large volume of LCZ data, the factors that limit the performance of the default WUDAPT workflow in cities of China have been revealed. Finally, this study sheds light on the directions of further optimization of the WUDAPT method by selecting suitable training samples, considering seasonal discrepancies between training samples and Landsat data, and involving external data sources. Among them, the urban DEM retrieved from freely available Sentinel-1 data shows great potential.The contributions of the work are multi-fold. First, LCZ classification maps with a mean overall accuracy of 76% for more than 50 Chinese cities were generated, which enriched the database of land cover and land use in China and can benefit the study of urban climate41, the LCZ maps of 20 individual cities and 3 major economic regions  were produced , which has a mean of 85% for the selected cities. Figure u) is 47%, and the lowest is only 26% in the Jing-Jin-Ji region. When compared to the number of training samples shown in Fig. Following the standard WUDAPT processing workflowced Fig. . The oveWhen examining the accuracy matrix of all classes in all cities/regions Fig. , it is o42 and from those published35. As a comparison, Fig. By investigating the confusion matrix, the accuracy discrepancy could be explained by misclassification among the LCZ classes. To obtain a general picture of the class mixture over all cities, we accumulate the confusion matrices by summing the corresponding elements Fig. . It is cWhen focusing on the quality assessment in each city, it is noticed that approximately half of the cities do not contain all the LCZ classes, among which, the city of Suzhou has the fewest classes (6 less). Poor classifications in the selected cities mainly occur in built-up areas, with exceptions in that Xian, Jian and Tianjin have the worst classifications of natural lands. In these cities, zero accuracy was observed in LCZ E (bare rock or paved) and LCZ C (low plants), where there were misclassifications with LCZ F in Xian, LCZ 10 in Jinan and LCZ B in Tianjin. To investigate the relationship of classification accuracies among cities, following Eq. , we calcThe analysis of the confusion matrices indicates that the WUDAPT method is insufficient for identifying distinct building classes and that height information is expected to be vital for refinement. There are several techniques to acquire urban elevation data ; however, the cost is usually prohibitive, especially for large-scale mapping. To improve the accuracy of classifying urban built-up classes and staying in line with the philosophy of WUDAPT, that is, using freely available imagery, we generated the urban DEM with 25 m resolution from C-band Sentinel-1 SAR data using our self-developed TCPInSAR processor to detect the impact of adding then urban DEM into the LCZ scheme. Figure Although the current accuracy of urban classes might not be sufficient to support a comprehensive analysis of the spatial pattern of each single class, it is possible to reveal the similarity among cities and regions with respect to class distribution by correlation analysis. Following Eq. , we calcThe generated LCZ maps have notable variations in classification accuracies . We attribute this result to the following reasons: (1) class incompleteness. It is commonly accepted that the WUDAPT method has relatively poor classification performance for compact and open buildings . Therefore, for cities that lack such classes, a higher classification quality can be anticipated. Examples in this work are Yantai and Suzhou. (2) Insufficient training samples. Although the significant relationship between the number of training samples and accuracy was not observed in the selected cities, we noticed that Hong Kong, owning the highest sample density in built-up areas, has the best urban classification performance, while the worst is the Jing-Jin-Ji region, where the sample density is lowest. Using LCZ 9 as another example, most cities performed poorly in this class except for Harbin, where the training samples were rather abundant, up to 28% of the total amount. (3) Similarity among classes. From the generated LCZ maps, it is found that, in addition to the misclassification among compact and open buildings, a large portion of classes were mistakenly recognized as LCZ 10 and LCZ D in the selected cities. Indeed, industrial structures could contain compact or open buildings. (4) Seasonal mismatch. When selecting training samples from Google Earth, seasonal differences with Landsat images deserve considerable attention. It is particularly risky for cities with four distinct seasons. Figure During the generation of LCZ maps, to improve the efficiency, we jointly processed some cities as a cluster. Among the three major economic regions, the Yangtze River Delta and Pearl River Delta have comparable (or even higher) classification accuracies, while the performance  over Jing-Jin-Ji is relatively poor, mainly due to insufficient samples (only 420 over the whole territory). Such practice confirms that large-scale processing is feasible provided that cities share similar biophysical backgrounds and the features of training samples are consistent.When looking into the confusion matrices, severe misclassification mainly occurred among urban classes in the selected cities. To distinguish them, information about the relative locations and heights of buildings is vital; however, optical images are insufficient to identify building heights, likely leading to the incorrect selection of training samples and thereby incorrect classifications. Building heights seem to be compulsory for accurate classification of urban landscapes, which was verified by a preliminary experiment with InSAR-derived urban DEM. Although the urban DEM has a moderate resolution of 25 m, it provides more reasonable height-related classifications, indicating that building classifications can be refined with extra height information.The correlation analysis among cities reveals that similarity in class proportions is regionally dependent. Cities within the same economic region have rather high correlation coefficients, indicating they have a higher similarity in the proportions of different built-up types, while the correlations among all cities are relatively low. The potential causes include similar social, economic and climate status. However, within each region, there are still cities with distinct urban structures, and this situation leads to low correlations. The distinction is mainly raised by one or more classes that have extraordinary proportions. For example, Nantong in the Yangtze River Delta region has 48.1% of sparsely built  land covers in urban areas, which is much higher than that observed in other cities. Hong Kong in the Pearl River Delta region is distinct from the others by having 30.6% of the compact high-rise class , the highest in the region. The high similarity in LCZ classes among cities within respective regions might indicate that the convergence of city development is serious, which can serve as a reference for regional planning and policy making.Following the standard WUDPT processing workflow, the LCZ maps were generated for 20 individual cities and 3 major economic regions in China based on site-specific operation. By examining the difference in classification accuracy, we conclude that the accuracy discrepancy among cities is mainly due to class incompleteness , the number and quality of training samples, the similarity among classes and the seasonal mismatches between samples and Landsat data. This result indicates that because Google Earth’s image does not include the height information of buildings, the standard WUDAPT method is inadequate for distinguishing height-related classes. Considering that the similarity among classes often makes the sample collection rather challenging, even for experienced operators, novel classifiers other than supervised ones should be developed. For cities with distinct seasonal land features , when collecting training samples, seasonal consistency should be ensured, as low plants (LCZ D) in summer could become bare soil (LCZ F) in winter in these cities.The experiment with the urban DEM demonstrated a reasonable refinement, which shows that the height information retrieved from satellite SAR images  has the potential for distinguishing and detecting the building heights of LCZ classes 1–10 in built-up areas and can improve the current WUDAPT method and level 0 product accuracy. However, although Sentienl-1 data are freely available around the world, it is challenging for non-InSAR experts to extract the urban DEM from these data. The main difficulty lies in the mitigation of building/ground deformations and turbulent atmospheric delays, which are mixed with building heights in InSAR measurements. Moreover, how to tightly integrate the data in the WUDAPT method  also needs further investigation. To facilitate the usage of the urban DEM in LCZ classification, the generation of annually updated national urban elevation datasets from interferometric Sentinel-1 measurements will be a part of our future work, which is also expected to benefit WUDAPT L1  data production.32, urban energy consumption estimation26, urbanization projection and air pollution distribution detection43. The correlation analysis results of the developed LCZ data show the morphological characteristics of both built-up areas and natural landscapes of Chinese cities and regions. The convergence of city development within respective economic regions might be useful for evaluating the collaborative/competitive relationship and degree of regional integration. At the international level, the developed database is also a crucial part of the WUDAPT database because approximately half of the training samples and LCZ maps are from this study. The developed database and training samples not only lead to the development of global and regional LCZ maps but also make data fusion and data transferability possible. For example, our data were adopted in the latest global transferability of local climate zone models44. Based on our findings, the study also indicates the use of open data to develop an urban DEM for improving the LCZ classification accuracy of built-up areas.It is worth noting that, although the building classes are currently of moderate accuracy, i.e., approximately 40%, the potential of LCZ maps can also be explored as they carry more detailed information on urban morphology than do other classification datasets and they are valued at both the domestic and the international levels. At the national level, the development of LCZ maps for 20 individual cities and 3 major economic regions in China can serve as a fundamental database for various applications, such as urban climate investigation32. Meanwhile, the active WUDAPT community is nurturing innovative methods to produce high-quality LCZ products, e.g., by involving other types of data sources and using advanced machine learning techniques. Accurate and publicly accessible LCZ maps for Chinese cities can be expected in the near future.Moreover, when studying the meteorological effects of urbanization, the LCZ maps can be reduced to 3 classes  as one of the inputs of the Weather Research and Forecasting (WRF) model to enhance the simulation20, the cities in China’s three major regions are jointly processed.A total of 20 individual cities and 3 major economic regions  were selected for LCZ map generation Fig. . Althoughttp://glovis.usgs.gov). They were used for LCZ map generation for the cities in China. Seamless mosaic and atmospheric correction were conducted if necessary, and all images were down-sampled to 100 m to reflect the local urban structures instead of single objects. For urban DEM generation, a set of Sentinel-1 radar data acquired in 2015 with a spatial resolution of 25 m were collected from the European Space Agency (https://scihub.copernicus.eu/dhus/#/home).The 30 m Landsat 8 level 1 images from 2014 to 2015 were downloaded from the U.S. Geological Survey , open low-rise (LCZ6) and lightweight low-rise (LCZ7)), Google Street View images were also employed. Considering that the Landsat images were acquired during a two-year period, when selecting training samples, we avoided digitizing polygons in places that were likely to change, such as construction sites, vacant development areas and bare land19. We selected a total of 9923 samples from these cities. The number of samples for each city/region is shown in Fig. Training data are vital for LCZ classification in the standard WUDAPT workflow. In view of efficiency, it is ideal to classify a large number of cities by using the training data collected in one or a few cities. However, such a strategy usually fails to produce satisfied classification data due to transferability, a common issue faced by machine learning methods, which has also been recently reported15: (1) acquisition and pre-processing of freely available satellite images ; (2) selection of training samples by experienced operators for each city; (3) conduction of supervised classification  embedded as an ‘LCZ classification tool’ in SAGA GIS. The workflow can be efficiently applied in each city while considering that a large portion of selected cities are located in the top 3 megacity clusters of China and that they share similar biophysical backgrounds; based on this information, we combined the samples in each cluster and generated the entire LCZ map for the region45.There are 3 steps involved in the standard WUDAPT processing workflow for the generation of LCZ maps14, where the training samples are divided into two portions for training and evaluation. Considering that the collection of a sufficient number of samples requires a long period and that only a limited number of samples can be selected for the classes that are rare in a given city, we conduct the quality assessment manually after the generation of LCZ maps using all samples. A new set of samples was randomly collected for each LCZ class as ground truths. The number of ground truths was set to 0.5% of each generated class. By cross-comparison between the ground truths and produced LCZ maps, the confusion matrices can then be determined, and these matrices reflect the misclassification of land covers of each city. To reflect the overall misclassification of all the selected cities/regions, an accumulated confusion matrix was also determined, where the number of evaluation samples was replaced by the percentage to address the ground truth imbalance among cities. Based on the single-city confusion matrix, a set of quality metrics can be calculated. We employed the following accuracies to evaluate the quality of the LCZ maps20: (1) overall accuracy (OA), which denotes the percentage of correctly classified pixels, regardless of the performance of each class; (2) OAb reflects the overall accuracy of the LCZ classes related only to built-up areas; and (3) OAn represents the overall accuracy of the LCZ classes of natural land covers. They can be calculated as follows:Nabu, which denotes the overall accuracy of the built versus natural LCZ classes, and the weighted accuracy (OAw), which is obtained by applying weights to the confusion matrix to account for the similarity and dissimilarity among classes. Considering that these two measures are usually high and could not reflect the discrepancies among cities, we discarded them in this work. To reflect the reliability of the generated LCZ maps , we also calculated the user’s accuracy by taking the total number of correct classifications for a particular class and dividing it by the row total. Because a large number of LCZ maps were generated, we attempted to investigate the relationship among the cities by calculating the correlation coefficients that are defined as follows46.xj and yj are the class accuracies or distributions in two cities, respectively.To evaluate the accuracy of the LCZ maps, a random sampling scheme  is used in the WUDAPT workflow47 and TanDEM-X DEM48 are typical examples. To retrieve the urban DEM from Sentinel-1 data acquired with a repeating cycle of 12 days, it is compulsory to mitigate the contribution of ground/building deformation and atmospheric artefacts in InSAR measurements. To this end, we adopted our self-developed multi-temporal InSAR technique termed TCPInSAR49 to process the data. The estimator has several advanced features, e.g., robust image coregistration50, adaptive coherent point selection44, quad-tree model for atmospheric delay mitigation51 and parameter estimation with no need for phase unwrapping52, which guarantees the quality of the retrieved DEM.The synthetic aperture radar interferometry (InSAR) technique has long been used for DEM generation. Global SRTM DEMSupplementary Table S1


Scientific Reports 10.1038/s41598-019-50343-5, published online 25 September 2019Correction to: This Article contains a typographical error in the Methods section under subheading ‘Image processing and automated supraglacial lake identification and mapping’ where,“This equation (Eq. 1) was therefore used to classify each image into ‘water’ or ‘no-water’ regions using a threshold value that was interactively selected:where ‘Green’ is Band 3 of the Sentinel imagery and Band 3 of the Landsat 8 imagery, and ‘NIR’ is Band 4 of the Sentinel imagery and Band 5 of the Landsat imagery”should read:“This equation (Eq. 1) was therefore used to classify each image into ‘water’ or ‘no-water’ regions using a threshold value that was interactively selected:where ‘Green’ is Band 3 of the Sentinel imagery and Band 3 of the Landsat 8 imagery, and ‘NIR’ is Band 8 of the Sentinel imagery and Band 5 of the Landsat imagery”


Stegastes baldwini, and the Clipperton angelfish, Holacanthus limbaughi, on Clipperton Atoll, tropical eastern Pacific. Visual surveys, performed over almost two decades and four expeditions, and genetic surveys based on genomic RAD sequences, allowed us to estimate kinship and genetic diversity, as well as to compare population size estimates based on visual surveys with effective population sizes based on genetics. We found that genetic and visual estimates of population numbers were remarkably similar. S. baldwini and H. limbaughi had population sizes of approximately 800,000 and 60,000, respectively. Relatively small population sizes resulted in low genetic diversity and the presence of apparent kinship. This study emphasizes the importance of small isolated islands as models to study population dynamics of marine organisms.Estimating population sizes and genetic diversity are key factors to understand and predict population dynamics. Marine species have been a difficult challenge in that respect, due to the difficulty in assessing population sizes and the open nature of such populations. Small, isolated islands with endemic species offer an opportunity to groundtruth population size estimates with empirical data and investigate the genetic consequences of such small populations. Here we focus on two endemic species of reef fish, the Clipperton damselfish,  Due to the habitat limitations in this spatially restricted system, comparing visual survey estimates and genetic estimates of population sizes was an attainable goal.The discovery of limited dispersal in marine systems, based on otolith microchemistry and genetic parentage analyses, offered a potential answer to this paradox, showing that populations, once thought to be open, often experience high levels of self-recruitment –6. It isen lower –10. Hoween lower . Endemicen lower –18. The 2, and a circumference of approximately 12 km [–2) [Myripristis gildi, Ophioblennius clippertonensis, Pseudogramma axelrodi, Thalassoma robertsoni, Xyrichtys wellingtoni and the two focal species of this study, H. limbaughi and S. baldwini. Studies made on small-island systems (including Clipperton Atoll) have proposed common characteristics of endemic fishes including small body sizes and limited dispersal potential, effectively “trapping” endemics due to the distance between oceanic islands and other distant reefs [Clipperton Atoll is an isolated atoll in the tropical eastern Pacific (TEP) that is approximately 1300 km to the west of mainland Mexico, and 1000 km south of the Revillagigedo Archipelago . Clipperly 12 km ,20. It i km [–2) . Clipper km [–2) , and sev km [–2) –25. The nt reefs –28.H. limbaughi, belongs to a genus that is found in the eastern tropical Atlantic (1 species), western tropical Atlantic (3 species), and the TEP (3 species) [H. passer, whose distribution is the widest, from Mexico to Ecuador, including the Galàpagos and Cocos islands; and two insular species, the Clipperton angelfish, H. limbaughi, which is restricted to Clipperton Atoll [H. clarionensis, which is mostly found at the Revillagigedo Islands and sporadically at the southern tip of the Baja California Peninsula. A single vagrant adult of this species was sighted at Clipperton in 1994, but not again in subsequent expeditions [The Clipperton angelfish, species) ,30. The on Atoll , and theeditions ,25.S. baldwini, belongs to a widespread damselfish genus of approximately 40 species that occurs in the Atlantic and the Indo-Pacific, with eight species in the TEP. S. baldwini belongs to a complex of three primarily insular, closely related species that include the whitetail damselfish, S. leucorus, which is primarily found at the Revillagigedo Islands, Rocas Alijos, and Guadalupe Island, and is occasionally found on the mainland from southern California to Mazatlan, Mexico, and the Galàpagos ringtail damselfish, S. beebei, which is primarily found in the Galàpagos Islands, but is also found commonly on the islands of Malpelo and Cocos, and as a vagrant in continental Costa Rica, Panamà, Ecuador and Peru [S. baldwini is the only member of its genus that has ever been recorded at Clipperton, and it has not been recorded at any other locations.The Clipperton damselfish, and Peru ,23,31. SH. limbaughi has a maximum length of 25 cm, while S. baldwini is much smaller with a maximum length of 9 cm. H. limbaughi primarily feeds on benthic, sessile invertebrates, including sponges, while the smaller S. baldwini is primarily omnivorous, feeding on algae and small invertebrates.The two focal species of this study exhibit some life history and ecological differences, in addition to having very different sizes. H. limbaughi, however, it was observed for the closely related H. passer which spawns in pairs or small harems [S. baldwini probably spawns in pairs on the benthos, and males guard a nest with hundreds of eggs until they hatch and pelagic larvae are released [H. passer, a close relative of H. limbaughi, could not find definitive evidence of such hermaphroditism. However, that study did find that smaller individuals were predominantly females and the largest fish exclusively males, and that the overall sex ratio was slightly female biased (1.32:1) [Stegastes is hermaphroditic, it is therefore assumed that this is also the case for S. baldwini.Spawning has not been recorded for l harems . Males al harems . In contreleased ,34. The released . Both sereleased . While sreleased , a study(1.32:1) . In contThe goal of this study was to capitalize on a system where endemic species of diurnally active reef fishes are found on a very small, highly isolated reef, thus providing a model of manageable size to assess population sizes using visual censuses and genetic tools. We used visual surveys on scuba to estimate fish population size, and Restriction site Associated DNA (RAD) genomic sequencing to assess population size based on genetic approaches. We then tested for inbreeding and lowered genetic diversity to investigate the genomic consequences of small population sizes.For depths within diving range (0-20m), benthic surveys were performed at 11 sites around the island in 2010  of the Haut-Commisariat de la République en Polynésie Francaise. Collections followed University of California Santa Cruz Institutional Animal Care and Use Committee (IACUC) protocol Berng1701.H. limbaughi (N = 35) and S. baldwini (N = 24) were collected at Clipperton Atoll using pole spears while scuba diving. In order to assess the taxonomic positions of our focal species, we used sister species H. clarionensis , H. passer , S. leucorus , and S. beebei . A description of these samples can be found in two previously published papers [0 C freezer.Samples of d papers ,29. Fin DNA was extracted using DNeasy Blood & Tissue kits (Qiagen) according to manufacturer’s protocol. We constructed RAD libraries using a variation of the original protocol with restriction enzyme SbfI –45. IndiRaw 100 bp reads were trimmed to 92 bp on the 3’ end, quality filtered, and then split according to the 6 bp unique barcode using custom Perl scripts  also av. Sequencdenovo_map.pl, which runs all three Stacks components in a pipeline . Parameter optimizations followed general published guidelines [populations to generate output files for input into downstream phylogenetic programs, retaining all SNPs. Due to high coverage across individuals, we increased the minimum stack depth (-m) to eight in populations runs. We created a stringent dataset by setting -p at 7, which means all individuals must retain the marker and with -r set to 80%, meaning that 80% of the individuals must retain the marker. All Fastq sequence files are available from the GenBank at the National Center for Biotechnology Information short-read archive database (accession number: SRP136950). Associated metadata are also available at GeOMe  [We used the software program Stacks version 1.29 ,47 to ididelines . We set idelines . We then47/BAY2) .We used outputs of the population script of Stacks to create different types of infiles. Genetic diversity of each group was estimated using Arlequin . There iIn order to assess introgression between sister species, we used a Bayesian approach implemented in Structure 2.2 ,53 to anConsidering that population sizes are presumably small for our focal species, there is a potential for related individuals to be present in our dataset. To test for potential relatedness, we calculated kinship coefficients  for each2) for each locus pair were first generated with NeEstimator with a minor allele frequency cutoff of 0.05. It is considered that locus pairs with r2 > 0.5 are likely to be physically linked, which may bias estimates downward [2 values; therefore no corrections were necessary in our dataset [−8 to 10−9 mutations per site [Pomacanthus semicirculatus , and 2.baldwini .Several sites were surveyed to assess the suitability of habitat around the atoll as shown in H. limbaughi and S. baldwini varied significantly with year and depth but not site (described in detail below). However, in all cases data consistently showed that the density of the two species differed by approximately one order of magnitude , while the average density for S. baldwini was 3,064 ind.ha-1   . In contrast, for S. baldwini, deep and shallow transects were found to be statistically different    . Both sp= 0.004) . Finally<0.0001) . In addictively) .H. limbaughi are found consistently to a depth of approximately 100 m, while S. baldwini, which occurs to 100 m, showed low densities below 60 m. We considered all coral reef substrate as suitable habitat for the species studied here, as there is only one sand patch of appreciable size (~20m diameter) on the reef above ~50m depth. Benthic visual assessments showed coral reef habitat to at least 50 m depth. Based on our hydrographic map, reef habitat to a depth of 50m was estimated to cover approximately 2.838 km2 (see 2 (see Deep surveys using ROV dives (in 1998) and manned submersible dives (in 2016) showed that large populations of  km2 see . Deeper ee 2 see .H. limbaughi of approximately 35,600 at 0–50 m depth, increasing to 64,400 when considering the additional habitat down to 100 m. The latter value is consistent with the estimate of 61,000 presented in the literature [S. baldwini was approximately 490,000 individuals. We estimated a reduction of density of S. baldwini by a factor of 5 in deeper water (50–100 m). Hence, adding habitat to 100 m depth for that species only increased the estimated census number to 565,000 individuals  and the calculated population sizes for each sampling year, the harmonic mean of these values resulted in an estimated total population size for terature . Populatividuals .Holacanthus, our approach yielded an average 49,454 loci per individual, with 4210 polymorphic loci and 5557 SNPs. For Stegastes, our approach yielded an average 47,898 loci per individual, with 3016 polymorphic loci and 4582 SNPs.RAD sequencing produced similar sequence yields for the two studied species. For Stegastes beebei and S. leucorus, At the outset, we wanted to make sure that the species used in this study were not being introgressed by sister species that could potentially bias our estimates of population size, inbreeding, and genetic diversity. We therefore included samples from closely related species that might potentially contribute to the genome of our study species. A population analysis showed that there was no evidence of gene flow between our species and their respective sister species, as shown by a Structure analysis , suggesting that the distribution of polymorphisms not only differs in the two species, but does so in a reverse manner to heterozygosity.Estimates of genetic diversity are presented in imbaughi . When loH. limbaughi and S. baldwini. In these two species, we found 5 pairs  and 1 pair  of genetic full sibs, respectively . When using estimates of Ne implemented by NeEstimator, populations of S. baldwini were found to be 3.4 times greater than for H. limbaughi  and direct values of Ne based on the LD method. Both approaches show population sizes to be greater in imbaughi .H. limbaughi, one for S. baldwini). The biological effect of this correction, however, was trivial. Corrected values of genetic diversity were also calculated and similarly remained virtually unchanged.These analyses were repeated after removing pairwise comparisons involving full-sibling relationships  resulted in very small population sizes, 109.0 and 375.9 individuals for ectively . HoweverH. limbaughi and its sister species, nor between S. baldwini and its sister species, and that the system is effectively closed from a genetic standpoint, thus validating endemism in these two species.In this study, we capitalized on the presence of endemic reef fishes at a small, extremely isolated atoll to address the evolutionary consequences of small population sizes in marine organisms. In this study, we confirmed that there is no evidence of gene flow between S. baldwini / H. limbaughi = 18.6), a value very similar to what was obtained using genetic estimates based on Tajima’s π (ratio = 14). These ratios were consistent despite slight inter-annual variations in visual census values.The two species used in this study are the only species in their respective genera at Clipperton Atoll, are easily identifiable because both have distinct coloration, do not behave cryptically, and do not resemble any other local species. Consequently, fish counts are likely to be accurate. Visual census data and genetic estimates of population size were consistent for both focal species. Census counts estimated that population size ratio between species are approximately one order of magnitude (−5), and the Atlantic cod (3.9 105) [In general for marine populations, the ratio between N and Ne reflects differences of several orders of magnitude ,75. In c3.9 105) –80. The H. limbaughi at 110 m depth, and S. baldwini at 67 m and 72 m, in areas that were not surveyed using scuba  with numbers comparable to the lowest recorded heterozygosities in naturally bottlenecked populations of cheetah (Acinonyx jubatus) and platypus (Ornithorhynchus anatinus) [Amphiprion (0.214–0.262), and Caribbean hamlets, genus Hypoplectrus (0.118–0.125) [H. limbaughi and S. baldwini, respectively), with numbers again comparable with the number of polymorphic loci in the cheetah genome (approximately 5%) [H. passer, Fistularia commersonii, Pomacentrus pavo, Stegastes nigricans, Pomachromis fuscidorsalis, Dascyllus aruanus, Chrysiptera brownriggii, Chromis margaritifer, Plectroglyphidodon lacrymatus, Chromis iomelas, Bernardi, unpublished), and also lower than values for closely related congeneric species that have large ranges and population sizes, H. passer (10.2%) and Stegastes nigricans (12.7%) .Indeed, both heterozygosity and amount of polymorphic loci were very reduced in these two species compared to species with much larger geographic distributions and population sizes. Heterozygosity values were extremely low for both species (0.05 and 0.18 for natinus) ,84,85. T8–0.125) ,87. The H. limbaughi and S. baldwini, respectively). Although these numbers seem surprising, considering that samples were collected randomly, they do make sense when one considers only genetic relatedness, rather than assigning classical values of kinship. Individuals are indeed genetically similar, but this may be due to the low levels of genetic diversity rather than true kinship, as has been observed in other studies of organisms in which inbreeding is rampant [actual siblings, but rather that all individuals share a large number of alleles and therefore represent apparent genetic siblings.The very low levels of genetic diversity described above unfortunately come with a practical consequence of lowering the power to detect true relatedness, because the lowered heterozygosity increases the sharing of alleles by chance alone. In this study, we found that a large proportion of individuals are apparently related Click here for additional data file.S2 FigData shown as box plots by year of census.(PDF)Click here for additional data file.S3 FigData shown as box plots by depth, shallow (6–12 m) and deep (20m).(PDF)Click here for additional data file.S4 Fig(JPG)Click here for additional data file.S1 Script(TXT)Click here for additional data file.S2 Script(TXT)Click here for additional data file.


Prunus spp.) are important deciduous fruit crops in the world. China is a major producer of P. salicina Lindl., but the genetic relationship of Chinese plums in key production regions remain unclear. In this study, 14 University of British Columbia (UBC) inter simple sequence repeats (ISSR) primers were used to analyze 33 plum varieties cultivated in Fujian Province to determine their genetic diversity and population structure. A total of 146 bands were generated, of which 130 were polymorphic. Mean percentage of polymorphic bands was 89.04%, Shannon’s information index value was 0.38, and the Nei’s genetic index value was 0.24. Using unrooted trees (Neighbor-Joining method), 33 varieties were classified into four groups. Split graph separated them into two major groups, each with two subgroups. The two phylogenetic trees indicate that environmental or natural selection pressure is an important factor influencing their genetic relationship. Analysis of population structure revealed that they have frequent genetic exchanges among closed subpopulations; thus, genetic variation mainly occurs within the population. Additionally, based on the phylogenetic analysis and unique morphological characteristics of fruits, we propose that the Chinese landrace Nai could contribute significantly to development of the famous variety Wickson.Plums ( Prunus salicina Lindl, 2n = 2x = 16) or European plum  [P. salicina to hybridize with other species, including P. americana, P. angustifolia, P. munsoniana, and P. simonii, resulting in the development of more than 100 varieties [P. salicina, but some may have genetic makeup of other species due to the introduction of Luther Burbank’s varieties [P. domestica remains unclear, but it is accepted that P. spinosa and P. cerasifera are involved in the genesis of European plum [P. domestica were released [Plums are deciduous fruit crops grown ubiquitously in China, America, Japan, and European countries . Most coarieties . Modern released .P. salicina are almost exclusively used for fresh consumption, whereas fruits of P. domestica are generally used for processing. Plums are used to make jam, juice liquor, and brandy. Concentrated juices of plum are also used for medicinal purposes as a laxative. Plum breeding has resulting in the release of yellow, red, purple, and black plums, and they are important sources of flavonoids, anthocyanins, carotenes, and polyphenolic acids [Fruits of ic acids . Some blic acids . Due to ic acids . The Fooic acids .P. salicina) and Simon (P. Simonii) or Kelsey [China has rich plum germplasm resources. Fujian Province, located on the southeast coast of China , has morr Kelsey . FurtherP. domestica genetic resources were assessed by random amplified polymorphic DNA (RAPD) [P. salicina were analyzed by RAPD [Molecular marker techniques are effective tools for analyzing genetic diversity and relationships of plants ,16. SomeA (RAPD) ,18,19 anA (RAPD) . Selecte by RAPD ,22, ampl by RAPD , and int by RAPD ,23,24,25 by RAPD . Plum ge by RAPD . Using I by RAPD . Fujian has over 700 years of history in plum cultivation and is still a major plum production province in China. Plum varieties or genetic resources from Fujian may represent Southern Chinese plum genetic resources. We believe that these genetic resources could significantly differ from those of Northern China. However, varieties cultivated in Southern China have not been well studied. The objective of this study was to use ISSR to analyze the genetic diversity and structure of plum varieties cultivated in Southern China, primarily Fujian. A better understanding of their genetic relationships could improve plum germplasm conservation and utilization and lead to new cultivar development. A total of 33 varieties were collected from two regions of Fujian province . One is v/v). The DNA suspension was mixed gently and centrifuged at 13,000× g (4 ℃) for 15 min. The top aqueous phase was collected into a new tube with addition of 2 µL of RNase (10 mg/mL), mixed, and incubated at 37 ℃ for 30 min. DNA was extracted once more with one volume of the chloroform/isoamyl mixture, the top aqueous phase was collected again, and DNA was precipitated with two volumes of cold ethanol. Resultant DNA pellet was washed three times with 70% ethanol and dissolved in 50 µL of sterile water after complete evaporation of ethanol. DNA quality was checked on a 1% agarose gel, and the concentration was determined by NanoDrop Lite Spectrophotometer with Printer . A portion of the DNA was diluted to 20 ng/µL for use; both the stock and diluted portions were stored at −40 ℃. DNA was extracted following the protocol of Liu  with modA total of 100 ISSR primers were initially screened, of which 14 University of British Columbia (UBC) primers that produced clear and highly polymorphic bands were selected . PCR reap distance were inferred, which provided a split graph through NNet analysis. Nei’s analysis of genes in subdivided populations via POPGENE v.1.32 [Amplified reproducible bands were scored as present (1) or absent (0) to create a binary matrix. To analyze genetic diversity of the varieties, the percentage of polymorphic bands (PPB), the observed number of alleles (Na) and effective number of alleles (Ne), Nei’s gene diversity index (h), Shannon’s Information index (I) were calculated using POPGENE v.1.32 . Gene flE v.1.32  was alsoK, the number of reconstructed panmictic populations (RPPs) of individuals, by testing K (log-likelihood) = 2–7 for all varieties, assuming that they were from unknown origin. Ten independent runs were conducted for each K. A burn-in period of 200,000 iterations was applied. Subsequently, structure harvester (http://taylor0.biology.ucla.edu/structureHarvester) [K value and Q value , the run with the maximum likelihood was used to assign genotypes to specific clusters [The Structure version 2.2.3 , which irvester) , which irvester) , was useclusters . P. salicina across 33 varieties was high (PPB = 89.04%). The observed and effective numbers of alleles (Na and Ne) were 1.95 and 1.39, respectively. The Nei’s genetic index value (h) was 0.24, and the Shannon’s information index value (I) was 0.38. The genetic diversity of varieties collected from Gutian region  was higher than those collected from Yongtai region   .An unrooted tree based on NJ method was constructed for 33 varieties A. The trp = 0.001), respectively (p = 0.001)  .K) by calculating the log probability of data for each value of K and using ΔK statistics described by Evanno et al. [K at K = 3  and clustering (2 or 4 main groups). A possible explanation about the difference could be due to the low resolution of cluster analysis. Further study is needed to clarify the difference. Nevertheless, 26 varieties were relatively single or genetically pure according the ‘Q > 0.6 relative to that of the single’ standard [The population genetic structures of the 33 varieties were evaluated by Structure 2.2.3. The results indicated that the varieties had three genetic components based on the probability of germplasm originated from a specific population. Thus, there is discrepancy between the results of structure  and Wickson (Burbank x Kelsey) in both phylogenetic trees (P. salicina was brought to Japan as a gift by Buddhism 2000 years ago. Based on the phylogenetic analysis and their morphology, we propose that Chinese plum Nai contribute greatly to development of Wickson. Another note is that Nai used to be considered a hybrid derived from peach and plum based on its inner plum’s quality and peach’s outer form. After isoenzyme and RAPD molecular analyses, Nai was reconsidered to be a local variety or landrace of salicina ,51. In tic trees . Based oPrunus salicina cultivated in Fujian Province, China. The 14 primers generated a total of 146 bands, of which 130 were polymorphic. Results showed that the mean percentage of polymorphic bands was 89.04%, Shannon’s information index value was 0.38, and the Nei’s genetic index value was 0.24, suggesting the 33 varieties are genetically diverse. Based on the NJ method, 33 varieties were categorized into four groups, whereas NNet analysis classified them into two major groups, each with two subgroups. Both classifications suggest the territoriality, or the location of their cultivation significantly influenced their genetic relationships. Population structure analysis indicated that frequent genetic exchanges occurred among closed subpopulations. As a result, genetic variation mainly occurs within the population. Additionally, based on unique morphological characteristics of fruits and phylogenetic analysis, we postulate that the Chinese landrace Nai could be the ancestor of the famous variety Wickson developed by Luther Burbank in 1892.This study used 14 UBC ISSR primers to analyze genetic diversity and structure of 33 varieties of


Euprymna albatrossae  and their Vibrio fischeri symbionts in the Philippine archipelago using a combined phylogeographic approach. Eleven separate sites in the Philippine islands were examined using haplotype estimates that were examined via nested clade analysis to determine the relationship between E. albatrossae and V. fischeri populations and their geographic location. Identical analyses of molecular variance (AMOVA) were used to estimate variation within and between populations for host and symbiont genetic data. Host animals demonstrated a significant amount of variation within island groups, while symbiont variation was found within individual populations. Nested clade phylogenetic analysis revealed that hosts and symbionts may have colonized this area at different times, with a sudden change in habitat. Additionally, host data indicate restricted gene flow, whereas symbionts show range expansion, followed by periodic restriction to genetic flow. These differences between host and symbiont networks indicate that factors “outside the squid” influence distribution of Philippine V. fischeri. Our results shed light on how geography and changing environmental factors can impact marine symbiotic associations at both local and global scales.Marine microbes encounter a myriad of biotic and abiotic factors that can impact fitness by limiting their range and capacity to move between habitats. This is especially true for environmentally transmitted bacteria that cycle between their hosts and the surrounding habitat. As geologic history, biogeography, and other factors such as water temperature, salinity, and physical barriers can inhibit bacterial movement to novel environments, we chose to examine the genetic architecture of  Vibrio  Nyholm, .Vibrio bacteria show distinct population breaks that are not necessarily driven by host specificity. Additionally, host‐mediated factors along with abiotic variables such as water temperature and salinity have been known to shape these mutualist assemblages  and their V. fischeri symbionts in the Philippine archipelago using a combined phylogeographic approach to determine whether host specificity or geographic location influence the distribution of symbiotic Vibrios in this region. The unique geographic origin of the Philippines, its proximity to deeper and colder water, as well as currents that move through the area allow for the investigation of what roles geography and host specificity have in the distribution of mutualistic associations.Earlier work on sepiolid squids has focused on the influence of geographic distance on symbiont prevalence and genotype in both sympatric and allopatric populations  and Marine Mix . Squid tissues were preserved in 95% ethanol for fixation and subsequent DNA extraction for Sanger sequence analysis at New Mexico State University (NMSU).Squids were collected in the months May, June, July, and August during the years 2010, 2012, 2013, and 2015 at eleven different sites around the Philippine islands . Each overnight culture was subcultured and allowed to reach log phase (2–3 hr at 28°C and 225 rpm), and the log‐phase cultures were used for DNA extraction and also frozen in 40% glycerol for storage at −80°C.2.2Euprymna albatrossae DNA was extracted using approximately 25 mg of ethanol preserved tissue that was dissected from the gill or mantle of each squid. Dissected tissues were washed with 100 μL of nuclease‐free water to remove any residual ethanol. E. albatrossae DNA was extracted using the DNeasy© blood and tissue protocol for animal tissues . All genomic DNA extractions were visualized on a 1% agarose gel and quantified using a Nanodrop 9600 . Total DNA extracted from each individual squid sample was used to amplify a 658‐bp fragment of the cytochrome c oxidase subunit I  Gram‐negative bacterial protocol. Approximately 2 × 109 cells were transferred from each log‐phase culture to the extraction tube for centrifugation. After, the remaining pellet was used for extraction using the Qiagen protocol. Purified V. fischeri DNA was visualized on a 1% agarose gel and quantified using a Nanodrop 9600 . Isolated DNA extracted from each V. fischeri isolate was used to amplify a portion of the glyceraldehyde phosphate dehydrogenase (gapA) locus (~900 bp) by PCR, using previously described Vibrio‐specific primers  contained 2–20 ng of template DNA [0.08–0.8 ng/μL], GoTaq DNA polymerase [0.05 U/μL] , 5× GoTaq buffer [1×] , a 10 mM deoxynucleoside triphosphate mix [0.8 mM] of each nucleotide , and both forward and reverse primers [0.5 μM each] . Host genetic data yielded three distinct unconnected haplotype networks, with one network containing only samples from the island of Palawan, while the other two networks exhibited introgression from the central island chain but no connection from the Palawan population . In contrast to host genetic architecture, symbiont populations are more connected compared to host populations. Vibrio genetic data produced a highly connected and diverse network showing continuity between Palawan and the central island chain populations of symbionts  lead as adults rarely migrate; however, the semipelagic nature of newly hatched squid (~3–5 mm) would allow water flow to transport juveniles to novel locations  as it approaches the Philippines from the east influences the directional flow of water through the central island chain, particularly north of the island of Panay and south of the islands of Negros and Cebu Figure . The amoWhile clade 4‐1 had no valid inference Table , had an 4.2Vibrio bacteria seem to be able to mitigate the barriers that restrict host genetic exchange. Analysis (AMOVA) of total symbiont genetic data reveals that most of the genetic variation observed lies within each population in contrast to the partitioning of variation from host genetic data  that allow for movement into novel areas where they are able to recruit into a new host. While bacteria alone cannot cross great expanses of ocean, the use of rafting has been shown to be an effective dispersal mechanism for marine bacteria like V. fischeri  are important drivers of microbial diversity in the region ; Vibrio fischeri .Final DNA sequences: GenBank accessions: