Coregonus lavaretus complex has created numerous morphs, posing significant challenges for taxonomy and conservation priorities. The highly endangered North Sea houting  has been considered a separate species from European lake whitefish  due to morphological divergence and adaptation to oceanic salinities. However, its evolutionary and taxonomic status is controversial. We analysed microsatellite DNA polymorphism in nine populations from the Jutland Peninsula and the Baltic Sea, representing NSH  and ELW (six populations). The objectives were to: 1) analyse postglacial recolonization of whitefish in the region; 2) assess the evolutionary distinctiveness of NSH, and 3) apply several approaches for defining conservation units towards setting conservation priorities for NSH.Adaptive radiation within fishes of the Bayesian cluster analyses of genetic differentiation identified four major groups, corresponding to NSH and three groups of ELW . Estimates of historical migration rates indicated recolonization in a north-eastern direction, suggesting that all except the Baltic Sea population predominantly represent postglacial recolonization via the ancient Elbe River. Contemporary gene flow has not occurred between NSH and ELW, with a divergence time within the last 4,000 years suggested from coalescence methods. NSH showed interbreeding with ELW when brought into contact by stocking. Thus, reproductive isolation of NSH was not absolute, although possible interbreeding beyond the F1 level could not be resolved.C. lavaretus complex in the Jutland Peninsula originate from the same recolonization event. NSH has evolved recently and its species status may be questioned due to incomplete reproductive isolation from ELW, but it was shown to merit consideration as an independent conservation unit. Yet, application of several approaches for defining conservation units generated mixed outcomes regarding its conservation priority. Within the total species complex, it remains one among many recently evolved unique forms. Its uniqueness and high conservation priority is more evident at a local geographical scale, where conservation efforts will also benefit populations of a number of other endangered species.Fishes of the  However, under this assumption Ne estimates differed considerably; 1139 in VID and 273 in RIN (Table Ne estimates with this method are highly sensitive towards inferred migration rates [Effective population size (igration  yielded on rate)  yielded on rates .θ values ranging between 0.31 (GUD) and 0.57 (ROS)  Fig. . Assumint, of 0.174 . The inferred estimates of historical Ne and m showed good correspondence with those obtained using MIGRATE . However, the results using STRUCTURE 2.2 Fig.  pointed q) in VAR94 and VAR04 using VID and RIN as learning samples. In the VAR94 sample there was limited evidence for hybridization, although two individuals showed intermediate q values  must be considered reproductively isolated at present but have nevertheless split recently, perhaps within the past few thousand years. Finally, NSH and ELW appear to interbreed where they have been brought into contact in one locality (VAR). We discuss these findings below along with their implications for species designation and for prioritizing populations for conservation.Coregonus lavaretus complex, northern pike (Esox lucius), burbot (Lota lota), ruffe (Acerina cernua) and smelt (Osmerus operlanus) have all been recovered from a lake deposit in northern Jutland and dated to approximately 11,000 years bp [sensu Hewitt [RST > θST [et al. [Subfossil remains of fish from the years bp . Hence, years bp ,66-69, tu Hewitt . There wST > θST  suggeste [et al.  based onThe Bayesian clustering of individuals Fig.  might atNe estimates (under the assumption of no gene flow) in RIN and VID, were also relatively high (point estimates exceeded 500). Detailed analyses of the demographic history of VID further showed that Ne has been stable over the past ca. 20 years [Ne is also high in the other samples for which temporal samples were not available. However, since historical Ne (θ) did not vary much among populations . The region inhabited by RIN and NIS is marshy with several smaller lakes and rivers, which could become connected at high water levels. Hence, occasional gene flow is certainly a possibility. Available temporal 20 years . We cannons Fig. , it is rCoregonus lavaretus complex is chaotic and numerous species have been described [synapomorphic characters, i.e. shared characters derived from a common ancestor. NSH has traditionally been considered a species (C. oxyrhynchus) separate from ELW (C. lavaretus) based particularly on its elongated snout and its ability to tolerate oceanic salinities [C. lavaretus populations the gill raker numbers must be considered intermediate; the mean gill raker number is 32.1 (s.d. 1.7) in VID vs. a range of mean values from 16.8 – 46.0 in other European populations, and a range from 29.1 to 35.5 in RIN, NIS, FLY and GUD  biologi i Vidå-systemet. Aarhus, Denmark: Institute of Biology, University of Aarhus; 1990. M.Sc. Thesis). All together, these characters might justify separate species designation of NSH adopting a Phylogenetic Species Concept, but the lack of demonstration of the genetic basis of these characters and absence of analysis of phenotype-environment associations would inevitably cast doubt on this classification.The taxonomy of the escribed . These sescribed , which dlinities . It has erial in ). Hence,erial in ; this stFrom an evolutionary biology perspective, adoption of the Biological Species Concept  is more The results suggest that NSH and ELW may not qualify as separate species according to a Biological Species Concept. The acid test would involve demonstration of interbreeding beyond the F1 generation and lack of postzygotic selection against hybrids, which could otherwise act to maintain species integrity despite interbreeding. There was insufficient statistical power in the STRUCTURE analyses to unambiguously identify backcrosses, which would require analysis of many more loci given the relatively low genetic differentiation . It shouA more pragmatic alternative to the Biological Species Concept would be Mallet's concept of Genotypic Clusters , where s-4  concept as defined by Waples  states tsee e.g. ,18). Hensee e.g. , but woureciprocal monophyly for mtDNA haplotypes and significant genetic differentiation at nuclear loci. This approach operates with a second category, management units (MU), i.e. populations showing significant differentiation at nuclear loci but not reciprocal monophyly at mtDNA. As this ESU definition stresses the time-frame over which populations have been reproductively isolated, it is unsurprising that NSH does not qualify as an ESU; mtDNA haplotypes in NSH vs. neighbouring ELW populations do not show reciprocal monophyly [et al. [The second approach by Moritz  adopts tonophyly . Instead [et al.  would beet al.'s [genetic exchangeability (reproductive isolation) and ecological exchangeability . Moreover, these criteria are considered on both a contemporary and historical time scale. We reject the hypothesis of contemporary genetic exchangeability between all populations. If we define historical exchangeability as dating back to postglacial recolonization, then we accept the hypothesis of historical genetic exchangeability of all populations. However, if "historical" is defined as pre-dating major anthropogenic influence, tentatively 500 years bp, then we reject the hypothesis for most populations, including VID vs. RIN.Crandall et al.'s  frameworThe data on ecological exchangeability is much scarcer. We are not aware of any adaptive divergence among the ELW populations of the study. This, however, does not preclude that adaptive divergence actually exists. The only feature that really stands out is high salinity tolerance in VID (NSH) and we therefore reject the hypothesis of contemporary ecological exchangeability between NSH and ELW. There are no long-term data relating to ecological exchangeability. If we consider a postglacial time-scale, then we would assume that ecological exchangeability existed, while it is most likely that high salinity tolerance was present in VID (NSH) if we consider a time scale of 500 years bp. Depending on the definition of historical exchangeability the recommendation would then be to treat VID (NSH) as a distinct species  or a distinct population .C. lavaretus complex, but on a local scale none of the studied ELW populations are considered endangered. The VID population appears stable and not immediately endangered [Finally, the Designatable Unit (DU) described by Green  is closedangered  but is nAltogether, NSH stands out as a separate conservation unit, although its distinctiveness varies considerably depending on the framework applied. Moreover, prioritization depends on the spatial or temporal scale considered, with highest prioritization granted on finer or shorter scales.Salmo salar) populations in the region from the Jutland Peninsula to Northern France [Alosa fallax) and sea lamprey (Petromyzon marinus), all of which would benefit from a NSH conservation program. Thus, an integrated approach combining conservation priority of NSH relative to other C. lavaretus forms with principles for identifying areas of importance for biodiversity conservation [The current rehabilitation program for NSH is based on restoring habitat in four rivers in the Danish region of the Wadden Sea, including the Vidaa River. These rivers harbour a number of other endangered species listed in the Berne Convention on the Conservation of European Wildlife and Natural Habitats. This includes the last remnants of indigenous Atlantic salmon  and migration rate between populations (M), estimated using MIGRATE 2.0.3 [Historical effective population size and migration rates. Estimates of historical effective population size (TE 2.0.3 .Click here for file


A total of 929 polymorphic SNPs in EB , and 1, 524 and 1, 403 polymorphic SNPs in WB and PB, respectively, were analysed. EB, WB and PB have all undergone recent drastic reductions in population size. Accordingly, they exhibited extremely depauperate genomes, deviations from genetic equilibrium and a genome organization consisting of a mosaic of haplotype blocks: regions with low haplotype diversity and high levels of linkage disequilibrium. No evidence for positive or stabilizing selection was found in EB, WB and PB, likely reflecting drift overwhelming selection. We suggest that utilization of genome-wide screening technologies, followed by utilization of less expensive techniques (e.g. VeraCode and Fluidigm EP1), holds large potential for genetic monitoring of populations. Additionally, these techniques will allow radical improvements of breeding practices in captive or managed populations, otherwise hampered by the limited availability of polymorphic markers. This result in improved possibilities for 1) estimating genetic relationships among individuals and 2) designing breeding strategies which attempt to preserve or reduce polymorphism in ecologically relevant genes and/or entire blocks.This article was reviewed by: Fyodor Kondrashov and Shamil Sunyaev Bison bonasus bonasus), 30 WB (Bison bison athabascae) and 26 PB (Bison bison bison) were used for genome screening. The PB samples derive from Elk Island National Park while WB samples come from Wood Buffalo National Park (Canada). The relationships  of three EB families were used in the SNP screening to verify Mendelian segregation. Genome-wide screening encompassed 54, 000 single nucleotide polymorphisms (SNPs) across the entire bovine genome. More than half of the SNPs were discovered using the sequencing system Genome Analyzer (Illumina®) [ftp://ftp.hgsc.bcm.tmc.edu/pub/data/Btaurus/fasta), the bovine reference genome, and the Bovine HapMap Consortium data set . The BeadChip has an MAF of 0.25 across all loci and has been validated in both dairy and beef cattle. SNPs were genotyped on the BovineSNP50 BeadChip according to the Infinium II Multi-Sample assay protocol provided by Illumina® .Fifty-four EB (lumina®) . The addBos and Bison lineages split about 1 million years ago [Despite the fact that the ears ago , a totalST used as a measure of differentiation between EB, PB and WB was estimated using FSTAT version 2.9.3 [ST = 0.114), albeit much lower compared to EB-PB (FST = 0.466) or EB-WB (FST = 0.474). Genetic differentiation estimated between EB, WB and PB might provide a relative estimate of the time elapsed since divergence in isolation.The P% for each bison species was calculated relative to the total number of loci that were polymorphic when EB, WB and PB samples were pooled. The pairwise FThe number of mapped, polymorphic SNPs in EB was 929 (P% = 42%). The distribution of these markers along the autosomal chromosomes  is shown in Figure After considering the distribution of the distance between the polymorphic loci the haplotypes were calculated according to the four gamete rule implemented in the Haploview 4.1 software [Inspection of the polymorphic SNPs  and EB and WB (1.89%) when compared to the overlap between PB and WB (41.91%). The number of shared polymorphisms is inversely related to the estimated Fe, which is negatively correlated to the speed at which the population re-establishes mutation-drift equilibrium and show therefore an L-shaped distribution. Given the distortion of the L-shaped distribution is transient, and likely to be detectable until genetic drift begin to re-establish mutation-drift equilibrium [e produced by the drastic bottleneck in EB, has allowed a rapid return of the population to genetic equilibrium.Simulations made with the software BOTTLENECK 1.2.02  assumingilibrium  we hypotST and divergence time between EB, WB and PB can be strongly biased. Additionally, the presence of blocks in the genome can probably partially be explained by this lacks of genetic equilibrium. Even if the EB population is now at equilibrium, we cannot exclude that previous bottlenecks have contributed to the observed pattern of a mosaic of haplotype blocks.As a consequence of the finding that WB and PB are not at genetic equilibrium we must expect that the relation between Fe has therefore increased slowly in bison. This is because the long term Ne is a function of the harmonic mean which is strongly influenced by the minimum population size reached [The presence of a mosaic blocks pattern and evidences for a mode-shift in the distribution of allele frequencies found in WB and PB agrees with theoretical expectations for populations which have undergone a severe bottleneck . The fin reached ,14.E for every locus in EB, PB and WB. The SNPs were considered outliers if their HE was > mean HE + 3 SD or < mean HE - 3SD. These tests were performed pooling all loci and testing every single chromosome singularly. Additionally, we tested (with a t-test) for EB, WB and PB if the HE estimated from the polymorphic SNPs shared among EB, WB and PB were significantly different from the HE estimated from the unshared polymorphic SNPs. These tests were conducted because we suspected that the reasons for this shared polymorphism could be due to stabilizing selection. However, no significant differences were found. The lack of evidence for positive selection in EB, WB and PB is not surprising as the selection intensity has to be larger than e is low for bison in general, all SNPs, even if not selectively neutral, behave neutrally until Ne reaches a certain threshold.In order to detect regions of bison chromosomes which show signs of positive selection we checked for the existence of "outlier SNPs" by creating distributions of Hfficient ,16. Givee) utilize only pedigree information, which describes the expected relationship among individuals. With the same pedigree, however, individuals still vary in the realized genetic relationship among them [By screening a large number of SNPs we considerably reduce the sampling error of the estimated overall genomic variability compared to traditional investigations that use a much more limited number of markers (SNPs or microsatellites). The SNPs can also be used in more limited numbers for genetic monitoring of populations. For many years, microsatellites have been the markers of choice for this purpose, using non-invasive techniques like extraction of DNA from scat or other organic material, and also using historical samples in order to add a temporal dimension to the survey. However, non-invasive DNA samples are often of poor quality and prone to genotyping errors due to DNA degradation, making long genetic markers like microsatellites difficult to use. Conversely, SNPs have the advantage that they are able to amplify short DNA sequences, decreasing therefore artifacts occurring due to degraded DNA. Additionally, the information from the BeadChip might allow genome-wide based breeding schemes designed to preserve rare alleles and minimize inbreeding . Traditional methods for making breeding decisions to reduce the level of inbreeding . Further research is needed to determine if existing SNP genotyping systems can be applied across whole taxonomic orders or families, or if they are only transferable at the species or genus level, and to what extent screening success depends on taxonomic distance from the source taxon or population history of the study taxon. A problem often associated with SNPs application in population studies is ascertainment bias ,20. It iE: expected heterozygosity; MAF: Minor allele frequency; MAS: Marker assisted selection; Ne : Effective population size; P%: Percent of polymorphic loci; PB: Plain bison; S: Selection gradient; WB: Wood bisonEB: European bison; HThe authors declare that they have no competing interests.All authors have conceived the study and drafted the manuscript. All authors have read and approved the final manuscript.Fyodor Kondrashov (Centre for Genomic Regulation Barcelona Spain).Reviewers' commentsThis reviewer provided no comments for publicationShamil Sunyaev .Reviewers' commentse and also depends on other parameters  Thus, a more sophisticated discussion of the observations would be appropriate instead of simply stating the low level of variation and, subsequently, Ne.This is an interesting study of genetic variation in bison. The authors characterized variation in the European bison and American wood and plains bisons using the BovineSNP50 Illumina Genotyping BeadChip. I have three comments listed below: 1)The manuscript seems to suggest that the observed small number of SNPs from the BovineSNP50 chip segregating in the bison population reflects a population bottleneck. However, the authors measured the level of shared polymorphism between bison and cow rather than the overall levels of variation in bison. The level of shared polymorphism has a more complex relationship with NAuthor's responsee following the method presented in Clark (1997). This method is based on the fact that the loss of a shared polymorphism of a neutral allele having an initial allele frequency of 1/2 occurs sooner than loss of polymorphism in a single species and has an expected time of 1.7 Ne generations, compared to 2.77Ne, which is the mean time necessary for one neutral allele to undergo fixation within a single population. Therefore, given the fact that in our study we have found several shared polymorphisms, we would have been able to estimate the historical Ne of EB, WB and PB. However, the method presented by Clark requires that the loci utilised for the estimation of Ne are unlinked. Hence, we performed tests for linkage disequilibrium and ran the program Haploview 4.1 which showed that the genome of the bison is mainly a mosaic of haplotype blocks where strong linkage disequilibrium between SNPs is observed  was significantly different from HE within a subspecies . The differences of mean HE was not significant. Therefore, we did not obtain evidence for the fact that the shared polymorphism could be due to stabilizing selection acting on these SNPs (please see the first paragraph on page 8) where the blocks are shown graphically). Therefore, we have not been able to use Clark's method, but we decided to utilise the shared polymorphism in order to test if H.in Clark 997. ThisST between the analyzed populations.2) The manuscript would benefit from the analysis of allele frequency distribution (excess of high frequency variants may be suggestive of a bottleneck) and FAuthor's responseST values as a measure of differentiation between EB, PB and WB have been added (see please the first paragraph on page 5)Population bottlenecks cause a characteristic mode-shift distortion in the distribution of allele frequencies at selectively neutral loci. In a stable population the distribution is expected to be L-shaped. Bottlenecks cause alleles at low frequency (< 0.1) to become less abundant than alleles in more intermediate allele frequency classes . We performed simulations with the software BOTTLENECK 1.2.02, assuming an infinite allele mutation model searching for mode-shift distortion in the distribution of allele frequencies. We found evidence for lack of genetic equilibrium in WB and PB (see please the third paragraph on page 5). Furthermore, the pairwise F.3) Is it possible to quantify the improvement of breeding strategies using genotyping instead of pedigree data?Author's responsee) utilize only pedigree information, which describes the expected relationship among individuals. With the same pedigree, however, individuals still vary in the realized genetic relationship between them. Therefore, information obtained from genetic markers can be useful in this respect as they will provide the realized genetic relationships. Additionally, information from genome-wide screening also enables detection of genes associated with hereditary genetic diseases and inbreeding depression (these issues have been discusses in the last paragraph on page 8)An exact quantification of the improvement of breeding strategy can only be made using empirical data comparing the improvement of a certain trait in mating individuals chosen on the basis of their pedigree or on the basis of their genetic similarity at the molecular level. Traditional methods for making breeding decisions to reduce the level of inbreeding (by increasing N.


Quercus liaotungensis and Q. mongolica, has been called into question. The objective of this study was to investigate the species status and to estimate the degree of introgression between the two taxa using different approaches.The species status of two closely related Chinese oaks, FST simulation analysis, suggesting a role for divergent selection in differentiating species.Using SSR (simple sequence repeat) and AFLP (amplified fragment length polymorphism) markers, we found that interspecific genetic differentiation is significant and higher than the differentiation among populations within taxa. Bayesian clusters, principal coordinate analysis and population genetic distance trees all classified the oaks into two main groups consistent with the morphological differentiation of the two taxa rather than with geographic locations using both types of markers. Nevertheless, a few individuals in Northeast China and many individuals in North China have hybrid ancestry according to Bayesian assignment. One SSR locus and five AFLPs are significant outliers against neutral expectations in the interspecific Q. liaotungensis and Q. mongolica maintain distinct gene pools in most areas of sympatry. They should therefore be considered as discrete taxonomic units. Yet, the degree of introgression varies between the two species in different contact zones, which might be caused by different population history or by local environmental factors.All results based on SSRs and AFLPs reached the same conclusion:  Natural hybridization occurs frequently in plants and animals Quercus) should be good models to evaluate the effects of hybridization and introgression on species delimitation. They have long been recognized as a challenge to the ideal standard of discrete biological species because of their propensity to intercross The oaks  DNA haplotypes are often shared in areas of sympatry Q. liaotungensis Koidz.) and the Mongolian oak (Q. mongolica Fisch. ex Lede). Taxonomists distinguish the two species by subtle morphological differences: Q. liaotungensis has smooth-cupule acorns and 5–7 pairs of lateral veins per leaf, whereas in Q. mongolica the acorn cupule is rough and there are 8–12 pairs of lateral veins per leaf Q. mongolica and 5–7, 5–8 and 5–9 for Q. liaotungensisFlora of ChinaHigher Plants of ChinaFlora of ChinaIn China, problems of taxonomic discrimination occur between the Liaotung oak  is another dominant broadleaved tree in the warm temperate zone, with a main distribution in northern China and partially in North Korea Q. liaotungensis has also been reported in the Russian Far East According to the description of Q. liaotungensis (smooth acorn cupule), or Q. mongolica (rough acorn cupule), following Higher Plants of ChinaQ. mongolica has smoother trunk bark than Q. liaotungensis. Therefore, the character of trunk bark was also considered where acorn cupule morphology alone was not sufficient to differentiate oak individuals . According to above rules, four ‘pure’ Q. liaotungensis populations were sampled. Populations ZW and WA are located in areas where Q. mongolica is completely absent ; CF2 and FS are located in areas from the sympatric range where Q. mongolica is locally absent. Three pure Q. mongolica populations were also collected. Populations HH and MR are located in areas where Q. liatungensis is completely absent ; Dan is within the sympatric range in an area where Q. mongolica is locally absent. At three locations both species were found within mixed forests. Individuals were randomly sampled and then categorized into different species as far as possible, thus resulting in three pairs of populations: SPl and SPm, THl and THm, and NAl and NAm, where the lower-case letter ‘l’ represents Q. liaotungensis and ‘m’ Q. mongolica. In addition, one Q. liaotungensis-type population (Dmy) and one morphologically intermediate population (Dtt) were sampled from the Dongling Mountain region where controversy has arisen among Chinese taxonomists because of the existence of a broad array of morphologically intermediate trees Leaf samples were collected from 419 individuals from a total of 15 oak populations across their distribution range in China . Individ2, 200 µM dNTP, 0.3 µM (each) primer, 20 ng of DNA template, and 0.5 U Taq polymerase . PCR amplifications were performed as follows: an initial denaturation step at 94°C for 4 min followed by 31 cycles of 45 s at 94°C, 45 s at an annealing temperature and 45 s at 72°C, and a final extension step at 72°C for 8 min.Total genomic DNA was extracted from 25 mg of leaf tissue from each individual oak and purified using a Plant Genomic DNA Extraction Kit . The oak samples were screened for variation at 19 nuclear SSR loci that had been developed for other oak species et al. EcoRI and 2 U MseI in 20 µL 1×NEB buffer  with 2 µg/ml BSA. Simultaneously, two adaptors, one for the EcoRI ends and one for the MseI ends, were ligated to cutting sites by adding 0.2 µM adapters, 80 U T4 DNA ligase and 1 mM ATP within the digesting mixture. Selective pre-amplification was performed with primers (E01 = 5′-GACTGCGTACCAATTCA-3′ and M02 = 5′-GATGAGTCCTGAGTAAC-3′) complementary to adapters, but with one base extension. Each PCR was performed in a 20-µL reaction volume using 2 µL ligated product, primer concentration 0.25 µM, 10 mM Tris-HCl (pH 8.0), 1.5 mM MgCl2, 0.2 mM dNTP each and 0.5 U Taq DNA polymerase . The selective pre-amplification was carried out in a PTC-200 thermal cycler (MJ Research Inc.) with the following thermo-cycling parameters: an initial denaturation step at 94°C for 2 min followed by 28 cycles of denaturation at 94°C for 45 s, annealing at 56°C for 45 s, extension at 72°C for 1 min, and a final extension step at 72°C for 10 min. The quality and quantity of the pre-amplified products obtained were determined on 1.0% agarose gels and diluted (1∶19) with ddH2O. The selective amplification was performed with four primer combinations . Each PCR was performed in a 20-µL reaction volume using 3 µL diluted pre-amplification product, 0.25 µM E-primer labeled fluorescence with 6-FAM , 0.3 µM M-primer, 10 mM Tris-HCl (pH 8.0), 1.5 mM MgCl2, 0.2 mM dNTP and 0.5 U Taq DNA polymerase . Amplification was performed with a touch down cycling process: an initial denaturation step at 94°C for 2 min, then 1 cycle of 30 s at 94°C, 30 s at 65°C, 1 min at 72°C, followed by 11 cycles in which the annealing temperature decreases 0.7°C per cycle, followed by 22 cycles of 30 s at 94°C, 30 s at 56°C and 1 min at 72°C, and a final extension step at 72°C for 5 min.AFLP fingerprinting was performed according to the original protocol of Vos The SSR genotyping of all individuals was performed by assessing allele size on an ABI 3100 automated Genetic Analyzer (Applied Biosystems), using forward primers labeled with 6-FAM, HEX, or TAMRA  and the ROX 500 (Applied Biosystems) as an internal standard. Allele sizing was performed using the GENEMAPPER software version 3.7 (Applied Biosystems). In the AFLP analysis, selective PCR products were also separated on an ABI 3100 automated sequencer (Applied Biosystems) with a genescan ROX 500 internal size standard. Electropherograms were subsequently analyzed using GENEMAPPER software version 3.7 (Applied Biosystems). The intensity of each individual peak was normalized on the basis of the total signal intensity and the peak was considered only if its intensity exceeded a fixed threshold. The multilocus AFLP profiles were scored as present (1), absent (0) or ambiguous (?) to create binary matrices. Each set of 48 reactions included a positive (known genotype) and a negative (water) control carried from restriction digest through to the final automated sequencer analysis for AFLP and from PCR through to the final automated sequencer analysis for SSR. Allele size determinations were performed twice manually to reduce scoring errors.OH and EH) were calculated using the program FSTAT 2.9.3 Descriptive statistics for SSR such as the number of alleles, allele frequencies, observed and expected heterozygosities  and differentiation statistics were calculated using the AFLP-SURV 1.0 software For AFLP, percentage of polymorphic loci (5% level), unbiased estimates of genetic diversity  were calculated in FSTAT 2.9.3 ΦPT-values with significance level (obtained by 999 times permutation) were calculated in GenAlEx6 Genetic differentiation of population pairs was estimated with SDDGenetic distances, A principal coordinate analysis (PCo) was performed for both AFLP and SSR data sets to calculate principal co-ordinates from pairwise Euclidian distance estimates between individual genotypes. Analyses were executed in GenAlEx6 K (number of possible clusters), and every cluster pattern from 1 to 10 was simulated. After fitting both the admixture and non-admixture models and an initial test of varying the burn-in and run length, ten replicates for each K were analyzed using the following parameters: we assumed correlated allele frequencies and an admixed origin of populations; burn-in was set to 100,000 with 1,000,000 additional cycles. STRUCTURE output, Pr(X|K), can be used as an indication of the most likely number of groups. In addition, following Evanno et al.ΔK, where the modal value of the distribution is located at the real K, was also calculated using the software Structure2A model-based clustering method implemented in the program STRUCTURE version 2.2 K = 2; see 36 (−33651.2 vs −33734.2) and 7.7×10131 (−41280.9 vs −41584.5) for SSR and AFLP, respectively. Under the admixture model, the posterior probability approximates the proportion of each individual's genome that is derived from each species K = 2. A threshold value 0.8 was used as a compromise between efficiency and accuracy For the most likely STRUCTURE run  for the SSR data set under an infinite-alleles model and a symmetrical two-island model of population structure. Simulations employed the median of observed FST and the same sample sizes as used in the empirical study. Outlier loci were detected by comparing the empirical distribution of FST's with a simulated distribution derived from 50,000 paired values of FST and heterozygosity at 99th quantiles. The significant AFLP outlier loci were identified by plotting FST against heterozygosity under the assumption of Hardy-Weinberg equilibrium using the program Dfdist FST values that fell outside the 99th quantiles threshold were obtained by generating a null distribution of FST-values based on 50,000 simulated loci with a mean FST equivalent to ‘neutral’ mean FST of empirical distribution, which was obtained by trimming the 30% highest and lowest FST values.To address the question of whether adaptive divergence occurs between EH) across all loci for each population varied from 0.713 to 0.797 (OH), expected heterozygosity (EH) across all loci were similar between Q. liaotungensis and Q. mongolica. However, there were some differences between species at some loci, e.g., ssrQpZAG15, ssrQpZAG110, quru-GA-0C11, ssrQrZAG87, and ssrQrZAG112 .All SSR loci studied were highly variable, with 5–45 alleles per locus (21 on average) and a total of 406 alleles found across all loci in our sample of 419 individuals. The mean expected heterozygosity (to 0.797 . The numQrZAG112 . Q. liaoongolica . In factQ. liaotungensis and 124 (64%) in Q. mongolica. The unbiased estimate of genetic diversity (jH) for each population varied from 0.204 to 0.234 (wH) or the whole-sample level (tH), were strikingly similar , of which 133 (69%) were polymorphic at the 5% level in to 0.234 . The vas similar .Q. liaotungensis and Q. mongolica was low but significant across all SSR loci . At most SSR loci, the interspecific θ was low, with 8 of the 19 loci displaying values <0.02 and only 3 having values >0.10 (θintra_ = 0.046 (p<0.01) for Q. liaotungensis; θintra_ = 0.019 (p<0.01) for Q. mongolica]. When the analysis was restricted to ‘pure’ individuals , the interspecific differentiation across all SSR loci was larger . The differentiation among populations within species was also higher . The genetic differentiation between Q. liaotungensis and Q. mongolica across the 194 AFLP markers was low but significant  for Q. liaotungensis; FintraST_ = 0.014 (p<0.01) for Q. mongolica; Fpure interST_ = 0.131, p<0.01) and was much higher than the differentiation among populations within species . The higher differentiation within Q. liaotungensis than within Q. mongolica was mainly caused by the divergence of THl and of ZW and WA, the two allopatric Q. liaotungensis populations (see The Weir and Cockerham's (1984) estimator of genetic differentiation between es >0.10 . The difions see .θ and ΦPT values with corresponding significant levels are presented in Q. mongolica population pairs; whereas among Q. liaotungensis populations, the THl, ZW and WA population were significantly differentiated from other Q. liaotungensis populations.Pairwise population Q. mongolica populations and the Dtt population shared a common node with a bootstrap value over 60% in both trees; all Q. liaotungensis populations shared the other common node in the AFLP tree. In the SSR tree, Q. liaotungensis populations were separated in two groups: the sympatric group vs the allopatric group; the sympatric Q. liaotungensis group, which is comprised of populations from northeast China and Chifeng, was more closely related to Q. mongolica populations than to the allopatric Q. liaotungensis group comprised of ZW, WA and Dmy populations. When the Dtt and Dmy populations were excluded from the analysis, the SSR and AFLP trees had exactly the same topology, with the two species separated at very strong bootstrap support . With both SSR and AFLP datasets, the majority of individuals from pure sites were assigned to their respective clusters, except in Chifeng, where the percentage of hybrids reached 30% (according to SSR) and 37% (according to AFLP). Most individuals from the three mixed sites where both species are present were also successfully assigned to their respective clusters, with only a few individuals being assigned to the alternative cluster . The percentage of hybrids in the three mixed sites varied among different sites  and Ning'an , whereas the percentage of hybrids in Dongling Mountain (56.8% for SSR and 79.5% for AFLP) was significantly higher than at any of the three mixed sites . Most hybrids there were from Dtt population. In contrast, a total of 7 (according to SSR) and 4 (according to AFLP) of the 12 Dmy individuals had a posterior probability >0.8 of belonging to the Q. liaotungensis cluster.More specifically, SSRs results indicated that 42% of all individuals had a posterior probability >0.8 of belonging to  and 38% . The assnt sites . TonghuaFST against heterozygosity  had a large difference in allelic frequency between Q. liaotungensis (95%) and Q. mongolica populations (14%). However, the interspecific FST of most SSR and AFLP loci exhibited very low values, suggesting substantial genetic exchanges at loci unlikely to be under disruptive selection  and five AFLPs are significant outliers on a plot of interspecific zygosity . Therefozygosity . The AFLelection .Q. liaotungensis and Q. mongolica, dominant members of warm temperate forests in northern China and the surrounding regions. A previous molecular study of the two oaks inferred strong gene flow between species and did not resolve the species status issue Q. liaotungensis and Q. mongolica. Acorn cupule and trunk bark characteristics were helpful to discriminate the species; in contrast, the number of lateral veins did not appear helpful for taxonomic purposes.The taxonomic status of closely related oak species has long been an issue of controversy because of continuous variation in morphological, ecological, and genetic traits due to interspecific hybridization and/or shared ancient polymorphisms Q. petraea) and pedunculate oaks (Q. robur). However, the bootstrap support for each group in our SSR study was not 100%, as in Muir et al.Although clustering based on population genetic distance relies on a priori classification of individuals or populations, it can provide useful insights. In our study, high bootstrap values and consistent topology between SSR and AFLP trees  indicateK = 2) also revealed that the taxonomical signal was much stronger than the geographic signal. The Bayesian clustering approach that was developed to identify genetic structure in the mixed populations has often been applied to test for the presence of a taxon without assigning individuals to a predefined group Q. liaotungensis and Q. mongolica, with only a few mismatched individuals and hybrids. When each of three pairs of mixed populations was analyzed separately, AFLP could also categorize individuals into the respective species .The most likely clustering in STRUCTURE , in agreement with their taxonomic status. However, in their study, only one marker displays a difference in frequency up to 71%. With comparable polymorphic markers, we identified one fragment with a difference in frequency between Q. liaotungensis and Q. mongolica of 81% (95% vs 14%); this difference would be even higher if the individuals suggested by STRUCTURE as having been misidentified had been corrected, suggesting that the two species studied are at least as valid as the well-investigated Q. robur and Q. petraea.The existence of outlier loci indicated that, although FST between Q. liaotungensis and Q. mongolica was slightly higher for AFLP (FST_inter = 0.093) than for SSR (_interθ = 0.049), in line with findings for Q. robur and Q. petraea by Mariette et al. Although AFLP and SSR markers have been widely used in describing interspecific differentiation in oaks, direct comparisons of genotype assignment using AFLP and SSR are still lacking. In our study, the interspecific differentiation estimated by Although the AFLP dataset provided higher resolution than the SSR dataset, both types of markers provided comparable and unbiased results. The overall mean species assignments were very close with each type of markers and the correlation between individual assignment values for the two types of data was high, although it was much lower for admixed individuals. These results indicate that similar conclusions regarding species delimitation can be arrived at using independent marker sets but also show that precise estimates of individual introgression rates require sizeable genome-wide datasets.Q. liaotungensis and Q. mongolica suggested that hybridization occurs between the two species in their sympatric range. First, the approximate Bayes factors  was greater than 100:1 for both the SSR and AFLP data sets, which could be considered as ‘decisive’ FST values were remarkably variable across markers, with many loci displaying low FST values (<0.02). Fourth, past hybridization between the two species is suggested by the extensive sharing of cpDNA in North China and Northeast China Our investigation of Q. liaotungensis and Q. mongolica  environment uncharacteristic of either parental species, such as in Dtt, a touristic place located in the suburb of Beijing. Further artificial pollination and transplant experiments are needed to test the mechanism by which reproductive isolation and habitat selection affects species delimitation of Q. liaotungensis and Q. mongolica.Alternatively, the differences between contact zones might be explained by geographically variable natural selection against hybrids. The infrequency of hybrids in Northeast China might result from their having lower fitness than the parental species. In fact, in Tonghua, where rates of introgression were very low, more loci under disruptive selection were identified in comparison with the two other mixed sites , even afQ. liaotungensis and Q. mongolica in Northeast China could have reinforced their reproductive isolation barrier, resulting in stronger barriers than in the new contact zone between Q. liaotungensis and Q. mongolica in northern China.Finally, to evaluate the hypothesis of reproductive character displacement, it will be necessary to reconstruct the history of the two species in their different contact zones. For instance, long time co-occurrence of Q. liaotungensis and Q. mongolica have maintained distinct gene pools, even in mixed stands, and should be considered as discrete taxonomic units, despite the existence of interspecific hybridization. Results based on SSRs and AFLPs were highly congruent, indicating that the conclusions reached regarding species delimitation and hybridization are of general value. Interestingly, hybridization rates were not uniform in different contact zones. More work is needed to tease apart the mechanisms underlying this heterogeneity of interspecific genetic differentiation across the species' ranges.Our molecular analysis led to the conclusion that Figure S1Distribution of per-locus interspecific FST values against heterozygosity in each study site.(TIF)Click here for additional data file.


Dog breeds lose genetic diversity because of high selection pressure. Breeding policies aim to minimize kinship and therefore maintain genetic diversity. However, policies like mean kinship and optimal contributions, might be impractical. Cluster analysis of kinship can elucidate the population structure, since this method divides the population in clusters of related individuals. Kinship-based analyses have been carried out on the entire Icelandic Sheepdog population, a sheep-herding breed.Analyses showed that despite increasing population size and deliberately transferring dogs, considerable genetic diversity has been lost. When cluster analysis was based on kinships calculated seven generation backwards, as performed in previous studies, results differ markedly from those based on calculations going back to the founder-population, and thus invalidate recommendations based on previous research. When calculated back to the founder-population, kinship-based clustering reveals the distribution of genetic diversity, similarly to strategies using mean kinship.Although the base population consisted of 36 Icelandic Sheepdog founders, the current diversity is equivalent to that of only 2.2 equally contributing founders with no loss of founder alleles in descendants. The maximum attainable diversity is 4.7, unlikely achievable in a non-supervised breeding population like the Icelandic Sheepdog. Cluster analysis of kinship coefficients can provide a supporting tool to assess the distribution of available genetic diversity for captive population management. Closed populations with high levels of genetic drift suffer from reduction of genetic diversity. Genetic diversity is essential to maintain the adaptive potential of populations, and confers higher resistance to pathogens. In the end, reduction of genetic diversity causes higher levels of inbreeding, which can cause inbreeding depression as well as high incidences of particular heritable (often recessive) diseases. Managing genetic diversity within populations is necessary to avoid high incidences of deleterious alleles and to preserve adaptive potential.In managed populations, such as domestic animals, genetic diversity can be maximised by selection according to optimal contributions, giving each reproductive animal a specific contribution for the next generations ,2. Howevet al. [Ubbink et al. -5 have uet al. , they usA dog breed is an example of an 'unsupervised' closed population  in whichIcelandic Sheepdogs are bred in several European countries by many individual breeders. It is well known that the current population of Icelandic Sheepdogs descends almost entirely from only a few founders that were selected from remote areas in Iceland between 1955 and 1965.In the work presented here, we investigate the amount of genetic diversity lost and the possibilities to maintain or increase genetic diversity within the Icelandic Sheepdog population considered as a typical closed dog population. Furthermore, cluster analysis is evaluated as a tool and for its potential to identify genetic diversity.We received pedigree data via ISIC  of the pmki) for individual i is calculated by Ballou and Lacy [Unless otherwise stated, inbreeding and kinship coefficients were calculated using the tabular method. Except for optimal contributions, which were calculated using Fortran, all measures were calculated using Visual Basic. Mean kinship was proposed by Ballou and Lacy  and is tand Lacy  as:N is the number of candidates and fij is the kinship between individual i and individual j. The mean kinship of an animal is a measure of the relationship of that individual with a population; animals with a low mean kinship are more valuable for genetic diversity. Mean kinship depends on the population which means that the mean kinship of an animal might change over time when a population changes. In conservation genetics, mean kinship is an important tool to maintain genetic diversity [where iversity .The following population diversity measures were used:Average inbreeding  is defined as the number of equally contributing founders with no random loss of founder alleles in descendants that would be expected to produce the same average mean kinship (and therefore genetic variation) as in the population under study. Nmk is Nmk = 1/2In this work, genetic diversity  is defined as half the number of distinct alleles that are still present in the population under study if all founder alleles were unique. The number of unique founder alleles that survive each year was determined by genedrop [NAD is also expressed in founder genome equivalents and can therefore be compared with Nmk and NOC (see below). For example, if the frequencies of all alleles were equal, NAD would be equal to Nmk. NAD monitors the loss of genetic diversity due to extinction of unique (founder-) alleles.In this work, allelic diversity  is defined as the maximum genetic diversity the population under study can achieve . NOC is the genetic diversity obtained when average mean kinship is minimised using Optimal Contribution Selection. NOC is calculated as described in Oliehoek et al. [In this work, potential diversity .For each year a 'current population' was defined as all the animals expected to be alive and the following population-parameters were determined: the current population size; the number of progeny born during that year; the number of founder introductions; and the following diversity measures: R2, the cubic clustering criteria and pseudo-F statistic were all examined . These clusters are displayed in a dendrogram, which is referred to as the all-gen-tree. (2) The second cluster-analysis was performed as described by Ubbink et al. [et al. [Cluster-analysis was performed twice on the current population. (1) The first analysis was based on kinship calculated using the tabular method starting with the founders and then UPGMA was applied for clustering all animals . To detek et al. . Kinshipk et al.  until se [et al. . This deOf the 4680 dogs in the data, 36 did not have any parents registered and were recognised as founders by the breeding organisations. All other dogs in the pedigree file descended from these 36 founders. Most founders lived in Iceland and were registered there, except for four animals that lived in Germany.The current population contained 2554 dogs and represented 512 unique parent combinations. For dogs in the current population, the most 'distant' founders appeared in their pedigree 10 to 20 generations back . The equivalent complete generations  traced wAll the animals of the current population can only carry alleles from the 36 founders. In the Icelandic Sheepdog, just three of the 36 founders contributed more than 80% of the alleles of the current population (results not shown). In other words, in about 80% of cases, the pedigree of every animal in the current population will end with one of these three over-represented founders.Nmk), potential diversity (NOC), and the allelic diversity (NAD). In 1955, the first 20 founders were selected for breeding. These animals were chosen from remote areas in Iceland.Figure Nmk, NOC and NAD . (2) Nmk has decreased since 1955, despite 10 founder introductions up till 1973 and six more after 1979. Each newly introduced founder can potentially increase genetic diversity but clearly in this case, founder introductions have not increased Nmk. (3) However, each founder introduction increases NOC and NAD by one. (4) Between 1960 and 1964, NOC and NAD have decreased from 24 to less than 10. This remarkable drop is explained by the fact that most of the 20 founders that were introduced in 1955 only produced one offspring and then died during this period. (5) Nmk has strongly decreased from 6.9 in 1967 to 3.2 in 1970. This is contemporaneous with the start of the first population size growth. NOC and NAD did not decrease as much during that period. Therefore, the decrease of Nmk is caused by unequal allele frequencies and not by extinction or mixing of unique alleles with over-represented alleles. The strong decrease of Nmk is due to a disproportional contribution of a small number of individuals to the future generation. (6) Unequal representation of founder animals in offspring is also responsible for the decrease of Nmk during the first years. (7) The distance between NOC and NAD has increased ever since 1963 and reached 5.2 in 1997, which means that it became increasingly difficult to equalise allele frequencies. In other words, 5.2 founder genome equivalents were lost because of unique alleles mixing with over-represented alleles within individuals. Optimal Contribution Selection cannot restore this loss. (8) The difference between Nmk and NOC shows that this population has the potential to increase genetic diversity.Figure Nmk), in order to compare Figure R2-values for different numbers of clusters (1 to 25). At cluster numbers of 3, 5 and 8, R2 shows a jump. Up to eight cluster numbers, cubic clustering criterion is around zero or less. However, when the number of clusters equals 8, it increases to 26.2, which means that the R2 is larger than can be expected from a normal distribution. The pseudo-F statistic was highest at a cluster number of 8 (1066). Eight clusters were selected based on these three criteria.Figure Figure et al. [et al. [Ubbink et al.  have sho [et al.  have sugNmk, NOC, NAD for each of the eight clusters treating each cluster as a separate 'population'. Note that mean kinship depends on this cluster. In Table Nmk is between 1.7 and 2.0. Only the cluster F, which contains only two animals, has an Nmk of 1.2. This is because kinship of an animal with itself has a higher effect on the total kinship in small populations. No single cluster can contain all the potential diversity. Moreover, within each cluster, the potential diversity NOC is hardly higher than Nmk, whereas for the population as a whole NOC is more than double Nmk (4.7 vs. 2.2). This indicates that an increase of genetic diversity in the current population can be achieved by optimisation between clusters but not by breeding within clusters. Each cluster could potentially contribute to genetic diversity. The small difference of Nmk and NOC within clusters also indicates that all dogs within the cluster are strongly related to each other.Table Nmk) of the current population of the Icelandic Sheepdog was only 2.2, the potential diversity (NOC) was 4.7. In other words, Nmk could be increased from 2.2 to Nmk = 4.7. However, this value can be achieved within a few generations only if specific animals are used for breeding according to their specific optimal contribution (as in vector: cOC) as calculated for each of the 2554 animals. Table Although genetic diversity  within each country. Breeders often think that within one country dogs are more related to each other and belong to the same cluster and they are often unaware that dogs from other countries might also belong to the same cluster. Since importing a dog is a large investment, breeders always selected the 'best dogs' from Iceland. Without knowing, Scandinavian mainland-countries imported highly related dogs time and again. This close relationship was not obvious on the standard pedigree forms given out by studbooks, because they indicate only three or at the most five generations. This lack of knowledge about true kinship among animals explains the occurrence of one large highly related cluster. Undetected relatedness is also the cause for the significant difference between cluster-analysis based on seven or on all generations Figure  and 2. Fwithin each cluster. The distance of each cluster to cluster A decreases mean kinship of animals of that cluster. This means that a conservation strategy based on selecting animals from distant clusters would give similar results than that based on selecting animals with a low mean kinship. While selection by optimal contributions is not possible within a multi-breeder population, cluster analysis could help in increasing genetic diversity. Cluster analysis can provide insight in the population structure for individual breeders, which helps to persuade them to select dogs from distant clusters.Mean kinship per animal was calculated for the current population. Figure et al. [In the populations of other breeds studied by Ubbink et al. ,4, speciNmk = 20 to guaranty adequate genetic variability. Nmk of the Icelandic Sheepdog was only 2.2. Leroy et al. [Nmk = 5.2 to 25) for nine French dog breeds. However, these results are difficult to compare since the correction for 'related animals with unknown parents' was not implemented because they were treated as founders [Nmk of 1.3 and concludes that Polish hound has a dramatic low level of genetic variability. Overall, it is surprising that, at the time of our study, the Icelandic Sheepdog did not show any genetic disease considering its level of inbreeding. Fortunately, the population size is still increasing, which usually lowers genetic drift.Lacy  has recoy et al.  have foufounders . GłażewsNOC was only 4.7 and genetic diversity was less than half of that and equalled Nmk = 2.2. Thus, in other words: the current population had a genetic diversity equal to 2.2 equally contributing founders with no random loss of founder alleles in descendants. An increase of genetic diversity to Nmk = 4.7 is not possible within a few generations in a multi-breeder population like the Icelandic Sheepdog.The overall picture of the Icelandic Sheepdog breed is as follows. The Icelandic Sheepdog breed was built from founders, located on remote areas of Iceland between 1955 and 1970. A good part of the diversity was already lost during the first years of the development of the breed. Figure Breeding with animals having a low mean kinship is an important conservation method . ClusterAlthough conservation of genetic diversity by means of optimal contribution selection is unlikely to happen within a multi-breeder population, preservation of potential diversity may be the second best option, when few animals are involved. In the Icelandic Sheepdog, optimal contributions show that the number of individuals with the highest potential genetic diversity equals about 50. It remains to be seen whether it is possible to convince some breeders to use those animals for breeding or for cryo-conservation of semen and oocytes.This research underlines that dog breeds suffer from genetic drift continuously. Often dog breeding is only authorized with animals meeting specific criteria. These selection criteria, like show-qualifications and health status reports, often strongly limit the number of animals used in breeding. Moreover, certain specific animals are genetically important (see also Table The authors declare that they have no competing interests.PA conceived the study and carried out the research, PB participated in its design and AM coordinated and helped to draft the manuscript. All authors read and approved the final manuscript.


The per capita land consumption (m2/person) of most cities decreased on average over the decade from 1,564 to 1,454 m 2/person, but there was substantial regional variation and some cities even increased. Cities with greater conservation funding or more reform-minded zoning tended to decrease in per capita land consumption more than other cities. The majority of developed area in cities is in low-density neighborhoods housing a small proportion of urban residents, with Gini coefficients that quantify this developed land inequality averaging 0.63. Our results suggest conservation funding and reform-minded zoning decrease per capita open space loss.Urban growth reduces open space in and around cities, impacting biodiversity and ecosystem services. Using land-cover and population data, we examined land consumption and open space loss between 1990 and 2000 for all 274 metropolitan areas in the contiguous United States. Nationally, 1.4 million ha of open space was lost, and the amount lost in a given city was correlated with population growth (r(272) = 0.85, P<0.001). In 2000, cities varied in per capita land consumption by an order of magnitude, from 459 m Urban population in the United States has climbed dramatically in recent decades, from 84.5 million in 1950 to 226 million in 2000 Rapid urban growth has raised public concern about the ecological and social impacts of “sprawl,” which has been defined as new settlements relatively far from city centers, as opposed to “densification,” which raises the density of already developed neighborhoods There have been dozens of books and hundreds of papers about sprawl In this paper we measure the open space lost from urban growth in all 274 metropolitan statistical areas (MSAs) in the contiguous United States from 1990 to 2000 . For breMost MSAs in the United States increased in population from 1990 to 2000 , with th2/person), Miami (476 m2/person), Philadelphia (519 m2/person), Los Angeles (535 m2/person), and Washington, DC (536 m2/person). Conversely, many small cities have large per capita land consumption, with the five biggest in 2000 being Grand Forks, ND (5394 m2/person), Bismark, ND (3913 m2/person), Flagstaff, AZ (3381 m2/person), Enid, OK (3249 m2/person), and Cheyenne, WY (3073 m2/person).American cities vary by more than an order of magnitude in their MSA-wide per capita land consumption. Generally large cities have small per capita land consumption, with the five smallest in 2000 being New York , while the city with the greatest increase in per capita land consumption was Pittsburgh (an increase of 47 m2/person).Most cities decreased in per capita land consumption from 1990 to 2000, measured in developed ha per person . The aveGreater amounts of conservation funding are associated with a greater decrease in per capita land consumption  = 20.9, P<0.001). Cities with any level of conservation funding are denser than cities with no conservation funding, although the reasons for this correlation are unclear. Conservation funding may force developments to be denser by setting aside some land as off limits to development. Alternatively, or in addition, cities that care about conservation enough to approve ballot initiatives may have citizens who prefer living in denser neighborhoods. In contrast to the correlation between conservation funding and density, zoning type is not significantly correlated with the proportion of housing in dense neighborhoods.2 while there were proportionally more houses in the smaller density categories  = 4.8, P = 0.006). The only significant contrast is between the “Reform” and “Traditional” zoning categories, with “Reform” cities slightly increasing in the proportion of houses in high-density neighborhoods while “Traditional” cities decreased. The change in proportion of houses in high density neighborhoods was not correlated with the median house value, the city size, or the degree of conservation funding.Neighborhoods containing a relatively small proportion of urban residents at low density usurp most of the developed area in a city. For instance, in Detroit, MI  80% of hOver the decade, the Gini coefficient of land inequality did not change much. Ninety-eight cities out of the total 274 increased in G by at least 0.01, while 32 cities decreased by at least that much. Cities that increased in the proportion of houses in high-density neighborhoods had greater increases in land inequality (r(272) = 0.43, P<0.001). Cities that had a greater MSA-scale decrease in per capita land consumption had greater increases in land inequality (r(272) = −0.41,P<0.001). While absolute levels of G in 2000 were not related to conservation funding, changes in G from 1990 to 2000 were  = 3.8, P = 0.01). Only one contrast is marginally statistically significant, with cities with greater than $100/person spending having a greater increase in land inequality than did cities with no conservation funding. The change in G was not related to city size, the median house value, or the zoning category.Our results show that patterns of urban growth from 1990 to 2000 are partially the result of contemporary factors and partially the result of historical factors. For instance, our results show that conservation funding and zoning over the decade correlated with patterns of urban growth. More conservation funding is correlated with lower per capita land consumption, an increase in the proportion of houses in dense neighborhoods, and a slight increase in land inequality. Cities with “Reform” style zoning, characterized by efforts to restrict development and sometimes to channel it to existing urban areas, also have lower per capita land consumption than other zoning styles. Note that correlation does not necessarily imply causation, and other factors might explain the development patterns in conservation friendly cities, such as their relatively fast rate of population growth or relatively high median house value. However, our results suggest that as cities protect land and tighten zoning restrictions, there is proportionally less development in more suburban areas, presumably because more dense residential development is comparatively favored by these actions. This then lowers the per-capita impact of urban residents on open space.The process of development plays out differently in cities with different socioeconomic histories. Moreover, cultural differences exist among and within many U.S. cities, leading to varying spatial patterns of development. However, a general historical pattern exists. In many U.S. cities, an urban core existed in the decades or centuries prior to the widespread use of the automobile, and these neighborhoods have high population density and small amounts of developed area per capita. The surrounding suburban and exurban areas, created predominately after WWII, contain residents living at lower population density and consume more land per capita Southeastern U.S. cities, excluding Florida, are often newer and have less of a legacy of a dense urban core. They do not appear to be getting markedly denser, and the relatively fast population growth of these cities implies that their total impact on natural habitat in coming decades will be large. In contrast to the Southeast, Western cities appear to be getting denser, including those that do not have a historical legacy of a dense urban core such as Phoenix. These Western cities are often still growing quickly and consuming a great deal of land, but contemporary development is making these cities denser than they were previously. Many of these Western cities have a strong conservation culture, and the degree of conservation funding and reform-minded zoning correlates with how much denser they are getting. However, it should be noted that contemporary development in Western cities is still well below the densities found in the dense urban core of Northeastern U.S. cities, posing problems for designing effective public transit systems Interestingly, measurements of land inequality show that low-density neighborhoods that house a small proportion of Americans contain much of the developed area of our cities. This suggests that the preferences and economic choices of a relatively small number of urban residents are associated with much of the natural land-cover lost to development. Efforts to increase the density of existing neighborhoods  may reduce urban expansion somewhat, but our results suggest that the strength of this effect will be diminished because a relatively small proportion of urban residents still desire to live in a more suburban setting and choose housing accordingly For our analyses we defined urban areas using the 2003 metropolitan statistical areas (MSA) and consolidated metropolitan areas (CMSA) of the US Census Bureau Land-cover data for our analysis was taken from the National Land Cover Database (NLCD) change product, which shows land-cover in 1990 and 2000 using a consistent classification methodology. NLCD information derived from a supervised classification of Thematic Mapper imagery (30m resolution) to a modified Anderson Our data on population and household density is from the Wildland-Urban Interface Database, version 2 Information on all open space initiatives passed by municipalities is maintained by the Trust for Public Land (Landvote). We calculated the total conservation funding for each MSA from municipal and county ballot initiatives for the period 1990–2000. Note that we summed up total conservation funding in each MSA as the amount passed by each component county or municipality within the MSA. We then calculated average per-capita conservation funding for the MSA by dividing by its total population. This approach is appropriate for our comparative analysis of America's MSAs, but does average over variation in conservation funding within MSAs.Information on general zoning patterns for the largest MSAs was taken from a large study by the Brookings Institution 2/person:All data was clipped to MSA boundaries and projected to an Albers Equal-Area projection. For each MSA we tabulated open space loss and population change. The total open space lost, summing over all four categories, was correlated with the change in population as well as the median house value of the MSA, as determined by the U.S. Census. One simple measure of the tendency toward sprawl is simply the change in the per capita land consumption, in mAn increase in this number implies a city is getting more spread out, while a decrease means a city is getting denser.2). We converted the polygon Wildland-Urban Interface Database to a 30m raster representation, and calculated the total developed area and number of households in each household density category. From these data we could calculate the proportion of urban residents who live in dense neighborhoods (>1250 units/km2), as well as the change in this proportion over the decade.To gain a more detailed understanding of both urban expansion and densification, we classified census blocks into categories of household density  or have a few workers earning significantly more than others . In an analogous fashion, land inequality may be quantified as the disparity in land consumption among households. A city might have all households consuming similar amounts of land  or have a few households consuming significantly more than others. We quantify land inequality using the Gini index Both MSA-wide metrics and fine-scale metrics were correlated with potentially explanatory continuous variables using the standard Pearson product-moment correlation coefficient. Zoning and the amount of public funding were treated as categorical variables (see the


With urban population increasing dramatically worldwide, cities are playing an increasingly critical role in human societies and the sustainability of the planet. An obstacle to effective policy is the lack of meaningful urban metrics based on a quantitative understanding of cities. Typically, linear per capita indicators are used to characterize and rank cities. However, these implicitly ignore the fundamental role of nonlinear agglomeration integral to the life history of cities. As such, per capita indicators conflate general nonlinear effects, common to all cities, with local dynamics, specific to each city, failing to provide direct measures of the impact of local events and policy. Agglomeration nonlinearities are explicitly manifested by the superlinear power law scaling of most urban socioeconomic indicators with population size, all with similar exponents ( How rich, creative or safe can we expect a city to be? How can we establish which cities are the most creative, the most violent, or most effective at generating wealth? The conventional answer is to use the rank order of per capita measures of performance Fortune, Forbes and The Economist, rely primarily on linear combinations of per capita quantities.Per capita measures of urban performance are ubiquitous in official statistics, policy documents and in the scientific literature. For example, official statistics on wages, income or gross domestic product (GDP) compiled by governmental agencies and international bodies worldwide The use of per capita indicators assumes implicitly that, on average, specific urban characteristics, These empirical regularities strongly suggest that underlying these apparently diverse phenomena there is a universal socioeconomic dynamic reflecting average organizational behavior of human interactions in cities expected average characteristics that a city of a given size should manifest in the absence of any specific local features. However, it is very often local characteristics, represented by how particular cities deviate from their expected baseline behavior, that are the most interesting for both policy and scientific analyses. Here, we show how deviations from scaling laws can be used to construct truly local measures of a city's organization and dynamics. As a result, we are able to address several fundamental questions such as how exceptional can a city be relative to its peers, what timescales are relevant for local policy to take effect, what are the local relationships between quantities such as economic development, crime and innovation, and whether each city is unique, or if there are identifiable (geographic) organizing principles expressed as shared patterns of urban development across families of cities.Thus, scaling laws provide the average baseline behavior and, by extension, the null model necessary for addressing the long-standing problem of how to rank specific cities meaningfully and assess the effects of local events, historical contingency, and policy, independently of population size. These agglomeration laws provide the i) economies of scale in material infrastructure ii) as increases in per capita temporal rates Agglomeration effects in cities are typically manifested as  is motivated by the more general observation that diverse characteristics of many complex adaptive systems, and especially those of biological organisms relative quantities rather than on their absolute values. Eq. (1) is analogous to a mean-field description and expresses the average behavior of urban metrics, Scale-Adjusted Metropolitan Indicators (SAMIs). Unlike per capita indicators, SAMIs are dimensionless, independent of city size  . These are socioeconomic units defined via commuting flows, in contrast to more arbitrary political divisions such as counties or administrative cities. This definition emphasizes social interactions as the defining feature of cities. It attempts to circumscribe the city geographically as a mixing population where all residents can come into contact with each other, a familiar concept in epidemiology and ecology We find that the variation in local quantities corresponding to different cities in the same year is well characterized statistically by a Laplace  distribution densityInterestingly, this Laplace distribution for SAMIs implies that the statistics of the urban indicators themselves also follow a power-law distribution density. Substituting, the definition of SAMIs, Eq. (2), into the Laplace distribution (3), and accounting for the change in measure in the probability density size see . Violenthttp://www.santafe.edu/urban_observatory/). Compared to per capita indicators, which place 7 of the largest 20 MSAs in the top 20 for GMP, SAMIs show no population size bias, ranking none of these cities in the top 20. SAMIs also reveal that New York is quite an average city, marginally richer than its size might predict , not very inventive (178th in patents) and quite safe (267th in violent crime). San Francisco is the most exceptional large city, being rich (11th in income), creative (19th in patents) and fairly safe (181th in violent crime). The truly exceptional MSAs are smaller, such as Bridgeport for income, Corvallis and San Jose  for patents and Logan or Bangor for safety.The first use of SAMIs is to provide a meaningful way to rank cities. The probability distribution of SAMIs, Eq. 3), might suggest that they behave much like random fluctuations. However, as illustrated in , might s see , and by In general, higher rates of violent crime positively correlate with higher average incomes. However, this is primarily because both quantities scale similarly with city population size. SAMIs allow us to factor out these dominant general size effects and identify local relationships. http://www.santafe.edu/urban_observatory/). for more maps and years). Such maps reveal regions with clusters of cities that under or over-perform relative to size expectations, resulting in short-distance positive correlations between local dynamics. However, these effects average out among neighboring cities with different characteristics so that significant average spatial correlations disappear for distances Place and geography are important in the development of cities nces  km . Consequkindred cities exhibiting common characteristic advantages and challenges as they evolve. These clusters of similar cities can be visualized using a heatmap, which is a correlation matrix sorted by similarity, grouping together sets of similar MSAs (denoted in red) and separating anti-correlated ones (blue); see This lack of greater spatial similarity in socioeconomic SAMIs raises the question of whether the local dynamics of different cities are idiosyncratic and unique , or whether there are common patterns across the urban system. To investigate this question we ask more specifically if the SAMI histories of different cities, see In this paper, we have proposed a systematic procedure for solving the long-standing problem of constructing meaningful, science-based metrics for ranking and assessing local features of cities quality from quantity in his studies of the emergence of organizational forms in small human societies Population size plays a fundamental role in this approach. In the spirit of the successful application of scaling analysis to many other system - from collective physical phenomena From this viewpoint, the general statistically stable properties of cities emerge as a hierarchy of interrelated fundamental quantities. First, it has been known for some time that the population size distribution of cities has remained relatively stable over time and across many different nations and is well-described by a Zipf power law distribution before the birth of Silicon Valley. Other examples that deal with population loss are also illuminating. Former industrial cities, such as Pittsburgh or Buffalo, have now experienced almost four decades of slow population loss, despite massive interventions to reverse such trends. The recent histories of these cities are also characterized by negative SAMIs  and by their lowest levels coinciding with the greatest population loss. These examples suggest that, at least in part, we should think of cities as sets of socio-economic processes with a temporal persistence much longer than that of typical policy initiatives or the participation of particular individuals. In this sense, urban policy that promotes population growth as a means to benefit from the effects of agglomeration leaves the character of a city, including most of its challenges, unresolved, and may, in fact, contribute to exacerbate them. Policies that focus instead on establishing beneficial fundamental change in local urban dynamics will be very difficult to achieve but very much worth creating, as they will position a city for a long run of prosperity and innovation. It would be interesting to investigate whether similar long term memory and persistence of urban dynamics is also a property of fast changing urban systems such as those in China or India. We intend to explore some of these important questions in future work.Secondly, perhaps the most conspicuous property of SAMIs is that they do not randomly fluctuate over time but, instead, show long temporal persistence. This indicates that, even though the size and structure of a city's population may change considerably over time, any initial advantage or disadvantage that it has relative to its scaling expectation tends to be preserved over decades. In this sense, either for good or for bad, cities are remarkably robust. Examples are Phoenix, which has remained a mild economic under-performer over the last four decades maintaining a similar value of te 1960s . Or, thete 1960s . Put sliOur analyses show that average spatial correlations between cities in the US are relatively short ranged  among cities, indicating that beneficial periods in specific sectors of the urban system coincide with negative developments in others, as Finally, it is important to emphasize that the average properties of most socioeconomic quantities such as wealth creation, crime and innovation are strongly predicted by the scaling laws expressed in Eq. (1), which are non-linear functions of population size and account for 65–97% of the variance in the data see . The shaIn summary, we have used the empirical manifestations of the underlying principles of agglomeration and the implicit network structures and dynamics responsible for the formation of cities to account systematically for urban dynamics at different scales. This paradigm allows us to separate measures of true local dynamics and organization in cities from their generic universal behavior. We have shown that these local indicators (SAMIs) have well defined statistics and that the consideration of their temporal and spatial properties is an essential element of models and theory of urban evolution and a new tool for the formulation of improved urban policy.Our spatial unit of analysis is the metropolitan statistical area (MSA). MSAs are defined by the U.S. Office of Management and Budget and are standardized county-based areas having at least one urbanized area of 50,000 or more population, plus adjacent territory that has a high degree of social and economic integration with the core, as measured by commuting ties. Data on Gross Metropolitan Product (GMP) was recently made available by the US Department of Commerces Bureau of Economic Analysis and is a measure - in 2001 chain-weighted dollars - of the market value of final goods and services produced within a metropolitan area in a particular period of time. Data on the number of violent crimes is provided by the US Federal Bureau of Investigation (Uniform Crime Reports). Metropolitan patent counts were constructed using data provided by the U.S. Patent and Trademark Office, see Data for GMP, personal income, violent crime and patents for each MSA corresponding to the same year were transformed logarithmically and fitted using Ordinary Least Squares to the logarithm of population, according to (1). Residuals from these fits, http://www.santafe.edu/urban_observatory/).The magnitude of the SAMIs corresponding to a given quantity and year for each city were used to rank cities. Two examples are shown in The temporal autocorrelation is defined asIn see also . Resultihttp://simile.mit.edu/wiki/Exhibit) and Google maps (http://maps.google.com). The figure shows one example. The full set can be viewed online (http://www.santafe.edu/urban_observatory/).Interactive maps and tables of SAMIs for each quantity and year were produced using Exhibit  and Gaussian distributions to residuals for personal income in 2005. Both distributions give an excellent fit, but the exponential (Laplace) distribution is better, especially for residues around zero.(3.33 MB TIF)Click here for additional data file.Figure S2Dendrogram of U.S. metropolitan areas grouped by incidence of violent crime, for cities with population above 1 million. Only cities reported by the FBI every year between 2001–06 are shown.(1.92 MB TIF)Click here for additional data file.Figure S3Heatmap of U.S. metropolitan areas grouped by incidence of violent crime for cities with population above 1 million. Only cities reported by the FBI every year from 2001 to 2006 are shown.(11.97 MB TIF)Click here for additional data file.Figure S4Dendrogram of U.S. metropolitan areas grouped by patenting rates for cities with population above 1 million. Data covers the period of 1975–2005.(8.04 MB TIF)Click here for additional data file.Figure S5Heatmap of U.S. metropolitan areas grouped by patenting rates for cities with population above 1 million. Data covers the period of 1975–2005.(12.02 MB TIF)Click here for additional data file.Figure S6Dendrogram of U.S. metropolitan areas grouped by Gross Metropolitan Product (GMP) for cities with population above 1 million. Data covers the period of 2001–2006.(7.62 MB TIF)Click here for additional data file.Figure S7Heatmap of U.S. metropolitan areas grouped by Gross Metropolitan Product (GMP) for cities with population above 1 million. Data covers the period of 2001–2006.(2.94 MB TIF)Click here for additional data file.Table S1Summary statistics for 2005. Scaling exponent with 95% confidence interval and R-squared for log-log fits of total urban indicator versus total population. Two fits to the residual distribution using an exponential (Laplace) and Gaussian distributions. The parameter s measures the width of the Laplace distribution. Similarly, σ is the standard deviation of the Gaussian. Values of R-squared shown for these parameters indicate goodness of fit of the cumulative residual distributions to the data see .(0.05 MB DOCX)Click here for additional data file.Text S1This describes in greater detail our methodology for assigning patents to metropolitan statistical areas.(0.11 MB DOCX)Click here for additional data file.Text S2This contains a summary of statistical analysis of correlations between SAMIs and population growth rates of Metropolitan Statistical Areas.(0.08 MB DOCX)Click here for additional data file.


Management and conservation can be greatly informed by considering explicitly how environmental factors influence population genetic structure. Using simulated larval dispersal estimates based on ocean current observations, we demonstrate how explicit consideration of frequency of exchange of larvae among sites via ocean advection can fundamentally change the interpretation of empirical population genetic structuring as compared with conventional spatial genetic analyses. Both frequency of larval exchange and empirical genetic difference were uncorrelated with Euclidean distance between sites. When transformed into relative oceanographic distances and integrated into a genetic isolation-by-distance framework, however, the frequency of larval exchange explained nearly 50 per cent of the variance in empirical genetic differences among sites over scales of tens of kilometres. Explanatory power was strongest when we considered effects of multiple generations of larval dispersal via intermediary locations on the long-term probability of exchange between sites. Our results uncover meaningful spatial patterning to population genetic structuring that corresponds with ocean circulation. This study advances our ability to interpret population structure from complex genetic data characteristic of high gene flow species, validates recent advances in oceanographic approaches for assessing larval dispersal and represents a novel approach to characterize population connectivity at small spatial scales germane to conservation and fisheries management. In many cases, however, no linear fit is found despite high variance in pairwise genetic difference values indicating diversity in population connectivity .In the marine environment, currents can be circuitous and oceanographic features like eddies and fronts can prevent mixing and diffusion of pelagic larvae, decoupling pelagic larval dispersal from Euclidean distance . Two adjper se), but only the knowledge of the (relative) distance between samples’. The challenge then is to use a model of oceanographic forces to map frequencies of dispersal between genetic sampling sites, and translate them into relative distances that can be tested for a linear fit with pairwise genetic difference. We estimated frequencies of larval exchange among populations of a marine species based on simulations of dispersal trajectories of the species' larval stage in a data-assimilated oceanographic circulation model produced from ocean temperature, salinity, current and wind observations in the study area. We focused our analysis on Kelletia kelletii, a subtidal whelk in US and Mexico Pacific waters that is a significant predator in kelp forest ecosystems (K. kelletii using the isolation-by-distance framework. Our approach enabled the construction of genetic isolation by ‘derived oceanographic distance’ (DOD) plots that were far more effective at organizing the pattern of population genetic structure than conventional isolation-by-distance methods based on Euclidian distance.This study represents an important advancement in the evaluation of spatial marine population genetic data (i.e. seascape genetics) by applying a new approach for incorporating ocean circulation observations directly into the isolation-by-distance analysis. As osystems . Recogni2.K. kelletii at nine microsatellite loci, K13, Kk2b, Kk7a, Kk28a, Kk33a, Kk34a, Kk41a, Kk48b and Kk52a, described and amplified with laboratory methods published previously . Particles were advected by simulated currents (e.g. as done by b) and were allowed to settle during K. kelletii's natural age-at-settlement competency window . Variability in the resulting connectivity matrices reached an asymptote at 25 particles per grid unit per day, prompting our use of this sample size here. To estimate potential larval connectivity, we assumed constant larval production for all release locations and no larval mortality (see §4).Simulations of larval trajectories for the Santa Barbara Channel, California, and the surrounding southern California area were used to estimate probability of larval dispersal and ultimately DOD among genetic sampling sites. Data-assimilated models of ocean currents for the study region were produced by combining available observations of ocean temperature, salinity, currents and winds in a numerical model to provide a dynamic interpolation of the near-surface ocean flow field . The observational period included the 1997/1998 El Niño event and thus represented both ‘normal’ and ‘exceptional’ oceanographic conditions. For each year, we recorded the proportion of larvae released from each grid cell that dispersed to a given grid cell. This procedure resulted in a 448 × 448 source–destination (row–column) matrix, K. kelletii larvae to grid cell i from grid cell j.Estimation of DOD for each pair of genetic sampling locations was done after subjecting the source–destination matrices to five transformations. First, we calculated a single, all-years 448 × 448 matrix as the element-by-element mean of the frequencies in the annual matrices. Second, we standardized the values in the all-years matrix so that for each source, destination probabilities sum to one . This procedure generated a forward transition matrix , which n2–104 generations, dependent on the demographics, marker mutation rate and other factors  intermediary sites. Thus, the Markov chain captures effects of both single- and multiple-generation dispersal processes on the long-term probability of dispersal among locations. Let M represent the original forward transition matrix that was used to set the initial condition. When n is sufficiently large, Mn = Mss, the Markov chain output of steady-state (long-term) dispersal probabilities between pairwise locations that is independent of the initial state of the population. We iterated the Markov chain until it reached numerically computed convergence, which took about 103 iterations.In the third transformation, we incorporated the effects of multi-generational gene flow on the long-term probability of dispersal between locations. Gene flow data from neutral markers represents a long-term average of dispersal integrated over 10 factors . In cont factors . The Mar factors . The Mar factors . We usedFST and Dest values represent genetic differences between subpopulations without indicating directionality of gene flow. In contrast, both M and Mss contain two values for each pair of locations, each representing a uni-directional probability of dispersal. To correspond with our empirical estimates of population genetic differences between each pair of sampling locations, in the fourth transformation step we averaged uni-directional dispersal probabilities to estimate mean probability of dispersal between pairwise locations. We performed this transformation step for both M and Mss in order to measure the effect of consideration of multi-generational dispersal events (transformation step 3) on our ability to explain the observed pattern of population genetic structure.Pairwise The isolation-by-distance analysis tests for a positive linear relationship between genetic differentiation and distance, requiring conversion of the matrix of mean dispersal probability into a distance matrix for the final step of the transformation process. Because each row in the matrix sums to one, the collection of dispersal probabilities for each source can be considered a probability density function (PDF). This type of PDF is termed a ‘larval dispersal kernel’ and provides a mathematical framework for transforming dispersal probabilities into relative dispersal distances . No two geo-referenced sites were closest to the same centroid; however, if this had been the case, we would have calculated DOD in relation to the mean probability of dispersal between that grid cell and itself. DODs could have been calculated for any number of pairs of grid cells, but was limited to only those pairs corresponding with the empirical genetic data.The dispersal kernel of FST and Dest) with Euclidean and derived oceanographic distance metrics. Correlations were tested using IBDWS Mantel tests with 10 000 permutations . Genetic differentiation between pairwise sites ranged from −0.0023 to 0.0068 (FST) and from −0.0048 to 0.023 (Dest) , compared with approximately 2 expected by chance. Results from pairwise FST and Dest were correlated , and the interpretation of the patterns was unchanged regardless of which measure of population structure was applied. For both FST and Dest, we did not detect a fit with a conventional isolation-by-distance model based on pairwise Euclidean distance  . Six of distance . In eachM, representing dispersal over a single generation), mean probabilities of dispersal among coastal grid units representing the 10 geo-referenced genetic sampling sites ranged from zero to 0.51 per cent (a). Using Siegel et al.'s dispersal kernel, we transformed the probabilities into DODs that ranged from ∞ − 47 km, respectively .In the original forward transition matrix . From these probabilities, we calculated DODs 171–82 km, respectively . Jackknifing the data found the observed pattern to be largely independent of any single locus or sampling location . Although convergence in Mss took many generations, corresponding with evolutionary time scales, similar regression correlation strengths were obtained when the Markov chain was run for fewer iterations . Setting negative FST and Dest values to zero had a minimal effect on the correlation with DOD, and did not improve the significance of the correlation with Euclidean distance .The steady-state transition matrix, per cent b. From tectively . Inclusi4.This study demonstrates the value of considering dispersal from the perspective of the organism for estimating population connectivity. Calculation of oceanographic distance in relation to the frequency of dispersal enabled us to construct genetic isolation-by-distance plots that were more effective at explaining population genetic structure than those based on Euclidian distance. Euclidean distance was uncorrelated with the probability of dispersal or DOD . Thus, it is not surprising that Euclidean distance was a poor predictor of genetic structure because probabilities of larval exchange, and presumably gene flow, among sites have little to do with the physical distance between them. The decoupling of dispersal probability and DOD from Euclidean distance between sites is probably due to the complex geography and circulation of the region.post hoc analysis highlighted variation in how well the consideration of larval dispersal in the oceanographic model explained genetic structure among different groups of sites. For example, model-based probabilities of dispersal were high between several cross-channel pairs linked by a within-channel eddy . This nel eddy   distance was a poor explanatory variable compared with DOD. Given the relatively low and equivalent levels of gene diversity observed among the sampling sites  of Dest and FST with DOD based on M. Consideration of Anacapa North may compromise the strength of the isolation by oceanographic distance regression because the oceanographic model poorly characterizes currents flowing to and from this location. Thus, limitations in the oceanographic model in relation to the experimental design of study sites may have generated results that undervalue the potential for single-generation dispersal probabilities to contribute significantly to the interpretation of population genetic structure. Alternatively, oceanography may simply be less influential on Anacapa Island's genetic structure compared with at other sites. Use of an improved circulation model and consideration of additional environmental factors  and demonstrations of the effect of vertical migration on dispersal . In transformation step 4, we represented mean dispersal probability as an average of uni-directional dispersal probabilities. This necessary simplification of the output provided by the oceanographic model is accurate given the isolation-by-distance approach we have taken, but future improvements on this approach might consider asymmetrical uni-directional probabilities of dispersal among sites. Additional explanatory power may be gained using genetics models that estimate directionality in migration, because asymmetry in migration rate can influence genetic structure (Our simulation model makes several important simplifying assumptions. Larvae are assumed passive, despite laboratory observations of diel vertical migration in ispersal . Also, atructure . OverallIt is likely that there are many situations where a stepping-stone model is an inadequate framework for describing the gene flow of marine species. A conventional stepping-stone model assumes that diffusion dominates ocean circulation over equilibrium population genetic time scales. However, ocean flow simulations with even simple but real coastlines can generate complex spatial patterns of connectivity whose spatial and temporal average departs considerably from expected Gaussian dispersal kernels based on homogeneous flow conditions . In the FST ≈ Dest ≈ 0.001, there is significant patterning organized by ocean currents on scales less than 30 km. Furthermore, our results serve as validation for estimating population connectivity using an oceanographic approach, one of the few methods for simulating larval dispersal in marine systems (This study advances the lower bound of seascape genetics for interpreting fine-scale population structure from seemingly chaotic genetic patchiness characteristic of marine species ( systems . Finally systems . Our dem