Madracis decactis (hermatype) and Tubastraea coccinea (invasive ahermatype). We sampled 28 platforms along four transects from 20 km offshore to the continental shelf edge off 1) Matagorda Island, TX; 2) Lake Sabine, TX; 3) Terrebonne Bay, LA; and 4) Mobile, AL. The entire population of M. decactis was sampled between depths of 5 m and 37 m. T. coccinea populations were sub-sampled. Genetic variation was assessed using the PCR-based Amplified Fragment Length Polymorphisms (AFLPs). Data were analyzed via AFLPOP and STRUCTURE. Genetic connectivity among M. decactis platform populations was highest near the FGB and decreased to the east. Connectivity increased again in the eastern sector, indicating isolation between the populations from different sides of the Mississippi River (Transects 3 and 4). A point-drop in genetic affinity (relatedness) at the shelf edge south of Terrebonne Bay, LA indicated a population differing from all others in the northern GOM. Genetic affinities among T. coccinea were highest in the west and decreased to the east. Very low genetic affinities off Mobile, AL indicated a dramatic difference between those populations and those west of the Mississippi River, apparently a formidable barrier to larval dispersal.The 3,000 oil/gas structures currently deployed in the northern Gulf of Mexico (GOM) provide hard substratum for marine organisms in a region where such has been rare since the Holocene. The major exception to this are the Flower Garden Banks (FGB). Corals are known to have colonized oil/gas platforms around the FGB, facilitating biogeographic expansion. We ask the question, what are the patterns of genetic affinity in these coral populations. We sampled coral tissue from populations of two species occurring on oil and gas platforms:  Prior to the 1940s, the bottom of the Gulf of Mexico (GOM) was characterized primarily by terrigenous, sandy muds with low habitat diversity i.e., composed of calcium carbonate that has been accreted by corals). The FGB are now surrounded by hundreds of platforms. It is possible that coral populations on the deeper banks could be a source of larvae that might colonize the platforms, but the abundance of coral on those banks is much lower than those on the FGB. The potential of the banks being a larval source for recruitment for the platforms in is probably relatively low.The only true coral reefs in the northern Gulf of Mexico are the Flower Garden Banks  Madracis decactis  and Tubastraea coccinea . Both of these reproduce via brooding. Madracis decactis is a simultaneous hermaphrodite and planulates monthly between March and December, with a broad peak occurring from Sept. to Nov. Tubastraea coccinea produces planulae sexually In this study, we focused on one hermatypic (zooxanthellate and reef-building) scleractinian coral species and one ahermatypic (azooxanthellate and non-reef building) one which occur on the platforms in the northern GOM and also on the FGB. We attempted to determine the degree of genetic connectivity (or relatedness) among the natural and platform populations on a large geographic scale, covering most of the northern GOM. Through earlier surveys, we found that these species were abundant enough to provide sample sizes sufficient for meaningful comparative molecular genetic analysis. The corals were Tubastraea coccinea is the single, most successful invasive coral and one of the most successful of all species to invade the Atlantic Ocean Fungia scutariaTubastraea micranthus (Ehrenberg 1834), a recent introduction to the region just south of the Mississippi River mouth (the Grand Isle – GI- lease area) T. tagusensis to Brazil, along with that of T. coccinea. Thus, the total number of introduced coral species to the western Atlantic Ocean is now four. T. coccinea was first recorded in Puerto Rico in 1943 and then in Curacao, Netherlands Antilles in 1948, occurring on ships’ hulls Tubastraea coccinea is clearly the most abundant scleractinian coral, hermatypic or ahermatypic, in the northern Gulf of Mexico on artificial substrata 2) T. coccinea is not as successful a competitor for space on natural well-established reefs as on artificial substrate.Porites astreoides) The two target coral species are brooders and reproduce by producing fully developed larvae which have the capability to settle in ≥4hrs Madracis decactis and Tubastraea coccinea, respectively. The populations sampled werefrom the Flower Garden Banks and a large number of platforms across the continental shelf of the northern Gulf of Mexico. The platforms cover ∼800 km of coastline from Matagorda Island, TX to Mobile, AL. The FGB occur west of the center of this range. We will examine the degree of connectivity between coral populations on the platforms in this region, and between those platforms and the natural reefs. We will also attempt to infer information about the comparative effectiveness of dispersal and colonization by these two brooding species, and relate it to an island-hopping strategy of colonization as described by MacArthur and Wilson  in this region. We also utilize a more conservative statistical analytical approach to analyze the data, based on extensive simulations. The specific objectives of this study are: To determine the degree of genetic connectivity between adult coral populations of Madracis decactis (hermatype) and of Tubastraea coccinea (ahermatype) on oil/gas platforms throughout the northern Gulf of Mexico and the Flower Garden Banks; to compare variation in genetic affinity between conspecific populations on different platforms; to determine the degree of genetic affinity between conspecific populations on the platforms vs. the FGB; and, to utilize data on patterns of genetic variation to infer comparative colonization potentials for these species.Here we examine genetic affinities among populations of the scleractinian corals d Wilson . We willWe initiated a set of field surveys followed by laboratory analyses. Surveys were conducted by SCUBA on offshore platforms extending between Madagorda Island, TX and Mobile, AL – a distance of 780 km. We sampled an area from ∼20 km offshore to the edge of the continental shelf and beyond. Platforms were sampled along four cross-shelf transects spaced at approximately equal intervals along the GOM coast ; Table 1These transects were chosen, firstly because they covered the breadth and width of the shelf where platforms exist. particularly the shelf edge. Secondly, they cover enough of the shelf to potentially provide northern boundary information regarding coral colonization and survival. In addition, they would provide information on coral colonization and growth on platforms near the shelf edge.Fling, Freeport, Texas) and conducted surveys over a period of three years  to preserve the DNA. This preservative allows tissue samples to be stored at room temperature, eliminating the need for storage in liquid nitrogen. The samples were then placed in additional SED buffer, placed in ice chests, and returned to the laboratory.Amplified Fragment Length Polymorphism (AFLP) is a DNA-“fingerprinting” technique i.e., 5–10 pg of zooxanthellar DNA in a background of coral DNA) It is possible that some genetic variation detected using AFLPs may not be derived from the target organism DNA was isolated by macerating samples lightly in SED buffer and spinning at 16Xg for 5 min to pellet the zooxanthellae from the homogenate. The DNA was then purified using the Wizard® SV Genomic DNA Purification System , following the manufacturer’s instructions for animal tissue. All samples were checked for zooxanthellae DNA contamination using the PCR techniques described in Brazeau et al. The AFLP technique, like other similar molecular genetic techniques, generate a subset of markers from a large population of markers. Of the subset obtained from a given AFLP experiment, a portion is often sensitive to specific reaction conditions. Thus, extra caution is required in processing samples through all procedural steps to maximize repeatability of results. Here, we processed samples in large lots containing members from all populations. This helped to distribute any error potentially introduced by reaction conditions uniformly between populations in an unbiased fashion. Also, all PCR reactions were replicated using one machine and the same thermal cycle profiles.4 DNA ligase 10X buffer /1 mM ATP), 1.1 µl of 0.5 M NaCl, 0.5 µl bovine serum albumin , 1.0 µl Mse I adapters (50 µM), 1.0 µl EcoRI adapter (5 µM), 0.25 µl Mse I , 0.25 µl of EcoRI , and 0.33 µl of T4 ligase . Sequences for the Mse I and EcoRI adapters and PCR primers are listed in A restriction-ligation “master mix” was prepared using the following reagents (measures are per sample): 1.1 µl TA second “master mix” was made for pre-selection (PS) amplification, using the following reagents (per sample measure given): 8.1 µl of nuclease-free water, 2.0 µl of 10X PCR buffer (15 mM Mg++ in buffer), 0.8 µl of 5 mM dNTP’s, 2.0 µl of EcoRI PS primer (2.75 µM), 2.0 µl of MseI PS primer (2.75 µM), and 0.1 µl of Thermostable (Taq) DNA polymerase (5 U/µl), for a total volume of 15.0 µl. Fifteen µl of the pre-selective amplification master mix was combined with 5 µl of each of the diluted restriction ligation reaction in a 0.5 ml tube. Samples were vortexed and centrifuged for 15 s. Amplification was performed using a 2-min initial incubation at 72°C, followed by 20 cycles of 20 s denaturation at 94°C, 30 s annealing at 56°C, and 2 min extension at 72°C. Last steps were 2 min final extension at 72°C, and 30 min final incubation at 60°C. After the cycling was completed, 180 µl of TE buffer was added to each tube, which consisted of the templates for the final step, selective amplification.In the final step, a selective amplification “master mix” was made, containing the following components: 8.1 µl of nuclease-free water, 2.0 µl of 10X PCR buffer (with Mg++ at 15 mM), 0.8 µl of 5 mM dNTP’s, 2.0 µl of EcoRI selective primer (0.46 µM), 2.0 µl of MseI selective primer (2.75 µM), and 0.1 µl of Taq DNA polymerase (5 U/µl) for a total volume of 15.0 µl. To each 0.5 ml micro-centrifuge tube, 5 µl of the diluted pre-selection PCR reaction was added to each corresponding tube, mixed, and centrifuged for 15 s. Samples were placed in the thermocycler, and the cycle profile was performed as indicated: 2 min initial denaturation at 94°C, followed by 1 cycle of 20 s denaturation at 94°C, 30 s annealing at 66°C, and 2 min extension at 72°C. Next, there were 9 cycles: 20 s at 94°C, initial 30 s at 66°C (reduced 1°C/cycle), and 2 min at 72°C. Final cycle consisted of 20 cycles: 20 s at 94°C, 30 s at 56°C, and 2 min at 72°C, followed by a 30 min final incubation at 60°C.Products for the selective PCR were run on an Amersham MegaBACE 1000 96 capillary sequencer at the University of Florida’s Interdisciplinary Center for Biotechnology Research. Resulting electropherograms were analyzed using SoftGenetics GeneMarker® (ver 1.51) for bands ranging from 50 to 400 bp in size in 20 bp increments.The Selective PCR reactions were repeated three times for each sample. These “repeat” reactions were run on different days with populations mixed in each run. Bands were considered present if they appeared in two of the three runs; conversely, bands were scored as absent if two out of the three reactions yielded no band. Of the bands included in the study, >90% yielded the same result in all three PCR runs. These inclusion criteria helped to identify bands that were sensitive to reaction conditions.Two statistical analyses were used to assess population differentiation: AFLPOP population allocation analysis , a statistical analytical procedure designed particularly to analyze data generated by AFLPs, and STRUCTURE V 2.0 The AFLPOP analysis uses AFLP presence/absence data to calculate log-likelihood values for any individual’s membership in a reference population, based upon their banding patterns. The reference population is that target population  against which all other colonies from other sites are compared for genetic affinity. Each individual is allocated to the population showing the highest likelihood for that genotype The AFLPOP program allows the user to set a log-likelihood threshold for each assignment. If set to 0.0, a colony will always be assigned to the population with the highest likelihood value. Atchison ST, and probable paternity, grand-paternity, etc. relationships. The program can accommodate dominant marker data such as those generated by the AFLP technique. Data were analyzed using a burn-in period of 500,000 iterations, followed by another 100,000 Markov Chain Monte Carlo (MCMC) repetitions.STRUCTURE uses Bayesian techniques and Monte Carlo simulations to assign samples to populations. Unlike AFLPOP, in which assignment is based solely upon marker frequencies, STRUCTURE makes assignments that minimize deviations from the Hardy-Weinberg (H-W) equilibrium, which assumes that the population giving rise to the recruits constitutes a large, randomly mating population. Using this approach, the program calculates probabilities of individual assignment, estimates of FSTRUCTURE also calls for definition of the parameter MIGPRIOR before running. This parameter was the prior probability of a spat being identified as coming from an external source. It was run at two levels, for comparative purposes – 0.05 and 0.50, taking into account different potential estimated migration rates.Nm and STF can in themselves be problematic as indirect genetic indices of assessing population connectivity see We chose not to use AMOVA for our analysis. Although AFLP data can be treated like allelic data, since these markers are co-dominant and multi-locus, we chose to take a more conservative approach by using genetic assignment tests. Genetic assignment tests are also more appropriate to the types of questions we have addressed here. Finally, AFLP data can be problematic in attempting to estimate “allele” frequency distributions among populations, plus classic parameters like Madracis decactis, the results derived from STRUCTURE revealed a clear pattern. The highest levels of genetic affinity occurred between coral populations on platforms in Transect II, off of Port Arthur/Lake Sabine, TX, and on platforms around the FGB , it became clear that not only was there little similarity between the two populations, there was also little similarity among platform populations within a transect .The analytical program STRUCTURE yielded clear results for vs. Transect IV  using AFLPOP with a log-likelihood threshold value of 0.0 (forcing assignment), there was once again little similarity anong populations across the mouth of the Mississippi River  pi River . A highepi River . In geneMadracis decactis on platforms in the western GOM, as determined by STRUCTURE, confirms that these populations most likely originated from the Flower Garden Banks (FGB). The platform populations show high affinity for those on the FGB and those on platforms around them. Also, as the platform populations become more distant from those in the west and the FGB, they exhibit less genetic affinity to each other. The slight increase in genetic affinity in the eastern sector, beyond the mouth of the Mississippi River mouth, underscores the lack of affinity between the populations on the eastern and western sides of the river. The mouth of the Mississippi River appears to represent a strong geographic barrier to larval dispersal, as is known to be the case in other organisms in the vicinity of river mouths, particularly the Mississippi  had general similarities to that of Madracis decactis; that is, there was a general decrease in affinity from west to east. The variations in this pattern observed in M. decactis, however, were much more striking in T. coccinea. Firstly, the strong decrease in genetic affinity near the mouth of the Mississippi River indicates a sharp differentiation between that far-eastern population and all of those to the west of the Mississippi River mouth. In addition, a deep point depression in genetic affinity of T. coccinea was also noted in Transect II. It was similar to that observed in Madracis decactis, but more marked. The population on that platform, was clearly unrelated to the others in the northern GOM. This may represent a second introduction of this species to the northern Gulf of Mexico. Potential sources include the Caribbean, as described above, or a second successful introduction to the western Atlantic from a commercial vessel from the Indo-Pacific. Once again, in either case, this population is unrelated to the others in this region.The geographic pattern of genetic affinity exhibited by Madracis decactis and Tubastraea coccinea.The Mississippi River appears to represent a formidable east-west barrier to coral larval dispersal in this region. This was evidenced through the lack of genetic affinity between coral populations on either side. This pattern was evident in two distantly related coral species - There are two possible explanations for this anomaly which are not mutually exclusive. The first is that the lower salinities, higher sediment, and higher nutrient concentrations associated with the Mississippi River plume Tubastraea coccinea has higher larval dispersal and recruitment capabilities than Madracis decactis. The latter species is clearly the most abundant and widely dispersed native hermatypic species on the platforms,yet its populations showed almost no genetic affinity to each other when compared across the two sides of the Mississippi River mouth. In addition, the populations showed little if any affinity to each other within a transect on a given side. Populations of T. coccinea, although exhibiting a similar high degree of separation across the river mouth, showed more recognition between platforms within a transect – suggesting higher dispersal between them.Our results also revealed that the invasive ahermatypic Madracis decactis and other corals are highly isolated on these platforms and that their variation from each other most likely results from Founder Effect  leading to Port Fourchon, and New Orleans via the Mississippi River. We are currently attempting to determine the extent of the invasion The second inference that may be drawn from these genetic affinity patterns is that some other Indo-Pacific species may be similarly better adapted to reproduce and disperse than our native species, and that all precautions should be taken to eliminate them should they be successfully introduced in our waters. Sammarco et al.


Various explanations have been offered for this phenomenon. Here we argue that these spatial forms are continuous with varieties of non-synesthetic visuospatial imagery and share their central characteristics. This includes their dynamic and elaborative nature, their involuntary feel, and consistency over time. Drawing from literatures on mental imagery and working memory, we suggest how the initial acquisition and subsequent elaboration of spatial forms could be accounted for in terms of the known developmental trajectory of visuospatial representations. This extends from the formation of image-based representations of verbal material in childhood to the later maturation of dynamic control of imagery. Individual differences in the development of visuospatial style also account for variation in the character of spatial forms, e.g., in terms of distinctions such as visual versus spatial imagery, or ego-centric versus object-based transformations.Sequence-space synesthetes experience some sequences  as arranged in  Skill iAlthough the common claim that sequence-space synesthesia is strongly automatic has been challenged , spatialIn both healthy and clinical populations, imagery experiences commonly result from involuntary as well as deliberate recall processes . RegularThe content of recurrent intrusive imagery is usually very consistent in terms of what is depicted in the image . AlongsiIn the attempt to establish sequence-space synesthesia as a legitimate and defined topic within cognitive neuroscience, the heterogeneity of spatial forms has tended to be overlooked. Spatial forms vary not merely in their vividness, personal importance, complexity, and involuntariness, but also in their spatial frame of reference, in whether they feel like static depictive visual images or like spatial models, and in the types of spatial transformation that can most naturally be applied to them . Aspectsobject visualizers, favoring a more visual style of imagery that is associated with vivid high resolution imagery and superior performance on visual memory tasks, or spatial visualizers, favoring good spatial analysis and dynamic image transformations such as mental rotation (For example, the term “visuospatial imagery” blurs the partial separability, at experiential, behavioral, and neural levels, between “visual” versus “spatial” aspects of visuospatial imagery . While irotation . A distirotation .There is ongoing debate over whether the experiential character of spatial forms and the behavioral skills of sequence-space synesthetes correspond more closely to the traits of object or spatial visualizers see . For exaFor those spatial forms which reach the developmental sophistication of a spatial model, their dynamic transformation is another area where individual differences can be informed by fractionation of visuospatial imagery skills. Some synesthetes report object-centered transformation – e.g., as the months go by, their spatial model of the calendar months rotates around or in front of them. Others report ego-centric transformations – e.g., they feel they navigate through, or around, their spatial model. A practical implication of these kinds of individual differences is that visuospatial skills will differ among synesthetes. Unless synesthetes’ individual profiles are carefully aligned, group comparisons of their behavioral skills versus control samples stand in danger of confusion and inconsistency.Our approach differs from suggestions  that spaCentral hallmarks of sequence-space synesthesia, including involuntary feel, consistency over time, dynamic nature, and elaborative imagery, are not unique to this variety of imagery, but shared by everyday visuospatial imagery, intrusive imagery, and mnemonic imagery. Furthermore, a visuospatial developmental perspective illuminates why and when sequence-space synesthesia originates in some people, and why its character varies between synesthetes. We therefore suggest strong continuity between synesthetic spatial forms and normal processes of visuospatial imagery.Interest in the relationship between mental imagery and synesthesia is by no means confined to sequence-space synesthesia, and there is growing evidence that other varieties of synesthesia are also associated with above-average visual imagery . HoweverThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Sebastes melanops, and compared this estimate with the distance between RCAs that would protect this species. Within each country, we found that the distance between RCAs was generally within the confidence intervals of mean dispersal per generation. The distance between these two RCA networks, however, was greater than the average dispersal per generation. The data were also consistent with a genetic break between southern Oregon and central Oregon. We discuss whether additional nearshore RCAs in southern Oregon and Washington would help promote connectivity between RCA's for shallow-water rockfishes.Marine reserves networks are implemented as a way to mitigate the impact of fishing on marine ecosystems. Theory suggests that a reserve network will function synergistically when connected by dispersal, but the scale of dispersal is often unknown. On the Pacific coast of the United States and Canada, both countries have recently implemented a number of rockfish conservation areas (RCAs) to protect exploited rockfish species, but no study has evaluated the connectivity within networks in each country or between the two countries. We used isolation-by-distance theory to estimate the scale of dispersal from microsatellite data in the black rockfish,  Marine reserve or marine protected area networks are intended to protect a species or a suite of species from overexploitation. In contrast to most terrestrial species, many marine species have a bipartite life cycle in which pelagic larvae have the potential to disperse long distances by ocean currents before settling into a benthic (and sometimes sedentary) adult phase. As a consequence of this life history, results from terrestrial reserves do not provide useful principles for the design of marine reserves  are a speciose genus consisting of over 100 species, most of which occur in the northeast Pacific .In this study, we evaluate connectivity between recently established rockfish conservation areas (RCAs) in Canada  and used isolation-by-distance theory to estimate the scale of dispersal. Isolation by distance (IBD) is a phenomenon in which genetic distance between populations increases with geographic distance between them. The IBD slope can be used in combination with information about the effective density to estimate the scale of dispersal . Female rockfish store sperm and use that sperm to fertilize their eggs around December to January, and then brood those eggs for about a month before releasing from 125 000 to 1 000 000 well-eyed larvae in parturition from January to March , Spi6 (AY192600), Spi10 (AY192603), and Spi12 (AY192604)  with the Benjamini–Hochberg sequential procedure  analysis on populations in GENODIVE 2.0  is estimated from the relationshipWe used isolation-by-distance (IBD) theory to estimate the scale of dispersal in black rockfish. The IBD slope can be used to estimate the standard deviation of the dispersal distribution or dispersal kernel, which is a function that describes the probability of dispersal at different distances from the source. For one-dimensional habitats such as coastlines, the standard deviation of the dispersal kernel (De is the effective density and m is the slope of the relationship between FST/(1 − FST) and geographic distance . We implemented the Mantel test with 999 iterations in R version 3.0.1 .ONeSAMP calculates eight summary statistics from a microsatellite dataset and uses approximate Bayesian computation to infer θ = 4 Neμ. The MIGRATE analysis strategy estimated θ with Bayesian inference with mutation rate estimated independently for each locus. Proposal distributions for θ used slice sampling, with uniform priors from 1 to 600. We used Markov chain settings of one long chain with 5000 recorded steps (long-sample), 10 concurrent chains (replicates), and 15 000 discarded trees per chain (burn-in). Multiple Markov chains were performed with four chains at temperatures of 105, 3, 1.5, and 1 with a swapping every 10 chains and an update frequency of 0.75. All other parameters were left at default values. Runs were performed at the high-performance computational facility at Florida State University. We extracted Ne from θ using mean mutation rates published in other fish species. We surveyed the mutation rates observed in fish in nature and found that they ranged from 9.4*10−4 to 9.1*10−3 with an average of 7.2*10−4 over all 61 loci surveyed  estimates Ellegren .De by dividing the mean and confidence intervals of Ne from each program by the span of distance covered by our sampling.We computed the mean and confidence intervals on http://www.reef.org, REEF We obtained census density estimates from Barkley Sound, BC  in British Columbia, Washington and Oregon that were likely to protect black rockfish in the analysis. We read the regulations for each conservation area and included it if: (i) it was on the outer coast , (ii) it contained habitat <50 m in depth, and (iii) rockfish were protected from both commercial and recreational fishing.In British Columbia, RCAs prohibit commercial and recreational fisheries that target or lead to by-catch of rockfish and most are within the depth range of black rockfish  is a large MPA on the open coast. Black rockfish, however, are not protected from recreational or commercial long line fishing in the Olympic Coast NMS  so it was also excluded from the analysis. Although there are some deep water RCA's on the Washington outer coast, no areas protect nearshore species like black rockfish.Oregon is in the process of designating a system of nearshore MPAs and marine reserves. Black rockfish are protected from fishing in the marine reserves that prohibit all extractive activities. We included the marine reserves that are currently in place (Otter Rock and Redfish Rocks) as well as reserves that will come into effect by 2016  . Commercial fishing and/or recreational fishing are also prohibited in a number of areas designated by the National Marine Fisheries Service; however, most of these areas are offshore and do not intersect with the typical depth range of black rockfish. Areas shallower than 50 m are found in two of these Essential Fish Habitat Conservation Areas: Hecata Bank and Newport Rockpile-Stonewall Bank. Although no black rockfish have been found in drop camera studies of Hecata Bank and the Newport Rock/Stonewall Bank , we included them in our analysis as they contain some potential black rockfish habitat.We plotted the RCAs and marine reserves in ArcMap 10.1 (ESRI 1999–2012) along with the 50-m contour lines using the GCS North American 1983 coordinate system and the Canada Albers Equal Area Conical projection. To map the distance between adjacent conservation areas, we created a line shapefile and drew lines or multipart lines using the ‘snap to nearest feature’ tool to find the closest edge of each conservation area. The length of each line was then calculated in kilometers using the calculate geometry tool. The number of adjacent reserves included in our calculation depended on geographic location of the focal reserve, but varied from one to three. The reserve areas and mean distance among RCAs in BC and Marine Reserves in Oregon were then compared with the estimated dispersal distance of black rockfish.After the reamplification of failed PCRs, the dataset had 0.45% missing data, corresponding to 0.17–0.86% missing per locus.FIS among loci  and all other samples, as well as between Newport, Oregon (pop. 2) and the most northern samples .The first two axes of the PC analysis explained approximately 49% of the total variance. The first PC axis explained 28.5% of the variance  and separated populations approximately according to latitude  and a significant IBD slope RMA slope = 1.25*10−5 with 95% CI in , ] than in the north . Likewise, there was no significant correlation between latitude and He , and a positive but nonsignificant relationship between latitude and allelic richness . These data suggest that the population is at or near equilibrium and that IBD theory can be used to estimate the scale of dispersal.Across all sampled populations, we observed a significant correlation between geographic distance and genetic distance (Mantel test 2 transect to be 7.16 ± 1.00 (st. error) averaged over 2010 and 2011. The REEF index for Barkley Sound, BC was 2.6 . This is close to observed estimates of the abundance of black rockfish. A mark–recapture study around Newport, OR from 2002 to 2006  estimated the abundance of black rockfish to be 0.13*107–0.21*107 fish (Sampson 7–7.41*107 fish).The extrapolated census density from the transects gave a population size of 2.85*10 Sampson . ExtrapoNe from the three programs are shown in Table Ne (>108) were assumed to equal infinity. MIGRATE and ONeSAMP produced very different point estimates, although their confidence intervals generally overlapped. LDNe produced finite estimates in less than half the samples. LDNe and ONeSAMP failed when all individuals were pooled for analysis. For each method, we averaged the finite values of Ne over all the samples to calculate De, assuming that the Ne of each sample was near the metapopulation Ne because of high gene flow.Means and confidence intervals on Ne from LDNe were finite in four of the nine samples and ranged from 342 to 1063 . We also noticed that using a different cutoff for minor allele frequency in LDNe could produce a negative or positive Ne estimate for the same locus. As LDNe seemed to fail with these data because of the numerous negative estimates and confidence intervals including infinity, we did not use them to calculate σ.Point estimates for 3 to 2.05∗106, with 95% confidence intervals in . The point estimate of De from ONeSAMP equaled 299 fish/km with 95% density in  . After including confidence intervals on mutation rate, this corresponds to a point estimate of Ne equal to 2632 with 95% density in . The point estimate of De from MIGRATE equaled 1.94 fish/km with 95% density in .From the coalescent method in MIGRATE, similar estimates of θ were obtained for each sampled population and for all samples combined Table . Mean θ σ) from MIGRATE and ONeSAMP, with confidence limits common to both programs. The point estimate on σ from MIGRATE was 101 km  and from ONeSAMP was 8.2 km   reasons for this are that (i) the assumed mutation rate for microsatellite markers was not representative of the loci used in this study, (ii) the historical Ne may have been much smaller because of population expansion since the last glacial maximum, or (iii) the temporal and thus spatial scale of Ne estimates is different. Our mean mutation rate was based on 61 loci from three species of fish; if the true mutation rate for the loci in this study was much smaller , we would have obtained similar point estimates from MIGRATE and ONeSAMP. The second possibility is that Ne estimates from each program are simply reflecting different timescales: MIGRATE estimates a historical population size based on the coalescent and ONeSAMP estimates a more contemporary population size. Black rockfish are a nearshore species whose northern range was limited near the United States/Canada border by the Cordilleran ice sheet 14 000 years ago . On the third point, contemporary estimates of Ne like those from ONeSAMP may produce estimates on a more local scale  than those from a coalescent estimator like MIGRATE, but we observed the opposite pattern with our data. Still, confidence intervals on contemporary Ne were large, and in the subsequent discussion on reserve design, we assume dispersal may be as high as 184 km per generation. The accuracy of Ne estimators in populations of large census size is an area in need of more research.A major limitation of this study that the precision Ne/N in black rockfish to be on the order of 10−4 (from MIGRATE) to 10−2 (from ONeSAMP). The ratio of effective population size to census size is an important metric because it describes how much genetic diversity is reduced relative to the number of census individuals in the population. It is of interest to note here the trade-off between Ne and the σ estimated from IBD: either genetic diversity in this species is severely reduced and mean dispersal is around 100 km per generation (MIGRATE), or genetic diversity is only slightly reduced but the scale of dispersal is <10 km per generation (ONeSAMP). Either case could be used as an argument for greater protection of the species.We estimated Ne/N ≪ 1 may be due to several nonideal conditions such as multiple paternity and overlapping generations , IBD theory reflects average dispersal distance per generation and is therefore closer to ecologically-relevant dispersal distances. The IBD slope is expected to be heavily affected by dispersal during the several most recent generations, and is not affected by rare dispersal events  population 1 was an outlier in the PC analysis, and (iii) the IBD slope was higher when only sites in OR and WA were included. These lines of evidence are in agreement with a previous studies on black rockfish  and the rest of the sampled range: (i) the pairwise So what are the putative barriers to dispersal within the Oregonian province? In other rockfish species, the distribution of genetic diversity has been partitioned by large-scale oceanographic barriers , and the representation of retention zones  that may affect reproductive success and larval retention (criteria iv). There is only one reserve located south of Cape Blanco at Redfish Rocks, and this reserve is located 144–171 km from the nearest reserves at Hecata and Cape Perpetua – distances near the upper limit on our estimate of mean dispersal for black rockfish. Given the lower genetic connectivity observed in many Sebastes spp. in this area, it is likely that criterias i and iv are violated in the southern part of Oregon.Our population structure analysis suggested that there is a genetic break near Cape Blanco, Oregon, and we discussed how this location corresponds to a shift in upwelling dynamics (criteria Sebastes ruberrimus) rockfish, and are outside the depth range of nearshore-to-shallow-shelf species like black rockfish. Many other rockfish species share this shallow depth preference . Although there are some deep water RCAs in Washington, these areas are designed to protect yelloweye (ce Table  and are Sebastes species).Black rockfish are just one of a suite of species that are protected by RCAs, and optimal reserve design for a suite of species becomes more complicated. In a review dispersal distances for a variety of taxa, Shanks et al.  found a a priori prediction for asymmetric dispersal in S. melanops and other rockfish species, as their PLD spans a seasonal switching from predominately downwelling and southerly winds that drive northward transport, to predominately upwelling and northerly winds that drive southward transport (OKs and Markel Our analysis was limited because it focused mainly on dispersal distance, and IBD theory assumed a well-mixed larval pool with uniform dispersal and ignored other important aspects of uncertainty. The assumption of a single dispersal distribution that applies across an entire marine landscape is unlikely to be justified given the asymmetry of oceanographic currents and the potential effect of larval behavior on dispersal (Cowen et al. We found that the average dispersal per generation in black rockfish was smaller than the distance between reserve networks that protect this species in Canada and Oregon. From the perspective of gene flow, the distance between reserves is probably sufficient to maintain genetic integrity of the species. But from the perspective of demographic connectivity, some areas of the network are unlikely to experience demographically relevant exchange. If additional reserves were to be implemented, we suggest that they be located in southern Oregon and on the Washington coast. Additional reserves in this area could improve connectivity among RCAs in both countries. Marine reserves play an important role in not only enhancing biodiversity (Russ and Alcala Although models have heuristic value in understanding the processes that determine the success of a reserve network, careful monitoring and evaluation will be required to evaluate RCAs as a tool for conservation and increasing fishery yields (Hilborn et al.


Models of genetic effects integrate the action of genes, regulatory regions and interactions among alleles across the genome. Such theoretical frameworks are critical for applied studies in at least two ways. First, discovering genetic networks with specific effects underlying traits in populations requires the development of models that implement those effects as parameters—adjusting the implementation of epistasis parameters in genetic models has for instance been a requirement for properly testing for epistasis in gene-mapping studies. Second, studying the properties and implications of models of genetic effects that involve complex genetic networks has proven to be valuable, whether those networks have been revealed for particular organisms or inferred to be of interest from theoretical works and simulations. Here I review the current state of development and recent applications of models of genetic effects. I focus on general models aiming to depict complex genotype-to-phenotype maps and on applications of them to networks of interacting loci. The phenotypes enter these models as genotypic values—the expected phenotype for each genotype—and the latter ones are the genetic effects. This connection between phenotypes and genetic effects enabled Fisher  to mergee.g. , 3).e.g. [It is noteworthy, however, that traditional applications of quantitative genetics were (and may keep on being) carried out using information about individual phenotypes and relatedness among individuals alone. Genotypes entail the pivotal ingredient of models of genetic effects but they were largely out of reach of researchers for a long time. The quantitative genetics theory was founded on individual genotypes, but initially treated them from a black-box perspective while allowing researchers to compute parameters of interest from visual observations. Two such parameters are heritability and the best linear unbiased predictor (BLUP) for breeding values. Restricted maximum likelihood (REML) has allowed estimation of heritabilities and BLUP under a wide range of experimental situations e.g. , 4)..e.g. [ 4e.g. [The advent of quantitative trait loci (QTL) analysis in the 1990s opened new fields for research. Many genetics research lines shifted focus to genes and genotypes as mapping phenotypes became available . The ulte.g. ). SimilaIn this communication, I review models of genetic effects with epistasis and current applications of them. I concentrate on models aiming to depict general genotype-to-phenotype maps and in applications of them to more or less particular cases. I begin by stressing the importance of implementing epistasis in genetic modelling. Next, I focus on models that are meant to analyse genetic properties at the population level, both from the perspective of traditional quantitative genetics and in the more recent context of QTL experiments. I draw special attention to the application of population-referenced genetic effects to genetic filtering. Then, I focus on genetic effects aiming to depict the genotype-to-phenotype map at the organism level. I recall why these models are necessary and review the terminology that has been used to express the duality between population- and individual-referenced genetic effects. I also develop an example to illustrate how individual-referenced genetic effects can be applied to model genetic networks entailing molecule interactions. Finally, I discuss current challenges in the development of models of genetic effects and their applications.The genetic architecture of a trait is the network of loci affecting that trait plus the effects of their alleles, both additive and interaction effects. The latter ones include within-locus interactions, like dominance and imprinting, and between- and among-locus interactions, which are pairwise and higher order epistasis, respectively. Sex-interaction effects may also occur and when different environments are considered, putative gene-by-environment interactions are included as well. Finally, pleiotropy should also be taken into account when considering more than one trait. It must be kept in mind that the genetic architecture of a trait cannot be detached from the population in which it is studied—which we will retake in the next section of this article. In other words, the loci affecting a trait in a population are the ones for which that population entails genetic variability affecting that trait. There could be, for instance, a gene holding great potential for functionally affecting a trait value that is fixed for one allele in a particular population. Such a gene could comprise an important component of the genetic architecture of the trait potentially, but it would show no effect on the trait in that population.e.g. [e.g. [Epistasis is known to also be able to conceal effects of genes—in this case, variable genes—in a population , 8. Signg. [e.g. , 12) andg. [e.g. -15. ThisRecently, epistasis has also been pointed out as an important issue to look at in genome-wide association studies (GWAS) -18. A mu1 and F2 hybrids are on average more vigorous than the midpoint of the two parental populations. Geneticists have tried to understand and exploit heterosis since long [e.g. [e.g. [e.g. [Midparent heterosis occurs when Fnce long  and keepng [e.g. , 26). Hong [e.g.  . Melchig. [e.g. -32). Sevg. [e.g. -36).et al., in preparation).Transgressive segregation (TS) is another deviation from what is expected under blending inheritance. It occurs when there are hybrid genotypic values lying outside the interval between the two parental populations. TS is frequent in both plant and animal populations  and it hHybrid incompatibilities occur when the offspring coming from the cross of two lines display reduced viability or fertility. This can also be described as TS on fitness since the offspring has lower fitness than any of the parental lines. Bateson, Dobzhansky and Muller -41 stresi.e. orthogonal, at the population in question. Population-referenced models are thus functions of the population frequencies and they would optimally be general in terms of both the genetic effects and the population frequencies they can implicate. Kempthorne [e.g. [As initially formalized by Fisher , 45, genmpthorne  and Cockmpthorne  took up ne [e.g. -51).e.g. [Assessing the behaviour of the components of the genetic variance under selection and drift has comprised a striking example of the use of those models. As a general principle for additive genetic architectures, the additive genetic variance should decrease and even eventually vanish in the face of drift . It is ae.g. -61). Thee.g. , 63). Ite.g. .e.g. [The theoretical study of the additive variance under selection has led to results similar to the aforementioned case of drift. Directional selection is initially assumed to remove genetic variation (e.g. ). Howevee.g. -70. As fe.g. -73. The e.g. , 75.e.g. [e.g. [Following Fisher’s statistical theory , 3, 45, g. [e.g. ). I findi.e. precluding overdominance and preserving the order of allele substitutions across backgrounds, at least in some loci) has been found to generate more realistic rates of epistatic variance [On the other hand, multilocus random genotype-to-phenotype maps can be built by assigning genotypic values to genotypes using a random distribution. It has very recently been shown that doing so generates rates of epistatic variance that are higher than the ones often measured in empirical situations . Therefovariance . It woulBeyond classical applications of the decomposition of the genetic variance to population and quantitative genetics, the advent of QTL analysis further fuelled the improvement of models of genetic effects. Orthogonal models of genetic effects have in general been found convenient for QTL analysis. I hereafter address three particular advantages of them.2 population and particuarly stressed this point. Estimation of genetic effects with orthogonal models will not be affected by the presence/absence of other genetic effects in the model, which for instance makes estimates of marginal effects of particular loci to remain constant regardless of whether putative epistatic effects with other loci are considered in our model or not . ThAs a second advantage of orthogonal models of genetic effects, we here elaborate on why and how to use them to perform genetic filtering of genotypic values in a QTL study. After a model selection procedure in a QTL mapping experiment, a genetic architecture will optimally have been chosen entailing the putative genetic factors that underlay the expression of a trait in a population. This includes not only a set of loci but also a set of genetic effects, namely (data permitting) additive, dominance, imprinting, and epistatic effects as well as gene-sex and gene-environment interactions. It is worth noting that not all possible effects of the selected loci will be included in the resulting genetic architecture. Some loci may for instance show dominance effects whereas others may not; some epistatic loci may even show no additive effects under a certain genetic background, as pointed out above. Similarly, some epistatic effects between/among the selected loci shall be found to be significant whereas others shall not.Focussing now on the genotypic values, they may be computed for each genotype as the mean phenotype of the individuals having that genotype. Two different problems may arise when doing so. On the one hand, the genotypic values computed this way will entail all kinds of genetic effects, including those that we may have discarded by a model selection procedure. Had for instance four loci been selected to significantly affect a trait, the genetic effects would entail up to four-order epistasis. This can be due to four order epistasis to actually occur—in which case we will most surely not be able to prove statistically—and/or just because our genotypic values are affected by sampling errors that show up, amongst other things, as all levels of genetic interaction. Therefore, researchers may often be interested in filtering out those genetic effects from our genotypic values.4=81 and the expected number of individuals of each of the 22 possible complete homozygotes in an F2 population of 800 individuals is just 3.125—hence, some empty genotypic classes will most surely occur. Thus, it will often not be possible to compute all genotypic values directly from the phenotypic data. Opportunely, it will still be possible to compute them using genetic modelling from the set of genetic effects picked up by the model selection procedure.On the other hand, above it has been assumed that it will be possible to compute the mean phenotype of each group of individuals with a particular genotype, that is, that in the data there are individuals with all possible genotypes. With a network of just four loci and two alleles per locus, the number of possible genotypes is 31). By doing so all genotypic values will be obtained entailing all genetic effects considered, and only those. However, this procedure will not make complete sense when the genetic effects that have been selected into our model change drastically after removing the ones discarded in the model selection procedure. Orthogonal models of genetic effects thus warrant genetic filtering to be performed in a consistent manner. As an example, applying genetic filtering with the natural and orthogonal interactions (NOIA) model, Le Rouzic et al. [Indeed, genetic filtering consists in using a set of genetic effects selected for a set of loci of interest to (re-)compute the genotype-to-phenotype map  as long as orthogonal models of genetic effects have been described for both populations . Overall∞ model because they can also be interpreted population-wise—they are orthogonal at an ideal population resulting after infinite generations of brother-sister mating [∞ population does not allow estimation of dominance effects—nor any kind of epistatic effects involving dominance, like additive-by-dominance effects.We have just discussed why models of genetic effects based on average effects of allele substitutions over populations are the core of quantitative genetics theory and we have in particular mentioned that, in QTL analyses, obtaining genotypic values that are coherent with a particular genetic architecture is one of the advantages we can gain from those models. It is at this point worth noting that genotypic values are however assumingly independent of the genotypic frequencies of the population in which they are measured. Consequently, it could be considered that providing genetic effects also in a population-independent way could enable researchers to attain further interpretation. Actually, Fisher  already r mating , 82, 83.In any case, the early attempts to map QTL  already Hansen and Wagner  further The multilinear model implements epistasis in an analytical tractable manner and it has proven to be useful for analyzing the potential evolutionary properties of the genetic architecture of a trait. In particular, Hansen and colleagues , 92 haveThe multilinear model has also been applied to studies involving stabilizing selection. The expected equilibrium genetic variance under mutation-selection-drift balance has been found to decrease with multilinear epistasis , 96. On e.g. not necessarily multilinear—epistasis. The traditional, statistical models cannot be accommodated to do this because their reference points are intercepts of regressions weighted by the population frequencies—hence, setting the population frequencies to use one individual genotype as intercept is equivalent to trying to perform a regression to a single point. Thus, a new set of formulae had to be developed for this task. For the biallelic case, Álvarez-Castro and Carlborg [1 shows a summary of the properties of several models of genetic effects.Functional genetic effects can also be modelled as natural effects of allele substitutions from individual genotypes in a theoretical framework with completely general—Carlborg  modelledCarlborg  to make Carlborg  connecteCarlborg  extendedet al. [Free software (noia ) has beeet al.  have obte.g. [One of the premises that enormously smoothes the progress of a field is to establish a common, unambiguous terminology. Above, we have on the one hand labelled the population-referenced models of genetic effects as statistical models, which describe the genotype-to-phenotype map orthogonally as a set of averaged allele substitutions from the mean of a population. On the other hand, we have labelled as functional models the ones describing the genotype-to-phenotype maps as sets of natural allele substitutions from the reference of individual genotypes. Indeed, epistasis raised a debate concerning the different approaches to formulating genetic effects e.g. , 90, 91), 91e.g. e.g. , 99.e.g. [e.g. [Both the above mentioned physiological  and multe.g. , 82, 91)e.g.  and evene.g. . More ree.g. . Indeed,g. [e.g. ) and keeg. [e.g. -111.∞ type models [e.g. [∞ model has been regarded both as a sort of arbitrary, population-independent model and as a population-dependent one, as explained above, which makes it confusing to use it as a label for one of these two types.The duality between formulations of genetic effects has also been expressed as Cockerham type versus Fe models . Althouge models  developme models  and on te models , 46 (seels [e.g. ). More ie.g. [sensu Hansen and Wagner [To sum up, a huge gap seems to be perceived between some self-proclaimed functional formulations of genetic effects and the modelling of the action of molecules at the physiological level . We.g.  deemed ed Wagner .1 and B1. Next, that population splits into two isolated populations where new alleles at the two loci, A2 and B2, arise by mutation and get eventually fixed—one allele in each population. Then, the two isolated populations merge and the “2” alleles of loci A and B, which were compatible with the “1” alleles, happen not to be completely compatible with each other. Two possible types of molecule interaction are considered that can underpin this lack of compatibility.Let us here consider a case of hybrid incompatibilities in accordance with the BDM model, with two loci, A and B, which code for two different protein monomers that bind into a functional enzyme dimmer. It will be assumed that one population is initially fixed for alleles “1” at both loci, A2 and B2 lead to enzyme monomers that bind easily but lead to non-functional enzyme dimmers—case C1. In this case, the concurrence of alleles A2 and B2 in one individual leads to a disadvantage due to a waste of resources in the production of non-functional enzyme dimmers. Second, consider that the monomers produced by alleles A2 and B2 bind difficultly—case C2. In this case, a disadvantage arises whenever an allele “2” is present at one locus only—either A2 or B2 but not both of them together—due to a waste of resources in the production of a certain amount of useless monomers.First, let us consider the possibility that alleles A2). Both charts 2A and 2B show that, in accordance with the assumptions, the populations in which the mutations appear and eventually get fixed  keep the same trait value as the ancestral population (A1A1B1B1). By chosing that trait value to be one, it can be regarded as a relative fitness value. In Figs. (2A and 2B) it can also be observed that some of the genotypes resulting from the hybridization of the two populations with fixed mutations get penalized phenotypes . The only difference between the two cases is the double heterozygote, which is penalized when considering the first case of molecule interaction, C1 , but not when considering the second one, C2 , in accordance with our assumptions. It is thus obvious that the two cases may have different evolutionary outcomes, since an F1 population would be penalized in one of the cases but not in the other one.These two cases are illustrated in Fig.  to one . Thus, this parameter takes a value of ½ in the examples of Fig. (2). Using the second parameter, e2, it is possible to consider the two extreme cases, C1 and C2 , plus all intermediate instances. Let us now consider a population at equilibrium in which the frequencies of alleles coming from the population where B2 got fixed are half of the frequencies of the alleles coming from the population where A2 got fixed (Table 3). The statistical genetic effects for that population can easily be obtained using the transformation tool of NOIA [2.Sets of genetic effects that reflect the two extreme cases considered, C1 and C2, and also all intermediate cases, are shown in Table IA model . In the  of NOIA  and they2). I have used a functional formulation from an appropriate reference because this can be extremely helpful in modelling genetic architectures in general, and situations expressed in terms of molecule interactions in particular. Second, in spite of having used a particular (and convenient) reference point to formulate our model, we are not restricted to that choice. To the contrary, having obtained a particular formulation of genetic effects from a particular reference point, the transformation tool of the NOIA model allows easy transformation of these genetic effects into the ones fitting to a different formulation that could be convenient for the analysis—in this example, the statistical formulation at the mean of the population shown in Table 3. Finally, the expressions of the genetic effects obtained in this example could now be used to test whether genetic effects of pairs of loci detected through QTL analyses or GWAS could fit to the kind of interactions that I have considered in this model.I have developed this example in order to stress three points. First, note that the functional formulation from the reference of the ancestral genotype leads to simpler expressions for the genetic effects than the statistical formulation does .Several models that were initially proposed for restricted situations have later on been generalized in different ways. The FFisher’s , 45 popuockerham  introducockerham , 46 deveockerham  (see Tab∞ model (see [2 and related text for an example). The different formulations of genetic effects are required to be interchangeable because different ways of measuring genetic effects are required for addressing different particular issues and many issues are always involved in the resolution of a broad research query [Also Fisher’s  initial del (see )—eventuadel  set of indexes was chosen to develop a model to test for imprinting. That such a model could also have been built from Cheverud and Routman’s [et al. [Extending such a unifying framework to further genetic architectures and population facts remains a challenge. Concerning genetic effects, Wolf et al.  have recoutman’s  physiolo [et al.  as a paret al. [Concerning now population facts, several works have addressed the implementation of linkage disequilibrium (LD) in models of genetic effects. Cockerham  already et al.  extendedet al.  epistatiOverall, considering epistasis largely increases the conceptual and mathematical complexity of models of genetic effects. However, this turns out to be an inescapable endeavour because of the combination of two facts. First, as pointed out throughout this communication, the effects of epistasis on response to selection and other agents are in general far from negligible. Second, evidence has been gathered that points to epistasis as a ubiquitous phenomenon in natural and experimental populations  and even to detect marginal effects with relatively small contributions to the trait variance. The arrival of routine QTL analysis about two decades ago might have made some researchers believe that mechanically disentangling biological systems would be just around the corner and, particularly, that genetic applications to human disease and to livestock production would spring out naturally (see e.g. ). But ing. [e.g. ). Currenet al. [sensu Dekkers [In the meantime, from the pioneering developments by Meuwissen et al. , genomicet al. . It is het al.  have impet al. , 138—sup Dekkers ) the exp


The narrow genetic base of cultivars coupled with low utilization of genetic resources are the major factors limiting grain legume production and productivity globally. Exploitation of new and diverse sources of variation is needed for the genetic enhancement of grain legumes. Wild relatives with enhanced levels of resistance/tolerance to multiple stresses provide important sources of genetic diversity for crop improvement. However, their exploitation for cultivar improvement is limited by cross-incompatibility barriers and linkage drags. Pre-breeding provides a unique opportunity, through the introgression of desirable genes from wild germplasm into genetic backgrounds readily used by the breeders with minimum linkage drag, to overcome this. Pre-breeding activities using promising landraces, wild relatives, and popular cultivars have been initiated at International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) to develop new gene pools in chickpea, pigeonpea, and groundnut with a high frequency of useful genes, wider adaptability, and a broad genetic base. The availability of molecular markers will greatly assist in reducing linkage drags and increasing the efficiency of introgression in pre-breeding programs. Grain legumes are second in importance to human and animal diets after cereals and occupy an important place in the world's food and nutrition economy. Being the primary source of high quality dietary protein, grain legumes are important in alleviating protein deficiency and malnutrition prevailing among poor people in developing countries, as well as contributing significantly to global food and nutritional security. Further, grain legumes provide high quality nutritious fodder for animal consumption. In addition to food and fodder, the nitrogen fixing capacity of grain legumes decreases the need for direct application of N-fertilizers and makes them an important component in cropping systems for improving and sustaining soil fertility and texture , pigeonpea (Cajanus cajan Millsp.), and groundnut (Arachis hypogaea L.).From the nutritional viewpoint, grain legumes are usually deficient in sulphur-containing amino acids-methionine, cysteine and tryptophan, but are rich in other essential amino acid, lysine. The reverse is true with cereals , rust and aflatoxin contamination in groundnut are the most important and widely distributed diseases affecting yield and quality. Besides, pod borer (Helicoverpa armigera L.), drought, heat, salinity, water-logging are the other important stresses potentially limiting their productivity worldwide. Grain legumes, including chickpea, pigeonpea and groundnut have a narrow genetic base, due to the bottlenecks associated with their evolution and domestication, as well as due to the replacement of locally adapted crop landraces by the genetically advanced modern varieties. Low grain legume productivity due to biotic/abiotic stresses coupled with limited genetic variation in the cultivated gene pool necessitates the identification and utilization of diverse germplasm sources to develop new high-yielding cultivars with a broad genetic base.Grain legumes are cultivated mostly in marginal lands under rainfed conditions, with low and unstable productivity ; the International Centre for Agricultural Research in Dry Areas (ICARDA), Aleppo, Syria ; the United States Department of Agriculture (USDA), USA (9964 Arachis accessions); and the Directorate of Groundnut Research (DGR), Junagadh, India (8934 Arachis accessions) (updated report based on species in http://apps3.fao.org/wiews/germplasm_query.htm?i_l=EN).Plant genetic resources are reservoirs of natural genetic variation and provide raw material for crop improvement programs. About 7.4 million germplasm accessions of different crops have been collected and/or assembled and conserved in over 1750 ide FAO,  and the http://www.nordgen.org/sgsv/index.php?app=data_unit&unit= sgsv_by_species&PHPSESSID=1hoc3n9kkdmng79o5vlqmsgs25).At present, emphasis is on conserving accessions safely with duplication both within and outside the CGIAR. At global level, the Svalbard Global Seed Vault has been established in the Norwegian island of Spitsbergen to store seed samples of a wide variety of plant species in an underground cavern. This seed vault will provide an insurance against the loss of seeds in genebanks, as well as a refuge for seeds in the case of large-scale regional or global crises. Over the last 5 years, >780,000 seed samples belonging to about 840 genera have been deposited in this vault of which about 170,000 accessions belong to 19 legume genera/species  have been utilized in crop improvement programs such as in wheat  that cannot be used directly in breeding populations (exotic/wild species), and to transfer these traits into well-adapted genetic backgrounds (recipients) resulting in the development of an intermediate set of material which can be used readily by the plant breeders in specific breeding programmes to develop new varieties with a broad genetic base Figure .Enormous efforts are needed to evaluate germplasm for traits of economic importance, as well as to screen for biotic and abiotic stress related traits through reliable and standardized techniques for identifying potential donors. Owing to the large size of germplasm collections, this becomes a costly and resource-demanding task. Following the concept of Frankel and Brown  for sampCicer, Cajanus, and Arachis have been extensively screened and several of them were reported to have very high level of resistance/tolerance to various stresses. Among wild Cicer species, C. bijugum, C. judaicum, and C. pinnatifidum are the most important sources having the highest levels of resistance/tolerance to multiple stresses  have also been identified in C. scarabaeoides, C. acutifolius, and C. platycarpus  identification of promising donor with good expression of the trait; (2) type of germplasm (Cultivated/cross-compatible wild type/cross-incompatible wild type); and (3) agronomic performance of the donors. New and diverse sources of variation for agronomic and nutrition-related traits and resistant/tolerant sources for biotic/abiotic stresses are now available both in cultivated and wild type germplasm and can be utilized to develop new pre-breeding populations having greater variability for various traits. In the past, a few promising wild type accessions have been utilized by some researchers for the improvement of chickpea, pigeonpea and groundnut and are given hereunder.Chickpea. Of the eight annual wild Cicer species, only C. reticulatum is readily crossable with cultivated chickpea resulting in fertile hybrid. The exploitation of the remaining seven annual wild Cicer species requires specialized techniques such as the application of growth hormones, embryo rescue, ovule culture, and tissue culture techniques . The CMS systems have been developed with cytoplasm derived from wild Cajanus species namely C. sericeus  with a BB genome species, A. batizocoi, followed by colchicine treatment of the sterile triploid to produce fertile hexaploid, TxAG-6  resistance from A. cardenasii   in a hybridization program, several progenies were selected which took 8–21 days less to flower, 6–33 days less to mature, with 20–103% larger seeds, and 97–217% greater seed yield than respective cultivated (C. arietinum) parent   × A. batizocoi] with very low 100-seed weight (~12 g) and poor pod yield (2–5 g plant−1), breeding lines have been developed. These have higher 100-seed weight (up to 95 g) compared to 32 g of control cultivar TMV 2 and 23–68% more pod yields than TMV 2 (3343 kg ha−1). They also have 10–50% more pod yield than the high yielding control cultivar ICGV 91114   and Ascochyta blight in chickpea and Phytopthora and Alternaria blight in pigeonpea. Frequent droughts coupled with high temperature during flowering to pod filling stage would result in heavy incidence of dry root rot whereas cooler temperature with high humidity are associated with high incidence of Ascochyta blight in chickpea , which provides flexibility to genotype any number of samples with any number of SNPs. Thus, ICRISAT has developed KASPar assays for 2005 SNPs in chickpea , VeraCode assays have been developed for 96-plex and 48-plex SNP sets for chickpea and pigeonpea, respectively. Similarly, an alternative genotyping assay (KASPar assay) has been developed by KBiosciences , and aflatoxin in groundnut. Among the three SAT legumes, cultivated groundnut has the major need for pre-breeding activities due to a narrow gene pool. The tetraploidization event occurred during the evolution of cultivated groundnut has restricted the exchange of genomic regions between wild relatives and cultivated groups. Fortunately, wide crosses resulted in the development of highly diverse autotetraploids and allotetraploids. Simpson et al. . Phenotyping and genotyping of these (GWI and AB) populations should help in identifying the lines with enhanced genetic base and minimum linkage drag for use in future breeding programs, as well as to find out the markers associated with traits of interest.Due to the limited genetic variability available in cultivated germplasm, pre-breeding is gaining importance in most crop improvement programs including wheat Valkoun, , maize , and comn et al.  and Fáven et al.  reportedThe availability of genomic resources has provided a solution to monitor allele/genome-specific introgression in GWI lines, along with identification of marker-trait association using AB populations. Genomics tools provide efficient tracking for desired and non-desired alien alleles among breeding lines using AB-QTL based breeding approach. Thus, integration of genomic tools with conventional breeding approaches promises to enrich the cultivated gene pool through genomics-assisted pre-breeding. These efforts will help in harnessing the rich diversity of wild relatives possessing superior alleles lost either during domestication or breeding, as well as for exploiting novel alleles. In AB-QTL based breeding approach, genotypic and phenotypic data generated on AB populations are used for identification of QTLs for several economically important agronomic traits. In addition, it also provides an opportunity to select for QTL-near isogenic lines (NILs) in a short time span along with the development of introgression lines with desired features for use in hybridization programmes.C. cajanifolius while ICPW 12 to C. acutifolius species. From the above crosses, BC2F3 seeds have been generated for multilocation phenotyping and selection. In groundnut, using two of the above mentioned synthetics, two AB-QTL mapping populations from the crosses between ICGV 91114 (cultivated) and ISATGR 1212 ; and between ICGV 87846 (cultivated) and ISATGR 265-5A  have been developed at ICRISAT. Considerable variability has been observed in these two populations for pod and seed traits  lines carrying the alien genomic segments with maximum recurrent parent genomic regions provided optimal distribution of the synthetic genome introgressions  for conducting AB-QTL analysis and their subsequent use in AB-breeding. Among wild relatives used, ICPW 29 belongs to desi chickpea. Twenty-eight two-way, 14 four-way and seven eight-way crosses were made to develop this MAGIC population. The best progenies from each generation are being used in various national breeding programs by the breeders. Currently, 1200 F6 are in the field (crop season 2012-2013). A total of 44 pre-breeding populations have also been developed.In case of chickpea multi-parent advanced generation intercross (MAGIC) populations are being developed to enhance the genetic base. Eight elite lines/cultivars  were selected from Ethiopia, Kenya and India for development of a MAGIC population for The availability of large number of markers and cost-effective genotyping systems in recent years has ensured that linkage drag has been limited through stringent background selection and monitoring of the wild genomic regions. Hence, keeping in view the low genetic diversity in all the three legume crops, deployment of genomic-assisted pre-breeding is needed for accelerated and efficient enrichment of the cultivated gene pool. This should lead to the incorporation of desired/favorable alleles from wild relatives, increasing genetic gain and ultimately improving yield under adverse conditions and diverse cultural practices.For grain legume improvement, sufficient genetic diversity exists in the form of landraces and wild relatives, which carry several useful genes for cultivar improvement. However, utilization of these resources in breeding programs is time-consuming and resource-demanding. To overcome this, pre-breeding activities should be initiated to generate new genetic variability using promising landraces and wild relatives for use by the breeders in crop improvement programs. Pre-breeding should focus on the continuous supply of useful variability into the breeding pipeline to develop new high-yielding cultivars with a broad genetic base; pre-breeding should not focus on increasing yield. Though pre-breeding is useful to enrich the primary gene pool for cultivar improvement, it is a time-consuming and difficult affair as well. As mentioned by Nass and Paterniani , promisiThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Ecklonia radiata. There was no genetic differentiation of kelp between estuaries and the open coast and the presence of estuaries did not increase genetic differentiation among open coast populations. Similarly, there were no differences in level of inbreeding or genetic diversity between estuarine and open coast populations. The presence of large estuaries along rocky coastlines does not appear to influence genetic structure of this kelp and factors other than physical heterogeneity of habitat are likely more important determinants of regional connectivity. Marine reserves are currently lacking in this bioregion and may be designated in the future. Knowledge of the factors that influence important habitat forming organisms such as kelp contribute to informed and effective marine protected area design and conservation initiatives to maintain resilience of important marine habitats.With marine protected areas being established worldwide there is a pressing need to understand how the physical setting in which these areas are placed influences patterns of dispersal and connectivity of important marine organisms. This is particularly critical for dynamic and complex nearshore marine environments where patterns of genetic structure of organisms are often chaotic and uncoupled from broad scale physical processes. This study determines the influence of habitat heterogeneity (presence of estuaries) on patterns of genetic structure and connectivity of the common kelp,  Knowledge of the physical and biological factors that structure patterns of connectivity and dispersal of nearshore marine organisms is critical for effective marine conservation Marine Protected Areas (MPAs) are commonly designated in nearshore marine environments which are inherently physically dynamic and spatially complex. Further, individual MPAs are designed on local spatial scales where dispersal and connectivity of marine organisms is rarely structured by the broad scale physical processes traditionally known to impart structure along entire coastlines Nearshore marine environments are often characterised by rocky reefs that are interspersed with habitat discontinuities and potential barriers to dispersal such as sandy beaches, estuaries and bays. Such heterogeneities of habitat have the potential to restrict dispersal and gene flow of rocky reef organisms and create complex patterns of genetic structure among populations. The presence of estuaries and beaches has been shown to increase genetic differentiation among rocky coast populations of intertidal Ecklonia radiata. Ecklonia radiata (C. Agardh) J. Agardh is the dominant form of biogenic habitat on Australia's temperate reefs E. radiata mostly inhabits open coast sites and is the most abundant macroalga on temperate rocky reefs of temperate Australia. However, in estuarine habitats this species can live on the small amount of available hard substrata and in the upper reaches of estuaries where suitable rocky substrata is often lacking, it is commonly found growing on artificial structures such as pontoons, pilings and breakwalls. E. radiata has a typical Laminarian alternation of generations life history strategy with conspicuous, macroscopic sporophytes  alternating with microscopic gametophytes , Broken Bay , Port Jackson  and Botany Bay   and positioned to the north and south of the entrance to each estuary  , Table 1 estuary . All est estuary  E. radiaHo) and expected heterozygosities (He) were generated using GENETIX ver. 4.04 FST and FIS estimates FST estimates were also estimated between sites. A sequential Bonferroni correction F statistics. A stepwise mutation model was not assumed and P<0.05 was used. We identified potential first generation migrants using GeneClass 2 P<0.01. Tests of isolation by distance for each estuary and surrounding open coast sites were done via Mantel tests using the program IBD Prior to conducting statistical analyses, data were checked for typographical and genotyping errors using Microchecker P>0.05, FIS estimates were mostly non-significant indicating random mating and there were no apparent patterns in the magnitude of FIS estimates between open coast and estuarine sites  and among open coast sites . Similarly, comparisons of estimates of FST for each individual estuary were similar to nearby open coast sites . This result may be a reflection of low replication as graphical representation of data  and south  were greater than for the region in the present study (FST = 0.023 for open coast sites).Estimates of mean pairwise P<0.01). These represented 9% of all individuals from estuaries and 13% of individuals from the open coast. Individuals growing in estuaries that were identified as migrants were mostly (62%) assigned to populations of origin that were open coast sites rather than estuarine sites. Conversely, individuals growing on the open coast that were identified as migrants were mostly assigned to other open coastal sites (66%).Tests for first generation migrants revealed that 11% of all individuals are likely migrants from another site  and populations that would otherwise not exist. In addition, the unique environmental conditions  within estuaries may extend or alter phenological factors (such as timing of reproduction) relative to open coast populations Interestingly, there was generally no genetic differentiation between open coast and estuarine populations of this species indicating that small populations of kelp within estuaries are not reproductively isolated and frequently exchange propagules with surrounding open coast populations. Indeed, large volumes of kelp from open coast habitats are often washed up inside estuaries after storms Despite weak genetic differentiation, spatial patterns of genetic structure appeared to be weakly associated with type of estuary with trends  for positive relationships between genetic structure and geographic distance in wave- compared to tide-dominated estuaries. Hydrodynamics within these estuaries and water exchange between the estuaries and nearby open coast sites are vastly different between these different estuarine morphologies and may explain these differences in spatial genetic structuring. In wave dominated estuaries, waves and water motion originating from the open ocean are the dominant structuring influences and this may promote one-way, linear patterns of dispersal. In contrast, tide-dominated estuaries are structured by flushing of tidal currents as water moves back and forth and this may enhance mixing of propagules and prevent the formation of spatial patterns of genetic structure. Although this pattern was only weakly demonstrated here, consideration of the influence of estuarine morphology and associated hydrodynamics on genetic structure may be important for designing MPAs (particularly those in estuarine areas).E. radiata is cleared from small patches of established forest by storms, grazing or other anthropogenic impacts may result in genetic patchiness because migrants may only be able to colonise bare patches within existing forests or rare areas of unoccupied rocky reef Together, these results suggest that the presence of estuaries is not a barrier to dispersal in kelp, either among open coast populations or between estuarine and open coast habitats. Indeed, estuaries may enhance connectivity via provision of additional habitat. Moreover, it is likely that the strength and complex nature of prevailing currents  combined with the multiple These results are particularly pertinent given that the bioregion (Hawkesbury) studied here is the only one in NSW without a marine park. It also represents the most urbanised and developed coastline in Australia and is home to Australia's largest city (Sydney). Protection of marine habitats in this region is currently achieved via numerous small, shallow Aquatic Reserves where marine macrophytes are protected. MPAs may be designated in this region in the future given the need to adequately protect biodiversity along the NSW coastline. Incorporating estuarine areas and open coast into MPA designs and considering linkages among these important habitats, as is currently done in other NSW MPAs, will be key for designing future MPAs in this bioregion. Nearshore and estuarine habitats are inherently complex with a suite of physical and biological factors determining dispersal and connectivity of marine organisms. Teasing apart the relative influence of such factors is important for effective MPA design and to ensure that species and habitats are protected into the future.


Stegodyphus lineatus. This species experiences relatively high levels of inbreeding as a result of its spatial population structure, philopatry and limited male mating dispersal. Polyandry may provide an opportunity for post mating inbreeding avoidance that reduces the risk of genetic incompatibilities arising from incestuous matings. However, multiple mating carries direct fitness costs to females suggesting that genetic benefits must be substantial to counter direct costs.Polyandry is widespread throughout the animal kingdom. In the absence of direct benefits of mating with different males, the underlying basis for polyandry is enigmatic because it can carry considerable costs such as elevated exposure to sexual diseases, physical injury or other direct fitness costs. Such costs may be balanced by indirect genetic benefits to the offspring of polyandrous females. We investigated polyandry and patterns of parentage in the spider Genetic parentage analyses in two populations from Israel and a Greek island, showed mixed-brood parentage in approximately 50% of the broods. The number of fathers ranged from 1–2 indicating low levels of multiple parentage and there was no evidence for paternity bias in mixed-broods from both populations. Microsatellite loci variation suggested limited genetic variation within populations, especially in the Greek island population. Relatedness estimates among females in the maternal generation and potentially interacting individuals were substantial indicating full-sib and half-sib relationships.S. lineatus is thus unlikely to be maintained through adaptive female choice.Three lines of evidence indicate limited potential to obtain substantial genetic benefits in the form of reduced inbreeding. The relatively low frequency of multiple parentage together with low genetic variation among potential mates and the elevated risk of mating among related individuals as corroborated by our genetic data suggest that there are limited actual outbreeding opportunities for polyandrous females. Polyandry in  Polyandry - female mating with multiple males - may carry considerable costs of mating from elevated exposure to predation, sexual diseases or physical injury Natural mating rates are poorly known in invertebrates and polyandrous mating systems are mostly studied in laboratory settings. Moreover, the number of males that a female mates with does not necessarily represent the number of sires that gain a share in paternity, as post-copulatory sexual selection may cause fertilization biases and differential patterns of parentage within a brood. Data on patterns of parentage in natural populations may provide insights to the potential for sperm competition and sperm selection as forces underlying the evolution of polyandry Stegodyphus lineatus (Araneae: Eresidae) to investigate whether polyandry and mixed paternity is part of the reproductive biology of this species, and to assess the potential for polyandrous females to acquire genetic benefits in the form of reduced risk of inbreeding. Stegodyphus lineatus are philopatric, which results in natural aggregations of related individuals In this study, we performed genetic parentage analysis in two natural populations of the spider S. lineatus (from Israel and Greece) by genotyping wild-caught females and their broods using microsatellite markers For polyandry to be adaptive in the face of these substantial direct costs, the magnitude of indirect genetic benefits should be large Stegodyphus lineatus (Latreille 1817) is a web-building spider with an annual life cycle occupying arid and semiarid habitats around the Mediterranean basin. Individuals occupy silk retreats built on shrubs, which are abandoned by adult males that start wandering in search of sedentary females. The mating season starts in March and eggs are laid from April to June We collected spiderlings at a pre-dispersal stage from their natal nest together with (where possible) remnants of the eaten mother (i.e. the exoskeleton). Sampling occurred from two geographically separated populations during summer 2006, from the Negev Desert (Israel) and at the island of Karpathos (Greece). Spiders are not evenly distributed within habitats, they are found in patches or sites where a number of individuals are in close proximity of each other reflecting philopatric dispersal 3 of size  was dried at 60°C for 30 minutes, homogenized and 200 µl of 20% Chelex ® resin solution (Bio-Rad) was added. Samples were vortexed briefly, boiled at 100°C for 10–12 minutes in a heating block and centrifuged for 3 minutes at 13000 rpm. Samples were genotyped using five nuclear polymorphic microsatellite loci We collected a total of 10 broods with a mean number of offspring per brood (±SE) of 46.8 (±8.6) from 2 sites in the Negev Desert; 7 broods from Sede Boker (SB-) and 3 from Lehavim (L-) . We collected a total of 40 broods with a mean number of offspring of 25.17 (±2.5) from 4 sites in Karpathos; 12 broods in site 1 (S1-), 15 in site 2 (S2-), 7 in site 3 (S3-) and 6 in site 4 (S4-) . All samples were preserved in 96% ethanol. DNA was extracted using a Chelex protocol The study did not require any specific permit and did not involve endangered or protected species.Prior to analyses, genotypes of offspring were screened and loci that showed absence of a maternal allele from the known mother were removed from the analysis. Presence and frequency of null alleles was tested for each locus using the software MICRO-CHECKER We adopted three different methods to obtain estimates of parentage within broods and to compare estimates among methods, allele count, GERUD 2.0 o), expected (He) heterozygosity and deviations from Hardy-Weinberg equilibrium. We applied FSTAT Fis, the inbreeding coefficient. Calculations were performed by random sampling of one offspring from each nest. Relatedness estimates among the parental and offspring generation within each site of collection were obtained by determining the mean of all pair wise relatedness values among adult females and among offspring of different broods (one offspring randomly sampled by each nest) separately by using the software ML-RELATE Levels of genetic variation of sires and inbreeding were calculated within each population using GENEPOP 3.3 2 = 25.52; d.f. = 10; P = 0.0046) due to heterozygote deficiency for locus R5N  and in the Greek population  due to heterozygote deficiency for loci R4 and R5N . Fis inbreeding coefficient values did not differ significantly among populations and were significantly greater than zero when estimated over all loci  indicating non-random mating within the population . Overall, observed heterozygosity varied considerably among loci (from 0 to 0.65). No linkage disequilibrium was detected between any loci (P>0.05) in any population. Population genetic variables are showed in pulation .The summary of results for number of fathers estimated with allele count, GERUD 2.0 and PARENTAGE 1.0 are shown in Relatedness values among adult females and among offspring of different broods are shown in We note that while the model system used here is particularly useful for studying indirect selection for genetically dissimilar males, it also raises challenges in terms of low marker polymorphism due to a history of inbreeding in natural populations Stegodyphus lineatus. We estimated parentage using three different methods and found differences among the methods. Allele count and GERUD, produced the lowest estimates of multiple parentage. These are conservative methods that may lead to an underestimation of number of fathers when different males may carry the same allele. This is likely to be the case for this species because of low population genetic variation Our data show evidence for polyandry and mixed paternity broods in two natural populations of the spider S. lineatus populations The molecular genetic estimates of multiple paternity may be conservative since genetic variation within populations was low, especially in the Greek island population, which is consistent with previous findings S. lineatus populations experience inbreeding which may depress fitness Given the direct costs of re-mating documented in this species (see references above), adaptations such as post-copulatory fertilization biases would allow polyandrous females to ameliorate these costs through indirect genetic benefits to their offspring S. lineatus, it is possible that adequate genetic variation is not present, as genetic variation was low . Due to the low genetic variability of mating partners the potential for polyandrous females to gain indirect genetic benefits through post-mating sexual selection may be low, suggesting that genetic benefits in the form of reduced inbreeding are unlikely to compensate entirely for the direct costs of re-mating. Polyandry in Table S1Skew analyses. PARENTAGE 1.0 estimates the contribution of each father to the offspring. For each iteration we calculated paternity skew as the sum of squared proportion of offspring assigned to each father . For each number of fathers in the estimated range we tested the average skew against a simulated distribution. Simulations were done using the actual number of offspring in the given nest and equal probability for each father to sire the offspring. Each father was assigned 1 offspring before simulations to avoid fathers siring no offspring.(DOC)Click here for additional data file.