After one generation (9-years), this would be equivalent to 14.23 mm and 21.97 kg/m3 for DBH and wood density respectively. Deploying clones of the selected individuals always resulted in higher additional genetic gain than deploying progeny/seedlings. Deploying genetic material selected from genomic selection with top-grafting for early coning appeared to be the best option. Application of genomic selection to conifer breeding programs, combined with deployment tools such as top-grafting and embryogenesis are powerful tools to speed the delivery of genetic gain to the forest.Genomic selection is a proven technology in animal and plant breeding to accelerate genetic gain, but as yet is to be fully realised in forest tree breeding. This paper examines, through stochastic simulation, the potential benefits of genomic selection (GS) over forward selection (FS) in a typical conifer breeding program. Methods of speeding the deployment of selected material were also considered, including top-grafting onto mature seed orchard ortets, using additional replicates of clones in archives for crossing, and embryogenesis and clonal propagation. Genetic gain per generation was found to increase considerably when the size of the training population was larger (800 c.f. 3000 clones), or when the heritability was higher (0.2 c.f. 0.5). The largest genetic gain, of 24% was achieved where large training populations (3000 clones) and high heritability traits (0.5) were combined. The accuracy of genomic breeding values (GEBVs) increased with the increase in the number of clones in the training population, the heritability of the trait and the density of the SNP markers. Calculated accuracies of simulated GEBVs and genetic gain per unit of time suggested that 2000 clones in the training population is the minimum size for effective genomic selection for conifers. Compared with forward selection, genomic selection with 2000 clones in the training population, and a 60K SNP panel, an increase of 1.58 mm per year in diameter-at-breast-height (DBH) and 2.44 kg/m Many animal and plant breeders now use genomic selection routinely , 2, 3. IIn practice, the accuracy of genomic selection directly depends on four factors , 11: 1) Genomic tools have been used in research for a number of forestry species now , 19–28. Abies, Larix, Picea, Pinus, and Pseudotsuga [Clonal forestry is promising to provide quality planting material to forest growers in the shortest time. It is defined as the commercial production and deployment of plants of field-tested individual genotypes of forest tree species . Clonal udotsuga  and contudotsuga , 33–35. udotsuga  and reduudotsuga , 37. Cloudotsuga , 39 or budotsuga . SomaticThis paper will simulate the combination of breeding, genomics and deployment methods in order to critically examine the potential benefits of genomic selection in a typical conifer breeding program. Key variables tested include training population sizes, heritability and length of breeding cycles. Genetic gains will be estimated for combinations of variables for tree breeding and genomics approaches. Benefits from the different deployment options employed for the different breeding approaches will be important for the realisation of genetic gain . We willWe consider two primary conifer breeding approaches; forward selection with clonal testing FS;  and forwOne time-saving option for shorter breeding cycle relevant to the FS approach, is the establishment of a clonal archive (CA) at the time of progeny testing, where ramets of each clone under progeny testing are also kept in an archive for crossing on a site known to promote good coning. Clones are therefore available with cones for crossing right after selection is made. With this clonal archive option, step 4 is not needed  and the Two deployment pathways were simulated to ensure the best selections reach the forest: deploying progeny or deploying clones . The firThe second option is to deploy clones of selected trees, where immature seeds (embryos) of all progeny are collected during step 1 of the breeding cycle as described in Figs There are eight combinations for deployment between the four breeding and selection schemes  and two generic deployment options (via seed or clones). Three steps were used to simulate a population for conducting forward selection or genomic selection and implemented using the QMSim software  based onThe first step created a base population that was named “Historical Generations”. This step consisted of running 500 generations with 300 female parents and 300 male parents. Random mating was used to undertake a random union of gametes. No selections were made in this step. This step had a constant population size of 1000. The second step, called “Extended Generations”, consisted of 10 generations with 100 female and 100 male parents. This step had a constant population size of 500.P. radiata [After “Extended Generations”, ten “Recent Discrete Generations” were simulated in the third step, by selecting 32 female parents and 32 male parents from the last generation. A circular mating design was implemented and each parent was crossed twice. Fifteen offspring were generated in each family and each offspring had 4 ramets. These parameters used in the recent generations mimicked more closely to a conifer breeding program such as the Elite Population in  radiata . Selecti radiata  and impl radiata .2 = 0.2) and with high heritability (h2 = 0.5) were simulated. Both traits had additive genetic variance of 300 based on similar genetic variances for diameter-at-breast-height (mm) and wood density (kg/m3), respectively. Three-hundred and fifty bi-allelic QTLs were assumed to control the traits. The true breeding value of an individual was calculated as the sum of the individual QTL additive effects. Non-additive genetic effects between ramets of the same genotype were sampled from a normal distribution with mean of zero and 15% additive genetic variance. Phenotypes were simulated as the sum of the true breeding value, non-additive genetic effects and random residuals.Two quantitative traits with low heritability  were used as the training population. All individuals in generation 10 were used as the prediction population. The prediction equations of SNPs or SNP effects were estimated in the training population. We then tested the power of the prediction equations of SNPs estimated from the training population to predict performance on the prediction population.−5 in the Historical Generations and the Extended Generations but no mutations in markers or QTLs were simulated in the Recent Generations.In this study, one 150 cM chromosome and five SNP panels of 600, 1,200, 2,500, 4,900 and 8,000 SNP markers randomly distributed on the chromosome were simulated. This was equivalent to a SNP density of 7,000, 14,000, 30,000, 60,000 and 90,000 SNPs at the whole genome level . The marth generation.Four training population with a size of 800, 1000, 2000 or 3000 clones were randomly chosen from the Recent Generations set (generations 4 to 8) to reflect conifer breeding populations under selection. The prediction population were composed of 960 clones from the 10y is a vector of trait phenotypes, μ is the overall mean, a is a vector of additive genetic effects, d is a vector of non-additive genetic effects, e is a vector of random residuals. Za and Zd are incidence matrices relating the additive and non-additive genetic effects to phenotype. It was assumed that A is the additive genetic relationship matrix, D is the non-additive genetic relationship matrix and I is the identity matrix, The EBVs of individual clones were used as input parameters for deriving individual-clone GEBVs, estimated using linear individual tree mixed models :y=μ+Zaavariance .yi is a vector of EBVs estimated from trait phenotypes and pedigree data, Xij is the incidence matrix related to copy number of a given allele of individual i at SNP j, aj is a vector of additive effects of SNP j, and e is a vector of random residuals. The priors for aj were a mixture of normal distributions as described by Habier et al. [va = 4.2 and scale t-distribution, t  rapidly decays with increasing distance between closely located SNPs in the 7K SNP panel.This simulation assumed that the conifer genome has low level of linkage disequilibrium and that linkage disequilibrium rapidly decayed within 1 centimorgan. The average LD was 0.17, 0.20, 0.22, 0.22 and 0.24 for 7K, 14K, 30K, 60K and 90K SNP panels, respectively. In the simulated breeding population, nine clones were selected from 960 clones in the prediction population, which was equivalent to a selection proportion of 1%. Genetic gain was given as the average true breeding value of the selected clones, representing the increase above the population mean of zero. Genetic gain per generation and per year was compared among selections based on their EBVs and GEBVs. Additional genetic gain from selection methods of FSCA, GS and GSTG over FS was calculated in the same manner.i was calculated as g(bi − 1), where g was the average true breeding value of selected parents and bi was ith breeding cycle. The genetic merit of ith rotation was the genetic merit of genetic material that was ready for deployment of the latest breeding cycle. The total genetic gain from a deployment for 8 rotations was calculated as the summation of the average genetic merit of genetic material deployed in 8 rotations. s) and for genomic selection (GSs). For instance, the genetic gain in rotation 3 for FSs (2gf) indicates that genetic material from the latest breeding cycle (breeding cycle 2) was deployed. The total genetic gain from a deployment for 8 rotations was estimated as 37gf for FSs and 72gg for GSs. Additional genetic gain from deployment options of FSCA, GS and GSTG over FS was calculated for two deployment options, deployed as progeny and as clones.Genetic gain from deployment was estimated as the total genetic gain from a plantation of 8 rotations in a hectare of forest. It was assumed that one rotation started immediately after the previous rotation had finished. Each rotation was assumed to be 25 years. The first rotation was assumed to start at the same time that the breeding cycle was started in the breeding population, either for GS or FS. At the start of a new rotation, it was assumed that any deployment could only use the genetic material available from the latest breeding cycle. Genetic merit was the average true breeding value of individual trees selected to be used as parents in the breeding population. The increment of genetic merit in each breeding cycle was assumed to be constant. Genetic merit in breeding cycle The accuracy of GEBVs increased with the increase in the number of individuals in the training population, heritability of the trait of interest, and the density of SNP markers . The accGenetic gain was used to evaluate the efficiency of genomic selection when compared with forward selection using phenotypic data and field testing. Genetic gain per generation increased from 4.22% to 16.02% for a trait with low heritability and 18.90% to 24.20% for a trait with high heritability when the number of clones used in the training population increased from 800 to 3000 . For a tc) led to 8.11% additional genetic gain over deploying progeny (seedlings) of the selected individuals (FSs) (s) was 21.62%. Deploying individuals via a clonal archive followed by clonal propagation (FSCAc) provided an additional 24.32% compared with FSs.Deploying clones through somatic embryogenesis after FS (FSls (FSs) . Use of s), the additional genetic gains obtained from deploying genetic material selected from GS increased with increasing heritability and training population size  was 1–4% and 4–6% higher than that from deploying progeny of the selected individuals (GSs) for a trait with low heritability and for a trait with high heritability, respectively. The additional genetic gain obtained from deploying clones of the individuals selected from GSTG (GSTGc) was 3–10% and 9–12% higher than that from deploying progeny of the individuals selected from GSTG (GSTGs) for a trait with low heritability and high heritability, respectively.With genomic selection (GS and GSTG), deploying clones was always superior to deploying progeny (seedlings) of the selected individuals . The adds led to 12–47% and 50–65% higher additional genetic gain than deploying progeny selected from GSs for a trait with low heritability and high heritability, respectively. Deploying clones selected from GSTGs led to 14–53% and 56–71% higher additional genetic gain than deploying clones selected from GSs for a trait with low heritability and high heritability, respectively.When comparing genetic gains obtained from deploying genetic material selected from GS, the use of top-grafting to accelerate coning remarkably increased genetic gains from deployment . DeployiPinus taeda L.) [Picea glauca) [Picea mariana) [The objective of this study was to simulate the effectiveness of genomic selection over traditional selection using phenotypes and pedigree information in conifer species. There are several key components to achieve this: increasing the accuracy of estimated breeding values, shortening the generation interval of the breeding population and shortening the time frame from the point of selection to deployment in the forest. The accuracy of genomic breeding values increased with the training population size and the number of markers included in developing genomic breeding values. These accuracies were in the range of those achieved in genomic selection in the literature. The accuracy of GEBVs for growth and wood density was 0.30–0.83 in loblolly pine (aeda L.) ; 0.52–0. glauca) , 59 and;mariana) . The simmariana) , 57–59.While this study focussed on increasing accuracies with increasing marker densities, there are other considerations when designing genomic breeding programmes. This includes designing the genotyping tools to be tailored to the population and traits. In this study higher marker density and higher heritabilities gave higher accuracies of estimated breeding values. Other genomic selection simulations have shown that increasing marker density will better describe the genetics of traits when compared with traditional genetic analysis, for example the recent simulation by . High ma2 = 0.2 and h2 = 0.5 were simulated. These two traits were representative of the growth trait diameter-at-breast-height (DBH) and wood density. Extrapolating from the current simulation under forward selection, and single-trait selection, we can expect an increase of 1.13 mm per year for DBH and 1.25 kg/m3 per year for wood density. Over a 17-year breeding cycle, the expected increases would be 19.13 mm for DBH and 21.3 kg/m3 for wood density. With genomic selection, using 2000 individuals in the training population and 60K SNPs for developing GEBVs, we can expect an increase of 1.58 mm per year in DBH and 2.44 kg/m3 per year for wood density. After one generation (9-years), this would be equivalent to 14.23 mm for DBH and 21.97 kg/m3 for wood density.The heritability of a trait is an important as it effects the efficiency of genomic selection over traditional selection . It has 2≈0.5 to r2≈0.25 over 2000 base pair segments in Pinus taeda [2≈0.25 to r2≈0.10 over 2000 base pair segments in Pseudotsuga mensiesii [This simulation is a generic study for evaluating the efficiency of genomic selection in conifer breeding, assuming very simple situations, such as simulating a single chromosome, no missing phenotypic and genomic data, and discrete recent generations. In reality, wood density has a high heritability and is expensive to measure. It has not been normal, historically, for example to assess wood density for all individuals in a trial. The level of linkage disequilibrium (LD) in a genome is also a key factor that affects the accuracy of genomic selection. This simulation therefore attempted to mimic linkage disequilibrium in conifers using knowledge from radiata pine. Low levels of LD and a rapid decay of LD have been observed in conifer genomes. LD decays about 50% from rus taeda  and fromensiesii . The aveensiesii . Howeverensiesii , a genetensiesii . Howeverensiesii  and 2300ensiesii .Pinus mugo [Pinus species such as P. banksiana Lamb., P. virginiana Mill., and P. contorta Dougl. var. contorta begin coning within 3–5 years of germination [Fagus sylvatica L. and Picea abies (L.) Karst [P. taeda L., where generation interval can be shortened to a minimum of three years and breeding cycle to five years [Juvenility is generally defined as the period during which a plant cannot be induced to produce cones , 69. Somnus mugo . Pinus smination . In most.) Karst . Researc.) Karst . The curve years –76.Cupressus arizonica Greene), from 20–25 years to 12 years for Norway spruce (Picea abies (L.) Karst), from 10–20 years to 6 years for Douglas-fir  Franco) from 20–30 years to 3 years for western hemlock (Tsuga heterophylla (Raf.) Sarg.) [Pinus banksiana Lamb) can be induced from 5–10 years in naturally regenerated stands under open-grown conditions to 12 months under near optimum growing conditions in the greenhouse and nursery [Pinus sylvestris (L.)) [The onset of sexual reproduction will no doubt be a major barrier to shortening the generation interval even further than simulated in this study. The current study assumed that generation interval can be reduced by two years. The time reduced through the use of top-grafting might be different for different conifer species. Coning can, however, be accelerated in conifers using a number of methods which could also be considered. Coning can be accelerated through wide spacing, fertilization and irrigation, the development of full tree crowns and rapid early growth are promoted so that trees reach coning size as soon as possible . Gibbere) Sarg.) . Coning  nursery , 80. Timis (L.)) . Early-cis (L.)) –84.One impediment that prevents somatic embryogenesis to be applied commercially in clonal forestry for most of conifers is its high costs . This prTherefore, somatic embryogenesis was simulated as a pathway to deploy genetic gain to the forest. There are likely to be at least two ways of using somatic embryogenesis in conifer breeding programmes, outlined in Due to the high cost of genotyping, other studies have often only genotyped part of a training population using a high SNP density assay. The remainder of the population is genotyped using a low SNP density assay. Imputation is often used to ‘fill in the blanks’ through SNP associations, and predict genotypes to the levels of a high SNP density assay . We haveThe accuracy of GEBVs increased with the increase in the number of individuals in the training population, heritability of the interested trait and the density of SNP markers. Accuracy of GEBVs and genetic gain per unit of time for the trait with lower heritability was a key factor determining the minimum training population size, suggesting that 2000 clones in the training population is the minimum size for effective genomic selection for conifers. Deploying clones of the selected individuals always resulted in higher additional genetic gain than deploying progeny. Deploying genetic material selected from genomic selection with top-grafting for early coning resulted in 12–71% additional genetic gain in the deployment than genomic selection without an early coning option. Application of genomic selection to conifer breeding programs, combined with deployment options such as top-grafting and somatic embryogenesis are powerful tools to speed the delivery of genetic gain to the forest.


Cetorhinus maximus, in the Northeast Atlantic (NEA) affords an opportunity to resolve individual re-encounters at key sites with population connectivity and patterns of relatedness. Genetic tagging (19 microsatellites) revealed 18% of re-sampled individuals in the NEA demonstrated inter/multi-annual site-specific re-encounters. High genetic connectivity and migration between aggregation sites indicate the Irish Sea as an important movement corridor, with a contemporary effective population estimate (Ne) of 382 (CI = 241–830). We contrast the prevailing view of high gene flow across oceanic regions with evidence of population structure within the NEA, with early-season sharks off southwest Ireland possibly representing genetically distinct migrants. Finally, we found basking sharks surfacing together in the NEA are on average more related than expected by chance, suggesting a genetic consequence of, or a potential mechanism maintaining, site-specific re-encounters. Long-term temporal genetic monitoring is paramount in determining future viability of cosmopolitan marine species, identifying genetic units for conservation management, and for understanding aggregation structure and dynamics.Migratory movements in response to seasonal resources often influence population structure and dynamics. Yet in mobile marine predators, population genetic consequences of such repetitious behaviour remain inaccessible without comprehensive sampling strategies. Temporal genetic sampling of seasonally recurring aggregations of planktivorous basking sharks,  Gaining insight into repeated long-distance movement, its drivers and evolutionary consequences, is fundamental to understanding the ecology of these species. It is also central in evaluating current environmental concerns during an era of rapid climate change and extensive marine habitat modification, to inform the management and design of effective networks of marine protected areas (MPAs).The movement and spatio-temporal patterns of large, mobile marine predators can be highly dynamic with frequent, ocean-scale movements prevalent among cetaceans, seabirds, turtles, teleost fish, and sharks2. The filter-feeding basking shark, Cetorhinus maximus, and the whale shark, Rhincodon typus, are the world’s largest fish. Their horizontal movement is primarily driven by zooplankton dynamics and typically exceeds thousands of kilometres, precluding knowledge of natal origins and the potential existence of mating and breeding areas for either of these planktivores4. Identifying recurring spatial patterns, such as seasonal migration between distinct habitats or behavioural fidelity to ecologically important sites  therefore remains a challenge.Chondrichthyan  extinction risk is substantially higher than for most other vertebrates, particularly for large–bodied sharks frequenting coastal waters5, only a few telemetry studies have achieved multi-year tracks to detect migratory behaviour6. Migration, typically defined as the more persistent, seasonal movement of animals from one place to another, is often driven by transitory availability of resources7 and may play an especially important role in shaping the spatial ecology of planktivores given their dependence on such highly heterogeneous prey distributions.While site fidelity, returning to an area over time, has been noted in more than 30 elasmobranch species11. There is some evidence of courtship within aggregations11, but general timing and areas for breeding or pupping have yet to be determined, with the possibility that these life history events are less structured in time and space compared with other sharks12. Basking sharks forage selectively on zooplankton13, and within the Northeast Atlantic (NEA) generally use deeper waters near shelf edges during winter, forming surface/temporal aggregations in shallower coastal waters during summer14.The basking shark is circumglobally distributed in temperate seas, but the western European shelf provides key habitats supporting relatively predictable, seasonal coastal aggregations between April and September off Southwest England, Ireland, Northwest Scotland and the Isle of Man (Irish Sea)15 and trans-equatorial16. Increasingly, satellite telemetry suggests that these latitudinal, long-range movements are less likely to be random , but instead represent seasonal movement patterns, characteristic of migration17. In the NEA, most documented return migrations are to the general vicinity of the Exclusive Economic Zone (EEZ) of UK and Irish waters17, with two tagged sharks returning to within 30 km of the previous centre of activity, within the area encompassing a proposed Scottish MPA18. As an adaptation to the seasonal, temperate habitat of the NEA, basking sharks may exhibit seasonal migration patterns where inter-annual site-fidelity to aggregation sites with elevated zooplankton densities19 (‘hotspots’ hereafter) could have genetic consequences, such as spatial patterning in genetic connectivity. Investigating the existence, extent and persistence of such patterns is fundamental to understanding the consequences of migratory behaviour.In addition to extensive basking shark movement across the European shelf, there is evidence for ocean-scale transits, both trans-Atlantic20. Through repeated genetic sampling of basking sharks at key sites, questions pertaining to multiannual site fidelity and genetic connectivity can be addressed in the context of species management measures, such as assessing the spatial adequacy of MPAs.Contrasting with the temporary attachment of satellite tags, the permanence of genetic identity enables lifelong “tracking” of individuals, resampling of which can indicate site fidelity21. However, this earlier study was limited by a small number of opportunistic samples and the use of a single, maternally-inherited genetic marker (mtDNA). In contrast, the use of bi-parentally inherited markers, such as microsatellites, allows a more contemporary genetic assessment of connectivity and population structure. Although evaluating demographic independence among marine populations using genetic markers remains challenging22, it can assist in the identification of conservation management units23. With analyses of genetic structure influenced by marker properties, models of mutation, selection and sample size, finding a lack of structure can seldom be regarded as definitive24. For instance, in the whale shark, the assumption of global panmixia using mtDNA was revised when microsatellite markers indicated genetic differentiation between Indo-Pacific and Atlantic populations25.While temporary aggregations may constitute an appreciable proportion of a NEA population, it has not been established if they represent genetically distinct management units, or a random genetic assemblage of a wider, global population, spatially drawn together by prey availability. Nonetheless, the prevailing perspective is that basking sharks lack global population structure and display low worldwide genetic variability26, including effective population size (Ne), the number of breeding individuals within a population; an evolutionary analogue of adult census size27. This parameter complements abundance estimates and is critical for informed long-term conservation management. Formerly extensively harvested as a commercial species, basking sharks are classified as ‘Endangered globally’28 (IUCN 2018). In spite of these concerns, no long-term regional population abundance estimates are available in the NEA due to the low number of re-sightings. In an attempt to estimate local abundance through mark-recapture in the NEA, a closed population model (assuming demographic closure) was applied previously, generating an estimate of 985 sharks in a 50 km diameter study area29.Further, genetic monitoring can identify temporal changes in population genetic metrics affecting population viability17 remain only poorly understood. Here, we use a panel of recently developed, species-specific microsatellite loci30 to test the null hypothesis of global panmixia using >400 basking shark samples collected worldwide. Secondly, using temporal samples collected at three known NEA aggregation sites from 2009 to 2014 inclusive, we assess genetic re-encounters, contemporary gene flow and migration rates between sites to infer regional connectivity, complementing and expanding previous studies using mtDNA21. Further, we provide a preliminary examination of average within-group relatedness to determine if aggregations are on average more related than expected by chance. Finally, we provide the first estimates of effective population size to assess contemporary Ne at a time when increasing anthropogenic activity  is rapidly changing the NEA seascape.To date, the population genetic consequences  of the basking shark’s complex migration strategiesA minimum of 13 out of 22 microsatellite loci were successfully amplified in the 460 DNA samples screened, which included 394 samples from the NEA  collected over 6 years (2009–2014) and 66 samples opportunistically collected over 20 years from elsewhere in the world . Genotypes of individuals sexed at the time of sampling  suggested all 22 microsatellites behaved as autosomal, co-dominant loci. Duplicate individuals  were removed, leaving a total of 407 unique individuals to characterise the microsatellites. Initial and final sample sizes can be found as Supplementary Table Cmax07 in three out of nine populations. None of the loci showed any evidence for stuttering, large allelic dropout or the presence of null alleles, except for Cmax12. However, Cmax13 and Cmax14 displayed significant linkage disequilibrium with several loci . Therefore, Cmax12–Cmax 14 were removed and all following analyses performed with the 19 validated loci.None of the loci were out of Hardy Weinberg equilibrium (HWE) overall (across populations). When HWE was assessed within global putative populations based on geographic origins  was removed following matching analyses. Across all 407 individuals, the number of alleles per microsatellite locus ranged from 3 to 18 with a mean number of alleles per locus of 8 . Observed and expected heterozygosity per microsatellite ranged from 0.22 to 0.87 (mean = 0.66) and 0.24 to 0.87 (mean = 0.66), respectively (see Supplementary Table The genotyping error rate was 0.0097 (i.e. 16 incorrect genotypes/1635 reprocessed genotypes), where the error rate per locus ranged from 0–0.0230 (see Supplementary Table Cmax16 and Cmax17) and three loci  under balancing selection were identified. However, the significance of pairwise estimates of population differentiation remained unchanged whether non-neutral loci were either included or excluded, justifying their incorporation in all subsequent analyses to maximize resolution.Two loci possibly under positive selection (ID(unbiased) = 5.25 × 10−17 and PID(sibs) = 3.11 × 10−07, indicating the high discriminatory power of this marker set.Based on 407 individuals, the probability of two individuals sharing the same multi-locus genotype across all 19 loci was very low with PThere were 51 ‘genetically confirmed re-encounters’  in the NEA region throughout the course of this study. Of these, 82% (n = 42) occurred within the same site and year , mostly within a day or week (max. 31 days) of the first sampling  were re-sampled in different years at the same or different localities within the NEA. Two of these individuals were re-sampled at locations >100 km distant from their original site and represent inter-annual site fidelity on a regional scale. One individual, first sampled in 2012 on the Scottish West Coast, was re-sampled off Ireland (north) two years later (SCO12_60 & IRE14_06). Another, originally encountered off the Scottish West Coast (SCO12_42) in 2012 , was re-sampled in the Moray Firth in 2013 (MF13_11).The remaining 7 individuals 14%) resampled at the same locality  between years, are indicative of inter-annual site-fidelity  remained significant  following bootstrapping. All but three GST pairwise values were initially significant, yet only the following comparisons involving IRE_12  remained significant following bootstrapping, IoM_10 , IoM_12  and SCO_13  .None of the pairwise comparisons of Overall, Irish samples were differentiated from other NEA sites, shown in the Principal Coordinates Analysis  suggested relatively high and uni-directional movement into the Irish Sea  and SCO (r = 0.009) was higher than other ‘population’ samples Table . Among-gGST and DST identified weak genetic structuring among the nine putative populations , IRE_12 and IRE , IRE_12 and SCO  and IRE and SCO , maintained using locpriors and without a priori information on population origin.Similarly, a PCoA of Nei’s genetic distances by population separated IRE_12, SA, NWA and MED on the principal axis (53.31%) from the remaining NEA and PAC samples , with IRE and PAC samples significantly out of HWE; probably a consequence of combining temporal samples of opportunistically collected individuals.Table Ne, rare allele frequencies were excluded to avoid bias (Pcrit < 0.02), an approach not adversely affecting precision31. Most Ne estimates exceeded the sample size used in the estimates  individuals from the IoM temporal data set gave an estimated Ne (using Pcrit = 0.02) of 382 individuals (parametric CIs = 241–830). If this point estimate were to be corrected by 0.74–0.86 to account for age-structure, the upwardly adjusted Ne comes to 516, which would suggest over 800 (Ne:N ratio used = 0.6) individuals frequenting IoM waters.When computing Ne (Ne = infinite) and parametric CIs from 3239 – infinity (Pcrit = 0.02). Using the same adjustments as above, a minimum of Ne = 3239 would suggest at least 4,000 individuals frequenting IoM and SCO waters.For a separate analysis, IoM samples were combined with SCO samples, resulting in no accurate estimates of 32, mark-recapture methods at a localised site29, and more recent satellite tracking18.Our analyses of the largest NEA temporal (2009–2014) set of basking shark DNA samples collected to date, provides the first supporting genetic evidence for site-specific re-encounters in the basking shark, with 18% of individuals re-sampled inter/multi-annually at or near the same location. These findings accord with previous evidence from photo-ID re-sightings within a region34. Recent satellite tracking identified summer (July-October) residency within the Sea of the Hebrides18, followed by various migration strategies into more southerly latitudes, showing a displacement range of 455km-2354km based on archival tags17. Neither study suggests individuals are year-around inshore residents found within a few kilometres of their tagging location. Rather, corroboration of all available evidence makes a compelling case that basking sharks leave an area to return on an inter- or perhaps multi-annual basis. Inter-annual returns of two satellite-tagged sharks to the Scottish MPA18 are concordant with wider genetic re-sampling of individuals from both Scottish and Manx (Isle of Man) waters within a few kilometres of where they were first encountered, often after several years. Our complementary evidence from Scottish, Irish and Manx aggregation sites suggests individuals may exhibit inter-annual site fidelity in the NEA, although data remains insufficient to suggest site fidelity on a population-level. To determine the scale and frequency of inter-annual basking shark returns at a population level, the calculated probability of recaptures would need to exceed that of random expectations20, where the chance of resampling an individual is inversely proportional to population size.Notably, we resampled seven individuals in close proximity (<20 km) to their first sampling location, suggesting inter-annual returns to these areas. While estimates of basking shark energetic requirements suggest over-wintering at the northerly limits of our survey is possible, inshore habitats of seasonally enhanced productivity are exploited only temporally, after which they move to other favourable, usually deeper habitats12, pupping35, mating36 or feeding37. Seasonality in prey availability is a common driver of migratory movement, and repeated use of an area for feeding is considered ‘feeding site fidelity’39. A strong association with thermal fronts and chlorophyll a in NEA shelf seas and low inter-annual variability of seasonally persistent fronts likely encourages basking sharks to return on an inter-annual basis19. Already documented in large-bodied lamniform sharks returning seasonally to areas supporting abundant prey6, this phenomenon is also observed in other large planktivores, such as the whale shark37 and the giant oceanic manta, Mobula birostris40.The scale, frequency and nature of return movements in elasmobranchs is complex and has been variously attributed to gestation41, there is little knowledge of the spatial and temporal variation of such events in planktivorous elasmobranchs, and reacquisition of individuals between years at the same site requires continuous and unambiguous quantification.Confirmation of inter-annual site fidelity can inform seasonal/dynamic protection measures around aggregation sites, particularly in wide-ranging marine predators. Yet, compared with some marine mammals16 of basking sharks is for gestation purposes or a consequence of mating dispersal (involving temporarily/permanently leaving a home range). However, only the latter could lead to effective dispersal and hence gene flow, as it requires dispersers to reproduce in a ‘new’ location42. Determining the number of migrants required to homogenize population structure, counteracting drift and selection, is a contentious issue. The basking shark, like the similarly long-lived whale shark25, would require only a few breeding migrants every 2–4 years to homogenize population structure.Seasonal affinity to oceanic regions has potential to promote population structure, but not if there are sufficient migrants to counter genetic drift. It is unclear if documented ocean-scale movementGST, with early-season sharks sampled off SW Ireland in 2012 (IRE_12) differentiated from most other temporal samples. Further, IRE12 shows a significantly higher mean group relatedness than expected, and departure from HWE consistent with the notion of genetically discrete kin groups moving en masse.Our temporal sampling regime in the NEA afforded an opportunity to examine the regional population structure of this species in unprecedented detail. Subtle but significant temporal structuring of populations in the NEA was apparent from estimates of 43. Whereas the migratory route of IRE_12 individuals cannot be ‘retraced’, their genetic similarity to earlier samples of NWA sharks in all analyses of population differentiation , these findings should be interpreted as a merged Irish Sea (IoM) sample set representing all NEA individuals at one time point, rather than source-sink population dynamics. That the Irish Sea (IoM), increasingly influenced by anthropogenic activity, could represent an important transit corridor during seasonal movements within the NEA deserves further investigation. Concordant with this interpretation is the limited but significant differentiation between merged temporal samples from Irish (excluding IRE_12) and Scottish waters . However, as some assumptions of the inference model were not met  samples, as represented by IRE_12 and IRE_14. All-female groups had higher kinship than all-male groups or mixed assemblages  is costly, social learning from conspecifics or kin can be advantageous, particularly in heterogeneous environments55. Larger, more experienced basking sharks may be more proficient at locating patchy food than juvenile sharks, due to ontogenetic differences in habitat selection34, as recently demonstrated in other marine predators56. Therefore, social information transfer and the potential to learn optimal foraging may benefit kin in loose aggregations57. However, that not all aggregations comprised significant kin groups, and overall exhibit a lack of evidence for inbreeding, raises the possibility of assortative mating in the basking shark. Differentiating between kin and unrelated conspecifics might be a mechanism to tolerate or seek kin during foraging, but to avoid breeding with close relatives. Kin recognition has been shown in salmonids58, and an experimental study demonstrated male guppies’ reproductive investment (courtship) was reduced when placed with siblings59. Although it is unknown if basking sharks are similarly capable, olfaction-mediated pairing behaviour has been suggested as a mechanism in sharks60.Understanding the spatial and temporal patterns of aggregations can contribute to long-term conservation efforts7. Our analysis of available samples from outside the NEA suggests lack of population differentiation, often interpreted as evidence of global panmixia, which contrasts with subtle but significant structuring within the NEA. Ocean-scale tracking studies indicate that neither the Mid-Atlantic Ridge nor tropical waters are barriers to migration16. Basking sharks appear to have a broad thermal range14 and are therefore relatively unrestricted by temperature. Considering their average cruising speed3, individuals could travel a straight-line distance of over 30,000 km annually. This supports the possibility of ocean-wide dispersal, suggesting movement among all sample locations is not beyond their known physical/physiological capabilities.Long-distance migration is common in mobile, long-lived species occurring at many spatial and temporal scales4. However, population genetic analyses using microsatellite and mitochondrial DNA found significant structure between Indo-Pacific and Atlantic populations25. This study emphasised inadequate population samples and marker choice had influenced previous interpretations of whale shark population structure61. Similarly combining our Pacific samples across years could have degraded statistical differentiation of the Pacific and NEA populations; although the possibility that their apparent genetic similarity has its historic origins in trans-Arctic migration during an ice-free period62 cannot be refuted. Adequate sampling, by contrast, revealed weak population structure in both species; the genetically most discrete whale sharks from the isolated Holbox Island, Gulf of Mexico61, and similar weak differentiation of the temporally discrete IRE_12 sharks.Similar movement analyses of the more thermally restricted whale shark inferred they existed as a global ‘meta-population’61, it remains questionable whether lack of differentiation between small, unrepresentative and perhaps most importantly, arbitrary ‘population’ samples, collected opportunistically from strandings and fishery captures over tens of years and across five oceanic regions, really can indicate high contemporary genetic connectivity and lack of population structure.Consequently, at a larger scale the prevailing view that basking sharks exhibit panmixia, lacking global population structure, cannot be rejected immediately by our microsatellite analysis of combined temporal samples collected worldwide. However, that these samples, where large enough for analysis to be meaningful, depart significantly from HWE suggests they could comprise individuals from genetically discrete temporal populations, biasing downward estimates of differentiation. This inference is supported by our findings of some temporal genetic differentiation and unexpectedly high levels of relatedness within basking shark aggregations in the NEA. Therefore, as in earlier studies of whale sharksNe) influences long-term evolutionary processes and estimates of Ne are generally lower than census size reflecting reproductive variability27. Most of our estimates for the NEA were at least three-fold the sample size. Larger sample sizes meant Ne could not be estimated or had infinite confidence limits, suggesting that the linkage disequilibrium (LD) method could not distinguish larger Ne from infinity, possibly reflecting a lack of true, independent populations.Effective population size. Effective population size , and based on our microsatellite-derived Ne estimates, the number of basking sharks in NEA waters are unlikely to exceed 10,000 individuals.Aerial surveys based on the Bay of Fundy, Canada, estimated the total population size of eastern Canada as 6512 (CI: 4040–11886)Ne, rather than number of breeders within the population. Finally, Ne/Nc ratios in large-bodied elasmobranchs are relatively unexplored, but a suggested ratio similar to those of marine and terrestrial mammals seems appropriate, considering elasmobranchs share similarly slow life history traits64. Merged Irish Sea samples from four successive years produced an estimated Ne of 382 (95% CI = 241–830), inferring >800 individuals frequent the Irish Sea (Ne:N ratio used after correcting point estimate = 0.6), similar to a local abundance estimate from neighbouring Scottish waters of 985 (95% CI = 494–1683)29.Despite efforts to reduce bias in our estimates, two requisites of the LD model, closed populations and random mating, were violated. Therefore, our sampling design and adjustments likely reflect a per-generation, local 18, and revealed high levels of gene flow among NEA aggregations. However, we have also found an early-season sample (IRE_12) whose genetic composition was temporally heterogeneous. There was a tendency for a spatial association of kin groups, perhaps drawn together by prey availability following ocean scale migration. Findings which do not contradict the inference of some level of global connectivity. Maintaining the integrity of these aggregations of related individuals undertaking focused seasonal return movements to feeding sites together, supports the case for area protection; while highlighting the global risks accruing to the basking shark’s characteristic adherence to sites and transit routes crossing multiple jurisdictions. Our analysis of migration rates associated with the Irish Sea, an area of intense marine renewable development, exemplifies the problem of protection during transit between aggregation sites. Nevertheless, knowledge of the spatial ecology and population connectivity of highly migratory species can inform spatial planning. Identification of aggregation ‘hotspots’, areas associated with the presence of persistent fronts, informed the recent proposal for a basking shark MPA in Scottish waters10. Its potential importance is underscored by observations of suspected mating activity within summer aggregations11.We report site-specific re-encounters of individuals, which supports suggestions of inter-annual site fidelity23. Evidence of relatedness, most marked in early season samples, suggests kin association may be characteristic. Activities depleting kin groups can erode selectable genetic variation rapidly; an important consideration in a slowly reproducing long lived species exhibiting limited genetic diversity.Finally, our study suggests an important corollary to the well documented catch rate declines of the 1950s to 1990s associated with area-focused basking shark fisheries65. All mucus sampling methodology was approved by, and conducted with the knowledge of the Wildlife and Conservation Division of the Isle of Man Government, Scottish Natural Heritage licence (13921 and 58660) and the UK Home Office ASPA licence . Three common aggregation sites within the NEA region were sampled repeatedly between 2009 and 2014 to investigate inter- and intra-annual connectivity within and among the waters of the Isle of Man (n = 118), Ireland (n = 124), and the West Coast of Scotland (n = 124). In addition, 22 samples were collected from individuals in the Moray Firth, Scotland, in 2013, representing the only North Sea sampling location. Finally, six individuals were sampled from Southern England off Cornwall, 1994–2004. Associated data, such as sampling location, total length, sex and dorsal fin-ID were acquired when possible and samples for DNA extraction were preserved in Analytical Reagent Grade absolute ethanol. Total genomic DNA was extracted using proteinase K digestion and standard phenol-chloroform procedures. DNA concentration was quantified using a fluorometer  and its quality assessed by electrophoresis on a 1% agarose gel stained with ethidium bromide.A total of 460 basking shark samples (skin/mucus) were collected from five oceanic regions during the last two decades (1994–2014). Tissue samples from dead, mostly stranded or trawl bycaught specimens were obtained opportunistically from the Northwest Atlantic (n = 12), South Africa (n = 5), the Mediterranean (n = 11) and New Zealand, representing the South Pacific (n = 38). The majority (>85%) of samples were collected from the Northeast Atlantic  using mainly mucus swabs of free-swimming basking sharkset al.30 (Cmax01–19) and three loci from Noble et al.66 , following the multiplex design described in Lieber et al.30. Products were amplified in a 2-µl PCR reaction, including 1 µl (30–80 ng) DNA (air dried), 1 µl primer mix (forward and reverse primer at 0.2 or 0.4 mM) and 1 µl QIAGEN Multiplex PCR Master Mix (including HotStar Taq DNA polymerase) with the following profile: 95 °C for 15 min, followed by 35 cycles of 94 °C for 30 s, 56 °C for 90 s, 72 °C for 60 s and finally 60 °C for 6 min. PCR products were diluted (1:160) and separated on an ABI 3730 48-well capillary DNA Analyser using ROX GS500 size standard (Applied Biosystems Inc.). Genotypes were scored using the GENEMAPPER v3.7 software.All samples were genotyped for 22 dinucleotide microsatellites, including 19 loci from Lieber 67. Individuals that failed to amplify at more than six loci were removed from the data set.To assess genotyping error rates, a random set of 87 individuals (19% of the entire data set) was re-amplified and re-scored at all 22 loci, producing 1,914 multilocus genotypes . The mean error rate per locus was calculated as the ratio between the number of single-locus genotypes including at least one allelic mismatch and the number of replicated single-locus genotypes. Each microsatellite locus was checked for scoring errors, allelic dropout and null alleles using Microchecker 2.2.368. The probability of randomly drawing two individuals from the population sharing identical multilocus genotypes was calculated in GenAlEx using both, PID(unbiased) and PID(sibs), to determine the upper and lower boundaries of probability of identity69. Fisher’s exact tests of Hardy-Weinberg equilibrium (HWE) using a Markov Chain Monte Carlo (MCMC) approach with 10,000 iterations and tests for linkage disequilibrium (LD) per locus and putative population were performed in GenePop 4.270. Significant values for multiple comparisons were adjusted by calculating false discovery rate (FDR) adjusted p-values.Observed and expected heterozygosities and mean number of alleles per locus were calculated in GenAlEx 6.5FST outlier method using the software Lositan71 was applied to evaluate if all loci were neutral. The method evaluates the relationship between FST and expected heterozygosity (HE) to identify outliers that have disproportionately high or low FST values compared to neutral expectations. The first run estimates the mean neutral FST using all loci, a consecutive run uses only putative neutral loci to compute neutral FST, and following the simulation, Lositan then reports the estimated selection status  of each locus. Lositan was run with 100,000 simulations using the infinite allele model, using the ‘neutral mean FST’ and ‘Force mean FST’ options. In order to assess if the inclusion or exclusion of suspected non-neutral loci had an effect on estimates of population differentiation, genetic pairwise distances were generated with and without loci under selection, and with only ‘neutral’ loci as identified by Lositan.As population genetic assessments require neutral loci, an Due to the non-destructive sampling procedures used on live animals, there was a possibility that the same individuals were sampled repeatedly. Matching analyses were performed in GenAlEx to assess intra- and inter annual genetically confirmed re-encounters of individuals within sites  or between years. Exact matches and near-matches (up to three mismatching loci) were considered as genetically confirmed re-encounters, genetic recaptures, and removed for subsequent population analyses. Near-matches with >3 mismatching loci were assessed one by one to evaluate if mismatches were likely a result of allelic drop-out or stutter. When available, genetically confirmed re-encounters were also evaluated with dorsal fin-ID photographs, size and sex of the individual.72 relatedness coefficient was determined by 1,000 bootstraps and 95% confidence determined by 999 permutations to test the null hypothesis of ‘no differences across populations’ using GenAlEx. To allow for a detailed investigation into basking shark genetic group structure with respect to relatedness, a genetic marker-based approach was applied. For this, a subset of spatio-temporal groupings  from IoM, SCO (the West Coast and the Moray Firth), and IRE was used to further test the null hypothesis that groups represent, on average, random associations with respect to relatedness. Defined as spatio-temporal sampling events, groups were defined as affiliations between two or more individuals that were within the same cluster at the surface when sampling commenced. In short, simulations were performed using the allele frequencies of the data set to generate 200 simulated pairs for each degree of relatedness  using four relatedness estimators (dyadic likelihood method: di73); and three moment estimators: LR74; QG72; and W75 as implemented in the R package ‘related’ v.1.0 using the function compareestimators76. Following evaluation of the four estimators, the degree of resolution that should be expected  was analysed using another simulation implemented in the familysim function in ‘related’. Relatedness estimates per group were calculated as the average within-group relatedness (R) derived from all pairwise comparisons within the group using the current sample set as the underlying reference population51. Following the simulations, variances of LynchRD and DyadML for pairs of relatedness indicated that they performed well in differentiating unrelated pairs from related ones. To compare estimators, R was calculated using both, the dyadic likelihood method DyadML, which constraints estimates to the range of , and the moment estimator LynchRD whose range also yields negative estimates74, using the grouprel function in ‘related’ with a 1000 iterations to compare the average observed R value with the expected distribution. Similar to estimates derived from the software ‘Storm’ v. 2.077, the grouprel function calculates the average pairwise relatedness values within each observed (pre-defined) group, resulting in an average within-group relatedness estimate. The expected distribution of average within-group relatedness values is then generated by shuffling individuals between groups while keeping each group size constant using a 1000 Monte Carlo simulations. This generates 1000 iterations of each group, where each iteration is one realisation of the expected relatedness value of the data set. The observed mean relatedness is then compared to the distribution of average simulated values to test the null hypothesis of groupings being random associations with respect to relatedness. Both relatedness estimators used above assume non-inbred individuals. However, possible non-random mating due to demonstrated patterns of site fidelity could violate the assumption of a large outbred population. Therefore, average inbreeding coefficients were computed per group , using the coancestry function in ‘related’. Additionally, within-group R computations were repeated with the option ‘allow inbreeding’ using DyadML (denoted as ‘DyadML_F’) to account for possible inbreeding using 1000 iterations.Mean group relatedness based on the Queller and Goodnight (1989)Ne (estimates apply to the time period encompassed by the sample) was estimated using the bias-corrected single-sample molecular method based on linkage disequilibrium (LD)31 as implemented in NeEstimator v278. Estimates were made excluding alleles with frequencies less than 0.02 and 0.05 (denoted as ‘Pcrit’). The analysis was performed on eight global sites  and then repeated on a set of merged NEA samples. For the latter, Irish sharks were excluded to avoid any downward bias in Ne caused by a potential Wahlund effect , as using samples from a ‘meta’-population with local subdivision can violate the main assumptions of the LD method. Merged SCO and IoM temporal samples resulted in 207 individuals of all age-classes. Simulations have shown that estimates of Ne performed best for mixed-age samples (if the number of age classes approximated generation length), thereby approaching Ne per generation79. Remaining downward-bias due to age structure was corrected according to mixed-age samples of species whose reproductive capacities might be most comparable to basking sharks . Therefore, an adjustment of 1/(0.74–0.86) = 1.16–1.35 was used to increase the LD point estimate. Finally, the Ne/N ratio (effective population size to adult census population size ratio) which needs to be interpreted with caution, was based on estimates for mammals: Ne/N = 0.6–1.0 (Ne/N ratio in fish: 0.4–1.0)80. A rather conservative Ne/N ratio of 0.6 was considered to be applicable to basking sharks in the absence of reliable estimates of life history parameters and was used to infer adult census population size based on NEA samples.Contemporary DST81 and GST82 were calculated for 15 NEA putative populations based on sampling site and year to test for site fidelity, and to assess whether temporal samples from the same site could be merged for subsequent global comparisons. GST  is considered the most appropriate metric when mutation rates are low relative to migration rates, as might reasonably be expected in basking sharks, and patterns of population structure are driven primarily by migration81. Bias-corrected 95% confidence intervals using 1,000 bootstraps in the diveRsity (function ‘fastDivPart’) package83 in R84 were estimated to test concordance of differentiation statistics. Using FSTAT v2.9.3.285, FST pairwise comparisons were also calculated for the 15 NEA putative populations, for comparative purposes only and reflecting this metric’s widespread historic use; however, when using highly variable microsatellites, levels of heterozygosity within populations can be underestimated. Significant p-values for multiple comparisons were adjusted using a sequential Bonferroni correction. After merging populations within the NEA into five populations , based on either sampling site or Gst differentiation level (in the case of IRE_12). GST, DST and FST pairwise comparisons were repeated, including the five NEA sites in addition to four global sites , resulting in nine global, putative populations  using all eight discriminant functions. DAPC is a dimension-reduction approach and is constructed using alleles which most reflect the between-group variance, thereby reducing variance within putative populations. In the resulting DAPC graph, individuals were represented as dots and the groups as inertia ellipses.A Discriminant Analysis of Principal Components (DAPC) in the Adegenet v1.3.4. package87 was performed in GenAlEx for the NEA samples as well as for all nine putative, global populations.A DAPC was repeated using merged samples from the NEA  to investigate if individuals sampled from Irish waters were genetically more distinct compared to SCO and IoM individuals. A principal coordinate analysis (PCoA) visualising Nei’s pairwise genetic distances88 to infer possible undetected posteriori genetic clusters using the nine global putative populations. Run length parameters included 500,000 MCMC iterations  with assumed genetic clusters (K) set from 1–10 (15 runs per K) using the admixture ancestry model with correlated allele frequencies. The analysis was repeated with the same parameters, however this time, a priori sampling locations as prior information using the LOCPRIOR parameter setting89 were considered to detect possible weak population structure signals in this highly mobile shark. In order to determine the most likely K (ΔK), the Evanno method90 was applied in Structure Harvester91.Another Bayesian cluster algorithm was used as implemented in Structure v2.3m) over the last several generations, a Bayesian approach based on Markov Chain Monte Carlo (MCMC) methods to estimate the posterior probabilities of the migration matrix among sub-populations was used as implemented in BayesAss v.3.046. The sub-populations were based on the NEA aggregation sites, consisting of merged samples from the IoM (n = 97), SCO (n = 133), IRE (n = 89) and the temporal sample of IRE_12 (n = 18) in order to assess migration rates and directionality within the NEA. BayesAss uses multi-locus genotypes and does not require the populations to be in migration drift or Hardy–Weinberg equilibrium. As suggested by the software authors, more accurate results are obtained when the number of proposed changes for the parameters m (migration rate), P , and F (inbreeding coefficient) yield an acceptance rate between 20% and 60%. Initial runs used the following parameter settings: m = 0.3, P = 0.15 and F = 0.2. However, parameters were adjusted in the final run to achieve acceptance rates <60% and to ensure optimal mixing, using the following parameters: m = 0.5, P = 0.6 and F = 0.6, resulting in acceptance rates ranging from 0.38–0.57%. The number of iterations for the MCMC analysis was set to 5 × 10−06 with a burn-in period of 5 × 10−05 (number of MCMC iterations discarded before sampling commences) and a thinning interval of 1,000 iterations. Convergence was diagnosed through multiple runs and by plotting the log-probability of the iterations , number of different alleles, mean number of alleles, private and effective alleles, observed and expected heterozygosity, and mean group relatedness (r) were computed in GenAlEx. Exact tests for HWE per site using a Markov Chain Monte Carlo (MCMC) approach with 10,000 iterations were performed in GenePop.Genetic diversity measures were calculated per global sampling site and within the NEA. Allelic richness, using the rarefaction method  was calculated in FSTAT. Wright’s inbreeding coefficient Supplementary information.


Felis catus) and red foxes (Vulpes vulpes) in Western Australia is the aerial broadcast of toxic baits, but assessing its efficacy can be difficult. Here, we report on a method of evaluating the effectiveness of this method for the abatement of feral cats using genetic analysis techniques. However, our results were unable to provide robust evidence that, over a five-year program, baiting had a detrimental impact on both genetics and demography in this population, and the results were not significant. Monitoring the impact of control programs in this way may provide valuable information to managers on the effectiveness of their management strategy, but further refinement of the methodology is recommended.The management of invasive species is a major challenge for the conservation of biodiversity globally. One technique that has been widely used to control feral cats (® baits was undertaken at Fortescue Marsh in the Pilbara region of north-western Australia, for the protection of biodiversity in this important wetland area. This program has been shown to have had a significant detrimental effect on cats in this landscape, but the long-term impact is difficult to ascertain. We assessed population genetics across three cohorts of feral cats sampled as part of the control program. We also compared cat populations in natural habitats and around human infrastructure. A key challenge in any study of wild animal populations is small sample sizes and feral cats are particularly difficult to capture and sample. The results of this study superficially appear to suggest promising trends but were limited by sample size and many were not statistically significant. We find that the use of genetic techniques to monitor the impact of invasive species control programs is potentially useful, but ensuring adequate sample sizes over a long enough time-frame will be critical to the success of such studies.The feral cat has been implicated in the decline and extinction of many species worldwide and a range of strategies have been devised for its control. A five-year control program using the aerial broadcast of toxic Eradicat Felis catus) is listed as one of the world’s worst invasive species [Vulpes vulpes) in reserves managed by the department across the state [® baits in several locations in Western Australia [® baiting program had a significant detrimental impact on cats on an annual basis [Invasive species are listed as one of the key threatening processes for biodiversity globally  and have species  and thei species ,6. Amonghe state . Aerial ustralia . In thesustralia . For exaal basis . MonitorGenetic diversity in vertebrate populations is understood to be an important factor in extinction risk ,11. PopuTo evaluate whether the success of the Fortescue Marsh control program on an annual basis resulted in a measurable inter-annual impact on population genetics, 11 polymorphic microsatellites were used to genotype a suite of feral cats captured as part of this program ,24. The 2 in the last 20 years, with an overall catchment size of 1300 km2 [Eucalyptus spp. and Acacia aneura at the margins of the Marsh to spinifex (Triodia spp.) grassland to halophytic chenopod shrubland in the centre of the Marsh [Pezoporus occidentalis) and greater bilby (Macrotis lagotis) [Fortescue Marsh is a large wetland that is subject to periodic inundations as large as 1000 km1300 km2 . The Mar1300 km2 . Inundat1300 km2 . Vegetathe Marsh ,26. Forthe Marsh ). Severalagotis) ,29 as welagotis) ,30,31. M® baits  once a year for five years (2012–2016) and monitoring of both feral cats and native fauna was to be conducted to assess the efficacy of the program. Eradicat® baits contain 4.5 mg directly-injected sodium monofluoroacetate (compound 1080) and were broadcast at the approved prescription of 50 baits per km2. Flight transects 1 km apart aimed to achieve a spread per bait drop of 200 × 40 m. The baiting program was conducted in winter when conditions were cool and dry and when live prey was understood to be least abundant [2 baited in 2012 and 2013 and approximately 920 km2 in 2014–2016.Fortescue Marsh is adjacent to the Cloudbreak and Christmas Creek iron ore mining areas, which are operated by Fortescue Metals Group Limited (FMG). On behalf of FMG, DBCA implemented the Fortescue Marsh Baiting Plan  to satisabundant . Low hum®  using a combination of cat urine and faeces as the attractant. Captures took place between 2012 and 2016, during late autumn-early winter at least three weeks prior to baiting, with a total of 65 individuals being sampled from the Marsh area (‘marsh’ cats) ; 2015 (n = 20); 2016 (n = 19). While these sample sizes were relatively small, they were comparable to those used in a previous study [One technique for monitoring the impact of baiting on feral cats involved the capture and fitting of GPS telemetry collars to individual feral cats in the baiting area to monitor behaviour  and ultimately the uptake of baits ,24. Indih’ cats) . In addih’ cats)  using cous study  which idExtraction of DNA from tissue samples and subsequent analysis was conducted by Y. Hitchen (Helix Molecular Solutions). Genotypes were generated using 11 polymorphic short-tandem repeat (microsatellite) markers characterised for felids ,34 Tabl and wereGenetic diversity was quantified using mean alleles per locus (AL) across all 11 microsatellites, as well as mean allelic richness (AR) between spatial and temporal cohorts and these were calculated using HP-RARE  using thGENEPOP   was usedEstimates of Ne were calculated for ‘marsh’ and ‘camps’ cohorts, as well as across the three temporal cohorts, in NeESTIMATOR   using thTo assess how genetic distance correlates with geographic distance across the landscape, predictions of spatial autocorrelation were made using GENALEX  ,45 usingAllele rarefaction curves were constructed for both populations  and indiAllelic diversity was considerably higher in the ‘marsh’ population rather than the ‘camps’, with a value for AL of 6.18 (±SD 2.31) for the ‘marsh’, compared to 4.64 (±SD 1.50) for the ‘camps’ population . Accountp < 0.05 level but not after a Bonferroni correction was applied. FST values were generally low across all loci and a mean value of 0.012 indicates that just 12% of observed variance was down to population subdivision ) (LD method) and 33.0 (0.0–165.7 (95% CI)) (MC method) . OverallEstimates of migration rate using the private allele method in GENEPOP estimated Nm as 3.28 effective migrants per generation between the ‘marsh’ and ‘camps’ populations. However, the FST method produced a much higher estimate of 20.58. Modelling in STRUCTURE found no evidence for geographic structure across the Marsh area, which was largely supported by the construction of a Neighbor-Joining phylogenetic tree , with inSpatial autocorrelation analysis in GENALEX appeared to support the lack of genetic structure across the landscape, with values for relatedness (r) not fluctuating outside the confidence limits, indicating no correlation between genetic and geographic distance . HoweverThe Fortescue Marsh area was subject to the broadcast of toxic baits over a five-year period, targeting the feral cat population in the vicinity of the Marsh. The success of annual baiting implementation was monitored using camera traps and occupancy modelling ,24 and fDifferences in genetic diversity were observed between the putative ‘marsh’ and ‘camps’ populations of feral cats, with the ‘marsh’ population having greater variation across all loci analysed. Generally feral cat populations on the Australian continent exhibit high genetic diversity , making One key aim of this study was evaluating impacts on genetic diversity between temporal cohorts, but the resulting data were unable to show significant trends that supported any detrimental effect from the baiting program. When estimates were calculated for the three temporal cohorts for the ‘marsh’ population from 2012–2016, allelic diversity appeared to decline with length of time since baiting commencement and FIS also showed an increase over the same period, potentially indicating an increase in inbreeding. However, it was impossible to draw robust conclusions from these results due to the lack of statistical power, which is likely due to a combination of small sample sizes, numbers of markers or the timeframe of the study. Capturing and monitoring feral cats is highly challenging  and therEffective population size is a measure of the contribution of reproductive individuals and is an important population metric. When Ne across all cohorts within the ‘marsh’ population was broken down into three temporal cohorts between 2012 and 2016, Ne estimates appeared to decline with increasing time since baiting commencement in 2012. This, combined with a decline in genetic diversity and/or an increase in FIS, would be indicative of a population that has undergone a rapid contraction or bottleneck ,51. HoweWe found no evidence of significant differentiation between the two populations or population substructure. This suggests that the arbitrary allocation of the ‘marsh’ and ‘camps’ populations is not representative of two genetically separate populations. Values for Nm varied widely between models with the private allele method predicting 3.28 effective migrants per generation , compareThere was limited evidence of a link between relatedness and geography, with a Neighbor-Joining phylogenetic tree finding little correlation in relatedness between the ‘marsh’ and ‘camps’ populations. However, a group of five individuals from the vicinity of Karntama Village (Christmas Creek) all clustered on the same branch, indicating that populations around mining infrastructure may be related to a higher degree than elsewhere on the Marsh . This might account for the higher FIS values for the ‘camps’ population, although these results would benefit from a larger sample size for the reasons previously discussed.Estimates of spatial autocorrelation found no evidence for a relationship between genetic and geographic distance for the overall population and for males as a discrete cohort. However, there was strong evidence for relationship between genetic and geographic distance for female cats. This suggests that female cats in this landscape are at least somewhat philopatric, whereas males appear to disperse more widely, accounting for the absence of any spatial autocorrelation for the male cohort. This is supported by male cats exhibiting, on average, larger home-range sizes than females at Fortescue Marsh over the five years of the program . This diCanis familiaris dingo; red fox Vulpes vulpes) may influence the behaviour of feral cats [While there is some evidence that canids  species. The relationship between cats on the Marsh and the mining infrastructure is suggested to be fluid, with high rates of migration per generation which could indicate the mining infrastructure represents a source population that needs to be controlled as part of a broader threat-abatement strategy . Finally, we uncover evidence in differential dispersal behaviour between male and female cats, which could have implications for control strategies in this landscape and others.


Quercus liaotungensis across its distribution range. Coalescent-based Bayesian analysis, approximate Bayesian computation (ABC), and ecological niche modeling (ENM) were integrated to investigate the genetic patterns and demographical history of this species. Association estimates including Mantel tests and multiple linear regressions were used to infer the effects of geographical and ecological factors on temporal genetic variation and diversity of this oak species. Based on multiple loci, Q. liaotungensis populations clustered into two phylogenetic groups; this grouping pattern could be the result of adaptation to habitats with different temperature and precipitation seasonality conditions. Demographical reconstructions and ENMs suggest an expansion decline trend of this species during the Quaternary climatic oscillations. Association analyses based on nuclear data indicated that intraspecific genetic differentiation of Q. liaotungensis was clearly correlated with ecological distance; specifically, the genetic diversity of this species was significantly correlated with temperature seasonality and soil pH, but negatively correlated with precipitation. Our study highlights the impact of Pleistocene climate oscillations on the demographic history of a tree species in Northern China, and suggests that climatic and soil conditions are the major factors shaping the genetic diversity and population structure of Q. liaotungensis.Past and current climatic changes have affected the demography, patterns of genetic diversity, and genetic structure of extant species. The study of these processes provides valuable information to forecast evolutionary changes and to identify conservation priorities. Here, we sequenced two functional nuclear genes and four chloroplast DNA regions for 105 samples from 21 populations of  Past environmental changes, such as orographic events and climatic oscillations during the Quaternary, have left complex imprints on the structure and genetic diversity of present day species e.g., . AdditioInsights from phylogeography and population genetics suggest that stable regions during the glacial period act as species shelters  and play an important role in post-glaciation recolonization because some glacial populations may harbor high levels of genetic diversity ; in thesIn Northern China, paleogeological evidence suggests the absence of extensive ice sheets during the Quaternary glaciation; however, very cold and dry conditions with a strong winter monsoon occurred during the glacial period and had a remarkable impact on the demographic history and genetic variation of temperate tree species . PalaeovNorthern China is a mega-diverse area characterized by complex topography and relatively mild Pleistocene climates. The Yanshan, Taihang, and Qinling mountains form a north–south vegetation transect with many microclimates ; thus, tQuercus liaotungensis Koidz. (Quercus L.) is an important economic and dominant deciduous tree species in Northern China  to reconstruct demography patterns of Q. liaotungensis in Northern China  and a predicted stress-associated protein gene (SAP). These two nuclear genes were selected based on a comparative transcriptome work in three Chinese oak species . Sequences of four chloroplast regions  for the same samples were compiled from our previous research , nucleotide diversity (π) and variance of segregating sites  was calculated for each locus using DnaSP 5.0; a neutrality test using the maximum frequency of derived mutations (MFDM) method (F-statistics with 1000 permutations in Arlequin 3.5 (Genetic diversity of ) method  was cond) method . Haplotyquin 3.5 .P-distance and 1000 bootstrap simulations.The neighbor-net method (NN network) in SplitsTree4  was usedQ. liaotungensis were estimated using the Bayesian method in BEAST 2  and phylogenetic relationships were inferred based on the best evolutionary models (Supplementary Table 7 Markov chain Monte Carlo (MCMC) generations and sampled every 20,000 generations to ensure effective sample size (ESS > 200). Each population size model was performed with four different time seeds and checked for convergence. Molecular dating of divergence among populations and major groups was estimated with selected mutation rates of chloroplast (7.905 ± 0.3 × 10-10 per site per year) and nuclear genes (2.20 ± 0.2 × 10-9 per site per year) following a previous study  tree with posterior probability (PP) limit >0.5 using TreeAnnotator of the BEAST 2 package after removing the first 20% of trees as burn-in. The MCC tree was edited and visualized in FigTree 1.4.2.Phylogenetic relationships among populations of  BEAST 2 . The Aka BEAST 2  was usedus study . Four ruus study . The besus study  was execQ. liaotungensis population size changes during the Quaternary, we applied ABC in DIY-ABC 2.1.0 . To select the most likely model under direct and logistic approaches, we selected 1% of the simulated datasets closest to the observed data to evaluate model accuracy and estimate the relative PP with 95% confidence intervals (95% CI) for each scenario. To estimate type I and II errors on the power of model selection, we assessed confidence in scenario choice with 500 simulated pseudo-observed data sets (PODs) for the seven plausible scenarios. For the best-supported model, performance of all parameter estimations was determined by evaluating the relative median of absolute errors (RMAE) with 500 PODs from the simulated datasets. To assess whether the chosen scenario could explain the observed data correctly under the five summary statistics, we conducted a goodness-of-fit analysis with 1000 PODs of the simulated data by using a principal component analysis (PCA)  mutation model was used for both datasets. We generated a reference table using 7 × 10is (PCA) .Q. liaotungensis were retrieved from the Chinese Virtual Herbarium (CVH)1. We retained 41 records for ENM analyses after removing duplicate and anomalous sites. For the climatic variables, the following six bioclimatic factors from global climate models (GCMs) were selected due to their relative importance in shaping plant phylogenetic patterns and geographical distribution of Quercus  with BIO1 and BIO4. Initially, eight simulations of the current geographic distribution of Q. liaotungensis were conducted based on present climatic conditions; the regularization multiplier was set from 0.8 to 1.5 and the threshold rule of maximum training sensitivity plus specificity in Maxent v3.3.3k was applied  and 0.8 (optimum) in ArcGIS 10.2.To check for multicollinearity, a Pearson correlation analysis was performed on the six climatic factors, and BIO11 was removed due its strong correlations  between the two phylogenetic groups.Climatic distributions of the two phylogenetic groups (see results of population structure) were described based on current bioclimatic variables (BIO4 and BIO15) using two dimensional kernel density estimation with the kde2d function of the MASS package  in R 3.4Q. liaotungensis habitat. Chloroplast region genetic data was not used due to relatively low genetic diversity and differentiation. Potential patterns of IBD or IBE among Q. liaotungensis populations were assessed with Mantel tests, which were used to evaluate the correlation between genetic and geographical/ecological distances. Pairwise FST distance calculated in Arlequin 3.5 was used as genetic distance. A matrix of geographical distances among locations was estimated using the Geographic Distance Matrix Generator 1.2.3  was found, a partial Mantel test was also performed to evaluate correlations between genetic differentiation and geographical/ecological distances when one of the abiotic matrices was controlled. All Mantel tests were performed using PASSaGE v2.We used correlation and regression analyses to test whether there was a relationship between genetic variation of two nuclear genes and or 1.2.3 . The ecotability . Ecologitability . To estiQ. liaotungensis. Variables with the lowest AIC value and significant correlation were retained and considered as appropriate explanatory factors. All linear regressions were performed in R 3.4 using the MASS package.Associations of genetic diversity  with geographical factors (latitude and longitude) and ecological variables (bioclimatic and soil factors) were assessed using multiple linear regression analysis. Two linear regressions including the generalized linear model (GLM) and the robust linear model (RLM) were used to fit genetic data with each variable and variable combinations. A stepwise method (stepAIC) with forward and backward search was used to estimate the best-fit model based on the AIC algorithm. The two linear models generated a total of 3096 regressions for the geographical factors (36) and ecological variables (3060) in relation to genetic diversity of Q. liaotungensis  compiled from our previous study were 404, 308, 713, and 389 bp long, respectively. Concatenation of the four chloroplast regions resulted in an 1814 bp alignment with five indels located in the psbA-trnH (three) and trnL-trnF (two) regions  among the 21 populations for the RPc data  and nucleotide diversity (π); also, variance of segregating sites (𝜃-W) in the chloroplast data was lower than in the two nuclear genes. For the nuclear data, the summary statistics revealed higher genetic diversity for the RPc gene than for the SAP gene for all rs Table . No locirs Table .Q. liaotungensis populations, based on multiple loci, revealed two highly divergent groups, each with two subclades showing low branch support   across 0.10-0.53 Ma. Population diversification in each cluster was almost synchronous and the mean split time of each group was around 0.15 Ma (95% HPD: 0.05-0.30 Ma), while establishment of the present populations began at Q. liaotungensis global population changes under direct estimate  and logistic regression tests   for the best-supported scenario 4 were compared, suggesting reliable results for the ABC simulation. For the best supported scenario 4, power analyses of confidence scenario choice showed low values of type I  and type II errors  based on 500 PODs; the RMAE values for all parameters of scenario 4 were moderately low   of the ABC simulation indicated that the ancestor of Q. liaotungensis could, at least date back to ca. 1.92 Ma (95% HPD: 1.09-2.87 Ma) with a relatively low population size . Under these conditions, the effective population size of the ancestral population expanded by ca. five times  from 1.92 Ma until 70.2 ka (95% HPD: 25-115 ka), and was later followed by a population decline to nearly half the largest population size , which has continued up to the present  and occupied the largest suitable habitat areas compared to LGM, MH and the present. During the LGM, the species moved northward to the Qinling Mountains and its distribution range decreased, consistent with the shrinkage of suitable habitat areas. The predicted distribution of Q. liaotungensis during the MH and the present suggest that this species had a relatively stable habitat after the LGM; however, the estimated habitat areas increased from the LGM to the MH. Also, the areas of current habitat are slightly smaller than those estimated for the MH  and precipitation seasonality (BIO15). Populations in cluster 1 seem to have adapted to low levels of temperature and precipitation seasonality, while populations in cluster 2 might have adapted to habitats with higher temperature seasonality and broader niche breadth of precipitation seasonality  between genetic and ecological distance for Q. liaotungensis; interestingly, when geographical distance was controlled using a partial Mantel test, the correlation between genetic differentiation and ecological distance was enhanced . Separate analysis of SAP data for cluster 1 revealed marginal significant correlations between genetic and ecological distances when Mantel and partial Mantel tests were conducted; by contrast, no significant correlations were found for cluster 2. Similarly, the RPc data showed no correlations between genetic and ecological/geographical distances for clusters 1 and 2  for the 21 locations tested  to explain the differentiation of 𝜃-W among Q. liaotungensis populations  and soil pH , and a negative correlation with BIO17 (precipitation of driest quarter)    during ca. 1.3-3 Ma  Figure ; this poongolica , and wit1.3-3 Ma . Further Juglans .Q. liaotungensis, which seems to have been predated by a small effective population size  to present time  and temperature seasonality, and precipitation of the driest quarter Figure . By contQ. liaotungensis and that the adaptive differentiation among populations could be mirrored by the neutral loci used in this study. However, as neutral markers are less affected by intraspecific gene flow during ecological divergence, this hypothesis will require further investigation using markers from divergently selected genomic regions , suggesting that habitat instability mediated by climate seasonality is a major factor in driving historical range shifts and shaping genetic variation of tree species having long distance gene flow , is a prerequisite for stabilization of ecological systems . Our resene flow .Q. liaotungensis (P = 0.0126), and suggest a marginally significant negative correlation of 𝜃-W with annual precipitation (P = 0.0597) (Figure Q. liaotungensis populations. This pattern appears to be supported by population GQ, which is located at the center of the Loess Plateau, where extreme and arid climate may have induced local adaptation resulting in high levels of genetic diversity and the presence of private haplotypes (Figure A previous study using variation partitioning simulations indicated that precipitation is one of the most important environmental factors determining the geographical ranges of ungensis . Similarn P = 0.097 (Figurs P = 0.026, and ss Figure . Additios Figure  as well s Figure .Q. liaotungensis in the face of climate changes during the Quaternary using multiple loci and coalescent-based simulations and ENMs. Our results suggest that climatic oscillations during the Pleistocene have greatly affected the effective population sizes and genetic structure of Q. liaotungensis. Habitat stability can maintain species genetic diversity but might not act as a direct factor in generating population differentiation of this oak during the glacial-interglacial periods. By comparison, ecological factors, such as the variation of climate seasonality and soil conditions seem to be more important in shaping the genetic structure and genetic diversity of Q. liaotungensis. Based on these results, our study highlights the effects of past and present ecological factors on the historical population dynamics and temporal genetic properties of tree species in North China.This investigation explores the demographical history and genetic variation of JY and GZ conceived the research. JY, LF, and ZL collected the samples. JY, LV, LF, and ZL analyzed the data. JY, LV, and GZ wrote the manuscript. All authors read and approved the final manuscript.The 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.