The New Zealand green-lipped mussel aquaculture industry is largely dependent on the supply of young mussels that wash up on Ninety Mile Beach , which are collected and trucked to aquaculture farms. The locations of source populations of Kaitaia spat are unknown and this lack of knowledge represents a major problem because spat supply may be irregular. We combined genotypic (microsatellite) and phenotypic (shell geochemistry) data in a geospatial framework to determine if this new approach can help identify source populations of mussels collected from two spat-collecting and four non-spat-collecting sites further south. Genetic analyses resolved differentiated clusters (mostly three clusters), but no obvious source populations. Shell geochemistry analyses resolved six differentiated clusters, as did the combined genotypic and phenotypic data. Analyses revealed high levels of spatial and temporal variability in the geochemistry signal. Whilst we have not been able to identify the source site(s) of Kaitaia spat our analyses indicate that geospatial testing using combined genotypic and phenotypic data is a powerful approach. Next steps should employ analyses of single nucleotide polymorphism markers with shell geochemistry and in conjunction with high resolution physical oceanographic modelling to resolve the longstanding question of the origin of Kaitaia spat. However, once acquired, such knowledge has helped to inform management in disciplines including fisheries, aquaculture, conservation and biodiversity protection, and more recently in understanding/forecasting the impacts of climate change8.One of the biggest challenges marine biology has sought to address over the last 40 to 50 years has been to track the movement of larvae through the water column. Identifying source and sink populations and quantifying distance of dispersal as well as the rates and routes of migration amongst sites have been longstanding problems10. Identification of source populations, in particular in a coastal setting, may be critical to the longevity of the fishery or the aquaculture operation. However, because most marine larvae are so small and so many larvae are produced it is generally impractical to mark larvae at their origin and then to follow them as they move through the water column. This problem is compounded by the often very large spatial scale of realised and potential dispersal, its significant temporal variability, and its apparent ’chaotic ‘ patchy nature13. As a consequence, several different forms of ’natural tags ‘ have been employed to track individuals, most usually not the larval stage itself, but post-metamorphic stages such as juveniles or adults. Such tags have included genetic markers16, fish otoliths19, molluscan shell geochemistry22, and others such as lipid content23, stable isotopes26, and radioisotopes27. In most studies the different tagging methods have usually been used in isolation. However, improving power of resolution by combining two or more of these tagging methods is now a realistic option. A multi-marker approach may provide new insights into the spatial sources and temporal availability of larvae or spat, and the routes that they travel to reach settlement sites.The sustainable management of many fisheries and also some aquaculture operations may depend on knowledge of the source of the individuals entering the fishery  or contributing to aquaculture Perna canaliculus Gmelin, 1791 is now the country’s most valuable aquaculture export. By 2017, total production was estimated to be ~ 100,000 tonnes pa, with a value of ~ NZ $350 M pa28. The industry has undergone rapid growth over the last 30 years, placing increasing demands on supplies of early stage juveniles, called spat30. The industry is largely reliant on wild caught spat, with ~ 80% of spat coming from Te Oneroa-a-Tōhē—Ninety Mile Beach (henceforth Ninety Mile Beach), in the far north30. In the New Zealand Quota Management System mussel spat may be collected by quota holders from area GLM9 which covers Ninety Mile Beach. Spat-bearing material is harvested after juvenile mussels are washed ashore attached to algae and hydroids30. A reliance on wild caught spat is a constraint to further industry growth, in particular given that the source populations that supply the spat are yet to be identified31. However, while hatchery production methods for this species are well established33, there remains a strong interest in the use of wild spat due to their ease of availability and low cost. To date, spat supply at Ninety Mile Beach has been surprisingly reliable and this availability has not hindered the expansion of the industry. However, in early 2019, spat supply to Ninety Mile Beach was low to non-existent for months at a time . More recently (early to mid 2020) spat supply has resumed, but there is still uncertainty around supply dynamics and a lack of knowledge of the locations of source populations.Farming of New Zealand’s endemic green-lipped mussel, 30. The coastal and offshore physical oceanography of the northwest of New Zealand is poorly understood30. The most recent study of the region indicates that, in general, flows are weak and highly variable31. Offshore, beyond the 1000-m isobath, the mean flow is to the southeast, whereas inshore of the 1000-m isobath and close to the coast the mean flow is northwestward31. Examination of a long-term data set revealed that the supply of Kaitaia spat is positively associated with strong offshore winds, with low swell height in the onshore direction, and with the number of storm events per year32. Overall, these observations32 suggest that the natural supply of mussel spat to Ninety Mile Beach is a complex interaction of numerous different biological, physical oceanographic and meteorological factors.The green-lipped mussel has a pelagic larval duration (PLD) of approx 28–35 days depending on environmental conditions and season, produces a planktotrophic (feeding) larval stage that stays near the water column surface, and which is generally a passive particle 30. Previous work33 using traditional genetic clustering approaches failed to resolve any genetic structure (differentiation) amongst mussels collected from this northern region, whilst analyses of shell geochemistry revealed large differences amongst mussels from different sites33. The identification of source populations has remained problematical. Striking a balance between what at present appear to be uninformative microsatellite data and perhaps overly variable shell geochemistry data seems to be necessary if our knowledge of source populations is to be advanced, but is proving to be a difficult next step. However, using a new analytical method that combines genotypic with phenotypic information, and placing these in a geospatial context34, may provide new insights into the origin of Kaitaia spat at Ninety Mile Beach.Assessments of PLD and coastal physical oceanography suggest that spat arriving at Ninety Mile Beach may be derived from one or more source populations to the south, in the region from Hokianga Harbour to Taranaki34, to better understand how powerful this approach is as a method for identifying putative source populations.In the present paper we describe geospatial analyses of separate and combined genotypic and phenotypic data sets of green-lipped mussels collected from six sites in northern New Zealand. Two of our sites (Scott Point at the northern end and Ahipara at the southern end of Ninety Mile Beach) are representative of Kaitaia spat, the mussels used by industry. We use these samples as our ‘target’ mussels. The four other sites (listed north to south), Tanutanu Beach, Mitimiti, Whatipu and Oakura Beach, are representative of sites that may be sources of Kaitaia spat. We use these samples as our putative ‘source’ mussels. These four sites span ~ 640 km of coastline to the south of Ninety Mile Beach. Our testing of ‘target’ and ‘source’ mussels is subject to some possible error or bias because pronounced temporal variation in spat supply  may result in loss of signal definition for this work. To try to mitigate this we used mussels of different sizes (ages) to reduce the likely affect of one large atypical pulse of spat arriving at Ninety Mile Beach. That is, we have taken a time-averaged sample of mussels (mussels of different ages). Additionally, mussels were collected from Ahipara in January, February and March of 2015 for shell geochemistry analyses to better understand the extent of temporal variation in Kaitaia spat supply. We quantified genotypic and phenotypic variation amongst mussels from these six sites, and then analysed these combined data sets in a geospatial framework that allows for correlated alleles, for spatial testing, and for inclusion of null allelesIn total, genotypic (microsatellite variation at 10 polymorphic loci) data were collected for 288 mussels from six sites and phenotypic (shell geochemistry variation of 12 elements) data from 395 mussels from six sites, with one site having three temporal samples. Mussels collected from Scott Point (SCO) and Ahipara (AHI) were expected to be most like the spat collected for the aquaculture industry from the region of Ninety Mile Beach, whereas mussels from the four southern sites  are representative of putative natal populations from which the Ninety Mile Beach mussels may be derived.Pcan1-27, Pcan1-29, Pcan6-17, Pcan10-44 and Pcan22-11. No long allele dropout was detected. After false discovery rate (FDR) correction, Pcan1-27 was identified as being significantly out of HWE at all populations and Pcan10-44 at more than half of the samples. No evidence of significant linkage disequilibrium was detected between locus pairs. Site-specific values of mean number of alleles, observed and expected heterozygosities, FIS and also AP were all very similar across sample locations . Genotypic differentiation was observed for only 1 of 15 pairwise comparisons, after correction for multiple testing .Allelic (genic) differentiation was observed in 9 of 15 pairwise sample comparisons after correction for multiple testing Table . Overallng Table . OverallFST were low   revealed clear separation amongst most samples Fig. . There wMean ± SD assignment probabilities for mussels from the six sites were 49.8% ± 18.5 Table a. The twF0) migrants were identified. Scott Point contributed and received the greatest number of F0 migrants , whereas Tanutanu contributed no F0 migrants and, along with Ahipara and Oakura, received only one migrant each  and 47 for Ahipara (Feb) were used in subsequent statistical analyses.Running the Q-DFA with a step-wise procedure revealed that six elemental ratios  explained the majority of the classification success (96.7%) of the Q-DFA Table . These rWhilst the ability of the Q-DFA to correctly assign mussels to their site of collection varied from sample to sample, classification success was high Table . MusselsThe plot of the Q-DFA canonical scores revealed no overlap of the 95% confidence interval ellipses for the ratios of B, Co, Li, Mg, Mn and Ni to Ca in shells collected in January 2015 from the six sites, as well as the additional temporal samples collected from Ahipara in February and March, 2015  = 253.9, P < 0.001) than at other sites. Lithium  = 190.1, P < 0.001) and manganese  = 230.2, P < 0.001) ratios were significantly higher in shells from Whatipu than all other sites. Cobalt ratios were significantly higher in Ahipara mussel shells in January 2015  = 248.4, P < 0.001) but not in February or March. Finally, the ratio for nickel was significantly higher in shells from Tanutanu  = 162.7, P < 0.001) than in shells from all other sites, and although magnesium was highly variable in shells collected from each site (particularly Ahipara in January), it was greatest in Mitimiti shells  = 259.2, P < 0.001). These results highlight the spatial  and temporal (three samples from Ahipara) variation that exists in shell geochemistry of green-lipped mussels.Ratios of elements to Ca within the shells varied significantly amongst the sites Fig.  for multK = 2 (4 times), K = 3 (12 times) and K = 4 (4 times), where K is the number of clusters, based on genotypic variation of the mussels from the six sites. The top four models were K = 3 (twice) and K = 4 (twice). These results indicate that Geneland cannot resolve the K = 3 or K = 4 question, that the most likely interpretation is K = 3, but also that the mussels from the six sites do not constitute one homogenous group. Whatipu was placed into its own cluster in 16 runs, Scott Point in 14 runs, and Mitimiti in six runs. Ahipara, Oakura and Tanutanu clustered together in 18 of 20 runs. Pairwise FST values were low and ranged from < 0.001 to 0.036  (Table In 20 independent runs Geneland resolved 4) Table .K = 6 in every instance. Based on shell geochemical variation the mussels from all six sites were very different and could easily be differentiated.In 10 independent runs Geneland resolved K = 3 in every instance for the monthly samples  of mussels from Ahipara. Likewise, in 10 independent runs Geneland resolved K = 8 in every instance when all eight samples  were analysed together. These results indicate that geochemical differences amongst shells collected from Ahipara at monthly intervals are sufficient to permit identification of month-specific samples: there is pronounced temporal variability in shell geochemical signatures.In 10 independent runs Geneland resolved K = 6 in every instance. These results indicate that the mussels from all six sites are very different.In 10 independent runs Geneland resolved 34 may help with the identification of previously undetected clusters or the novel identification of source populations. The ability to utilise both phenotypic and genotypic information in assignment testing is important because phenotypic variation may be high even when genotypic variation is low within the same individuals. This phenomenon is often reported36 and may occur because the genetic markers being used do not adequately reflect phenotypic variation or because genotypic variation and phenotypic variation are influenced by different processes37. Thus, given the differing levels of information they can provide, a combined genetic and geochemical approach may be particularly informative in identifying green-lipped mussel source populations. This approach has been used to delineate stocks of Mediterranean hake (Merluccius merluccius) and it was reported that microsatellite methods provided broad scale geospatial information of gene flow on evolutionary timescales, whereas geochemical methods could provide fine scale geospatial information on ecological relevant timescales38.Analysis of genotypic and phenotypic variation in a geospatial framework31 and the 28–35 day PLD32 the natal sites could be 10s to 100s of km away. Balanced against this are the high levels of self-recruitment (in the range 40–70%) reported for green-lipped mussels39, and in addition factors such as offshore winds, storminess and low swell height in the onshore direction will all contribute to variation in natural spat deposition on Ninety Mile Beach32. Previous work in the North Island of New Zealand (including in the far north region) has been unable to resolve any population genetic structure  despite using a range of different genetic marker types41. These studies did not employ geospatial analyses, and the apparent absence of spatial genetic differentiation suggested that it would be very hard, if not impossible, to identify source populations for the Kaitaia spat, given that all mussels in the region belonged to one homogeneous ‘northern’ group. The new analyses reported here resolve genetic differentiation between/amongst mussels from six sites in this northern region. Whilst different results were resolved by Geneland for the genotypic data alone, the most frequently reported was K = 3 clusters, with the sites of Ahipara, Mitimiti, Oakura and Tanutanu forming one group, and the sites of Scott Point and Whatipu each forming separate groups. There is no obvious association between a site’s geographic location and its group membership. In terms of determining the origin of spat collected from the Ninety Mile Beach sites  these results indicate that we have not collected source mussels for the spat arriving at Scott Point, but that spat arriving at Ahipara may be derived from two neighbouring sites of Tanutanu and Mitimiti, which are 15 km and 48 km south, respectively, and also from the most distant site of Oakura, which is ~ 640 km south. Mussels from the northernmost Kaitaia spat site of Scott Point were consistently identified as being genotypically distinct, as were mussels from the geographically intermediate site of Whatipu, which suggests that both receive limited larval supply (gene flow) from the other four sites. Pairwise FST estimates amongst the K = 3 clusters confirm this interpretation. Based on the microsatellite markers alone, these results indicate that Kaitaia spat are most probably not derived from a single source population/site, but are a mix of mussels derived from multiple different sites. The estimate of the total number of first generation migrants amongst all sites was 12 of 288 individuals analysed. Estimates of the number of first generation migrants between pairs of sites were all in the range 1 to 3. The two Kaitaia spat sites of Scott Point and Ahipara had the highest (n = 3) and the lowest (n = 1) number of first generation migrants, respectively. When grouped according to the Geneland results, the number of first generation migrants was 4 for Ahipara, Tanutanu, Mitimiti and Oakura, 3 for Scott Point, and 3 for Whatipu. There is no obvious geographic explanation for the patterns of gene flow (source populations versus destination sites) revealed by these analyses, but for the first time we have evidence that microsatellite variation can be used to accurately differentiate amongst samples collected 10s to 100s km apart within what was previously recognised as one large genetically homogenous ‘northern’ group41.In terms of larval connectivity, new mussel recruits to any site are expected to originate from source sites to the south: given the weak mean northwestward flow of water along the coastK = 6 clusters  in every run for the shell geochemistry data, confirming the distinct nature of the six samples. However, the PCoA plot revealed that shells from Scott Point and Ahipara (the sites at Ninety Mile Beach) exhibited the greatest variation in shell geochemistry. Thus, mussels from sites that are only a few kilometres apart can be differentiated with a very high degree of confidence. Clearly, this finding needs to be tested against samples from other sites to determine the extent of site specificity versus some sort of regional specificity. In a management sense, when trying to trace the source of Kaitaia spat, such high levels of shell geochemical variability are really only informative when mussels collected from Ninety Mile Beach cannot be differentiated from mussels collected from another site (the putative source) because absence of differentiation implies similarity of mussels from source and recipient sites. To resolve mussel sources would require high resolution subsampling of adult shell at the umbo using laser ablation ICP-MS. The composition of the umbo represents larval shell geochemistry which can be compared to the spatial variation in overall shell geochemistry shown in this study. Recently, this approach was used to determine level of larval spill over from mussel aquaculture to adjacent mussel beds42. Future studies using LA-ICP-MS analyses of shells along the northwest coast of New Zealand are planned to address this.Analysis of the shell geochemistry of mussels collected from the six sites revealed very high levels of elemental variation. In total, 17% (739 of 4396) of analysed geochemical values were imputed which could potentially introduce bias in the analysis. However, there was no correlation between importance of an element within the Q-DFA and the level of imputation (unpub. data). Assignment test success results were ~ 95% (range 90–100%), and are much higher than those reported for the same mussels when based on genotypic variation . High levels of assignment success based on shell geochemistry suggest that mussels from each site experience environmental conditions that result in different shell chemical fingerprints. Geneland recognised K = 6 clusters, with each site-specific sample being different from all other samples. That is, the addition of the genotypic data set to the phenotypic data set did not increase the power of the analysis to detect differences amongst the clusters. At the moment, our analyses suggest that shell geochemical signal is more informative than the genetic signal in assigning mussels to site. Nonetheless, we view the combined application of genotypic and phenotypic data sets to be an important tool and a major step forward in terms of information  and management options43. This will be particularly true when the next generation of molecular markers is developed and employed (see subsequent section).Whilst we may not have been able to identify the source population(s) of Kaitaia spat our results indicate that combined genotypic and phenotypic data sets, when analysed in a geospatial framework, probably have the necessary discriminatory power to identify source populations. However, it is equally apparent that these analyses are, to a large extent, only as good as the source sites that are sampled. In the present study we focussed on six sites, two of which represent the Kaitaia spat collecting region and four of which are further south and could reasonably have been sources. Subsequent work will require more focussed spatial coverage of sample sites, and ideally this work will be a targeted approach to site selection based on, for example, physical oceanographic models8 that can be used to help identify source populations, rather than the haphazard site selection process involved here. Subsequent work may also be informed by our results. For example, mussels from Whatipu were identified consistently as being different from all other mussels. This may be because the site’s location on Manukau Harbour, a large body of seawater contained in a coastal embayment to the west of the city of Auckland, is expected to be characterised by a chemical composition that is very different from that of all other sites sampled. The results from Whatipu are helpful in that they suggest that further detailed examination of the geochemistry signals in the shells of mussels from Scott Point and Ahipara may permit the identification of environmental signatures that can be used to help identify source sites.Geneland analysis of combined genotypic and phenotypic variation revealed M. edulis) populations at ~ 50 km distance44, populations of M. californianus and M. galloprovincialis 20 km apart20, and New Zealand green-lipped mussel populations at a distance down to 12 km21. These studies highlight the power and utility of the shell geochemistry approach in identifying source populations, and also in addressing questions of spatial and temporal settlement variability at ecological relevant scales.Six elemental ratios  provided the majority of classification success in discriminating amongst the six west coast mussel samples along the northern North Island of New Zealand. These six elemental ratios assigned mussels to their site of origin with 92–100% success: this information is valuable because it limits the number of elements needing to be assayed in future geochemical tag studies on this species along this coast. Reliable discrimination of mussels from sites encompassing similar distances to those in our study (10s-100s of km) have been recorded using geochemical tags for a number of marine taxa, including blue mussel  revealed that mussels could be assigned back to their site of origin (Ahipara) with a high degree of confidence (0.995–1.000), both in the context of the other five sites and also in terms of the month of collection . There was, however, a comparatively high degree of variability in elemental ratios (e.g. Mg:Ca) amongst the mussels of the three-monthly samples. Comparing shell lengths of mussels sampled during these three months reveals a significant increase in shell length between January to February  = 5.931, P < 0.003), which presumably contributed to differential incorporation of elements into growing shells. The three samples from Ahipara illustrate the extent of short-term temporal variability in elemental composition  at any one site. Such variation may be highly informative in identifying spat source populations, but only as long as this shell geochemistry signal can be linked back to the source population(s).Understanding the spatio-temporal stability of geochemical tags is vital if such tags are to be informative. Some species, such as the cockle, 26.The shell geochemistry data in particular, and the population genetics data to a lesser extent, highlight spatial variability  that may, in the future, be informative in identifying Kaitaia spat source populations for the New Zealand green-lipped mussel industry. Temporal variation in geochemistry likely reflects changing environmental conditions on short temporal timeframes at the sampling sites. These results present a challenge to the future identification of the source populations, given that the geochemical and genetic signals of the spat at Ninety Mile Beach will be mixed, reflecting the differing spatial and temporal contributions from different source populations. Interpreting this mixed signal will be difficult, but not impossible, and is akin to the use of mixed source models employed to test the origin of stable isotopes in diet analysisMytilus edulis53. The use of LA-ICP-MS methods combined with a thorough sampling regime  will be necessary to trace green-lipped mussel spatfalls back to their site(s) of origin.The ICP-MS method is an accurate approach to be able to discriminate populations separated by as little as 5 km along an open coast. In this study, the elemental ratios of B, Co, Li, Mg, Mn, and Ni:Ca appear to consistently discriminate mussel populations along the west coast of northern New Zealand. It is now vital that the spatio-temporal stability of these geochemical tags along this coastline be confirmed. Furthermore, if determining provenance of harvested mussels is of interest then shell geochemistry has a demonstrated role to play here as has been shown for other bivalve species e.g. 55. However, in the meantime, the supply of spat to Kaitaia that underpins the New Zealand green-lipped mussel aquaculture industry remains unpredictable, even if it is reasonably cheap to collect and distribute around the country. It seems likely that over the next several decades many mussel growers will continue with Kaitaia spat as their preferred source of juvenile mussels for stocking their farms. In addition, Aquaculture NZ has plans to expand coastal and open water aquaculture in New Zealand, and a large part of this strategy is focussed on growth of the green-lipped mussel sector56. Where the spat supply to meet this demand will come from is unclear, again suggesting that Kaitaia spat will remain an important resource for the green-lipped mussel aquaculture in New Zealand in the foreseeable future.From a management perspective the identification of the Kaitaia spat source populations remains important, even as the industry moves towards commercial-scale hatchery production of spat. Hatchery production will help to secure year-round availability of spat and will permit selective breeding of spat with fitness characteristics of interest to the industry59 and are expected to outperform microsatellite markers for information content. This is expected to translate into far greater power to discriminate amongst samples in a spatial and temporal context. In addition, the results of the present study highlight the need for a more focussed approach to site selection. Future work will therefore be guided by newly developed high definition physical oceanographic models that will provide much more guidance for site selection than was available to us at the time of this study. Finally, Norrie et al. (2019)22 analysed shell geochemistry in selectively bred green-lipped mussel and demonstrated a genetic component in the incorporation of elements within the shell of this species. If such population differences could be uncovered in wild mussels this could further strengthen future analyses seeking to determine site of larval origin.Future research to identify source populations of Kaitaia spat will include the use of more informative genetic markers  than the microsatellite markers employed here. SNPs are highly polymorphic co-dominant markers that can now be developed reasonably cheaply and in very large numbers (thousands to hundreds of thousands). They have proven to be useful in a range of different approachesPerna canaliculus, 14–20 mm shell length, were collected in January 2015 from six intertidal reef sites along the west coast of the North Island of New Zealand  green-lipped mussels, and Fig. . At thesth Table  via Imag61. Microsatellite loci were PCR-amplified in 15 μL reactions containing 70 ng of DNA template, 0.5 units/μL Taq DNA polymerase, 67 mM Tris–HCl pH 8.8, 16 mM (NH4)SO4, 2 mM MgCl2, 0.2 mM dNTPs, 0.3 μM of Forward and Reverse primers and ddH2O to volume. PCR products were visualised with an automated sequencer  with the GeneScan-500 (LIZ) internal size standard. The software GeneMarker V2.2.0 (SoftGenetics) was used to analyse electropherograms for allele scoring and alleles were binned with manual checking using the AutoBin program62. Genotyping artefacts  were assessed using the software Micro-Checker v.2.2.0.363.Total DNA was extracted from mantle or gill tissue using Geneaid genomic DNA kits following manufacturer´s instructions. DNA concentrations and A260/A280 ratios were quantified using a NanoDrop ND-1000 (Thermo Scientific). Specimens were genotyped at 10 polymorphic microsatellite lociA), observed (HO) and expected (HE) heterozygosities per site and the number of private alleles (AP) were calculated using GenePop v4.7 on the web (https://genepop.curtin.edu.au/). Analysis of allelic and genotypic differentiation, departure from Hardy–Weinberg equilibrium (HWE) and linkage disequilibrium (LD) were performed via GenePop using the Markov chain method and Fisher´s exact test64. False discovery rate (FDR) control65 was applied to P-values in all statistical analyses that included multiple comparisons. A principal coordinates analysis (PCoA) of allelic data with site as the grouping variable was carried in PRIMER using the MDS function66 to examine variation in the multilocus data set.Number of alleles , the individual was assigned to the site with the highest probability. Thus, self-assignments are ‘home’ individuals (self recruits), whereas the first-generation migrants are ‘away’ individuals (a migrant from one known site to another).To determine the most likely source of individuals at each site, self-assignment tests were performed in the software GeneClass23 (Sigma-Aldrich trace element grade) and rinsed three times in de-ionised water. To standardise analyses amongst sites, only the left shell of each individual was analysed. Shells were cleaned69. To remove the periostracum and any remaining organic matter, each shell was soaked in a 1% H2O2  solution buffered in 1 N NaOH  at 80 °C for 10 min. Shells were then rinsed by running under de-ionised water for 10s and stored in acid-washed plastic vials, until digestion.Individual shells were thawed and placed into 5 ml plastic vials and sonicated for 2 min before the water was drained. All plastic ware used to handle and store mussels after the sonication step was acid washed for 12 h in 10% HNO3 (65%) and 1 ml HCl (37%). Shells were digested using a Milestone ETHOS-UP microwave digestion system. Digests were diluted with 50 ml dH2O and the final weights obtained. Final solutions were clear and the shells were completely dissolved. Solutions were diluted 10 × with matrix matched diluent and elemental ratios were then analysed using an Agilent 7700 ICP-MS . Instrument settings were RF Power (W) = 1550, with a He gas flow rate of 4.3 L min−1. Based on the results of Dunphy et al. (2011)21 the elemental suite analysed consisted of: 43Ca, 66Zn, 55Mn, 11B, 88Sr, 25Mg, 138Ba, 63Cu, 7Li, 60Ni, 47Ti, and 59Co. Raw elemental concentrations were converted to molar concentrations i.e. mmol mol−1, and trace element:calcium (TE:Ca) ratios were calculated to standardise elemental concentrations.After weighing, shells were placed in Maxi-44 80 ml Teflon vessels and combined with 5 ml HNO70. An imputation procedure was applied when the concentration of an element was below detection limits of the instrument. This consisted of substituting the recorded 0 value with half of the minimum value of that element recorded in all shells, from all sites combined . The Q-DFA was performed stepwise by firstly running the analysis on all elemental ratios and recording the overall classification success. Then the elemental ratio with the least discriminatory influence was removed and the Q-DFA run again with the difference in classification success noted. This was repeated for all elemental ratios to identify the point at which no increase in the classification success was observed and thus which combination of elements would prove to be successful in discriminating amongst shells from the different sites. Mean canonical scores were calculated for shells from each site and a classification success table generated in order to gauge the success of the Q-DFA in assigning shells back to their site of collection based on elemental ratios. Areas of Receiver Operating Characteristic (ROC) curves were calculated to provide a measure of the sensitivity of the Q-DFA by comparing true positive classifications against the false positive classifications. Kruskall-Wallis ANOVAs were used to test elemental ratios of shells amongst the sites and the temporal samples. All analyses were carried out in JMP 13.0 software71.To assess the efficacy of geochemical markers in discriminating amongst shells from the six sites, a quadratic discriminant function analysis (Q-DFA) was performed. A Q-DFA was selected due to differing covariance estimates amongst samples. Due to non-normality, data were rank transformed and the Q-DFA was performed on the rank valuesK) within the six-site data set34. Geneland is a spatial Bayesian analysis method that can include not only genotypic data (co-dominant microsatellite variation) but also phenotypic data  and geographic data (latitude and longitude). It is implemented in the R language code72. We conducted four separate sets of analyses in Geneland: (1) the genetic data only were analysed in a geospatial setting, (2) the shell geochemistry data only were analysed in a geospatial framework, (3) the three samples from Ahipara  were analysed in a geospatial context as a separate test of temporal variation in shell geochemistry i.e., we tested the hypothesis that shell geochemistry varied enough amongst the three samples to permit their identification as K = 3 clusters in Geneland, and we also analysed all eight samples together in a geospatial context  to test the extent of temporal variability in the range of spatial variability, and (4) the genetic and the shell geochemistry data were analysed together in a geospatial framework. This stepwise approach permits identification of clustering within the genetics dataset, within the shell geochemistry dataset, and then within the combined genotypic and phenotypic dataset. Geneland assumes approximate Hardy–Weinberg equilibrium (HWE) and linkage equilibrium between loci for the genotypic data. Because molluscan populations are rarely in HWE and are often characterised by null alleles73 we accounted for this in the analysis using the ‘null alleles’ module. We employed the ‘spatial’ and ‘correlated allele frequencies’ modules because previous work has shown that allele frequencies in one population are similar to those in other neighbouring populations39. Finally, spatial uncertainty was set to zero because mussels are sessile and we know their exact site of collection. We ran multiple independent runs  to test for the number of clusters (K) and to ensure that the results converged. In all three cases  the results of these runs were assessed based on the frequency of the different K values and also on a ranking of the run-specific probability density values. For the genotypic data analysis we calculated the pairwise FST values between the different clusters to help quantify the extent of differentiation amongst clusters.The software package Geneland v4.0.8 was employed to identify the number of clusters (Supplementary Information 1.Supplementary Information 2.


Ostrea chilensis , the flat oyster, is native toChile and New Zealand. In Chile, it occurs in a few natural beds, from thenorthern part of Chiloé Island (41 ºS) to the Guaitecas Archipelago (45 ºS).This bivalve is slow growing, broods its young, and has very limited dispersalpotential. The Ostrea chilensis fishery has been over-exploitedfor a number of decades such that in some locations oysters no longer exist. Theaim of this study was to study the genetic diversity of the Chilean flat oysteralong its natural distribution to quantify the possible impact of the dredgefishery on wild populations. The genetic structure and diversity ofOstrea chilensis from six natural beds with differenthistories of fishing activity were estimated. Based on mitochondrial (Cytb) andnuclear (ITS1) DNA sequence variation, our results provide evidence that geneticdiversity is different among populations with recent history of wild dredgefishery efforts. We discuss the possible causes of these results. Ultimately,such new information may be used to develop and apply new management measures topromote the sustainable use of this valuable marine resource. Ostrea chilensis occurs in only a fewnatural beds, from Calbuco (41º 45’S) to the Guaitecas Archipielago (45º 10’S). Althoug5º 10’S).Within its naturally limited spatial range in Chile, the oyster has been harvestedfor at least 77 years. As early as 1943, Pullinque, which is located in the interiorarea of the Gulf of Quetalmahue (Chiloé Island) was declared a Marine Reserve forthe conservation of the flat oyster , due to st to March 31st) with a minimun legal size for oysters with different histories of fishing pressure.Samples of oysters were collected by dredging or diving from six sites that wesubsequently refer to as wild source populations , coverin2 piece of tissue was excised from the mantle border ofeach individual and was fixed in 95% ethanol and stored at 4 °C before DNAextraction. Total DNA was extracted using the genomic DNA mini-kit , according to manufacturer’s instructions. DNA samples were diluted50-fold with ultrapure water, and PCR amplifications for the mitochondrial DNAcytochrome b (Cytb - 704 bp) and the nuclear DNA InternallyTranscribed Spacer 1 (ITS1 - 494 bp) markers were performed using an AppliedBiosystems® (Model Veriti) thermal cycler. For the amplification of the Cytb region,new primers were designed (Cytb Fost- F5’ TGT ATT CCC AGG TGG CTC TC 3’ andCyt-b Rost -R5’ CTG CAC TCG CAT TCC TGA TA 3’). ITS1amplification was performed using primers designed by 2, 200 mM dNTPs, 0.5 µlof each primer (10 pg/µl), 1 U Taq (Invitrogen), 17.5 µl ofdouble-distilled water plus 20 ng of DNA. Thermal cycling parameters for Cytbincluded an initial denaturation step at 95 °C for 3 min, followed by 30 cycles at95 °C for 1 min, 54.4 °C for 1 min, and 72 °C for 1:30 min, and ended with a final10 min extension at 72 °C. Thermal cycling parameters for ITS1 included an initialdenaturation step at 95 °C for 3 min, followed by 30 cycles at 95 °C for 30 s, 60 °Cfor 20 s, and 72 °C for 30 s, and ended with a final 10 min extension at 72 °C. Thesamples did not exhibit double band amplifications as previously reported for thisspecies by other authors  was trimmed from the posterior in thepost-processing. A contour map of the posterior mode of population membership wascreated to visualise the spatial genetic structure of the six populations withoutregard to their fishing histories. Past population dynamics in Ostreachilensis was analysed using the Bayesian Skyline Plot in the programBEAST 1.8.1 (Standard diversity indices including number of haplotypes (K), number of segregatingsites (S), haplotype diversity (H), mean number of pairwise differences (∏), andnucleotide diversity (π) were estimated for each population without regard to theirfishing histories using DnaSP v.5.1 . To measThe data that support the findings of this study are available on request fromthe corresponding author. The data are not publicly available due to Fundingprivacy restrictions.Ostrea chilensis. For Cytb the sequences wereA-T rich (62.7%) compared to G-C content (37.3%). In contrast, for ITS1 thesequences were G-C rich (60.3%) compared to A-T content (39.7%).Sequence data  were obtained for 165 individuals fromsix populations of Population genetic diversity: For Cytb, 11 nucleotide sites were polymorphic and 12haplotypes were identified. For ITS-1, 13 nucleotide sites were polymorphic and 25haplotypes were identified .HaplotyAt the regional level, there was an apparent decrease in the total number ofhaplotypes from north to south, with 10 haplotypes (Cytb) and 18 haplotypes (ITS1)for cluster 1, and 5 haplotypes (Cytb) and 11 haplotypes (ITS1) for cluster 2 . These gΠ = 0.303; π = 0.00049;Cluster 2: H = 0.700; Π = 0.957; π = 0.00155 / Cluster 1(ITS1): Π = 1.374; π = 0.00294; Cluster 2: Π= 3.640; π = 0.00755 (see Differences in diversity indices were observed between the north and the south. These D and Fu’s FS values were negative, providing evidence of recent population expansion orselective sweeps  and ITS1 (nDNA) analysis, including their significant values,was carried out which gives a better understanding of the population structure contained the 4 most northerly populations plus the most southerly,whilst the secondary group (Cluster 2) contained only the Bahía Low population. Theassignment probabilities of individuals to their respective clusters were 0.90 that enclose themarine area. The same general area has been suggested as the most likely westernrefuge for other Patagonian taxa . This evidence of a genetic discontinuitybetween Bahía Low and the other locations in this area is consistent with the knownimpacts of glacial cycles on Patagonian biota  25-18 Ka - Ostrea chilensispopulations because of the almost permanent presence of harmful algal blooms (HABs)in this region  on thenorthern Guaitecas Archipielago (Melinka) s region  and3, ws region .Also, tOstrea chilensis larvae havea short pelagic life  are expected because Ostrea chilensis cluster that wascomposed of five sites exhibited reduced genetic diversity  compared to the other cluster (one site only - Bahía Low) locatedon Melinka Island. These results may suggest that fishing pressure has contributedto changes in Ostrea chilensis genetic diversity, principally onthose natural beds that experienced elevated fishing extraction pressure  aretherefore very hard to make and the results are not as robust as we might like, butnonetheless, such comparisons are critically important if we are to understand howfishing pressure has impacted flat oyster genetic diversity and if improvedmanagement of the stock(s) is to take place in the immediate future.As identified from Geneland, the Genetic diversity has a fundamental role in the evolution of a species. Populationsneed a high level of genetic diversity to rapidly adapt to change or to stress. Reduced


Bouteloua gracilis) in 48 populations. The genetic evaluation was performed through amplified fragment length polymorphism (AFLP) molecular markers. The maximum entropy method was used to model the past, present, and future environmental niches of the three clusters derived from the genetic analysis. The environmental niches of the three genetic clusters showed dynamic overlaps and isolations during the last interglacial and glacial maximum. The paleoclimatic events, which occurred during those periods, may have reinforced genetic exchange among populations and affected their genetic structure. Genetic clusters also presented different environmental niches in the present. Thus, they can be considered as three distinct ecotypes and restoration programs must be carried out using local germplasm from each environmental niche to increase their chance of success. Based on the environmental niches of the genetic clusters, changes are expected in the near and mid-century future. Therefore, climate change must be considered for species conservation management and future restoration programs.Understanding the genetic structure adopted by natural populations and its relation to environmental adaptation is critical for the success of restoration programs. We evaluated the genetic structure and temporal environmental niche dynamics of blue grama ( The assessment of genetic structures provides insights into genetic differences within species occurring in response to environmental adaptation pressures. Knowledge about the genetic structure allows researchers to quantify the number of genotypes within species and determine their geographical distribution. This information may be useful for modeling the migration patterns shown by different genotypes through time. It may also be useful to determine the interactions that may have influenced the adaptative genetic differentiation within species .Environmental niche models are statistical and mathematical models used to infer and predict the geographic distribution of species . AlthougSeveral recent studies have incorporated genetic structure information into environmental niche modeling to obtain accurate predictions and support genetic conservation strategies ,11,12,13Bouteloua gracilis (Kunth) Lag. ex Griffiths). This grass has been widely used due to its proven adaptability to a wide range of environmental conditions and its high forage quality [One of the species most utilized for grasslands restoration in North America is blue grama  and ΔK values . The populations belonging to Cluster I are located in the semi-arid (42.1%) and arid (57.9%) regions of the state. Populations from Cluster II are located in the semi-arid (73.3%) and mild (26.7%) regions, while most of the populations (92.9%) integrating Cluster III are from the arid region . Such differences only explained 16.6% of the total genetic variation and the majority of it (83.3%) exists within clusters. Accordingly, results from the AFLP analysis suggested a gene flow (Nm) value of 1.25 and an Fst of 0.167. These values indicate there is a relatively high genetic exchange and a low differentiation among genetic clusters. In addition, the diversity statistics showed that Cluster III exhibited the highest (p < 0.05) genetic diversity compared to the other genetic clusters  differences among the present ENMs of the three clusters . The degree of niche overlapping among genetic clusters was similar in the past and present models. Nevertheless, the niche overlapping of Cluster III with the rest of the clusters is expected to be lower in the future.The ENMs revealed quite different potential distribution ranges among the three genetic clusters in the past, present, and future scenarios . The areclusters . The degAccording to the results of the genetic analysis, the blue grama populations of northern Mexico exhibit a genetic structure that can be split into three genetic clusters. Genetic structures in blue grama populations were previously reported by Tso et al. ; these rEvolutionary processes, such as environmental adaptation, gene flow, habitat fragmentation, and population isolation determine a population’s genetic structure. Accordingly, the reason for the divergence among genetic clusters might be adaptation to the different ecoregions of the state, since an association between the ecoregions of the state and the genetic clustering pattern was found. The populations from Cluster I are located in the central part of the state, in the transition zone between the semi-arid and the arid regions, while populations from Cluster II are located in the mild and semi-arid regions. Finally, populations belonging to Cluster III are mainly located in the arid region. Therefore, populations from Cluster III are better adapted to areas with lower precipitation and higher temperatures than the populations from Clusters I and II. Meanwhile, populations from Cluster II may be better adapted to lower temperatures than the other clusters. These results agree with previous studies, which found that adaptation to arid environments can produce genetic divergences among lineages in plants ,31,32.The AMOVA partitioning of the genetic variation revealed there is a high level of genetic diversity within clusters, since a quite high partition (83.3%) of the variation exists within clusters. This suggests the possibility of selecting outstanding populations from each cluster, instead of only selecting them from all the populations evaluated.Psathyrostachys junceain and found that most of the variation (87.6%) occurred within populations. Similar results were found by Wu et al. [Elymus tangutorum explained 26.1% of the global variation. Likewise, Tso et al. [Furthermore, the high level of diversity within clusters observed in this study agrees with patterns of genetic diversity found in other grass species. For example, Xiong et al.  analyzedu et al. , who repo et al.  found thFestuca ovina. Similarly, Coppi et al. [Phragmites australis populations. Furthermore, the Fst obtained in this study (0.167) revealed a relatively low differentiation among genetic clusters. Mitchell et al. [Fst of 0.02 among 85 Australian populations of Microlaena stipoides. In the study by Tso et al. [Fst values of 0.15 to 0.22 for 44 populations and 5 cultivars of blue grama were reported.The low level of genetic differentiation among clusters suggests a high genetic exchange exists among the blue grama populations. Accordingly, the Nm among genetic clusters was 1.25, which can be considered high compared with those obtained for other grasses. For instance, Zhang et al.  found ani et al.  reportedl et al.  obtainedo et al. , Fst valp < 0.05) for all the diversity statistics evaluated. Populations integrated into this cluster are mainly distributed throughout the arid region, suggesting blue grama populations from dry habitats tend to have a high genetic diversity. This agrees with previous studies, which concluded that populations from adverse environments tend to have a high genetic diversity [Stipa krylovii populations from drier environments tend to have a high genetic diversity as an evolutionary mechanism of adaptation to drought stress. Furthermore, previous research has reported a relationship between genetic diversity and environmental variables in grass species. For example, Zhang et al. [Dactylis glomerata.Cluster III obtained the highest values  compared to Cluster III. This suggests that populations from Clusters I and II are adapted to a broader range of environmental conditions than Cluster III. Furthermore, the differences among the environmental niches of the three genetic clusters indicate they are three ecologically distinct units and can be considered as distinct ecotypes.In contrast, the present ENMs resulted in quite different geographical patterns among the three clusters. Clusters I and II have larger surface areas with a probability of habitat suitability >75%  as they were collected. The plants from each population were also identified with a number from 1 to 3 .The DNA extraction was carried out according to the method of Doyle and Doyle . DNA froK) from 1 to 10. For each K, 30 independent replicates were conducted. A burn-in period of 10,000 and a run length of 100,000 Markov Chain Monte Carlo (MCMC) replications were implemented for each replicate. The optimal number of population genetic clusters was estimated based on the mean log-likelihood (L(K)) and the delta K (ΔK) statistics, using the Structure Harvester  [The genetic structure was inferred using the model-based Bayesian clustering method, and implemented in the STRUCTURE software, ver. 2.3.4 ,60. Thisry 2022) ,62. The FST) using GenAIEx, ver. 6 [FST)/(FST)) [An analysis of molecular variance (AMOVA) was performed to determine the genetic differentiation within and among the genetic clusters resulting from the STRUCTURE analysis . This an, ver. 6 . Gene fl)/(FST)) .I), and Nei’s genetic diversity (He) were estimated using the software GenAIEx, ver. 6 [For each genetic cluster, the diversity indices percentage of polymorphic loci, average of alleles per locus, average effective number of alleles per locus, Shannon information index  were obtained to perform the modeling [The ecological niche modeling approach was applied to quantify the temporal ecological differentiation among the clusters derived from the genetic analysis. The past, present, and future suitable areas for the distribution of the genetic clusters were predicted based on the maximum entropy method using MaxEnt, ver. 3.4.4 ,66. The modeling . Then, PThe environmental niche models from the past were projected based on paleoclimatic data of the Last Interglacial  and the Last Glacial Maximum . The LIG bioclimatic layers were utilized at 2.5 arc-min resolution . The LGMThe environmental niche models were also projected to future scenarios to assess how climate change will affect the potential distributions of the genetic clusters. The future bioclimatic variables with a 2.5 arc-minutes resolution were obtained from the MIROC-ES2L climatic model  for two The models were constructed with a maximum number of interactions of 5000 and 10,000 background pseudo-absence points. The output format used was Cloglog. Most of the Maxent features were configurated as standard. Models were constructed by using 50 replicate runs, and the bootstrap method was set as the replicated run type. The models were built with 75% of sampling points, and the remaining 25% were randomly selected as test data. Fifty replicate runs were used to obtain a consensus model prediction and to calculate the area under the receiver operating curve (AUC) . The modSD) [WI) [SD and WI ranged from 0 to 1, where 0 indicates null overlap and 1 total overlap. To determine significance for the niche identity test, we ran 100 permutations in which sample labels were randomized to generate a null distribution of the identity statistics. If the empirical value of the statistics SD and WI fell below the 95% confidence interval of the null distribution, we considered the niches of the two clusters to be significantly different. The niche identity test was conducted through the ENM Tools available in the R software, ver. 4.0.4. The niche identity test was performed to evaluate whether the genetic clusters would occupy different niches in the geographical space in the past, present, and future scenarios. The degree of niche overlapping existing between a pair of genetic clusters was quantified using the identity statistics Schoener’s D (SD)  and WarrSD) [WI) . The SD The combination of genetic structure analysis and environmental niche modeling allowed us to quantify the major genetic lineages within blue grama populations from northern Mexico and predict their potential distribution over different periods. The blue grama populations analyzed in this study exhibit a special genetic structure pattern, and can be split into three distinct genetic clusters. The genetic structures of these lineages can be partially explained by the past paleoclimatic events of the LIG and LGM periods. The environmental niches of the three genetic clusters showed dynamic overlaps and isolations during the LIG and LGM, which may have affected the gene exchange among populations. Therefore, environmental adaptation may have contributed to shaping the genetic structure of the blue gamma populations during the history of the species.Under the present scenario, each genetic cluster exhibits a different environmental niche. Hence, they can be considered as three genetically and ecologically distinct units, which means they are different ecotypes with different utilization potentials. Thus, restoration programs with blue grama in northern Mexico should be performed by using local germplasms from the environmental niche of each cluster.According to the projections of future environmental niches, climate change will modify the suitable areas for the distribution of two of the genetic clusters. The potential distributions of Clusters I and III will be broader in the middle of the century, which suggests these populations are more resistant than populations from Cluster I to the anthropogenic drying projected as a consequence of climate change. Thus, climate change must be considered in species conservation management and future restoration programs. Finally, the high genetic diversity found within each cluster represents an opportunity to select outstanding germplasms, which may be used in future restoration programs.


Gymnocypris chilianensis, D-loop region of the mitochondrial DNA was sequenced in 50 individuals of G. chilianensis obtained from 2 geographic locations (Heihe River and Shule River) and 25 individuals of G. przewalskii (Qinghai Lake). Twenty-five homologous sequences of another G. eckloni (Yellow River) downloaded from GenBank were analyzed together. The sequences were analyzed by using the MEGA (version 7.0) and DnaSP (version 6.0) software. The results revealed that 82 haplotypes were detected among 100 individuals. The haplotype diversity (Hd) and nucleotide diversity (Pi) of G. chilianensis of the Shule River were 0.963 ± 0.029 and 0.00414 ± 0.00069, which were lower than those of 3 other populations. The genetic distance of G. chilianensis in both Heihe River and Shule River was 0.0013. The genetic distances between the 2 G. chilianensis populations and the G. eckloni were 0.0148 and 0.0141, respectively. Population differentiation values (Fst) and gene flow (Nm) showed that 4 population had occurred obvious genetic differentiation . Compared with G. przewalskii and G. eckloni, the differentiation degree was more significant between Heihe River G. chilianensis and Shule River G. chilianensis . Maximum Likelihood (ML) phylogenetic tree showed that G. chilianensis had further genetic distance with G. eckloni and G. przewalskii. In conclution, G. chilianensis (HH&SL) had lower genetic diversity and further genetic distance than G. przewalskii (QH) and G. eckloni (YL). We suggest strengthen the protection of genetic resources of G. chilianensis.In order to analyze the genetic diversity and genetic differentiation of  Gymnocypris chilianensis is a species of the genus Gymnocypris which belongs to the subfamily Schizothoracinae of the family Cyprinidae. It is mainly distributed in the Shiyang River, Heihe River and Shule Rivers, inland river basin of Hexi Corridor in Gansu province of China  to assess the phylogeographic structure of G. chilianensis in the inland river watersheds of the Hexi Corridor in Gansu Province, China. Moreover, we have studied the genetic diversity and taxonomic status of G. chilianensis based on the mitochondrial DNA cytochrome b gene . It was concluded that Gymnocypris przewalskii has high genetic diversity, and there is a certain degree of genetic differentiation among populations, especially the Keruke Lake population has been highly differentiated, but the level of genetic diversity is very low, it should be priority to protect.At present, there are few studies on the genetic diversity and population structure of o et al.  used mitg et al.  used theG. chilianensis and using the mtDNA D-loop gene as a marker, with the aim of providing a theoretical basis for resource conservation and utilization of G. chilianensis.In this study, we analyzed the genetic diversity and population structure by focusing on G. chilianensis were collected, among which 25 individuals each were obtained from Heihe (HH) and Shule (SL)  rivers. Besides, 25 individuals of G. przewalskii were collected from Qinghai Lake (QH) .A total of 50 individuals of D-loop region sequences of G. eckloni (YL) which were derived from the mainstream of the Yellow River in Qinghai Province of China, were downloaded from the GenBank database (GenBank accession No. FJ601074-FJ601098)All individuals were identified based on morphological characteristics , 2.5 mM of 1 × PCR reaction buffer, 1 U of Taq DNA polymerase (Takara), 0.4 µM of each primer and 19.3 μL of ddH2O. PCR was carried out under the following conditions: an initial denaturation at 95 °C for 3 min, followed by 30 cycles of denaturation at 94 °C for 30 s, annealing at 57 °C for 30 s, and extension at 72 °C for 90 s, followed by a final extension at 72 °C for 10 min.Escherichia coli (TianGen) according to the manufacturer’s instructions. Colonies of E. coli harboring recombinant clones were screened for the presence of inserts of the expected size by LacZ blue-white selection and identified by double digestion with EcoRI and PstI. The positive recombinant clones were sequenced from both directions .Subsequently, PCR products were analyzed using 1% agarose gel and purified using the Universal DNA Purification Kit . Purified PCR products were subsequently cloned into pMD-19T Simple Vector (TaKaRa) and the ligation products transformed into DH5α Chemically Competent Cell D-loop sequences were separately aligned and trimmed to equal lengths using the BioEdit program (http://www.mbio.ncsu.edu/bioedit/bioedit.html) and ClustalX 2.1 program . The nucleotide composition and genetic distance among four populations (the average genetic distances based on Kimura’s two-parameter model) were computed using MEGA 7.0 software (http://www.megasoftware.net/), with the standard errors estimated by bootstrapping using 1000 replicates (Kimura http://www.ub.edu/dnasp/). For phylogenetic analysis, Gymnodiptychus pachycheilus (GenBank accession No. of D-loop: FJ601172) and Ptychobarbus dipogon (GenBank accession No. of D-loop: FJ601178) were taken as the out-group, Maximum Likelihood (ML) phylogenetic tree based on D-loop haplotypes was constructed using MEGA 7.0, and statistical support was estimated using 1000 bootstrap replicates. Haplotype network (median-joining) was generated using Network 5 software (http://www.fluxus-engineering.com/) , compared with the lowest haplotype diversity of the Yellow River G. eckloni (Hd = 0.943). The nucleotide diversity of the Qinghai Lake G. przewalskii was the highest (Pi = 0.00831), compared with the lowest nucleotide diversity of the Shule River G. chilianensis (Pi = 0.0014).A total of 82 haplotypes were recovered from 100 aligned sequences and the haplotype diversity and nucleotide diversity indices were shown in G. chilianensis population and the G. eckloni population were found to be the highest (0.0148), followed by the genetic distance between Heihe River G. chilianensis population and the G. przewalskii population (0.0143), while the genetic distance between the Heihe River G. chilianensis population and the Shule River G. chilianensis population were the lowest (0.0013).Genetic distance within and among populations was estimated using the Kimura’s two-parameter model, with values ranging from 0.0042 to 0.0084 and from 0.0013 to 0.0148, respectively , suggestFst and Nm among the 4 populations, and the results were shown in Fst (Fst > 0.25) and Nm (Nm < 1) of the four populations reached the level of high differentiation, with the highest Fst and the lowest Nm between Heihe River G. chilianensis and Shule River G. chilianensis, the lowest Fst and the highest Nm between Qinghai Lake G. przewalskii and Yellow River G. eckloni.DNASP 6 software was used to calculate the G. chilianensis. Another branch contains Shule River G. chilianensis, G. przewalskii and G. eckloni which the Shule River G. chilianensis from a haplotype clade, bootstrap values were 84%, G. przewalskii and G. eckloni from another haplotype clade, bootstrap values were 73%  clustered on haplogroups a, G. eckloni (YL) and G. przewalskii (QH) clustered on haplogroups b and some haplotype cross distribution each other . ch other .D-loop region sequence analysis showed that the haplotype diversity (Hd =0.9970) and nucleotide diversity (Pi = 0.00831) of G. przewalskii population were the highest among the four groups. The nucleotide diversity index of the Shule River G. chilianensis population was 0.00414, which was lower than the other populations analyzed in our study, but still well above the research results on the Shule River G. chilianensis (Pi = 0.0002) by Zhao et al. (Spinibarbus yunnanensis (Pi = 0.0014) by Li and Lü∨ (Pi of genetic diversity proposed by Lan and Shi (G. chilianensis population was lower (Pi < 0.0047). Moreover, the combination of high haplotype diversity (Hd = 0.9630) with low nucleotide diversity (Pi = 0.00414) of the Shule River G. chilianensis population reveals possible mechanisms for the formation of this population, which multiply rapidly from a small effective fish population  and nucleotide diversity (Pi) are important indicators of population genetic variation. They are a valuable indicator for estimating the abundance or scarcity of the genetic diversity of a population , which indicated that obvious genetic differentiation had occurred in the four populations. Nm < 1 between populations indicates that the population may be differentiated due to genetic drift. Nm > 1 between populations indicates that the level of gene flow between populations is higher and the genetic differentiation between populations is smaller. When Nm > 4, the gene exchange between populations is more sufficient and the genetic differentiation is more smaller  clustered on one branch, G. chilianensis (SL) clustered on one branch, G. eckloni (YL) and G. przewalskii (QH) clustered on the other branch, and the bootstrap values of which were 83–99%. G. chilianensis had further genetic distance with G. eckloni and G. przewalskii. G. chilianensis should be recognized as an independent species, which is widely accepted . The established Maximum Likelihood (ML) phylogenetic tree also showed that G. przewalskii and G. eckloni clustered into one branch and haplotype crossed each other.The Maximum Likelihood (ML) phylogenetic trees showed that the populations of G. chilianensis (HH&SL) have developed genetic differentiation, but there was some cross distribution between haplotypes, indicating that the degree of genetic differentiation is not high. Other haplogroup contain G. eckloni (YL) and G. przewalskii (QH), and some haplotype cross distribution each other. In conclusion, G. przewalskii and G. eckloni might be subspecies of each other, however, further studies are required. The result of this study shows that the G. chilianensis (HH&SL) had lower genetic diversity than G. przewalskii (QH) and G. eckloni (YL). We suggest strengthen the protection of genetic resources of G. chilianensis. According to different groups, effective protection measures should be set up to actively protect the ecological environment in the basin and improve its habitat environment.Eighty-two haplotypes we observed were clustered into two clades and cross distribution each other . Cluster


Various parental characteristics, including psychiatric disorders and parenting behaviours, are associated with offspring mental health and related outcomes in observational studies. The application of genetically informative designs is crucial to disentangle the role of genetic and environmental factors (as well as gene–environment correlation) underlying these observations, as parents provide not only the rearing environment but also transmit 50% of their genes to their offspring. This article first provides an overview of behavioural genetics, matched-pair, and molecular genetics designs that can be applied to investigate parent–offspring associations, whilst modelling or accounting for genetic effects. We then present a systematic literature review of genetically informative studies investigating associations between parental characteristics and offspring mental health and related outcomes, published since 2014. The reviewed studies provide reliable evidence of genetic transmission of depression, criminal behaviour, educational attainment, and substance use. These results highlight that studies that do not use genetically informative designs are likely to misinterpret the mechanisms underlying these parent–offspring associations. After accounting for genetic effects, several parental characteristics, including parental psychiatric traits and parenting behaviours, were associated with offspring internalising problems, externalising problems, educational attainment, substance use, and personality through environmental pathways. Overall, genetically informative designs to study intergenerational transmission prove valuable for the understanding of individual differences in offspring mental health and related outcomes, and mechanisms of transmission within families. However, although observed associations between parental factors and offspring outcomes are often interpreted as direct environmental influences, in truth parents provide both the rearing environment and genes to their children. Thus, observed parent–offspring associations may be wholly or partially explained by genetic factors shared between the parent and child; i.e. in a gene–environment correlation (rGE), when exposure to specific environments depends on an individual’s genotype. The potential mechanisms  underlying associations between parental characteristics and offspring outcomes are described in detail in Fig. Parents are considered a driving force in the development of their children and parental factors are associated with various mental health outcomes in offspring, including emotional and behavioural problems2. Twin-based research shows that most mental health and related traits are moderately heritable (under the influence of additive genetic effects), with additional variance explained by the unique environment , and for some traits also the shared environment (environments that the twins have in common)3. However, classical twin studies say little about mechanisms of transmission within families where, in addition to genetic transmission, parental effects may be transmitted through both the shared environment via parentally provided rearing factors, and to a lesser extent, the unshared environment through specific parent–child interactions. Consequently, genetically informative designs that include both the parent and offspring generations are required to disentangle genetic and environmental effects underlying parent–offspring associations.In genetic epidemiology, the classical twin design is generally used to decompose the contribution of genetic and environmental effects underlying human traits4. However, several novel methodologies that investigate within-family transmission using innovative techniques have emerged in the past few years. Consequently, there is a gap in the literature for a broad systematic overview that incorporates all genetically informative designs that can be applied to study parent–offspring associations. Here, we focus on studies published from 2014 onwards, as these have not been covered by previous reviews. We first provide a brief overview of the types of genetically informative designs that can be employed to investigate parent–child associations. This is followed by a systematic review of studies investigating associations between parental characteristics and offspring mental health and related outcomes, including internalising behaviours (such as anxiety and depression), externalising behaviours (such as attention-deficit/hyperactivity disorder), educational attainment, substance use and personality.The present review aims to synthesise literature investigating the association between parental characteristics and offspring mental health and related outcomes in genetically informative designs. An earlier systematic review published in 2014 focused on the children-of-twins methodDesigns that can be used to separate genetic and environmental mechanisms of transmission from parents to offspring broadly fall into the following three categories: behavioural genetics designs, matched-pair designs, and molecular genetics designs. In this section, we summarise the principles underlying these approaches Fig. , describ5 and children-of-twins6 designs  than adoptive (rearing only) families, this indicates the contribution of both genetic and environmental effects; i.e. passive rGE. If unaccounted for, this rGE, reflected in increased similarity between biological parents and lived-with offspring, could potentially lead to an inflated estimation of genetic transmission in adoption studies.Behavioural genetics designs leverage knowledge of relatedness among individuals within a family to make inferences about the contribution of genetic and environmental factors underlying parent–offspring associations. The adoptiongns Fig.  are key GE Table . This is7 studies, genetically related or genetically unrelated parents are analogous to the biological and adoptive parents in an adoption design, whereas in triparental family8 and multiple parenting relationships9 designs, the rearing effect of step-parents and genetic effect of not-lived-with biological parents are examined 17 or trio-GCTA18 , parent variables , offspring variables (“offspring” or “child*”) and topic variables (“gene*” or “environment”). The search did not include predictor or outcome-specific search terms, so as not to limit the review to a particular set of traits. We restricted the search to scientific articles published in English. Through the results of the initial search, we identified additional designs that were relevant  diagnosis23. This is in line with twin literature which shows that depression is a heritable phenotype3. After accounting for genetic effects, parental depression was associated with offspring internalising behaviours through environmental pathways, and these associations were observed throughout childhood25, adolescence26, and adulthood23. Similarly, associations between parental anxiety and offspring internalising behaviours also showed evidence of environmental transmission across development, from toddlerhood to early adulthood32. However, unlike depression, this association was not partly explained by shared genes, as there was no evidence of genetic overlap between parental anxiety and offspring internalising behaviours32. The lack of evidence for genetic transmission of anxiety is at odds with findings from twin literature which estimate that 40% of individual differences in anxiety are explained by genetic factors3. However, there are some possible explanations of why genetic transmission is not evident within the adoption and children-of-twins studies reviewed here. Measures of inherited risk in the adoption studies could lack validity, and may not adequately capture the genetic risk of anxiety from birth parents. Alternatively, as longitudinal studies show that genetic factors involved in anxiety change across the lifespan33, different genes could be relevant for the occurrence of anxiety in early life and adulthood. Therefore, parental anxiety and offspring internalising symptoms may share fewer common genetic factors that are not easily captured using adoption or children-of-twins designs. Even if different genes are involved in childhood internalising symptoms and adult anxiety, the observed environmental association indicates that exposure to an anxious parent is a risk factor for offspring internalising symptoms.Studies investigating the association between parent and offspring internalising behaviours Table , includi34. This finding stands in contrast to the substantial body of literature that interprets associations between perinatal maternal distress and offspring mental health outcomes in causal terms35. Based on the current findings, such parent–offspring associations detected in previous observational studies are likely to be attributable to unmeasured rGE, or concurrent parental depression. In investigating the presence of gene–environment correlation, several adoption studies found no evidence of evocative rGE, although some child-to-parent effects were identified36. These studies highlight the dynamic nature of parent and offspring relationships, where associations can be bidirectional, with both parent and offspring behaviour influencing the other.Overall, environmental associations between parental factors and offspring internalising behaviours were generally driven by exposure to concurrent parental anxiety or depression, whereas prenatal and post-natal symptoms did not have a long-lasting effect40  did not overlap with genes involved in offspring internalising behaviours40 Table . After a43, whereas the subsequent study with three times the sample size identified a genetic nurturing effect on offspring depressive symptoms that was mediated by maternal emotional symptoms44. This finding is in line with the studies reviewed above which showed environmental associations between maternal depression and offspring internalising behaviours24 and shows that seemingly environmental associations between parental factors and offspring outcomes may nonetheless be driven by genetically influenced parental traits.Genetic nurture is a relatively new topic within psychiatric genetics, and as such, we identified only two studies that investigated environmentally mediated effects of parental genes on offspring internalising behaviours Table . Both st45  showed correlations between birth parent and offspring externalising behaviours51, and between birth parent antisocial behaviour and offspring callous–unemotional behaviours52, although oppositional and attentional-deficit behaviours were uncorrelated with birth parent antisociality52. Findings from previous literature show substantial heritability of externalising behaviours3 and highlight the important role of genetic transmission in explaining parent–offspring simarlity4. It is likely that the detection of genetic transmission in adoption studies requires more power, especially if the specific parent and offspring phenotypes under investigation are related, but not identical traits.Several adoption studies investigating the intergenerational transmission of externalising behaviours Table  were bas4. Here, we identified one adoption study which found no robust evidence for an association between parent antisociality and offspring disruptive behaviours53. In addition, three large Swedish population-based studies of criminal behaviour found robust evidence of both genetic and environmental transmission of criminal behaviour55 and showed that risk of criminal behaviour was strongest in families where the same parent provided both the genes and the rearing environment55.The role of environmental transmission in externalising behaviours has also been previously implicated56 78. Evidence of passive rGE was also found for the overall genetic nurturing effect in a within-family PGS study of adoption samples, where parental PGS of educational attainment was more strongly associated with offspring educational attainment in biological families than adoptive families85. This particular passive rGE has also been reported outside of the reviewed work91.Research into genetic nurture has gained traction in the last two years, starting with the publication of three landmark studies with novel designs to identify genetic nurturing effects on offspring educational attainment87 Table . These s67 56. As well as indicating genetic transmission of similar traits, these findings indicate that the same genetic factors may be relevant for the development of several distinct mental health problems92, and could also partly explain the comorbidity between mental health disorders that is widely observed in literature113. Without accounting for genetic transmission within families, observational studies run a serious risk of misinterpreting these associations as causal environmental influences. For instance, it was observed that after accounting for shared genetic effects, perinatal maternal depression did not hold any long-lasting associations with offspring internalising or externalising behaviours in childhood34. This is in contrast to the substantial body of literature that interprets associations between perinatal maternal distress and offspring mental health outcomes in causal terms35. We urge future studies investigating parent–offspring associations to err on the side of caution in interpreting their results and consider evidence from multiple methodologies in forming their conclusions. Even genetically informative designs can be skewed towards non-genetic findings if there is insufficient power in the study. Triangulating evidence from multiple methodologies is required before a general conclusion can be reached on whether a given parent–offspring association is likely to be truly present, after accounting for shared genetic effects or rGE.The reviewed literature highlights that genetically informative designs must be implemented to model or control for genetic effects in studies investigating parental influences on offspring development. There was substantial evidence of genetic overlap between parental and offspring phenotypes for both similar traits 3. This suggests that the largest way through which parents influence offspring mental health outcomes is through the passing on of their genes. In addition, estimates of heritability for mental health phenotypes within classical twin literature tend to increase with age, while the influence of the shared family environment decreases114. From a developmental perspective, this indicates that genetic influences on offspring mental health become increasingly important as the child gets older while the overall environmental impact of parental characteristics on offspring behaviour is likely to be small. In the current review, effect sizes showing the relative contribution of genetic and environmental factors in parent–offspring associations were not consistently reported and the available statistics are hard to compare between studies. Some studies reported higher effect sizes for genetic or environmental transmission, while others reported equal effect sizes for genetic and environmental effects in parent–offspring associations (Tables 117. Genetic nurture is a new way to index the environmentally mediated effect of parental genes on offspring behaviour. The reviewed studies provide evidence of genetic nurture effects on offspring internalising symptoms and educational attainment (Table Even so, the reviewed studies indicate that both genetic and environmental factors are important in associations between parental factors and offspring mental health outcomes Table . These os Tables . Based ont Table . This isrGE is also prevalent within genetically informative designs (Table rGE can also explain parent–offspring associations. The reviewed studies showed evidence of evocative rGEs underlying associations between parental characteristics and offspring internalising symptoms, externalising symptoms and personality (Table 119. In instances where evocative rGE effects were not observed, child-to-parent effects were sometimes still present65. These findings highlight the bidirectional and dynamic nature of parent–offspring associations, with child-to-parent effects, as well as parent-to-child effects, and also show the importance of cross-lagged models in modelling parent–offspring associations over time.As well as demonstrating genetic overlap and environmental transmission within parent–offspring associations, the reviewed studies showed that confounding by passive ns Table . If unmoty Table . These f120. In addition, several reviewed studies showed that positive parental environments, such as parental warmth and positive reinforcement, were protective against externalising and substance use behaviours in children with high inherited risk109. Whilst preventative interventions for externalising problems already include a family component, current preventative strategies for substance use incorporate school-based and skills training approaches121. A family-based approach could be a valuable addition to preventative interventions of substance use behaviours in early life.Reviewed findings with clinical implications are worth highlighting further. Parents with depression, anxiety, substance use problems, and externalising behaviours appeared to pass on these traits to the offspring through both genetic and environmental mechanisms. This information can be used to extend preventative and early intervention services to high-risk children of parents with internalising, externalising, or substance use disorders in healthcare settings. Family-based interventions, including cognitive, behavioural, and psychoeducational components, are already shown to be effective in children of parents with internalising and externalising disordersTo conclude, parental factors are important predictors of offspring mental health and related outcomes. Both genetic and environmental processes are important in these associations. Further clarification of these processes requires more research. Exciting opportunities for parent–offspring research are increasingly present, with the availability of more datasets and ongoing advances in methodologies.