Oxytropis diversifolia (Fabaceae), to investigate population genetic structure of this taxon, as well as potential hybridization events with closely related taxa in this genus.Microsatellite primers were developed for a perennial legume from northern China, Oxytropis species from northern China  revealed that all of these loci can be amplified successfully and show polymorphism.One hundred and five primer pairs were designed from Illumina sequence data and screened for suitability. Fifteen of these primer pairs were polymorphic, and these primers amplified tri‐, tetra‐, and pentanucleotide repeats with 10–56 alleles per locus. Cross‐amplification tests in three other O. diversifolia, as well as studies of potential hybridization events with closely related taxa in this genus.These primer pairs can be used to assess the genetic diversity and population structure in future studies of  Oxytropis diversifolia E. Peter (Fabaceae) is a perennial herb occurring in dry Stipa L. grasslands/semi‐desert regions of northern China and Mongolia  DC., O. neimonggolica C. W. Chang & Y. Z. Zhao, and O. squammulosa DC. We are particularly interested to test for potential hybridization of O. leptophylla with O. diversifolia .Here, we describe the development of microsatellite markers that will facilitate future research on leaf shape variation in SRP131738, BioProject ID PRJNA431827) were obtained. The paired‐end reads were then processed using Trimmomatic version 0.35  and additional populations of O. leptophylla (n = 19), O. neimonggolica (n = 20), and O. squammulosa (n = 16)  BioSample accession SAMN08408037, (2) BioProject ID PRJNA431827, (3) GenBank Short Read Archive accession  BioSampl


Schoenoplectus americanus, a foundational brackish marsh sedge, to determine whether it can serve as a resource for reconstructing historical records of demographic and population genetic variation. After assembling profiles of the seed bank from radionuclide‐dated soil cores, we germinated seeds to “resurrect” cohorts spanning the 20th century. Using microsatellite markers, we assessed genetic diversity and differentiation among depth cohorts, drawing comparisons to extant plants at the study site and in nearby and more distant marshes. We found that seed density peaked at intermediate soil depths. We also detected genotypic differences among cohorts as well as between cohorts and extant plants. Genetic diversity did not decline with depth, indicating that the observed pattern of differentiation is not due to attrition. Patterns of differentiation within and among extant marshes also suggest that local populations persist as aggregates of small clones, likely reflecting repeated seedling recruitment and low immigration from admixed regional gene pools. These findings indicate that persistent and stratified soil‐stored seed banks merit further consideration as resources for reconstructing decadal‐ to century‐long records that can lend insight into the tempo and nature of ecological and evolutionary processes that shape populations over time.Stratigraphic accretion of dormant propagules in soil can result in natural archives useful for studying ecological and evolutionary responses to environmental change. Few attempts have been made, however, to use soil‐stored seed banks as natural archives, in part because of concerns over nonrandom attrition and mixed stratification. Here, we examine the persistent seed bank of  EphippiBiased representation and poor stratification are two well‐recognized concerns that have deterred use of soil‐stored seed banks for reconstructing records of genetic variation and other aspects of organismal evolution  Volkart ex Schinz & R. Keller (Cyperaceae) for studying paleoecological responses to environmental change , S. americanus has been the focus of more than three decades of research on coastal marsh responses to climate change . Elevation of the marsh is 40–60 cm above mean low water, with 20% of high tides flooding the site  and C4  aboveground biomass were used to quantify a vertical profile of S. americanus seeds . We filled 32 pots with the mixture and arrayed the pots in a rectangular grid within a 6 cm deep tray (24 × 48 cm2). The tray was filled with water, and water levels were kept at approximately 1 cm below the soil surface. Seeds from each of the five soil layers were randomly assigned to 2–4 pots per layer with 10–30 seeds placed in each pot. The tray was placed in a growth cabinet with a 15 hr light:9 hr dark photoperiod and 30°C constant temperature . The number of germinating seeds was recorded daily for the first 7 days and again at 10, 12, 14, and 19 days after planting. The second germination assay was conducted from May to July 2004 involving (a) 328 additional seeds recovered from depths 2–4, 8–10, 14–16, 20–22, and 22–24 cm of the 2002 soil monolith; (b) 1,136 seeds recovered from all depths (0–64 cm) of core 2004‐A; and (c) 480 seeds recovered from depths 8–10, 14–16, 20–22, and 22–24 cm of core 2004‐B . The assay was conducted in a growth room with a 15‐hr light:9‐hr dark photoperiod and 30°C constant temperature. Germination success was recorded as in the first assay. Differences in germination among seed cohorts were assessed using analysis of variance (ANOVA) in Systat v.13 . Bonferroni‐corrected post hoc least‐squares means tests were conducted to compare cohorts. Approximately 0.30 g of leaf tissue was taken from each of 75 individual seedlings resulting from the two germination assays for genetic analysis of the 2–4, 8–10, 12–14, 14–16, 20–22, and 22–24 cm depth cohorts , 75 μM of each dNTP, 1 pmole of each primer, and 1× PCR buffer . The forward primer was fluorescently labeled with HEX, 6‐FAM, or TET for each primer pair. All PCR amplifications were generated with a thermal regime of 35 cycles of 94°C for 45 s, 53°C for 30 s, and 72°C for 90 s, followed by a final extension stage at 72°C for 5 min. The labeled PCR amplicons were sized against a CST ROX 50‐500 standard  on an ABI 3100 Genetic Analyzer  and scored with Genemarker software .For each individual and each microsatellite locus, 10–50 ηg of genomic DNA was used as template in 15 μl PCR mixtures that also included 1U of 2.52.5.1G) and the proportion of samples exhibiting a distinct genotype (R) for each depth cohort and sample site using the program GenAlEx v.6.41  and Shannon diversity (S) including all samples  per depth cohort and sample site using Microsatellite Analyzer (MSA)  excluding putative clones to account for the possibility of repeated sampling of genetically identical specimens. In addition, we estimated effective population size (Ne) for each depth cohort and for sites sampled for extant S. americanus, based on Burrow's composite measure of disequilibrium as implemented in the program LDNe  extant S. americanus in Kirkpatrick Marsh first according to community type  and by CO2 regime ; (d) extant S. americanus from Kirkpatrick Marsh versus other Chesapeake marshes; and (e) extant S. americanus from Atlantic versus Gulf coast locations. The significance of the outcome of each test was determined by comparison of the observed value to 10,000 permutations of samples between groups, with α representing the proportion of randomized data sets giving a larger value than the observed value. All comparisons excluded sites with <3 distinct genotypes.With depth serving as a proxy for age, we determined whether genetic diversity differed according to age and location using post hoc least‐squares linear regressions in  Goudet, . We asseR v.3.4.0  to examine the distribution of genetic variation across depth cohorts. We also performed AMOVAs with samples grouped according to age  and location . In addition, we conducted AMOVAs to assess whether genetic variation in extant S. americanus reflects CO2 exposure regime and community type (respectively) across the sampled plots in Kirkpatrick Marsh.We used Genetix v.4.05  all samples from Chesapeake Bay; (d) all samples from the Atlantic coast; and (e) all samples from the Atlantic and Gulf coasts. A parallel series of analyses were completed with the full set of specimens for comparison to outcomes based on data sets excluding putative clones. For each STRUCTURE analysis, we allowed for admixture and correlated allele frequencies for three independent runs at iterative values of K, with the burn‐in period set to 30,000 iterations and data collected from an additional 500,000 iterations. Values of K were set to range from one to as high as 36 . The likeliest value of K was estimated according to the maximum Pr(X|K) value  values  Interpolation tool in ArcGIS (ESRI ArcMap v10.3). Inverse Distance Weighted utilizes a power function that assumes each sample site has a local influence that diminishes with increased distance; this function is used to weigh the points closer to the prediction location greater than those farther away. The result is a heat map of genetic relatedness between points based on cluster assignments and the distance between sites.We visualized patterns of differentiation with genetic heat maps of optimal 33.1S. americanus were recovered across the full length of the sediment cores taken in Kirkpatrick Marsh  to 2002 (±0.1).Seeds of sh Table . The maxsh Table . The denSD = 2.6) and no seeds germinated 14 days after planting. Germination rates differed according to seed age . Post hoc comparisons of seeds recovered from the soil monolith indicate that seeds deposited in the 8–10 cm (1984 ± 1.2) depth had a significantly higher germination rate (52.2% ± 10.6 SE) compared to all other depth cohorts  to 2002 (±0.1). At least one seed was recovered and germinated from soil layers spanning 0 to 24 cm depth intervals (Table 3.2We examined 75 “resurrected” plants from six horizons spanning the 20th century: 2–4 cm (1998 ± 0.4), 8–10 cm (1984 ± 1.2), 12–14 cm (1963 ± 3.0), 14–16 cm (1947 ± 4.2), 20–22 cm (1908 ± 25), and 22–24 cm (1900 ± 32.2) . Genetic diversity across the length of the core could not be distinguished from a null, even distribution (p = 0.329). Similarly, Ne was not related to depth , nor did it deviate from an even distribution of Ne (p = 0.081) .No relationship was found between measures of genetic diversity and depth according to post hoc least‐squares linear regressions , estimates of genetic diversity and Ne for individual depth cohorts were comparable to those estimated for extant S. americanus in Kirkpatrick Marsh and elsewhere , but mixed community plots exhibited significantly lower estimates of genetic diversity than both Schoenoplectus‐dominated plots  and Spartina‐dominated plots . No differences were found between Schoenoplectus‐ and Spartina‐dominated plots.We detected evidence of genetic similarity among historical and extant h Figure  illustrae Figure . STRUCTUh Figure . A Manteh Figure . An AMOVh Figure . The AMOS. americanus  in Kirkpatrick Marsh was comparable to levels of genetic diversity found in other Chesapeake Bay marshes . Estimates of genetic diversity also did not differ among Atlantic and Gulf coast sites , and no clear geographic patterns in diversity were observed across either coastline (Supporting Information Table Genetic diversity of depth cohorts and of extant FST ranged from 0.06 to 0.48 among sample sites in Chesapeake Bay. An AMOVA attributed 27% (p < 0.001) of genetic variance to differences among sample sites. A Mantel test indicated that genetic distance corresponds to geographic distance  revealed a more complex configuration of spatial differentiation in the embayment  recovered clusters reflecting biogeographic breaks , as well as regional differences along coastlines . STRUCTURE runs with all unique genotypes also demonstrated that depth cohorts bear the greatest resemblance to extant S. americanus in Kirkpatrick Marsh . We have shown that S. americanus seeds can be recovered from radionuclide‐dated sedimentary layers spanning 100+ years. Comparisons of genetic diversity among contemporary populations and depth cohorts constructed from recovered seeds also indicate that postburial attrition and potential germination bias exert little influence on genetic measures of local demography. Evidence of shifting abundance, alongside spatial and temporal patterns of differentiation, further illustrate that soil‐stored seed banks can lend insight into the tempo and nature of ecological and evolutionary processes that shape populations over time.Here we illustrate that, like other dormant propagule pools, soil‐stored seed banks can serve as a resource for studying demographic and genetic variation over time. Although concerns about biased representation and stratification have discouraged interest in soil‐stored seed banks as natural archives, our findings indicate that both can be constrained and potentially overcome. For example, concerns about biased representation can be minimized by examining species, like Schoenoplectus sedges. In addition, other buried contents  and attributes  of marsh sediments can be examined to obtain information about past environmental conditions  that determine plant performance . Our work further illustrates that genetic information can be extracted from soil‐stored seed banks and that it can be contextualized by a well‐constrained stratigraphic record as well as complementary information on local demography  to draw inferences about ecological and evolutionary processes that shape populations over time.This study explores the prospects of exploiting a virtually untapped dimension of soil‐stored seed banks. Prior studies have largely utilized soil‐stored seed banks as resources to reconstruct records of past geological, climate‐related environmental conditions  conditions likely exert a strong influence on genetic variation within marshes , as do the size, number, and distribution of S. americanus clones  structures genetic variation across shoreline gradients , enough to shift the balance of competition in mixed communities toward S. americanus dominance  might uncover signatures of responses to CO2 exposure, although it is also possible that responses to stressors that reduce fitness and elevate mortality  might supersede signatures of response to CO2.Like the observed patterns of spatial variation, shifts in genotypic composition across depth cohorts might reflect responses to local selective pressures. Although it is possible that the observed pattern is a consequence of stochasticity , relatively modest changes in stressor exposure can structure whole marsh communities . This would help overcome limitations set by shifts in abundance over time (Jarrell et al., Besides demonstrating that soil‐stored seed banks can offer perspectives on demographic and genetic change over time, our work illustrates that dormant soil‐stored seeds can be a resource for experimental “resurrection” approaches for studying ecological and evolutionary responses of plants to environmental change over time. In many ways, the process of reconstituting depth cohorts from soil‐stored seed banks parallels the steps required to assemble experimental cohorts from dormant zooplankton ephippia and curated seed collections (Franks & Weis, https://doi.org/10.5061/dryad.c76q3t7.Data available from the Dryad Digital Repository:  Click here for additional data file. Click here for additional data file.


I) and the mean expected heterozygosity were 2.528 and 0.879, respectively. At the population level, the regions of 42°N and 124°E had the highest genetic diversity among all latitudes and longitudes. The greater the difference in latitude was, the greater the genetic distance was, whereas a similar trend was not found in longitude populations. Three main clusters  were assigned to populations. AMOVA analysis showed that the genetic differentiation among latitude and longitude populations was 0.088 and 0.058, respectively, and the majority of genetic variation occurred within populations. The Mantel test revealed that genetic distance was significantly correlated with geographical distance . Furthermore, spatial autocorrelation analysis showed that there was a spatial structure  and the correlation coefficient (r) decreased as distance increased within a radius of 250 km.In this study, the genetic diversity and population structure of 205 wild soybean core collections in Northeast China from nine latitude populations and nine longitude populations were evaluated using SSR markers. A total of 973 alleles were detected by 43 SSR loci, and the average number of alleles per locus was 22.628. The mean Shannon information index ( Glycine soja Sieb. and Zucc.), the direct progenitor of the cultivated soybean (Glycine max (Linn.) Merr.), is a predominantly self-pollinated annual plant species [The annual wild soybean , Tris-HCl (pH 8.3), 500 mmol/L KCl, 200 mmol/L MgCl2 , 0.4 μL dNTP (2.5 mmol/L), 2.0 μL each of forward and reverse primers (10 pmol/L), 2.0 μL total DNA (20 ng/μL), 1.0 μL Taq DNA polymerase (2 U/μL), and 15.1 μL ddH2O. PCR was performed using the T100™ thermal cycler  with the following cycle conditions: an initial denaturing at 95°C for 5 min, followed by 35 cycles of 95°C denaturing for 45 s, 52–57°C annealing for 45 s, 68°C extension for 45 s, and a final extension at 72°C for 10 min.The 25 Amplified products were fractionated by electrophoresis through 6% denaturing polyacrylamide gels and stained with silver staining, which could detect 60 bp to 500 bp fragments and had a high resolution of 2 bp. The size of the stained band was analyzed based on its migration distance relative to the 100 bp DNA ladder (MBI Fermentas) using AlphaView software (version 1.3.0.7).Na), Shannon-Weaver index (I), expected heterozygosity (He), observed heterozygosity (H0), fixation index (Fis), genetic differentiation coefficient (Fst), genetic identity and genetic distance, molecular variation analysis of variance (AMOVA), Mantel tests, and spatial autocorrelation coefficients were computed by GenAIEx v6.5 [I) and expected heterozygosity (He) for evaluating the diversity were measured according to the formulas I = −1 × sum (pi × Ln (pi)) and He = 1–sum pi2, where pi is the frequency of the ith allele. H0 is generally lower than He due to inbreeding, and Fis = (1 − H0)/He. Outcrossing rate (t) was calculated using the equation t = (1 − Fis)/(1 + Fis) [The amplification fragments of genomic DNA by each SSR marker were scored based on the migration difference. The data format was converted accordingly in Microsoft Excel. The number of alleles (IEx v6.5 . The Sha + Fis) . Based o + Fis) , the den + Fis) . Spatialk value was set to 1 to 12, and ten runs were performed for each value of k to test stability of the results. The MCMC (Markov chain Monte Carlo) value was set as 100,000 burn-in with 200,000 iterations. The correct number of genetic clusters was inferred according to a value of Δk (Δk = mean|lnP(k + 1) − 2lnP(k) + lnP(k − 1)|/Sd|lnP(k)|) [The population genetic structure was predicted by STRUCTURE 2.3.4 . The deflnP(k)|) .I) and expected heterozygosity (He) were 2.528 and 0.879, respectively  to 36 (Satt286), with an average of 22.628. The mean Shannon information index (vely see .I) of nine latitude populations ranged from 1.300 to 2.419, with an average of 1.716; the expected heterozygosity (He) ranged from 0.661 to 0.884, with an average of 0.756. The region of 42°N had the highest genetic diversity among all latitudes. The Shannon information index (I) of nine longitudes varied from 1.659 to 2.368, averaging 1.919; the expected heterozygosity (He) varied from 0.751 to 0.880, averaging 0.805. The region of 124°E had the highest genetic diversity among all longitudes. As shown in Tables At the population level see , the Shak was 3, Δk was the highest, which indicated that 3 main clusters had been identified  [The  < 0.15)  was obser = 0.207, p < 0.05), which suggests that geographic distance limits gene flow among populations and influences the genetic structure. For further analyses, spatial autocorrelation analysis was performed by distance classes of 100 km, and a general decline was found in the correlation coefficient (r) with distance. The correlation values were negative and significant up to 250 km. This revealed that there is a clinal spatial structure in wild soybeans in Northeast China  (see The Mantel test indicated that there was a positive correlation between geographic and genetic distance (.01) see .I), and expected heterozygosity (He) were 22.628, 2.528, and 0.879, respectively, which were significantly higher than previously reported results [The wild soybean in Northeast China is an important ecotype, and its genetic diversity has been widely studied by using phenotypic traits , 30, 31  results –34. The  results , 36. On  results . These a results .Fst value was 0.088) and longitude populations  occurred   see , which cr = 0.207, p < 0.05), which indicates that geographical isolation has also been an important factor in forming the current genetic structure of wild soybeans in Northeast China. Spatial autocorrelation analysis revealed that the correlation between geographical distance and genetic distance is limited.The Mantel test revealed that there was a positive relationship between geographic and genetic distance (The wild soybean in Northeast China has become one of the most severely endangered wild plant species due to the interference of human activities . The genIn summary, the genetic diversity and geographic population structure of the wild soybean in Northeast China were fully investigated as a single population, or at different latitude or longitude populations for the first time. The distribution pattern of genetic variation is related to latitude, and the highest level of genetic diversity was found at 42°N, and protection in areas with higher genetic diversity should be prioritized. This study disclosed that natural selection to adapt temperature and photoperiod, selfing mating reproductive system, and distance isolation resulted in the current population structure of wild soybean in Northeast China.


The recent alarming reports on global climate change and the challenges facing the agricultural sector to meet the increase in meat consumption, impose research in biodiversity. An important genetic pool of local breeds might play a crucial role in the near future to address these challenges. Although Italy is considered as one of the richest countries in biodiversity, there are autochthonous cattle breeds under extinction. To safeguard biodiversity and increase genetic diversity within breeds, appropriate management tools must be developed. To achieve this, precise knowledge of the population structure and genetic diversity per breed are required. This study analyzed pedigree data of six local beef breeds: Calvana, Mucca Pisana, and Pontremolese (from the region of Tuscany), all under extinction, and Sarda, Sardo Bruna, and Sardo Modicana, from the island of Sardinia, that are larger in number but of lower productivity. In addition, the study investigated the population structure of the cosmopolitan beef breeds, Charolais and Limousine, reared in the same regions and undergoing selection. The high mating percentage between relatives for Mucca Pisana and Calvana is an alarming situation for these breeds. The population structure of the Sardinian breeds suggests the application of breeding programs.The aim was to investigate the population structure of eight beef breeds: three local Tuscan breeds under extinction, Calvana (CAL), Mucca Pisana (MUP), and Pontremolese (PON); three local unselected breeds reared in Sardinia, Sarda (SAR), Sardo Bruna (SAB), and Sardo Modicana (SAM); and two cosmopolitan breeds, Charolais (CHA) and Limousine (LIM), reared in the same regions. An effective population size ranges between 14.62 (PON) to 39.79 (SAM) in local breeds, 90.29 for CHA, and 135.65 for LIM. The average inbreeding coefficients were higher in Tuscan breeds  compared to the Sardinian breeds , while for CHA and LIM they were <1%. The highest rates of mating between half-siblings were observed for CAL and MUP , while the highest rate of parent–offspring mating was ~8% for MUP. Our findings describe the urgent situation of the three Tuscan breeds and support the application of conservation measures and/or the development of breeding programs. Development of breeding strategies is suggested for the Sardinian breeds. Cattle domestication started in Southwest Asia, in the 9th millennium BC ,2, whileThe Food and Agricultural Organization (FAO) has reported 1224 local cattle breeds worldwide . The ItaNevertheless, there exist advantages related to the presence and maintenance of local breeds . FirstlyF; per year or generation) has been used to evaluate how genetic diversity evolves during breed history [F summarizes the increase in inbreeding values per generation (or year) at a population level, providing an overview of genetic diversity and the risk of inbreeding depression. This last phenomenon is essential to be controlled in livestock species, because it can cause a decrease in performance  [Ne) by the equation ΔF = 1/2Ne. Ne is defined as the number of individuals that effectively participate in producing the next generation and is often lower than the census of the total number of individuals [Genetic diversity is a primary step for the establishment of a breeding program or to take conservation measures. It is defined as the measure of genetic differences between and within groups of animals and is highly related to the selection and adaptation of a breed to the local environment. There are several causes that influence genetic diversity, such as migration, mutation, selection, drift, bottleneck, and inbreeding . The fir history . In partuantity) ,19 and ruantity) . The ratividuals . ConsequPedigree analysis is a primary step that enables the characterization of the genetic diversity of populations: it identifies genetic variability and changes in the population structure in consecutive generations. It describes and quantifies the increase of homozygosity and the level of inbreeding in the population, important factors to be considered either for breeding or conservation schemes. Indeed, demographic analyses can also help to understand factors regarding genetic history, conservation status of a population, and relationships within and between breeds . AlthougOur aim was to investigate the population structure and genetic diversity of six Italian local beef breeds, three reared in Tuscany , Mucca Pisana (MUP), Pontremolese (PON)) and three in Sardinia (Sarda (SAR), Sardo Bruna (SAB), and Sardo Modicana (SAM)), utilizing pedigree information to support the development of strategies for conservation or breeding. Following FAO legislation, CAL, MUP, and SAM were classified as endangered breeds, while PON was set to a critical situation. No risk of extinction exists for SAR and SAB breeds, with each of these two breeds having a few thousand animals. In addition, to compare with cosmopolitan breeds, we analyzed pedigree data from the Italian populations of Charolais (CHA) and Limousine (LIM). Pedigree information of the local breeds was supplied by the Italian Breeders Association  and by the breeding association  for the two cosmopolitan breeds. Our full dataset included animals born between 1980 and 2018, and consisted of 2798 CAL, 3399 MUP, 328 PON, 97,163 SAR, 74,981 SAB, 25,355 SAM, 99,464 CHA, and 322,321 LIM cattle. n, where n is the number of generations separating the individual from each of its known ancestors [sireC and damC were contributions from the paternal and maternal lines calculated (separately for each line) asig is the proportion of known ancestors in generation i and d is the total number of generations. Generation interval (GI) was defined with two measures: (i) the average age of parents at the birth of all their progenies and (ii) the average age of parents at the birth of the progenies that were used for reproduction. Both parameters were calculated for the classical four pathways .Genetic analysis was carried out with the ENDOG v4.8 software . Pedigrencestors ); (ii) mncestors ); and  calculated according to Meuwissen and Luo [Ne is the effective population size. Ne was computed via regression as following:b is the regression coefficient of the individual F over equiGen. To overcome pedigree incompleteness, equiGen was used.Genetic diversity was described with three parameters: (i) inbreeding coefficient  av and Luo ; and , half-sibs, and parent with offspring—were calculated.fe) [fa) [fe/fa ratio and the number of ancestors explaining 50% [fe/fa = 1 shows the absence of bottleneck in the population under study, and low ANC_50 is an indicator of the founder effect [Also, the effective number of founders (fe) , and thefe) [fa)  were conning 50% ,24 of thning 50% , were alr effect . Pedigree content  was analyzed for each breed to estimate the contribution of each ancestor  up to the fifth parental generation .Ne calculated based on equiGen. Moreover, the relative change of population size, expressed both as average population size ratio , and average standard deviation of population size ratio (APSSD), were included. Past software was used for the PCA [Principal component analysis (PCA) summarizes correlated variables into a reduced set of mutually uncorrelated variables , allowing a dimensionally reduced visualization while keeping a certain amount of the original variance. Each of the PCs contains all the original variables. The PCs are constructed by maximum variability explained in the data and with the constrain to be orthogonal to each other. This helps to summarize information and to better study relationships among the samples . PCA was the PCA . The pattern of male to female ratio per generation was similar for CAL, MUP, SAM, SAR, CHA, and LIM, with more stable numbers between males and female during the generations, while SAB and PON had fluctuating trends . Table 2MUP had the highest PCI values (74%), followed by CAL and LIM . Intermediate PCI values were observed for CHA, PON, and SAM, while SAR and SAB had the lowest PCI values. As expected, pedigree quality increased over time with a similar pattern for both PCI and equiGen indices . GI in yIt should be noted, however, that there was variation within each breed, with standard deviation (SD) estimates of GI being equal to the mean or slightly higher. Moreover, the highest GI for the father–son and father–daughter paths was found for PON. The lowest values for the father–son path were observed for SAB and CHA, while CHA also had the lowest GI for the father–daughter path. The maternal intervals were shorter than the paternal for MUP and PON, while for SAB, SAM, CHA, and LIM, maternal pathways were greater. Equal father/mother to daughter GI was observed for SAR (10.7). CAL showed similar GI in the four pathways which varied between 9.55 (father–son) and 10.54 (mother–son) . The larF for the Tuscan breeds were 7.25%, 5.10%, and 3.64% for MUP, CAL, and PON, respectively. Sardinian breeds showed lower values , while CHA and LIM had inbreeding coefficients less than 1%. True mean inbreeding (TMI) was higher than F values in all the breeds studied.Estimated genetic parameters per breed are summarized in In general, inbreeding increased by generation. For CHA and LIM, the increase was relatively small .Ne also varied among breeds. For the local breeds, the values ranged from 14.62 (PON) to 39.79 (SAM). Ne was similar for MUP, SAB, SAR, and CAL. Concerning the two cosmopolitan breeds, Ne estimates were much higher  and 3.42% (PON). The AR values, expressed as a percentage, were generally higher in breeds with high F. The fe/fa ratio was practically around 1 for all the local breeds indicating the absence of narrow bottlenecks, whereas CHA and LIM had a higher ratio  (Estimates of h higher . The avectively) . The estimated ANC_50 reflects the size of the different populations, with extremely low values found for the Tuscan breeds, intermediate for SAM (~100), and high values (>200) for SAB, SAM, CHA, and LIM . APSR anRegarding P_CONT, CAL and MUP breeds presented the highest average values of the first generation , followed by LIM, whereas the lowest value was found for SAB (48%) . The perAll breeds had a very low percentage of mating between full-sibs (<1%) with the highest value being observed for PON (0.6%) . However, clear differences were observed for the half-sibs and the parent–offspring matings. In these cases, CAL and MUP had the highest percentages  for the matings between half-sibs; for parent–offspring percentages were 8.2% and 6.25% for MUP and CAL, respectively. The cosmopolitan breeds had the lowest values in all cases. Ne) and the fe/fa ratio. In PC1 all parameters were loaded except P_CONT, while PC2 was mainly described by the P_CONT and AVG_F, with Ne and fe/fa ratio having loadings close to zero.The biplot of the first two PCs, explaining together 78.31% of the total variation among breeds, is shown in To the best of our knowledge, this is the first study utilizing full pedigree records for the CAL, MUP, PON, SAR, SAB, and SAM local beef breeds together with two Italian beef populations of the CHA and LIM. Merging those breeds, our dataset consisted of three subgroups: three Tuscan breeds under extinction ; three local breeds from the island of Sardinia ; and two cosmopolitan beef breeds , that are mainly reared in Italy in the regions of Tuscany and Sardinia and have recently set up a national breeding scheme. The three Tuscan breeds are under extinction, hence drastic measures need to be taken for their conservation. At present, only 263 CAL , 346 MUP , and 52 PON  cattle are alive. A pedigree analysis to investigate the relationships among individuals and the levels of inbreeding within each breed is a primary step. n = 1231) as well as to higher pedigree incompleteness.The level of inbreeding within a breed is closely related and dependent on the pedigree completeness , becauseRegarding the cosmopolitan breeds, the number of equiGen found in LIM was similar to Slovenian Limousine (3.38) reported by Kadlečík et al. . In geneGutiérrez et al.  and CañaThe highest average inbreeding values were observed for Tuscan breeds (from 3.64% for PON to 7.25% for MUP) as shown in The TMI values were higher in breeds with a PCI lower than 40%  , confirmNe. Meuwissen [Ne than the threshold proposed, ranging between 14 and 40. However, a common problem related to the analysis of Ne is the amount of missing data in the pedigree. Boichard et al. [Ne. In our study, the Sardinian breeds had very low pedigree quality, as depicted by the estimation of equiGen and the PCI , suggesting that the situation could be more alarming in terms of loss of genetic variability. Another measure commonly used to assess the genetic variability within a breed is euwissen  proposedd et al.  argued t the PCI , and altNe. The low values found for the three populations, ranging between 14.6 (PON) and 19.7 (CAL), together with the small populations and the number of farms, report these breeds to be in an alarming situation. Notably, nowadays ~48% of the alive CAL cattle and ~70% of PON belong to four farms, while for MUP, five farms keep ~79% of the total population. This is a worrisome fact. In the case of an outbreak disease in the area there will be a thread on the existence of the Tuscan breeds.The situation is different for the Tuscan breeds. The higher pedigree quality  allowed for more accurate estimation of Ne estimates. In a more recent study, Mastrangelo et al. [Ne estimates differed from our estimates ; however, this discrepancy could be attributed to a different approach used from the authors, who estimated Ne from the relationship between linkage disequilibrium (LD), Ne, and recombination rate. Moreover, their analysis was based on a small sample of the total population per breed , which might not be representative of the population. Future genomic analysis utilizing a larger number of animals could help in reducing this discrepancy. Nevertheless, this is another indicator of the sensitivity of the Ne estimates upon the methodology applied.Compared to other Italian local beef breeds  analyzed by Bozzi et al. , the thro et al.  analyzedF with different approaches, estimating annual ΔF [F during the last generation [F greater than 1% per generation with the highest value observed for PON (3.42%). The AR between individuals of the Tuscan populations ranged from 6.4 to 10.5, indicating that animals shared a high percentage of alleles in relation to the population. The AR values observed in Sardinian breeds were low (<0.4%), but this could also be an artefact due to the lack of complete pedigree data.Regarding the level of inbreeding, FAO suggests a threshold of 1% per generation to maintain reproductive fitness . Severalnnual ΔF ,36 or pennual ΔF , while oneration . The sixfe/fa ratio close to 1 that was found in all local breeds indicates a high balance between the founders’ contributions and consequently, an absence of bottleneck effect [The k effect . As expek effect . The matThe proportions of mating between close relatives were also examined. The most alarming situations were observed for CAL, PON, MUP, and SAR, with rates of half-sibs matings >4% and up to 9.25% for CAL . AlthougPresent results suggest the necessity of safeguarding measures that will guarantee the physical, economic, and logistic viability of the three Tuscan breeds and thereby their existence. High rates of parent–offspring and half-sibs matings in MUP outline the necessity for the development of an appropriate mating scheme. Several ways exist to preserve animal populations from extinction and maintain genetic diversity within a population: (i) genetic/genomic tools, (ii) biotechnology, (iii) management, (iv) scientific support, (v) cultural relatedness, and (vi) political engagement. In the first two categories, cryoconservation of semen, embryos and oocytes could be reported ,49. In aOur analysis outlined the critical situation, in terms of population size and genetic diversity, for the Pontremolese, Calvana, and Mucca Pisana breeds. Concerning the breeds of Sardinia  low pedigree quality poses restrictions for an accurate assessment of the genetic diversity. However, trends of pedigree completeness in recent years are encouraging and towards the desired direction. Genomic data, favorably of a large and representative sample per breed, are expected to bridge this gap and to shed more light on the genomic background of the aforementioned breeds and the level of their genomic diversity.As expected, based on the history of the CHA and LIM breeds, there is space for intense selection. However, this should not be thoughtlessly applied, but under an optimized scheme taking into account both an increased genetic gain of the traits under selection and maintenance of genetic diversity.


Human infants, apes and capuchin monkeys engage in intuitive statistics: they generate predictions from populations of objects to samples based on proportional information. This suggests that statistical reasoning might depend on some core knowledge that humans share with other primate species. To aid the reconstruction of the evolution of this capacity, we investigated whether intuitive statistical reasoning is also present in a species of Old World monkey. In a series of four experiments, 11 long-tailed macaques were offered different pairs of populations containing varying proportions of preferred versus neutral food items. One population always contained a higher proportion of preferred items than the other. An experimenter simultaneously drew one item out of each population, hid them in her fists and presented them to the monkeys to choose. Although some individuals performed well across most experiments, our results imply that long-tailed macaques as a group did not make statistical inferences from populations of food items to samples but rather relied on heuristics. These findings suggest that there may have been convergent evolution of this ability in New World monkeys and apes (including humans). For instance, it might rain more in some months than in others, certain fruits will be more abundant in a specific habitat, and someone's relative will repeatedly be late to a meeting while another one will always be there in case of need. In other words, frequencies of past occurrences can be informative about existing relationships between events, as well as about the likelihood of their future occurrence. Using statistical regularities to reduce uncertainty and acquire knowledge about the state of the world, what we call statistical reasoning, is key to human learning and pervades disciplines from psychology to economics, biology, physics, law and medicine –4. It alThe nature and development of human statistical reasoning has long been the topic of much debate among various researchers. Some have advocated that humans become proficient in it only during later stages of childhood ,7 and arRecently, similar reasoning abilities were highlighted in non-human primates: Using a paradigm originally developed for children , it was Macaca fascicularis), a species of Old World monkey, are able to make statistical inferences from populations of food items to samples. Previous findings with infants, apes and capuchins could point towards an evolutionarily ancient origin of this trait, given that they shared a common ancestor over 30 Ma [Based on the same paradigm used with children  and apeser 30 Ma . Howeverer 30 Ma , the samer 30 Ma .Previous research on numerical cognition has shown that, similar to other species , long-taIn a series of four experiments (with seven test conditions), we presented long-tailed macaques with two transparent buckets containing populations with varying proportions of preferred (grapes) versus neutral (monkey chow) food items. Subjects watched an experimenter randomly (in appearance only) drawing a one-item sample out of each population and were given the choice between the two hidden samples. To receive a preferred food item as reward, therefore, subjects had to distinguish between the two populations in terms of their proportion of preferred to neutral food items, and use this relative frequency information to form expectations about the likely outcome of a sampling event. In Experiment 1, absolute and relative frequencies were confounded, thus, to differentiate whether monkeys really estimated probabilities, or rather relied on some simpler heuristics, several control experiments were administered. Experiments 2a–c disentangled absolute and relative frequencies of food items, while Experiment 3 controlled for the number of neutral items. If monkeys engage in statistical reasoning, they should have a preference for the samples stemming from the populations with the higher proportion of grapes. In case they rely on absolute number heuristics, they should have a preference for the hand drawing out of the populations with the higher quantity of grapes and/or the smaller quantity of monkey chow. If they do not expect any quantitative information to predict sampling events, they should have no consistent preference for either sample. Lastly, an additional control experiment checked for the use of olfactory cues (Experiment 4).2.2.1.N = 8)—aged 1–10 years  was adjacent to the indoor enclosure, and could be subdivided into six experimental compartments. Subjects were tested individually in one compartment  to which an attachable cage  was fixed, allowing subjects to have better access to the experiment. The cage was built in metallic mesh, except for the front part that separated the monkey and the experimenter, which consisted in a removable Plexiglas pane . The pane had two small holes  preceded by a short choice training, a preference test and a familiarization phase . Each test condition consisted of 12 test trials, evenly divided into two sessions. Test sessions always started with an additional preference test to make sure that monkeys’ preference for one of the two food types was consistent over time. Twice in a row, subjects were given the choice between one grape and one piece of monkey chow. Hence, each session consisted of two preference trials and six test trials. In each test trial, subjects were confronted with two populations consisting of a pre-determined mix of grapes (preferred food type) and monkey chow  contained in transparent buckets. Items of the two food types had the same approximate size but differed in colour so as to render them easily distinguishable one from the other. The experimenter presented both populations on a table, shook them one after the other  and tilted them slightly forward to give the subject a good overview. She then closed her eyes, reached into the buckets and simultaneously drew one item  out of each bucket in a way that kept the item hidden from the subject. While keeping the food concealed in her fists, the experimenter subsequently moved both hands towards the holes and allowed the monkey to make a choice . Once th2.4.Every session was video-recorded. The experimenter coded monkeys' choices live: Whenever monkeys chose the sample stemming from the favourable population, she coded it as a success on a paper sheet, and as a failure when they chose the alternative option. A second blind observer coded 25% of the sessions, using the video recordings. Agreement between the experimenter and the second coder was perfect for all experiments (100% agreement).2.5.t-tests. To analyse individual performances, we used binomial tests to calculate the probability to observe the number of successes or any higher number out of 12 trials, conditional on an underlying probability of success in one trial equal to 0.5. To adjust for an inflation of the family-wise significance level, we report p-values adjusted for multiple testing [To test whether monkeys’ mean group performance for each experiment was different from chance, we computed one sample two-tailed  testing . In addi testing .3.3.1.In Experiment 1, we wanted to investigate whether long-tailed macaques use quantitative information to make inferences about sampling events. As Experiment 1a was not fully randomized with regard to the side on which the favourable sample appeared , we added Experiment 1b to our study, which was corrected for this flaw. We used the same proportions of grapes and monkey chow in both experiments, but varied the absolute quantities of food items. Eleven subjects participated . Both bu= 1.8 trials) in Exp. 1a and in 7.18 trials  in Exp. 1b (t(10) = 3.01, p = 0.01, d = 0.91; Exp. 1b: t(10) = 2.55, p = 0.03, d = 0.77). At the individual level, only two individuals (Mila and Maja) performed significantly above chance . This suggests that, as a group, long-tailed macaques use some quantitative information (absolute and/or relative) to draw inferences from population to sample.On average, subjects selected the sample drawn out of the favourable population in 7.64 trials  from merely relying on absolute frequencies (choosing the sample drawn from the population with absolutely more preferred food items). As such, we added Experiment 2a–c to differentiate between these possibilities. We disentangled absolute and relative frequencies of grapes in two ways: In Experiment 2a, the absolute quantity of grapes was higher in the population with the less favourable proportion of grapes to monkey chow . If monkt(10) = 1.47, p = 0.17, d = 0.44; Exp. 2b: t(10) = 1.67, p = 0.13, d = 0.5). At the individual level, only Maja was significantly above chance in Exp. 2b (p < 0.01), while Sally's performance, with 10 out of 12 correct trials, was also good  and 7.09 trials  in Exp. 2a and Exp. 2b, respectively, which was not different from chance  the population with the higher absolute number of neutral food items. In Experiment 3, therefore, both populations contained the same absolute number of monkey chow pieces but different amounts of grapes . Hence, t(10) = 2.11, p = 0.06, d = 0.64). At the individual level, only Maja was significantly above chance (p < 0.01). These results suggest that at least Maja was not relying on the absolute number of neutral items to make her inferences. Her choice was consistent across the 12 trials of the experiment, suggesting that it was not affected by the rewarding pattern.On average, subjects selected the sample drawn out of the favourable population in 7.27 trials , not significantly different from chance  = −0.32, p = 0.76, d = 0.10; no individual performance was above chance). This suggests that none of our subjects based their decisions on olfactory cues.On average, subjects selected the sample drawn out of the favourable population in 5.9 trials, , not significantly different from chance . Furthermore, in every condition there was no significant correlation between group performance and trial number .Our results indicated no main effect of arms' positioning, whether they were straight or crossed  drawn from the populations. The main results were the following: As a group, long-tailed macaques performed above chance level only in the first experiment in which absolute and relative quantities were confounded, but did not perform above chance level in the experiments in which these quantities were deconfounded (Exp. 2 and 3). Comparing absolute quantities of grapes could have been sufficient to select the favourable sample above chance level in Experiment 1. By contrast, human infants, apes and capuchins performed above chance level in experiments in which absolute and relative quantities of the preferred items were deconfounded. Our results demonstrate differences in statistical reasoning abilities between long-tailed macaques on the one hand, and 12-month-old children ,39, apesWhen focusing on individual performance rather than on group means, a different picture emerged. Although most individuals performed at chance level, two adult females, Maja and Sally, performed well in all conditions testing for statistical reasoning, except in Experiment 2a, in which absolute and relative frequencies of preferred items were deconfounded. There are two possible and mutually exclusive explanations for this pattern of behaviour. The first one is that in Experiment 2a, the bucket with the less favourable proportion contained a much larger number of grapes. Thus, it took not only the ability to compare proportions to solve this task but also the ability to inhibit the choice of the larger amount of preferred food, which is a highly salient stimulus with the potential to interfere in rational decision-making –42. ThisThe second explanation for Maja and Sally's performance results directly from a possible confound within this bucket paradigm : In all conditions except Experiment 2a, the higher proportion of grapes was confounded with the higher quantity of visible grapes. Subjects could have succeeded in most conditions by selecting the sample drawn out of the population with the higher number of visible grapes, without taking into account the grapes hidden by monkey chow items and thus without considering proportions at all . In Experiment 2a, the difference between the quantities of visible grapes of both populations was less striking, which might explain why Maja and Sally performed at chance level. To rule out this alternative explanation, we added Exp. 2c. In this condition, the grapes were added to the buckets before the monkey chow so that Maja could have access to the complete information. Her performance in this experiment, even if not perfect, was still high  and suggests that she relied on proportions. To better decide between these alternatives, future studies should make sure to work with transparent populations, i.e. populations in which all items are visible at all times. Performance of previously tested species should maybe also be reassessed using appropriate controls, so as to definitely rule out this alternative explanation.Another improvement to this paradigm would be to switch from deterministic rewarding to probabilistic rewarding in all experiments. In the initial paradigm  tested oWhy did these two females, but especially Maja, perform better than any other monkey in this study? The reason is difficult to pin down as there might be several factors at play and as we did not make any specific prediction about this individual variation. It might have to do with variation in motivation and sustained attention rather than ability, as Maja in particular always seemed calm and attentive when being tested, while many of the other subjects often looked or momentarily moved away. We did always wait for our subjects to pay attention to the testing material before proceeding with a trial, but this looking and moving away might suggest some lack of interest in the experiment. For the sake of comparability with previous studies, we did not counterbalance the order of the conditions, to control for order effects on performance. We thus cannot rule out that group performance was better in Exp. 1 only because monkeys were more motivated in the beginning compared to the end of the study. Another less observable factor might be a variation in how monkeys interpreted the drawing, as inferences based on proportions should only have been made under the assumption that the drawing was blind. Some monkeys might not have made this assumption even if the experimenter closed her eyes during the drawing, either because they considered the human experimenter omniscient and almighty (not too unreasonable from their everyday experience), or because they assumed that she could haptically distinguish monkey chow from grapes during the drawing process. To address this concern, future studies could integrate a mechanical device performing the drawing into the paradigm. Finally, a pre-existing side-bias, or an impulse to choose whatever side on which preferred food was displayed, might have prevented our subjects from engaging in more sophisticated decision-making processes. In fact, several individuals were consistently side-biased across conditions , when looking at individual performances, similar patterns emerge between species. For example, only one of the capuchins (see Kato in the online resources of the capuchin study ), and noIn summary, at the group level, our subjects' performance did not match the capacities described in human children, great apes and capuchins. It remains an open question whether this observed difference was due to performance limitations such as a lack of sustained attention and motivation or to a failure to interpret the drawing as random, or due to a true competence limitation in their statistical reasoning capability. We found some evidence that two individuals used proportions to solve the tasks. However, this is not sufficient to generalize to the group level. Our findings, together with findings of studies with human children, apes and capuchins, suggest that there might have been convergent evolution of intuitive statistics (or any of the prerequisite components for this ability) in New World monkeys and apes, at least in a context of food choice. However, before reaching any sharp conclusion about the evolutionary origins of statistical reasoning, appropriate controls as well as analyses of individual performances should be added to previous studies to rule out the use of quantity-heuristics that could be involved in such decision-making and to rule out possible confounds that might also account for the data. This was not done thoroughly enough until now. Furthermore, more species of Old and New World monkey should be tested for intuitive statistics to help make precise any claim about the evolutionary origins of this ability.


Paramuricea clavata.In the ocean, the variability of environmental conditions found along depth gradients exposes populations to contrasting levels of perturbation, which can be reflected in the overall patterns of species genetic diversity. At shallow sites, resource availability may structure large, persistent and well-connected populations with higher levels of diversity. In contrast, the more extreme conditions, such as thermal stress during heat waves, can lead to population bottlenecks and genetic erosion, inverting the natural expectation. Here we examine how genetic diversity varies along depth for a long-lived, important ecosystem-structuring species, the red gorgonian, We used five polymorphic microsatellite markers to infer differences in genetic diversity and differentiation, and to detect bottleneck signs between shallow and deeper populations across the Atlantic Ocean and the Mediterranean Sea. We further explored the potential relationship between depth and environmental gradients  on the observed patterns of diversity by means of generalized linear mixed models.An overall pattern of higher genetic diversity was found in the deeper sites of the Atlantic Ocean and the Mediterranean Sea. This pattern was largely explained by bottom temperatures, with a linear pattern of decreasing genetic diversity with increasing thermal stress. Genetic differentiation patterns showed higher gene flow within sites  than between sites. Recent genetic bottlenecks were found in two populations of shallow depths.P. clavata. Differentiation patterns showed that the deep richer populations are isolated. Gene flow was also inferred across different depths; however, not in sufficient levels to offset the detrimental effects of surface environmental conditions on genetic diversity. The identification of deep isolated areas with high conservation value for the red gorgonian represents an important step in the face of ongoing and future climate changes.Our results highlight the role of deep refugial populations safeguarding higher and unique genetic diversity for marine structuring species. Theoretical regression modelling demonstrated how thermal stress alone may reduce population sizes and diversity levels of shallow water populations. In fact, the examination of time series on a daily basis showed the upper water masses repeatedly reaching lethal temperatures for  Extreme environmental conditions may change the distribution of intra-specific biodiversity . HoweverIn the marine environment, environmental gradients are known to affect the genetic diversity levels of the populations e.g.,  . ParticuIn the coastal zone, depth gradients are associated with increased environmental variability due to the stratification of wind-induced turbulence, light attenuation, nutrient availability, sedimentation and the presence of thermoclines and haloclines . TogetheParamuricea clavata (Risso) lives on shadowed rocky substrates down to 120 m in the Mediterranean Sea .The red gorgonian nean Sea  and to 1nean Sea . Shallownean Sea . Damage nean Sea . In the nean Sea . Howevernean Sea  and marinean Sea , which eWe sampled two populations at different depths  in three sites of the Atlantic Ocean (Portuguese coast) and two sites of the Mediterranean Sea  per population  were standardized to the smallest sample size found within sites using 10izations .4 randomizations. The estimates of diversity were modelled with linear regression against depth (null model) and a set of important environmental predictors affecting the physiology of P. clavata  and the Biogeochemistry Non-Assimilative Hindcast Simulation . Bottom environmental data for each population was obtained using trilinear interpolation  we adopted the Generalized Linear Mixed Models GLMM;  frameworFST estimator and an analysis of molecular variance (AMOVA) based on allele frequencies, computed with Genodive  using Genodive  under 10izations .4 simulations. We used the suggested and more appropriate  parameters for microsatellites: the Two-Phase Model (TPM) (Evidence of genetic bottlenecks were inferred by testing for heterozygosity excess . This isel (TPM)  with a sel (TPM) . Becauseel (TPM) .Genetic diversity as allelic richness (A) and number of private alleles (PA) was consistently higher in deeper populations, with the exception of only one site  was higher between than within sampling sites, with two exceptions for Sagres and Riou Sud  were detected in the shallow populations of Berlengas and Punta Mesco ; S3. AllP. clavata consistently showed less allelic richness, a smaller number of private alleles and bottleneck signs (for two sites) in shallow waters populations. This pattern found across the Atlantic Ocean and Mediterranean Sea was explained by the negative relationship between thermal stress and depth, and not by the used proxies of essential resources . In fact, the examination of daily time series of bottom temperatures for each site and depth range  did not correlated with depth, nor with the additional predictors considered in regression analyses. Gene diversity is not as sensitive as allelic richness to detecting historical population changes  since drr corals . Similarr corals .P. clavata followed the patterns of thermal stress, the bottleneck tests did not provide a strong empirical support of recent bottlenecks in shallow populations (exceptions for Berlengas e Punta Mesco). Although recent environmental changes may account for genetic diversity losses, the fluctuations of past climate changes may further shape the genetic structure of marine species, both latitudinally and along depth gradients , this study represents a timely baseline to conserve populations with higher conservation value in risk of disappearing.This study demonstrates that deeper marine populations can consistently harbour higher genetic diversity than those in shallower environments. Theoretical regression modelling using environmental data showed that such genetic pattern may arise from thermal stress alone, reducing population sizes and diversity levels. These findings raised the hypothesis of deep refugia for 10.7717/peerj.6794/supp-1Table S1Click here for additional data file.10.7717/peerj.6794/supp-2Table S2Click here for additional data file.10.7717/peerj.6794/supp-3S3Click here for additional data file.10.7717/peerj.6794/supp-4S4Click here for additional data file.10.7717/peerj.6794/supp-5Dataset S1Click here for additional data file.


We next used structural equation modeling to test if variation in Pgi mediated gene flow as measured by Fst at putatively neutral loci. In a year when the population was recovering following a large decline, individuals with a genotype associated with greater dispersal ability were found at significantly higher frequencies in populations isolated by water and forest, and these populations showed lower levels of genetic differentiation at neutral loci. These relationships disappeared in the next year when metapopulation density was high, suggesting that the effects of individual variation are context dependent. Together our results highlight that (1) more complex aspects of landscape structure beyond just the configuration of habitat can be important for maintaining spatial variation in dispersal traits and (2) that individual variation in dispersal plays a key role in maintaining genetic variation across fragmented landscapes.There is now clear evidence that species across a broad range of taxa harbor extensive heritable variation in dispersal. While studies suggest that this variation can facilitate demographic outcomes such as range expansion and invasions, few have considered the consequences of intraspecific variation in dispersal for the maintenance and distribution of genetic variation across fragmented landscapes. Here, we examine how landscape characteristics and individual variation in dispersal combine to predict genetic structure using genomic and spatial data from the Glanville fritillary butterfly. We used linear and latent factor mixed models to identify the landscape features that best predict spatial sorting of alleles in the dispersal‐related gene  Understanding how fragmentation affects dispersal and gene flow across human‐modified landscapes has long been a goal in evolutionary biology. Growing evidence suggests that individuals of the same species can vary considerably in their dispersal and movement traits. While the effects of this individual dispersal variation on range expansions and invasions have been well characterized, knowledge of how it might mediate genetic responses to landscape fragmentation are almost entirely lacking. Here, we demonstrate that individual variation in dispersal is key to the maintenance of genetic variation during recovery following a large decline in a butterfly metapopulation. We further show that spatial variation in dispersal is not maintained by the configuration of habitat patches alone, but by a more complex genotype–environment interaction involving the landscape matrix . This challenges the simplified landscape representations typically used in studies of dispersal evolution that ignore heterogeneity in the landscape matrix. More broadly, our results highlight the interplay of adaptive and neutral processes across fragmented landscapes, suggesting that an understanding of species vulnerability to landscape fragmentation requires consideration of both.Dispersal is key to the maintenance of genetic variation and adaptive potential in fragmented landscapes. Differences in species ability to maintain genetic diversity in fragmented landscapes can, in part, be explained by interspecific differences in dispersal capacity  in the Åland Islands, Finland. Importantly, individuals vary in their dispersal ability: butterflies heterozygous or homozygous for the “c” allele in a SNP associated with the gene phosphoglucose isomerase (Pgi) have higher flight metabolic rate, which translates to substantially increased dispersal propensity and dispersal distance in the field, especially at cooler temperatures  that contain one or both of its host plants, Pgi (Mc1:1687:14486), Mc1:1687:14486, Mc1:1873:36910, Mc1:1124:71239, Mc1:752:33517 , we excluded them from downstream analysis. We further excluded 98 patches in 2011 and 129 in 2012 with only a single larval nest where the effects of genetic drift are expected to be especially strong, giving a sample size of 152 patches  in 2011 and 193 patches  in 2012.In 2011 and 2012, three larvae per nest were collected from patches across Åland and were genotyped at 40 neutral markers and five putatively function markers from candidate genes related to flight or dispersal traits including M. cinxia individual—either a value of 1 (not resistant to movement) or 10 (restricts movement), generating a total of 20 surfaces with different combinations of resistant and nonresistant features  or resistance distance (from CIRCUITSCAPE) between focal patch i occupied in year t and source patch j occupied in the previous year (t – 1), and jN is the number of M. cinxia nests found in source patch j in year t – 1. The constant α scales the dispersal kernel and should be equal to 1/mean dispersal distance of the species, which has been estimated to be 1 km , isolated populations and at the lowest in old, isolated populations because dispersive individuals also have high emigration rates , and was determined for each year separately using BAPS6 . We used this approach since alternatives such as model averaging and summing akaike weights can lead to flawed interpretation of effects when variables are even weakly collinear  simultaneously. This applies a stronger control of background genetic structure, particularly when structure is complex and hierarchical , and also allowed us to confirm that we only see associations between connectivity and allele frequencies for Pgi and not for other loci. A drawback is that it cannot incorporate additive or interactive effects, and thus we used it only to confirm results in the partitions of the data that were found to be significant in linear mixed effect models . In addition to testing the individual Si variables identified as important in the mixed models, we explored potential additive effects by testing for association between loci and Si variables that incorporated the resistance of two or more of the landscape features. For example, the additive effects of Siwater and Siforest were included in the model as the predictor Siwater+forest , or indirectly through its effect on the spatial distribution of individuals with contrasting Pgi genotypes.Our second objective was to test if intraspecific variation at the st .Direct effects were tested using linear mixed effect models with neutral genetic differentiation as a response variable. A patch‐specific measure of genetic differentiation was calculated by taking the average pairwise Weir and Cockerham unbiased FPgi using structural equation modeling (SEM) implemented in the lavaan library  connectivity directly affects genetic differentiation, and (2) connectivity indirectly affects genetic differentiation through its effects on the frequency of the Pgi‐c allele. Connectivity was included in the model as a latent variable – i.e., a construct that is not easily measured directly but can be indicated by a number of observed, and potentially correlated, variables that have some level of measurement error . We included connectivity as a latent variable, with the five connectivity hypotheses Simetapop, Siwater, Siforest, Siagriculture, and Siroads as indicators. We estimated model parameters using maximum likelihood and assessed model fit with chi‐square tests, where a P‐value > 0.05 indicates the model‐implied covariance fits the observed covariance well . The relative effects of paths were evaluated from standardized path coefficients and individual significance of paths.We next tested for indirect effects of landscape structure on genetic differentiation in the partitions of data where we found strong association between connectivity and  Rosseel . Originaforest and Siwater, and seven of the models were embellishments of higher ranked models—i.e., they were the same as a higher ranked model but included uninformative parameters with little effect on model fit  in this partition. The full structural equation model including all five landscape connectivity indicator variables showed poor fit as indicated by a significant deviation of the observed covariance from the model‐implied covariance . Because Simetapop did not have strong effects in our earlier models, we removed it as an indicator variable. This model fit the data well as indicated by no significant deviation of the observed and modeled‐implied covariance . The removal of Simetapop did not affect the strength or significance of associations. Siwater and Siforest were the strongest and only significant indicators of connectivity . We further found that patch connectivity alone did not predict genetic differentiation at neutral markers, but rather the effect of landscape on genetic structure was mediated through individual variation in the Pgi locus; populations with higher frequencies of the Pgi‐c allele had lower Fst. In the following year when the density of populations increased, these relationships disappeared, suggesting that good dispersers only have an advantage when there are many empty patches to colonize. Together our results suggest that both individual variation in dispersal traits and landscape matrix heterogeneity are important for predicting spatial patterns of genetic variation.Here, we show that patch connectivity metrics incorporating landscape matrix best predicted the distribution of a dispersal polymorphism during a population recovery in the Glanville fritillary butterfly. In particular, newly colonized populations that were isolated by water and forest matrix had significantly higher frequencies of an allele associated with increased dispersal ability , and geographic distance might have been sufficient to capture patterns. It should also be noted that previous studies tested only a single model of patch connectivity, whereas we competed several models assuming different landscape structures.We found that spatial sorting of individuals based on their ers Fig. . In conters Fig. . This suPgi dispersal polymorphism in the M. cinxia system in the Åland islands is not maintained by variation in patch configuration alone , but that the landscape matrix further influence dispersal patterns. This is an important finding given that studies investigating the drivers of dispersal evolution almost exclusively use simplified landscape models that assume a homogenous matrix  at moderate and cool temperatures, but individuals without a c allele fly better at very warm temperatures ; an allelic combination at these two loci was associated with maximal metabolic endurance in M. cinxia. Linkage of Pgi to other functional loci have not been resolved, but the low frequency and fitness of individuals homozygous for the C allele suggest possible linkage to a deleterious mutation . Interestingly, the four other candidate loci included in our study did not show significant associations with landscape structure, despite being found as outliers in previous work . Future work would benefit from fine‐tuning landscape resistance surfaces to better account for these potential small additive effects , which were excluded from our analysis because estimating Fst requires larger samples  and 2012 (B). Each point is a patch and the colour indicates membership to a particular cluster.Figure S2. Associations between the frequency of the Pgi c allele and two indices of connectivity (Siwater+forest and log‐transformed Simetapop) for each genetic cluster in 2011 (A) and 2012 (B).Figure S3. Q‐Q plots showing observed and expected –log10 p‐values for latent factor mixed models testing for associations between 39 loci and connectivity metrics in new populations in 2011 (A‐E) and Supporting Figures – DiLeo et al. Evolution Letters 2012 (F‐G).Figure S4. Scatterplots showing the relationship between the frequency of the Pgi‐c allele and a patch connectivity metric incorporating roads as a facilitator to dispersal (A) and water as a barrier to dispersal (B) for the year 2012.Figure S5. Scatterplots showing relationships between population‐specific genetic differentiation (Fst) and the frequency of the Pgi‐c allele (A), Fst and Siforest (B) and Fst and Siwater (C) for newly colonized populations in 2011.Figure S6. Sources of variation in patch connectivity per year. Yearly differences in connectivity for newly colonized populations, Simetapop, (A), geographic distance between source and target patches for newly colonized populations, dij (B), number of nests in source patches, Nj (C), and total number of source patches (D).Figure S7. Relationships between connectivity and minor allele frequencies (MAF) of the four other candidate loci in newly colonized and old populations.Click here for additional data file.Table S1. Descriptions of landscape connectivity hypotheses.Table S2. Pearson correlation coefficients among the five landscape connectivity variables.Table S3. Posterior mean estimates, standard deviation, and quantiles of Bayesian nonspatial and spatial INLA models.Table S4. Results of model selection on linear mixed effect models testing for associations between the frequency of the Pgi‐c allele, population age (old or new), and competing metrics of patch connectivity (Si) for the year 2011.Table S5. Results of model selection on linear mixed effect models testing for associations between the frequency of the Pgi‐c allele, population age (old or new), and competing metrics of patch connectivity (Si) for the year 2012. Only the top 15 models are shown.Table S6. Genomic inflation factors of for each value of k and each predictor tested in latent factor mixed models.Table S7. Results of model selection on linear mixed effect models testing for associations between population genetic differentiation (Fst), population age (old or new), and competing metrics of patch connectivity (Si) for the year 2011.Table S8. Results of model selection on linear mixed effect models testing for associations between population genetic differentiation (Fst), population age (old or new), and competing metrics of patch connectivity (Si) for the year 2011 with a singly outlier with a high Fst value removed. Only the top 15 models are shown.Table S9. Results of model selection on linear mixed effect models testing for associations between population genetic differentiation (Fst), population age (old or new), and competing metrics of patch connectivity (Si) for the year 2012.Click here for additional data file.Appendix A – Supplementary methods and resultsClick here for additional data file.Appendix B: Information about the 5 candidate and 40 neutral SNPs used.Click here for additional data file.


The transcription factor signal transducer and activator of transcription (STAT)3 mediates the functions of cytokines, growth factors, and oncogenes under both physiological and pathological conditions. Uncontrolled/constitutive STAT3 activity is often detected in tumors of different types, where its role is mostly that of an oncogene, contributing in multiple ways to tumor transformation, growth, and progression. For this reason, many laboratories and pharmaceutical companies are making efforts to develop specific inhibitors. However, STAT3 has also been shown to act as a tumor suppressor in a number of cases, suggesting that its activity is strongly context-specific. Here, we discuss the bases that can explain the multiple roles of this factor in both physiological and pathological contexts. In particular, we focus on the following four features: (i) the distinct properties of the STAT3α and β isoforms; (ii) the multiple post-translational modifications  that can affect its activities downstream of multiple different signals; (iii) the non-canonical functions in the mitochondria, contributing to the maintenance of energy homeostasis under stress conditions; and (iv) the recently discovered functions in the endoplasmic reticulum, where STAT3 contributes to the regulation of calcium homeostasis, energy production, and apoptosis. Signal transducer and activator of transcription (STAT) factors are activated by tyrosine phosphorylation, mainly by receptor-associated Janus kinases (JAKs) downstream of cytokine and growth factor receptors ,2. TyrosThe activation/inactivation of STAT proteins is a crucial step for their biological activities, and the disruption of this control, leading to defective or constitutive STATs activation, usually correlates with the development of pathological conditions. Accordingly, STAT3 activation must be tightly controlled by negative regulators, which mainly belong to three groups, namely: phosphatases, suppressor of cytokine signaling (SOCS) proteins, and the protein inhibitor of activated STAT (PIAS) proteins ,29,30. PIn agreement with the observation that most of its numerous activators/negative regulators are frequently deregulated in cancer, STAT3 is frequently constitutively activated in many tumors both of hematopoietic and epithelial origin, and is mostly considered as an oncogene . AThe STAT3 constitutive activity in tumors can induce proliferation and resistance to apoptosis, down-modulate immune responses and promote angiogenesis, enhance invasion and metastasis by inducing epithelial to mesenchymal transition (EMT), alter the extracellular matrix inducing matrix metalloproteinases (MMP) and promote collagen cross-linking and tissue tension, regulate energy metabolism and mitochondrial activity, and confer cancer stem cell features in a number of liquid and solid tumors ,39,40,41However, in keeping with the complexity of its functions, recent evidence is accumulating that STAT3 can also act as a tumor suppressor under specific conditions (recently reviewed in ), even iCertainly, the multiple and even contrasting actions of STAT3 both under physiological and pathological conditions can be partly explained by its ability to activate different target genes in different cell types and under different conditions. This is in turn due to the cell- and context-specific accessibility of STAT3 binding sites on the genome , to its On the other hand, referring to STAT3 as a single factor is somewhat misleading. Indeed, STAT3 exists in two alternatively spliced isoforms, the full length STAT3α and the truncated STAT3β, which possess both specific and overlapping transcriptional activities ,53,54. AO-Tetradecanoylphorbol-13-acetate (TPA) and dextran sodium sulfate (DSS), resulting in accelerated skin and intestinal tumorigenesis in the well-known TPA/DMBA and AOM/DSS models of inflammation-driven carcinogenesis [While STAT3α is the full-length form of STAT3, the β isoform lacks the C-terminal transcriptional activation domain (TAD) including S727, replaced by a unique seven amino acid tail ,56. STATogenesis . DespiteAs already mentioned, STAT3 canonical activity as a transcription factor is mainly due to phosphorylation on Y705, which determines the ability to form functional dimers, to concentrate in the nucleus, and to bind DNA. This occurs downstream of the many activatory signals mentioned above, and is in turn controlled by the negative regulators that lead to STAT3 dephosphorylation. However, similar to STAT1, STAT3 can regulate transcription also when not phosphorylated. Indeed, unphosphorylated (U)-STAT3 can shuttle between the nucleus and the cytoplasm , and hasIn addition to its prevalent nuclear localization and function, YP-STAT3 has also been shown to localize to the focal adhesions in ovarian cancer cells, where it interacts with phosphorylated paxillin and focal adhesion kinase (FAK), thus regulating cell migration . STAT3 cIn addition to YP, STAT3 can undergo a number of different post-translational modifications correlated with either enhanced or reduced trans-activating potential. These can however also regulate non-transcriptional functions of this factor. The first of these modifications to be detected is phosphorylation on the serine residue in position 727 (SP), located in the carboxy-terminal transcriptional activation domain and thus lacking in the STAT3β isoform. SP can be carried out by several kinases including MAP kinases, PKCε and mTOR, downstream of both classical STAT3 activating cytokines/growth factors, of the PI3 kinases and of Ras proteins ,72. WhilIn response to cytokines and growth factors signaling, STAT3 can also be acetylated (Ac) on multiple lysine (K) residues by the CBP/p300 histone acetyltransferase. K685 acetylation can enhance the STAT3 transcriptional activity by increasing tyrosine phosphorylation and dimer stability , and K68STAT3 methylation can occur in the nucleus on residues K140 or K180, with different effects on its activities. The histone methyltransferase SET9 is able to methylate promoter-bound STAT3 on K140, leading to impaired transcriptional activation of the target genes .2O2 scavengers within the cell [Finally, STAT3 can become both oxidized and glutathionylated on multiple cysteine residues, impairing its transcriptional activity, either under conditions of oxidative stress or downstream of IL-6 signaling, which can raise reactive oxygen species (ROS) levels ,83,84,85the cell ,90.Tumor cells are known to rely on the so-called Warburg effect for their growth on metabolic rewiring  involvinIntriguingly, as mentioned above, STAT3 was described in a number of cell types to be able to localize to mitochondria, where its SP form is able to regulate the respiratory and signaling functions of this organelle ,97,98,992+ fluxes into the mitochondrion [2+ concentration to initiate MPTP opening [+ T cell by IL-6 requires mitochondrial STAT3 and coincides with the formation of ET super complexes, which, by optimizing coupling, are known to reduce electron leakage [2+ levels. In turn, these were required to ensure the prolonged stimulation of IL-4 production via the activation of the nuclear factor for activated T-cells (NFAT) and the induction of effector Th2 T-cells from naïve CD4+ T-cells [Mitochondrial STAT3 functions -stimulated ES cells [SP, which was shown to occur downstream of Ras-activated MAP kinases ,110 and survival . AdditioES cells ,107. As ES cells , leadingMany reports suggest that several known STAT3 pro-oncogenic activities previously ascribed solely to YP involve SP instead. Originally, SP STAT3 was reported to sustain the tumor transformation of MEF cells downstream of Harvey rat sarcoma virus oncogene (H-RAS) . Additio2+ ions into the mitochondria [MEF cells expressing constitutively active STAT3, in addition to a steady-state reduction of the mitochondrial membrane potential and of ETC protein levels, also featured reduced mitochondrial activation in response to ATP, assessed as a reduced flux of Cachondria .2+ homeostasis and the regulation of its fluxes between different subcellular compartments are of fundamental importance for the control of many cellular functions, including life/death decisions [2+ storage compartment [2+ pumps such as the SERCAs, which trigger ER Ca2+ entry, and of Ca2+ channels activated by the second messenger IP3 downstream of many different extracellular signals [2+ in the mitochondrial matrix regulates the activation of mitochondrial respiration, as the Ca2+ concentration is rate-limiting for the function of several enzymes of the Krebs cycle [2+ at the mitochondrial associated membranes (MAMs) [2+ overload, triggering the opening of the MPTP and the initiation of the intrinsic apoptotic program [2+ signals to mitochondria [2+-mediated cell death, like H2O2 and menadione [Caecisions . The endpartment . ER calc signals , the inobs cycle . Howevers (MAMs) , and spe program ,121 (Figchondria , is a keenadione . It is tenadione ,124. Indenadione ,126,127.2+ release from the ER, and indeed we observed a significantly reduced ER Ca2+ content and release in MEF cells [2+ content and release, while not affecting at all of the MDA-MB-453 or T47D cells, which do not display activated STAT3. Increased Ca2+ release upon STAT3 silencing occurred, only in the STAT3-dependent cells, also upon H2O2 or menadione treatment, correlating with a significant increased sensitivity to apoptotic cell death.With this in mind, we decided to assess whether STAT3 could somehow alter CaEF cells . Importa2+ homeostasis came from the observation that this factor, both phosphorylated on Y705 and on S727 abundantly, localizes to the ER and MAMs, where it physically interacts with the IP3R3 Ca2+ channel. Although neither SP nor YP are required for either ER localization or IP3R3 interaction, it is the phosphorylation on S727 that appears to play the regulatory role, as MEF cells expressing a STAT3 mutated on S727, but not a WT or Y-F mutant, displayed excessive Ca2+ release and apoptotic cell death upon H2O2 or menadione treatment. Finally, we could demonstrate that ER STAT3 acts by facilitating IP3R3 proteasomal degradation, likely via the ubiquitin E3 ligase FBXL2 [A possible explanation of how STAT3 could regulate ER Case FBXL2 . Indeed,se FBXL2  (Figure se FBXL2 .2+ fluxes.The well-known anti-apoptotic functions of STAT3, so far known to be based on the transcriptional activation of anti-apoptotic genes in the nucleus and on the maintenance of ETC activity in the mitochondrion, now include also its activities at the ER and MAMs, via IP3R3 degradation and modulation of CaSTAT3 has long been considered a pleiotropic factor, due to its multiple activators and its ability to regulate the transcription of different target genes under different conditions. In the past few years, it became clear that STAT3 multifaceted functions were even underestimated, as this factor acts in an unconventional way outside from the nucleus, regulating the functions of mitochondria and the ER independently of its transcriptional activity . Althoug


Just as there is inconsistency with respect to coverage of genomic testing with insurance carriers, there is interprovincial discrepancy in Canada. Consequently, the option of private pay  arises, which can lead to inequities in access, particularly when patients may not be aware of this option. There are currently no published data regarding how the Canadian genetics community handles discussions of private pay options with patients. The purpose of this study was to assess the attitudes of genetic healthcare professionals  practicing in Canada toward these discussions.An online survey was distributed to members of the Canadian College of Medical Geneticists and the Canadian Association of Genetic Counsellors to assess frequencies, rationale, and ethical considerations regarding these conversations. Quantitative data were analyzed using descriptive statistics.Of 144 respondents, 95% reported discussing private pay and 65% reported working in a clinic without a policy on this issue. There were geographic and practice‐specific differences. The most common circumstance for these discussions was when a test was clinically indicated  followed by when the patient initiated the conversation. The most frequently discussed tests included: multi‐gene panels (73% of respondents), noninvasive prenatal testing (62%), and pre‐implantation genetic diagnosis (58%). Although 65% felt it was ethical to discuss private pay, 35% indicated it was “sometimes” ethical.With the increasing availability of genomic technologies, these findings inform how we practice and demonstrate the need for policy in this area. These can include, but are not limited to, whether the GHP thinks the patient can afford to pay for the test or whether they believe that this option contributes to a two‐tier health system , and under which circumstances. This study provides insight into the decision‐making of GHPs with respect to discussing private pay options with patients, including the reasoning behind their decisions and any discordance that may exist with respect to the rationale regarding the dialog. Identifying these underlying issues can foster an open discussion on the issue of access to genetic services. It could lead to policy development addressing these discrepancies so that Canadians may be equally informed of available options, which may influence their own decision‐making when considering genetic testing.22.1Ethics approval for this study was obtained through the Children's and Women's Research Ethics Board and University of British Columbia in Vancouver, British Columbia (H17‐00225).2.2The authors developed the questionnaire and received feedback on a draft version from five GHPs. This feedback was incorporated into the final version, which consisted of 15 multiple choice and free response questions intended to gather information about participant demographics and frequencies, rationale, and ethical considerations around private pay discussions ; it does not refer to direct‐to‐consumer genetic testing.2.3https://www.ccmg-ccgm.org) and the Canadian Association of Genetic Counsellors . The survey was open for participation for 4 months.The online survey was administered using the REDCap tool  genetic counselors, medical geneticists, and other genetic healthcare providers actively practicing in Canada were eligible to participate. This was expected to encompass “regular” members of CCMG and “full” members of CAGC : there were 244 regular members of CCMG at the time of survey circulation and 306 full members of CAGC. Only responses that were complete and where participants clicked “submit” to provide implied consent were included.2.4Descriptive statistics were utilized to evaluate participant demographics, the frequency of private pay discussions by GHPs, genetics clinic policies on private pay, circumstances that influence private pay discussions, types of private pay genetic testing that are discussed Canada‐wide, and ethical considerations, as well as to identify any discrepancies that emerged amongst GHPs in different regions , Ontario, Quebec, and the Maritime provinces ), subspecialties of practice, or type of GHP. One participant reported being both a genetic counselor and a medical geneticist; this was reflected in the demographic table but this participant was considered as only a medical geneticist for the remainder of the analyses. One participant reported practising in all provinces and territories; this participant was excluded from analyses by geographical region. Some participants’ responses to the “other” option for some questions echoed options that were available within that question, so the responses of 10 participants were modified prior to the final analysis to reflect this Due to some “check all that apply” questions, some questions received more responses than the number of participants. Percentages are calculated as percent of participants included within each category rather than the percent of responses. Further, some participants’ responses were included in multiple categories within one comparison .Qualitative data were not formally analyzed, but were reviewed to identify common themes in order to provide context for quantitative findings.33.1In total, 153 responses were received for a response rate of 28%. Nine responses were excluded: four respondents indicated they were not currently practicing in Canada, four had incomplete questionnaires, and one was identified as a duplicate, leaving 144 responses . Demographic features are summarized in Table 3.2In total, 95% of participants reported discussing private pay options with patients Table ; 100% of3.3Twenty‐two percent of participants reported working in a clinic that has a policy on discussing private pay options Table . However3.4The most common circumstances under which GHPs would discuss private pay genetic testing Table  were wheThe three most common types of genetic tests discussed as private pay options Table  were mul3.5The majority (83%) of participants identified one of four factors as the most influential in their decision to discuss private pay Table : the imp3.6When deciding whether discussing private pay options within a publicly funded healthcare system was ethical Table , 65% of 4This is the first study to report on the practice of Canadian GHPs with respect to discussion of private pay genetic testing options. Our findings show that, while most GHPs report discussing these options with patients, there is inconsistency amongst practices, including the types of testing that may be discussed, the circumstances under which the option may be presented, the factors that may influence their decisions and whether policies are in place at the clinics where they practice. The manner in which private pay options are discussed may impact patients’ perceptions of the options and the decisions they ultimately make about whether to pay privately for genetic testing. Consequently, inconsistencies amongst GHPs may lead to inequities regarding patient care and different decision‐making patterns of patients. These results emphasize the need for uniformity of practice amongst Canadians GHPs.4.1When discussing private pay genetic tests, multi‐gene panels were reported to be the most commonly discussed type of genetic test. Panels may be ordered for a variety of indications, including hearing loss, cardiac conditions, and cancer. While our survey did not specifically ask about the indications for which GHPs were discussing panels, 100% of participants who practice solely in cancer genetics reported discussing private pay panels with patients. Panels are a common test in cancer genetics . To our knowledge, this test is not currently funded anywhere in Canada, although some provinces may have funding or tax deductions for in vitro fertilization (IVF) cycles. In general, guidelines on reproductive genetic testing require that the condition in question be severe enough to warrant the testing, but the level of risk required is not defined  to coordinate private pay testing.” GHPs may weigh pros and cons differently impacting whether to discuss private pay options. Consequently, patients may receive discrepant information about their options, resulting in unequal access to healthcare options. In addition, private pay testing may only be a feasible option for patients who can afford to pay for the tests (de Jong & de Wert, The demand for genetic counseling that comes with an increase in genetic testing cannot be met by the current number of GHPs in Canada (Gekas et al., 4.4Due to a low‐to‐average response rate Nulty,  from the4.5The purpose of this study was not to discuss the potential implications of specific genetic test results that may arise from private pay testing, such as incidental findings, variants of uncertain significance, or cascade testing. The purpose was to describe current practice in Canada. Our findings show that there is inconsistency of practice on the topic of private pay genetic testing amongst Canadian GHPs. However, this problem could be resolved by the development of practice guidelines to lessen the decision‐making burden for individual GHPs (Boycott et al., While willingness to pay was not the focus of our study, it is worth noting that patients in the United States and Australia have been shown to be willing to pay for genetic testing that is not covered by their insurance plans (Georgiou et al., None declared. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.