Economic weights in the index promoted high GY, seed oil, protein in meal, and Phoma stem canker (blackleg) disease resistance while maintaining plant height, flowering time, oleic acid, and seed size and decreasing glucosinolate content. After factor analytic modelling of the genotype-by-environment interaction for the additive effects, the linear rate of genetic gain in GY across cycles was 0.059 or 0.087 t ha−1 y−1 (2.9% or 4.3% y−1) based on genotype scores for the first factor (f1) expressed in trait units or average predicted breeding values across environments, respectively. Both GY and yield stability, defined as the root-mean-square deviation from the regression line associated with f1, were predicted to improve in the next cycle with a low achieved mean parental coancestry (0.087). These methods achieved rapid genetic gain in GY and other traits and are predicted to improve yield stability across global spring canola environments.Crop breeding must achieve higher rates of genetic gain in grain yield (GY) and yield stability to meet future food demands in a changing climate. Optimal contributions selection (OCS) based on an index of key economic traits should increase the rate of genetic gain while minimising population inbreeding. Here we apply OCS in a global spring oilseed rape (canola) breeding program during three cycles of S In some in 2050 . Plant bOne potential method of accelerating genetic gain in self-pollinating crops is the use of rapid cycles of early-generation recurrent selection . Such meex post facto analysis of recurrent selection programs in crops such as maize, oats, barley and rice [Genetic gain in plant breeding programs can be assessed by a variety of methods  and the and rice ,11,12, bAssessment of genetic gain may be based on linear mixed models ,14, but 1) of an FA(2) model were a measure of variety performance and scores for the second factor (f2) were a measure of variety stability across the environments. Scores for f1 and f2 were expressed in trait units in Figures 3 and 4 of Stefanova and Buirchell [−1 y−1 based on f1 scores expressed in trait units in Figure 4 of Stefanova and Buirchell [−1 y−1 in Figure 3 of Stefanova and Buirchell [Variety-by-environment interaction effects will also influence genetic gain over time and may be accounted for in MMM with factor analytic (FA) modelling of the variety-by-environment effects. Stefanova and Buirchell  used MMMuirchell . The ratuirchell . Howeveruirchell . 1 scores in MMM-FA models expressed in trait units. OP was equivalent to a generalised main effect of variety performance when the scale differed across environments [1. OP and RMSD were deemed to be the two main drivers in the selection process [Smith and Cullis  used a sronments . Variety process . r > 0.80) in non-inbred lines based on an individual model [In this study, we compare two methods of estimating rates of genetic gain for GY and other traits in an actively evolving spring oilseed rape breeding program based on MMM-FA models: the average predicted breeding values (PBV) across environments, and OP. For the first time in crop breeding, we use RMSD as a measure of yield stability in a selection index. Our estimate of genetic trends across cycles is based on an individual model  and depends on sufficient depth and connectedness of pedigrees across cycles . We accual model .Knowledge of current genetic trends in the breeding program allows timely decisions to be made to optimise the program, such as rapid responses to changes in technology, markets, or the environment . For exaOptimal contributions selection (OCS) was first used in self-pollinating crops in stochastic models of the additive effects in rapid cycles of early generation selection ,10,25. O0,1 family selection, the S0-derived S1 family provides an estimate of the breeding value of the S0 individual for selection purposes [0,1 family selection (two years per cycle) with OCS and optimised mating designs improved the rate of long-term genetic gain and reduced the rate of population inbreeding compared to truncation selection and random mating among selected parents [0,1 family seeds are grown in plots in field trials, and the agronomic and harvested grain traits recorded on these plots are used to predict the breeding value of the S0 individuals. Accurate PBV (r > 0.80) were generated on S0 individuals for GY, disease resistance, agronomic traits, and seed quality in stochastic models of S0,1 family selection based on a deep and highly interconnected pedigree [In Spurposes . In stoc parents . For prapedigree ,10. 0,1 family was grown in replicated plots at multiple sites and in multiple regions; we grew concurrent field trials of the same S0,1 families in Australia and Canada  to generate sufficient seed for phenotyping [Augmented Sn cycles  to genern cycles . As a sin cycles . Genomicn cycles . New gernotyping , such asnotyping .0,1 family selection. We compare the rate of genetic gain in GY across cycles by two methods, OP and average PBV across environments. For the first time in crop breeding, we include yield stability across global environments based on RMSD in the economic index. This study also represents the first major attempt to integrate spring oilseed rape germplasm across the southern and northern hemispheres.In this study, we use MMM-FA analysis of multi-environment trials across cycles to assess the rate of genetic gain for GY and several economic traits inside an active global spring oilseed rape (canola) breeding program during three cycles of augmented S1’ as progeny of crosses between near-homozygous founder varieties, and ‘S0’ as progeny of crosses between heterozygous parent plants. F1 progeny are heterozygous and genetically uniform, and segregation occurs in the F2 generation after self-pollination. S0 progeny are non-inbred and therefore heterozygous and heterogeneous, that is, each S0 progeny is a unique genotype and segregation occurs in the S0 for multiple traits. Each plant in the ancestral pedigree is a unique genotype and has a relationship with every other genotype in the pedigree, whether the genotype is derived from crossing or selfing , and this increases the number of collateral relatives and pedigree linkages across cycles of selection . For example, the F1 of AU2 × EU2 was intercrossed with the F1 of CA5 × AU5 to generate S0 progeny D1 (The founder population included 32 southern hemisphere (SH) elite spring canola lines from Australia (AU1 to AU32) and 32 northern hemisphere (NH) elite spring canola lines from Canada (CA1 to CA16) and Europe (EU1 to EU16) which were intercrossed in 32 pair-wise combinations (SH × NH) in 2012. Crossing occurred in glasshouses at The University of Western Australia Field Station, Shenton Park, Western Australia. Their Fogeny D1 . All fem0 and F2 progeny were grown in the field in Chile from October 2013 to February 2014 where selfing occurred inside pollination bags on single plants. S0,1 and F2,3 families were returned to Australia where they were sown in the cycle 1 field trial in May 2014  as described below in The cycle 1 field trial  was grown in Western Australia from May to November 2014 and included 668 Snt plant . In addi in 2014 . Remnant0,1 family selection continued in two-year cycles with phenotyping of S0,1 and S2,3 families in multiple field trials in Australia and Canada in cycles 2 (2016), 3 (2018) and 4 (2020) (The process of augmented S4 (2020) . There w4 (2020) a. Connec4 (2020) b.0,1 family selection   and coefF) (f)  were cal0 and S2 genotypes were evaluated as S0,1 or S2,3 families in field plots  with the default spatial model at sites in Australia and Canada  and plant height at maturity . At disease nursery sites (AU2), plots were also scored for blackleg resistance at maturity. The blackleg (BL) disease resistance score was based on the number of plants that were visibly dead or lodging as a result of blackleg disease in the plot at pod filling stage, and BL scores ranged from 1  to 9  . Two ass−1). Harvested grain from each plot was stored in a dry environment and one hundred seeds were randomly extracted from harvest bags to measure 100 seed weight .Field trial plots were harvested by small plot harvester and the weight of harvested seed per plot was converted to grain yield per hectare , and oleic acid  after pre-calibration against standard seed samples with known levels of moisture, Oil, ProM, GLS, and OL [Samples of harvested seed from each plot (5 g) were assessed by near-infrared radiation spectroscopy for moisture content (%), seed oil , protein in meal , glucosinolates  based on grain price US$550 t−1. The contribution of Oil to the economic index in US$ ha−1 was calculated as the PBV Oil of the genotype × economic weight for +1% Oil (US$8.25 t−1) × total GY of the genotype (2.343 t ha−1)  for each 1% seed oil above (or below) the base seed oil content of 42% . The eco t ha−1) .The economic weights on ProM, DTF, PlHt, GSL, OL, SW100, and BL in the economic index were subjectively assigned and modified to achieve the desired outcomes in the next cycle of the optimised mating design. Positive weights were assigned for traits where genetic gains were desired upwards , and negative weights where genetic gains downwards were desired , or zero weights where no change was desired (OL) .The inter-crossing of founders to begin cycle 1 is described above in Crossing designs for cycles 3, 4, and 5 were based on OCS in the implementation platform ‘MateSel’ for the construction of an optimised mating design . The selMateSel was instructed to design 250 matings (to initiate cycles 3 and 4) and 150 matings (to initiate cycle 5)  from canTwo types of genetic gain were assessed in this study — genetic gain in the breeding population over cycles, and genetic gain in historical cultivars included as controls in the trials over year of release. The rate of genetic gain in the breeding population was estimated from the linear regression over cycles of PBV or OP of candidate genotypes in cycles 2, 3, and 4 from the optimum MMM-FA analysis which included all data from cycles 2, 3, and 4. The rate of genetic gain in Australian historical cultivars was assessed by regressing the PBV or OP of historical cultivars based on the optimum MMM-FA analysis over their year of release. The Australian historical cultivars are listed in On average, 0.6% of plots at each trial site were invalid for various reasons and were excluded from the analysis of GY . −1 and fluctuated from 0.33 t ha−1  to 3.57 t ha−1   . −1, the average OL was 61.7%, the average SW100 was 0.325 g and the average BL score was 5.1  but low across cycles (between 1 and 9 control cultivars were common across cycles)  and S0 cross progeny , and this increased the accuracy of additive genetic values in the base model  b. Howevese model . A smallse model . The conse model b, and he1 and F2 progeny of elite homozygous founder lines AU1 × EU1  had an 0 genotype P (progeny of cross C6 × O2) had a much lower The of 0.969 .1 genotypes and not inbred and not related. The 1 parents E2 and H2  was relatively high due to the presence of several common ancestors , but the 0 genotypes J1 and I (0.080) was low since they shared no recent common ancestors . Average 0 genotypes exceeded 0.8 for all traits and exceeded 0.9 for DTF and OL , where the percentage genetic variance accounted for (%VAF) was 68.3% and there was a trivial increase in %VAF in the FA(3) model . FA(2) wMost of the %VAF occurred in FA(1) with minor increases in FA(2), and for most traits, the loadings for FA(1) were all positive . In thisThere were moderate to high genetic correlations of additive effects for GY across trials in Australia and Canada within and between cycles  and highThe genetic correlations of GY between sites in Canada and Australia in cycle 2 was 0.80 (2018CA1 vs. 2018AU2) and 0.73 (2018CA1 vs. 2018AU1), and 0.81 between the two Australian sites in cycle 2 (2018AU1 vs. 2018AU2) . LikewisGenetic correlations of GY between the disease nursery sites 2016AU2 and 2020AU2 and other sites in Australia or Canada were low to moderate, whereas genetic correlations of GY between non-disease sites were much higher . This maCluster analysis of pair-wise correlations of PBV across traits from the optimum MMM-FA model revealed two main groups of traits . GY, BL,r = 0.957), which confirms that both assess average performance of genotypes although the range of values was wider for PBV GY than for OP GY (PBV GY and OP GY from the optimum MMM-FA model were closely correlated across genotypes (or OP GY .−1) were perfectly correlated to the absolute value of f2 scores as expected from the FA(2) MMM-FA model for GY [2 scores since it is scaled in trait units and is applicable to FA models at all levels [RMSD values for GY  over cycles 2, 3, and 4 from 2016 to 2020 as assessed by PBV for candidates for crossing in each cycle  as assessed by PBV and 5.05% y−1 (US$47.71 ha−1 y−1) as assessed by OP over cycles 2, 3 and 4 from 2016 to 2020 (There was a very high rate of genetic gain in the economic index of 7.26% y to 2020 . Significant genetic gain (or loss) in PBV and OP was found for all traits under selection in the economic index except PlHt . There w−1) and Oil (0.276% y−1) when assessed by PBV. The genetic gain in ProM assessed by OP (+0.187% y−1) was superior to the assessment by PBV, but the genetic gain in Oil by OP (0.249% y−1) was inferior to the assessment by PBV  over their year of release from 1996 to 2015 as assessed by PBV (−1 y−1 (1.1% y−1) as assessed by OP  with no signs of decreasing across cycles, while PBV GY in the population increased  to 0.40 t hancreased . With a 6.1% y−1 . As a re6.1% y−1 . −1 or 4.1% y−1 relative to the population mean without RMSD GY in the index, and 0.170 t ha−1 or 4.2% y−1 with RMSD GY in the index  was achieved inside a spring oilseed rape (canola) breeding program based on field testing in Australia and Canada over three cycles of S0,1 family selection from 2016 to 2020, as assessed by average predicted breeding values (PBV) for GY in the optimum MMM-FA model (1 scores in the optimum MMM-FA model and expressed in trait units was lower at 0.059 t ha−1 y−1 (2.9% y−1) (A very high rate of genetic gain in GY of 0.087 t haFA model a. The ra.9% y−1) b. This v.9% y−1) . This hiBoth measures of genetic gain, PBV and OP, are valid and interesting outcomes of MMM-FA models, which help to explain the environmental impact on the assessment of genetic gain. Both PBV and OP support the conclusion that rapid genetic gain in spring oilseed rape was achieved across two disparate global regions . All the evidence, including high genetic correlations of additive effects across trials for most traits, suggests that the genetic gains in this population were realised in both Australia and Canada.The reasons for the lower rate of genetic gain assessed by OP vs. PBV in this study are complex but may reflect the relatively low loadings to FA(1) for GY in the optimum MMM-FA model allocated to disease nursery trials 2018AU2 and 2020AU2 in cycles 3 and 4 . At thesThe genetic gain was achieved in several agronomic and grain quality traits in these trials, including increases in Oil, ProM, and BL disease resistance and a decrease in GSL . These e−1 y−1 (1.3% y−1) from 1996 to 2015 as assessed by PBV (−1 y−1 (1.1% y−1) as assessed by OP , seed oil (+0.62% y−1), and protein in meals (+0.41% y−1), while seed glucosinolate content decreased (−3.4% y−1)  disease resistance (+8.3% y.4% y−1) . However.4% y−1) . This re.4% y−1) .0,1 family selection with OCS based on an economic index, where achieved parental coancestry was >0.20 with OCS after five cycles [1’s of SH × NH crosses) were added in cycles 1, 2, 3, and 4, which helped to reduce achieved parental coancestry in cycle 5  equivalent to full-sibs which helps to increase the accuracy of PBV.Interestingly, we achieved only a small increase in the accuracy of additive genetic values in inbred lines over non-inbred genotypes of approximately 0.02 , which sr = 0.957) (2 scores for GY in the FA(2) model in this study but this may not be the case in higher-order FA models.MMM-FA provides a means to assess yield stability (RMSD GY) and overall performance (OP) of genotypes in the presence of genotype by environment interactions ,22. OP i= 0.957) , and botFuture selection for adaption to global spring rapeseed environments may require economic weights on traits specific to regions , and this may be accommodated through ‘multiple end-uses’ in MateSel . The popThe integration of multiple traits into an economic index follows the logic of Hazel and Lush  that sel0,1 family selection with OCS resulted in very high rates of genetic gain in GY and other commercial traits in spring oilseed rape in both Australia and Canada, thereby confirming the predictions of stochastic modelling of the same process with OCS and optimised mating designs [Rapid cycles of S designs ,10. Yiel designs , and the designs ,3.


N = 54) imagined performing activities involving visually presented objects in a positive  or neutral  manner. They then completed a visual probe task using picture stimuli depicting one object per type of imagery. Positive compared to neutral imagery increased self-reported behavioral motivation and biased the direction, but not the duration, of gaze toward objects associated with the imagined activities. An exploratory analysis showed a positive association between the direction bias and depressive symptoms. Our findings build on existing literature on positive imagery as a motivational amplifier by highlighting early attention as an underlying cognitive mechanism.Positively imagined activities may capture visual attention due to an increase in positive value. Increasing attention toward activities, in turn, may prove useful for clinical interventions aiming to motivate behavioral engagement. Employing a within-subject experimental design, we examined the effect of positive imagery on attention using a visual probe task with concurrent eye tracking. Adults from the general population (The online version contains supplementary material available at 10.1007/s00426-022-01737-0. To imagine our future can be a source of motivation. Mental imagery, an internally generated perceptual experience, has long been thought to drive and guide behavior by representing a desired goal McMahon, . In the Previous work has shown that mental imagery has the capacity to evoke emotions on a subjective, physiological and neural level  were: (1) Self-reported motivational impact  will be greater for positive compared to neutral imagery, (2) attentional bias (direction and duration bias) will be greater for picture stimuli previously associated with positive compared to neutral imagery and (3) greater self-reported motivational impact of positive compared to neutral imagery will be positively associated with the attentional bias (direction and duration bias) toward picture stimuli from positive imagery trials.In the present study, the influence of positive versus neutral imagery of everyday activities on motivated attention was tested using a within-subject experimental design. Participants viewed pictures of objects while listening to brief audio recordings guiding the mental imagery of performing an activity involving the objects in either a positive or neutral manner. Attention bias for objects associated with positive imagery was then measured as a proxy of motivated attention using a visual probe task with concurrent eye tracking. Additionally, subjective ratings of the vividness and motivational impact (reward anticipation and motivation) of the mental imagery were recorded. Our preregistered hypotheses  were recruited from the German general public via online advertising and a community volunteer web portal. Educational achievement levels included high school (74.1%), university or higher (20.4%) and apprenticeship (5.6%). Eligible participants fulfilled the following criteria: (1) age between 18 and 65 years, (2) German language proficiency (native speaker or C1 CEFR level), (3) no diagnosis or treatment of a mental disorder within the last 6 months and (4) normal or corrected-to-normal vision and hearing. Participation was compensated with 10€ or course credit. The study was approved by the ethics committee of the German Psychological Society (2020-02-13VA).A total of 54 participants  between the age of 18 and 38 years (Eligible participants entered the study after they received study information and provided (written) informed consent. Prior to the experimental session, they completed a short battery of online self-report questionnaires. Upon arrival at the eye tracking laboratory, participants first indicated their current affective state on a self-report questionnaire. They then received a brief introduction to mental imagery and subsequently completed an experimental paradigm, the Positive Imagery Paradigm (PIP), in which the valence of mental imagery was manipulated . This was followed immediately by a visual probe task to assess attention bias for stimuli associated with positive imagery during the PIP. The study was conducted in line with the COVID-19 guidelines of the Institute of Psychology of the University of Freiburg. Participants and experimenters wore FFP-2 facemasks throughout the entire experiment.The PIP was developed based on a similar paradigm by Holmes et al.  to experThe PIP comprised 30 trials equally divided across conditions. All trials began with a fixation cross displayed for 1000 ms, followed by the  presentation of the individual objects and corresponding imagery scripts all varying between 10 and 13 s. Subsequently, self-reported vividness and motivational impact of the mental imagery were measured see “”. The paYou are at home and draw yourself a bath.), (2) action involving an activity-related object (e.g. You get into the bathtub and lean back.) and (3) positive emotional reaction to the activity  or description of a neutral circumstance of the activity . Activities were chosen based on the assumption that they are familiar to a large number of people (e.g. taking a bath or going for a run) to ensure the accessibility of activity-related detail to form a vivid mental image. Half of the audio-recorded imagery scripts were spoken by a male speaker and half by a female speaker. Picture stimuli of objects mentioned in the imagery scripts were used under license from Shutterstock.com. The imagery script and picture pairs were finalized after initial pilot testing.Imagery scripts guiding the mental simulation of performing an activity were developed for the purpose of the present study. All scripts were three sentences long and followed the same basic structure: (1) place and activity . The position of the objects and probe was counterbalanced so that each image pair was presented four times. All image pairs were matched on visual aspects, such as color and complexity.The present study primarily focused on eye movements recorded during the object pair presentations as a direct measure of attention rather than reaction times as an indirect measure of attention (presuming faster responses to probes replacing stimuli of attention). The task recently demonstrated good internal consistency and test–retest reliability of eye tracking-based attention bias for rewarding stimuli  were recorded with a desktop mounted EyeLink 1000 Plus system  following a 9-point calibration and subsequent validation procedure. Gaze fixations were defined as any period of at least 100 ms that is not a blink or saccade. Fixation data for periods of object pair presentations were divided into three interest areas, including the midsection (i.e. prior fixation cross location) and the left and right sections (i.e. object locations) of the presentation screen. Following standard procedures, fixations located on the midsection of the presentation screen and anticipatory fixations were discarded. Participants identified as “starer”  were excluded from analyses  and sustained (duration bias) attention allocation  and vividness (I imagined the activity vividly) of the mental imagery performed during the PIP were measured on continuous rating scales ranging from 0  to 100 (“completely true”) following each trial. The self-report questions on anticipatory reward, anticipated reward and motivation were based on prior research .The DASS-21 .The SUIS .The PSI-Q  between PI and NI (hypothesis 1) and differences in attention bias indices (direction and duration bias) between PI objects and NI objects (hypothesis 2). Pearson correlations were applied to explore the relationship between attention bias indices and self-reported motivational impact (hypothesis 3). A post-hoc exploratory correlation analysis was conducted between attention biases (direction and duration bias) and depressive symptoms as assessed with the DASS-21 depression subscale. Data from other questionnaires were not included in statistical analyses.t(53) = 7.34, p < 0.001, d = 1.00), anticipated reward (t(53) = 6.46, p < 0.001, d = 0.88) and motivation (t(53) = 4.96, p < 0.001, d = 0.68) in response to PI  compared to NI .α = 0.017. PI  and NI  did not significantly differ in vividness (t(53) = 0.09, p = 0.928, d = 0.01)  = 2.18, p = 0.034, d = 0.31).M = 0.52, SD = 0.05) compared to objects associated with NI . Results did not show a significant duration bias for PI objects (t(49) = − 0.36, p = 0.720, d = − 0.05). Participants did not significantly differ in their gaze duration on objects associated with PI  compared to objects associated with NI  . None of the inspected correlations were statistically significant .Pearson correlations were conducted between attention bias indices (direction and duration bias scores) and self-reported motivational impact ,r = 0.02, p = 0.903).Post-hoc exploratory correlation analyses revealed that depressive symptom severity (DASS-21 depression sum-score) correlated moderately positively with the direction bias  rewards has been recently found to increase motivated behavior in an effort-based decision-making task  or merely represent the difference in valence . It would also be important to further explore the mechanism driving the effect of positive imagery on motivated attention and whether the effect is also present for mental representations of activities. Additionally, findings need replication in clinically depressed individuals to solidify the exploratory finding of a greater susceptibility to positive imagery in this patient group.The present study elucidates early attention as a cognitive mechanism affected by positive imagery. As early attention plays an important role in motivated behavior, applying positive imagery to increase the saliency of adaptive behaviors in the environment may be a valuable instrument to promote behavioral activation. One potential target group for this treatment are individuals with depressive symptoms, as they appear to be especially susceptible to positive imagery.Supplementary file1 (DOCX 312 KB)Below is the link to the electronic supplementary material.


Phaseolus lunatus) in the Yucatan Peninsula, an important Mesoamerican diversity area for this crop, using a genotyping-by-sequencing approach  and two scales. At the local scale, STRUCTURE and NGSEP analyses showed predominantly crop-to-wild introgression, but also evidence of a bidirectional gene flow in the two wild-weedy-crop complexes studied (Itzinté and Dzitnup). The ABBA-BABA tests showed a higher introgression in Itzinté (the older complex) than in Dzitnup (the younger one); at the allelic level, the wild-crop introgression in Itzinté was similar in both directions, in Dzitnup it was higher from crop-to-wild; and at the chromosomal level, introgression in Itzinté was from wild-to-crop, whereas in Dzitnup it occured in the opposite direction. Also, we found HE values slightly higher in the domesticated accessions than in the wild ones, in both complexes , but %P and π estimators were higher in the wild accessions than in the domesticated ones. At a regional scale, STRUCTURE and MIGRATE showed a low gene flow, predominantly from crop-to-wild; and STRUCTURE, Neighbor-Joining and PCoA analyses indicated the existence of two wild groups and one domesticated group, with a marked genetic structure based in the existence of domesticated MI and wild MII gene pools. Also, at the regional scale, we found a higher genetic diversity in the wild accessions than in the domesticated ones, in all estimators used . Our results indicate that gene flow and introgression are playing an important role at the local scale, but its consequences on the structure and genetic diversity of the Lima bean are not clearly reflected at the regional scale, where diversity patterns between wild and domesticated populations could be reflecting historical events.We evaluated the role of gene flow and wild-crop introgression on the structure and genetic diversity of Lima bean ( Global While wild-crop gene flow and introgression can be studied at a regional scale, which allows us to understand the historical genetic consequences of both processes, it is also advisable to study both processes at a local scale, since this allows us to gain knowledge of the immediate genetic consequences and relate them with the agricultural management in the involved agroecosystems . The effPhaseolus lunatus L.) is one of the five domesticated species of the genus Phaseolus, comprising two botanical varieties  and P. lunatus var. silvester that include the wild populations. This species shows a very wide range of ecological adaptations throughout its natural distribution, from Mexico to Argentina; being considered a promising crop to improve food security under the climate change scenarios predicted for Latin America and other regions of the world where it is grown , and local MII wild populations have been reported . In thisIn the Yucatan Peninsula, Given the important role that gene flow and introgression play in the evolutionary dynamics of domesticated species and their wild relatives, the objectives of this study were to determine: (1) the degree and direction of introgression at a local scale in two wild-weedy-crop complexes of Lima bean of different ages, (2) the degree and direction of wild-crop gene flow in Lima bean at a regional scale in the Yucatan Peninsula, and (3) the consequences of these two processes on the structure and genetic diversity of Lima bean in this region of Mexico. To meet these goals, we used 15,168 SNP markers genotyped in 183 accessions and a population genomic approach.The study was conducted in the Yucatan Peninsula, Mexico, at a local and regional scale:P. lunatus is not in any risk category, all material plant used in this study was collected under scientific permits (SGPA/DGVS/008421/18 and SGPA/DGGFS/712/2913/17) issued by the Secretaría del Medio Ambiente y Recursos Naturales (SEMARNAT-Mexico) to researchers at Centro de Investigación Científica de Yucatán, A. C (CICY).(1) Local scale. A specific search for wild-weedy-crop complexes of Lima bean was carried out in 2019; two complexes were found, which were named as Itzinté and Dzitnup based on local place names . As species, i.e., plantation fields are used for many years continuously without a rest period). Within Itzinté, seeds were collected in three sites separated by a distance of 300–400 m: (a) site 1, the slope of a hill where only some wild plants were found; (b) site 2, a milpa where domesticated and wild plants were observed growing in close proximity (<1 m); and (c) site 3, a milpa with only one production cycle and whose seeds were brought from the town of Escárcega, located in the south of Campeche. A total of 27 seeds were sequenced from this complex, all from different plants: (a) three wild seeds from site 1, without morphological evidence of introgression; (b) 22 seeds from site 2, which spanned the entire range of morphological variation found and with possible introgression since they were collected from wild and domesticated plants growing near each other; and (c) two domesticated seeds from site 3, without morphological evidence of introgression  Regional scaleA total of 65 accessions in the broader Yucatan Peninsula were sequenced: 39 domesticated and 25 wild accessions; part of these accessions were collected in 2019 and others were obtained from the collection of Dr. Jaime Martínez-Castillo. In addition, we included data reported by Ape KI restriction enzyme which resulted in a population of fragments, most of them in the range of 200–300 bp. For library preparation, 100 ng of digested DNA and 3.6 ng of each adaptor were used for ligation. After ligation, 5 µl from each one of 96 libraries were mixed, amplified by PCR and sequenced in an Illumina sequencing lane. Average sequencing depth per accession ranged from 5X to 10X.Seedlings from the selected accessions were used to obtain DNA. Total genomic DNA was extracted from leaf tissue using a silica extraction protocol . DNA quahttps://phytozome-next.jgi.doe.gov/info/Plunatus_V1) with the Bowtie2 tool and with default parameters in NGSEP (ver. 4.02); we then used the MultiSampleVariantsDetector option of NGSEP (ver. 4.02) to identify and genotype the genetic variants with the following parameters: 100 as the maximum number of alignments per start position, SNP heterozygosity rate of 0.0001 and minimum genotyping quality of Q40. The SNP matrix was filtered with the FilterVCF option of NGSEP (ver. 4.02) with the following options: MAF of 5%, missing data at a maximum of 20% (for loci and genotypes), minimum genotyping quality of Q40, maximum heterozygosity of 0.2 and removing SNPs from the repetitive regions of the genome. This filtering process resulted in 15,168 SNPs and 184 accessions. SNP density per chromosome varied from 15.2 SNPs/Mbp for chromosome PL10 to 41.8 SNPs/Mbp for chromosome PL03, with an average SNP density of 29 SNPs/Mbp. The VCF file was converted to other formats using TASSEL  . Subsequg TASSEL  and PGDSg TASSEL .PRJNA596114 and PRJNA823361. Details about the SRX number for each accession can be found in the Raw sequence data generated and used in this study have been deposited in the NBCI Sequence Read Archive (SRA) under the Bioproject accessions numbers The degree and direction of introgression within each wild-weedy-crop complex were evaluated by four methods, using all the 15,168 SNPs found:(a) First, an individual assignment test based on global ancestry was applied, as implemented in STRUCTURE . To assi(b) Second, to compare the STRUCTURE results, a Principal Coordinate Analysis (PCoA) was performed using GenAlex .(c) Third, a sliding-window analysis was applied using the IntrogressionAnalysis module of NGSEP 4.02 , which ap-values at the 0.05 threshold were corrected by the Bonferroni method. After examining the results, we chose the models with the highest D values and f4-ratios to perform sliding window scans of introgression. For this, we used the Dinvestigate option in Dsuite to calculate the distance-based statistics df (distance fraction) , with gene flow between P2 and P3 df is positive (when d13 > d23), and with gene flow between P1 and P3 df is negative (when d13 < d23). Introgressed genomic regions (or blocks) were defined as those genomic windows within the top 10% of df outliers within each chromosome. Consecutive genomic windows complying with this definition were collapsed in the same introgressed block.(d) Fourth, ABBA and BABA tests were carried out with the software Dsuite . In thisraction)  in windoHE) and the nucleotide diversity (π) were calculated using ARLEQUIN 3.5.22 , the percentage of polymorphic loci (% P), the expected heterozygosity  A Neighbor-Joining (N-J) analysis rooted with the Andean accessions and using DARwin V6 , (2) a PHE) and nucleotide diversity (π), calculated using ARLEQUIN 3.5.22 , percentage of polymorphic loci (% P), expected heterozygosity  the STRUCTURE results obtained within the Yucatan Peninsula to evaluate recent gene flow; and (2) MIGRATE-N version 3.3  to evaluIn Itzinté, optimal K was 2; STRUCTURE recognized genetically pure wild (green color) and domesticated (red color) individuals and identified seven individuals collected at site 2 that showed different grades of ancestry of both gene pools, which can be considered as admixed individuals . In DzinFor Itzinté, NGSEP identified 54 introgression events in four domesticated and seven wild individuals, all of them (except two) collected at site 2 ; these iAlthough both STRUCTURE and NGSEP analyses indicated gene flow mainly from crop-to-wild in both complexes, these analyses also showed some evidence of gene flow in both directions. In order to delve into this, we conducted ABBA-BABA tests to assess whether the signals of introgression are stronger between wild and domesticated populations within the same complex than with other wild and domesticated populations of the Yucatan Peninsula. In effect, as it can be seen in df = 18%) is almost twice than in the opposite direction (median df = 10%). When comparing introgression proportions (median df) in both directions  at the chromosome level, df values are strikingly different in chromosome seven for Itzinté and in chromosome nine for Dzitnup, with df values larger in the direction domesticated to wild in both chromosomes , and vice versa (w-d), within both complexes at a fine genomic scale, we chose to investigate four models: m1, m3, m5 and m7. versus 158 Mb) ( 158 Mb) –S8. In cHE values were slightly higher in the domesticated accessions  than in the wild ones ; but the other estimators were greater in the wild accessions than in the domesticated ones  analysis  clearly K = 2  grouped 2  from the wild ones (integrated in two groups) : a pure HE (0.36) than the admixed wild group (HE = 0.26), and the latter showed a slightly higher value than the domesticated group (HE = 0.23); the admixed wild group showed higher values for the other estimators (P% and π), and the domesticated group showed the lowest values for all estimators  separated the wild accessions from the domesticated ones, with most accessions showing high ancestry coefficients (>90%). With K = 3, a group of wild accessions considered as genetically pure were collected in Tabasco and western Campeche where Lima bean is not planted and clustered apart from another group of wild accessions that showed some ancestry from the domesticated gene pool, and that were collected in areas of traditional agriculture of Yucatan, Quintana Roo and central-eastern Campeche, where wild and domesticated Lima bean populations grow in sympatry. Moreover, only three domesticated accessions showed some degree of ancestry from the wild gene pool, although it was low  from domesticated group (157.17) to admixed wild group (77. 07), slightly higher from the pure wild group (130.61) to admixed wild group (123.14), and higher from the domesticated group (114.07) to the pure wild group (55.93).STRUCTURE and NGSEP analyses showed predominantly a crop-to-wild introgression at the local scale, but also some evidence of a bidirectional gene flow in both studied complexes; ABBA-BABA tests showed bidirectional gene flow in both complexes, but predominantly in the direction wild-to-crop in Itzinté and in the opposite direction in Dzitnup. These tests also showed a higher introgression proportion in Itzinté than in Dzitnup.Itzinté is the oldest of the two complexes, it has existed for at least 20 years , suggestIn Dzitnup, the wild-crop contact site was located in a slash-and-burn milpa. This type of milpa is commonly used only for 1–3 years and then left for a rest period  that can last up to 15 years . It meanversus non-hybrid genotypes are expected to vary according to some characteristics, including distance between hybridizing individuals, relative size of each population, overlap in the flowering period, and pollen and seed dispersal . In hybrid zones, the relative frequencies of hybrid ispersal . These cispersal  and it iispersal ; furthere.g., agricultural, genetic or environmental management) may explain the differences observed in both studies.The asymmetry in the wild-crop introgression found at local scale , is expected when the relative size of wild or domesticated populations is different, or when populations are fragmented ; unfortuPhaseolus vulgaris L.) from other regions of Mexico and the Americas  did not showed genetic infiltration of the domesticated gene pool. This crop-to-wild asymmetry was also supported with the results from MIGRATE, which showed more historical gene flow from the domesticated gene pool to the wild one; the admixed wild group appears to be acting as a genetic bridge between the domesticated populations and the pure wild populations. Americas .Crop-to-wild gene flow and introgression asymmetry, as observed at the regional scale (and into Dzitnup), may depend on the fitness of several hybrid generations in relation to their wild counterpart , which ci.e., which occurs when a small population loses its genetic integrity and assimilates into a larger population, as a result of repeated episodes of hybridization and introgression; ; however, these analyses masked the existing genetic structure within this region . Although we detected a group of admixed wild individuals, Lima bean in the Yucatan Peninsula showed a strong genetic structure based on the existence of wild MII and domesticated MI gene pools. This is an interesting result considering that gene flow and introgression processes between wild and domesticated Lima bean may have been occurring for many centuries in this region as revealed by archaeological evidence . Even thHE values in the wild gene pool  than in the domesticated one. In contrast, although π values were higher for the wild gene pool compared with the domesticated one, for this estimator the Admixed wild group showed a higher nucleotide diversity than the Pure wild group, probably due to the higher percentage of polymorphic sites in the Admixed wild group. Although HE is a good estimator because it is directly related to genetic diversity and is not influenced by differences in sample size (such as P%), the differences found in HE values should be taken with caution considering that the statistical significance of this estimator was not evaluated among groups. Differences in genetic diversity between crops and their wild relatives can be explained by a founder effect due to domestication  than those found by us; although both studies considered different geographical scales, our results suggested that the Yucatan Peninsula is an important area of genetic diversity for Lima bean. Relatively high values of genetic diversity for wild and domesticated populations of Lima bean (considering its endogamous nature) from the Yucatan Peninsula have also been reported using SSR , do not present a high risk of local extinction, given that once milpas are abandoned, the generated hybrids will likely be eliminated by natural selection in a few generations. Even in Itzinté, where wild population maintain permanent contact with a domesticated population, does not show risk of local extinction due to swamping. Results from this study can also be useful for the genetic improvement of Lima bean in traditional agroecosystems. The existence of wild-weedy-crop complexes opens the possibility for farmers to benefit from the weedy variants; the presence of this type of complexes could be indicating an on-going domestication process.10.7717/peerj.13690/supp-1Figure S1K = 2 .The colors assigned to the observed groups correspond to those used in the STRUCTURE analysis with Click here for additional data file.10.7717/peerj.13690/supp-2Figure S2K = 2 .The colors assigned to the observed groups correspond to those used in the STRUCTURE analysis with Click here for additional data file.10.7717/peerj.13690/supp-3Figure S3In these models, wild MII populations from Mexico (outside the Yucatan Peninsula) were assigned as the sister group (P1) of wild populations within the complexes or within the Yucatan Peninsula assigned as P2, and wild MI populations from Mexico were assigned as the sister group (P1) of domesticated populations within the complexes or within the Yucatan Peninsula assigned as P2. In each model P3, the tested group, was appropriately chosen to test gene flow between P3 and P2. In all these models, the Andean wild gene pool (AI) was fixed as outgroup.Click here for additional data file.10.7717/peerj.13690/supp-4Figure S4The figure shows the proportion of introgression (df) per chromosome in the Itzinté (a) and Dzitnup (b) complexes in the direction domesticated to wild (d-w) (red bars) and wild to domesticated (w-d) (yellow bars). Gray dashed lines indicate df = 0, red and yellow dashed lines indicate median df values observed in the direction d-w and w-d, respectively.Click here for additional data file.10.7717/peerj.13690/supp-5Figure S5The colors assigned to the observed groups correspond to those used in the Neighbor-Joining analysis .Click here for additional data file.10.7717/peerj.13690/supp-6Figure S6The colors correspond to the observed groups using different K values: (A) K = 2; (B) K = 3; (C) K = 4. Colors with K = 4 match to those used in the Neighbor-Joining analysis  and in the PCoA .Click here for additional data file.10.7717/peerj.13690/supp-7Figure S7The colors assigned to the observed groups correspond to those used in the STRUCTURE analysis with K = 3 .Click here for additional data file.10.7717/peerj.13690/supp-8Table S1Click here for additional data file.10.7717/peerj.13690/supp-9Table S2Click here for additional data file.10.7717/peerj.13690/supp-10Table S3Click here for additional data file.10.7717/peerj.13690/supp-11Table S4Click here for additional data file.10.7717/peerj.13690/supp-12Table S6Click here for additional data file.10.7717/peerj.13690/supp-13Supplemental Information 13Click here for additional data file.10.7717/peerj.13690/supp-14Supplemental Information 14Click here for additional data file.10.7717/peerj.13690/supp-15Supplemental Information 15Click here for additional data file.


Petauroides volans, is an endangered species highly sensitive to the predicted changes in temperature under a changing climate and was recently severely impacted by a megafire natural disaster (85% estimated population loss). Baseline genetic data is essential for conservation management and for detecting detrimental changes in fire-effected populations. We collected genetic samples within 2 years post the 2019–2020 catastrophic Australian bushfires to examine adaptive potential, baseline genetic diversity and population structure, across their southern range in the state of New South Wales. Population genomic analyses were conducted using 8493 genome-wide SNPs for 86 greater glider individuals across 14 geographic locations. Substantial genetic structure was detected across locations, with low genetic diversity and effective population sizes observed in isolated areas. Additionally, we found signals of putative adaptation in response to temperature in greater gliders using a genotype-environment association analysis. These findings have important implications for the management of greater glider populations by identifying at-risk populations and identifying adaptive potential. We demonstrate the importance of baseline genetic information for endangered species as a practical approach to conservation. This is particularly important given the threat that changes in temperatures and megafire events, as predicted under a changing climate, poses for this species.The impacts of a changing climate threaten species, populations and ecosystems. Despite these significant and large-scale impacts on threatened species, many remain understudied and have little to no genetic information available. The greater glider,  Natural disasters are likely to also be exacerbated by climate change6, and their impact can be severe, as a single event can result in the sudden loss of millions of animals from populations across their range2. Populations that suddenly experience significant reductions in effective population size can result in a population bottleneck7. Changes to these populations include a loss of genetic diversity9, compromised ability to adapt to environment changes10, and an increase in extinction likelihood thorough various genetic and demographic processes14. As a result, conserving genetic diversity within a species population is critical to ensure resilience and evolutionary potential in response to anthropogenic climate change16.When natural disasters occur at large scales, such as tsunamis, earthquakes, hurricanes, or wildfires, they can have profound effects on a species not only in the short term18. Genetic information can identify management units for conservation, assess population size, quantify connectivity, detect hybridisation, and evaluate the potential for populations to adapt to environmental changes20. Genetic diversity, contemporary effective population size and adaptive potential are vital pieces of information that can aid in the conservation of threatened species, as it tells of a populations ability to persist and adapt to changes in their environment24. Despite the importance of these measurements, many threatened species have missing or very limited baseline genetic data that can be used to assess the conservation status of the species, inform the direct management of wild populations, or even delineate their range27.Conservation genetics as a management tool is becoming increasingly important, particularly for small populations and threatened speciesPetauroides volans) is a nocturnal, canopy-dwelling, gliding marsupial found along the east coast of Australia28. Greater gliders are highly dependent on eucalypt forests, as they have specialist eucalypt diets31 and are reliant on large hollows for sheltering and raising offspring33. The species has experienced significant decline as a result of habitat loss35, wildfire37 and the effects of climate change40. Over the past 20 years, long-term studies have identified rapid declines in populations43 as well as local extinction, such as at Booderee, New South Wales (NSW)42. Consequently, the species has recently been uplisted from vulnerable to endangered under the Australian Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) and is listed as vulnerable under the International Union for Conservation of Nature (IUCN) Red List of Threatened Species44.The greater glider  was associated with a decline (63% to 10%) in the total area of suitable climatic conditions40. This indicates that significant range contraction to cooler and wetter habitat at high elevations is likely to occur38. Furthermore, heatwave events are also strongly associated with a greater probability for catastrophic wildfires, and the Australian megafire event of 2019–2020 is likely to occur again46. Greater gliders experienced an immediate population loss of an estimated 85% following the 2019–2020 bushfires47. These events pose a serious threat for greater glider populations unless they can adapt to changing environmental conditions49. However, the absence of baseline population genetic data hinders any opportunity for informing practical management actions52.Increases in annual temperatures and frequency of heatwaves has been shown to have an over-riding, and devastating, impact on greater glider occupancy throughout its range26. For the endangered greater glider, there is no genome-wide single nucleotide polymorphism (SNP) data available across its entire range in the state of NSW . Genetic analysis has been performed at smaller scales in NSW, investigating greater glider metapopulation structure in only one fragmented forest, using microsatellite data55. Under NSW state legislation, the species was uplisted to endangered in 2022, and three small populations were listed as endangered under the NSW Biodiversity Conservation Act 2016. For these endangered populations, baseline genetic research is a priority due to their isolation and vulnerability to stochastic changes58.Baseline genetic data collected before populations decline offer the opportunity to inform conservation management and reduce extinction riskNSW Fig. . The spe60, and this study was conducted within 2 years of the 2019–2020 Australian bushfires. This meant that the adults captured in this study were likely conceived pre megafire. It is possible that populations were already experiencing genetic declines because of habitat fragmentation and isolation, thus the ongoing impacts of the megafire is likely to compound these effects. Baseline genetic data is essential for greater glider conservation and management, as it will allow researchers to monitor changes in the effective population size and genetic diversity of fire-effected genetic clusters, and act accordingly.Our aim was to provide crucial baseline genetic data for greater gliders before genetic impacts from the 2019–2020 catastrophic bushfires could be observed. Greater gliders have a generation time of 7–8 years2 , a throw line was launched over the branch and was used to shake the branch. The glider would then glide to the ground, where it was slow and readily captured by hand.Tissue was sampled from 85 wild greater gliders from 14 locations. An additional five samples were obtained from donations made by the public  . The metal ear punch was sprayed with 70% ethanol and flamed for sterilization. Once cooled, a clip was taken from the outside edge of the ear and stored in sterilized vials containing 80–95% ethanol. DNA was kept at − 20 °C prior to DNA extraction.All research was performed in accordance with relevant guidelines and regulations. The experimental protocols were approved by a University of Wollongong Animal Ethics committee (AE19/02). Research was conducted under a NSW DPIE Scientific Licence (SL101968).A total of 94 greater glider samples  were plated and sent to Diversity Arrays Technology Pty Ltd (DArT) , for DNA extraction and next generation sequencing.Petauroides genus. DNA digestion and ligation followed the steps outlined in Kilian et al. 64, except two different kinds of adaptors were used for compatibility with the PstI and SphI overhangs.For DNA extraction DArT removed the ethanol and incubated the tissue samples with T1 Buffer and proteinase K at 60 °C overnight. The lysates were aspirated to a new plate and the DNA was bound to NucleoMag B-beads before being washed and eluted with a Tecan 100 robot and DArT PL script (maximum concentration 50–100 ng/µl). To find the best genome complexity reduction method for sequencing, DArT tested and optimized different restriction enzyme combinations. They selected the PstI-SphI enzyme combination as the optimal method for the 64. Genomic regions around SNPs were aligned to the closest relative available: the Leadbeaters possum  reference genome ‘GCA_011680675.1 LBP_v1’ using an E-value of 5e−7 and a minimum sequence identity of 70%. SNP markers were scored “0”, “1” and “2” representing reference allele homozygote, SNP allele homozygote and heterozygote respectively.DNA fragments were amplified using Polymerase Chain Reaction (PCR) and the following thermal cycling conditions: initial denaturation step for 1 min at 94 °C, 30 cycles of  and a final elongation step at 72 °C for 7 min. Amplified DNA samples were pooled and underwent Illumina c-Bot bridge PCR. The library then underwent 77 sequencing cycles on an Illumina Hiseq2500, providing 2,500,000 sequences per barcode per sample. SNPs were called using DArT analytical pipelines (DArTsoft14), and an average read depth of 20 reads per locus ensured calling quality. Additional quality control procedures can be found in Kilian et al. dartR v 2.0.4 package65 in R 4.0.266. Loci were removed if (1) the average repeatability of alleles at a locus was < 0.99, (2) the call rate of loci was < 0.80, (3) minor allele frequency for a locus was < 0.01, (4) read depth was < 5 or > 100, and (5) if they were secondary SNPs with the possibility of being linked. It was ensured no monomorphic loci remained in the dataset.We stringently filtered the genome-wide SNPs from DArT using the pcadapt v 4.3.2 package in R68. Pcadapt uses principal components to detect candidate loci under selection68. The first two principal components were retained (K = 2) based on the inflection point of the screeplot and the proportion of explained genetic variation from K = 1 to 20 .Outlier loci  were detected using a differentiation-based analysis and an environmental association analysis. The differentiation-based analysis was conducted using the vegan v 2.5-7 package in R to detect outlier loci that may be associated with environmental variables70. This multivariate analysis has proved effective in identifying environmental variables potentially responsible for selection in many species73, including a potential temperature adaptation in another Australian folivore, the koala 74.A Redundancy Analysis (RDA) was conducted using the raster v 3.6 package in R75. Environmental variables were filtered to avoid high correlations (Pearson correlation coefficient > 0.6) while prioritizing variables of interest based on the knowledge that greater gliders are sensitive to heat and water availability45. As a result, three environmental variables were retained for the RDA: BIO5 (maximum temperature of the warmest month), BIO10 (mean temperature of the warmest quarter), and BIO16 (precipitation of the wettest quarter).RDA climate variables were obtained for each greater glider sampling location. These values were taken from the WorldClim database using a spatial resolution of 30 s and the LEA v 3.0.0 was used to impute missing data after testing 10 runs of K 1 to 10 (best K = 3 and run = 10). After running the RDA, the significance of each constrained axis was calculated using the “anova.cca” function in vegan. Outlier SNPs were identified on the first three RDA axes. When a SNPs loading was ± 3 standard deviation from the mean, it was flagged as an outlier (candidate adaptive loci)72, and these were assigned to either BIO5, BIO10 or BIO16 based on the strongest correlation72. The three environmental variables had a Variance Inflation Factor (VIF) less than five, so multicollinearity was not an issue (VIF range 1.09–1.56).As RDA requires a complete genetic data frame, the ‘snmf’ function in Pcadapt and RDA outlier loci were removed from the dataset to ensure neutral SNPs were used for the following population genomic analyses.hierfstat v 0.5-1076 and equations from Nei77.Greater glider population genetic diversity was measured in the form of observed and expected heterozygosity of SNPs. Average observed heterozygosity (Ho), expected heterozygosity (Hs) and the inbreeding coefficient (Fis = 1 − Ho/Hs) were calculated for each locus and greater glider sampling locations using the R package ST matrix, where values are produced for each population comparison. Values near 0 indicate no genetic differentiation between populations, while higher values indicate genetic differentiation and structure 79.Genetic distances of sampling sites (putative ‘populations’) can be quantified with a pairwise FStAMPP v 1.6.3 R package generates FST values that are unbiased by sample size81. Greater glider pairwise FST values were generated using StAMPP and 10,000 bootstraps across loci to generate p-values. A Bonferroni correction for multiple testing generated a new p-value of 0.0005 to infer significance82.The K). STRUCTURE detects genetic clusters (K) with a Bayesian iterative clustering algorithm83, while DAPC uses a multivariate method to assign individuals to genetic clusters with sequential K-means and model selection84. We decided to report the results of both programs here since they use different techniques and have their own limitations87.Geneticists use programs such as STRUCTURE and Discriminant Analysis of Principal Components (DAPC) to group individuals with similar genetic patterns into genetic clusters 89 where the optimal K was selected after analysing ∆K, the STRUCTURE plots and the posterior probability90.We ran STRUCTURE v 2.3.4 using 8 replicates of adegenet v 2.1.591 and a maximum K value of 15. The optimal number of clusters was chosen based on the lowest BIC value  retained in the DAPC analysis was determined through the ‘xvalDAPC’ function (maximum PC’s = 100).Greater glider DAPC genetic clusters were determined de novo using the ‘find.clusters’ function in poppr v 2.9.3 using 9999 permutations and the geographic locations as putative populations. We examined the proportion of genetic differentiation that was apportioned between greater glider sampling locations, between greater gliders within sampling locations, and between greater glider samples themselves. A second and third AMOVA was conducted after reassigning greater glider individuals to the de novo groupings detected with STRUCTURE and the DAPC analysis, to see if this explained more of the genetic variation.An Analysis of Molecular Variance (AMOVA) calculates the level of genetic differentiation within samples, between samples within populations, and between populations. We conducted an AMOVA in dartR, and individuals were plotted on the first four axes. The 14 sampling sites were colour coded so that patterns of genetic variation could be observed.Principal Component Analyses are multivariate analyses that are used to plot multi-dimensional population substructure. We ran a Pearson Principal Component Analysis (PCA) to visualise the genetic structure of NSW greater gliders using the ‘gl.pcoa’ function in dartR, the “mantel” function in vegan and 9999 permutations. The log of the Euclidean distances were compared to the genetic distances of populations (pairwise FST/1 − FST).Once genetic structure was identified in NSW greater gliders, it was tested to see if some of the genetic structure could be explained by geographic distance. To do this, an Isolation By Distance (IBD) analysis tested whether the geographic distances between greater glider sampling sites correlated with the genetic distances of the NSW sites. An IBD analysis was generated for the greater glider dataset using the “gl.ibd” function in 92. NeEstimator uses a single-sample linkage disequilibrium method to calculate contemporary effective population sizes (Ne) (i.e. the effective population size for the time the samples were obtained)93. Ne values were calculated for greater glider sampling sites with more than five individuals, using the RLDNe v 0.1.0 package93 and an allele frequency cutoff of 0.05. A random mating system was selected, and all other default parameters were used. Upper and lower confidence intervals were generated with the jackknife resampling method as it performs better than the parametric method94.Small effective population sizes can decrease genetic diversity, increase inbreeding and put a population at risk of extinction through genetic driftDArTseq returned 18,807 genome-wide SNPs from 90 greater glider tissue samples  detected 58 outlier loci potentially under selection in the greater glider genome, while the environmental-association analysis (RDA) detected 74 outlier loci as candidate adaptive loci Fig. . Two com2 = 0.108). Of the 74 outlier loci detected by the RDA, 66 loci were associated with maximum temperature of the warmest month (BIO5), four were associated with mean temperature of the warmest quarter (BIO10), and four were associated with precipitation of the wettest quarter (BIO16) , and together they explained 14.0% of the genetic variation  and Escarpment N had an excess of heterozygotes (FIS = − 0.279)  to 0.206 (MSA) Table . ObserveST value of 0.244 (± SD 0.166). Significant pairwise FST values ranged from low genetic differentiation between Gulguer and MSA (FST = 0.041) to extreme genetic differentiation between the greater glider at Gulguer and those at Eurobodalla (FST = 0.666) (Table ST = 0.041), it was significantly differentiated from all other geographic locations   . Most of the genetic variation was found to be within samples (76%), and only 3% of the genetic variation was apportioned between samples within sampling sites  detected patterns of genetic structure amongst the 14 sampling sites. The first axis explained 19.1% of the genetic variation, and when the first four axes were combined, they cumulatively explained 29.3% of the genetic variation Fig. . Axis 1 There was a significant IBD effect detected, with genetic distances strongly influenced by the geographic distances of greater gliders   were calculated for locations where five or more individuals were sampled. Ne ranged from 6  to 5058 (MSA), with an average Ne of 678 (± SD = 1771) Table . Meroo, 40, and have long lasting consequences on population size and thus ongoing genetic viability. Furthermore, because of these heat events, the probability for wildfires increases greatly and these stochastic events further impact the viability of populations. Our main findings identify a concerning pattern where all populations sampled, except one (MSA), had low effective population size and thus were at a heightened risk of local extinction. Additionally, we detected substantial genetic structure across NSW and a potential adaptation to temperature that may have implications for future management of greater gliders under a changing climate.The increase in heatwaves and wildfires as predicted under a changing climate, significantly threatens the greater glider. Changes in the suitability of habitat can occur rapidly95, primarily due to a lack of baseline data. Catullo et al.96 discuss the importance of having pre-fire within-species diversity estimates for the effective monitoring of species recovery following the 2019–2020 Australian bushfires, however such data is not always available. Conservation genomic studies are crucial for well-informed genetic management decisions16. We demonstrate that baseline genetics are needed before these events happen as this allows for prioritization of population management and recovery actions, especially for endangered species where genetic declines are ongoing, and events induced by climate change only further exasperate these issues. Mcmahon et al.51 highlights the importance of genomic data in developing a ‘conservation prior’ with the aim of avoiding ‘emergency room conservation’. This involves identifying early on, viable populations with adaptive potential for prioritized preservation51.There are few studies that have monitored changes to genetic diversity and effective population size of vertebrates after bushfires53. Our analyses provide crucial baseline data for which fire-effected greater glider populations  and endangered populations  can be monitored through time. Greater gliders are extremely susceptible to population decline and extinction, particularly in response to fire that partially or completely consumes the canopy97. Lindenmayer et al.36 reported low greater glider abundances in high severity burn sites in the Central Highlands of Victoria, and May-Stubbles et al.37 observed the same pattern within high severity burn sites of Monga National Park, NSW. This is due to the species’ specialist eucalypt diet and sensitivity to high temperatures98. Fire has also caused significant declines in the past, with greater gliders considered locally extinct after a fire burnt 90% of the Royal National Park (NSW) before they were rediscovered in 201299. The species distribution continues to shrink, with greater gliders declared locally extinct in Booderee National Park (NSW) after the last sighting was reported in 2006100. Isolation is believed to be the main cause of this local extinction, having entered an extinction debt (a time lag between past events and their extinction level impact), which was likely then further exasperated by a wildfire that burnt 50% of the park in 2003100. Considering the negative effects of fire and isolation on greater glider population persistence, baseline genetic data is vital for species conservation by identifying priority sites and to inform genetic management actions.Here we provide the first baseline genetic data for greater gliders across half of their distribution within New South Wales and summarize the conservation actions required for these locations (Supplementary IS = 0.116), despite having high genetic diversity and a large contemporary effective population size. Parts of MSA experienced a wildfire in 2001101, which may have contributed to this. Escarpment N and MSA are linked by more than 100,000 ha of continuous habitat that is primarily a Eucalyptus and Angophora canopy 101. This further reinforces the importance of large continuous habitat in allowing populations to recover and remain resilient after stochastic events7. We propose that the greater glider population within MSA should be considered a stronghold for the species given its continuous connected habitat and large effective population size, however this location must be monitored over time to ensure inbreeding does not reduce genetic diversity. Efforts should be made to protect the area from wildfire events.We obtained NSW genetic samples within 2 years of the 2019–2020 bushfires, with the goal of generating baseline genetic diversity estimates before significant generational genetic effects could be observed. Greater gliders at MSA displayed evidence of inbreeding . Since its listing as an endangered population by the NSW state government, no direct government intervention has been made for this population. Given its incredibly small effective population size, the Eurobodalla population is likely to go extinct. Despite being state-listed in the past as an endangered population,  this had no impact on direct conservation actions. Similarly, the population at Seven Mile Beach National Park (SMBNP), also previously state-listed as endangered, is highly isolated. However, the amount of available habitat appears to support relatively high numbers of gliders102 and moderate genetic diversity (Ho = 0.175). However, SMBNP may be a closed population (STRUCTURE and DAPC results both show that cluster two is predominantly SMBNP and Escarpment S), and thus vulnerable to future stochastic events. Direct action must be taken for SMBNP to avoid a repeat in genetic decline as observed in Eurobodalla. This baseline genetic data sets a pretext for future monitoring and informing management actions as they become required.Greater gliders on the far south coast  displayed low genetic diversity (Ho = 0.090–0.105). These locations have experienced habitat loss and fragmentation from NSW state forestry logging  and isolation due to urban areas, farmland, lakes and rivers . The Eurobodalla population is especially isolated as they are surrounded by various water bodies, a highway and cleared land, and as a result the greater gliders in this location were categorized as part of an endangered population in NSW104. Substantial Petauroides genetic structure has been observed in other states, however the species delineation in NSW remains unclear25. As anticipated, high genetic structure was observed in the NSW greater gliders. Analyses showed significant genetic differentiation of Gulguer, MSA, Escarpment N and Tallaganda greater gliders to all other locations, and limited gene flow was evident through a significant IBD analysis105. Notably, the two greater gliders from Tallaganda were clustered with greater gliders 125 km away (DAPC cluster three) and were differentiated from those located only 30 km away in Monga National Park . This could potentially be explained by the ~ 20 km stretch of cleared, agricultural land that has separated Tallaganda and Monga for over 200 years106.We expected to see genetic structure in the NSW greater gliders as their dispersal ability is dependent on trees and they have small home ranges of 1 to 3 hectaresPetauroides species.The single greater glider from Gulguer was significantly differentiated from far south coast greater glider sampling locations, however these results should be considered cautiously as only one greater glider was sampled from Gulguer. Similarly, MSA and Escarpment N greater gliders were significantly differentiated from far south coast greater gliders. The limited gene flow and high genetic structure should be taken into consideration when developing management plans for the species following the 2019–2020 bushfires. Additionally, future research should combine our genetic data with existing data from Queensland and Victoria to further delineate the genetic structure of the three 107. Here, we found maximum temperature of the warmest month may drive patterns of genetic variation in the greater glider genome. Gulguer, Escarpment N and SMBNP experienced the highest maximum temperature of the warmest month and mean temperature of the warmest quarter, while Eurobodalla and Broulee experienced the lowest. The isolated nature of the endangered Eurobodalla population is particularly concerning in the face of climate change, as remaining individuals may not be able to adapt fast enough and migration in/out of the area is currently not possible.Greater glider populations should be managed separately where possible, particularly as we have shown that locations may be adapted to local climatic conditions such as temperature and precipitation. To avoid outbreeding depression associated with translocations, attention should be focused on conserving and connecting patches of fragmented forest . The population at SMBNP, highly isolated with limited potential for immigration, does not meet these requirements (Ne = 45.1) and thus will be unlikely to persist long-term, without intervention. Other locations such as Meroo, Murramarang and Eurobodalla had extremely low effective population sizes (Ne < 10) and are subsequently at risk of inbreeding which is also reflected in the low observed heterozygosity values for these locations. Furthermore, low observed heterozygosity was observed in Mogo E, Mogo W and Broulee. Considering the low contemporary Ne values found in our study and the fact that greater gliders experienced an estimated 85% population loss as a result of the 2019–2020 catastrophic bushfires2, we support the up-listing of their conservation status across the entire state of NSW to endangered. Furthermore, greater gliders in unburnt, continuous forest habitat  should be considered a critical stronghold that maintains a source population of greater gliders, contributing to the long-term population viability of gliders in the larger landscape. Further sampling in the Blue Mountains World Heritage Area, west of MSA, is also important given its size, protected status, and potential to support another large stronghold population. Ne is a criterion that is often used to support endangered species classifications  with the IUCN Red List of Threatened Species112. We provide a detailed list of actions required for each location within our study , as these could provide the adaptive potential needed to overcome the effects of climate change. In a post fire context, we exemplify the importance of collecting genetic data to elucidate ongoing issues and pre-emptively identify populations of greater extinction risk. Repeated wildfire events induced by a changing climate may continue to compound existing genetic effects, and long-term, self-sustainable solutions are sought to ensure species persistence into the future. Greater gliders have limited dispersal and are highly reliant on hollow-bearing trees. Thus restoring landscape connectivity (through creating and conserving wildlife corridors) and improving forest quality  is essential for the long-term persistence of this endangered species. Enhanced connectivity and improving habitat quality through increasing hollow availability, would not only promote gene flow and migration following natural disasters, but also improve the species’ ability to adapt and recover under a changing climate.We provide crucial baseline genetic data for the species prior to the genetic impacts from this natural disaster. Such information is essential to effectively monitor and inform species recoverySupplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.


In the Sindhi population, the distribution of HLD81 and HLD97 alleles deviated from Hardy-Weinberg equilibrium after Bonferroni correction. The combined match probability ranged from 2.0E-12  to 1.0E-12 (Sindhi), and the mean paternity exclusion power varied from 0.995  to 0.996 . The high combined power of discrimination (0.999 999 999 999 97) and low combined match probability (1.7E-12) for all subpopulations studied support the utility of the 30 Indel markers for forensic identification in the studied subpopulations. The allele frequencies of the Indel markers in the Pakistani subpopulations were compared with those from 18 other populations. The results show that the populations clustered according to geography. The subpopulations investigated in this work showed a close genetic relationship with others from Pakistan, as well as with South Central Asian and Middle Eastern populations. The results suggest that the InvestigatorSupplemental data for this article is available online at https://doi.org/10.1080/20961790.2021.1933366 . The sThis study aimed to describe the allele frequency distribution of 30 insertion-deletion (Indel) markers in five major ethnic groups from Pakistan and investigate the forensic genetic effectiveness for future application in forensic casework. Indel markers consist of either a deletion or an insertion of nucleotides ,13. Inde® 3.0 Fluorometer  according to the manufacturer’s recommendation.Buccal swabs were collected on FTA cards from 529 unrelated healthy Pakistani individuals after acquiring signed informed consent. The individuals belonged to five major Pakistani subpopulations: 106 Punjabis from northern Punjab, 107 Pashtuns from Khyber Pakhtunkhwa, 103 Saraikis from southern Punjab, 104 Sindhis from Sindh, and 109 Balochs from Baluchistan. The study was approved by the Review Board/Ethical Committee of the University of the Punjab, Pakistan (D/No. 019/DFEMS). All samples were anonymized. Genomic DNA was extracted as previously described . Purifie® ID-X Software v1.4 (Thermo Fisher Scientific) using the allelic ladder and the set of bins and panels provided by the manufacturer of the kit. The DIPSorter software (Qiagen) was used for the interpretation of the results.The amplification of DNA samples was performed using the Investigator DIPplex Kit  that includes 30 bi-allelic autosomal Indels and amelogenin. PCR cycling was performed on a GeneAmp PCR System 9700 Thermal Cycler  following the manufacturer’s protocol. The PCR amplicons were separated by capillary electrophoresis using 3500xL Genetic Analyzer (Thermo Fisher Scientific) and the POP-4™ polymer (Thermo Fisher Scientific). Allele allocation was carried out with GeneMapperSTF values, and diversity measures were calculated using the Arlequin v3.5 software [Supplementary Table S1) and other populations, data of previously published populations were collected (Supplementary Table S2). The pairwise STF values were calculated using the POPTREE2 software [STF values were visualized in a multidimensional scaling (MDS) plot using the package MASS (version 7.3-51.1) [Population statistics including allele frequencies, Hardy-Weinberg equilibrium (HWE), linkage disequilibrium (LD), pairwise software . HWE anasoftware . The measoftware . To estisoftware . The pai.3-51.1)  and RStu.3-51.1) .Supplementary Table S1. The frequencies of the deletions varied from 0.259 to 0.741 in the Punjabi population, 0.299 to 0.668 in the Pashtun, 0.223 to 0.738 in the Sindhi population, 0.197 to 0.721 in the Saraiki population and 0.266 to 0.706 in the Baloch population as shown in Supplementary Table S1.The Indel frequencies, Ho, He, MP, TPI, PD, and PE in the subpopulations studied are shown in P <0.0017), except for HLD81 and HLD 97 in the Sindhi population (Supplementary Table S1). Both loci showed lower Ho than expected. Statistically significant LD was detected among the Indels after Bonferroni correction for three marker pairs  in the Pashtun, one marker pair (HLD58/HLD83) in the Sindhi, and one marker pair (HLD77/HLD128) in the Saraiki population.The Ho ranged from 0.311 for the markers HLD81, HLD97 (Sindhi) and HLD83 (Punjabi) to 0.575 for HLD58 (Punjabi). The average gene diversity over loci for all the subpopulations ranged from 0.465 (Punjabi and Saraiki) to 0.475 (Pashtun). All the loci were in HWE after Bonferroni correction for multiple testing .The PD ranged from 0.513 to 0.694. HLD128 was the most discriminating locus (Pashtun). The PE varied from 0.052 to 0.272. HLD58 had the highest PE value in Punjabis. In the studied subpopulations, the CMP ranged from 2.0E-12  to 1.0E-12 (Sindhi), the MPE ranged from 0.995  to 0.996 , and the combined power of discrimination (CPD) ranged from 0.999 999 999 999 90  to 0.999 999 999 999 98 (Pashtuns) . The comSTF genetic distances were calculated. To compare the genetic relationship among the Pakistani subpopulations, those from neighbouring territories, South Asia, East Asia, Middle East, Africa, and Europe, pairwise ® DIPplex kit is useful as a supplementary tool for human identification, especially in cases with genetic inconsistencies, and in supplementary testing in relationship testing, e.g., in cases with a few genetic inconsistencies in STR systems. The allelic data reported here can be used as population reference database for the studied subpopulations. The small degree of LD among some alleles at various loci is most probably due to non-random mating, as previously reported by Manzoor et al. [This study demonstrated that the Investigatorr et al. .Genetic differences among the investigated groups were small yet statistically significant among some groups. This study confirms the previous findings by Chishti et al. , who fouIt is important to carry out further studies to better understand the genetic structure of the Baloch subpopulation and its relation to the other subpopulations.Click here for additional data file.