Pleistocene environmental changes are generally assumed to have dramatically affected species’ demography via changes in habitat availability, but this is challenging to investigate due to our limited knowledge of how Pleistocene ecosystems changed through time. Here, we tracked changes in shallow marine habitat availability resulting from Pleistocene sea level fluctuations throughout the last glacial cycle  and assessed correlations with past changes in genetic diversity inferred from genome‐wide SNPs, obtained via ddRAD sequencing, in Caribbean hawksbill turtles, which feed in coral reefs commonly found in shallow tropical waters. We found sea level regression resulted in an average 75% reduction in shallow marine habitat availability during the last glacial cycle. Changes in shallow marine habitat availability correlated strongly with past changes in hawksbill turtle genetic diversity, which gradually declined to ~1/4th of present‐day levels during the Last Glacial Maximum . Shallow marine habitat availability and genetic diversity rapidly increased after the LGM, signifying a population expansion in response to warming environmental conditions. Our results suggest a positive correlation between Pleistocene environmental changes, habitat availability and species’ demography, and that demographic changes in hawksbill turtles were potentially driven by feeding habitat availability. However, we also identified challenges associated with disentangling the potential environmental drivers of past demographic changes, which highlights the need for integrative approaches. Our conclusions underline the role of habitat availability on species’ demography and biodiversity, and that the consequences of ongoing habitat loss should not be underestimated. Global Changes in habitat availability have often been invoked as a causal mechanism for the demographic changes associated with Pleistocene climate changes  are specialized herbivores that graze on seagrass meadows  are spongivores associated with feeding in coral reefs  during the last 125 thousand years (kya) in the wider Caribbean. We evaluated the association between changes in shallow marine habitat availability and changes in genetic diversity of the hawksbill turtle inferred from genome‐wide single nucleotide polymorphism (SNP) markers. The hawksbill turtle is a migratory tropical marine vertebrate closely associated with tropical coral reefs, where they fulfill a keystone role in structuring sponge communities in coral reefs in the Caribbean  to identify and bioinformatically remove PCR duplicates  did not restrict feeding habitat availability because tropical SSTs were only marginally lower during the LGM  and past changes in shallow marine habitat availability (relative to the present), as well as mean global surface air temperature and sea level anomalies , where an autoregressive process of order 1 was used to model autocorrelation among residuals. We initially included shallow marine habitat availability, global surface air temperature and sea levels as predictors of past changes in genetic diversity, and subsequently explored simpler models by excluding predictors. The models were fitted by maximizing the restricted log‐likelihood. Model performance was evaluated using the Aikaike information criterion (AIC) and Bayesian information criterion (BIC).To account for the time‐series nature of the data, we additionally performed a regression analysis using generalized least squares with the nlme R package . The mean alignment rate against a green turtle draft genome  and an alignment rate at only 37.5% was excluded from further analysis. We additionally excluded another sample with missing genotypes at more than 50% of the identified SNPs  or sea level changes (μ = 1.2 × 10−8 substitutions per site per generation), while the model including shallow marine habitat availability ranked third and second, respectively.Relatively minor decreases in sea levels resulted in considerable declines in shallow marine habitat availability; a 25%, 50% and 75% reduction in shallow marine habitat availability was achieved at sea levels 9, 26 and 55 m lower than present‐day levels, respectively Figure . Shallow4Balaena mysticetus), a post‐LGM population expansion was concordant with a threefold increase in suitable habitat area following the retreat of permanent sea ice in the northern Atlantic  showed no evidence of past demographic changes, consistent with relatively minor declines (15%) in suitable habitat availability during the LGM concordant with their broad distribution between the tropics and the arctic regions  and detected concordant declines in the genetic diversity of hawksbill turtles. Shallow marine habitat availability and genetic diversity showed a synchronous and rapid increase to present‐day levels after the LGM. However, sea level changes and surface air temperatures also showed strong correlations with Caribbean hawksbill genetic diversity, and we found no consistent support for a single driver of past demographic changes. In part, these findings can be attributed to non‐independence among the investigated drivers, i.e., temperature is indirectly related to sea levels via the formation and melting of continental ice sheets, and in turn to shallow marine habitat availability. Temperature probably also influenced other abiotic or biotic processes, e.g., the distribution and quality of nesting habitat for sea turtles Pike, , sex ratθ = 6.0 to θ = 0.3) showed a 7% and 50% decrease in genetic diversity after 200 and 2000 generations, respectively  at microsatellite loci and model‐based clustering suggested reduced gene flow between Caribbean and Southwest Atlantic green turtle nesting populations  is sufficiently high, it is possible that the estimated past changes in genetic diversity reflect demographic changes across larger spatial scales. Atlantic hawksbill turtle nesting populations are located in the Caribbean  demonstrated a high degree of juvenile fidelity to river systems (Feutry et al., Estimated kinship coefficients suggested the observed pairs of related individuals represented putative sibling‐, and parent‐offspring pairs (Weir et al., The genetic diversity of hawksbill turtles prior to the last glacial period was higher than contemporary genetic diversity. Immigration may introduce novel genetic diversity into a population, which, over long time scales implies that local levels of genetic diversity converges towards “global” species genetic diversity (Hudson et al., Due to the uncertainty in mutation rates, we investigated the timing of events using both the slower mutation rate estimated from crocodilians (Green et al., Despite recent heavy human exploitation of hawksbill turtles and severe habitat degradation (McClenachan et al., The relationship between Pleistocene climate changes, habitat availability and demographic change underlines that the consequences of contemporary habitat loss should not be underestimated. Tropical marine ecosystems have degraded considerably during the most recent decades as a result of human disturbances and climate change (Alvarez‐Filip et al., The authors declare no conflict of interest.Jurjan P. van der Zee conceived the study and wrote the manuscript with input from all authors. Per J. Palsbøll supervised the study. Laboratory work was done by Martine Bérubé. Jurjan P. van der Zee conducted bioinformatic‐, and data analyses. Mabel Nava, Leontine E. Becking, Marjolijn J.A. Christianen and Per J. Palsbøll obtained funding. Mabel Nava, Jessica Berkel, Sietske van der Wal, Melanie Meijer zu Schlochtern and Tadzio Bervoets provided tissue samples and logistic support in the field.Supplementary MaterialClick here for additional data file.


Balanus glandula is a common intertidal barnacle native to the northeastern Pacific. However, this species has been introduced to Japan, South America, South Africa, and Europe. While a latitudinal genetic cline is well known in its native range, it is unclear whether such a genetic cline occurs in introduced areas. Twenty years have passed since it was first identified in Japan and its distribution now ranges from temperate to subarctic regions.Large numbers of exotic marine species have been introduced worldwide. Monitoring of introduced species is important to reveal mechanisms underlying their establishment and expansion. B. glandula in Japan at high and mid intertidal zones.In the present study, we examined genotypes of cytochrome oxidase subunit I (COI) of mitochondrial (mt)-DNA and elongation factor 1a (EF1) across the distribution of At all sampling sites, native northern genotypes are abundant and I did not detect significant effects of latitude, tide levels, or their interaction on genotypic frequencies. Further, I did not detect any change of genotype composition between data collected during a study in 2004 and samples in the present study collected in 2019. Data from the present study offer an important baseline for future monitoring of this species and supply valuable insights into the mechanisms of establishment and expansion of introduced marine species generally. Marine organisms have been introduced beyond their native ranges by humans both intentionally and unintentionally at increasing rates over the last 200 years . Such inBalanus glandula, is a dominant species in the intertidal zone of the Northeastern Pacific coast from Alaska to California. It shows a strong latitudinal genetic cline both in mitochondrial COI and nuclear EF1 genes  of the Japanese populations was somewhere between Puget Sound and Alaska  following the manufacturer’s instructions. PCR of the cytochrome oxidase subunit I (COI) region of mitochondrial (mt)-DNA was performed using the universal primers LCO1490 and HCO2198 . PCR forFst values with pairwise differences between 2004 and 2019 were calculated based on both COI and EF1 sequences with using Arlequin 3.5 ). In these models, haplotype groups of COI-A and EF1-B were classified as the northern lineage, and other groups were classified as the southern lineage. The best-fit model was the one with the highest Akaike Information Criterion (AIC) weighting. These analyses were conducted in the R statistical environment  or tide level (P = 0.145), or interactions between latitude and tide levels (P = 0.136) with EF1 lineages. AMOVA detected no significant population genetic structure between the Honshu and Hokkaido regions, and among populations within these regions  or EF1  between 2004 and 2019. In addition, Fst values were not significantly different from zero between 2004 and 2019 in COI or EF1 . For EF1, AIC weighting indicated the best model to be all interactions . In this model, however, no significant associations were detected with latitude  is the main source of Japanese B. glandula, as suggested by While northern genotypes have increased in the native range through time , I did nB. glandula, either in regard to latitude or tide level in COI and EF1, and it is inferred to be a proxy for the global population structure of this species. Genotypic composition of these genetic markers has remained stable since 2004 in Japan. Data from the present study offer an important baseline for future monitoring of this species, which provide insight into mechanisms of establishment and expansion of introduced species.I detected no structuring of genetic variation in the introduced barnacle, 10.7717/peerj.14073/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.14073/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.14073/supp-3Supplemental Information 3Balanus glandula sampling locations and daily average temperatures in August. (D) Linear regression between latitude of Balanus glandula sampling locations and daily average temperatures in February. Summer (August) amd Winter (February) temperature data of Japanese locations in 2018 were obtained from the website of Japan Meteorological Agency . Data of Summer (August or September) and Winter (January) temperatures in Southern (San Diego Lindbergh Fld), Central (Point Arena), North (Crescent City) California were obtained from the website of Western Regional Climate Center .(A) Average of daily max. and min. temperatures in Summer. (B) Average of daily max. and min. temperatures in Winter. (C) Linear regression between latitude of Click here for additional data file.10.7717/peerj.14073/supp-4Supplemental Information 4(A) Haplotype diversity at COI. (B) Nucleotide diversity at EF1. (C)Nucleotide diversity at COI. (B) Nucleotide diversity at EF1. Bars indicate standard deviation.Click here for additional data file.10.7717/peerj.14073/supp-5Supplemental Information 5(A) Haplotype diversity at COI. (B) Nucleotide diversity at EF1. (C) Nucleotide diversity at COI. (B) Nucleotide diversity at EF1. Bars indicate standard deviation.Click here for additional data file.


Parkinson's disease (PD) is caused by the progressive loss of dopaminergic cells in substantia nigra pars compacta (SNc). The root cause of this cell loss in PD is still not decisively elucidated. A recent line of thinking has traced the cause of PD neurodegeneration to metabolic deficiency. Levodopa (L-DOPA), a precursor of dopamine, used as a symptom-relieving treatment for PD, leads to positive and negative outcomes. Several researchers inferred that L-DOPA might be harmful to SNc cells due to oxidative stress. The role of L-DOPA in the course of the PD pathogenesis is still debatable. We hypothesize that energy deficiency can lead to L-DOPA-induced toxicity in two ways: by promoting dopamine-induced oxidative stress and by exacerbating excitotoxicity in SNc. We present a systems-level computational model of SNc-striatum, which will help us understand the mechanism behind neurodegeneration postulated above and provide insights into developing disease-modifying therapeutics. It was observed that SNc terminals are more vulnerable to energy deficiency than SNc somas. During L-DOPA therapy, it was observed that higher L-DOPA dosage results in increased loss of terminals in SNc. It was also observed that co-administration of L-DOPA and glutathione (antioxidant) evades L-DOPA-induced toxicity in SNc neurons. Our proposed model of the SNc-striatum system is the first of its kind, where SNc neurons were modeled at a biophysical level, and striatal neurons were modeled at a spiking level. We show that our proposed model was able to capture L-DOPA-induced toxicity in SNc, caused by energy deficiency. Levodopa (L-DOPA), a precursor of dopamine (DA), is used as a symptom-relieving treatment for Parkinson's disease (PD) , slow-adapting RS (0.90 ±1.23 Hz), intrinsic bursting (3.1 ±2.6 Hz), and non-inactivating bursting (2.8 ±3.2 Hz)  with a time step of 0.1ms.The proposed model of L-DOPA-induced toxicity (LIT) consists of the cortico-basal ganglia system. We modeled a part of the basal ganglia system, comprising the following nuclei: SNc, striatum (STR), subthalamic nucleus (STN), and globus pallidus externa (GPe). Within the SNc, we separately considered SNc soma (cell body) and SNc terminal (bouton) that make contact with striatal neurons. The medium spiny neurons (MSNs) in the striatum are classified into two types based on the DA receptor present, namely, D1 and D2-types. In the proposed LIT model, we considered only D1-type MSNs because they only project GABAergic inputs to SNc neurons Gerfen, . Within Glucose and oxygen inputs to SNc cells were reduced to implement energy deficiency conditions in the proposed model. As the number of SNc neurons under energy deficiency increases, the dopaminergic tone to the striatum decreases due to SNc terminal loss. The dopamine deficiency leads to lesser excitation of MSN neurons by pyramidal neurons in the cortex; as a result, SNc neurons get disinhibited. Disinhibition from MSN leads to overactivity of SNc neurons, resulting in SNc neurons degeneration due to excitotoxicity.To examine L-DOPA's role in the degeneration of SNc neurons in PD, we administered L-DOPA after a certain percentage of SNc neuronal loss due to energy deficiency and investigated how L-DOPA changes the course of SNc cell loss.Ix) was adjusted to match the firing rate of nuclei with published data  and membrane recovery variable (ux):The Izhikevich neuron model of STN, GPe, CTX, and MSN neurons consists of two variables: membrane potential , respectively;kx is the membrane constant, Cx is the membrane capacitance, {a, b, c, d} are Izhikevich parameters;x being GPe or CTX or STN or MSN neuron.where, vSNc) is given as,The biophysical neuronal model of SNc in the proposed LIT model was adapted from Muddapu and Chakravarthy . The detF is the Faraday's constant, CSNc is the SNc membrane capacitance, vcyt is the cytosolic volume, Jm,Na is the sodium membrane ion flux, Jm,Ca is the calcium membrane ion flux, Jm,K is the potassium membrane ion flux, Jinp is the overall input current flux. The detailed information about the SNc neuron model is provided in where DAe]) was modeled as,The biochemical DA terminal of SNc in the proposed LIT model was adapted from Muddapu and Chakravarthy . The bioJrel represents the flux of calcium-dependent DA release from the DA terminal, JDAT represents the unidirectional flux of DA translocated from the extracellular compartment (ECS) into the intracellular compartment (cytosol) via DA plasma membrane transporter (DAT), and where, DAi]) was modeled asThe DA dynamics in the intracellular compartment  is given by,The DA dynamics in the cytosolic compartment , Jldopa represents the flux of synthesized cytosol DA from levodopa, which is induced by calcium.where, DAv]) is given by,The DA dynamics in the vesicular compartment  current was regulated by voltage-dependent magnesium channels, which were modeled as,x to y, τRecep is the decay constant for the synaptic receptor, x at time t, Wx→y is the synaptic weight from neuron x to y, y for the neuron at the location , ERecep is the receptor-associated synaptic potential (Recep = NMDA/AMPA/GABA), and [Mg2+] is the magnesium ion concentration. The time constants of gamma-amino butyric acid (GABA), alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA), and NMDA in GPe, CTX, MSN, SNc, and STN were chosen from Götz et al.  is the summation of the glutamatergic input from the STN neurons, considering both NMDA and AMPA receptor activation, comprising the GABAergic inputs from the D1 − MSN (GS) and D1 − MSN (G) neurons and the lateral GABAergic current from other SNc neurons.The total synaptic current received by an NMDA and AMPA receptors activation, respectively; D1 − MSN (G) and D1 − MSN (GS) neurons, respectively; SNc neurons; FSTN→SNc is the scaling factor in the glutamatergic current from the STN neuron; FD1 − MSN (G) → SNc is the scaling factor in the GABAergic current from D1 − MSN (G) neuron; FD1 − MSN (GS) → SNc is the scaling factor in the GABAergic current from the D1 − MSN (GS) neuron.where GPe neuron at the lattice position  is the summation of the glutamatergic input from the STN neurons, considering both NMDA and AMPA receptors activation and the lateral GABAergic current from other GPe neurons.The total synaptic current received by a STN neuron, considering both NMDA and AMPA receptors activation, respectively; GPe neurons.where STN neuron at the lattice position  is the summation of the GABAergic input from the GPe neurons and the lateral glutamatergic input from other STN neurons, considering both NMDA and AMPA receptors activation.The total synaptic current received by an GPe neuron; STN neurons, considering both NMDA and AMPA receptors activation, respectively.where D1 − MSN (GS) neuron at the lattice position  is the summation of the GABAergic input from the D1 − MSN (G) neurons and the glutamatergic input from CTX neurons, considering both NMDA and AMPA receptors activation.The total synaptic current received by a D1 − MSN(G) neuron, CTX neurons, considering both NMDA and AMPA receptors activation, respectively.where D1 − MSN (G) neuron at the lattice position  is the summation of the GABAergic input from the D1 − MSN (GS) neurons and the glutamatergic input from CTX neurons, considering both NMDA and AMPA receptor activation.The total synaptic current received by a D1 − MSN(GS) neuron, and CTX neurons, considering both NMDA and AMPA receptors activation, respectively.where The lateral connections in SNc, STN, and GPe were modeled as Gaussian neighborhoods , dij,pq is the distance from the center neuron , Rm is the variance of Gaussian, and Am is the strength of lateral synapse, m = GPe or STN or SNc.where The connections within SNc and GPe populations were considered inhibitory and within STN as excitatory , EGABA is the GABAergic receptor potential, and g, where d Terman  and giveThe effect of neuromodulators, such as DA and substance P (SP), in the proposed LIT model was modeled based on Buxton et al.  and MuddDA-modulated lateral connection strength in SNc, STN, and GPe populations. As the DA level increases, the lateral connection strength in SNc and GPe increases, whereas, in the case of STN, it decreases  is the instantaneous DA level, the spatial average DA concentration of all the terminals. All parameter values are given in where The post-synaptic effect of DA in SNc, STN, and GPe was modeled similar to Muddapu et al. ,wx → y is the synaptic weight , cd2 is the parameter that affects the post-synaptic current, and DAs (t) is the instantaneous DA level.where The effect of DA in the MSN population occurs on both synaptic and intrinsic ion channels , DAs (t) is the instantaneous DA level, y neuron [where y = D1 − MSN (GS) or D1 − MSN (G))], and βDA was adapted from Humphries et al.  is the instantaneous DA level;y neuron [where y = D1 − MSN (GS) or D1 − MSN (G))], and KMSN and LMSN were adapted from Humphries et al.  and D1 MSN (G) in the proposed LIT model . It was x to y, Wx→y is the synaptic weight from neurons x to y, x at time t, y for the neuron at the location , ERecep is the receptor-associated synaptic potential (Recep = NMDA/AMPA), sp is the gain factor, wsp is the influence of SP on wSTN→SNc, DAs (t) is the instantaneous DA level, bsp and λsp were adapted from Buxton et al. . Under such conditions, the particular SNc neuron gets eliminated as follows,The SNc neuron undergoes ER-stress-induced apoptosis when calcium levels in ER cross a certain threshold .where Caer]) dynamics is given by,The ER calcium concentration . Then, that particular SNc neuron will be eliminated as follows,The SNc neuron undergoes mitochondria-induced apoptosis when calcium levels in mitochondria cross a certain threshold , and i, j).where Camt]) dynamics is given by,The MT calcium concentration . Proapoptotic cytochrome-c is released from MT through MTPs, which triggers cytochrome-c-dependent apoptosis. In the proposed modeling study, when the apoptotic signal gets activated from either of the pathways in a particular neuron, we formulated an approach wherein that particular neuron was eliminated by making ROSthres) as follows,DA is the primary contributor to the oxidative stress in the neuron ;i, j).where The ROS concentration in the SNc terminal was given as,Jleak is the flux of oxidative stress due to mitochondrial leakage, Jenv is the flux of external oxidative stress , Jdopa is the flux of oxidative stress due to excess cytoplasmic dopamine, Jcat is the catabolizing flux of ROS by a catalase enzyme, and Jdox is the flux of the GSH-dependent ROS-scavenging pathway. The detailed information about the ROS formation is provided in where t till the end of the simulation since calcium plays an important role in the function of the terminal.When the ROS level crosses a certain threshold, excess ROS triggers degeneration of the terminal. In the proposed modeling study, when the ROS level crosses the threshold in a particular terminal, we formulate an approach wherein that particular terminal was eliminated by making To alleviate PD symptoms, the most potent drug, L-DOPA, a precursor of DA, is typically administrated  at which therapeutic intervention was employed (pcl = 0.25),z. In the present study, therapeutic intervention is given at 25% SNc cell or terminal loss.where It was reported that SP exacerbated dopaminergic neurodegeneration in mice .where The impaired DA metabolism causes oxidative stress (ROS), leading to PD pathogenesis , and [GSHz] is the GSH concentration.where The glutathione therapy was implemented in the proposed LIT model by the following criterion,GSH] therapy based on the number of surviving SNc neurons or terminals at the time GSH] concentration dosage under GSH therapy, and z = soma or terminal).where p-value is <0.05  firing rate of the pyramidal cortical neuron in the model was tuned by adjusting  − 15 Hz . SimilarHz, which falls in the range of 8 − 60 Hz observed experimentally , and bursting or high-frequency regular phasic firing (~ 20 Hz) )] was injected, SNc neuron exhibited a bursting type of firing, which lasted for more than 1 s after the pulse was removed )] was injected, SNc neuron exhibited quiescent state until stimulation was removed )] increase, the influence of cortical glutamatergic inputs on D1-type MSN increases, resulting in monotonously increasing firing frequency )] increases, the influence of STN glutamatergic inputs on SNc increases, resulting in monotonously increasing firing frequency  and terminals (in the striatum) are located far from each other: homogeneous  and heterogeneous . Homogeneous energy deficiency was implemented by reducing glucose and oxygen values by the same proportions in both SNc somas and terminals. The homogeneous energy deficiency causes soma loss at 70% energy deficiency . The somHeterogeneous energy deficiency was implemented by reducing glucose and oxygen values by different proportions in SNc somas and terminals. The heterogeneous energy deficiency causes soma loss at only 100% energy deficiency in terminals when the energy deficiency in somas is set at 0%, 25%, or 50% . When 75x 10−9mM to 36 mM in multiples of 10    L-DOPA on SNc somas and terminal loss under energy deficiency (100% energy deficiency), we have modified extracellular L-DOPA concentration in the range from 36 es of 10 . At an ef ~ 63%)  of SNc sf ~ 63%) . Contrarf ~ 95%)  was obsef ~ 95%) . At extrx 10−5mM) administered was near the basal value , it was observed that the percentage loss of SNc somas and terminals was not altered much. When external L-DOPA concentration administered was in the range from 36 x 10−4mM to 36 x 10−3mM, it was observed that the percentage loss of SNc somas was decreasing; however, the percentage loss of SNc terminals was not significantly altered. On the contrary, when administered external L-DOPA concentration was above 36 x 10−3mM, it was observed that the percentage loss of SNc somas and SNc terminals increased , it was observed that the percentage loss of SNc somas was decreasing with increasing inhibition of SP transmission (x 10−4mM), it was observed that the percentage loss of SNc somas and terminals was decreasing with increasing glutathione concentration , which targets overexcitation in SNc somas (by reducing SP-mediated excitatory inputs to SNc) and an ROS build-up in SNc terminals (by scavenging ROS), respectively. When SP antagonists are co-administered  terminals are more vulnerable to energy imbalance when compared to SNc cell bodies (somas), which was also observed experimentally, when an injury is initiated at axonal terminals  terminals are more vulnerable to energy deficiency than SNc somas.Basal extracellular L-DOPA concentration is needed to maintain for normal functioning of the neuron.Adjuvant therapies, along with L-DOPA, such as glutathione, result in evading L-DOPA-induced toxicity.L-DOPA-induced toxicity in cell bodies and axonal terminals of SNc neurons was autoxidation irrelevant and autoxidation relevant, respectively.In normal conditions, there is no SNc cell or terminal loss where SNc maintains the dopaminergic tone on its target regions, such as STN, D1-MSN(G), and D1-MSN(GS). In the first loop , normal Under pathological conditions, there is an SNc cell or terminal loss where SNc fails to maintain the dopaminergic tone in its target regions, such as STN, D1-MSN(G), and D1-MSN(GS). In the first loop , DA defiIn medication conditions, L-DOPA is administrated where dopaminergic tone to SNc target regions  is restored. If the administrated L-DOPA dosage goes beyond a certain threshold, overexcited D1-MSN(GS) through the DA-SP feedback mechanism makes SNc hyperactive, resulting in SNc cell loss due to excitotoxicity. Along with SNc cell body loss, SNc terminals also undergo degeneration due to excess DA-causing oxidative stress. To summarize, L-DOPA-induced toxicity in SNc does not occur when L-DOPA dosage is below the threshold, which results in the survival of remaining SNc cells. However, if L-DOPA dosage goes beyond a threshold, from that point onward, the aforementioned runaway effect kicks in, leading to a progressive and irrevocable cell loss in SNc. Thus, it is evident that L-DOPA might be toxic to SNc neurons under high dosage, which triggers a runaway effect, resulting in uncontrollable SNc cell loss.x 10−3mM; concentration levels beyond 36 x 10−3mM result in diminishing neuroprotective effect of L-DOPA  can be found at: VM contributed to conceptualization, model development, data curation, formal analysis, investigation, methodology, and manuscript writing. VC contributed to conceptualization, model development, data curation, formal analysis, investigation, methodology, manuscript writing, and supervision. KV and KR contributed to conceptualization and model development. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Lewontin's 1972 article ‘The apportionment of human diversity’ described a key feature of human genetic diversity that would have profound impacts on conversations regarding genetics and race: the typical genetic locus varies much less between classical human race groupings than one might infer from inspecting the features historically used to define those races, like skin pigmentation. From this, Lewontin concluded: ‘Human racial classification … is now seen to be of virtually no genetic or taxonomic significance’ (p. 397). Here, 50 years after the paper's publication, the goal is to understand the origins and legacy of the paper. Aided by insights from published papers and interviews with several of Lewontin's contemporaries, I review the 1972 paper, asking about the intellectual background that led to the publication of the paper, the development of its impact, the critiques of the work and the work's application and limitations today. The hope is that by gaining a clearer understanding of the origin and reasoning of the paper, we might dispel various confusions about the result and sharpen an understanding of the enduring value and insight the result provides.This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’. And so I just looked into the literature, and that literature was in books and so on.[O]ne day I was going to give a lecture, I think it was in Carbondale, Illinois, or somewhere south. I was working in Chicago at the time. So I took a couple of these books with me and a pad of paper, and a table of logarithms which I needed for this purpose, and a little hand calculator, and I sat on this bus trip for three or four hours looking at the books, picking out the data, looking it up in the table of logarithms, doing a calculation, and writing it down in tables. And when I got back after the round trip I had all the data I needed to write the paper about how much human genetic variation there was, and so I did it. […]Shows you it's worthwhile being afraid to fly, by the way, because you have lots of time on a bus to work. – R.C. Lewontin, Interview for ‘Race the Power of an Illusion’ Documentary With this story, Lewontin describes the origins of his paper on ‘The apportionment of human diversity’ , a paperAs is well known, the paper showed that at a typical genetic locus 85% of ‘[genetic variation is found within human groups]' and on that basis he concluded that ‘human races and populations are remarkably similar to each other with the largest part by far of human variation being accounted for by the differences between individuals’ and from this he judged that ‘Human racial classification is now seen to be of virtually no genetic or taxonomic significance’. The paper's main result is routinely cited in antiracist perspectives on race and genetics and has been featured in some of the most widely distributed public translations of genetic results e.g. ,3)..3]).Here, 50 years after the paper's publication, the goal is to understand its origins and legacy, including the nature of the critiques and concerns with it, and ultimately what power and limitations it has in discussions about genetics and race. Aided by insights from published papers and interviews with several of Lewontin's contemporaries (see Acknowledgements), I review the 1972 paper, asking about the intellectual background that led to the publication of the paper, the development of its impact, the critiques of the work and its enduring relevance. This review also benefits greatly from insights provided by other perspectives on Lewontin's 1972 –6), inclQuantitative Zoology  are genetically different and constitute distinct geographical races (under one commonly accepted criterion of race).’ (p.304)As an application of the analysis of variance, the chapter describes how ‘a problem common in experimental taxonomy is that of distinguishing geographical races' (p.304). Though it is not explicit about defining criteria for races, it presents an example from Apportionment paper. Both works apportion variances to make evaluative statements about taxonomic categories. The working logic is that a meaningful taxonomic assignment  is one where a large amount of variance between groups exists relative to the variance within.Though differing in detail (using indirect estimates of genetic variance in an example trait versus direct observation of genotypic data) and imprecise in defining a criterion for race, the approach foreshadows Lewontin's work on human genetic diversity in the 1972 In the real world, no two populations of objects can have precisely equal means or standard deviations. A difference between two such populations can always be established, provided the observer is willing to go to enough trouble to refine and increase the number of his measurements. Another relevant passage that is worth revisiting as background to the 1972 )In Lewontin's words, molecular data could ‘put the comparative differentiation within and between groups on a firm quantitative basis , p. 383. (c)A methodological curiosity of the 1972 paper is that although he was well versed in analysis of variance (as described above) and having titled the paper the ‘apportionment of human diversity’, Lewontin did not use a traditional analysis of variance nor the already well-known alternative based on Sewall Wright's heterozygosity-based measures . InsteadH] is widely used to characterize species diversity in community ecology, and since I am performing a kind of taxonomic analysis here, I will use H’  How many different European nationalities should be included as compared with many African peoples or Indian tribes?’ (p.384). He ends up choosing to use ‘as much as possible equal numbers of African peoples, European nationalities, Oceanian populations, Asian peoples, and American Indian tribes' (p.385) weighing them equally, with the rationale that the equal weighting should favour finding variation between groups versus within them. With regards to the racial groupings, ‘the boundary line must be arbitrary’ (p.385) but ‘I have chosen a conservative path and have used the classical racial groupings with a few switches based on obvious total genetic divergence. Thus the question I asked is ‘‘How much of diversity between populations is accounted for by more or less conventional racial classification?'’’ (p.386). To that end, he uses what he calls ‘the usual four’ , as well as ‘South Asian Aborigines', ‘Oceanians’ and ‘Australian Aborigines'.To carry out his analysis, Lewontin used published data ,42,44,45With those designations he found his well-known results: the average proportion of diversity within populations was 85.4% of the total, between populations within races was 8.3% of the total, and a final 6.3% was accounted for by diversity between the race groups. Given the arbitrariness of how to group populations into races, the 85.4% value is expected to be the more robust than the 8.3% and 6.3% values. Later results and re-calculations show that these numbers vary depending on grouping decisions . Over tiet al.  How reliably can an individual be placed in the correct population, if one makes use of all the information available? Spielman and Smouse then went on to show how a multivariate/multi-locus approach allows assignment of Amazonian individuals to villages or clusters of villages better than random expectation.Foreshadowing Edwards, in 1976, Richard Spielman and Smouse began their paper on the ‘Multivariate classification of human populations'  with a cIn a similar spirit, Mitton  criticizThese same points first raised in the late 1970s would be arrived at independently by Anthony Edwards in 2003, in his impactful paper titled: ‘Human genetic diversity: Lewontin's fallacy’. Edwards argued that even with most of the variation found within populations, genetics can be used for assignment of individuals to genetic populations . EdwardWith regards to the legacy of Lewontin's result, one passage from Chakraborty's response to Mitton is particularly illuminating: ‘if it is possible to score the entire human genome we would be able to infer that there is a vast amount of genetic diversity even between individuals of the same family. … To have a uniform unit for all evolutionary studies [a] ‘per cistron’ unit is probably more profitable. , p. 1137taxonomic value in the sense that they are predictive of meaningful differentiation at a typical genetic locus. For instance, in his retelling of the result in Human Diversity, after granting that loci with large frequency differences between populations like Duffy Fy exist, Lewontin stated, ‘The question is, are these large differences typical?’  really wasn't going from the variation problem to the phylogenetic problem’ , p. 422.For his part, Lewontin in his work and later writings, made clear that he recognized that some form of classification is possible e.g. ,72)—his —his 72]). 4 (a)An important antecedent for Edwards' critique, relative to the earlier critiques in the late 1970s, is that by the time Edwards was writing, the apportionment of diversity result had been taken up broadly and become an iconic, politicized and sometimes over-simplified sound bite.Science for the People. He repeated the results, for instance in his 1974 book The Genetic Basis of Evolutionary Change . Some ochard C. ), in a 1y Series , and in ur Genes .The Mismeasure of Man by Lewontin's Harvard colleague Stephen J. Gould, cited Lewontin's result in its closing chapter )For some of the most controversial traits of interest , many practising population geneticists think geographically varying selective pressures are unlikely and that any differences (due to selection or otherwise) will be negligible, especially relative to the rich prior knowledge of environmental factors and interactions that can shape such traits (Lewontin was a leader in expressing and arguing this perspective). Others think that differences between groups in some form (selected or otherwise) are more likely to exist than not For Lewontin's part, he seems to have understood the main limitations and the utility of his results ,72,104. As Lewontin knew, his result is not a silver bullet that defeats all possible racist positions, but it does set an important prior expectation. On a scientific basis, one can safely expect that the average variant is not substantially differentiated across race groups, and in turn that race is a poor proxy for genotype at any one locus of interest. For any discussion of human variation, this is an important ‘understanding of the situation’.. 5After considering the controversies and confusions that have arisen from Lewontin's 1972 ,105,106,While there are many intellectual frameworks to understand scientific controversies (e.g. ), the leIn the context of paper  and its Finally, in reflecting on the legacy of Lewontin's work, I hope this narrative has made clear some of the enduring and positive aspects of Lewontin's work. To summarize, his work helps us understand that the typical locus in the human genome is not as differentiated as one might guess from looking at our external features. This does not imply that classification using multi-locus genomic data is not possible; conversely the ability to use multi-locus data to study how individuals genetically relate to one another does not invalidate Lewontin's result and main conclusion. Fifty years after the publication, Lewontin's key empirical claim still continues to hold true. We continue to see that for a typical variant, most variance is found within human groups and little is found between them. This fact has never been a ‘silver bullet’ that ends all discussion of genetics and race. Yet, as a critique of race as a useful classification system, it still deepens our understanding of human variation, still challenges simple interpretations about human differences, and helps affirm the willful choice to see past the superficial in how we as humans relate to one another.


The results indicate that: (1) during 2007–2016, urban PM2.5 concentration exhibited declining trends in fluctuations concerning the development of the urban economy, accompanied by uncertainty under different city types; (2) population size has a significant effect on propelling PM2.5 concentration; (3) the effect of structure reformation on PM2.5 concentration is evident among cities with different populations and levels of economic development; and (4) foreign investment and scientific technology can significantly reduce PM2.5 emission concentration in cities. Accordingly, local governments not only endeavor to further control population size, but should implement a recycling economy, and devise a viable urban industrial structure. The city governance policies for PM2.5 concentration reduction require re-classification according to different population scales. Cities with large populations  should consider reducing their energy consumption. Medium population-sized cities (between 1 million and 10 million) should indeed implement effective population (density) control policies, while cities with small populations (less than 1 million) should focus on promoting sustainable urban development to stop environmental pollution from secondary industry sources.Based on the extended STIRPAT model, this paper examines social and economic factors regarding PM The Ministry of Environmental Protection adopted the Ambient Air Quality Standards (GB3095-2012) in 2012. It stipulates the annual average concentration limit of PM2.5, with the primary limit standard of 15 μg/m3 and the secondary limit standard of 35 μg/m3. As seen in 2.5 annual average concentration below the secondary concentration limit experienced an increase from 26.27% in 2007 to 36.86% in 2012, followed by a sharp decline from 31.32% in 2013, and a steady increase up to 47.06% in 2016. The proportion of PM2.5 annual average concentration is higher than 55 μg/m3 but did decline gradually to 36.86% in 2007, then 18.43% in 2016 after a slight rebound in 2010 (31.76%) and 2013 (33.21%). The national average of PM2.5 concentration was 47 μg/m3 in 2016, with a 29% decrease compared to 2013.With the rapid changes occurring in industrialization and urbanization, China’s economic development has been expanding in recent decades. Consequently, massive haze pollution has been generated from such rapid economic development and it can no longer be ignored. In response to the frequent and widespread haze pollution problem, the Chinese government is now paying much greater attention to it. The Action Plan for the Prevention and Control of Air Pollution was issued in 2013, aiming to reduce the concentration of fine particles. PM2), nitrogen oxide (NOX), sulfur dioxide (SO2) and fine particulate matter (PM2.5) [2 emissions of 128 countries from 1990 to 2014. The results indicate that causality varies among the variables across regions [2 emissions and found that pollutant emissions are correlated across regions [2 emissions in the Jiangsu Province and its south, middle and north regions during 2004–2016 [2, wastewater and dust emission intensity in prefecture-level cities [2.5 is one of the main particulate matters found in haze, which is evidenced as the main contributor of air pollution [2.5 can in fact seriously affect ambient air quality [Most current studies in China have stated the main pollutants of air pollution such as: carbon dioxide CO PM2.5) ,2,3,4. D.5 ,2,3,4 ,2,3,4.  regions . Miao et regions . Yang anollution . Wang et quality ,12.2.5 concentration at the city level. Furthermore, energy-related indicators also play an important role in PM2.5 concentration but the effects are rarely examined due to the lack of data captured and in cities [Most Chinese scholars utilize certain variables to measure population density , urbanizn cities , which hn cities ,18.2.5 concentration [2.5 concentration. The study finds that environmental regulations not only have a direct impact on haze pollution, but also affect haze pollution indirectly through coal consumption, as it relates to foreign direct investment (FDI), and industrial structure and technological innovation [2.5 concentration in China from 1998 to 2014. The results conclude that high PM2.5 concentration in the northwest of China were mainly caused by natural factors (sand and dust), while in the eastern region mainly resulted from human activities. Low PM2.5 concentration areas were mainly located in the less developed regions [2.5 concentration and potential factors. Both natural and socioeconomic factors and their interactive impacts on PM2.5 pollution are considered [2.5 concentration in 338 Chinese cities during 2014–2017, and further used structural equation modeling (SEM) to quantify the socio-economic driving factors of PM2.5 concentration changes. This concluded that urban population density contributes most significantly [2.5 concentration in 199 cities of China [Scholars have used different research methods, such as exploratory spatial data analysis (ESDA) , factor ntration . Zhang enovation . Lu et a regions . Yang etnsidered . Wang etficantly . Zhou etof China . 2.5 concentration among various population scales. The factors causing the problem of PM2.5 concentration vary in regions with different population sizes. Although previous scholars studied PM2.5 concentration mainly at the national level, it has gradually expanded to provide research into the regions and regional or cross-regional levels, such as the Yangtze River Delta region, Beijing-Tianjin-Hebei region and various provinces of China [2.5 concentration for 12 different regions in China, and ranked the influencing factors on PM2.5 concentration in descending order of importance [2.5 concentration in various geographic regions.Few scholars evaluated the determinants of PMof China ,29,30. Fportance . Cheng eportance . Accordi2.5 concentration, the quantile regression method is implemented. The ordinary least squares (OLS) method only analyzes the expected value of the effect of each explanatory variable. It cannot ascertain the influence of each factor on the distribution patterns alone of PM2.5 concentration and distinguish the different results from one factor using such varied types. Quantile regression as proposed by Koenker and Bassett (1978), however, can perhaps solve this problem [To differentiate the diverse effects of population sizes on PM problem . The qua problem ,35. Cons2.5 concentration using various models to support the existence of EKC in PM2.5 concentration. As referred to previously, a few scholars have combined the influential elements on PM2.5 concentration and the EKC of the economy and PM2.5 concentration, which is worth investigating further. The Environmental Kuznets Curve (EKC) theory is another important issue widely discussed in current literatures. The inverted U-shaped curve relationship between the level of environmental pollution and income level implies that environmental degradation increases with output during the early stages of economic growth. However, it declines with output after reaching a specified threshold. Some scholars further studied the EKC hypothesis and found that haze pollution and economic development not only have an inverted U-shaped curve relationship ,37, but 2.5 concentration in 255 cities and combines the EKC hypothesis to measure the determinants of haze pollution more comprehensively. Thirdly, total electricity consumption as a critical proxy of energy consumption is also involved in the model to measure the influence of energy consumption on PM2.5 concentration, which enriches the model with more variables.In summary, three aspects contribute to this research field. Firstly, instead of only considering the geological differences, cities in China are categorized into four types to explore the differentiated effect of variables on PM2.5 concentration classified by population scale. Secondly, the urban scale and EKC effect are both taken into account. This paper constructs the STIRPAT model of PMThe following is further discussed. I = PAT” model [e denotes random disturbance term. Both sides of the equation are taken logarithmically as:In 1971, Ehrlich and Holden first proposed the “T” model  to study2.5 concentration, an extended STIRPAT-based model is implemented to empirically analyze the influencing factors of PM2.5 concentration in 255 cities in China. The EKC curve between PM2.5 emission intensity and economic development is also investigated. The equation in the form of logarithm is as follows:i represents region, t stands for time, PM2.5 is PM2.5 concentration in region i at time t, PD denotes population density, GDP denotes gross domestic product, SE refers to science expenditure, TEC denotes total electricity consumption, FI indicates actual foreign investment amount, PSP denotes the proportion of secondary industry in GDP and itμ denotes the disturbance term.To measure the influence of energy on urban PM2.5 concentration in cities with different population sizes at different quartiles, the following quantile regression model is developed.In order to investigate the influence of PMitX is the independent variable; θβ is the coefficient vector; and 2.5 concentration corresponding to the quantile θ (0 < θ < 1) for a given θβ corresponding to In Equation (4), The bootstraps intensive algorithm technique is applied to estimate the quantile regression coefficients 2.5 concentration is adopted as the proxy variable of air pollution, which can directly represent the extent of haze pollution. Six explanatory variables are selected in this paper based on the STIRPAT model. The main explanatory variables in STIRPAT model include: (1) Population density (PD) is the number of people in per unit area. (2) Gross domestic product (GDP) measures the level of urban output. This paper uses 2007 as the base period to do the treatment of the invariant price. (3) Science expenditure (SE) represents the intensity of government R&D investment. The control variables are selected according to the existing literature using extended STIRPAT model. They include: (1) total electricity consumption (TEC) used to measure the degree of energy consumption [FI) calculated to measure the degree of openness to the world [PSP) used to measure the effect of industrial structure on PM2.5 concentration [Integrating domestic and foreign studies, PMsumption ; (2) acthe world ; and (3)ntration .2.5 emission concentration impacts:In addition, two effects are considered in this paper due to the complexity of PM2.5 concentration. Therefore, this paper adopts the variable of population as the classification criterion and divides 255 cities into four categories of cities, i.e., four groups of dummy variables, according to the Notice of the State Council on Adjusting the Criteria of City Size Classification issued by the State Council in 2014. Of these, cities with a population size of more than 10 million are Type I cities (Type I cities are the reference group), cities with populations between 5 million and 10 million are Type II cities, cities with populations between 1 million and 5 million are Type III cities, and cities with populations less than 1 million are Type IV cities. In the following analysis, the influence of each four city categories on PM2.5 concentration will be reported. The definitions and descriptive statistics of all the above variables are shown in (1) Urban scale effect. Population is a crucial indicator of city characteristics, which not only reflects the level of urban development, but also is one of the main factors affecting PM(2) EKC effect. The EKC effect is taken into consideration to verify the relationship between haze pollution and GDP. Before applying it, most studies based on a particular sample of data do not examine the random characteristics of the data and determine their suitability for the model. This brings about the deviation in the curve from the sample points within the sample interval . We also2.5 concentration in cities in China, 255 sample cities were selected for the period 2007–2016. To ensure that the logarithm value is positive, all ratios are calculated as percentages. The presence of large differences among variables makes the use of logarithms rational. Data sources are as follows. The PM2.5 concentration data of each prefecture-level city were obtained from the global raster data based on satellite monitoring published by the Center for Socioeconomic Data and Applications of Columbia University. Other data were obtained from China Urban Statistical Yearbook (2008–2017), China Environmental Statistical Yearbook (2008–2017) and the provincial statistical yearbooks in (2008–2017).In order to explore the factors influencing PMt-tests, the Levin–Lin–Chu (LLC) test [lnPD for horizontal series, all the results in p-value less of than 0.01. Moreover, all variables for first-difference series are stationary at the 1% significance level.To avoid the problem of pseudo-regressions and invalid LC) test  and FishLC) test  assess tp-value less than 0.01. Hence, long-term stable equilibrium relationships exist among PM2.5 concentration, foreign direct investment, electricity consumption, GDP, population density, share of secondary industry and science expenditure. A mixed OLS regression is illustrated in According to the co-integration theory, if all variables are single integers of the same order, co-integration relationships may exist between variables. Based on the Eangle and Granger two-step method, the Kao co-integration test of homogeneity and Padroni co-integration test of heterogeneity are used ,51,52. TBased on the panel data of 255 cities in China from 2007–2016, a mixed OLS regression is conducted. Both the random effect and the fixed effect are considered in panel data analysis. A Hausman test is adopted for modeling selection See in . The reslnPD (coefficient 0.172) passes the significance test at the 1% level, which means that for every 1% rise in the population density, PM2.5 concentration increases by 0.172%. The coefficients of lnGDP and (lnGDP)3 are 0.558 and 0.003, respectively, statistically significant at the 5% and 10% levels. Furthermore, the coefficient of (lnGDP)2 is −0.08 at the 10% significance level. It indicates an inverted N-shaped relationship between PM2.5 concentration and GDP, confirming the existence of the Kuznets curve. Based on the trends of PM2.5 concentration in 255 cities from 2007 to 2016, the inflection points of PM2.5 concentration in each city will occur when economic progress is made. The coefficient of lnSE is −0.048 at the 1% significance level, illustrating that the PM2.5 concentration will increase by 0.048% when the scientific expenditure decreases by 1%. As for the control variables, the coefficients of lnTEC (0.011) and lnFI (0.011) are, respectively, statistically significant at the 10%, and 1% levels, which means that both total electricity consumption and foreign direct investment function to promote PM2.5 concentration in 255 Chinese cities. The PM2.5 concentration rises by 0.011% when total electricity consumption and actual use of foreign direct investment expands by 1%. On the contrary, lnPSP (coefficient −0.058) is negative at the 5% level, which shows that the share of secondary production will lead to a decline in PM2.5 concentration.Column 3 in 2.5 concentration for cities by various population-size categories and different quantile of city-size categories in every city size. The quantile regression model is an essential reference for the formulation and piloting implement of PM2.5 concentration control policies in different cities. It is regressed that based on the conditional quantile of the dependent variable, the conditional quantile describes the variation of each indicator on PM2.5 concentration more accurately. The 25%, 50% and 75% quantile represent the different PM2.5 concentration from low to high in the same type cities. For example, the 25%, 50% and 75% quantile in type I mean the lower, middle and higher PM2.5 concentration cities in this type, respectively.The estimated coefficients in the expanded STIRPAT model can reflect the overall situation for all cities. Considering substantial city samples and long observation times in this paper, the overall OLS regression is not able to reflect the heterogeneity of the influential factors in various city categories based on population scale. A quantile regression is carried out to distinguish the differential effects of influential indicators on the PMlnPD (0.141) is only significant at 10% significance when tested by the 50% quantile regression. This illustrates that population density worsens air pollution only for the middle PM2.5 concentration cities. lnGDP, (lnGDP)2 and (lnGDP)3 are significant at 25%, or 50% quantile regression, the coefficients of which are positive, negative and positive at the 1% significance level. It indicates an inverted N-type Kuznets curve in cities with low and medium PM2.5 concentration. This is consistent with the national situation. The coefficients of lnTEC are 0.557, 0.363 and 0.166, respectively, at 25%, 50% and 75% quantile regression. However, only coefficients at 25%, or 50% quantile pass the 1% significance level. This indicates that in Type I cities, the effect of energy consumption on air pollution is weakening as the PM2.5 concentration rises. The coefficients of lnFI and lnPSP at 25% quantile regression are 0.087 and −0.549, which pass the significance test at 10% and 1%, respectively. It indicates that for every 1% increase in foreign direct investment, haze pollution is raised by 0.087%. Otherwise, PM2.5 concentration rather declines by 0.549% as the share of secondary production advances every 1%. lnPD passes the significance test at 1% level, the coefficients of which are 0.353, 0.428 and 0.451 at 25%, 50% and 75% quantile regression, respectively. Such results illustrate that the enhanced population density does accelerate PM2.5 concentration in Type II cities. For instance, the higher the PM2.5 concentration is, the stronger the effect of population density on haze pollution is. The coefficient of lnSE is significantly negative at 25% and 75% quantile, which is 0.091 and −0.057, respectively. lnFI are significant at 25%, 50% and 75% quantile regression. The coefficient at 25% quantile is positive (0.096), while the coefficients at 50% and 75% quantile are both negative . It indicates that in Type II cities, the science expenditure and foreign direct investment will accelerate PM2.5 concentration at lower PM2.5 concentration cities, but hinder the PM2.5 concentration at higher PM2.5 concentration cities. lnPSP are significant at 1% and 5% level, respectively, the coefficients of which are −0.254 at 50% quantile and −0.234 at 75% quantile. These results indicate that the share of secondary production will exert a negative influence on air pollution at middle and higher PM2.5 concentration cities in Type II. In Type II cities (population size between 5 million and 10 million), lnPD all pass the significance test at the 1% level, which are 0.343, 0.333 and 0.289 at 25%, 50% and 75% quantile regression, respectively. These results illustrate that population density will increase PM2.5 concentration in Type III cities. However, the higher the PM2.5 concentration is, the weaker the effect of population density on haze pollution. lnGDP, (lnGDP)2 and (lnGDP)3 are significant at 25% quantile regression, and the coefficients of which are 3.766, −0.524 and 0.025, in fact indicate the existence of a N-type Kuznets curve in cities with lower PM2.5 concentration. The coefficients of lnSE are −0.068, −0.056 and −0.052, which all pass the significance test at the 1% level. Expenditure on science will encourage technological innovation, which helps to improve the end-treatment measures and reduce polluting emissions and can to some extent improve air quality. Only lnTEC at 50% quantile regression passes the significance test in Type III. Total electricity consumption can alleviate air pollution only at middle PM2.5 concentration cities. The coefficients of lnFI are −0.019 and −0.013 at 25%, 50% quantile regression, respectively, which pass the 5% significance level. PM2.5 concentration will abate by 0.019% when foreign direct investment falls by 1% in cities with lower PM2.5 concentration. For middle PM2.5 concentration cities, PM2.5 concentration drops by 0.013% when foreign direct investment decreases by 1%. This indicates that in Type III cities, increases in foreign direct investment will restrict air pollution. The coefficients of lnPSP are 0.421, 0.319 and 0.279, all passing the significance test at the 1% level. The share of secondary production does exacerbate haze pollution, and the influence will grow as the PM2.5 concentration becomes higher in Type III cities.In Type III cities (population between 1 million and 5 million), the coefficients of lnPSP (0.876) at 50% quantile is significant, which means that the secondary production will exacerbate air pollution in cities with small population size. In Type IV cities (population size less than 1 million), only the coefficient of 2.5 concentration. During urbanization, having a much larger and concentrated population is one of the important manifestations. The relationship between PM2.5 concentration and population density is shown in GDP of cities also rises, which brings about the increment in PM2.5 concentration. Cities with different urban population scales have significant diverse effects on PM2.5 concentration. PM2.5 concentration in Type I (cities over 10 million people), Type II  and Type III cities (cities between 1 million and 5 million people) are significantly higher compared to Type IV (cities less than 1 million people) cities, as can be seen in On a national scale, population density exerts the greatest positive influence on PM2.5 concentration are correspondently higher. The economic growth has an inverted N-shaped EKC curve with air pollution, which indicates the following experiences in Chinses cities. First, urban economic progress is accompanied by a reduction in haze pollution. Then, the higher economic expansion jeopardizes the environment and increases emissions. Finally, the air pollution gradually declines again when further economic development is accomplished. Expenditure on science has a significant effect on PM2.5 concentration reduction. Research into pollution reduction technology and on haze emission reductions to improve industries can effectively mitigate urban PM2.5 concentration. Social economic activities in cities with larger populations are more frequent, thus the PMlnTEC (0.011) means that electricity consumption surges by every 1% when PM2.5 concentration rises by 0.011%, due to the high correlation between society-wide electricity consumption and energy consumption. Foreign direct investment exerts a significant positive effect on PM2.5 concentration, i.e., the PM2.5 concentration rises by 0.011% when the actual utilization of foreign direct investment expands by 1%. This shows that foreign direct investment will accelerate haze pollution emissions, indicating that FDI inflow contributed to air pollution and the “pollution shelter” hypothesis was established in China. From the perspective of industrial distribution in foreign direct investment in 2015, the secondary industry accounts for the most of it, mainly the manufacturing (31.32%) and real estate (22.96%) industries. The unabated expansion of these industries and their current polluting processes will certainly compromise the quality of urban air. Meanwhile, the coefficient of 2.5 concentration for the following reasons. At the outset, the proportion of secondary industry is closely related to the level of urban economic development. When cities’ secondary industry expands, urban economic development increases. Secondly, as the latter increases  and GDP 2, (lnGDP)3)) on different population sizes, the population density of Type I cities only influences PM2.5 concentration at the 50% quantile, indicating that in megacities with more than 10 million people, the population density does not show a significant effect on PM2.5 concentration. On the other hand, this indeed enhances PM2.5 concentration in Type II and III cities, for the reason that the regression coefficients of population density are all significantly positive at 25%, 50% and 75% quantile in these two types. In the middle-sized cities, the influx of residents accelerates urbanization and the consumption of private cars and houses, resulting in the rise of PM2.5 concentration. The coefficients of economic development are significant at the 25% and 50% quantile in Type I cities. In Type I cities, there is an inverted N-type Kuznets curve in cities with lower and medium PM2.5 concentration cities. In general, population density plays a more important role on PM2.5 concentration in Type II and III cities, while economic development has a more crucial effect on PM2.5 concentration in Type I cities comparatively.When it comes to the effect of two main variables , and energy consumption has a more crucial effect on PM2.5 concentration in Type I cities (large populations over 10 million) comparatively. The secondary industry will enhance the PM2.5 concentration in most Type IV cities .The main conclusions of this paper are as follows. On the whole, a inverted N-shaped Kuznets curve between economic development and PMThe above research conclusion provides policy recommendations for air pollution abatement in cities as follows. Firstly, policymakers should treat the relationship between environmental emission reduction and economic development rationally. From a national aspect, an EKC relationship exists between GDP and the environment; however, from the perspective of cities of different sizes, the EKC curve does not really exist between GDP and the environment. The relationship between economic development and air pollution in megacities is in line with the law of the Kuznets curve, and at the peak of the haze pollution. The medium and large population-sized cities coexist with the scale effect and technology effect, and the peak of haze pollution is not yet ascertained. The pollution in medium and large population-sized cities is influenced by changes in population size, so medium cities are in fact considered as diseconomies of scale caused by the expansion of population size when economic progress occurs. Consequently, policymakers should help balance the population size of cities with economic development, and pay more attention to the environment, technological improvement and better use of local natural resources. 2.5 concentration, it is necessary to reasonably determine the scale of urban development and city size, promote industrial transformation and upgrade small and medium-sized cities synergistically in order to promote their functioning, state of industry and the environment, and promote the green economy and circular economy. Energy consumption has a more significant impact on the air quality of megacities; thus, policymakers should improve the concept of green consumption by enterprises and residents.Secondly, the proportion of the secondary industry has a great influence on urban PMThirdly, government should emphasize science and technology instead of focusing on better targeted investment. This is especially the case when introducing green capital to improve the situation of “pollution shelter” and increase investment in research on how to reduce pollution and improve remediation strategies.


Knowledge of the genetic diversity and genetic relationship is important in crop improvement. The objective of this study was to determine the genetic diversity of 31 sweetpotato genotypes and furthermore to select distantly related individuals for breeding of superior parental clones. The genotypes  originating from Africa and American continent were genotyped using eight highly polymorphic SSR markers. The SSR markers generated a total of 83 putative alleles. The polymorphic information content (PIC) of the tested simple sequence markers varied from 0.73 to 0.91, with a mean of 0.85. At least 11 different alleles were found in 8 loci within the population, with 7 effective alleles per locus. Although high diversity was found among the genotypes, genetic distances among the genotypes were relatively low. Cluster analysis revealed the existence of three distinct genetic groups, and the clustering patterns follow to some extent the geographic origin and pedigree of the genotypes. High gene flow was observed among different sweetpotato accessions. The selected SSR markers were found to be highly polymorphic with high discriminatory power for genetic characterization studies and are useful genomic tool to complement phenotyping of sweetpotato genotypes. Two heterotic groups were found in the study. The heterotic group A was composed of 14 genotypes mainly of South African origin, while the heterotic group B consisted of 17 genotypes of American origin. The two distinct groups were important for the selection of breeding clones that were distantly related to be used as parental clones in the advancement of traits of interest. Ipomoea batatas (L.) Lam, is an important root crop cultivated in 117 countries worldwide [I. batatas is the species of economic interest [Sweetpotato, orldwide ,3. Sweetorldwide . It origorldwide ,6. The gorldwide ,9,10. Mainterest , being aSweetpotato is grown as a food crop in most developing countries for its roots and leaves, and has multiple uses in industrial processing . In SoutThrough genetic diversity studies, comprehensive information on the magnitude of variation between and within germplasm collection is obtained. This information is fundamental and a precondition for successful breeding and achieving breeding goals . AmongstGenetic diversity and phylogenetic analyses using molecular tools are very useful for effective selection and use of germplasm in the planning and execution of breeding programs . Simple A comprehensive germplasm collection is preserved at the ARC-VIMP, with more than 360 sweetpotato genotypes from the Americas, Asia, and Africa. However, these genetic resources are yet to be systematically characterised using DNA markers. Few studies on the genetic diversity using molecular markers were performed for most of the ARC sweetpotato genotype collection, and therefore limited information is available on the genetic relatedness of the genotypes. The DNA fingerprinting was performed by Ref.  on 25 ARFor the scope of the study and due to the limitation of time and resources, various pre-selection approaches were implemented. The plant material used in the present study was selected based on the results from morpho-agronomical characterization of local and international sweetpotato germplasm from South Africa collection at ARC-VIMP in a field trial conducted from January–June 2015 [−1.Genomic DNA was extracted from samples of two leaf discs obtained from a young and healthy leaf of three individual plants of each genotype using the modified Cetyl trimethylammonium bromide (CTAB) method, described by . DNA conA set of 10 polymorphic SSR markers  was used to amplify individual reactions. Conditions entailed initial denaturation at 95 °C for 5 min, then 45 cycles of denaturation 95 °C for 30 s. Primer annealing was done at optimal temperature for 30 s and primer extension at 72 °C for 1 min. Lastly, a final extension for 20 min at 72 °C was included.The microsatellite region was amplified using EconoTaq PLUS 2x Master Mix Catalog No. 30035 . A PCR reaction was performed using a total volume of 20 µL consisting of 10 ng of genomic DNA, 10 mM of forward primer and reverse primer, 10 μL of Econotaq PLUS 2x Master Mix , and nuclease free water. A Gene AmpTM. For data analysis, GeneScan™ Software was used, and for data interpretation GeneMapper® v5.0.  was used.A 1:10 dilution of the fluorescently labelled PCR amplicons, LIZ500 sizing standard and Hi-DiTM Formamide  was prepared and denatured at 95 °C for 5 min. Following denaturation, the fragments were run on an ABI PRISM™ 3500xl Genetic Analyser , 50 cm capillary array, POP-7a), number of effective alleles per locus (Ne), allelic richness (Ar), observed heterozygosity (Ho), and average gene diversity (He) and inbreeding coefficient (FIS) were determined using the protocol of [PIC of a marker i; jth pattern for marker i, and the summation extends over n patterns.Two approaches were adopted to investigate the genetic structure and diversity among the 31 sweetpotato genotypes. In the first approach, the co-dominant nature of the marker (fragment length polymorphism) was used to determine the genetic parameters such as total number of alleles per locus  was calculated using the formula:ST is the F-statistic for genetic differentiation calculated according to Wright’s original derivation [To examine the population differentiation pairwise Frivation .In the second approach, cluster analysis with Ward’s method was performed to established similarity between the entries/samples. Ward’s method is a criterion applied in hierarchical cluster analysis. Ward’s minimum variance method is a special case of the objective function approach originally presented by Ref. . Ward suThe estimated genetic diversity parameters measured in 31 sweetpotato genotypes using 8 SSR markers are presented in IS).A total of 83 putative alleles of different sizes were detected. The number of alleles per locus varied from 6 (IB-242) to 15 (IBSSR19), with a mean of 11.9, while the effective number of alleles per locus varied from 3 to 11, with a mean of 7 alleles per locus. The high number of observed and effective alleles implied high levels of polymorphism (high variability of the clones) and good marker choice. A significantly high variation was detected on observed heterozygosity, ranging from 0.59 to 1.00, with a mean of 0.88 among the tested genotypes. The average gene diversity varied from 0.74 to 0.93, with a mean 0.86. The highest gene diversity (He) was scored from marker IB-286 and the smallest by IB-242. A highly significant variation ranged from −0.37 to 0.32, with the mean of −0.04 also observed on fixation index  levels  and root flesh colour . The genetic diversity within and among the 31 sweetpotato genotypes classified by root protein content and flesh colour is presented in o, signifying that there is a high level of genetic diversity maintained in these sub-groups. The mean number of detected alleles (Na) was higher for clones with a high RPC, followed by clones with an intermediate RPC. Clones with a low RPC showed the highest Ho, suggesting that this sub-group is highly heterozygous as compared to the two other sub-groups. The highest private allele (17) was detected from the sub-group with high RPC followed by intermediate RPC (14).No significant variation was observed between sub-groups with high and intermediate RPC, but highly significant variation was observed between these two sub-groups and the sub-group with low RPC for all the genetic parameters. Clones with high and intermediate RPC levels had significantly higher levels of variation for most of the genetic diversity parameters except HSignificantly high levels of genetic variation were also detected among the three sub-groups based on flesh colour in all the genetic parameters studied. The number of observed, effective, and private alleles and Shannon’s information index (I) were significantly higher for the sub-group with orange flesh colour, while the white flesh colour genotypes revealed significantly higher values of observed and expected gene diversity.Clones with orange flesh were genetically diverse in terms of the number of observed and private allele, suggesting that the orange fleshed clones may contain genetically unique and rare alleles. However, the clones with white flesh were the most genetically divers in terms of the number of allele and allele frequency. Additionally, the large number of alleles observed in the orange flesh sub-group might partly be attributed to the large number of samples. Clones with yellow/cream flesh colour had the lowest values for all the genetic parameters. This could either be attributed to the small number of genotypes sampled, or their low level of genetic diversity.ST), genetic distance (GD), and genetic identity (GI) estimates across sub-groups based on root flesh colour and RPC is presented in The gene flow (Nm), genetic differentiation  were observed for the overall fixation index (Fit) and were significant (p = 0.023) for the inbreeding coefficient (Fis). No significant variation was observed among the subpopulation due to high gene flow and the use of common breeding stocks.Analysis of molecular variance (AMOVA) among and within sweetpotato populations is presented in Ward’s method, agglomerative hierarchical clustering (AHC) based dendrogram constructed using the Euclidean’s dissimilarity coefficient from binary data of 8 SSR markers, is presented in P1 consists of two sub-clusters and is comprised of nine genotypes including ‘Bophelo’, ‘2007-17-1’, ‘199062.1 x Ndou’, ‘2010-5-4’, ‘1990-10-2’, ‘2011-10-2’, ‘1988-7-7’, ‘2012-8-4’, and ‘Impilo’. P2 consists of two sub-clusters with a total of five genotypes, namely ‘Mvuvhelo’, ‘2000-12-16’, ‘2005-12-5’, ‘1988-20-1’, and ‘1986-35-1’. P1 and P2 consisted mainly of genotypes from South Africa except for ‘2012-8-4’, a genotype originating from Mozambique. P3 consisted of 17 genotypes that were sub-divided into 3 sub-clusters. The genotypes grouping within Cluster 3 consisted of a mixture of introduced genotypes , South African ’, ‘1987-2-1’, ‘2005-5-5’, ‘1985-7-1’, and ‘1987-19-5’) and African genotypes . Most of the South African genotypes in this group are somehow related to the introduced genotypes.P1 and P2, containing 14 genotypes, were best combined to form heterotic group A (mainly South African genotypes) while P3, consisting of 17 genotypes, was allocated to heterotic group B (mostly introductions from South and North America). Further selection based on the flowering ability were implemented on the 2 heterotic groups and 11 genotypes from each group were identified to be the female and male parental clones for the subsequent implementation of an accelerated breeding scheme, and genetic analysis of quantitative and nutritional traits. The female parents include: ‘1988-7-7’, ‘1988-20-1’, ‘1990-10-2’, ‘2000-12-16’, ‘2005-12-2’, ‘2007-17-1’, ‘2010-5-4’, ‘2012-8-4’, ‘199062.1 x Ndou’, Bophelo, and Impilo. The male parents include: ‘1981-27-1204’, ‘1984-3-66’, ‘1987-2-1’, ‘1985-7-1’, ‘1987-19-5’, ‘2005-5-5’, ‘2008-3-1’, ‘2008-8-5’, ‘2012-29-4’, ‘Monate x 1999-5-1’, and ‘Melinda’.This study analyzed the genetic diversity and relatedness of selected sweetpotato genotypes using SSR markers. SSRs are abundant in plant genomes, co-dominantly inherited, suitable for automation, and are easily transferable across laboratories, making them very useful for genetic diversity studies in hexaploid species like sweetpotato. The SSR markers used in the present study were highly polymorphic, revealing a mean of 11 polymorphic alleles per locus. Similar results were reported by Refs. ,40,41,42In this study, the selected SSR markers were discriminatory and informative, as it was revealed by their high PIC values, which ranged from 0.73 to 0.91, the mean being 0.85. The high discriminatory power of SSR markers in hexaploid sweetpotato was demonstrated in the studies by Refs. ,45, usina = 11.9) with small major allele frequency (mean MAF = 0.22), which indicated that the alleles were evenly distributed among the tested genotypes, suggesting the presence of wide genetic diversity among sweetpotato. Similarly, the high-observed heterozygosity (mean Ho = 0.88) and the low inbreeding coefficient (mean FIS = −0.04) values confirmed that sweetpotato is a highly heterozygous species. This is likely attributable to its outcrossing and hexaploid nature [In the present study, the selected SSR loci revealed large number of observed allele  as well as RPC (4.72 to 9.14), and were higher than the values of 0.36 to 2.18 reported by Ref. . The narCluster analysis revealed the presence of three distinct groups among the studied sweetpotato genotypes, suggesting the existence of a wide range of variation for breeding and strategic conservation . The twoIn the present study, genotypes from genetic clusters P1 and P2 were linked through geographical origin, both comprising of genotypes of African origin, and were therefore combined to form heterotic Group A. Genetic cluster three P3) formed by American exotic cultivars as well as their progenies from either direct or polycrosses involving these cultivars, constitute heterotic Group B. As reported by Ref.  formed b.Although exploitation of heterosis is well known in other crops such as maize , it is b1 progenies will be used as a study population to which an accelerated breeding scheme and genetic analysis of important nutrient traits and yield components will be applied. SSR markers proved to be robust and informative markers capable of determining the genetic diversity and relatedness of sweetpotato genotypes and sub-populations. SSR markers were used successfully and effectively to discriminate heterotic groups within the sweetpotato germplasm maintained in South Africa. This information can be used in future sweetpotato breeding and conservation activities and to advance the sweetpotato breeding program at ARC-VIMP of South Africa. However, a further study is required to confirm the heterotic grouping with a large number of sweetpotato accessions.SSR markers proved to be robust and very informative markers for genetic diversity and relatedness studies on selected sweetpotato genotypes. In this study, a total of 83 putative alleles, with a mean of 11 alleles per loci were observed. Cluster analysis revealed the existence of two heterotic groups. The identified heterotic groups A and B will allow for further selection of parental clones that are genetically diverse and distantly related, which will enable the breeders to exploit heterosis or hybrid vigour. The generated F


Prunus genus. Plum × apricot  are the most widespread interspecific hybrids, which include plumcots, pluots, and apriums. In this work, 115 accessions of interspecific hybrids from different origins and 27 reference genotypes of apricot and other diploid plum species were analyzed using eight simple sequence repeat (SSR) markers to assess the population structure and current genetic diversity. A total of 149 alleles were obtained, with an average of 19 alleles per locus. The overall polymorphic information content (PIC) mean value of SSR markers was 0.81, indicating a high degree of polymorphism of the SSR. The genetic analysis revealed 141 unique genotypes and two synonyms. The unweighted pair group method with arithmetic averages (UPGMA) dendrogram and the population structure with five groups inferred through the discriminant analysis of principal components (DAPC) revealed a clear genetic differentiation between apricot genotypes and the rest of the accessions since the interspecific hybrids clustered with the Japanese plum genotypes. Repeated backcrosses between interspecific hybrids with plum genotypes could be the cause of the higher genetic proximity of the hybrids with respect to plum than with apricot genotypes. This corresponds to the fruit morphology and agronomic behavior observed in most interspecific hybrids, which also resemble plums more than apricots.The main objective of many fruit-breeding programs around the world is the release of new cultivars from interspecific hybridizations between species of the  Prunus comprises 400–430 species and is one of the most diverse genera within the Rosaceae family, with species of great commercial interest such as Prunus armeniaca L. (apricot), Prunus avium L. (sweet cherry), Prunus domestica L. (European plum), Prunus dulcis Mill. , Prunus persica (L.) Batsch (peach), and Prunus salicina Lindl. (Japanese plum) [Prunus that have been cultivated for generations in several regions of the world, such as the hybrids P. armeniaca × Prunus cerasifera Ehrh., traditionally grown in southwest Asia [Prunus dasycarpa Ehrh. [Prunus species allows the development of interspecific hybridizations for breeding purposes [The genus se plum) . The cloest Asia . These hP. salicina and at least 15 different diploid plum species between the late 19th and early 20th centuries [Luther Burbank obtained more than 100 cultivars from interspecific crosses between enturies , introduenturies  and ‘Rutenturies .Prunus species for objectives such as self-compatibility and resistance to Sharka disease  [P. salicina and P. cerasifera are usually used as female parents to ensure fruit set and obtain a greater number of offspring [P. armeniaca, P. persica, and P. avium as male parents [Currently, many fruit-breeding programs around the world use interspecific hybridizations between us, PPV)  as well us, PPV) . In thesffspring ,11, and  parents . Plumcot parents . These h parents . However parents ,12. In t parents . In 1991 parents . However parents ,15.Plumcot fruits have a higher content of sugars, such as glucose and fructose, than plums or peaches, although they do not exceed the sugar content of apricots . Pluot fPrunus [Prunus [Although most of the currently grown Japanese plum-type cultivars are actually hybrids , modern Prunus . The relPrunus ,21,22,23 [Prunus ,21,24,25Prunus species, mainly apricot. For this purpose, 115 accessions of interspecific hybrids, including commercial cultivars and advanced selections, and 27 reference genotypes have been analyzed using eight simple sequence repeat (SSR) markers.The objective of this work is to assess the genetic diversity and population structure of interspecific hybrids of plum with other AN), with an average value of 19. These values were similar to those reported in previous works in apricot , which can be related to the genetic bottleneck as a consequence of the use of common parents in breeding programs of apricot [Prunus species of the eight SSR markers used in this study and previously observed in other works [All the SSR markers used in this study showed correct amplification and turned out to be polymorphic in the analysis of 115 accessions of interspecific plum × apricot hybrids and 27 reference genotypes. The genetic profiles of each accession obtained with the eight SSR markers are included in NA = 4.1 ; 890 acc = 24.36 ) and Jap[NA = 13 ). Howeve apricot . The smaer works  and the P. salicina , P. domestica , and P. insititia  [Ho and He) of the analyzed accessions was lower than that observed in apricot cultivars, a crop in which a decrease in genetic diversity has been observed [FIS values ranged from −0.13 (CPPCT033) to 0.37 (CPSCT026), with a mean of 0.08, indicating that there is no inbreeding in the whole population since values were observed close to zero. The FST ranged from 0.06 (BPPCT007) to 0.29 (BPPCT039), with a mean value of 0.13, showing low genetic differentiation due to the gene flow between accessions [The polymorphic information content (PIC) ranged from 0.61 (CPPCT033) to 0.90 (CPSCT005), with a mean value of 0.81; therefore, the alleles were considered to be highly polymorphic, showing their usefulness for the study of genetic diversity . The obs = 0.70) , indicatobserved . The FISThe analysis for the detection of homonyms and synonyms allowed the identification of 141 unique genotypes and 2 synonyms: ‘IBG047’ and ‘IBG057’, both accessions from the Ibergen breeding program, which were grouped in the subgroup B6. The genetic relationships between the interspecific hybrids and the reference genotypes were assessed using the unweighted pair group method based on arithmetic averages (UPGMA) to generate a dendrogram based on the Nei and Li similarity index. The dendrogram showed the first node with a high bootstrap value (100), separating the accessions into two groups: group A, formed by 12 accessions (8.5%); and group B, formed by 130 accessions (91.5%) a. These Group A was divided into two subgroups. The subgroup A1 was formed by the ten reference genotypes of apricot. In subgroup A2, two advanced selections from different breeding programs were grouped, ‘IBG024’ and ‘Z029’, which could be aprium hybrids since they showed a greater genetic relationship with the apricot reference genotypes than with those of plum . P. cerasifera, P. salicina, and P. simonii). Plumcots, pluots, and the reference genotypes were allocated into different groups, mixing and grouping mainly according to their genealogical origin. This grouping trend has been found between pluots, plumcots, plums, and apricots in a previous study, suggesting that the grouping is mainly due to the relationship between the accessions and their parents [Group B, the larger of the two groups, was made up of the rest of the interspecific hybrids and the reference genotypes of diploid plums ( parents . P. persica) with yellow flesh [P. persica. Subgroup B2 was a small but diverse group, grouping the reference genotypes ‘Kelsey’ , ‘Mitard’ (P. cerasifera), and ‘Songold’ (‘Golden King’ (unknown origin) × ‘Wickson’ ). ‘Songold’ could be a hybrid between P. salicina and P. cerasifera since it is grouped close to ‘Mitard’ (P. cerasifera). ‘Songold’ was developed by ARC-Infruitec, a public breeding program of South Africa that frequently uses ‘Methley’  as a parent [P. salicina), and the pluots of Zaiger Genetics: ‘Flavor Fusion’, ‘Flavor Finale’, and ‘Flavor Grenade’. These accessions were located near to ‘Red Beaut’ (‘Burmosa’ × ‘Eldorado’) [P. simonii). Subgroup B5 contained some commercial cultivars from Zaiger Genetics. In subgroup B6, 16 accessions were allocated, including the reference genotype ‘Sweet Treat’ (pluerry). Subgroup B1 was separated a greater distance from the rest, grouping five Zaiger Genetics accessions. Two of them were reference genotypes: ‘Honey Top’, a nectarine  , which wP. salicina) [n = 35), including four reference genotypes  and eight commercial cultivars. The genealogy of ‘Glory Red’ includes ‘Queen Ann’ [‘Red Beaut’ is one of the cultivars most used by Zaiger Genetics as a parent , which calicina) ,34,35. ‘een Ann’ , which ween Ann’ . The threen Ann’ . FinallyThe advanced selections were allocated in the subgroups A2 and B1–B9, showing the same diversity observed among the commercial cultivars. K = 5) according to the lowest BIC value (166.17) (K = 5) would be obtained with 25 principal components (PCs)  was performed. Despite the high degree of introgression in this type of interspecific hybrids ,12,34,36(166.17) A. The crts (PCs) b, so 25 In the scatterplot of the DAPC analysis , groups K = 5) , the reference genotypes ‘Dapple Jack’ (pluot) and ‘Mariposa’  were grouped with ‘Queen Rosa’ and’ Santa Rosa’, which are considered simple hybrids of P. salicina [n = 32, 23%) included reference genotypes of different species, such as ‘Abundance’ and ‘Kelsey’ , ‘Honey Sun’ and ‘Honey Top’ (P. persica), and ‘Mitard’ (P. ceracifera), and some hybrids of P. salicina, such as ‘Angeleno’, ‘Methley’ , and ‘Red Beaut’. Group G4 included the largest number of accessions , including four reference genotypes: ‘Queen Ann’, ‘Simon’ (P. simonii), ‘Songold’, and ‘Sweet Treat’ (pluerry). Finally, group G5 was formed exclusively by the 10 reference genotypes of P. armeniaca (apricot). The accessions were allocated into five groups (K = 5) . Group G n = 38, 7%, the rThe composition of groups G1, G2, G3, and G4 was homogeneous, probably due to their accessions being obtained from the selection of crosses and backcrosses in which common parents such as ‘Mariposa’, ‘Queen Ann’, ‘Queen Rosa’, ‘Friar’, and ‘Red Beaut’ were used ,31,37.K = 5) was attributed to the variability within accessions with significant differences (p < 0.01)  on genetic differentiation among the five groups based on DAPC and within accessions revealed that 80% of the total variation in the genetic structure ( < 0.01) , a perceK = 5) (NA PER LOCUS) ranged from 6 (G5) to 12 (G3). The total number of alleles for each group ranged from 49 (G5) to 95 (G3). The same trend was observed in allelic richness (AR), with values between 6.25 (G5) and 8.14 (G3). Alleles observed in only one group were considered private alleles (PA), with the smallest value (4) in group G2 and the largest value (24) in group G5. These results showed moderate and relatively homogeneous levels of genetic diversity, except in group G3, which showed a greater number of alleles (NA PER LOCUS and NA TOTAL), greater allelic richness (AR), and a value of the coefficient of inbreeding notably higher than the rest (IFS = 0.21), revealing a heterozygosity deficit, as observed in a diverse group of apricots from different geographical origins [Ho) ranged from 0.61 (G2) to 0.75 (G1 and G5), and the expected heterozygosity (He), from 0.67 (G2) to 0.86 (G3). Ho was slightly lower than He in groups G1, G2, G3, and G4, which could be attributed to the exhaustive breeding activity developed in these hybrids. The heterozygosity (Ho and He) of G5, which includes the apricot cultivars, was similar to that observed in a previous work including traditional and new apricot cultivars [The parameters of genetic diversity were calculated for each of the five groups (K = 5) . The numultivars .STF) were determined , ‘Simon’ (P. simonii), and ‘Robinson’ (P. munsoniana)), and the first simple hybrids  [Although many species are involved in the genealogy of the interspecific hybrids, our results suggest that the observed diversity is lower than expected, probably due to the use of the same parents, such as ‘Friar’, ‘Mariposa’, ‘Queen Ann’, ‘Queen Rosa’, and ‘Red Beaut’, in different breeding programs . In addiickson’) . In previckson’) ,11. ThisA group of 142 accessions was analyzed, including 27 reference genotypes , 18 commFor each accession, DNA was extracted from young leaves collected in spring and preserved in silica gel . Once drPrunus species in previous reports [For SSR analysis, eight SSR markers were selected from the previously reported studies in Japanese plum  and peac reports ,20,24,25The amplification was performed using five sets of multiplex PCR reactions (M01 to M05). Each multiplex reaction was designed according to the protocol described by Guerrero et al. . All reaThe data of alleles generated by the SSR markers were converted into an object of the class ‘genind’ using the ‘df2genind’ function of the ‘adegenet’ package v. 2.1.2.  and anal(NA), polymorphism information content (PIC), private alleles (AP), allelic richness (AR), observed heterozygosity (Ho), expected heterozygosity (He), inbreeding coefficient (FIS), and Wright’s fixation index (FST)) were determined on the whole population using the packages ‘adegenet’ v. 2.1.2 [The genetic diversity parameters  cluster analysis was used to determine the genetic relationships among accessions according to Nei and Li ; a UPGMAK) in the whole population was inferred using the ‘find.clusters’ function and according to the lowest Bayesian information criterion (BIC) value. The correct numbers of principal components (PCs) to be retained were determined using the cross-validation function ‘xvalDapc’. The membership probabilities were obtained from the DAPC objects in the slot posterior (‘class(dapc1$posterior)’). The slot ‘assign.per.pop’ was used to indicate the proportions of successful reassignment (based on the discriminant functions) of accessions to their original groups [The population structure was analyzed by a discriminant analysis of principal components (DAPC) using the ‘adegenet’ package v. 2.1.2. ,57. The ST values using the ‘pp.fst’ function. The genetic diversity analysis at the group level was performed using the same parameters determined in the whole population (3.4.1.). The package ‘hierfstat’ v. 0.5-7  was usedFinally, the variance components among the inferred groups and the accessions were calculated with an analysis of molecular variance (AMOVA) using the ‘poppr’ package v. 2.8.5 .Prunus due to their commercial potential [The molecular characterization allowed structuring the interspecific plum × apricot hybrids into five groups that clearly corresponded with the genealogical background. The growing interest in obtaining new interspecific hybrids of otential  led to totential ,12, whicotential  and Japaotential . These r