Among sepsis biomarkers, procalcitonin resulted to be a specific indicator of bacterial infection or severity of infection, and to be a good control of the success of a therapeutic procedure. The clinical studies on the relevance of procalcitonin as a sepsis predictor in veterinary patients are few, likely due to the total absence of validated assays. For this reason, this study aimed to investigate commercial ELISA kits for the detection of canine and equine procalcitonin. Validation was performed evaluating linearity, limits of detection (LOD), recovery, and intra-assay and inter-assay variability; furthermore, clinical samples were analyzed. The results of the present study demonstrate that the human PCT ELISA kit is suitable to detect equine procalcitonin with a LOD of 56 ng/mL, and the canine recombinant PCT ELISA kit can be used to measure canine procalcitonin in plasma samples, showing an intra-assay and inter-assay coefficient of variation less than 20% and a LOD of 11 pg/mL.In human medicine, procalcitonin (PCT), the precursor of calcitonin, is used for the rapid identification of the origin and severity of sepsis. In veterinary medicine, PCT has been studied in horses, cattle, and dogs, but the use of PCT in diagnostic and/or prognostic settings is not possible because of the lack of validated assays to obtain reference ranges. The aim of the present study was the investigation of commercially available ELISA kits for the detection of canine and equine PCT in plasma samples. Validation of the ELISA kits was performed by using species-specific recombinant proteins spiked both in plasma and buffer samples; linearity, limit of detection (LOD), recovery, and intra-assay and inter-assay variability were calculated. Moreover, clinical samples obtained from sick and healthy animals were also analyzed with the tested kits. Canine PCT was measured with a recombinant canine and a canine PCT ELISA kit. Equine PCT was measured with an equine and a human ELISA PCT kit. Our data demonstrate that the canine recombinant PCT ELISA kit can be used to measure canine PCT in plasma samples, showing an intra-assay and inter-assay coefficient of variation less than 20% and a LOD of 11 pg/mL, whereas the present results do not support the use of the canine PCT ELISA kit. The human PCT ELISA kit is suitable to detect equine PCT with a LOD of 56 ng/mL, whereas the equine PCT ELISA kit did not detect recombinant equine PCT. Procalcitonin (PCT) is a precursor of the hormone calcitonin and consists of 116-amino acids, which are normally processed by a specific protease to calcitonin, katacalcin, and an N-terminal residue . PCT is The aim of this study was to evaluate commercially available ELISA kits targeted for dog and horse for the detection of their respective plasma PCT levels. The validation of the kits was performed by using species-specific recombinant PCT. PCT-spiked samples in buffer and in plasma were analyzed. Finally, a first approach on the use of the validated kits in clinical samples was performed.n = 5) and horses (n = 5) with a positive SIRS score (≥2 criteria) plus diagnosis of bacterial infection in order to only include animals truly affected by SIRS due to bacteria . An owner’s written consent was obtained for plasma collection.All investigated ELISA kits used in this study are sandwich immunoassays. Canine PCT was measured with two different commercial ELISA kits: the recombinant canine procalcitonin ELISA kit  and the canine procalcitonin ELISA kit . Equine PCT was measured with two different commercial ELISA kits: the equine procalcitonin ELISA kit  and the human procalcitonin ELISA kit . The preparation of reagents and all incubations and washes were performed according to the manufacturer’s instructions. The optical density (OD) of the samples was determined with a microplate reader set to a wavelength of 450 nm for all kits except for the rcPCT ELISA kit. In this case, the absorbance was determined subtracting the readings at 630 nm from the readings at 450 nm. The OD of the blank well was subtracted from each sample OD. The standard curve was constructed by plotting the mean absorbance of standards against the known concentration of standards for all ELISA kits except for the rcPCT ELISA kit. In this case, the concentration was expressed in logarithmic scale. This was subsequently used to determine the PCT concentration in samples. Samples with absorbances exceeding the absorbance of the highest standard have been measured with higher dilution. The final concentration of samples calculated from the standard curve has been multiplied by the respective dilution factor. The validation parameters  of the tested kits supplied by the manufacturer are reported in n = 3). Recombinant equine PCT  was reconstituted in water and dilutions of 100, 500, 1000, 5000, 10,000, and 25,000 pg/mL were used to construct calibration curves for both the ePCT and hPCT ELISA kit (n = 3). Moreover, the following dilutions of recombinant equine PCT were tested in hPCT ELISA kit: 500, 1000, 5000, 10,000, and 25,000 ng/mL. LOD has been calculated as the concentration of PCT giving absorbance higher than the mean absorbance of the blank (buffer) plus three standard deviations of the absorbance of the blank: A blank + 3 × SD blank. LOQ has been calculated as concentration of PCT giving absorbance higher than the mean absorbance of the blank (buffer) plus ten standard deviations of the absorbance of the blank: A blank + 10 × SD blank. LOB has been calculated as concentration of PCT giving absorbance higher than the mean absorbance of the blank (buffer) plus 1.645 standard deviations of the absorbance of the blank: A blank + 1.645 × SD blank. Canine and equine plasma samples obtained from healthy subjects were pooled and spiked with recombinant proteins at 100, 500, and 1000 pg/mL and used to obtain validation parameters for cPCT, rcPCT, and ePCT ELISA kits. Equine plasma samples were also spiked with recombinant equine PCT protein at 0.1, 0.5, 1, 1000, 5000, and 10,000 ng/mL and were used to obtain validation parameters for the hPCT ELISA kit. Plasma spiked samples were analyzed in 5 replicates and the mean, standard deviation (SD), coefficient of variation (CV), and recovery were calculated. Intra-assay CV was calculated for each plate and for each measured concentration. Inter-assay CV was determined on three plates at each concentration. Acceptance criteria for CV were set within 20% of the nominal value. Cross-reactivity was calculated by comparing standard curves of human recombinant PCT and equine recombinant PCT obtained in the hPCT ELISA kit by using the formula: EC50 human PCT/EC50 equine recombinant PCT. EC50 is defined as the concentration of PCT corresponding to 50% of the maximum OD in the calibration curve.The ELISA kits were validated evaluating linearity, limits of detection (LOD), limits of quantitation (LOQ), limits of blank (LOB), recovery, and intra-assay and inter-assay variability. Three calibration curves based on the analysis of duplicate standard solutions supplied by the manufacturer were constructed for each PCT ELISA kit. Recombinant canine PCT  was reconstituted in water and dilutions of 12.5, 25, 50, 100, 200, 400, and 800 pg/mL were used to construct calibration curves for both the cPCT and rcPCT ELISA kit  for Mac. The Kolmogorov–Smirnov test was performed to evaluate data distribution. The Mann–Whitney test was used to evaluate differences between healthy and sick animals. The Spearman test was used to compare results obtained with different ELISA kits. A level of The validation parameters of the rcPCT ELISA kit obtained in the plasma samples spiked with recombinant canine PCT are reported in 2 of 0.9432 (n = 3) (2 of 0.9814 (n = 3). The recombinant canine PCT not supplied with the kit and reconstituted in water showed corresponding results (mean r2 = 0.9820) (2 of 0.9200 (The cPCT ELISA kit did not detect recombinant canine PCT at any assayed concentrations in both the buffer and spiked plasma samples (OD < LOD) and it could not be validated by using a recombinant protein. Analysis of the dilutions of the calibration standard supplied in the cPCT ELISA kit generated standard curves with a mean r (n = 3) . Analysi 0.9820) . The ePC 0.9820) . Recombif 0.9200 . The val2 of 0.9904 (n = 3) (2 of 0.9470 (n = 3)  . Analysi (n = 3) .The plasma PCT concentrations measured by using the cPCT and rcPCT ELISA kits in the samples of healthy and sick dogs are reported in p = 0.7494). Plasma PCT concentrations measured by the rcPCT ELISA kit were statistically higher in sick  than in healthy dogs  (p < 0.03) (p > 0.05).No correlation was found between the two assayed kits  , whereasThe plasma PCT concentrations measured by using the ePCT and hPCT ELISA kits in the samples of healthy and sick horses are reported in p = 0.0283). Plasma PCT concentrations measured by the ePCT ELISA kit were statistically higher in sick  than in healthy horses  (p < 0.009) (p < 0.04)  a. Plasma < 0.04) b.The present study is a first investigation regarding the validation of commercially available ELISA kits used to detect PCT in dogs and horses. Validation data are extremely useful to allow the kits to be used to obtain the reference ranges. The immunochemical quantification of PCT in human medicine is state of the art for sepsis diagnosis, course control of the disease, and antibiotic stewardship. The same use has been proposed for this sepsis biomarker in veterinary medicine. Although several ELISA kits are commercially available, the use of PCT in diagnosis and/or prognosis settings is not possible because of the lack of species-specific reference ranges and validated assays. The present study showed that the cPCT ELISA kit used was not able to detect and measure canine recombinant PCT either in buffer or plasma samples. Different dilutions of recombinant canine PCT showed OD lower than the blank samples, confirming a lack of binding of PCT to the kit antibodies. The cPCT ELISA kit could not be validated. The cPCT ELISA also generated inconsistent results for canine plasma of both healthy and septic dogs. These results do not support the use of this commercial ELISA for the detection of PCT in dogs. Dog PCT has a homology within the amino acid sequence to the human PCT of 67% ,24 Figu.The results regarding canine the rcPCT ELISA kit are in agreement with the study of Goggs and collaborators , confirmHuman PCT consists of 116 amino acids with 13kDa molecular weight . Equine The present study investigated commercial ELISA kits for the quantitative determination of PCT in dogs and horses. Based on this study, canine PCT can be measured in plasma by using the canine recombinant PCT ELISA kit . The human PCT ELISA kit  is suitable to detect PCT concentrations in equine plasma samples, whereas the equine PCT ELISA kit  used did not detect recombinant standard protein and cannot be fully validated.


Enterocytozoon bieneusi is one of common intestinal pathogens in humans and animals including non-human primates (NHPs). Many zoonotic pathogens including E. bieneusi have been found in these animals. However, there are few studies on the population structure of E. bieneusi in NHPs. To infer the gene diversity and population genetics of E. bieneusi, we selected 88 E. bieneusi-positive samples from crab-eating macaques for multilocus characterizations in this study.E. bieneusi isolates examined belonged to three common genotypes with different host ranges by sequence analysis of the ribosomal internal transcribed spacer (ITS): Type IV (n = 44), Macaque3 (n = 24) and Peru8 (n = 20). They were further characterized by sequence analysis at four microsatellite and minisatellite loci . DnaSP, Arlequin and LIAN were used to analyze the sequence data together with those from the ITS locus to infer the population genetics. Subpopulation structure was inferred using phylogenetic and STRUCTURE analyses.The Seventy-two (81.8%), 71 (80.7%), 76 (86.4%) and 79 (89.8%) samples were amplified and sequenced successfully at the MS1, MS3, MS4 and MS7 loci, respectively, with 53 having sequence data at all five MLST loci including ITS. Altogether, 33 multilocus genotypes (MLGs) were produced based on concatenated sequences from the 53 samples. In phylogenetic analyses of sequences and allelic data, four major subpopulations (SPs) were observed with different ITS genotypes in each of them: Type IV and Peru8 in SP1 and SP2; Type IV, Macaque3 and Peru8 in SP3; and Type IV and Macaque3 in SP4. SP3 and SP4 were phylogenetically related and might be NHP-specific based on the fact that Macaque3 is mostly found in NHPs. A strong linkage disequilibrium (LD) was observed among the multilocus sequences and allelic data.E. bieneusi in crab-eating macaques. The inconsistent segregation of MLGs among ITS genotypes suggested some occurrence of genetic recombination. These observations should improve our understanding of the population genetics of E. bieneusi in NHPs.The significant LD in the multilocus sequence analysis indicated the presence of an overall clonal population structure of  Enterocytozoon bieneusi is an obligate intracellular pathogen infecting humans, domestic animals and wildlife [E. bieneusi in AIDS patients in 1985 for the first time, this pathogen has been found in other immunocompromised/immunodeficient individuals, such as organ transplant recipients, children, and life-threatening and persistent diarrhea often occur in these populations [wildlife . Since tulations –7. It haulations .E. bieneusi genotypes have been identified based on sequence analysis of the internal transcribed spacer (ITS) region of the rRNA gene [E. bieneusi infections whereas host adaptation seems to be more common in genotypes of Groups 3 to 11 according to a recent review of E. bieneusi [E. bieneusi genotypes have been detected in NHPs, mostly belonging to Group 1 [Approximately 500 RNA gene , 16. The Group 1 , 17–21.  Group 1 . Convers Group 1 –26.E. bieneusi in humans and some animals, including pigs, sheep, rabbits, calves, black bears, giant pandas and horses [E. bieneusi isolates in NHPs, and only one study has found the presence of an overall clonal population structure and an epidemic population structure in subpopulations of this pathogen in NHPs [Multilocus sequence typing (MLST) based on sequence analysis of four markers  with simple tandem repeats and the ITS has been used in investigating the transmission dynamics of d horses –35. Thus in NHPs , 36, 37.E. bieneusi-positive samples from crab-eating macaques (Macaca fascicularis) in Hainan Province, China were used for MLST analysis and population genetic characterization of E. bieneusi. The present study aims at evaluating the genetic diversity and population structure of E. bieneusi in NHPs to better understand the transmission and epidemiology of E. bieneusi in these animals.In this study, 88 E. bieneusi-positive samples used in this study were from crab-eating macaques in Hainan, China. They consisted of ITS genotypes Type IV (n = 44), Macaque3 (n = 24) and Peru8 (n = 20), which were identified by sequence analysis of the ITS region of the rRNA gene in a previous study [E. bieneusi genotypes from that study were not included in the present study, as the small numbers of samples for each of them made examinations of intra-genotypic variations of them difficult.The 88 http://technelysium.com.au/ChromasPro.html), and aligned with each other and homologous sequences downloaded from GenBank using ClustalX . The sequence alignments at the four genetic loci  were used in the assessment of sequence polymorphism within the ITS genotypes, determination of MLGs and phylogenetic analyses of the MLST data.Four established MLST loci were targeted in this study, including three microsatellite loci  and one minisatellite locus (MS4). These loci were amplified by nested PCR using published primers and conditions . A positE. bieneusi using the software DnaSP version 5.10.01 (http://www.ub.edu/dnasp/) [E. bieneusi was assessed by intragenic and intergenic linkage disequilibrium (LD), neutrality , and recombination events (Rms).The multilocus sequences data  generated were used in the analysis of the population genetics of /dnasp/) . The popISA) was measured using LIAN 3.7 [Pairwise intergenic LD based on allelic profiles at the five genetic loci was evaluated using the exact test and Markov chain parameters implemented in Arlequin version 3.5.1.2 . The staLIAN 3.7 .https://www.megasoftware.net). The reliability of clusters formed was assessed using bootstrap analysis with 1000 replicates. An un-weighted pair group mean average (UPGMA) tree of the allelic data was generated using MEGA 6. Wright’s fixation index (FST) of E. bieneusi isolates was calculated using software Arlequin version 3.5.2.2.The phylogenetic relationship among MLGs including both SNPs and INDELs was assessed using the maximum likelihood (ML) analysis and general time reversible model implemented in MEGA 6  from each of the 53 samples were concatenated. Genetic diversity was assessed based on both total polymorphic sites (finite population variance estimates) and segregating sites only (infinite population variance estimates). Together they formed 33 MLGs with an Hd of 0.98  indicated a possible occurrence of genetic recombination, which was confirmed by results of the recombination analysis (10 Rms) (Table P (Fs < − 5.56) = 0.158) and − 6.91 (P (Fs < − 6.91) = 0.122) based on polymorphic sites and segregating sites, respectively  was obtained using the Monte Carlo method based on the null hypothesis of panmixia  and had the epidemic population structure.We calculated tic loci . Resultsia Table . All theMaximum likelihood analysis of the concatenated sequences of the 33 MLGs showed four main subpopulations (SPs). SP1 contained samples of three MLGs of ITS genotype Type IV and two MLGs of ITS genotype Peru8, SP2 contained six MLGs of ITS genotype Type IV and two MLGs of ITS genotype Peru8, SP3 contained seven MLGs of ITS genotype Type IV, four MLGs of ITS genotype Peru8, and five MLGs of ITS genotype Macaque3, while SP4 contained two MLGs of ITS genotype Type IV and two MLGs of ITS genotype Macaque3  to measure population differentiation within the E. bieneusi population. Two subpopulations (SPs) with alpha (α) value of 0.104 were produced . At K = 3, the differentiation between SP1 and SP2 , SP1 and SP3 , and SP2 and SP3  were also significant. Similarly at K = 4, the differentiation between SP1 and SP2 , SP1 and SP3 , SP1 and SP4 , SP2 and SP3 , SP2 and SP4 , and SP3 and SP4  were all significant.E. bieneusi around the world [E. bieneusi isolates from domestic, wild, and zoo animals [E. bieneusi-positive samples from macaque monkeys, indicating a high genetic diversity within some common ITS genotypes , and supporting high-resolution of the multilocus sequencing tool established by Feng et al. [Currently, multilocus sequence typing has been used to analyze the genetic diversity of he world , 42, 43.he world , 37. In  animals , 42–48. g et al. .E. bieneusi in this study. In previous studies, the strong and significant intragenic LD obtained was used as an indication for the presence of a clonal population structure of E. bieneusi in the AIDS patients, giant pandas, pigs, fur animals and NHPs [E. bieneusi in the study population.A strong and significant intragenic LD was observed in the analysis of the MLST data, indicating an overall clonal population structure of and NHPs , 49, 50.E. bieneusi population studied. In previous investigations, E. bieneusi MLGs were segregated by ITS genotypes for some genotypes with narrower host ranges, such as A, EbpA, SCO2 and Cs-4, but not for zoonotic genotypes such as Type IV, D, WL11 and Peru11 [E. bieneusi in crab-eating macaques were also not segregated by ITS genotypes, especially for the zoonotic genotypes Type IV and Peru8. Genetic recombination appears to be common in some microsporidia of insects, including Kneallhazia solenopsae in fire ants and Andreanna caspii in mosquitoes. In a previous study in China, the MLGs of some common ITS genotypes  in NHPs were not segregated by ITS genotypes in phylogenetic analysis, suggesting a possible occurrence of sexual recombination in E. bieneusi [E. bieneusi genotypes in NHPs.Nevertheless, the MLGs found in crab-eating macaques were not segregated by ITS genotypes, indicating the existence of genetic recombination in the d Peru11 , 16. Simbieneusi . TherefoFour SPs were obtained in this study among MLGs isolates within Group 1, according to results of phylogenetic and subpopulation genetics analyses. The four SPs all appear to have an epidemic population structure. In previous studies, the ITS genotype Macaque3 was mostly found in NHPs, and only occasionally in dogs , 26, 51.E. bieneusi isolates from crab-eating macaques by multilocus sequence typing (33 MLGs) than by ITS genotyping (three ITS genotypes). This appears to have resulted from genetic recombination among common ITS genotypes despite an overall clonal population structure in E. bieneusi. Nevertheless, the MLGs generated from the analysis have formed four subpopulations with different host ranges and an epidemic population structure. These results provide data on the population and subpopulation structure of E. bieneusi in NHPs, and would improve our perception of E. bieneusi epidemiology in these animals.A much higher genetic diversity was revealed in


Ostreopsis cf. siamensis, along a 1,500 km north–south gradient in southeastern Australia. Sixty‐eight strains were established from eight sampling sites. The study revealed long‐standing genetic diversity among strains established from the northern‐most sites, along with large phenotypic variation in observed physiological traits such as growth rates, cell volume, production of palytoxin‐like compounds, and photophysiological parameters. Strains from the southern populations were more uniform in both genetic and functional traits, and have possibly colonized their habitats more recently. Our study reports significant genetic and functional trait variability in a benthic harmful algal species, indicative of high adaptability, and a possible climate‐driven range extension. The observed high trait variation may facilitate development of harmful algal blooms under dynamic coastal environmental conditions.Genetic diversity in marine microbial eukaryotic populations (protists) drives their ecological success by enabling diverse phenotypes to respond rapidly to changing environmental conditions. Despite enormous population sizes and lack of barriers to gene flow, genetic differentiation that is associated with geographic distance, currents, and environmental gradients has been reported from planktonic protists. However, for benthic protists, which have reduced dispersal opportunities, phylogeography and its phenotypic significance are little known. In recent years, the East Australian Current (EAC) has intensified its southward flow, associated with the tropicalization of temperate waters. Benthic harmful algal species have been increasingly found in south‐eastern Australia. Yet little is known about the potential of these species to adapt or extend their range in relation to changing conditions. Here, we examine genetic diversity and functional niche divergence in a toxic benthic dinoflagellate,  Genetic diversity and phenotypic diversity in marine microbial eukaryotic populations are critical for adaptability, range expansion, and potential bloom formation. In this study, we report significant functional trait variability in a benthic harmful algal species, indicative of high adaptability, and a possible climate‐driven range extension along the East Australian Current region in the South Pacific. The observed high trait variation may facilitate development of harmful algal blooms under dynamic coastal environmental conditions. The EAC has experienced a southern range extension of approximately 350 kilometers in the recent decades, and a significant increase in ocean temperature at the mid‐latitudes of up to 2.0°C, making it a global “hot spot” of climate variation Ridgway, . In reces Figure  is a coss Figure . O. cf. O. cf. siamensis strains established from eight sampling sites along a 1,500 km north–south gradient, ranging from subtropical to temperate coastal regions of the EAC. We first determined long‐standing genetic variability and phylogeographic trends among these strains based on ribosomal gene phylogeny and haplotype networks. Then, we examined phenotypic variation in growth rates, cell volume, light harvesting, and utilization parameters and production of PLTX‐like compounds. We observed that populations of this benthic HAB species in the northern subtropical regions are likely to harbor more genetic diversity and phenotypic variation compared to the more “uniform” populations in the southern temperate regions, possibly due to their recent colonization caused by the southward intensification of the EAC. Results from this study provide novel insights into the phylogeographic structure and large phenotypic trait variation within benthic HAB populations along the EAC, which potentially facilitate bloom development under dynamic environmental conditions and promote the success of HABs during global environmental change.Here, we assessed the functional and genetic diversity among 22.1Ostreopsis cells were identified microscopically from the epiphytic suspension. Nonaxenic monoclonal cultures were established according to single cell isolation protocol as described in Verma, Hoppenrath, Harwood, et al. (−2 s−1 set to a 12:12‐hr (light: dark) photoperiod, in 70ml sterile tissue culture flasks, up to a volume of 50 ml (Becton Dickinson). Although irradiance measurements showed that the light field was uniform, positions of the flasks were changed randomized every other day. Cultures were maintained in semicontinuous batch mode and diluted to maintain exponential growth as per Hennige, Suggett, Warner, McDougall, and Smith  extraction buffer , followed by amplification and sequencing of partial regions of the large ribosomal subunit (LSU) of rRNA gene, that is, D1–D3 and D8–D10, the internal transcribed spacer regions and 5.8S rRNA gene (ITS/5.8S) as described in Verma, Hoppenrath, Dorantes‐Aranda, et al.  (see TabO. cf. ovata HER27 and O. rhodesae strains HER6 and HER20 as outgroups. ML trees were produced using Tamura‐Nei (T92) + 5 gamma (G) category substitution model with 1,000 bootstrap replications. Bayesian analysis was performed in Geneious v6 using MrBayes v3.2  and Bayesian inference (BI) analyses on individual alignments of ITS/5.8S, D1–D3, and D8–D10 rDNA LSU regions were performed in MEGA v6  between and within sites were estimated from individual and concatenated sequence datasets using the p‐distance model  in MEGA v6. All positions containing gaps and missing data were eliminated for the analyses. Mean number pairwise differences, nucleotide diversity (π), no. of polymorphic sites (S), and haplotype diversity (Hd) for each site were calculated with concatenated sequences using Arlequin v3.5  were determined during exponential growth (8–10 days of subculturing) using a FastOcean (Chelsea Technologies Group) Fast Repetition Rate Fluorometry (FRRf) measurements as described in Suggett et al. , and anterior–posterior diameter (AP) were performed using ImageJ v1.48 . Calculations of cell volume were performed assuming ellipsoid cell shape using equation of Sun and Liu  equipped with brightfield optics. Samples were fixed in 1% Lugol's solution for imaging, and measurements of dorso‐ventral diameter (DV), trans‐diameter width ( and Liu :(1)V=π/62.4Ostreopsis cultures were harvested during late‐stationary phase (Day 19–21) via centrifugation  for screening PLTX‐like compounds using a quantitative LC‐MS/MS method at the Cawthron Institute, New Zealand, as described in Selwood et al.  for 20 min to relax nonphotochemical quenching (NPQ) and minimize chlororespiratory effects  and maximum  PSII fluorescence (instrument units), PSII functional absorption cross section, σPSII(ʹ) (nm2 PSII−1), and PSII photochemical efficiency ϕPSII(ʹ) (unitless), where prime notations denote measurements performed in the light‐acclimated state  protocol  at each light step were calculated as:E is irradiance (µmol photons m−2 s−1), [RCII] is the concentration of PSII reaction centers (mol RCII m−3), and qP (where qP = [Fm′–F′]/[Fm′–Fo′]) is an estimate of the fraction of “open” [RCII] −1, [cells/m3] is the mean cell count of the sample, and C is a factor converting σPSIIʹ from units of nm2 PSII−1 to m2 mol PSII−1, and seconds to hours (=2.168 × 109). [RCII] was estimated fluorometrically using the algorithm of Oxborough et al. (Karepresents an instrument‐specific constant (m−1). PE curves were fitted to the hyperbolic tangent function of Platt, Gallegos, and Harrison, (max), light utilization efficiency (α), and light saturation parameter (kE).Photosynthesis‐irradiance (PE) response was assessed using triplicate fluorescence light curves. The FRRf was programmed to deliver 15 steps of increasing irradiance from 0 to 1,208 μmol photons mh et al.  as:(3)RCarrison, , using MC) versus non‐photochemical quenching (NPQ) in governing photosynthetic efficiency across strains, we followed the procedure of Suggett et al. (Fv′/Fm′]/[Fv/Fm]), hereafter referred to as (1–Q). Importantly, while the RLC is a powerful approach to probe photosynthetic response, its relatively short length (20 s) provides limited time for the generation of NPQ mechanisms; and thus, in this study, values of (1–Q) are most useful to identify strains capable of rapid upregulation of non‐photochemical energy dissipation  and stored overnight at 4°C to allow for pigment extraction. Absorbance of extracts was measured at 665 and 750 nm  to determine chlorophyll‐a (Chl‐a) concentration using the following equation , co = cell path length (cm), and V = filtered sample volume (ml). Size of the photosynthetic unit (PSU) was calculated from Chl‐a concentration and [RCII] measures as per Hughes et al. Chl-as et al. :(5)PSUSi2.8µ, total cell volume, Fv/Fm, σPSII, ETRmax, α, and Ek. Analyses were conducted using SPSS v24 (IBM Corp.). Correlation of certain phenotypic traits was determined using the Pearson correlation coefficient (r) using SigmaPlot v12 . When data failed to meet assumptions of normality, the nonparametric Kruskal–Wallis test was applied. To identify various phenotypic functional groupings among strains based on light harvesting and utilization parameters , and (1–Q), together with µ, cell volume and quantity of PLTX‐like compounds, cluster analysis, and multidimensional scaling (MDS) were performed using PRIMER‐E v6.1 (PRIMER‐E Ltd.). Values of fluorescence ratios, that is, Fv/Fm, (1–C) and (1–Q), were arcsine transformed to stabilize variance for MDS analysis. Prior to analysis, variables were standardized to account for order of magnitude differences in value ranges. The correlation matrix was used for a two‐dimensional principal component analysis (PCoA), and principal components were displayed as an ordination plot.One‐way analysis of variance (ANOVA) and Tukey's HSD post hoc tests were applied to identify differences across strains from each site for 33.1O. cf. siamensis strains was strongly supported by both ML and BI phylogenetic analyses for all three molecular markers  ranged from 0.12 ± 0.03 (GB16) to 0.27 ± 0.04 d−1 (MW9) among the 53 strains (mean: 0.18 ± 0.004 d−1) in this study with significant differences among strains isolated from different locations  to 2.76 ± 0.69 × 104 µm3 (FR10) with significant differences among strains isolated from different sampling sites  = .27; p > .05; Table Cell volumes ranged from 9.17 ± 0.47 × 10O. cf. siamensis strains did not correlate with their corresponding cell volumes  but showed a weak, yet significant correlation to growth rates –11.19 ± 1.5 µmol e− cell−1 hr−1  among the 53 strains and varied significantly among isolates from each location with the exception of MER  – 5.6 ± 0.7 × 10–2 µmol e− cell−1 hr−1 , that is, the irradiance at which photosynthesis becomes light‐saturated, ranged from 90 to 240 μmol photons m−2 s−1 among isolates, despite cultures being maintained under a growth irradiance of 60 ± 15 µmol photons m−2 s−1  to 0.59 ± 0.005 , thus falling within the expected range for dinoflagellate cultures maintained under steady‐state growth  to 3.97 ± 0.17 nm2 PSII−1 . Chl‐a concentration varied from 30 ± 2 (MW7) to 105 ± 3 pg per cell (MW5) among Ostreopsis isolates (mean: 60 ± 3 pg Chl‐a cell−1) which are similar values to those reported from other Ostreopsis species , while normalization to cellular Chl‐a concentration yielded a PSU size range of 482 ± 32 (MW7)–1,850 ± 40 mol Chl‐a (mol [RCII])−1 (GB8) −1) and neither appeared to show an obvious pattern linked to geographic origin  and light utilization, that is, (1–C) and (1–Q), is presented in Figure −2 s−1, representing a saturating irradiance level for all strains. The variability among functional groups was predominately driven by cellular [RCII] concentration and σPSII  modifying the size of the PSII antenna complex serving the core reaction center (σ‐type strategy); (b) increasing the number of PSII units (n‐strategy); or (c) a combination of both.The MDS analysis based on photoacclimation  is represented in Figure σPSII  is a significant variable in population ecology, as it integrates numerous biochemical processes to yield a single “output” that reflects the ability of individuals to adapt to the changing environments  are beneficial under conditions likely to cause photoinhibition, such as high irradiance  that have been linked with their bloom formation capabilities by providing mechanical resistance to dynamic wave motion  in a select few strains Figure . This su5O. cf. siamensis, over a north–south gradient along the east coast of Australia, with a larger number of “eco”‐ and “geno‐” types from the northern geographic populations. Among the investigated phenotypic traits, the prevalence of different light harvesting strategies highlights a greater potential for a benthic species such as O. cf. siamensis to cope with highly dynamic light regimes. Such high degree of functional trait variability among isolates may have enabled this species to colonize new environments successfully, which could aid the potential southward range expansion, and can also facilitate its rapid proliferation under varying environmental conditions. Trait variation maintained through shifting selection pressures can contribute to the prevalence and success of protistan populations under the changing global environment, and might also facilitate in bloom formation among harmful algal species.This study reports considerable interspecific genetic divergence and functional trait variation among strains of the benthic marine microbial eukaryote, The authors declare that they have no conflict of interest.Arjun Verma: Conceptualization (lead); Data curation (lead); Formal analysis (lead); Investigation (lead); Methodology (lead); Validation (lead); Visualization (lead); Writing‐original draft (lead); Writing‐review & editing (lead). David J. Hughes: Data curation (supporting); Formal analysis (supporting); Investigation (supporting); Methodology (supporting); Validation (supporting); Writing‐review & editing (supporting). D. Tim Harwood: Data curation (supporting); Formal analysis (supporting); Investigation (supporting); Methodology (supporting); Writing‐review & editing (supporting). David J. Suggett: Resources (supporting); Writing‐review & editing (supporting). Peter J. Ralph: Supervision (supporting); Writing‐review & editing (supporting). Shauna A. Murray: Conceptualization (supporting); Funding acquisition (lead); Investigation (supporting); Resources (lead); Supervision (lead); Writing‐review & editing (supporting).Figure S1Click here for additional data file.Figure S2Click here for additional data file.Figure S3Click here for additional data file.Figure S4Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.Table S4Click here for additional data file.Table S5Click here for additional data file.Table S6Click here for additional data file.


Pterois volitans) have rapidly invaded the tropical Atlantic and spread across the wider Caribbean in a relatively short period of time. Because of its high invasion capacity, we used it as a model to identify the connectivity among nine marine protected areas (MPAs) situated in four countries in the Gulf of Mexico and the Caribbean Sea. This study provides evidence of local genetic differentiation of P. volitans in the Gulf of Mexico and the Caribbean Sea. A total of 475 lionfish samples were characterized with 12 microsatellites, with 6–20 alleles per locus. Departures from Hardy–Weinberg equilibrium (HWE) were found in 10 of the 12 loci, all caused by heterozygous excess. Moderate genetic differentiation was observed between Chiriviche, Venezuela and Xcalak, México localities (FST = 0.012), and between the Los Roques and the Veracruz (FST = 0.074) sites. STRUCTURE analysis found that four genetic entities best fit our data. A unique genetic group in the Gulf of Mexico may imply that the lionfish invasion unfolded both in a counterclockwise manner in the Gulf of Mexico. In spite of the notable dispersion of P. volitans, our results show some genetic structure, as do other noninvasive Caribbean fish species, suggesting that the connectivity in some MPAs analyzed in the Caribbean is limited and caused by only a few source individuals with subsequent genetic drift leading to local genetic differentiation. This indicates that P. volitans dispersion could be caused by mesoscale phenomena, which produce stochastic connectivity pulses. Due to the isolation of some MPAs from others, these findings may hold a promise for local short‐term control of by means of intensive fishing, even in MPAs, and may have regional long‐term effects.Lionfish ( Pterois volitans in the Gulf and the Caribbean showed that the number of organisms that originated the invasion was much higher than the one mentioned in the literature.Our findings demonstrate that there is genetic differentiation at local and regional scales.We suggest that the colonization of each geographic area has been temporary pulses, probably caused by mesoscale phenomena, and intensified with the presence of self‐recruitment. The genetic diversity of  The first sighting occurred in 1985 , Veracruz (13), Cozumel (76), Chinchorro (77), and Xcalak (89); one locality in Guatemala: Punta Manabique (44); one locality in Honduras: Utila, (54); and two localities in Venezuela: Los Roques (32) and Chiriviche (55)  following manufacturer's instructions. To ensure that every individual had been correctly identified as González .2.3P. volitans and P. miles published by Schultz et al. , (b) average expected total heterozygosity, (c) average expected heterozygosity by population, and (d) coefficient of gene differentiation (GST) for each locus. Finally, frequency of null alleles (Na) for each population and locus was estimated. Genetic structure analysis started with computing a principal coordinate analysis (PCA) on genetic frequencies to plot the samples in a reduced dimensional map and identify the most divergent locations . We then computed a hierarchical analysis of molecular variance (AMOVA) carried out with 999 permutations to corroborate the statistical baseline of the main divergence observed in the PCA. In addition, we calculated the FST values between all sites also to validate the structure and we made a sequential Bonferroni adjustment for multiple tests  average number of private alleles per locus; (b) allelic richness, both per locus and population; and (c) Nei's genetic diversity. Based on heterozygosity of allelic frequencies we estimated: (a) Nei's fixation index  and number of alleles (Na) in each locus were slightly wider than published by Schultz et al. , with a FIS) values, which indicated that individuals were less related than expected under a random mating model. The total number of alleles, average alleles, and number of private alleles were higher in the Mexican Caribbean sites . The highest differentiation values were found between Los Roques and Veracruz MPAs . All genetic differences between sites were significant, even after a sequential Bonferroni adjustment for multiple tests. Additionaly we obtained pairwise Hedrick's standardized FST′ index . The distribution of P. volitans genetic frequencies of our study, suggests strong self‐recruitment and that the main dispersal factor, may be the influence of mesoscale phenomena, transporting larvae from one site to another  is constant and increasing. It is likely that at some point all haplotypes will be dispersed throughout the entire Caribbean Sea.The genetic structure can be linked in some way to the local Mesoamerican Reef System currents that coincide with a mesoscale temporal gyre associated with the meandering of the Cayman Current that can connect between south Cuba and the West Yucatan peninsula , it was mentioned of possible slight restrictions on the genetic flow of The invasion of the Gulf of Mexico has been slower than that of the Caribbean, its genetic group differs and has a unique allele not identified in Caribbean samples. Additionally, there is only 63% of the allelic similarity between samples from the Gulf of Mexico and those from Cozumel—the geographically closest collection MPA. Based on these facts and the results of the recent study by Pérez‐Portela et al. , we hypo4.2P. volitans to sell. Considering that P. volitans is a territorial organism  may be showing that the intensive removal of organisms has an effect on settled populations. In this light, we might expect that, depending on the intensity of the removal of organisms, some areas with greater dynamics of migrants will have a fluctuating variability of genetic groups derived from the settlement of new migrants.The genetic structure we found shows a particular pattern regarding the distribution of the Cozumel samples. When we considered four genetic units, the Cozumel samples get divided in two units: 48 individuals belonging to one genetic group and another unit of 28 individuals. This difference is quite pronounced and clearly due to the sampling process that involved a collection of 48 individuals at a single time and in a unique location, while the other 28 were collected at a different time, on the same day, but about 3 km north of the first collection site, at the borderline of the Marine Protected Area, adjacent to the area where local fishermen constantly catch 4.3K = 4). The Gulf of Mexico reefs present characteristics totally different from those of the Caribbean, due to a greater introduction of fresh water, sediments from rivers, estuaries, and lagoons that flow into its waters. It is predominantly rocky with low coral cover, poor visibility, and relatively shallow waters (González‐Gándara, It is worth mentioning that the Marine Protected Areas where the collections were made differ in the type of reefs they contain, and those that have some similar physical characteristics also show a similarity in the genetic group identified in Figure 4.4P. volitans suggests that the invasion began with six to eight individuals that escaped from an aquarium in Miami, Florida, USA (Courtenay, The first hypothesis about the spread of P. volitans is nine (Freshwater et al., Present microsatellites data show HW disequilibrium for excess of heterozygotes. Taking into account the study of Selwyn et al.  and compP. volitans population, because it violates the criteria of an ideal population (Selkoe & Toonen, The excess of heterozygotes found in the study could be attributed to biological realities of this Although it is known that populations that present a recent bottleneck show apparent excess of heterozygotes (Cornuet & Luikart, P. volitans from the same geographical area, and that were released in a single exhibition, that diverse introductions of organisms that probably took place at different chronologic moments within the last 30 years. Which makes us think about the probability that the black market of ornamental marine fish was behind of the cause of the massive liberation in an entrance port. Which of these hypotheses is close to reality? It is material for another study. But the history of the stepwise invasion in the Atlantic should be revisited in light of this approach.Overall, our data clearly show that the early invasive history of the lionfish in the Atlantic is not as simple as a few individuals released around the 1980s. Also, this species is extremely faithful to its territory and that its movement as an adult is relatively limited to a few hundred meters (Jud & Layman, 5The origin of the Atlantic Lionfish invasion, the number of individuals that initiated it, and the connectivity patterns in the Caribbean and the Gulf of Mexico is more complex than has been previously suspected. Our findings demonstrate that there is genetic differentiation on both regional, local, and fines scales. We suggest that the colonization of each geographic area has been in temporary pulses, probably caused by mesoscale phenomena, and intensified by self‐recruitment. The presence of private alleles in the Mexican Caribbean points to the need to carry out further studies in that area, since it presents greater dispersal dynamics of larvae. These results open new challenges in research seeking to understand the functioning, the evaluation, the efficiency, and the management strategies of MPAs in the Caribbean and the Gulf of Mexico. Integrating all the components, these results are promising toward controlling this invasive species, acting at local scales that may have real short‐term effects at regional scale.The authors declare no conflict of interest.Authors whose names appear on the paper have contributed sufficiently to the scientific work and therefore share collective responsibility and accountability for the results. The authors of this manuscript contributed in this project proportionally to the location in which their name is presented in the article. All authors have approved the work, and all authors have agreed to the last version of the manuscript.https://openscience.com for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at https://datadryad.org/stash/share/OF-FgZjZer-iMwDVoglD4C5-Ikr7pIFeoW9Evldhg_s.This article has earned an  Click here for additional data file.


Modern urban science views differences in attractiveness of residential suburbs as the main driver of resettlement within a city. In particular, certain suburbs may attract residents due to lower commute costs, and this is believed to lead to compactification of a city, with highly populated central business district and sprawled suburbia. In this paper we assess residential resettlement patterns in Australian capital cities by analyzing the 2011 and 2016 Australian Census data. Rather than explicitly defining a residential attractiveness of each suburb in subjective terms, we introduce and calibrate a model which quantifies the intra-city migration flows in terms of the attractiveness potentials (and their differences), inferring these from the data. We discover that, despite the existence of well-known static agglomeration patterns favouring central districts over the suburbia, the dynamic flows that shape the intra-city migration over the last decade reveal the preference directed away from the central districts with a high density of jobs and population, towards the less populated suburbs on the periphery. Furthermore, we discover that the relocation distance of such resettlement flows plays a vital role, and explains a significant part of the variation in migration flows: the resettlement flow markedly decreases with the relocation distance. Finally, we propose a conjecture that these directional resettlement flows are explained by the cities’ structure, with monocentric cities exhibiting outward flows with much higher reluctance to long-distance relocation. This conjecture is verified across the major Australian capitals: both monocentric  and polycentric (Darwin and Canberra). Growing complexity of cities demands new fundamental understanding and rigorous models of the urban dynamics, including diverse and transient intra-urban migration patterns. At the same time, the increasing availability of large-scale spatiotemporal data  provides an additional impetus for quantitative modelling of urban development and related dynamics4. A consistent modeling framework for settlement formation does not yet exist2, despite an abundance of stable empirical patterns12 and theoretical insights22 that can potentially improve our understanding of how cities grow and self-organise.The modern world undergoes rapid urbanisation25. However, heterogeneous preferences of individual residents cannot be easily integrated into their aggregate migration flows. The latter is a collective phenomenon which we believe is determined mainly by the spatial city structure2. Furthermore, such migration patterns are usually persistent in time. In particular, we observe strong correlation between migration flows in two different periods of 2006–2011 and 2011–2016 40, the housing affordability41, and perhaps, other publicly known but not explicitly defined subjective factors. Our model treats the attractiveness as a revealed quantitative characteristic of a suburb, inferring it from data, rather than being based on a particular subjective characterization.The notion of subjective suburb’s attractiveness is often considered in the context of identifying the city structure43 and, in particular, in patterns of the resettlement dynamics.Using our resettlement model, we analyze intra-urban migration flows in the Australian capital cities, represented by the corresponding Greater Capital Areas (GCA): Sydney, Melbourne, Brisbane, Adelaide, Perth, Hobart, Darwin, and Canberra Fig. . These cWe use the latest Census data to estimate the spatial distribution of revealed attractiveness and its contribution to the migration flows. As a result, we discover that the role of the suburbs’ attractiveness is opposite to what may be expected by a naive perspective: the migration flows are directed away from the central districts with a high population density and infrastructure accessibility, towards sparsely populated suburbs . In addition, the attractiveness of suburbs alone does not allow one to predict the resettlement flows with an adequate precision. In particular, we observe that, to a large extent, the migration flows are determined by the relocation distance: the short-distance relocations occur much more often than the long-distance ones.reluctance to long-distance relocations, with a clear center-to-periphery migration flow. In contrast, two out of eight capital cities (Darwin and Canberra) are non-monocentric, while exhibiting a low reluctance to long-distance relocations without a well-defined flow direction. While it is hard to make a definite conclusion based on small number of cities, we make a conjecture: not only does the human resettlement affect the spatial structure of cities, but the city structure itself may also affect the intra-urban migration. A proper verification of this conjecture is subject to future research and data availability.Finally, we observe and quantify a specific relationship between the dynamic migration patterns and the static city structure, defined in terms of its polycentricity  provides the worst fit, both in terms of the coefficient of determination and the number of parameters. In contrast, the extended hypothesis (3) uses only one parameter more, but provides the best fit in terms of the coefficient of determinations. Finally, the spatial hypothesis (4) isolates the differences between the extended and the baseline hypothesis and uses 2 parameters only, providing the goodness of fit comparable to the extended hypothesis. This indicates that the spatial hypothesis captures the main pattern in relocations, while the extended hypothesis provides additional insights. Below we describe each of these hypotheses in detail.40 which is based on the analogy between the processes of human resettlement and chemical reactions. For each suburb i we calculate a value of the potential i. These characteristics may include population density, distance to centers of business and retail activity, quality of public services , transportation connectivity, and other hedonic indicators of the suburb. The notion of the suburb’s potential was introduced in the analysis of inter-regional migration44, and its role in the dynamics of residential resettlement has been investigated in our earlier study40. In particular, we demonstrated, that the model incorporating the suburb’s potentials contains critical regimes where small fluctuations in the model parameters may result in sharp changes in the spatial population structure. This aspect of the approach is comparable to the Boltzmann-Lotka-Volterra model  which describes an evolution of the urban structures27, predicting a qualitatively similar long-term outcome40.To asses the impact of suburb attractiveness on residential migration flows, we adopt the migration model of Weidlich and Haagi to suburb k is modelled asinfer their values directly from the data. In particular, the suburb’s potentials N suburbs there are N+1 parameters which are fitted to i to suburb k depends only on the difference in individual characteristics of these suburbs , and does not depend on other pairwise characteristics (such as distance or other types of proximity). In other words, we test whether the attractiveness k equally enhances the migration flows i, regardless of their proximity to k.In the baseline hypothesis, the migration force The results of calibration for the period of 2011–2016, and the corresponding number of model parameters are presented in Table [i to suburb k depends also on the distance k and the source i of the corresponding migration flow k is determined not only by its intrinsic attractiveness, but also by the proximity of a source i to the destination k.We next extend the baseline hypothesis to include additional characteristics which may affect the relocation process. In particular, we assume that the force driving a migration flow from suburb The factor 47, daily commutes6, shopping activity48, trafficking of goods50, inter-city phone calls51. There are various functional forms of the distance dependence. The most common ones are the inverse quadratic51, exponential53, their combination50 and Gaussian-like54 functions. We adopt this approach to describe the internal city migration, and use the exponential form of the distance dependence.Distance-dependent migration is investigated in various areas of social and biological sciences, such as population biologyThe results of the calibration include now the value of the relocation impedance, which is listed in Table In addition, we observe that the fitted values of the relocation impedance 40 where it is assumed that the attractiveness potential increases with the population density only until a certain threshold level, beyond which the attractiveness goes down. This means, in particular, that for the majority of Australian capitals this threshold value is quite low. This is fairly surprising, since even the largest Australian capitals have very low population density compared with European and Asian cities which have not reached such threshold yet .Finally, comparing the values of the attractiveness potentials in Fig. It is worth noting that this pattern becomes visible only in the extended model (3), whereas the values of potentials In order to contrast the roles of the relocation distance and the attractiveness potentials, we may consider a simplification of the extended hypothesis, by assuming that the migration force In this way we abstract away a large number of parameters , and keep only two parameters: the relocation impedance and the normalization factor.The calibrated values of the relocation distance are shown in Table The presented results lead to two important conclusions. Firstly, we observe that temporal stability in intra-urban migration flows 58, monocentricty versus polycentricity59, population density profile60, etc. In a recent study10, two main indicators capturing the whole diversity of the possible urban structures have been introduced: the degree of heterogeneity and the degree of spreading. Here we adopt the latter, and investigate to what extent the suburbs with a high population density are localized together, and how far they are spread across a city.There exists a number of approaches which identify the most important characteristics of urban spatial structures, including compactness and spreading10 is defined as the ratio of the average distance between the highly populated areas to the average distance between all areas in a city:i, S is the total area of suburbs, and 61. To evaluate particular threshold values of the density 61. The threshold densities for each GCA are listed in Table 4 of the supplementary material.The spreading index10). Conversely, a low relocation impedance is observed in cities with high 10). We can identify the cluster of monocentric cities with a high relocation impedance, for which The spreading index Most of non-capital Australian cities have less than 30 suburbs, with only two exceptions (Gold Coast and Sunshine Coast). The relocation impedance values for Gold Coast and Sunshine Coast are 0.68 and 0.81 respectively (not shown in the figure), and the corresponding values of spreading index are 0.51 and 0.41. This means that these two cities belong to the monocentric cluster with the high relocation impedance as well.61  of the corresponding city, and is identified as being high (blue) or low (yellow). We observe that a clear majority of the cities  include high-density suburbs, predominantly clustered around a single center. In contrast, the two remaining cities (Darwin and Canberra) include high-density suburbs clustered around multiple centers. Comparing Fig. The spreading index analysis is illustrated in Fig. The spreading index reflects structural properties of a city, while the relocation impedance reflects its dynamic properties. The observed correlation between these two quantities suggests that the population dynamics is likely to be influenced by urban spatial structure.Our analysis of intra-urban resettlement in the Australian Capital Cities leads to several important conclusions. In particular, we observe that the revealed attractiveness of individual suburbs  alone cannot explain the migration flows with a satisfactory precision. In contrast, we discover that the relocation distance alone provides much higher determination of migration flows than the suburbs’ attractiveness.62, intra-urban commutes52, or even migration of animals65, which obey the so-called “gravity law”), it is generally believed that the relocation distance is not important for intra-urban resettlement. In particular, the traditional understanding of how the distance affects migration relies on the associated transportation costs62, which are supposed to be negligible for relocations within the same city. Here we show that this is not the case for resettlement in Australian capital cities, and that the relocation distance alone can explain circa 50% of the variation in migration flows. Moreover, we observe that all capital cities except for Darwin and Canberra form a cluster of monocentric cities with high relocation impedance. In particular, we see that with respect to intra-urban resettlement, the cities which differ in size, population, transportation network and other dynamical details, reveal similar migration patterns which are independent of these features.It is interesting to note that while the importance of the geographical distance has been shown for various types of migration .We report two exceptions to these more general observations: Darwin and Canberra. In particular, in these GCAs i) the migration flows are directed towards more densely populated areas; ii) the relocation impedance is low; iii) the spreading index is high. This indicates that the migration dynamics in these cities is significantly different from the one across all other GCAs. While this can be simply a coincidental feature, we speculate that this is a consequence of historical development of these cities, in which centralized governmental planing played a much larger role than in the other GCAs which followed more than 100 years of essentially self-organized evolution. Indeed, a large part of Canberra was built in the period during 1961–198135. We analyze the Greater Capital Areas (GCAs), which represent urbanized contiguous areas around the corresponding cities. The Census data are organized by the so-called “statistical areas” (SAs), where people reside. The data also contain information on where people resided at the Census date and 5 years before the Census date. The information is provided on the aggregate level in terms of the number of people, and not on the individual level. The data are available in various resolutions, and we use the “Statistical Areas Level 2” (SA2s) resolution. There are 2,310 SA2 regions in entire Australia and between 30–300 SA2s in each GCA. Each SA2 has a population generally within a range of 3,000–25,000 persons35, while some of SA2s within each GCA, such as national parks or industrial objects, have negligible population. In our analysis, we discard suburbs with the population density less than 10 persons/km2, setting the difference between the urban and the rural suburbs. This particular value is chosen to indicate a threshold beyond which the spreading index To perform our analysis, we use the data about internal migration in eight Australian capital cities  from the Australian Census datasets (2011 and 2016), provided by the Australian Bureau of Statisticsi to k. The values of i and k. To take into account the fact that the sizes of suburbs are not uniform , we normalize i and k respectively. This means that We built the dataset of migration flows, which makes a correspondence between each pair of statistical areas In our analysis we do not consider people who do not change their place of residence within the observation period (the case when 71 (method scipy.optimize.leastsq): i to suburb j, and The fitting procedure is performed numerically using the ordinary least square technique and the trust region reflective algorithm implemented in SciPy 1.3.0 library in PythonSupplementary Material.


The algorithm was personalized to obtain a FI with high discrimination ability in the prediction of mortality. The resulting FI included 40 deficits and showed areas under the curve consistently higher than 0.80 (range 0.81-0.90) in the prediction of 3-year and 6-year mortality in the whole sample and in sex and age subgroups. This methodology represents a promising opportunity to optimize the exploitation of medical and administrative databases in the construction of clinically relevant frailty indices.The frailty index (FI) is one of the most widespread tools used to predict poor, health-related outcomes in older persons. The selection of clinical and functional deficits to include in a FI is mostly based on the users’ clinical experience. However, this approach may not be sufficiently accurate to predict health outcomes in particular subgroups of individuals. In this study, we implemented an optimization algorithm, the  With age, an individual’s likelihood of experiencing chronic disorders, loss of functionality, cognitive problems, and of dying increases. Similarly, the ability to recover from acute events, such as a hip fracture or pneumonia, decreases or is even absent , 2. ThisDuring the last two decades, a number of different clinical operationalizations of frailty have been developed, among which is the frailty index (FI) , 7. The A FI is calculated as the ratio between the number of clinical deficits presented by an individual and the overall number of deficits taken into consideration . Althouggenetic algorithm, a well-known tool employed to solve optimization problems [Genetic algorithms have been extensively employed to find near-optimal solutions to complex problems in the presence of a non-manageable number of possible alternatives [genetic algorithm to create a FI for mortality prediction in older people, characterized by a high prognostic accuracy both in the whole population and in sex and age subsamples. We used data from the population-based Swedish National Study on Aging and Care in Kungsholmen. In this article, we provide proof of concept, namely, that the FI built through a genetic algorithm exhibits a higher predictive performance—stable across subsamples and time windows—than that shown by a FI whose deficits were clinically selected.With this study, we propose the application of an optimization algorithm—the problems — to crearnatives . For thegenetic algorithm is an optimization algorithm inspired by Darwin’s theory of evolution; it is employed to identify solutions to computationally complex problems through the principle of the “survival of the fittest” [genetic algorithm . The resulting FI was described and compared with a clinical FI in the remaining 30%   . From th = 1009) . We ran lgorithm  in the tgenetic algorithm-derived FI (ga-FI) was obtained after 63 iterations  and 0.88 (95% CI 0.86-0.91), respectively . When apgenetic algorithm included 40 deficits. In order to investigate the meaning of such a finding, we randomly generated 100 FIs that included 40 deficits, and compared their AUCs with the ga-FI. As shown in As shown in genetic algorithm led to a FI exhibiting AUCs higher than most of those reported in previous literature for the prediction of mortality, stable across different age and sex subgroups, and both in the short and long term [In the present study, we showed that an optimization algorithm can be used to guide the selection into which and how many deficits are to be included in a highly performant FI. To the best of our knowledge, this is the first time robust and standardized optimization methodology has been employed for this purpose. The adoption of the ong term , 32–34.In recent decades, several authors adopted the FI as a tool to quantify the risk of developing poor, health-related outcomes in different settings and among persons affected by different diseases. Such proliferation may be explained by the fact that several studies showed that once a minimum number of deficits has been included in a FI, it is strongly associated with poor outcomes, irrespective of the list of variables included , 26, 35.genetic algorithm included 40 deficits in the fittest FI in our dataset, and that FIs including more or less deficits had a higher probability to be discarded by the genetic algorithm. Indeed, almost all the best FIs  included 40 deficits, even if no limitations in the number of deficits (after the first iteration) were introduced. However, upon generating 2000 random FIs, which included between 25 and 108 deficits, we observed that the median AUC increased up to within 65-70 deficits, and then plateaued. Arguably, the inclusion of too few variables may lead to a less precise FI, more sensible to missing values. At the same time, our simulation suggests that the inclusion of too many deficits would not translate into a higher discriminant capacity; indeed, this is likely to generate statistical noise, limiting the further improvement of the FI. Lin S. and colleagues [genetic algorithm represents promising methodology for the creation of highly-performing FIs in different settings and datasets, without a-priori knowledge about the optimal number of deficits to be included.The inclusion of a minimum of 30-40 deficits has been recommended to obtain a reliable FI. However, it has been suggested that a higher number of variables may improve the precision of a FI’s estimates . While alleagues  showed tlleagues , a multigenetic algorithm was implemented to be blinded to these recommendations and to deficit distribution in the population. Thirty-seven out of the 40 deficits included in the ga-FI were fully compliant with the aforementioned recommendations, while three deficits presented a similar prevalence among younger and older individuals. Among the 109 deficits we selected to run the genetic algorithm, 80% (N = 87) presented a prevalence among older individuals higher than that exhibited among younger ones, and none exhibited a risk of premature saturation; all these deficits could, in theory, be included in the resulting FI. However, the ga-FI included only 40 deficits and, interestingly, the genetic algorithm excluded several deficits exhibiting well-known associations with aging, mortality, and frailty . These deficits are typically included in the FIs proposed in the literature [genetic algorithm maximizes the information included in a FI, which is likely to increase the FI’s discriminant capacity in mortality prediction.Clinicians and researchers build their FIs selecting the deficits on the basis of their clinical experience, relying on the recommendations provided by Searle SD et al.  In accor80% N = 8 presenteterature , 25, 36.terature –34, 37),genetic algorithm provided a FI characterized by a consistently higher discriminative capacity in different subsamples using the same 40 deficits.The lowest difference in the AUCs between the ga-FI and the c-FI was found in the prediction of 3-year mortality among older individuals, where 24 deficits were shared between these two FIs. These findings corroborate the idea that the clinical selection of deficits may lead to the construction of an accurate FI in specific subsamples. However, the genetic algorithm in the construction of a FI. Genetic algorithms can be highly personalized according to researchers’ or clinicians’ aims and needs—both the characteristics of the generated FI and the fitness criteria can be adapted to the goals of the users developing it (see methods for further details). Genetic algorithms were first proposed several decades ago [genetic algorithms [genetic algorithms may be implemented in different settings, using commonly employed statistical software. Furthermore, genetic algorithms, although strongly related to random numbers (see methods for further details), propose solutions according to user pre-defined criteria. Researchers can still infer about the resulting solutions—this is not always possible using high accuracy “black-box” methods  [genetic algorithm in our study should be understood, bearing certain limitations in mind. Firstly, genetic algorithms  tend to overfit. We limited overfitting by employing two separate subsamples (a training and test sample), and we presented the results through bootstrapping techniques. Nevertheless further studies are needed to inspect the predictive accuracy of the ga-FI in different datasets, such as follow-up waves of SNAC-K. Secondly, we tested the genetic algorithm for the computation of an optimized FI using only SNAC-K data. These data are gathered through a set of interviews, tests and examinations performed by trained nurses, physicians and neuropsychologists, which guarantees more exhaustive and comprehensive data collection compared to automatically generated administrative data. Further studies are needed to understand the optimization power a genetic algorithm has on data coming from different settings. Lastly, it is possible that the parameters presented in this paper  allowed us to obtain a good accuracy-to-computing speed ratio in SNAC-K, but may require further testing in other datasets.Box 1 shows the main advantages offered by the implementation of a ades ago  and are ades ago –42. Sevegorithms , 44. Indetworks) . In spitgenetic algorithm may be helpful in selecting those deficits responsible for an optimal predictive capacity, whether it is in the general population or in specific subsamples. The genetic algorithm might be used to minimize the number of deficits needed to obtain a reliable FI in different subsamples; this may strongly contribute to cost reductions when a FI is used to stratify risk in a large population, for example. Genetic algorithms can be personalized with different fitness functions; in other words, it would be possible to select other outcomes  to adapt the FI to their prediction. Furthermore, the genetic algorithm could be used to create a highly accurate FI in a particular subsample, for example, patients undergoing specific surgical procedures or groups affected by the same index disease (e.g. HIV). Additionally, the metrics used to assess fitness can also be modified—a FI with higher sensitivity might be used for screening purposes, for example. Genetic algorithms are well known algorithms, although seldom used in medical research. In consideration of the increasing number of available large databases [genetic algorithm may be employed to find the most suitable weights for the deficits to be included in a non-linear form of FI, in order to increase its accuracy.In this study, we showed that a atabases , these aatabases . Interesatabases  showed hgenetic algorithm generates a FI with a consistently high accuracy in the prediction of mortality in different subsamples, obtaining higher AUCs than the ones exhibited by a FI whose deficits were selected based on our own expertise. This methodology represents a promising opportunity to optimize the exploitation of clinical and administrative databases in the construction of highly performant frailty indices.The frailty index is a recognized clinically relevant tool to support medical decisions in the treatment and care of older adults. In this study, we present the implementation of an algorithm that allows to overcome the limitations posed by feature selection, based mostly on clinicians’ expertise, which may lead to wrong or biased results. We show that the Box 1genetic algorithm in the optimization of a frailty index.Advantages offered by the Genetic algorithms’ strength-points:Is user-focused: the main characteristics of candidate solutions can be specified by the user.Is goal-directed: the definition of “goodness” of a solution is defined by the user.Is data-driven: it might be used where clinical experience is insufficient but data are available.Is transparent: although largely based on random numbers, the selection of solutions is user-determined. In contrast with black-box methods , inference about the selected solution is possible.Data were obtained from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K), an ongoing population-based study started in 2001. Persons aged at least 60 years old and living in a central area of Stockholm (Sweden), either at home or in institutions, were asked to participate in the study. Standard questionnaires, interviews, medical examinations, instrumental evaluations, as well as physical and neuropsychological tests were employed to retrieve information for the 3363 individuals enrolled in the study. Further details are available in a previous study . The Reg32.A variable was considered to be a potential deficit if it was likely associated with individuals’ health status. For the aim of the present study, we decided not to limit the selection of potential deficits to those whose prevalence grew with age, as suggested by Searle SD . Our hypA genetic algorithm starts from a limited number of randomly generated solutions and applies a selective pressure. Solutions more adherent to pre-defined criteria  have higher chances to be combined with each other, generating new solutions. These newly generated solutions (i.e. children solutions), likely incorporating parents’ characteristics responsible for the increased fitness, replace the less fit solutions from the previous iteration. Iteration by iteration, the algorithm produces solutions that are increasingly adherent to the pre-defined criteria set by the investigators.genetic algorithm.Creation of the initial set of solutions: in this phase, the genetic algorithm randomly creates a set of solutions . These solutions represent the starting point used by the genetic algorithm to search (and create) more fit solutions to the problem. Our genetic algorithm created 1100 FIs. We set the initial number of FI to 10 times the number of potential deficits. After different trials, we found that this number offered a good balance between late convergence and time of execution. Each potential deficit had a uniform probability to be included (or excluded) from each FI. Each randomly created FI was represented by a vector  with dimension 109 : a 1 or 0  was recorded for each vector component, according to the status of the deficit . In this phase, the number of deficits included (i.e. the number of “1”s recorded) in each FI ranged between 30 and 70. To note, we did not introduce any constraints on the number of deficits to be considered in the next phases of the algorithm. A FI was built from each vector; the sum of the deficits included was divided by the total number of non-missing deficits taken into consideration. If the number of missing deficits exceeded 10% of those included in the FI, a missing value for the FI was registered. If the total number of missing values for the FI was higher than 177 (7.5% of the training sample), the FI was excluded.Fitness evaluation: in this phase, the genetic algorithm assesses the fitness of each solution according to the definition of goodness set by the developing team. Our genetic algorithm was coded to find the most accurate FI for the prediction of mortality in the whole study population and different subsamples. The accuracy was assessed calculating the mathematical mean between the Areas Under the Receiver-Operating-Characteristic (AUC) in the prediction of 3-year and 6-year mortality, exhibited in the whole study sample and in four subsamples . The AUCs were obtained from non-parametric ROC analysis.Selection: in this phase, the genetic algorithm selects the parent solutions (i.e. the fittest solutions out of the 1100) to be re-combined in new solutions, likely increasing the general fitness of the population of solutions. We employed a proportionate fitness selection operator for this. Shortly, the probability of selection for each FI was obtained according to the following formula:i is the considered FI and fitness evaluation phase. This method allows the increase of the probability in selecting the most accurate FIs  and to decrease the probability in selecting those FIs with a lower accuracy . This method is also known as “roulette wheel selection”. Similar to the casino game, where a ball is used to determine the winning number among all possible numbers, a random probability ip is used to select candidate solutions for re-combination. The probability for a FI to be selected is proportional to its accuracy . We obtained 1067 couples of FIs for the re-combination phase: the 33 most accurate FIs in every iteration were maintained for the subsequent iteration (elitism = 3%). There were no constraints on the number of times a FI could be selected and the same FI could also be selected for both elements of the couple .where Recombination (crossover): in this phase, the genetic algorithm re-combines the selected solutions (parents) to generate children solutions . Each child solution includes sections of the parents’chromosome. In our study, two random numbers were generated for each couple of selected FIs. These numbers, called crossing-over points in genetic algorithm terminology, identified the “switching” positions in the vectors representing the FIs. A new vector (i.e. a new FI) was created by combining the components between the first component and the component identified by the first random number of the first vector, the components between the positions identified by the first and the second random numbers of the second vector, and the components between the second random number and the last component of the first vector again .Population replacement and termination: in this phase, the genetic algorithm replaces the solutions included in the initial population with those generated in the re-combination phase. In our genetic algorithm, re-combined FIs replaced the 97% least accurate FIs in the initial population (see point 3). The genetic algorithm ran until at least one of the following conditions was met: 1) 150 iterations were reached, or 2) the average AUC of the best FI did not show any AUC increase for seven consecutive iterations.genetic algorithm 10 times, therefore starting from 10 different initial populations, on the same training sample. The FI showing the highest average AUC most frequently across the whole population and different subsamples in the training sample was considered our best ga-FI, and was compared head-to-head with the c-FI (see below). We run the The c-FI was created based on a previous published work . It inclDifferences in the characteristics of the study population and its subsamples were described using means and standard deviations or absolute numbers and proportions and investigated using t-tests and chi-squared tests, as appropriate. The performance of the ga-FI and the comparison with the c-FI were conducted in the test sample. AUCs, their confidence intervals, and their comparison were obtained through non-parametric ROC analyses, employing stratified bootstrapping replacement technique  . Access to these original data is available to the research community upon approval by the SNAC-K data management and maintenance committee. Applications for accessing these data can be submitted to Maria Wahlberg (Maria.Wahlberg@ki.se) at the Aging Research Center, Karolinska Institutet.Data are from the SNAC-K project, a population-based study on aging and dementia (The lead author (AZ) affirm that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as originally planned have been explained.Supplementary Tables 1, 2Supplementary Table 3


Mutant zebrafish exhibit different behaviours depending on the genetic background of the fish they were raised with. Related research article Ribeiro D, Nunes AR, Teles M, Anbalagan S, Blechman J, Levkowitz G, Oliveira RF. 2020. Genetic variation in the social environment affects behavioral phenotypes of oxytocin receptor mutants in zebrafish. eLife9:e56973. doi: 10.7554/eLife.56973Behaviours emerge under the combined influence of the environment (nurture) and the genetic information an individual inherited from its ancestors (nature). However, it is still difficult to tease apart the respective contribution of these different factors, which are often deeply intertwined. This is particularly the case with regards to social behaviours.When animals with a mutation in a gene show a change in a specific behaviour, it is tempting to conclude that said gene is somehow involved in that behaviour. But this is not always the case. Animals are usually raised by their parents and grow up with siblings, who may share the same environment and genetic background (including this mutation). This makes it difficult to pinpoint exactly which elements, or combination of elements, are responsible for the emergence of these ‘behavioural phenotypes’ – that is, behaviours that are associated with a specific genotype.To understand the direct effect of a specific mutation on the behavioural phenotype of an individual, the environment must be controlled for, including the genetic background of the individual’s social group – its genetic social environment . Now, inZebrafish are a good model to study the indirect effects of social genetic variation because they are highly social animals with a genome that can easily be modified. Ribeiro et al. first generated a mutant zebrafish line that lacks the gene for the oxytocin receptor, a protein involved in social-bonding behaviours in animals . A mutanRegardless of whether they were raised with mutants or non-mutants, fish that lacked the gene for the oxytocin receptor were always worse at discriminating between a familiar and an unfamiliar fish – a result predicted by previous studies . HoweverThis study may help researchers to understand how the genetic social environment can influence the impact of specific mutations on social interactions. It could also be relevant to work on other forms of behaviour, such as fear conditioning in mice: researchers wishing to investigate this behaviour would normally generate a mouse line lacking a gene thought to be involved in fear conditioning, and then examine how the mutation affects the behaviour of the mice. Variations in fear conditioning in the mutants would then be attributed to the genetic change rather than the social genetic environment. The work of Ribeiro et al. shows that researchers need to be aware of this effect, and control for it whenever possible.These results also demonstrate the need to be cautious about the many human genetic studies that suggest potential links between a gene and the propensity to develop certain conditions. For instance, the general public now has easy access to DNA tests, which can link variations in certain genes to higher risks of becoming obese, being a smoker, or living a shorter life. However, a gene apparently associated with an increased risk for obesity may in fact be connected to increased parental anxiety. In this case, the weight gain would be a secondary effect of being raised by anxious parents. The impact of the social genetic environment should therefore be carefully assessed for all of these genes.Finally, Ribeiro et al. show that specific social environments could potentially rescue or promote specific behavioural phenotypes, a finding that could be used to better study human behaviours and socialisation.


Osteobrama belangeri is a freshwater inhabitant, highly endemic, threatened and economically important minor carp for its food value. In the present investigation, population structure of O. belangeri was examined using mitochondrial ATPase 6/8 gene from geographically distinct locations along the Indo-Burma biodiversity hotspot. A total of 80 individuals were collected belonging to natural and farm populations. The hierarchical analysis of molecular variance and conventional Fst values  indicated significant genetic structure among populations. The result showed that ATPase 6/8 genes are potential marker in determining the genetic divergence between natural and farm populations of O. belangeri from North-East India.Pengba fish,  Osteobrama belangeri , commonly known as Pengba, is highly endemic average size carp with restricted distribution range in India and Myanmar  marker has been used increasingly for quantifying levels of genetic differentiation among populations  and Tamu , a part of Chindwin basin and Hiyangthang  and Kodompokpi  belonging to different geographical sites in Myanmar and India  were performed using Peqstar Universal Gradient (Peqlab) with initial denaturation at 94 °C for 5 min, 30 cycles consisting of denaturation at 94 °C for 30 s, annealing at 50 °C for 1 min and extension at 72 °C for 1 min 30 s and final extension at 72 °C for 10 min. The amplified PCR products were checked through 2% agarose gel and were sequenced bidirectionally, to check the validity of the sequence data, using an automated capillary sequencer (ABI 3730Xl) following the manufacturer’s recommendations.The total genomic DNA was extracted from muscle tissue excised from each fish specimen using phenol–chloroform method  and nucleotide diversity (π) were estimated using DnaSP 5.10 . It was observed that out of 842 bases, conserved sites, variables sites, parsimony informative sites and singleton were 838, 4, 2, and 2, respectively. Out of the five haplotypes obtained, h01 and h04 were found in samples from Moreh and Tamu; h02 and h03 in Moreh and h04 in Hiyangthang and Kodompokpi. The mean haplotype diversity (h) for samples collected from four populations was 0.574, while nucleotide diversity (π) obtained was 0.00077. AMOVA revealed that 82.55% was attributed to variation among population and 17.45% was due to within the population. Fst value was found to be significant at 0.825 (p < .05). Population pair wise Fst values ranged from 0.0 to 0.870  observed in this study is higher than that reported (32.4%) for non-migratory species (Vrijenhoek O. belangeri was abundant in the Imphal river which drained the adjoining plain area and finally formed the river Manipur, which flows directly from southern part of Manipur into the major river basin Chindwin in northern Myanmar. The migration of O. belangeri from Chindwin river to Imphal river was enabled due to the construction of Ithai Barrage in the middle of Imphal river course, the main route for entering into the Indian province. This gives rise to total disconnection of short distance migration of O. belangeri during breeding season between the two rivers system which can also be the reason for such genetic variation. Findings on mtDNA support presence of two genetic stocks, river and farm fish and it requires important management program for this species.In recent past, O. belangeri, an important fish having food value and threatened species. It must be taken into account that genetic divergence obtained in farm and wild population could be attributed to the environmental condition of the culture fish. This study will be useful in selecting the fish stocks that maintain better genetic diversity for being used in planning conservation and management strategies of O. belangeri.In conclusion, ATPase 6/8 region has proven its ability to detect population sub-structure in