Effects of virtual agents on interaction efficiency and environmental immersion in MR environments

Background Physical entity interactions in mixed reality(MR)environments aim to harness human capabilities in manipulating physical objects,thereby enhancing virtual environment(VEs)functionality.In MR,a common strategy is to use virtual agents as substitutes for physical entities,balancing interaction efficiency with environmental immersion.However,the impact of virtual agent size and form on interaction performance remains unclear.Methods Two experiments were conducted to explore how virtual agent size and form affect interaction performance,immersion,and preference in MR environments.The first experiment assessed five virtual agent sizes(25%,50%,75%,100%,and 125%of physical size).The second experiment tested four types of frames(no frame,consistent frame,half frame,and surrounding frame)across all agent sizes.Participants,utilizing a head mounted display,performed tasks involving moving cups,typing words,and using a mouse.They completed questionnaires assessing aspects such as the virtual environment effects,interaction effects,collision concerns,and preferences.Results Results from the first experiment revealed that agents matching physical object size produced the best overall performance.The second experiment demonstrated that consistent framing notably enhances interaction accuracy and speed but reduces immersion.To balance efficiency and immersion,frameless agents matching physical object sizes were deemed optimal.Conclusions Virtual agents matching physical entity sizes enhance user experience and interaction performance.Conversely,familiar frames from 2D interfaces detrimentally affect interaction and immersion in virtual spaces.This study provides valuable insights for the future development of MR systems.

A Study on the Effects of Different Interaction Combinations and Language Levels on Continuous Writing in Online Environments

This study was conducted with non-English sophomore students,aiming to explore the effects of different interaction combinations and language levels on continuous writing in an online environment,and compare the differences in lexical alignments and composition quality of learners with different interaction combinations and language levels in the same continuous writing task through experiments.The results show that the mean values of the word-phrase alignment of the paired group were higher than those of the individual group in different interaction combinations,and the two groups showed significant differences;in terms of composition quality,the individual group was better than the paired group,but there was no significant difference between the two groups in terms of task continuation.Secondly,the word-phrase alignment and composition scores of the different language-level groups were higher than those of the same language-level groups,and there was a significant difference between the two groups in terms of word-phrase alignments,but not in terms of composition scores.The results of this study can be useful and informative for second language teachers in future continuous teaching in online environments.

Analysis of Commuting Modal Shift in Consideration of Social Interaction of Consciousness for Environment

It is the matter for achievement of the low carbon transport system that the excessive use of private vehicles can be controlled appropriately.Not only improvement of service level of modes except private vehicle,but also consciousness for environmental problem of individual trip maker is important for eco-commuting promotion.On the other hand,consciousness for environment would be changed by influence of other person.Accordingly,it is aimed in the study that the structure of decision-making process for modal shift to the eco-commuting mode in the local city is described considering environmental consciousness and social interaction.For the purpose,the consciousness for the environment problem and the travel behavior of the commuter at the suburban area in the local city are investigated by the questionnaire survey.The covariance structure about the eco-consciousness is analyzed with the database of the questionnaire survey by structural equation modeling.As the result,it can be confirmed with the structural equation model that the individual environmental consciousness is strongly related with the intention of self-sacrifice and is influenced with the local interaction of the individual connections.On the other hand,the intention of modal shift for the commuting mode is analyzed with the database of the questionnaire survey.It can be found out that the environmental consciousness is not statistically significant for commuting mode choice with the present poor level of service of public transport.However,the intention of self-sacrifice for the prevention of the global warming is statistically confirmed as the factor of modal shift with the operation of eco-commuting bus service with the RP/SP integrated estimation method.As the result,the multi-agent simulation system with social interaction model for eco consciousness is developed to measure the effect of the eco-commuting promotion.For the purpose,the carbon dioxide emission is estimated based on traffic demand and road network condition in the traffic environment model.On the other hand,the relation between agents is defined based on the small world network.The proposed multi-agent simulation is applied to measure the effect of the eco-commuting promotion such as improvement of level of service on the public transport or education of eco-consciousness.The effect of the promotion plan can be observed with the proposed multi-agent system.Finally,it can be concluded that the proposed multi-agent simulation with social interaction for eco-consciousness is useful for planning of eco-commuting promotion.

Effects of erect panicle genotype and environment interactions on rice yield and yield components

The dense and erect panicle (EP) genotype conferred by DEP1 has been widely used in the breeding of high-yield Chinese japonica rice varieties.However,the breeding value of the EP genotype has rarely been determined at the plant population level.Therefore,the effects of the interaction of EP genotype and the environment at different locations and times on rice yield and its various components were investigated in this study.Two sets of near-isogenic lines (NILs)of EP and non-EP (NEP) genotypes with Liaojing 5 (LG5) and Akitakomachi (AKI) backgrounds were grown in the field in 2016 and 2017 in Shenyang,China,and Kyoto,Japan.In 2018,these sets were grown only in Kyoto,Japan.The average yields of the EP and NEP genotypes were 6.67 and 6.13 t ha^(-1)for the AKI background,and 6.66 and 6.58 t ha^(-1)for the LG5 background,respectively.The EP genotype positively affected panicle number (PN) and grain number per square meter (GNPM),mostly resulting in a positive effect on harvest index (HI).In contrast,the EP genotype exerted a negative effect on thousand-grain weight (KGW).The ratio of the performance of the EP genotype relative to the NEP genotype in terms of yield and total biomass correlated positively with mean daily solar radiation during a 40-day period around heading.These results indicate that the effectiveness of the EP genotype depends on the availability of solar radiation,and the effect of this genotype is consistently positive for sink formation,conditional in terms of source capacity,and positive in a high-radiation environment.

Influence of Epistasis and QTL×Environment Interaction on Heading Date of Rice(Oryza sativa L.)

QTLs for heading date of rice （Oryza sativa L.） with additive, epistatic, and QTL × environment （QE） interaction effects were studied using a mixed-model-based composite interval mapping （MCIM） method and a double haploid （DH） population derived from IR64/Azucena in two crop seasons. Fourteen QTLs conferring heading date in rice, which were distributed on ten chromosomes except for chromosomes 5 and 9, were detected. Among these QTLs, eight had single-locus effects, five pairs had double-locus interaction effects, and two single-loci and one pair of double-loci showed QTL × environment interaction effects. All predicted values of QTL effects varied from 1.179 days to 2.549 days, with corresponding contribution ratios of 1.04%-4.84%. On the basis of the effects of the QTLs, the total genetic effects on rice heading date for the two parents and the two superior lines were predicted, and the putative reasons for discrepancies between predicted values and observed values, and the genetic potentiality in the DH population for improvement of heading date were discussed. These results are in agreement with previous results for heading date in rice, and the results provide further information, which indicate that both epistasis and QE interaction are important genetic basis for determining heading date in rice.

Root Sprouts of Domesticated Phaseolus coccineus L. Overwinter Plants and the Environment at Its Collection Sites

Buds emerge from the roots of domesticated runner bean plants (Phaseolus coccineus L.) as sprouts and give the crop its persistence. This particularity of the species seems to be associated with the environment where the species was cultivated during long time. Data on regrowth traits were recorded on 70 accessions of domesticated P. coccineus after overwintering at a site in the central highlands of Mexico. The characteristics recorded included fresh weight of vegetative regrowth, root length, root crown diameter, and diameter of the main root. Most accessions that sprout the following spring, after the grain harvest of the previous year, come from temperate and semi-cold sites with cambisol and andosol soils in mixed Quercus pine forest and high mountain fir trees. Those that do not sprout are from sites of semiarid temperate climate, haplic phaeozem soil in shrub grasslands with deciduous broadleaved trees, and semiwarm subhumid climate, eutrophic regosol soil in deciduous broadleaved forest. Two types of roots were differentiated: branched and thickened taproots, also associated with a different climate, and the type of soil of the collection sites.

Mapping QTLs with epistatic effects and QTL×environment interactions for plant height using a doubled haploid population in cultivated wheat

Quantitative trait loci （QTLs） for plant height in wheat （Triticum aestivum L.） were studied using a set of 168 doubled haploid （DH） lines, which were derived from the cross Huapei 3/Yumai 57. A genetic linkage map was constructed using 283 SSR and 22 EST-SSR markers. The DH population and the parents were evaluated for wheat plant height in 2005 and 2006 in Tai＇an and 2006 in Suzhou. QTL analyses were performed using the software of QTLNetwork version 2.0 based on the mixed linear model. Four additive QTLs and five pairs of epistatic effects were detected, which were distributed on chromosomes 3A, 4B, 4D, 5A, 6A, 7B, and 7D. Among them, three additive QTLs and three pairs of epistatic QTLs showed QTL×environment interactions （QEs）. Two major QTLs, Qph4B and Qph4D, which accounted for 14.51% and 20.22% of the phenotypic variation, were located similar to the reported locations of the dwarfing genes Rhtl and Rht2, respectively. The Qph3A-2 with additive effect was not reported in previous linkage mapping studies. The total QTL effects detected for the plant height explained 85.04% of the phenotypic variation, with additive effects 46.07%, epistatic effects 19.89%, and QEs 19.09%. The results showed that both additive effects and epistatic effects were important genetic bases of wheat plant height, which were subjected to environmental modifications, and caused dramatic changes in phenotypic effects. The information obtained in this study will be useful for manipulating the QTLs for wheat plant height by molecular marker-assisted selection （MAS）.

QTL mapping and QTL × environment interaction analysis of multi-seed pod in cultivated peanut(Arachis hypogaea L.)

To dissect the genetic mechanism of multi-seed pod in peanut, we explored the QTL/gene controlling multi-seed pod and analyzed the interaction effect of QTL and environment. Two hundred and forty eight recombinant inbred lines(RIL) from cross Silihong × Jinonghei 3 were used as experimental materials planted in 8 environments from 2012 to 2017. Three methods of analysis were performed. These included individual environment analysis, joint analysis in multiple environments, and epistatic interaction analysis for multi-seed pod QTL. Phenotypic data and best linear unbiased prediction(BLUP) value of the ratio of multi-seed pods per plant(RMSP) were used for QTL mapping. Seven QTL detected by the individual environmental mapping analysis and were distributed on linkage groups 1, 6, 9, 14, 19(2), and 21. Each QTL explained 4.42%–11.51% of the phenotypic variation in multi-seed pod, and synergistic alleles of5 QTL were from the Silihong parent. One QTL, explaining 4.93% of the phenotypic variation was detected using BLUP data, and this QTL mapped in the same interval as q RMSP19.1 detected in the individual environment analysis. Seventeen additive QTL were identified by joint analysis across multiple environments. A total of 43 epistatic QTL were detected by ICIM-EPI mapping in the multiple environment trials(MET) module, and involved 57 loci. Two main-effect QTL related to multi-seed pod in peanut were filtered. We also found that RMSP had a highly significant positive correlation with pod yield per plant(PY), and epistatic effects were much more important than additive effects. These results provide theoretical guidance for the genetic improvement of germplasm resources and further fine mapping of related genes in peanut.

Characterization of the Main Effects, Epistatic Effects and Their Environmental Interactions of QTL on the Genetic Basis of Plant Height and Heading Date in Rice

Main-effect QTL, epistatic effects and their interactions with environment are important genetic components of quantitativetraits. In this study, we analyzed the QTL, epistatic effects and QTL by environment interactions (QE) underlying plantheight and heading date, using a doubled-haploid ( DH) population consisting of 190 lines from the cross between anindica parent Zhenshan 97 and a japonica parent Wuyujing 2, and tested in two-year replicated field trials. A geneticlinkage map with 179 SSR (simple sequence repeat) marker loci was constructed. A mixed linear model approach wasapplied to detect QTL, digenic interactions and QEs for the two traits. In total, 20 main-effect QTLs, 9 digenic interactionsinvolving 18 loci, and 5 QTL by environment interactions were found to be responsible for the two traits. No interactionswere detected between the digenic interaction and environment. The amounts of variations explained by QTLs of maineffect were 53.9% for plant height and 57.8% for heading date, larger than that explained by epistasis and QEs. However,the epistasis and QE interactions sometimes accounted for a significant part of phenotypic variation and should not bedisregarded.

Mapping epistasis and environment × QTX interaction based on four-omics genotypes for the detected QTX loci controlling complex traits in tobacco

Using newly developed methods and software, association mapping was conducted for chromium content and total sugar in tobacco leaf, based on four-omics datasets. Our objective was to collect data on genotype and phenotype for 60 leaf samples at four developmental stages, from three plant architectural positions and for three cultivars that were grown in two locations. Association mapping was conducted to detect genetic variants at quantitative trait SNP(QTS) loci, quantitative trait transcript(QTT) differences,quantitative trait protein(QTP) variability, and quantitative trait metabolite(QTM) changes,which can be summarized as QTX locus variation. The total heritabilities of the four-omics loci for both traits tested were 23.60% for epistasis and 15.26% for treatment interaction.Epistasis and environment × treatment interaction had important impacts on complex traits at all-omics levels. For decreasing chromium content and increasing total sugar in tobacco leaf, six methylated loci can be directly used for marker-assisted selection, and expression of ten QTTs, seven QTPs and six QTMs can be modified by selection or cultivation.

Genetic and Genotype × Environment Interaction Effects for Appearance Quality of Rice

This study was conducted to generate genetic information in rice varieties based on a complete diallel crosses over two years. The results indicated that genotype effect was significant for all traits. Genotype × environment interaction effects were significant only for cooked grain length （CGL） and cooked grain shape （CGSH）. General combining ability （GCA） and specific combining ability （SCA） effects were significant for entire traits, which indicated the important roles of both additive and non-additive gene actions. GCA x environment interaction effects were significant for CGL, CGSH and grain elongation index （GEI）. In the controlling of the inheritance of milled grain shape （GSH）, milled grain width （MGW）, GEI, milled grain length （MGL）, CGSH and cooked grain width （CGW）, the additive gene effects were more important than non-additive one. The average degree of dominance was within the range of partial dominance for all of the traits. The narrow-sense heritability was ranged from 0.65 （GSH） to 0.36 （CGL）. GCA effects were significant for all of the parents in milled grain length and it was significant for some of the parents in other traits. The crosses of Deilmani × IRFAON-215 exhibited significant SCA for GEI. The positive mean of heterosis was observed for CGW. The highest maximum values of heterosis were revealed in GEI, flowed by GSH, MGW and CGW. GCA and MPV were significantly and positively correlated together for all traits.

Genotype-by-environment interaction for grain yield among novel cowpea(Vigna unguiculata L.) selections derived by gamma irradiation

This study determined the effects of genotype-by-environment(G × E) interaction and stability of yield among elite cowpea(Vigna unguiculata L.) selections derived by gamma irradiation. The study was conducted in Namibia at three selected sites: Bagani, Mannheim,and Omahenene, during 2014/2015 and 2015/2016. Thirty-four newly developed mutant genotypes and three local checks were evaluated using a randomized complete block design with three replications. Grain yield data were analyzed using the additive main effects and multiplicative interaction(AMMI) and the genotype main effect plus genotype-by-environment interaction(GGE) biplot methods. The AMMI and GGE biplot models explained 77.49% and 75.57% of total observed genotypic variation, respectively.Bagani and Omahenene were the environments best discriminating the test genotypes during 2014/2015 and 2015/2016, respectively. Four promising mutant genotypes: G9(Sh L3 P74), G10(Sh R3 P4), G12(Sh R9 P5), and G4(Sh L2 P4), showed wide adaptation and grain yields of 2.83, 2.06, 1.99, and 1.95 t ha^(-1), respectively. The novel mutant lines are useful genetic resources for production or future cowpea breeding programs in Namibia or similar environments.

Interaction of methylenetetrahydrofolate reductase C677T,cytochrome P4502E1 polymorphism and environment factors in esophageal cancer in Kazakh population

AIM： To evaluate the association and interaction of genetic polymorphisms in methylenetetrahydrofolate reductase （MTHER） and cytochrome P4502E1 （CY- P4502E1）, environment risk factors with esophageal cancer （EC） in Kazakh, a high EC incidence area of Xinjiang Uygur Autonomous Region, China. METHODS： A 1：2 matched case-control study was conducted with 120 cases of EC and 240 populationor hospital-based controls. The controls were matched for sex, nationality, area of residence and age within a 5-year difference. MTHER and CYP4502E1 genotypes were identified by PCR-based restriction fragment length polymorphism （RFLP）. A conditional logistic regression model was established to identify risk factors. The strata method was adopted in interaction analysis. RESULTS： Low consumption of green vegetables and fresh fruits, alcohol drinking, and unsafe water （shallow well, or river） were found to be the risk factors for EC. Individuals with the MTHFR677 （C/T ＋ T/T） genotype had a 2.62-fold （95% CI： 1.61-4.28） risk of developing EC compared with those who carried the C/C genotype. Individuals with the CYP4502EIC1/C1 genotype had a 3.00-fold （95% CI： 1.82-4.96） risk compared with those who carried the CYP4502E1 （C1/C2 ＋ C2/C2） genotype. Gene-environment interaction analysis showed that MTHFR677 gene polymorphism was correlated with consumption of green vegetables and fresh fruit, while CYP4502E1 C1/C1 was correlated with alcohol drinking and unsafe drinking water. MTHFR and CYP4502E1 analysis of gene-gene interaction showed that individuals with the MTHFR677 （C/T ＋ T/T） and CYP4502EIC1/ C1 genotypes had a 7.41-fold （95% CI： 3.60-15.25） risk of developing EC compared with those who carried the MTHFR677C/C and CYP4502E1 RsaI C1/C2 ＋ C2/C2 genes, and the interaction rate was higher than that of the two factors alone. CONCLUSION： Low consumption of green vegetables and fresh fruits, alcohol drinking, and unsafe water （shallow well, or river） and polymorphisms in MTHFR and CYP4502E1 genes are important risk factors for EC. There is a synergistic interaction among polymorphisms in MTHFR and CYP4502E1 genes and environment factors. MTHFR and CYP4502E1 genes can be used as biomarkers for prevention of EC in Kazakh, Xinjiang Uygur Autonomous Region, China.

Genetic background effects on QTL and QTL × environment interaction for yield and its component traits as revealed by reciprocal introgression lines in rice

QTLs for quantitative traits are influenced by genetic background(GB) and environment.Identification of QTL with GB independency and environmental stability is prerequisite for effective marker-assisted selection(MAS). In this study, QTLs and QTL × environment interactions affecting grain yield per plant(GY) and its component traits, filled grain number per panicle(FGN), panicle number per plant(PN) and 1000-grain weight(TGW) across six environments were dissected using two sets of reciprocal introgression lines(ILs) derived from the cross Lemont × Teqing and SNP genotypic data. ANOVA indicated that the differences among genotypes and environments within each set of ILs were highly significant for all traits. A total of 72 distinct QTLs for GY and its component traits including 15 for GY, 25 for FGN, 18 for PN, and 29 for TGW were detected over the six environments. Most QTLs(87.4%) showed significant QTL × environment interactions(QEIs) and appeared to be more or less environment-specific. Among 72 QTLs, 15(20.8%) QTLs and 12(16.7%) QEIs were commonly identified in both backgrounds, indicating QTL especially QEI for yield and its component traits had strong GB effects. Four QTL regions affecting GY and its component traits, including S1269707–S4288071, S16661497–S17511092, and S35861863–S36341768 on chromosome 3, and S4134205–S7643153 on chromosome 5, were detected in both backgrounds and coincided with cloned genes for yield-related traits. These regions can be the targeted in rice breeding for high yield potential through MAS. Application of QTL main effects and their environmental interaction effects in MAS was discussed in detail.

Mixed Model, AMMI and Eberhart-Russel Comparison via Simulation on Genotype ×Environment Interaction Study in Sugarcane

Brazil is the world leader in sugarcane production and the largest sugar exporter. Developing new varieties is one of the main factors that contribute to yield increase. In order to select the best genotypes, during the final selection stage, varieties are tested in different environments (locations and years), and breeders need to estimate the phenotypic performance for main traits such as tons of cane yield per hectare (TCH) considering the genotype × environment interaction (GEI) effect. Geneticists and biometricians have used different methods and there is no clear consensus of the best method. In this study, we present a comparison of three methods, viz. Eberhart-Russel (ER), additive main effects and multiplicative interaction (AMMI) and mixed model (REML/BLUP), in a simulation study performed in the R computing environment to verify the effectiveness of each method in detecting GEI, and assess the particularities of each method from a statistical standpoint. In total, 63 cases representing different conditions were simulated, generating more than 34 million data points for analysis by each of the three methods. The results show that each method detects GEI differently in a different way, and each has some limitations. All three methods detected GEI effectively, but the mixed model showed higher sensitivity. When applying the GEI analysis, firstly it is important to verify the assumptions inherent in each method and these limitations should be taken into account when choosing the method to be used.

Gene-gene,gene-environment,gene-nutrient interactions and single nucleotide polymorphisms of inflammatory cytokines

Inflammation plays a significant role in the etiology of type 2 diabetes mellitus(T2DM).The rise in the pro-inflammatory cytokines is the essential step in glucotoxicity and lipotoxicity induced mitochondrial injury,oxidative stress and beta cell apoptosis in T2 DM.Among the recognized markers are interleukin(IL)-6,IL-1,IL-10,IL-18,tissue necrosis factor-alpha(TNF-α),C-reactive protein,resistin,adiponectin,tissue plasminogen activator,fibrinogen and heptoglobins.Diabetes mellitus has firm genetic and very strong environmental influence; exhibiting a polygenic mode of inheritance.Many single nucleotide polymorphisms(SNPs) in various genes including those of pro and antiinflammatory cytokines have been reported as a risk for T2 DM.Not all the SNPs have been confirmed by unifying results in different studies and wide variations have been reported in various ethnic groups.The inter-ethnic variations can be explained by the fact that gene expression may be regulated by gene-gene,gene-environment and gene-nutrient interactions.This review highlights the impact of these interactions on determining the role of single nucleotide polymorphism of IL-6,TNF-α,resistin and adiponectin in pathogenesis of T2 DM.

Evaluation of Genotype × Environment Interaction in Rice Based on AMMI Model in Iran

Identification of high-yielding stable promising rice lines and determination of suitable areas for rice lines would be done by additive main effects and multiplicative interaction(AMMI) model. Seven promising rice genotypes plus two check varieties Shiroudi and 843 were analyzed using a randomized complete block design with three replications in three consecutive years(2012, 2013 and 2014). Homogenous error variance was indicated in the nine environments for grain yield. The combined analysis of variance indicated significant effects of environment, genotype and genotype × environment(GE) interactions on grain yield. The significant effect of GE interaction reflected on the differential response of genotypes in various environments and demonstrated that GE interaction had remarkable effect on genotypic performance in different environments. The application of AMMI model for partitioning the GE interaction effects showed that only the first two terms of AMMI were significant based on Gollob's Ftest. The lowest AMMI-1 was observed for G7, G2 and G6. G7 and G6 had higher grain yield. According to the first eigenvalue, which benefits only the first interaction principal component scores, G1, G6, G2 and G9 were the most stable genotypes. The values of the sum of first two interaction principal component scores could be useful in identifying genotype stability, and G6, G5 and G2 were the most dynamic stable genotypes. AMMI stability value introduced G6 as the most stable one. According to AMMI biplot view, G6 was high yielding and highly stable genotype. In conclusion, this study revealed that GE interactions were an important source of rice yield variation, and its AMMI biplots were forceful for visualizing the response of genotypes to environments.

Genotype × Environment Interaction for Iron Concentration of Rice in Central Java of Indonesia

To explore the effect of genotype and genotype x environment interaction on Fe concentration in rice grains, Fe concentrations of 10 genotypes were analyzed across eight paddy field environments during 2007-2008 using the AMMI-biplot method. Experiments were conducted using a randomized completely block design with three replications in eight environments. Results indicated that environment （E）, genotype （G） and genotype x environment interaction （GE） significantly affected Fe concentration in rice grains. Environment explained 74.43 % of total （G＋E＋GE） variation, whereas G and GE captured 5.60% and 19.67%, respectively. Rice genotype Barumun was desirable in terms of the highest ability and stability for Fe concentration in rice grains. Environment in genotype Cilongok was the best representative of the overall environments and the most powerful to discriminate rice genotypes.

The Roles of Biotic Interactions and Environmental Factors on Multispecies Dynamics

The distributions of species in their habitats are constantly changing. This phenomenon is thought to be determined by species’ environmental tolerance and biotic interactions for limited resources and space. Consequently, predicting the future distribution of species is a major challenge in ecology. To address this problem, we use mathematical model to study the combined effects of biotic interactions (e.g. competition) and environmental factors on multiple species community assembly in a heterogenous environment. To gain insights into the dynamics of this ecological system, we perform both analytical and numerical analyses of the range margins of the species. We observe that the range margins of the species can be influenced by biotic interactions combined with environmental factors. Depending on the strength of biotic interactions, our model exhibits coexistence of species and priority effects;mediated by weak and intense biotic interactions respectively. We also show the existence of bifurcation points (i.e. the threshold values of competition coefficient) which lead to the presence—absence of different species. Thus, we suggest that adequate knowledge of biotic interactions and changes in the environments is important for effective maintenance of biodiversity and conservation management.

RGxE: An R Program for Genotype x Environment Interaction Analysis

Genotype x environmental interaction (GxE) can lead to differences in performance of genotypes over environments. GxE analysis can be used to analyze the stability of genotypes and the value of test locations. We developed an Rlanguage program (RGxE) that computes univariate stability statistics, descriptive statistics, pooled ANOVA, genotype F ratio across location and environment, cluster analysis for location, and location correlation with average location performance. Univariate stability statistics calculated are regression slope (bi), deviation from regression (S2d), Shukla’s variance (σi2), S square Wricke’s ecovalence (Wi), and Kang’s yield stability (YSi). RGxE is free and intended for use by scientists studying performance of polygenic or quantitative traits over multiple environments. In the present paper we provide the RGxE program and its components along with an example input data and outputs. Additionally, the RGxE program along with associated files is also available on GitHub at https://github.com/mahendra1/RGxE, http://cucurbitbreeding.com/todd-wehner/publications/software-sas-r-project/? and http://cuke.hort.ncsu.edu/cucurbit/wehner/software.html.