一种基于Hypervolume指标的自适应邻域多目标进化算法

通过定义反映个体之间邻近程度的指标(个体的树邻域包含关系),在考虑个体间支配关系的基础上,利用个体与其周边个体的树邻域密度进行适应度赋值;提出了一种2,3维情况下个体独立支配区域的Hypervolume指标的计算方法,该方法用于评价个体对群体的贡献时只需要1次计算(同类方法需要2次计算);当外部种群中非支配个体数目超过规定规模时,根据个体独立支配区域的Hypervolume指标的大小对其进行修剪;在此基础上,提出了一种基于Hypervolume指标的自适应邻域多目标进化算法ANMOEA?HI.对比实验结果表明,ANMOEA?HI在保证了解集收敛性的同时亦拥有良好的分布性.

Detection of functional diversity gradients and their geoclimatic filters is sensitive to data types(occurrence vs.abundance)and spatial scales(sites vs.regions)

Functional diversity(FD)reflects within-and between-site variation of species traits(α-and β-FD,respectively).Understanding how much data types(occurrence-based vs.abundance-weighted)and spatial scales(sites vs.regions)change FD and ultimately interfere with the detection of underlying geoclimatic filters is still debated.To contribute to this debate,we explored the occurrence of 1690 species in 690 sites,abundances of 1198 species in 343 sites,and seven functional traits of the Atlantic Forest woody flora in South America.All FD indices were sensitive and dependent on the data type at both scales,with occurrence particularly increasing a richness and dispersion(occurrence>abundance in 80%of the sites)while abundance increased β total,β replacement,and α evenness(abundance>occurrence in 60%of the sites).Furthermore,detecting the effect of geoclimatic filters depended on the data type and was scale-dependent.At the site scale,precipitation seasonality and soil depth had weak effects on α-and β-FD(max.R^(2)=0.11).However,regional-scale patterns of a richness,dispersion,and evenness strongly mirrored the variation in precipitation seasonality,soil depth,forest stability over the last 120 kyr,and cation exchange capacity(correlations>0.80),suggesting that geoclimatic filters manifest stronger effects at the regional scale.Also,the role of edaphic gradients expands the idea of biogeographical filters beyond climate.Our findings caution functional biogeographic studies to consider the effect of data type and spatial scale before designing and reaching ecological conclusions about the complex nature of FD.

Leaf nitrogen and phosphorus are more sensitive to environmental factors in dicots than in monocots,globally

Leaf nitrogen(N)and phosphorus(P)levels provide critical strategies for plant adaptions to changing environments.However,it is unclear whether leaf N and P levels of different plant functional groups(e.g.,monocots and dicots)respond to environmental gradients in a generalizable pattern.Here,we used a global database of leaf N and P to determine whether monocots and dicots might have evolved contrasting strategies to balance N and P in response to changes in climate and soil nutrient availability.Specifically,we characterized global patterns of leaf N,P and N/P ratio in monocots and dicots,and explored the sensitivity of stoichiometry to environment factors in these plants.Our results indicate that leaf N and P levels responded to environmental factors differently in monocots than in dicots.In dicots,variations of leaf N,P and N/P ratio were significantly correlated to temperature and precipitation.In monocots,leaf N/P ratio was not significantly affected by temperature or precipitation.This indicates that leaf N,P and N/P ratio are less sensitive to environmental dynamics in monocots.We also found that in both monocots and dicots N/P ratios are associated with the availability of soil total P rather than soil total N,indicating that P limitation on plant growth is pervasive globally.In addition,there were significant phylogenetic signals for leaf N(λ=0.65),P(λ=0.57)and N/P ratio(λ=0.46)in dicots,however,only significant phylogenetic signals for leaf P in monocots.Taken together,our findings indicate that monocots exhibit a“conservative”strategy(high stoichiometric homeostasis and weak phylogenetic signals in stoichiometry)to maintain their growth in stressful conditions with lower water and soil nutrients.In contrast,dicots exhibit lower stoichiometric homeostasis in changing environments because of their wide climate-soil niches and significant phylogenetic signals in stoichiometry.

Fagus sylvatica and Quercus pyrenaica:Two neighbors with few things in common

Background:The Iberian Peninsula comprises one of the largest boundaries between Mediterranean and Eurosiberian vegetation,known as sub-Mediterranean zone.This ecotone hosts many unique plant species and communities and constitutes the low-latitude(warm)margin of numerous central European species which co-occur with Mediterranean vegetation.Two of the main species found in this region are the Eurosiberian European beech(Fagus sylvatica L.)and the Mediterranean Pyrenean oak(Quercus pyrenaica Willd.).It remains unclear how the different physiological and adaptive strategies of these two species reflect their niche partitioning within a subMediterranean community and to what extent phenotypic variation(intraspecific variability)is driving niche partitioning across Eurosiberian and Mediterranean species.Methods:We quantified functional niche partitioning,based on the n-dimensional hypervolume to nine traits related to resource acquisition strategies(leaf,stem and root)plus relative growth rate as an additional wholeplant trait,and the environmental niche similarity between Pyrenean oak and European beech.Further,we analyzed the degree of phenotypic variation of both target species and its relationship with relative growth rates(RGR)and environmental conditions.Plant recruitment was measured for both target species as a proxy for the average fitness.Results:Species’functional space was highly segregated(13.09%overlap),mainly due to differences in niche breadth(59.7%)rather than niche replacement(25.6%),and beech showed higher trait variability,i.e.,had larger functional space.However,both species shared the environmental space,i.e.,environmental niches were overlapped.Most plant traits were not related to abiotic variables or RGR,neither did RGR to plant traits.Conclusions:Both target species share similar environmental space,however,show notably different functional resource-use strategies,promoting a high complementarity that contributes to maintaining a high functionality in sub-Mediterranean ecosystems.Therefore,we propose that conservation efforts be oriented to preserve both species in these habitats to maximize ecosystem functionality and resilience.

Functional trait space and redundancy of plant communities decrease toward cold temperature at high altitudes in Southwest China

Plant communities in mountainous areas shift gradually as climatic conditions change with altitude. How trait structure in multivariate space adapts to these varying climates in natural forest stands is unclear. Studying the multivariate functional trait structure and redundancy of tree communities along altitude gradients is crucial to understanding how temperature change affects natural forest stands. In this study, the leaf area, specific leaf area, leaf carbon, nitrogen, and phosphorous content from 1,590 trees were collected and used to construct the functional trait space of 12 plant communities at altitudes ranging from 800 m to 3,800 m across three mountains. Hypervolume overlap was calculated to quantify species trait redundancy per community. First,hypervolumes of species exclusion and full species set were calculated, respectively. Second, the overlap between these two volumes was calculated to obtain hypervolume overlap. Results showed that the functional trait space significantly increased with mean annual temperature toward lower altitudes within and across three mountains, whereas species trait redundancy had different patterns between mountains. Thus, warming can widen functional trait space and alter the redundancy in plant communities. The inconsistent patterns of redundancy between mountains suggest that warming exerts varying influences on different ecosystems. Identification of climate-vulnerable ecosystems is important in the face of global warming.

Design optimization of multilayer perceptron neural network by ant colony optimization applied to engine emissions data

A multilayer perceptron(MLP) artificial neural network(ANN) model has been optimized by the multi-objective ant colony optimization(MOACO) algorithm, which uses three objective functions. A sensitivity analysis to choose MOACO parameter values is carried out by calculating hypervolume metric, and the proposed approach adopts the Vlsekriterijumska Optimizacija I Kompromisno Resenje(VIKOR) decision method to choose final compromised solution on the Pareto front obtained from MOACO. As a result, we used the MLP-MOACO developed model to estimate the value of engine emissions of NOxin a four stroke, spark ignition(SI) gasoline engine and observed acceptable correlation coefficient(R^2) of 0.99978.

Ecological niche modelling of a critically endangered species Commiphora wightii (Arn.) Bhandari using bioclimatic and non-bioclimatic variables

Background The aim of this study is to examine the effects of four different bioclimatic predictors(current,2050,2070,and 2090 under Shared Socioeconomic Pathways SSP2-4.5)and non-bioclimatic variables(soil,habitat heterogeneity index,land use,slope,and aspect)on the habitat suitability and niche dimensions of the critically endangered plant species Commiphora wightii in India.We also evaluate how niche modelling affects its extent of occurrence(EOO)and area of occupancy(AOO).Results The area under the receiver operating curve(AUC)values produced by the maximum entropy(Maxent)under various bioclimatic time frames were more than 0.94,indicating excellent model accuracy.Non-bioclimatic characteristics,with the exception of terrain slope and aspect,decreased the accuracy of our model.Additionally,Maxent accuracy was the lowest across all combinations of bioclimatic and non-bioclimatic variables(AUC=0.75 to 0.78).With current,2050,and 2070 bioclimatic projections,our modelling revealed the significance of water availability parameters(BC-12 to BC-19,i.e.annual and seasonal precipitation as well as precipitation of wettest,driest,and coldest months and quarters)on habitat suitability for this species.However,with 2090 projection,energy variables such as mean temperature of wettest quarter(BC-8)and isothermality(BC-3)were identified as governing factors.Excessive salt,rooting conditions,land use type(grassland),characteristics of the plant community,and slope were also noticed to have an impact on this species.Through distribution modelling of this species in both its native(west-ern India)and exotic(North-east,Central Part of India,as well as northern and eastern Ghat)habitats,we were also able to simulate both its fundamental niche and its realized niche.Our EOO and AOO analysis reflects the possibility of many new areas in India where this species can be planted and grown.Conclusion According to the calculated area under the various suitability classes,we can conclude that C.wight-ii’s potentially suitable bioclimatic distribution under the optimum and moderate classes would increase under all future bioclimatic scenarios(2090>2050≈current),with the exception of 2070,demonstrating that there are more suitable habitats available for C.wightii artificial cultivation and will be available for future bioclimatic projections of 2050 and 2090.Predictive sites indicated that this species also favours various types of landforms outside rocky environments,such as sand dunes,sandy plains,young alluvial plains,saline areas,and so on.Our research also revealed crucial information regarding the community dispersion variable,notably the coefficient of variation that,when bioclimatic non-bioclimatic variables were coupled,disguised the effects of bioclimatic factors across all time frames.

Transmission risk of Oropouche fever across the Americas

Background Vector-borne diseases(VBDs)are important contributors to the global burden of infectious diseases due to their epidemic potential,which can result in signifcant population and economic impacts.Oropouche fever,caused by Oropouche virus(OROV),is an understudied zoonotic VBD febrile illness reported in Central and South America.The epidemic potential and areas of likely OROV spread remain unexplored,limiting capacities to improve epidemiological surveillance.Methods To better understand the capacity for spread of OROV,we developed spatial epidemiology models using human outbreaks as OROV transmission-locality data,coupled with high-resolution satellite-derived vegetation phe‑nology.Data were integrated using hypervolume modeling to infer likely areas of OROV transmission and emergence across the Americas.Results Models based on one-support vector machine hypervolumes consistently predicted risk areas for OROV transmission across the tropics of Latin America despite the inclusion of diferent parameters such as diferent study areas and environmental predictors.Models estimate that up to 5 million people are at risk of exposure to OROV.Nevertheless,the limited epidemiological data available generates uncertainty in projections.For example,some out‑breaks have occurred under climatic conditions outside those where most transmission events occur.The distribu‑tion models also revealed that landscape variation,expressed as vegetation loss,is linked to OROV outbreaks.Conclusions Hotspots of OROV transmission risk were detected along the tropics of South America.Vegetation loss might be a driver of Oropouche fever emergence.Modeling based on hypervolumes in spatial epidemiology might be considered an exploratory tool for analyzing data-limited emerging infectious diseases for which little understand‑ing exists on their sylvatic cycles.OROV transmission risk maps can be used to improve surveillance,investigate OROV ecology and epidemiology,and inform early detection.

Spatial patterns and determinants of common root-associated fungi in a subtropical forest of China

Aims Spatial patterns of fungal populations are affected by plant distribu-tion,abiotic factors,fungal dispersal ability and inter-species interac-tions.While several studies have addressed spatial patterns of some mycorrhizal,saprotrophic and pathogenic fungi,the method based on fruit-body surveys is not efficient and unreliable to study the spa-tial pattern of root-associated fungal species because most fungi in plant roots do not have sporocarps and cannot be identified based only on morphological traits.Our aims are(i)to determine the spa-tial pattern of common root-associated fungi;(ii)to evaluate whether the abundance and spatial pattern of root-associated fungi and cat-egories of fungi,reflect their biotic and abiotic niche constraints.Methods About 828 soil cores were collected from a 24-ha plot in a sub-tropical forest and Illumina Miseq was carried out to determine fungal composition in root samples and spatial patterns of 1009 common fungal Operational Taxonomic Units(OTUs)were studied using point pattern analyses.Biotic(plant community composition)and abiotic niche constraints on the presence/abundance of a fun-gal OTU was assessed as the n-dimensional niche hypervolumes of biotic and abiotic characteristics.Important Findings Our results showed that(i)most fungal OTUs were highly spa-tially aggregated at small scales(less than 30 m),but that the aggregated pattern decreased,while regular and random patterns increased,with the increasing distance;(ii)A significant positive correlation was found between fungal abundance and aggrega-tion intensity of fungal OTUs,indicating that the dominant fungi tended to be more aggregated in natural forests;(iii)Mean abun-dance and spatial aggregation intensity of ectomycorrhizal and pathogenic fungi were relatively higher than those of saprotrophic fungi,indicating that host plants may play an important role in determining spatial pattern of root-associated fungi;(iv)Spatial patterns of root-associated fungi depended on fungal abundance,fungal functional group,fungal taxa,biotic and abiotic niche hypervolumes of fungal OTUs.

Chaotic Simulator for Bilevel Optimization of Virtual Machine Placements in Cloud Computing

The drastic increase in engineering system complexity has spurred the development of highly efficient optimization techniques.Many real-world optimization problems have been identified as bilevel/multilevel as well as multiobjective.The primary aim of this work is to present a framework to tackle the bilevel virtual machine(VM)placement problem in cloud systems.This is done using the coupled map lattice(CML)approach in conjunction with the Stackelberg game theory and weighted-sum frameworks.The VM placement problem was modified from the original multiobjective(MO)problem to an MO bilevel formulation to make it more realistic albeit more complicated.Additionally comparative analysis on the performance of the CML approach was carried out against the particle swarm optimization method.A new bilevel metric called the cascaded hypervolume indicator is introduced and applied to measure the dominance of the solutions produced by both methods.Detailed analysis on the computational results is presented.

Multi-category diagnostic accuracy based on logistic regression

We provide a detailed review for the statistical analysis of diagnostic accuracy in a multi-category classification task.For qualitative response variables with more than two categories,many traditional accuracy measures such as sensitivity,specificity and area under the ROC curve are no longer applicable.In recent literature,new diagnostic accuracy measures are introduced in medical research studies.In this paper,important statistical concepts for multi-category classification accuracy are reviewed and their utilities are demonstrated with real medical examples.We offer problem-based R code to illustrate how to perform these statistical computations step by step.We expect such analysis tools will become more familiar to practitioners and receive broader applications in biostatistics.Our program can be adapted to many classifiers among which logistic regression may be the most popular approach.We thus base our discussion and illustration completely on the logistic regression in this paper.