Assessing temporal scales and patterns in time series: Comparing methods based on redundancy analysis

Time-series modelling techniques are powerful tools for studying temporal scaling structures and dynamics present in ecological and other complex systems and are gaining popularity for assessing resilience quantitatively. Among other methods, canonical ordinations based on redundancy analysis are increasingly used for determining temporal scaling patterns that are inherent in ecological data. However, modelling outcomes and thus inference about ecological dynamics and resilience may vary depending on the approaches used. In this study, we compare the statistical performance, logical consistency and information content of two approaches: (i) asymmetric eigenvector maps (AEM) that account for linear trends and (ii) symmetric distance-based Moran's eigenvector maps (MEM), which requires detrending of raw data to remove linear trends prior to analysis. Our comparison is done using long-term water quality data (25 years) from three Swedish lakes. This data set therefore provides the opportunity for assessing how the modelling approach used affects performance and inference in time series modelling. We found that AEM models had consistently more explanatory power than MEM, and in two out of three lakes AEM extracted one more temporal scale than MEM. The scale-specific patterns detected by AEM and MEM were uncorrelated. Also individual water quality variables explaining these patterns differed between methods, suggesting that inferences about systems dynamics are dependent on modelling approach. These findings suggest that AEM might be more suitable for assessing dynamics in time series analysis compared to MEM when temporal trends are relevant. The AEM approach is logically consistent with temporal autocorrelation where earlier conditions can influence later conditions but not vice versa. The symmetric MEM approach, which ignores the asymmetric nature of time, might be suitable for addressing specific questions about the importance of correlations in fluctuation patterns where there are no confounding elements of linear trends or a need to assess causality.     

Effects of wind‐driven spatial structure and environmental heterogeneity on high‐altitude wetland macroinvertebrate assemblages with contrasting dispersal modes

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Statistical methods for temporal and space–time analysis of community composition data

This review focuses on the analysis of temporal beta diversity, which is the variation in community composition along time in a study area. Temporal beta diversity is measured by the variance of the multivariate community composition time series and that variance can be partitioned using appropriate statistical methods. Some of these methods are classical, such as simple or canonical ordination, whereas others are recent, including the methods of temporal eigenfunction analysis developed for multiscale exploration (i.e. addressing several scales of variation) of univariate or multivariate response data, reviewed, to our knowledge for the first time in this review. These methods are illustrated with ecological data from 13 years of benthic surveys in Chesapeake Bay, USA. The following methods are applied to the Chesapeake data: distance-based Moran''s eigenvector maps, asymmetric eigenvector maps, scalogram, variation partitioning, multivariate correlogram, multivariate regression tree, and two-way MANOVA to study temporal and space–time variability. Local (temporal) contributions to beta diversity (LCBD indices) are computed and analysed graphically and by regression against environmental variables, and the role of species in determining the LCBD values is analysed by correlation analysis. A tutorial detailing the analyses in the R language is provided in an appendix.     

Environmental and spatial processes influencing phytoplankton biomass along a reservoirs‐river‐floodplain lakes gradient: A metacommunity approach

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Multifaceted aspects of synchrony between freshwater prokaryotes and protists

Community composition of freshwater prokaryotes and protists varies through time. Few studies contemporarily investigate temporal variation of these freshwater communities for more than 1 year. We compared the temporal patterns of prokaryotes and protists in three distinct habitats for 4 years (2014–2017) in Lake Tovel, a cold‐water lake. This lake showed a marked temperature increase in 2017 linked to altered precipitation patterns. We investigated whether microbial communities reflected this change across habitats and whether changes occurred at the same time and to the same extent. Furthermore, we tested the concept of hydrological year emphasizing the ecological effect of water renewal on communities for its explanatory power of community changes. Microbe diversity was assessed by Illumina sequencing of the V3–V4 hypervariable region of the 16S rRNA gene and 18S rRNA gene, and we applied co‐inertia analysis and asymmetric eigenvector maps modelling to infer synchrony and temporal patterns of prokaryotes and protists. When considering community composition, microbes were invariable in synchrony across habitats and indicated a temporal gradient linked to decreasing precipitation; however, when looking at temporal patterns, the extent of synchrony was reduced. Small‐scale patterns were similar across habitats and microbes and linked to seasonally varying environmental variables, while large‐scale patterns were different and partially linked to an ecosystem change as indicated by increasing water transparency and temperature and decreasing dissolved oxygen. Our advanced statistical approach outlined the multifaceted aspect of synchrony when linked to community composition and temporal patterns.     

Asymmetric oceanographic processes mediate connectivity and population genetic structure,as revealed by RADseq,in a highly dispersive marine invertebrate (Parastichopus californicus)

Marine populations are typically characterized by weak genetic differentiation due to the potential for long‐distance dispersal favouring high levels of gene flow. However, strong directional advection of water masses or retentive hydrodynamic forces can influence the degree of genetic exchange among marine populations. To determine the oceanographic drivers of genetic structure in a highly dispersive marine invertebrate, the giant California sea cucumber (Parastichopus californicus), we first tested for the presence of genetic discontinuities along the coast of North America in the northeastern Pacific Ocean. Then, we tested two hypotheses regarding spatial processes influencing population structure: (i) isolation by distance (IBD: genetic structure is explained by geographic distance) and (ii) isolation by resistance (IBR: genetic structure is driven by ocean circulation). Using RADseq, we genotyped 717 individuals from 24 sampling locations across 2,719 neutral SNPs to assess the degree of population differentiation and integrated estimates of genetic variation with inferred connectivity probabilities from a biophysical model of larval dispersal mediated by ocean currents. We identified two clusters separating north and south regions, as well as significant, albeit weak, substructure within regions (F_ST = 0.002, p = .001). After modelling the asymmetric nature of ocean currents, we demonstrated that local oceanography (IBR) was a better predictor of genetic variation (R² = .49) than geographic distance (IBD) (R² = .18), and directional processes played an important role in shaping fine‐scale structure. Our study contributes to the growing body of literature identifying significant population structure in marine systems and has important implications for the spatial management of P. californicus and other exploited marine species.     

Genetic analysis of seed size, yield and days to flowering in a chickpea recombinant inbred line population derived from a Kabuli × Desi cross

Quantitative traits, seed size, yield and days to flowering were studied in a chickpea intraspecific recombinant inbred line (RIL) population (F_6:7) derived from a Kabuli × Desi cross. The population was evaluated in two locations over 2 years. Days to flowering was also evaluated in the greenhouse under short-day conditions. Seed size was the most heritable trait (0.90), followed by days to flowering (0.36) and yield (0.14). Negative and significant correlation was found between yield and seed size in the second year where environmental homogeneity was tested by analysing the controls included in each assay. During the first year, the environment was not considered homogeneous for yield in either location. Quantitative trait loci (QTLs) for the three characters were detected in linkage group (LG) 4. In relation to seed size, two QTLs were located in LG4 (QTL_SW1) and LG8 (QTL_SW2). QTL_SW1 accounted 20.3% of the total phenotypic variation and QTL_SW2 explained 10.1%. A QTL for yield (QTL_YD) was located in LG4 explaining around 13% of variation. QTL_YD might be pleiotropic with QTL_SW1. For days to flowering, a QTL (QTL_DF1) was located in LG4 for all environments analysed explaining around 20% of variation. QTL_DF1 was closely linked to QTL_SW1 and QTL_YD in LG4.     

Estimating and controlling for spatial structure in the study of ecological communities

Aim Variation partitioning based on canonical analysis is the most commonly used analysis to investigate community patterns according to environmental and spatial predictors. Ecologists use this method in order to understand the pure contribution of the environment independent of space, and vice versa, as well as to control for inflated type I error in assessing the environmental component under spatial autocorrelation. Our goal is to use numerical simulations to compare how different spatial predictors and model selection procedures perform in assessing the importance of the spatial component and in controlling for type I error while testing environmental predictors. Innovation We determine for the first time how the ability of commonly used (polynomial regressors) and novel methods based on eigenvector maps compare in the realm of spatial variation partitioning. We introduce a novel forward selection procedure to select spatial regressors for community analysis. Finally, we point out a number of issues that have not been previously considered about the joint explained variation between environment and space, which should be taken into account when reporting and testing the unique contributions of environment and space in patterning ecological communities. Main conclusions In tests of species‐environment relationships, spatial autocorrelation is known to inflate the level of type I error and make the tests of significance invalid. First, one must determine if the spatial component is significant using all spatial predictors (Moran's eigenvector maps). If it is, consider a model selection for the set of spatial predictors (an individual‐species forward selection procedure is to be preferred) and use the environmental and selected spatial predictors in a partial regression or partial canonical analysis scheme. This is an effective way of controlling for type I error in such tests. Polynomial regressors do not provide tests with a correct level of type I error.     

森林害虫发生的空间模式研究进展

森林昆虫种群表现出多样的时空模式,空间同步性是其中最常见的。回顾了森林昆虫空间同步性的特点、形成机制及研究方法方面的进展。森林害虫发生的同步性是广泛存在的,但不同昆虫种类的同步性大小不同。空间同步性常随距离的增大而下降,还与时间尺度有关。Moran效应和扩散是解释空间同步性的两种主要机制,通常Moran效应的影响要比扩散大。从虫害发生数据的获取、同步性的度量及成因3个方面介绍了空间同步性的研究方法方面的进展。利用树轮生态学原理重建森林虫害发生历史的方法可在事后获取可靠的数据,很值得国内研究者借鉴和应用。在空间自相关度量上,空间统计学方法和地统计学方法都是非常有力的手段,但由于不能处理多时间点数据而限制了其在同步性研究中的应用。在同步性成因研究中,利用变异分解将基于距离的Moran特征向量图(dbMEM)作为空间变量研究害虫发生的驱动力是比较新颖的研究方法。     

Social and spatial effects on genetic variation between foraging flocks in a wild bird population

Social interactions are rarely random. In some instances, animals exhibit homophily or heterophily, the tendency to interact with similar or dissimilar conspecifics, respectively. Genetic homophily and heterophily influence the evolutionary dynamics of populations, because they potentially affect sexual and social selection. Here, we investigate the link between social interactions and allele frequencies in foraging flocks of great tits (Parus major) over three consecutive years. We constructed co‐occurrence networks which explicitly described the splitting and merging of 85,602 flocks through time (fission–fusion dynamics), at 60 feeding sites. Of the 1,711 birds in those flocks, we genotyped 962 individuals at 4,701 autosomal single nucleotide polymorphisms (SNPs). By combining genomewide genotyping with repeated field observations of the same individuals, we were able to investigate links between social structure and allele frequencies at a much finer scale than was previously possible. We explicitly accounted for potential spatial effects underlying genetic structure at the population level. We modelled social structure and spatial configuration of great tit fission–fusion dynamics with eigenvector maps. Variance partitioning revealed that allele frequencies were strongly affected by group fidelity (explaining 27%–45% of variance) as individuals tended to maintain associations with the same conspecifics. These conspecifics were genetically more dissimilar than expected, shown by genomewide heterophily for pure social (i.e., space‐independent) grouping preferences. Genomewide homophily was linked to spatial configuration, indicating spatial segregation of genotypes. We did not find evidence for homophily or heterophily for putative socially relevant candidate genes or any other SNP markers. Together, these results demonstrate the importance of distinguishing social and spatial processes in determining population structure.     

FINE‐SCALE GENETIC STRUCTURE IN A WILD BIRD POPULATION: THE ROLE OF LIMITED DISPERSAL AND ENVIRONMENTALLY BASED SELECTION AS CAUSAL FACTORS

Individuals are typically not randomly distributed in space; consequently ecological and evolutionary theory depends heavily on understanding the spatial structure of populations. The central challenge of landscape genetics is therefore to link spatial heterogeneity of environments to population genetic structure. Here, we employ multivariate spatial analyses to identify environmentally induced genetic structures in a single breeding population of 1174 great tits Parus major genotyped at 4701 single‐nucleotide polymorphism (SNP) loci. Despite the small spatial scale of the study relative to natal dispersal, we found multiple axes of genetic structure. We built distance‐based Moran's eigenvector maps to identify axes of pure spatial variation, which we used for spatial correction of regressions between SNPs and various external traits known to be related to fitness components (avian malaria infection risk, local density of conspecifics, oak tree density, and altitude). We found clear evidence of fine‐scale genetic structure, with 21, seven, and nine significant SNPs, respectively, associated with infection risk by two species of avian malaria (Plasmodium circumflexum and P. relictum) and local conspecific density. Such fine‐scale genetic structure relative to dispersal capabilities suggests ecological and evolutionary mechanisms maintain within‐population genetic diversity in this population with the potential to drive microevolutionary change.     

Environmental and spatial effects on coastal stream fishes in the Atlantic rain forest

Contemporary and historical factors influence assemblage structure. The environmental and spatial influences acting on fish organization of rain forest coastal streams in the Atlantic rain forest of Brazil were examined. Fish (and functional traits such as morphology, diet, velocity preference, body size), environmental variables (pH, water conductivity, dissolved oxygen, temperature, stream width, flow, depth, substrate), and altitude were measured from 59 stream reaches. Asymmetric eigenvector maps were used to model the spatial structure considering direction of fish movements. Elevation played an important role—fish abundance, biomass, and richness all decrease with increasing elevation. Fish communities are influenced by both environmental and spatial factors, but downstream movements were shown to be more important in explaining the observed spatial variation than were bidirectional and upstream movements. Spatial factors, as well as environmental variables influenced by the spatial structure, explained most of the variation in fish assemblages. The strong spatial structuring is probably attributable to asymmetric dispersal limitation along the altitudinal profile: Dispersal is likely to be more limiting moving upstream than downstream. These fish assemblages reflect scale-dependent processes: At the stream-reach scale, fish respond to local environmental filters (habitat structure, water chemistry, and food supply), which are in turn influenced by a larger scale, namely the altitudinal gradient expected in steep coastal mountains. Thus, environmental drivers are not independent of spatial factors, and the effects of local factors can be confounded across the altitudinal gradient. These results may have implications for conservation, because downstream reaches are often neglected in management and conservation plans.     

金沙江干旱河谷植物群落的数量分类及其结构分异的环境解释

植物群落的空间分异格局是异质生境条件下物种性状、种间相互作用等生态学过程共同作用的结果, 对其分析有助于深入理解群落构建进程。本文基于金沙江流域干旱河谷116个样点562个样方的植物群落调查数据, 采用自适应仿射传播聚类的方法进行群落数量分类, 运用莫兰特征向量地图, 和方差分解的方法对影响群落结构的空间和环境因子进行分析。结果表明: (1)自适应仿射传播聚类将金沙江干旱河谷的植物群落分为30组, 可归为7个植被型, 23个群系, 以稀树草原(30.0%)、暖性落叶阔叶灌丛(55.7%)为最主要的植被类型。(2)年均温和干燥指数是限制金沙江干旱河谷植物群落分布的主要环境因子。稀树草原、肉质灌丛、常绿阔叶灌丛是典型的干热河谷植被类型; 暖性落叶阔叶灌丛、常绿硬叶林是干暖河谷植被的优势类型; 暖性针叶林、落叶阔叶林则主要在干温河谷环境占优势。(3)纯环境因子可以解释群落物种组成变化的5.5%, 纯空间因子可以解释的物种组成变化为22.5%, 有空间结构的环境因子部分为6.6%, 未解释的部分为65.4%。在诸多环境因子中, 年均温及干燥指数的不同显示了不同群落生境的重要差异, 并显著影响到群落的分布格局。大尺度的空间因子则主要通过地理隔离对群落结构的差异产生影响。     

Effect of field sampling design on variation partitioning in a dendritic stream network

Variation partitioning is one of the most frequently used method to infer the importance of environmental (niche based) and spatial (dispersal) processes in metacommunity structuring. However, the reliability of the method in predicting the role of the major structuring forces is less known. We studied the effect of field sampling design on the result of variation partitioning of fish assemblages in a stream network. Along with four different sample sizes, a simple random sampling from a total of 115 stream segments (sampling objects) was applied in 400 iterations, and community variation of each random sample was partitioned into four fractions: pure environmentally (landscape variables) explained, pure spatially (MEM eigenvectors) explained, jointly explained by environment and space, and unexplained variance. Results were highly sensitive to sample size. Even at a given sample size, estimated variance fractions had remarkable random fluctuation, which can lead to inconsistent results on the relative importance of environmental and spatial variables on the structuring of metacommunities. Interestingly, all the four variance fractions correlated better with the number of the selected spatial variables than with any design properties. Sampling interval proved to be a fundamentally influential sampling design property because it affected the number of the selected spatial variables. Our findings suggest that the effect of sampling design on variation partitioning is related to the ability of the eigenvectors to model complex spatial patterns. Hence, properties of the sampling design should be more intensively considered in metacommunity studies.