Small-scale association measures in epibenthic communities as a clue for allelochemical interactions

The small-scale associations in a rocky subtidal community in the northwestern Mediterranean were studied by a development of the continuous line transect method. This method allowed the overall measurement of non-randomness in interspecific contacts and the assignment of an association index to each species-pair, whose, significance was tested by Monte Carlo procedures. At the same time, the continuous recording allowed the study of the weakening of the interactions with increasing distances. Our purpose was to uncover evidence for allelochemical mechanisms of space occupation and maintenance. A strong non-randomness was found in the interspecific associations. This was mostly due to the interactions of the poecilosclerid sponge Crambe crambe (Schmidt) with its neighbours, especially its negative associations with other sponge species. The strength of the relationships fell drastically over the first few centimeters from the contact borders of the different species. The results pointed strongly to an allelochemical mechanism. The extracts of this sponge featured high bioactivity in laboratory assays, and field experiments demonstrated that the sponge can inhibit the growth of species in the community studied. Standard sampling techniques would have overlooked the spatial structure present in the data. The study emphasizes the need for both contact data and distance data in order to identify the underlying processes reliably. The line transect method provides both types of information easily and allows testing of models and identification of organisms likely to use chemical defenses in space competition. Its use as a preliminary step in studies of chemical ecology might help to detect presumptive allelochemical processes prior to experimental work on the potentially active species.     

Using dense locality sampling resolves the subtle genetic population structure of the dispersive fish species Plecoglossus altivelis

In dispersive species with continuous distributions, genetic differentiation between local populations is often absent or subtle and thus difficult to detect. To incorporate such subtle differentiation into management plans, it may be essential to analyse many samples from many localities using adequate numbers of high‐resolution genetic markers. Here, we evaluated the usefulness of dense locality sampling in resolving genetic population structure in the ayu (Plecoglossus altivelis), a dispersive fish important in Japanese inland fisheries. Genetic variability in, and differentiation between, ayu populations around the Japan–Ryukyu Archipelago were investigated in 4746 individuals collected from 120 localities by genotyping 12 microsatellite markers. These individuals represented the two subspecies of ayu, namely the Ryukyuan subspecies (Plecoglossus altivelis ryukyuensis) and both amphidromous and landlocked forms of the nominotypical subspecies (P. a. altivelis) along the archipelago. We successfully detected an absence of genetic differentiation within the landlocked form and subtle but significant differentiation and clear geographic patterns of genetic variation among populations of the amphidromous form, which had been considered genetically homogeneous. This suggests that dense locality sampling effectively resolves subtle differences in genetic population structure, reducing stochastic deviation in the detection of genetic differentiation and geographic patterns in local populations of this dispersive species. Resampling analyses based on empirical data sets clearly demonstrate the effectiveness of increasing the number of locality samples for stable and reliable estimations of genetic fixation indices. The genetic population structure observed within the amphidromous form provides useful information for identifying management or conservation units in ayu.     

The program structure does not reliably recover the correct population structure when sampling is uneven: subsampling and new estimators alleviate the problem

Inferences of population structure and more precisely the identification of genetically homogeneous groups of individuals are essential to the fields of ecology, evolutionary biology and conservation biology. Such population structure inferences are routinely investigated via the program structure implementing a Bayesian algorithm to identify groups of individuals at Hardy–Weinberg and linkage equilibrium. While the method is performing relatively well under various population models with even sampling between subpopulations, the robustness of the method to uneven sample size between subpopulations and/or hierarchical levels of population structure has not yet been tested despite being commonly encountered in empirical data sets. In this study, I used simulated and empirical microsatellite data sets to investigate the impact of uneven sample size between subpopulations and/or hierarchical levels of population structure on the detected population structure. The results demonstrated that uneven sampling often leads to wrong inferences on hierarchical structure and downward‐biased estimates of the true number of subpopulations. Distinct subpopulations with reduced sampling tended to be merged together, while at the same time, individuals from extensively sampled subpopulations were generally split, despite belonging to the same panmictic population. Four new supervised methods to detect the number of clusters were developed and tested as part of this study and were found to outperform the existing methods using both evenly and unevenly sampled data sets. Additionally, a subsampling strategy aiming to reduce sampling unevenness between subpopulations is presented and tested. These results altogether demonstrate that when sampling evenness is accounted for, the detection of the correct population structure is greatly improved.     

Comparing mixed models and Random Forest association tests using naturalgwas and a Striped Bass SNP data set

In this study, we used the phenotype simulation package naturalgwas to test the performance of Zhao's Random Forest method in comparison to an uncorrected Random Forest test, latent factor mixed models (LFMM), genome-wide efficient mixed models (GEMMA), and confounder adjusted linear regression (CATE). We created 400 sets of phenotypes, corresponding to five effect sizes and two, five, 15, or 30 causal loci, simulated from two empirical data sets containing SNPs from Striped Bass representing three and 13 populations. All association methods were evaluated for their ability to detect genotype–phenotype associations based on power, false discovery rates, and number of false positives. Genomic inflation was highest for uncorrected Random Forest and LFMM tests and lowest for Gemma and Zhao's Random Forest. All association tests had similar power to detect causal loci, and Zhao's Random Forest had the lowest false discovery rate in all scenarios. To measure the performance of association tests in small data sets with few loci surrounding a causal gene we also ran analyses again after removing causal loci from each data set. All association tests were only able to find true positives, defined as loci located within 30 kbp of a causal locus, in 3%–18% of simulations. In contrast, at least one false positive was found in 17%–44% of simulations. Zhao's Random Forest again identified the fewest false positives of all association tests studied. The ability to test the power of association tests for individual empirical data sets can be an extremely useful first step when designing a GWAS study.     

Investigating structure and temporal scale in social organizations using identified individuals

Studies of individually identified animals can produce substantialdata sets containing information on the structure and temporalscale of social organizations. However, methods of analyzingsuch data are not well established. Important features of asocial organization are revealed by plotting the rate of persistenceof the associations between pairs of individuals over a rangeof time lags (lagged association rate). The consistency of long-termrelationships can be characterized using the rate of associationof pairs of individuals between their first and last observedassociations (intermediate association rate). A hierarchicalseries of models featuring exponentially decaying lagged associationrates may be fitted to these data. This technique retrievedthe essential parameters of five simulated social organizationsand, when used on real data, portrayed the essential featuresof the patterns of temporal change in relationships betweenanimals. The method should be especially useful for analyzingfissionfusion societies containing 10–10, 000 individuallyidentifiable animals.     

A Robust GWSS Method to Simultaneously Detect Rare and Common Variants for Complex Disease

The rapid advances in sequencing technologies and the resulting next-generation sequencing data provide the opportunity to detect disease-associated variants with a better solution, in particular for low-frequency variants. Although both common and rare variants might exert their independent effects on the risk for the trait of interest, previous methods to detect the association effects rarely consider them simultaneously. We proposed a class of test statistics, the generalized weighted-sum statistic (GWSS), to detect disease associations in the presence of common and rare variants with a case-control study design. Information of rare variants was aggregated using a weighted sum method, while signal directions and strength of the variants were considered at the same time. Permutations were performed to obtain the empirical p-values of the test statistics. Our simulation showed that, compared to the existing methods, the GWSS method had better performance in most of the scenarios. The GWSS (in particular VDWSS-t) method is particularly robust for opposite association directions, association strength, and varying distributions of minor-allele frequencies. It is therefore promising for detecting disease-associated loci. For empirical data application, we also applied our GWSS method to the Genetic Analysis Workshop 17 data, and the results were consistent with the simulation, suggesting good performance of our method. As re-sequencing studies become more popular to identify putative disease loci, we recommend the use of this newly developed GWSS to detect associations with both common and rare variants.     

A Novel Test for Gene-Ancestry Interactions in Genome-Wide Association Data

Genome-wide association study (GWAS) data on a disease are increasingly available from multiple related populations. In this scenario, meta-analyses can improve power to detect homogeneous genetic associations, but if there exist ancestry-specific effects, via interactions on genetic background or with a causal effect that co-varies with genetic background, then these will typically be obscured. To address this issue, we have developed a robust statistical method for detecting susceptibility gene-ancestry interactions in multi-cohort GWAS based on closely-related populations. We use the leading principal components of the empirical genotype matrix to cluster individuals into “ancestry groups” and then look for evidence of heterogeneous genetic associations with disease or other trait across these clusters. Robustness is improved when there are multiple cohorts, as the signal from true gene-ancestry interactions can then be distinguished from gene-collection artefacts by comparing the observed interaction effect sizes in collection groups relative to ancestry groups. When applied to colorectal cancer, we identified a missense polymorphism in iron-absorption gene CYBRD1 that associated with disease in individuals of English, but not Scottish, ancestry. The association replicated in two additional, independently-collected data sets. Our method can be used to detect associations between genetic variants and disease that have been obscured by population genetic heterogeneity. It can be readily extended to the identification of genetic interactions on other covariates such as measured environmental exposures. We envisage our methodology being of particular interest to researchers with existing GWAS data, as ancestry groups can be easily defined and thus tested for interactions.     

Stakeholder perceptions regarding the environmental and socio-economic impacts of the Algarve artificial reefs

The artificial reef (AR) complex of the Algarve (Southern Portugal), deployed for the purpose of restoring and enhancing fisheries resources, is currently the largest structure of its kind in Europe, extending for over 43.5 km². Such a structure can be expected to have had both positive and negative impacts. To evaluate the overall perception of the effects of deployment, a survey of stakeholders’ opinions was undertaken based on a set of questions addressing various dimensions (environmental, social, and economic). The survey covered 44 key-stakeholder representatives distributed in six groups: commercial fishermen associations, anglers associations and clubs, diving schools and clubs, fisheries and environmental administrators, natural and social scientists, and local council representatives in the fisheries and/or environmental sectors. The opinions of stakeholders were measured using summated rating scales. The results obtained reflect the most important issues be impacted and the possibility of using them as indicators of relative success or failure. From a total of 12 factor-sets of impacts, the results showed that in general the environmentally related were the ones having had the most positive results. The overall perception of the environmental factor-sets specified as the ‘deployment area use’ revealed that the artificial reefs were an incentive to users and that the structures were perceived as a satisfactory tool to support the fishery and its management. In both cases divers were the strongest supporters. A closer look at the results presented in the form of an AMOEBA plot showed that there were other factor-sets perceived as impacting positively in other dimensions. Such examples are the factor-sets ‘opinion’ and ‘production and benefits’ lying respectively in the social and economic dimensions. The latter factor-set was even the only one having the support of five out of six stakeholder-types. As expected, in general different stakeholder-types take somewhat different positions and attitudes towards AR impacts: usually scientists are the most optimistic, whereas fishermen take the most sceptic view.     

PUMA: A Unified Framework for Penalized Multiple Regression Analysis of GWAS Data

Penalized Multiple Regression (PMR) can be used to discover novel disease associations in GWAS datasets. In practice, proposed PMR methods have not been able to identify well-supported associations in GWAS that are undetectable by standard association tests and thus these methods are not widely applied. Here, we present a combined algorithmic and heuristic framework for PUMA (Penalized Unified Multiple-locus Association) analysis that solves the problems of previously proposed methods including computational speed, poor performance on genome-scale simulated data, and identification of too many associations for real data to be biologically plausible. The framework includes a new minorize-maximization (MM) algorithm for generalized linear models (GLM) combined with heuristic model selection and testing methods for identification of robust associations. The PUMA framework implements the penalized maximum likelihood penalties previously proposed for GWAS analysis (i.e. Lasso, Adaptive Lasso, NEG, MCP), as well as a penalty that has not been previously applied to GWAS (i.e. LOG). Using simulations that closely mirror real GWAS data, we show that our framework has high performance and reliably increases power to detect weak associations, while existing PMR methods can perform worse than single marker testing in overall performance. To demonstrate the empirical value of PUMA, we analyzed GWAS data for type 1 diabetes, Crohns''s disease, and rheumatoid arthritis, three autoimmune diseases from the original Wellcome Trust Case Control Consortium. Our analysis replicates known associations for these diseases and we discover novel etiologically relevant susceptibility loci that are invisible to standard single marker tests, including six novel associations implicating genes involved in pancreatic function, insulin pathways and immune-cell function in type 1 diabetes; three novel associations implicating genes in pro- and anti-inflammatory pathways in Crohn''s disease; and one novel association implicating a gene involved in apoptosis pathways in rheumatoid arthritis. We provide software for applying our PUMA analysis framework.     

A comparison of five methods for selecting tagging single-nucleotide polymorphisms

Our goal was to compare methods for tagging single-nucleotide polymorphisms (tagSNPs) with respect to the power to detect disease association under differing haplotype-disease association models. We were also interested in the effect that SNP selection samples, consisting of either cases, controls, or a mixture, would have on power. We investigated five previously described algorithms for choosing tagSNPS: two that picked SNPs based on haplotype structure (Chapman-haplotypic and Stram), two that picked SNPs based on pair-wise allelic association (Chapman-allelic and Cousin), and one control method that chose equally spaced SNPs (Zhai). In two disease-associated regions from the Genetic Analysis Workshop 14 simulated data, we tested the association between tagSNP genotype and disease over the tagSNP sets chosen by each method for each sampling scheme. This was repeated for 100 replicates to estimate power. The two allelic methods chose essentially all SNPs in the region and had nearly optimal power. The two haplotypic methods chose about half as many SNPs. The haplotypic methods had poor performance compared to the allelic methods in both regions. We expected an improvement in power when the selection sample contained cases; however, there was only moderate variation in power between the sampling approaches for each method. Finally, when compared to the haplotypic methods, the reference method performed as well or worse in the region with ancestral disease haplotype structure.     

Variance and spatial scales in a tropical rain forest: changing the size of sampling units

The size of a sampling unit has a critical effect on our perception of ecological phenomena; it influences the variance and correlation structure estimates of the data. Classical statistical theory works well to predict the changes in variance when there is no autocorrelation structure, but it is not applicable when the data are spatially autocorrelated. Geostatistical theory, on the other hand, uses analytical relationships to predict the variance and autocorrelation structure that would be observed if a survey was conducted using sampling units of a different size. To test the geostatistical predictions, we used information about individual tree locations in the tropical rain forest of the Pasoh Reserve, Malaysia. This allowed us to simulate and compare various sampling designs. The original data were reorganised into three artificial data sets, computing tree densities (number of trees per square meter in each quadrat) corresponding to three quadrat sizes (5×5, 10×10 and 20×20 m⁽²⁾). Based upon the 5×5 m⁽²⁾ data set, the spatial structure was modelled using a random component (nugget effect) plus an exponential model for the spatially structured component. Using the within-quadrat variances inferred from the variogram model, the change of support relationships predicted the spatial autocorrelation structure and new variances corresponding to 10×10 m⁽²⁾ and 20×20 m⁽²⁾ quadrats. The theoretical and empirical results agreed closely, while the classical approach would have largely underestimated the variance. As quadrat size increases, the range of the autocorrelation model increases, while the variance and proportion of noise in the data decrease. Large quadrats filter out the spatial variation occurring at scales smaller than the size of their sampling units, thus increasing the proportion of spatially structured component with range larger than the size of the sampling units.     

Empirical comparison of tests for differential expression on time-series microarray experiments

Methods for identifying differentially expressed genes were compared on time-series microarray data simulated from artificial gene networks. Select methods were further analyzed on existing immune response data of Boldrick et al. (2002, Proc. Natl. Acad. Sci. USA 99, 972-977). Based on the simulations, we recommend the ANOVA variants of Cui and Churchill. Efron and Tibshirani's empirical Bayes Wilcoxon rank sum test is recommended when the background cannot be effectively corrected. Our proposed GSVD-based differential expression method was shown to detect subtle changes. ANOVA combined with GSVD was consistent on background-normalized simulation data. GSVD with empirical Bayes was consistent without background correction. Based on the Boldrick et al. data, ANOVA is best suited to detect changes in temporal data, while GSVD and empirical Bayes effectively detect individual spikes or overall shifts, respectively. For methods tested on simulation data, lowess after background correction improved results. On simulation data without background correction, lowess decreased performance compared to median centering.     

Not so different after all: a comparison of methods for detecting amino acid sites under selection

We consider three approaches for estimating the rates of nonsynonymous and synonymous changes at each site in a sequence alignment in order to identify sites under positive or negative selection: (1) a suite of fast likelihood-based "counting methods" that employ either a single most likely ancestral reconstruction, weighting across all possible ancestral reconstructions, or sampling from ancestral reconstructions; (2) a random effects likelihood (REL) approach, which models variation in nonsynonymous and synonymous rates across sites according to a predefined distribution, with the selection pressure at an individual site inferred using an empirical Bayes approach; and (3) a fixed effects likelihood (FEL) method that directly estimates nonsynonymous and synonymous substitution rates at each site. All three methods incorporate flexible models of nucleotide substitution bias and variation in both nonsynonymous and synonymous substitution rates across sites, facilitating the comparison between the methods. We demonstrate that the results obtained using these approaches show broad agreement in levels of Type I and Type II error and in estimates of substitution rates. Counting methods are well suited for large alignments, for which there is high power to detect positive and negative selection, but appear to underestimate the substitution rate. A REL approach, which is more computationally intensive than counting methods, has higher power than counting methods to detect selection in data sets of intermediate size but may suffer from higher rates of false positives for small data sets. A FEL approach appears to capture the pattern of rate variation better than counting methods or random effects models, does not suffer from as many false positives as random effects models for data sets comprising few sequences, and can be efficiently parallelized. Our results suggest that previously reported differences between results obtained by counting methods and random effects models arise due to a combination of the conservative nature of counting-based methods, the failure of current random effects models to allow for variation in synonymous substitution rates, and the naive application of random effects models to extremely sparse data sets. We demonstrate our methods on sequence data from the human immunodeficiency virus type 1 env and pol genes and simulated alignments.     

Data requirements and data sources for biodiversity priority area selection

The data needed to prioritize areas for biodiversity protection are records of biodiversity features — species, species assemblages, environmental classes — for each candidate area. Prioritizing areas means comparing candidate areas, so the data used to make such comparisons should be comparable in quality and quantity. Potential sources of suitable data include museums, herbariums and natural resource management agencies. Issues of data precision, accuracy and sampling bias in data sets from such sources are discussed and methods for treating data to minimize bias are reviewed.     

Association of the GNAS locus with severe malaria

Functional studies have demonstrated an interaction between the stimulatory G protein alpha subunit (G-alpha-s) and the malaria parasite at a cellular level. Obstruction of signal transduction via the erythrocyte G-alpha-s subunit reduced invasion by Plasmodium falciparum parasites. We sought to determine whether this signal pathway had an impact at the disease level by testing polymorphisms in the gene encoding G-alpha-s (GNAS) for association with severe malaria in a large multi-centre study encompassing family and case–control studies from The Gambia, Kenya and Malawi, and a case–control study from Ghana. We gained power to detect association using meta-analysis across the seven studies, with an overall sample size approximating 4,000 cases and 4,000 controls. Out of 12 SNPs investigated in the 19 kb GNAS region, four presented signals of association (P < 0.05) with severe malaria. The strongest single-locus association demonstrated an odds ratio of 1.13 (1.05–1.21), P = 0.001. Three of the loci presenting significant associations were clustered at the 5-prime end of the GNAS gene. Accordingly, haplotypes constructed from these loci demonstrated significant associations with severe malaria [OR = 0.88 (0.81–0.96), P = 0.005 and OR = 1.12 (1.03–1.20), P = 0.005]. The evidence presented here indicates that the influence of G-alpha-s on erythrocyte invasion efficacy may, indeed, alter individual susceptibility to disease.     

Linkage disequilibrium based association mapping of fiber quality traits in <Emphasis Type="Italic">G. hirsutum</Emphasis> L. variety germplasm

Cotton is the world’s leading cash crop, but it lags behind other major crops for marker-assisted breeding due to limited polymorphisms and a genetic bottleneck through historic domestication. This underlies a need for characterization, tagging, and utilization of existing natural polymorphisms in cotton germplasm collections. Here we report genetic diversity, population characteristics, the extent of linkage disequilibrium (LD), and association mapping of fiber quality traits using 202 microsatellite marker primer pairs in 335 G. hirsutum germplasm grown in two diverse environments, Uzbekistan and Mexico. At the significance threshold (r ² ≥ 0.1), a genome-wide average of LD extended up to genetic distance of 25 cM in assayed cotton variety accessions. Genome wide LD at r ² ≥ 0.2 was reduced to ~5–6 cM, providing evidence of the potential for association mapping of agronomically important traits in cotton. Results suggest linkage, selection, inbreeding, population stratification, and genetic drift as the potential LD-generating factors in cotton. In two environments, an average of ~20 SSR markers was associated with each main fiber quality traits using a unified mixed liner model (MLM) incorporating population structure and kinship. These MLM-derived significant associations were confirmed in general linear model and structured association test, accounting for population structure and permutation-based multiple testing. Several common markers, showing the significant associations in both Uzbekistan and Mexican environments, were determined. Between 7 and 43% of the MLM-derived significant associations were supported by a minimum Bayes factor at ‘moderate to strong’ and ‘strong to very strong’ evidence levels, suggesting their usefulness for marker-assisted breeding programs and overall effectiveness of association mapping using cotton germplasm resources. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Comparison of High-Level Microarray Analysis Methods in the Context of Result Consistency

Motivation

When we were asked for help with high-level microarray data analysis (on Affymetrix HGU-133A microarray), we faced the problem of selecting an appropriate method. We wanted to select a method that would yield "the best result" (detected as many "really" differentially expressed genes (DEGs) as possible, without false positives and false negatives). However, life scientists could not help us – they use their "favorite" method without special argumentation. We also did not find any norm or recommendation. Therefore, we decided to examine it for our own purpose. We considered whether the results obtained using different methods of high-level microarray data analyses – Significant Analysis of Microarrays, Rank Products, Bland-Altman, Mann-Whitney test, T test and the Linear Models for Microarray Data – would be in agreement. Initially, we conducted a comparative analysis of the results on eight real data sets from microarray experiments (from the Array Express database). The results were surprising. On the same array set, the set of DEGs by different methods were significantly different. We also applied the methods to artificial data sets and determined some measures that allow the preparation of the overall scoring of tested methods for future recommendation.

Results

We found a very low level concordance of results from tested methods on real array sets. The number of common DEGs (detected by all six methods on fixed array sets, checked on eight array sets) ranged from 6 to 433 (22,283 total array readings). Results on artificial data sets were better than those on the real data. However, they were not fully satisfying. We scored tested methods on accuracy, recall, precision, f-measure and Matthews correlation coefficient. Based on the overall scoring, the best methods were SAM and LIMMA. We also found TT to be acceptable. The worst scoring was MW. Based on our study, we recommend: 1. Carefully taking into account the need for study when choosing a method, 2. Making high-level analysis with more than one method and then only taking the genes that are common to all methods (which seems to be reasonable) and 3. Being very careful (while summarizing facts) about sets of differentially expressed genes: different methods discover different sets of DEGs.     

Microsatellite analysis of the natterjack toad (Bufo calamita) in Denmark: populations are islands in a fragmented landscape

The European natterjack toad (Bufo calamita) has declined rapidly in recent years, primarily due to loss of habitat, and in Denmark it is estimated that 50% of the isolated populations are lost each decade. To efficiently manage and conserve this species and its genetic diversity, knowledge of the genetic structure is crucial. Based on nine polymorphic microsatellite loci, the genetic diversity, genetic structure and gene flow were investigated at 12 sites representing 5–10% of the natterjack toad localities presently known in Denmark. The expected heterozygosity (H _E) within each locality was generally low (range: 0.18–0.43). Further analyses failed to significantly correlate genetic diversity with population size, degree of isolation and increasing northern latitude, indicating a more complex combination of factors in determining the present genetic profile. Genetic differentiation was high (overall θ = 0.29) and analyses based on a Bayesian clustering method revealed that the dataset constituted 11 genetic clusters, defining nearly all sampling sites as distinct populations. Contemporary gene flow among populations was undetectable in nearly all cases, and the failure to detect a pattern of isolation by distance within major regions supported this apparent lack of a gene flow continuum. Indications of a genetic bottleneck were found in three populations. The analyses suggest that the remaining Bufo calamita populations in Denmark are genetically isolated, and represent independent units in a highly fragmented gene pool. Future conservation management of this species is discussed in light of these results. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

海南岛吊罗山热带山地雨林两个演替阶段的种间联结性

种间联结一直是群落演替理论研究的焦点之一, 关于物种间相互作用与群落演替之间的动态关系仍然存在争议。本文中, 作者通过出现-不出现数据的方差比例、基于2×2列联表的种间联结分析和 ²检验, 对海南岛吊罗山热带山地雨林演替前期和演替后期群落的种间联结性进行了研究, 以揭示种间联结与群落演替的相互作用。结果表明: (1)在演替前期, 群落内所有树种间总体呈正联结, 但不显著; 发展至演替后期达到显著正联结。群落内所有物种间正、负联结种对数占总种对数的比例随演替进程呈现下降趋势, 而无联结种对数的比例则大幅上升; 优势种和伴生种间呈现相同的趋势。这表明群落演替正朝着有利于物种稳定共存的方向发展。(2)演替前期建群种和后期侵入种间正联结与无联结种对数的比例(分别为37.8%与41.5%)远大于负联结的比例(20.7%), 但正联结均不显著; 前期定居树种和后期侵入种通过分割资源而共存, 而且也趋于独立存在。(3)后期侵入种间不存在负联结, 所有正联结(占总对数的33.3%)均达显著水平, 显示它们对生境有相似的适应和相互重叠的生态位。