scholarly journals STUDYING BETA DIVERSITY: ECOLOGICAL VARIATION PARTITIONING BY MULTIPLE REGRESSION AND CANONICAL ANALYSIS

2007 ◽  
Vol 31 (5) ◽  
pp. 976-981 ◽  
Author(s):  
Pierre Legendre ◽  
Keyword(s):  
Multiple Regression ◽  
Beta Diversity ◽  
Variation Partitioning ◽  
Canonical Analysis ◽  
Ecological Variation

scholarly journals rdacca.hp: an R package for generalizing hierarchical and variation partitioning in multiple regression and canonical analysis

2021 ◽  
Author(s):  
Jiangshan Lai ◽  
Yi Zou ◽  
Jinlong Zhang ◽  
Pedro Peres-Neto
Keyword(s):  
Multiple Regression ◽  
Variation Partitioning ◽  
R Package ◽  
Canonical Analysis ◽  
Predictor Variables ◽  
List Type ◽  
Multiple Response ◽  
Hierarchical Partitioning ◽  
Commonality Analysis ◽  
Single Response

SummaryCanonical analysis, a generalization of multiple regression to multiple response variables, is widely used in ecology. Because these models often involve large amounts of parameters (one slope per response per predictor), they pose challenges to model interpretation. Currently, multi-response canonical analysis is constrained by two major challenges. Firstly, we lack quantitative frameworks for estimating the overall importance of single predictors. Secondly, although the commonly used variation partitioning framework to estimate the importance of groups of multiple predictors can be used to estimate the importance of single predictors, it is currently computationally constrained to a maximum of four predictor matrices.We established that commonality analysis and hierarchical partitioning, widely used for both estimating predictor importance and improving the interpretation of single-response regression models, are related and complementary frameworks that can be expanded for the analysis of multiple-response models.In this application, we aim at: a) demonstrating the mathematical links between commonality analysis, variation and hierarchical partitioning; b) generalizing these frameworks to allow the analysis of any number of responses, predictor variables or groups of predictor variables in the case of variation partitioning; and c) introducing and demonstrating the usage of the R package rdacca.hp that implements these generalized frameworks.

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

2014 ◽  
Vol 281 (1778) ◽  
pp. 20132728 ◽  
Author(s):  
Pierre Legendre ◽  
Olivier Gauthier
Keyword(s):  
Statistical Methods ◽  
Community Composition ◽  
Beta Diversity ◽  
Variation Partitioning ◽  
Regression Tree ◽  
Space Time ◽  
Time Variability ◽  
Ecological Data ◽  
Canonical Ordination ◽  
Temporal Beta Diversity

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.

Leaders' Behavior and Subordinates' Task Perceptions: A Multivariate Investigation

1987 ◽  
Vol 64 (3_suppl) ◽  
pp. 1171-1184 ◽  
Author(s):  
Bronston T. Mayes ◽  
Mary E. Barton
Keyword(s):  
Multiple Regression ◽  
Role Conflict ◽  
Goal Setting ◽  
Role Stress ◽  
Canonical Analysis ◽  
Work Setting ◽  
Role Perceptions ◽  
Heterogeneous Sample ◽  
Multivariate Relationships ◽  
Alpha Error

Recent studies have demonstrated that leaders' behavior can affect the task and role-perceptions of subordinates. This study extends prior research by demonstrating multivariate relationships between sets of leaders' behaviors and subordinates' task or role-perceptions. Canonical analysis was used to test for set relationships and to protect against inflation of alpha error. Multiple regression was then used to decompose the canonical relationships into more interpretable data. For this heterogeneous sample of rank and file employees, leaders' participation was significantly related to subordinate perceptions of task scope. The effect of participation on task scope seems to be through provision of feedback and autonomy. Contrary to previous findings, participation was not significantly related to role conflict and ambiguity after controlling for the effects of leaders' consideration and initiating structure. Consideration and structuring behavior were negatively related to role stress. It is concluded that a cluster of leaders' behaviors may be useful in the work setting and that different leaders' behaviors might be employed to alter subordinates' task and role perceptions. The functions of leaders' participation in goal-setting approaches to motivation is also discussed.

scholarly journals Local environmental variables are key drivers of ant taxonomic and functional beta-diversity in a Mediterranean dryland

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Clara Frasconi Wendt ◽  
Ana Ceia-Hasse ◽  
Alice Nunes ◽  
Robin Verble ◽  
Giacomo Santini ◽  
...  
Keyword(s):  
Beta Diversity ◽  
Environmental Variables ◽  
Environmental Gradients ◽  
Variation Partitioning ◽  
Environmental Filters ◽  
Spatial Variables ◽  
Β Diversity ◽  
Key Drivers ◽  
Main Determinants ◽  
Functional Replacement

AbstractThe decomposition of beta-diversity (β-diversity) into its replacement (βrepl) and richness (βrich) components in combination with a taxonomic and functional approach, may help to identify processes driving community composition along environmental gradients. We aimed to understand which abiotic and spatial variables influence ant β-diversity and identify which processes may drive ant β-diversity patterns in Mediterranean drylands by measuring the percentage of variation in ant taxonomic and functional β-diversity explained by local environmental, regional climatic and spatial variables. We found that taxonomic and functional replacement (βrepl) primarily drove patterns in overall β-diversity (βtot). Variation partitioning analysis showed that respectively 16.8%, 12.9% and 21.6% of taxonomic βtot, βrepl and βrich variation were mainly explained by local environmental variables. Local environmental variables were also the main determinants of functional β-diversity, explaining 20.4%, 17.9% and 23.2% of βtot, βrepl and βrich variation, respectively. Findings suggest that niche-based processes drive changes in ant β-diversity, as local environmental variables may act as environmental filters on species and trait composition. While we found that local environmental variables were important predictors of ant β-diversity, further analysis should address the contribution of other mechanisms, e.g. competitive exclusion and resource partitioning, on ant β-diversity.

scholarly journals Beta diversity patterns of Bromeliaceae growing on rocky cliffs within the Atlantic Forest in southern Brazil

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Edilaine Andrade Melo ◽  
Jorge Luiz Waechter
Keyword(s):  
Environmental Factors ◽  
Species Composition ◽  
Atlantic Forest ◽  
Beta Diversity ◽  
Variation Partitioning ◽  
Floristic Composition ◽  
Spatial Effects ◽  
Environmental Predictors ◽  
Diversity Patterns ◽  
Β Diversity

Abstract: In recent years there has been increasing attention in patterns of β-diversity and mechanisms related to variations in species composition. In this study, we evaluated beta diversity patterns of bromeliads growing on cliffs immersed in Atlantic Forest. We hypothesized that the species composition varies according to the spatial scale, inferring that there is a replacement of species influenced mainly by environmental factors. The study was carried out on sandstone cliffs included in contiguous but distinct vegetation formations: Evergreen and Seasonal forests. Twenty-four vertical rocky outcrops were sampled. The spatial variation in species composition was evaluated by two β-diversity components, turnover and nestedness. Multivariate analysis and variation partitioning were performed to distinguish niche and stochastic processes. We recorded 26 bromeliad species and a significantly higher contribution of turnover explaining beta diversity. Environmental factors affect β-diversity patterns of Bromeliaceae. However, individually, the environmental predictors do not explain the data variation. Environmental variations spatially structured, and spatial variables determinate the dissimilarity in the composition of bromeliads on cliffs. Thus, our results revealed that both environmental and spatial effects can act together to define the floristic composition of rock-dwelling bromeliad communities.

scholarly journals Determinants of rain-forest floristic variation on an altitudinal gradient in southern Costa Rica

2012 ◽  
Vol 28 (5) ◽  
pp. 463-481 ◽  
Author(s):  
Adina Chain-Guadarrama ◽  
Bryan Finegan ◽  
Sergio Vilchez ◽  
Fernando Casanoves
Keyword(s):  
Costa Rica ◽  
Beta Diversity ◽  
Altitudinal Gradient ◽  
Species Turnover ◽  
Variation Partitioning ◽  
Dispersal Limitation ◽  
Forest Composition ◽  
Lowland Forest ◽  
Palm Species ◽  
Floristic Variation

Abstract:The degree to which geographical location rather than environment affects the maintenance of high tropical forest beta diversity on altitudinal gradients is not well understood. Forest composition and its relationship to climate, soil, altitude and geographical distance were determined across an 1114-km2 landscape in south Pacific Costa Rica spanning an altitudinal gradient (0–1500 m asl). In 37 0.25-ha plots, > 200 species of dicot trees (≥ 30 cm dbh) and canopy palms (≥ 10 cm dbh) were found. Ordination analysis showed strong species composition patterns related to altitude; plot coordinates on the main axis showed negative correlations to the abundance of lowland-forest species Iriartea deltoidea (r = −0.54) and Brosimum utile (r = −0.65), and positive correlations to higher-altitude species Alchornea glandulosa (r = 0.63), Quercus sp. (r = 0.50) and Ocotea sp. 2 (r = 0.48). Mantel correlations, correlograms and variation partitioning analysis of relationships between floristic composition and spatial and environmental factors indicated that spatial location of the plots – potentially, dispersal limitation – was the single most important (R2adj = 0.149) driver of beta diversity, but that environmental heterogeneity also plays an important role. In particular, palm species turnover was strongly related to soil chemical properties. The effects of dispersal limitation on floristic assembly could determine the future distribution of plant communities as a result of climate change.

scholarly journals Examining residual spatial correlation in variation partitioning of beta diversity in a subtropical forest

2019 ◽  
Vol 12 (4) ◽  
pp. 636-644 ◽  
Author(s):  
Ke Cao ◽  
Xiangcheng Mi ◽  
Liwen Zhang ◽  
Haibao Ren ◽  
Mingjian Yu ◽  
...  
Keyword(s):  
Spatial Correlation ◽  
Beta Diversity ◽  
Variation Partitioning ◽  
Subtropical Forest ◽  
Residual Variance ◽  
Spatial Structures ◽  
Data Set ◽  
Strong Argument ◽  
Canonical Ordination ◽  
Spatial Variables

Abstract Aims The relative roles of ecological processes in structuring beta diversity are usually quantified by variation partitioning of beta diversity with respect to environmental and spatial variables or gamma diversity. However, if important environmental or spatial factors are omitted, or a scale mismatch occurs in the analysis, unaccounted spatial correlation will appear in the residual errors and lead to residual spatial correlation and problematic inferences. Methods Multi-scale ordination (MSO) partitions the canonical ordination results by distance into a set of empirical variograms which characterize the spatial structures of explanatory, conditional and residual variance against distance. Then these variance components can be used to diagnose residual spatial correlation by checking assumptions related to geostatistics or regression analysis. In this paper, we first illustrate the performance of MSO using a simulated data set with known properties, thus making statistical issues explicit. We then test for significant residual spatial correlation in beta diversity analyses of the Gutianshan (GTS) 24-ha subtropical forest plot in eastern China. Important Findings Even though we used up to 24 topographic and edaphic variables mapped at high resolution and spatial variables representing spatial structures at all scales, we still found significant residual spatial correlation at the 10 m × 10 m quadrat scale. This invalidated the analysis and inferences at this scale. We also show that MSO provides a complementary tool to test for significant residual spatial correlation in beta diversity analyses. Our results provided a strong argument supporting the need to test for significant residual spatial correlation before interpreting the results of beta diversity analyses.

scholarly journals Taxonomic and functional anuran beta diversity of a subtropical metacommunity respond differentially to environmental and spatial predictors

2019 ◽  
Author(s):  
Diego Anderson Dalmolin ◽  
Alexandro Marques Tozetti ◽  
Maria João Ramos Pereira
Keyword(s):  
Beta Diversity ◽  
Variation Partitioning ◽  
Spatial Distance ◽  
Environmental Predictors ◽  
Environmental Filters ◽  
Abundance Data ◽  
Behavioural Characteristics ◽  
Metacommunity Structure ◽  
Total Turnover ◽  
Spatial Predictors

AbstractThe relative contributions of environmental and spatial predictors in the patterns of taxonomic and functional anuran beta diversity were examined in 33 ponds of a metacommunity along the coast of south Brazil. Anurans exhibit limited dispersion ability and have physiological and behavioural characteristics that narrow their relationships with both environmental and spatial predictors. So, we expected that neutral processes and, in particular, niche-based processes could have similar influence on the taxonomic and functional beta diversity patterns. Variation partitioning and distance-based methods (db-RDA) were conducted with presence/absence and abundance data to examine taxonomic and functional facets and components (total, turnover and nestedness-resultant) in relation to environmental and spatial predictors. Processes determining metacommunity structure were similar between the components of beta diversity but differed among taxonomic and functional diversity. While taxonomic beta diversity was further accounted by environmental predictors, functional beta diversity responded more strongly to spatial predictors. These patterns were more evident when assessed through abundance data. These opposing patterns were contrary to what we had predicted, suggesting that while there is a taxonomic turnover mediated by environmental filters, the spatial distance promotes the trait dissimilarity between sites. Our results reinforce the idea that studies aiming to evaluate the patterns of structure in metacommunities should include different facets of diversity so that better interpretations can be achieved.

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