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 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.

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 A Rank-Based Nonparametric Method for Mapping Quantitative Trait Loci in Outbred Half-Sib Pedigrees: Application to Milk Production in a Granddaughter Design

Genetics ◽  
1998 ◽  
Vol 149 (3) ◽  
pp. 1547-1555 ◽  
Author(s):  
Wouter Coppieters ◽  
Alexandre Kvasz ◽  
Frédéric Farnir ◽  
Juan-Jose Arranz ◽  
Bernard Grisart ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Real Data ◽  
Interval Mapping ◽  
Mapping Method ◽  
Residual Variance ◽  
Data Set ◽  
Wilcoxon Rank Sum Test ◽  
Holstein Friesian ◽  
Trait Loci

Abstract We describe the development of a multipoint nonparametric quantitative trait loci mapping method based on the Wilcoxon rank-sum test applicable to outbred half-sib pedigrees. The method has been evaluated on a simulated dataset and its efficiency compared with interval mapping by using regression. It was shown that the rank-based approach is slightly inferior to regression when the residual variance is homoscedastic normal; however, in three out of four other scenarios envisaged, i.e., residual variance heteroscedastic normal, homoscedastic skewed, and homoscedastic positively kurtosed, the latter outperforms the former one. Both methods were applied to a real data set analyzing the effect of bovine chromosome 6 on milk yield and composition by using a 125-cM map comprising 15 microsatellites and a granddaughter design counting 1158 Holstein-Friesian sires.

scholarly journals A short geostatistical study of the three-dimensional spatial structure of fumonisins in stored maize

2010 ◽  
Vol 3 (1) ◽  
pp. 95-103 ◽  
Author(s):  
M. Rivas Casado ◽  
D. Parsons ◽  
N. Magan ◽  
R. Weightman ◽  
P. Battilani ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Structure ◽  
Spatial Correlation ◽  
Three Dimensional ◽  
Sample Collection ◽  
Data Set ◽  
Research Areas ◽  
Geostatistical Approach ◽  
Sampling Protocols ◽  
Current Sample

The heterogeneous three-dimensional spatial distribution of mycotoxins has proven to be one of the main limitations for the design of effective sampling protocols. Current sample collection protocols for mycotoxins have been designed to estimate the mean concentration and fail to characterise the spatial distribution of the mycotoxin concentration due to the aggregation of the incremental samples. Geostatistical techniques have been successfully applied to overcome similar problems in many research areas. However, little work has been developed on the use of geostatistics for the design of sampling protocols for mycotoxins. This paper focuses on the analysis of the two and three-dimensional spatial structure of fumonisins B1 (FB1) and B2 (FB2) in maize in a bulk store using a geostatistical approach and on how results help determine the number and location of incremental samples to be collected. The spatial correlation between FB1 and FB2, as well as between the number of kernels infected and the level of contamination was investigated. For this purpose, a bed of maize was sampled at different depths to generate a unique three-dimensional data set of FB1 and FB2. The analysis found no clear evidence of spatial structure in either the two-dimensional or three-dimensional analyses. The number of Fusarium infected kernels was not a good indicator for the prediction of fumonisin concentration and there was no spatial correlation between the concentrations of the two fumonisins.

Assessing components of the model-based mean square error estimator for remote sensing assisted forest applications

2018 ◽  
Vol 48 (6) ◽  
pp. 642-649 ◽  
Author(s):  
Ronald E. McRoberts ◽  
Erik Næsset ◽  
Terje Gobakken ◽  
Gherardo Chirici ◽  
Sonia Condés ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Spatial Correlation ◽  
Laser Scanning ◽  
Satellite Image ◽  
Error Estimator ◽  
Residual Variance ◽  
Mean Square ◽  
Model Parameter ◽  
Model Based ◽  
Residual Covariance

Model-based inference is an alternative to probability-based inference for small areas or remote areas for which probability sampling is difficult. Model-based mean square error estimators incorporate three components: prediction covariance, residual variance, and residual covariance. The latter two components are often considered negligible, particularly for large areas, but no thresholds that justify ignoring them have been reported. The objectives of the study were threefold: (i) to compare analytical and bootstrap estimators of model parameter covariances as the primary factors affecting prediction covariance; (ii) to estimate the contribution of residual variance to overall variance; and (iii) to estimate thresholds for residual spatial correlation that justify ignoring this component. Five datasets were used, three from Europe, one from Africa, and one from North America. The dependent variable was either forest volume or biomass and the independent variables were either Landsat satellite image bands or airborne laser scanning metrics. Three conclusions were noteworthy: (i) analytical estimators of the model parameter covariances tended to be biased; (ii) the effects of residual variance were mostly negligible; and (iii) the effects of spatial correlation on residual covariance vary by multiple factors but decrease with increasing study area size. For study areas greater than 75 km2 in size, residual covariance could generally be ignored.

scholarly journals Beta Diversity Partitioning and Drivers of Variations in Fish Assemblages in a Headwater Stream: Lijiang River, China

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 680 ◽  
Author(s):  
Liangliang Huang ◽  
Jian Huang ◽  
Zhiqiang Wu ◽  
Yuanmin Mo ◽  
Qi Zou ◽  
...  
Keyword(s):  
Species Composition ◽  
Beta Diversity ◽  
Fish Assemblages ◽  
Headwater Streams ◽  
Species Turnover ◽  
Diversity Partitioning ◽  
Spatial Variables ◽  
Lijiang River ◽  
Abiotic Variables ◽  
Hydrological Variables

Beta diversity partitioning has currently received much attention in research of fish assemblages. However, the main drivers, especially the contribution of spatial and hydrological variables for species composition and beta diversity of fish assemblages are less well studied. To link species composition to multiple abiotic variables (i.e., local environmental variables, hydrological variables, and spatial variables), the relative roles of abiotic variables in shaping fish species composition and beta diversity (i.e., overall turnover, replacement, and nestedness) were investigated in the upstream Lijiang River. Species composition showed significant correlations with environmental, hydrological, and spatial variables, and variation partitioning revealed that the local environmental and spatial variables outperformed hydrological variables, and especially abiotic variables explained a substantial part of the variation in the fish composition (43.2%). The overall species turnover was driven mostly by replacement (87.9% and 93.7% for Sørensen and Jaccard indices, respectively) rather than nestedness. Mantel tests indicated that the overall species turnover (ßSOR and ßJAC) and replacement (ßSIM and ßJTU) were significantly related to hydrological, environmental, and spatial heterogeneity, whereas nestedness (ßSNE or ßJNE) was insignificantly correlated with abiotic variables (P > 0.05). Moreover, the pure effect of spatial variables on overall species turnover (ßSOR and ßJAC) and replacement (ßSIM and ßJTU), and the pure effect of hydrological variables on replacement (ßSIM and ßJTU), were not important (P > 0.05). Our findings demonstrated the relative importance of interactions among environmental, hydrological, and spatial variables in structuring fish assemblages in headwater streams; these fish assemblages tend to be compositionally distinct, rather than nested derivatives of one another. Our results, therefore, indicate that maintaining natural flow dynamics and habitat continuity are of vital importance for conservation of fish assemblages and diversity in headwater streams.

scholarly journals Edge effects and beta diversity in ground and canopy beetle communities of fragmented subtropical forest

PLoS ONE ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. e0193369 ◽  
Author(s):  
Marisa J. Stone ◽  
Carla P. Catterall ◽  
Nigel E. Stork
Keyword(s):  
Edge Effects ◽  
Beta Diversity ◽  
Subtropical Forest

Pixel-level Signal Modelling with Spatial Correlation for Two-Colour Microarrays

2005 ◽  
Vol 4 (1) ◽  
Author(s):  
Claus T Ekstrøm ◽  
Søren Bak ◽  
Mats Rudemo
Keyword(s):  
Spatial Correlation ◽  
Substantial Improvement ◽  
Model Fit ◽  
Oligonucleotide Microarrays ◽  
Data Set ◽  
Pixel Intensity ◽  
Spherical Models ◽  
Correlation Structures ◽  
Signal Modelling ◽  
Better Than

Statistical models for spot shapes and signal intensities are used in image analysis of laser scans of microarrays. Most models have essentially been based on the assumption of independent pixel intensity values, but models that allow for spatial correlation among neighbouring pixels can accommodate errors in the microarray slide and should improve the model fit. Five spatial correlation structures, exponential, Gaussian, linear, rational quadratic and spherical, are compared for a dataset with 50-mer two-colour oligonucleotide microarrays and 452 probes for selected Arabidopsis genes. Substantial improvement in model fit is obtained for all five correlation structures compared to the model with independent pixel values, and the Gaussian and the spherical models seem to be slightly better than the other three models. We also conclude that for the data set analysed the correlation seems negligible for non-neighbouring pixels.

Alpha, beta, or gamma: where does all the diversity go?

Paleobiology ◽  
1988 ◽  
Vol 14 (3) ◽  
pp. 221-234 ◽  
Author(s):  
J. John Sepkoski
Keyword(s):  
Beta Diversity ◽  
Alpha Diversity ◽  
Taxonomic Richness ◽  
Soft Bottom ◽  
Gamma Diversity ◽  
Data Set ◽  
Alpha And Beta Diversity ◽  
Community Or ◽  
Global Diversity ◽  
Measured Change

Global taxonomic richness is affected by variation in three components: within-community, or alpha, diversity; between-community, or beta, diversity; and between-region, or gamma, diversity. A data set consisting of 505 faunal lists distributed among 40 stratigraphic intervals and six environmental zones was used to investigate how variation in alpha and beta diversity influenced global diversity through the Paleozoic, and especially during the Ordovician radiations. As first shown by Bambach (1977), alpha diversity increased by 50 to 70 percent in offshore marine environments during the Ordovician and then remained essentially constant for the remainder of the Paleozoic. The increase is insufficient, however, to account for the 300 percent rise observed in global generic diversity. It is shown that beta diversity among level, soft-bottom communities also increased significantly during the early Paleozoic. This change is related to enhanced habitat selection, and presumably increased overall specialization, among diversifying taxa during the Ordovician radiations. Combined with alpha diversity, the measured change in beta diversity still accounts for only about half of the increase in global diversity. Other sources of increase are probably not related to variation in gamma diversity but rather to appearance and/or expansion of organic reefs, hardground communities, bryozoan thickets, and crinoid gardens during the Ordovician.

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