Spatial scale of GIS‐derived categorical variables affects their ability to separate sites by community composition

2008 ◽  
Vol 11 (3) ◽  
pp. 421-430 ◽  
Author(s):  
Emily A. Holt ◽  
Bruce McCune ◽  
Peter Neitlich
Keyword(s):  
Spatial Scale ◽  
Community Composition ◽  
Categorical Variables

scholarly journals Similar Processes but Different Environmental Filters for Soil Bacterial and Fungal Community Composition Turnover on a Broad Spatial Scale

PLoS ONE ◽  
2014 ◽  
Vol 9 (11) ◽  
pp. e111667 ◽  
Author(s):  
Nicolas Chemidlin Prévost-Bouré ◽  
Samuel Dequiedt ◽  
Jean Thioulouse ◽  
Mélanie Lelièvre ◽  
Nicolas P. A. Saby ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Community Composition ◽  
Fungal Community ◽  
Fungal Community Composition ◽  
Environmental Filters ◽  
Soil Bacterial ◽  
Broad Spatial Scale

scholarly journals Spatial scale structure soil bacterial communities across an Arctic landscape

2020 ◽  
Author(s):  
Lucie A Malard ◽  
Muhammad Zohaib Anwar ◽  
Carsten S Jacobsen ◽  
David A Pearce
Keyword(s):  
Bacterial Community ◽  
Spatial Scale ◽  
Community Composition ◽  
Bacterial Communities ◽  
Bacterial Community Composition ◽  
Arctic Region ◽  
Dispersal Limitation ◽  
Sampling Strategies ◽  
Autocorrelation Distance

Bacterial community composition is largely influenced by environmental factors, and this applies to the Arctic region. However, little is known about the role of spatial factors in structuring such communities. In this study, we evaluated the influence of spatial scale on bacterial community structure across an Arctic landscape. Our results showed that spatial factors accounted for approximately 10% of the variation at the landscape scale, equivalent to observations across the whole Arctic region, suggesting that while the role and magnitude of other processes involved in community structure may vary, the role of dispersal may be stable globally in the region. We assessed dispersal limitation by identifying the spatial autocorrelation distance, standing at approximately 60 m, which would be required in order to obtain fully independent samples and may inform future sampling strategies in the region. Finally, indicator taxa with strong statistical correlations with environment variables were identified. However, we showed that these strong taxa-environment associations may not always be reflected in the geographical distribution of these taxa. IMPORTANCE The significance of this study is threefold. It investigated the influence of spatial scale on the soil bacterial community composition across a typical Arctic landscape and demonstrated that conclusions reached when examining the influence of specific environmental variables on bacterial community composition are dependent upon the spatial scales over which they are investigated. This study identified a dispersal limitation (spatial autocorrelation) distance of approximately 60 m, required to obtain samples with fully independent bacterial communities, and therefore, should serve to inform future sampling strategies in the region and potentially elsewhere. The work also showed that strong taxa-environment statistical associations may not be reflected in the observed landscape distribution of the indicator taxa.

scholarly journals Integrating over uncertainty in spatial scale of response within multispecies occupancy models yields more accurate assessments of community composition

2017 ◽  
Author(s):  
Luke Owen Frishkoff ◽  
D. Luke Mahler ◽  
Marie-Josée Fortin
Keyword(s):  
Spatial Scale ◽  
Community Composition ◽  
Spatial Scales ◽  
Error Rates ◽  
Environmental Drivers ◽  
Type I ◽  
List Type ◽  
Occupancy Models ◽  
Species Detection ◽  
Scale Of Response

AbstractSpecies abundance and community composition are affected not only by the local environment, but also by broader landscape and regional context. Yet determining the spatial scale at which landscapes affect species remains a persistent challenge that hinders ecologists’ abilities to understand how environmental gradients influence species presence and shape entire communities, especially in the face of data deficient species and imperfect species detection.Here we present a Bayesian framework that allows uncertainty surrounding the ‘true’ spatial scale of species’ responses (i.e., changes in presence/absence) to be integrated directly into a community hierarchical model.This scale selecting multi-species occupancy model (ssMSOM) estimates the scale of response, and shows high accuracy and correct type I error rates across a broad range of simulation conditions. In contrast, ensembles of single species GLMs frequently fail to detect the correct spatial scale of response, and are often falsely confident in favoring the incorrect spatial scale, especially as species’ detection probabilities deviate from perfect.Integrating spatial scale selection directly into hierarchical community models provides a means of formally testing hypotheses regarding spatial scales of response, and more accurately determining the environmental drivers that shape communities.

scholarly journals Livestock grazing-induced large-scale biotic homogenization in arid Mediterranean steppe rangelands

2021 ◽  
Author(s):  
Merdas Saifi ◽  
Yacine Kouba ◽  
Tewfik Mostephaoui ◽  
Yassine Farhi ◽  
Haroun Chenchouni
Keyword(s):  
Species Composition ◽  
Spatial Scale ◽  
Community Composition ◽  
Plant Community ◽  
Plant Communities ◽  
Beta Diversity ◽  
Large Scale ◽  
Livestock Grazing ◽  
Plant Community Composition ◽  
Arid Steppe

Despite many studies explored the effect of livestock grazing on plant communities the response of species composition and diversity to livestock grazing in arid rangelands remain ambiguous. This study examined the effects of livestock grazing vs grazing exclusion on plant communities in arid steppe rangelands of North Africa. Plant diversity of annual species perennial species and all species combined was measured and compared between grazed and grazing-excluded areas. We also verified whether the difference in plant community composition between the two management types was due to species spatial turnover or community nestedness. Besides the effects of livestock grazing on beta diversity at local among transects and landscape among sites scales were examined using the multiplicative diversity partitioning. Results revealed that livestock grazing significantly decreased the alpha diversity of all species combined and the diversity of annual plants. Livestock grazing induced a shift in plant community composition where the most of species composition variation ~74% was due to infrequent species replacement between the two management types rather than community sub setting ~26%. The analysis of beta diversity at different spatial scales revealed that livestock grazing significantly increased beta diversity at the local scale but decreased it at the landscape scale. Our findings suggest that livestock grazing in arid steppe rangelands increases the variation of plant composition at local spatial scale and engenders vegetation homogeneity at coarse spatial scale. Therefore, the implementation of appropriate management practices such as short-term grazing exclusion is mandatory to prevent these ecosystems from large scale biotic homogenization.

scholarly journals Linking spatial scale dependence of land-use descriptors and invertebrate cave community composition

2016 ◽  
Vol 18 ◽  
pp. 17-38 ◽  
Author(s):  
Thais Pellegrini ◽  
Lilian Patrícia Sales ◽  
Polyanne Aguiar ◽  
Rodrigo Lopes Ferreira
Keyword(s):  
Land Use ◽  
Spatial Scale ◽  
Community Composition ◽  
Scale Dependence

Influence of General and Crystallized Intelligence on Vocabulary Test Performance

2002 ◽  
Vol 18 (1) ◽  
pp. 78-84 ◽  
Author(s):  
Eva Ullstadius ◽  
Jan-Eric Gustafsson ◽  
Berit Carlstedt
Keyword(s):  
Hierarchical Model ◽  
Test Performance ◽  
Verbal Ability ◽  
Testing Procedure ◽  
Intellectual Ability ◽  
Analysis Of Covariance ◽  
Categorical Variables ◽  
Vocabulary Test ◽  
Crystallized Intelligence ◽  
General Ability

Summary: Vocabulary tests, part of most test batteries of general intellectual ability, measure both verbal and general ability. Newly developed techniques for confirmatory factor analysis of dichotomous variables make it possible to analyze the influence of different abilities on the performance on each item. In the testing procedure of the Computerized Swedish Enlistment test battery, eight different subtests of a new vocabulary test were given randomly to subsamples of a representative sample of 18-year-old male conscripts (N = 9001). Three central dimensions of a hierarchical model of intellectual abilities, general (G), verbal (Gc'), and spatial ability (Gv') were estimated under different assumptions of the nature of the data. In addition to an ordinary analysis of covariance matrices, assuming linearity of relations, the item variables were treated as categorical variables in the Mplus program. All eight subtests fit the hierarchical model, and the items were found to load about equally on G and Gc'. The results also indicate that if nonlinearity is not taken into account, the G loadings for the easy items are underestimated. These items, moreover, appear to be better measures of G than the difficult ones. The practical utility of the outcome for item selection and the theoretical implications for the question of the origin of verbal ability are discussed.

An Alternative to Cohen's κ

2006 ◽  
Vol 11 (1) ◽  
pp. 12-24 ◽  
Author(s):  
Alexander von Eye
Keyword(s):  
Simulation Study ◽  
Null Hypothesis ◽  
Categorical Variables ◽  
Alternative Measure ◽  
Rater Agreement ◽  
Verbal Processing ◽  
Heavy Tailed ◽  
Applicant Selection

At the level of manifest categorical variables, a large number of coefficients and models for the examination of rater agreement has been proposed and used. The most popular of these is Cohen's κ. In this article, a new coefficient, κ s , is proposed as an alternative measure of rater agreement. Both κ and κ s allow researchers to determine whether agreement in groups of two or more raters is significantly beyond chance. Stouffer's z is used to test the null hypothesis that κ s = 0. The coefficient κ s allows one, in addition to evaluating rater agreement in a fashion parallel to κ, to (1) examine subsets of cells in agreement tables, (2) examine cells that indicate disagreement, (3) consider alternative chance models, (4) take covariates into account, and (5) compare independent samples. Results from a simulation study are reported, which suggest that (a) the four measures of rater agreement, Cohen's κ, Brennan and Prediger's κ n , raw agreement, and κ s are sensitive to the same data characteristics when evaluating rater agreement and (b) both the z-statistic for Cohen's κ and Stouffer's z for κ s are unimodally and symmetrically distributed, but slightly heavy-tailed. Examples use data from verbal processing and applicant selection.

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