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Multivariate Hypotheses (multivariate + hypothesis)

Distribution by Scientific Domains

Life Sciences	100%

Selected Abstracts

Geographic patterns of diversity in streams are predicted by a multivariate model of disturbance and productivity

JOURNAL OF ECOLOGY, Issue 3 2006
BRADLEY J. CARDINALE
Summary 1Univariate explanations of biodiversity have often failed to account for broad-scale patterns in species richness. As a result, increased attention has been paid to the development and testing of more synthetic multivariate hypotheses. One class of multivariate hypotheses, founded in successional diversity theory, predict that species richness is jointly influenced by periodic disturbances that create new niche opportunities in space or time, and the production of community biomass that speeds displacement of inferior by superior competitors. 2While the joint response of diversity to disturbance and productivity has gained support from theoretical and small-scale experimental studies, evidence that corresponding patterns of biodiversity occur broadly across natural systems is scarce. 3Using a data set that employed standardized methods to sample 85 streams throughout the mid-Atlantic United States of America, we show that biogeographical patterns of primary producer diversity in stream ecosystems are consistent with the predictions of a multivariate model that incorporates disturbance frequency and community biomass production as independent variables. Periphyton species richness is a concave-down function of disturbance frequency (mean no. floods year,1) and of biomass production (µg of biomass accrual cm,2 day,1), and an increasing function of their interaction. 4Changes in richness across the disturbance × productivity response surface can be related to several predicted life-history traits of the dominant species. 5Our findings complement prior studies by showing that multivariate models which consider interactive effects of community production and ecosystem disturbance are, in fact, candidate explanations of much broader patterns of richness in natural systems. Because multivariate models predict synergistic effects of ecological variables on species diversity, human activities , which are simultaneously altering both the disturbance regime and productivity of streams , could be influencing biodiversity more than previously anticipated. [source]

Separating the influence of resource ,availability' from resource ,imbalance' on productivity,diversity relationships

ECOLOGY LETTERS, Issue 6 2009
Bradley J. Cardinale
Abstract One of the oldest and richest questions in biology is that of how species diversity is related to the availability of resources that limit the productivity of ecosystems. Researchers from a variety of disciplines have pursued this question from at least three different theoretical perspectives. Species energy theory has argued that the summed quantities of all resources influence species richness by controlling population sizes and the probability of stochastic extinction. Resource ratio theory has argued that the imbalance in the supply of two or more resources, relative to the stoichiometric needs of the competitors, can dictate the strength of competition and, in turn, the diversity of coexisting species. In contrast to these, the field of Biodiversity and Ecosystem Functioning has argued that species diversity acts as an independent variable that controls how efficiently limited resources are utilized and converted into new tissue. Here we propose that all three of these fields give necessary, but not sufficient, conditions to explain productivity,diversity relationships (PDR) in nature. However, when taken collectively, these three paradigms suggest that PDR can be explained by interactions among four distinct, non-interchangeable variables: (i) the overall quantity of limiting resources, (ii) the stoichiometric ratios of different limiting resources, (iii) the summed biomass produced by a group of potential competitors and (iv) the richness of co-occurring species in a local competitive community. We detail a new multivariate hypothesis that outlines one way in which these four variables are directly and indirectly related to one another. We show how the predictions of this model can be fit to patterns of covariation relating the richness and biomass of lake phytoplankton to three biologically essential resources (N, P and light) in a large number of Norwegian lakes. [source]

Exact multivariate tests for brain imaging data

HUMAN BRAIN MAPPING, Issue 1 2002
Rita Almeida
Abstract In positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) data sets, the number of variables is larger than the number of observations. This fact makes application of multivariate linear model analysis difficult, except if a reduction of the data matrix dimension is performed prior to the analysis. The reduced data set, however, will in general not be normally distributed and therefore, the usual multivariate tests will not be necessarily applicable. This problem has not been adequately discussed in the literature concerning multivariate linear analysis of brain imaging data. No theoretical foundation has been given to support that the null distributions of the tests are as claimed. Our study addresses this issue by introducing a method of constructing test statistics that follow the same distributions as when the data matrix is normally distributed. The method is based on the invariance of certain tests over a large class of distributions of the data matrix. This implies that the method is very general and can be applied for different reductions of the data matrix. As an illustration we apply a test statistic constructed by the method now presented to test a multivariate hypothesis on a PET data set. The test rejects the null hypothesis of no significant differences in measured brain activity between two conditions. The effect responsible for the rejection of the hypothesis is characterized using canonical variate analysis (CVA) and compared with the result obtained by using univariate regression analysis for each voxel and statistical inference based on size of activations. The results obtained from CVA and the univariate method are similar. Hum. Brain Mapping 16:24,35, 2002. © 2002 Wiley-Liss, Inc. [source]