Université de Montréal

About: Université de Montréal is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Population & Poison control. The organization has 45641 authors who have published 100476 publications receiving 4004007 citations. The organization is also known as: University of Montreal & UdeM.

SUDS, LID, BMPs, WSUD and more – The evolution and application of terminology surrounding urban drainage

Abstract: The management of urban stormwater has become increasingly complex over recent decades. Consequently, terminology describing the principles and practices of urban drainage has become increasingly diverse, increasing the potential for confusion and miscommunication. This paper documents the history, scope, application and underlying principles of terms used in urban drainage and provides recommendations for clear communication of these principles. Terminology evolves locally and thus has an important role in establishing awareness and credibility of new approaches and contains nuanced understandings of the principles that are applied locally to address specific problems. Despite the understandable desire to have a ‘uniform set of terminology’, such a concept is flawed, ignoring the fact that terms reflect locally shared understanding. The local development of terminology thus has an important role in advancing the profession, but authors should facilitate communication between disciplines and between regio...

Analyzing beta diversity: partitioning the spatial variation of community composition data

Abstract: Robert H. Whittaker defined beta diversity as the variation in species com- position among sites in a geographic area. Beta diversity is a key concept for understanding the functioning of ecosystems, for the conservation of biodiversity, and for ecosystem management. This paper explains how hypotheses about the origin of beta diversity can be tested by partitioning the spatial variation of community composition data (presence- absence or abundance data) with respect to environmental variables and spatial base func- tions. We compare two statistical methods to accomplish that. The sum-of-squares of a community composition data table, which is one possible measure of beta diversity, is correctly partitioned by canonical ordination; hence, canonical partitioning produces correct estimates of the different portions of community composition variation. In recent years, several authors interested in the variation in community composition among sites (beta diversity) have used another method, variation partitioning on distance matrices (Mantel approach). Their results led us to compare the two partitioning approaches, using simulated data generated under hypotheses about the variation of community composition among sites. The theoretical developments and simulation results led to the following observations: (1) the variance of a community composition table is a measure of beta diversity. (2) The variance of a dissimilarity matrix among sites is not the variance of the community com- position table nor a measure of beta diversity; hence, partitioning on distance matrices should not be used to study the variation in community composition among sites. (3) In all of our simulations, partitioning on distance matrices underestimated the amount of variation in community composition explained by the raw-data approach, and (4) the tests of significance had less power than the tests of canonical ordination. Hence, the proper statistical procedure for partitioning the spatial variation of community composition data among environmental and spatial components, and for testing hypotheses about the origin and maintenance of variation in community composition among sites, is canonical parti- tioning. The Mantel approach is appropriate for testing other hypotheses, such as the var- iation in beta diversity among groups of sites. Regression on distance matrices is also appropriate for fitting models to similarity decay plots.

Discrepancies between Meta-Analyses and Subsequent Large Randomized, Controlled Trials

Abstract: Background Meta-analyses are now widely used to provide evidence to support clinical strategies. However, large randomized, controlled trials are considered the gold standard in evaluating the efficacy of clinical interventions. Methods We compared the results of large randomized, controlled trials (involving 1000 patients or more) that were published in four journals (the New England Journal of Medicine, the Lancet, the Annals of Internal Medicine, and the Journal of the American Medical Association) with the results of meta-analyses published earlier on the same topics. Regarding the principal and secondary outcomes, we judged whether the findings of the randomized trials agreed with those of the corresponding meta-analyses, and we determined whether the study results were positive (indicating that treatment improved the outcome) or negative (indicating that the outcome with treatment was the same or worse than without it) at the conventional level of statistical significance (P<0.05). Results We identi...