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Donald A. Jackson
Researcher at University of Toronto
Publications - 131
Citations - 14799
Donald A. Jackson is an academic researcher from University of Toronto. The author has contributed to research in topics: Species richness & Principal component analysis. The author has an hindex of 50, co-authored 124 publications receiving 13486 citations. Previous affiliations of Donald A. Jackson include Queen's University.
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Stopping rules in principal components analysis: a comparison of heuristical and statistical approaches'
TL;DR: In this article, the authors compared several approaches to determining the number of components to interpret from principal components analysis (PCA) using simulated data matrices of uniform correlation structure and data sets of lake morphometry, water chemistry, and benthic invertebrate abundance.
What controls who is where in freshwater fish communities — the roles of biotic, abiotic, and
TL;DR: Evidence for the structuring of fish communities from stream and lake systems and the roles of biotic, abiotic, and spatial factors in determining the species composition are examined.
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Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks
TL;DR: By extending randomization approaches to ANNs, the “black box” mechanics of ANNs can be greatly illuminated and by coupling this new explanatory power of neural networks with its strong predictive abilities, ANNs promise to be a valuable quantitative tool to evaluate, understand, and predict ecological phenomena.
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What controls who is where in freshwater fish communities the roles of biotic, abiotic, and spatial factors
TL;DR: In this paper, the authors examine evidence for the structuring of fish communities from stream and lake systems and the roles of biotic, abiotic, and spatial factors in determining the species composition.
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How well do multivariate data sets match? The advantages of a Procrustean superimposition approach over the Mantel test.
TL;DR: This study contrasts the effectiveness, in terms of power and type I error rates, of the Mantel test and PROTEST and illustrates the application of Procrustes superimposition to visually examine the concordance of observations for each dimension separately.