Disentangling community patterns of nestedness and species co‐occurrence

By Werner Ulrich, Nicholas Gotelli. 2007

202 citations recorded by the source corpus.

Subfields: Ecology, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/50d071bd6f8f47e269bfb96c13ebdcf355c7f866

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

In Ecology, Evolution, Behavior and Systematics

In Nature and Landscape Conservation

Citations in the corpus

A consumer's guide to nestedness analysis (2009, methodology): “On all three points, no consensus has yet been reached among ecologists, which has hindered a general understanding of the frequency, causes, and consequences of nestedness (Ulrich and Gotelli 2007a, Almeida-Neto et al. 2008, Timi and Poulin 2008).”
Testing the spatial phylogenetic structure of local communities: statistical performances of different null models and test statistics on a locally neutral community (2008, methodology): “Similar problems were reported in related randomization tests, for instance to assess whether patterns of species co-occurrence is random (Gotelli 2000; Ulrich & Gotelli 2007a,b), or with partial Mantel tests (Raufaste & Rousset 2001).”
Differentiating between niche and neutral assembly in metacommunities using null models of β‐diversity (2016, background): “Null models, which compare observed statistical patterns to those expected in the absence of a particular assembly mechanism (Colwell and Winkler 1984, Gotelli and Graves 1996, Ulrich and Gotelli 2007), represent one common approach for connecting statistical patterns to assembly mechanisms.”
The virtual ecologist approach: simulating data and observers (2010, background): “Patterns tested have considered nestedness (Fischer and Lindenmayer 2002, Greve and Chown 2006, Higgins et al. 2006, Ulrich and Gotelli 2007a, 2007b) and trait, phylogenetic and species diversity (Kraft et al. 2007).”
Statistical challenges in null model analysis (2012, methodology): “To our knowledge, the eff ect of autocorrelation on matrix structure has not been studied systematically, although Ulrich (2004) demonstrated that a neutral model with limited spatial dispersal can generate binary presence–absence matrices that are statistically segregated.”