Statistical challenges in null model analysis

By Nicholas Gotelli, Werner Ulrich. 2012

274 citations recorded by the source corpus.

This review identifies several important challenges in null model testing in ecology: 1) developing randomization algorithms that generate appropriate patterns for a specified null hypothesis; these randomization algorithms stake out a middle ground between formal Pearson–Neyman tests (which require a fully-specified null distribution) and specific process-based models (which require parameter values that cannot be easily and independently estimated); 2) developing metrics that specify a particular pattern in a matrix, but ideally exclude other, related patterns; 3) avoiding classification schemes based on idealized matrix patterns that may prove to be inconsistent or contradictory when tested with empirical matrices that do not have the idealized pattern; 4) testing the performance of proposed null models and metrics with artificial test matrices that contain specified levels of pattern and randomness; 5) moving beyond simple presence–absence matrices to incorporate species-level traits (such as abundance) and site-level traits (such as habitat suitability) into null model analysis; 6) creating null models that perform well with many sites, many species pairs, and varying degrees of spatial autocorrelation in species occurrence data. In spite of these challenges, the development and application of null models has continued to provide valuable insights in ecology, evolution, and biogeography for over 80 years. ‘A null model is a pattern generating model that is based on randomization of ecological data or random sampling from a known or imagined distribution. The null model is designed with respect to some eco logical or evolutionary process of interest’. (Gotelli and Graves 1996) From its origins in the analysis of species/genus ratios (Jarvinen 1982), there is a long history of using null models to analyze patterns and test hypotheses in ecology, evolution and biogeography (Harvey et al. 1983). Although the general controversy in the 1970s over null models and competition has died down (Gotelli and Graves 1996), there are still many disputed aspects of testing and implementing null models. In this paper, we review some of the more recent challenges and controversial issues in the implementation and interpretation of null models in ecology. We focus primarily on the use of null models in biogeography, ecology, and macroecology. 1. Hypothesis testing and constraints in null model analysis

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/47da40b93b834af6dde58f000604dc1b9fc6daf0

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In Ecological Modeling

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Citations in the corpus

Stochastic Community Assembly: Does It Matter in Microbial Ecology? (2017, methodology): “Null model analysis may be not well suited for such large data sets due to false-positive significant pairs and data autocorrelation (201).”
A framework for evaluating the influence of climate, dispersal limitation, and biotic interactions using fossil pollen associations across the late Quaternary (2014, unknown): “…Anikó Tóth, Amelia Villaseñor, and Scott Wing This article is available at ScholarWorks @ UVM: https://scholarworks.uvm.edu/casfac/53 IB S special issue little attention has been devoted to understanding the underlying causes of non-random patterns (Collins et al. 2011, Gotelli and Ulrich 2012).”
On the meanings of nestedness: back to the basics (2012, methodology): “When applied in combination with the standard fixed–fixed null model (Gotelli and Ulrich 2012), PRN (which includes tied ranks) identified significantly more (t-test, P(H 0) 0.01) matrices with a high percentage of tied ranks as being nested.”
Disentangling biotic interactions, environmental filters, and dispersal limitation as drivers of species co‐occurrence (2018, background): “More recent approaches view individual species pairs as a more informative unit of co-occurrence, allowing for a classification of all of the unique species pairs in an assemblage as random, aggregated, or segregated (Boulangeat et al. 2012, Gotelli and Ulrich 2012, Veech 2014).”
Quantifying and interpreting nestedness in habitat islands: a synthetic analysis of multiple datasets (2015, methodology): “…to compare the observed nestedness metric value with a distribution of values generated using a null model, with much debate arising about the choice of algorithms (Wright et al., 1998; Miklós & Podani, 2004; Ulrich & Gotelli, 2007, 2012, 2013; Gotelli & Ulrich, 2012; Strona & Fattorini, 2014).”