Difficulties in benchmarking ecological null models: an assessment of current methods

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/89124 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/89124 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/89124 archive 353 disk0/00/01/62/30 2019-12-19 07:42:31 2021-08-27 17:32:30 2019-12-19 07:42:31 article show Molina C. 2171 0000-0001-9722-4446 Stone L. Difficulties in benchmarking ecological null models: an assessment of current methods pub prog_asa prog_eep Benchmarking; Community structure; Null models; Power; Robustness; Type I error; Type II error Identifying species interactions and detecting when ecological communities are structured by them is an important problem in ecology and biogeography. Ecologists have developed specialized statistical hypothesis tests to detect patterns indicative of community-wide processes in their field data. In this respect, null model approaches have proved particularly popular. The freedom allowed in choosing the null model and statistic to construct a hypothesis test leads to a proliferation of possible hypothesis tests from which ecologists can choose to detect these processes. Here, we point out some serious shortcomings of a popular approach to choosing the best hypothesis for the ecological problem at hand that involves benchmarking different hypothesis tests by assessing their performance on artificially constructed datasets. Terminological errors concerning the use of Type-I and Type-II errors that underlie these approaches are discussed. We argue that the key benchmarking methods proposed in the literature are not a sound guide for selecting null hypothesis tests, and further, that there is no simple way to benchmark null hypothesis tests. Surprisingly, the basic problems identified here do not appear to have been addressed previously, and these methods are still being used to develop and test new null models and summary statistics, from quantifying community structure (e.g., nestedness and modularity) to analyzing ecological networks. 2020-03 published Wiley-Blackwell 10.1002/ecy.2945 3055 public Ecology 101 3 e02945 TRUE 1939-9170 FALSE no info:eu-repo/semantics/article