Hypothesis-Driven Research on Multiple Stressors: An Analytical Framework for Stressor Interactions

Hypothesis-Driven Research on Multiple Stressors: An Analytical Framework for Stressor Interactions | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Ecology and Evolution This is a preprint and has not been peer reviewed. Data may be preliminary. 16 April 2025 V1 Latest version Share on Hypothesis-Driven Research on Multiple Stressors: An Analytical Framework for Stressor Interactions Authors : Iris Madge Pimentel 0000-0002-8252-7660 [email protected] , Dania Albini 0000-0003-4236-1536 , Arne Beermann , Florian Leese 0000-0002-5465-913X , Sam Macaulay , Christoph Matthaei , James Orr 0000-0002-6531-5623 , Jeremy Piggott , and Ralf Schäfer Authors Info & Affiliations https://doi.org/10.22541/au.174480136.61924343/v1 Published Ecology and Evolution Version of record Peer review timeline 757 views 333 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract 1. Identifying and characterizing stressor interactions is central to multiple-stressor research. Such interactions refer to stronger (synergism) or weaker (antagonism) joint effects of co-occurring stressors on biological entities, when compared to the predictions of a theoretical null model. Various null models have been developed and selection of the most appropriate null model for a specific research question is ideally based on assumptions on co-tolerance patterns in communities, and mechanisms of stressor effects. 2. Statistical models are commonly used to evaluate the statistical significance of interaction terms. However, they introduce constraints by imposing a specific null hypothesis on stressor combinations that cannot be flexibly changed. This can introduce a mismatch between the null model that the analyst wants to test, and the one imposed by the statistical model. 3. Here, we show under which conditions the statistical null hypothesis for interaction terms misaligns with a multiple-stressor null model and propose to resolve such misalignments using post-estimation inference. Null-model specific interaction estimates can be calculated from adjusted predictions of a fitted regression model, and associated standard errors are derived using the delta method, posterior simulations or bootstrapping. We illustrate the suggested approach with three case studies and validate statistical conclusions through data simulations. 4. Post-estimation inference has the potential to advance hypothesis-driven research on stressor interactions by flexibly testing any a priori defined null model independent from regression model structure. Supplementary Material File (manuscript_submission_v4.docx) Download 1.33 MB Information & Authors Information Version history V1 Version 1 16 April 2025 Peer review timeline Published Ecology and Evolution Version of Record 12 Aug 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Ecology and Evolution Keywords community ecology ecological experiment method development multiple statistical Authors Affiliations Iris Madge Pimentel 0000-0002-8252-7660 [email protected] University of Duisburg-Essen View all articles by this author Dania Albini 0000-0003-4236-1536 University of Essex View all articles by this author Arne Beermann University of Duisburg-Essen View all articles by this author Florian Leese 0000-0002-5465-913X University of Duisburg-Essen View all articles by this author Sam Macaulay Oxford University View all articles by this author Christoph Matthaei University of Otago View all articles by this author James Orr 0000-0002-6531-5623 The University of Queensland View all articles by this author Jeremy Piggott Trinity College Dublin View all articles by this author Ralf Schäfer University of Duisburg-Essen View all articles by this author Metrics & Citations Metrics Article Usage 757 views 333 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Iris Madge Pimentel, Dania Albini, Arne Beermann, et al. Hypothesis-Driven Research on Multiple Stressors: An Analytical Framework for Stressor Interactions. Authorea . 16 April 2025. DOI: https://doi.org/10.22541/au.174480136.61924343/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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