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multiplestressR: An R package to analyse factorial multiple stressor data using the additive and multiplicative null models

Murrell, David; Burgess, Benjamin; (2022) multiplestressR: An R package to analyse factorial multiple stressor data using the additive and multiplicative null models. bioRxiv: Cold Spring Harbor, NY, USA. Green open access

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Abstract

Globally, ecosystems are being affected by multiple simultaneous stressors (also termed drivers, factors, or perturbations). While the effects of single stressors are becoming increasingly well understood, there remains substantial uncertainty regarding how multiple stressors may interact to affect ecosystems. Accordingly, there is substantial interest in documenting how stressors combine to impact individuals through to entire communities. Indeed, understanding how stressors interact represents one of the grand challenges currently facing ecologists and conservationists. Popular methods used to classify stressor interactions comprise multiple steps, including complex mathematical equations. Accordingly, there is the potential for errors to occur at multiple points, any of which can result in erroneous conclusions being drawn. Furthermore, there are frequently minor methodological differences between studies which may limit, or even prevent, direct comparisons of their results from being made. Here, we introduce the multiplestressR R package, a statistical tool which addresses the above issues. The package allows researchers to easily conduct a rigorous analysis of their multiple stressor data and provides results which are simple to interpret. The multiplestressR package can implement either the additive or multiplicative null model using iterations of these tools which are commonplace within multiple stressor ecology. The multiplestressR package can classify interactions as being synergistic, antagonistic, reversal, or null and requires minimal experience in either R or statistics to implement. Additionally, we provide example R code which can be easily modified to analysis any given factorial multiple stressor dataset. Indeed, widespread use of this software will allow for an easier and more robust comparison of results. Ultimately, we hope that the multiplestressR package will provide a stronger understanding of how stressors combine to affect individuals, populations, communities, and ecosystems.

Type: Working / discussion paper
Title: multiplestressR: An R package to analyse factorial multiple stressor data using the additive and multiplicative null models
Open access status: An open access version is available from UCL Discovery
DOI: 10.1101/2022.04.08.487622
Publisher version: https://doi.org/10.1101/2022.04.08.487622
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Genetics, Evolution and Environment
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
URI: https://discovery.ucl.ac.uk/id/eprint/10158342
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