Analysing the impact of multiple stressors in aquatic biomonitoring data: A ‘cookbook’ with applications in R

Abstract

Multiple stressors threaten biodiversity and ecosystem integrity, imposing new challenges to ecosystem management and restoration. Ecosystem managers are required to address and mitigate the impact of multiple stressors, yet the knowledge required to disentangle multiple-stressor effects is still incomplete. Experimental studies have advanced the understanding of single and combined stressor effects, but there is a lack of a robust analytical framework, to address the impact of multiple stressors based on monitoring data. Since 2000, the monitoring of Europe's waters has resulted in a vast amount of biological and environmental (stressor) data of about 120,000 water bodies. For many reasons, this data is rarely exploited in the multiple-stressor context, probably because of its rather heterogeneous nature: stressors vary and are mixed with broad-scale proxies of environmental stress (e.g. land cover), missing values and zero-inflated data limit the application of statistical methods and biological indicators are often aggregated (e.g. taxon richness) and do not respond stressor-specific. Here, we present a ‘cookbook’ to analyse the biological response to multiple stressors using data from biomonitoring schemes. Our ‘cookbook’ includes guidance for the analytical process and the interpretation of results. The ‘cookbook’ is accompanied by scripts, which allow the user to run a stepwise analysis based on his/her own data in R, an open-source language and environment for statistical computing and graphics. Using simulated and real data, we show that the recommended procedure is capable of identifying stressor hierarchy (importance) and interaction in large datasets. We recommend a minimum number of 150 independent observations and a minimum stressor gradient length of 75% (of the most relevant stressor's gradient in nature), to be able to reliably rank the stressor's importance, detect relevant interactions and estimate their standardised effect size. We conclude with a brief discussion of the advantages and limitations of this protocol.