Analysing the impact of multiple stressors in aquatic biomonitoring data: A ‘cookbook’ with applications in R
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 in...
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| Vydáno v: | The Science of the total environment Ročník 573; s. 1320 - 1339 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Netherlands
Elsevier B.V
15.12.2016
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| Témata: | |
| ISSN: | 0048-9697, 1879-1026, 1879-1026 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | 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.
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•Biomonitoring schemes such as the EU Water Framework Directive result in hundreds of thousands of samples taken from about 120,000 water bodies in Europe.•This data allows for unprecedented analysis of the biological impacts of multiple pressures, yet an analytical framework addressing biomonitoring data in particular is missing.•Here we present a 'cookbook' for multiple stressor analysis that provides such an analytical framework, accompanied by guidance on the analysis and the interpretation of results.•Annotated R scripts allow the user to cook the analysis based on his/her own data.•Simulated data suggest that reliable rankings of stressor hierarchy and interactions are achieved, if sample site number is ≥150 and stressors gradient lengths encompasses ≥75 of its observable gradient in nature. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 0048-9697 1879-1026 1879-1026 |
| DOI: | 10.1016/j.scitotenv.2016.06.243 |