Predicting the effects of multiple global change drivers on microbial communities remains challenging

Abstract

Microbial communities in many ecosystems are facing a broad range of global change drivers, such as nutrient enrichment, chemical pollution, and temperature change. These drivers can cause changes in the abundance of taxa, the composition of communities, and the properties of ecosystems. While the influence of single drivers is already described in numerous studies, the effect and predictability of multiple drivers changing simultaneously is still poorly understood. In this study, we used 240 highly replicable oxic/anoxic aquatic lab microcosms and four drivers (fertilizer, glyphosate, metal pollution, antibiotics) in all possible combinations at three different temperatures (20, 24, and 28°C) to shed light into consequences of multiple drivers on different levels of organization, ranging from species abundance to community and ecosystem parameters. We found (i) that at all levels of ecological organization, combinations of drivers can change the biological consequence and direction of effect compared to single drivers, (ii) that effects of combinations are further modified by temperature, (iii) that a larger number of drivers occurring simultaneously is often quite closely related to their effect size, and (iv) that there is little evidence that any of these effects are associated with the level of ecological organization of the state variable. These findings suggest that, at least in this experimental ecosystem approximating a stratified aquatic ecosystem, there may be relatively little scope for predicting the effects of combinations of drivers from the effects of individual drivers, or by accounting for the level of ecological organization in question, though there may be some scope for prediction based on the number of drivers that are occurring simultaneous. A priority, though also a considerable challenge, is to extend such research to consider continuous variation in the magnitude of multiple drivers acting together.

Identification of microbial communities and their removal efficiency of multiple pharmaceutical micropollutants combined in Membrane-Bioreactors

“…This could be explained by the fact that the microbial communities within the MBRs are confronted to multiple substrates with easier biodegradability than diclofenac. This demonstrates the high complexity of dealing with multiple substrates (like in real scenarios) instead of just one driver (Suleiman et al, 2022).…”

“…The choice of applying a mixture of pharmaceuticals was motivated by previous studies showing that multiple drivers can affect microbial communities differently (Orr et al, 2022;Suleiman et al, 2022). In order to analyze key players for the degradation of each pharmaceutical, MBR communities were transferred to batch cultures incubated with one single micropollutant.…”

Disentangling climate change from air pollution effects on epiphytic bryophytes

“…Since the beginning of the industrial era, terrestrial ecosystems have experienced anthropogenic disturbance, involving complex factors such as land use changes, pervasive levels of air pollution from fossil fuel combustion, and climate change. Disentangling how these factors, or the combination thereof, affect ecosystem functioning and diversity remains, however, challenging (Esseen et al, 2022; Mathias & Thomas, 2018; Suleiman et al, 2022).…”

Predicting effects of multiple interacting global change drivers across trophic levels

“…A primary goal of the multiple driver research is to explore and describe the frequency and distribution of multiple driver effects across a wide range of ecological systems. To do so, researchers usually measure a quantity of interest (e.g., population size or community biomass) and estimate the effects of the individual drivers alone and jointly with statistical models that capture the magnitude and sign of the main and interactive effects ( Rillig et al, 2019 ; Suleiman et al, 2022 ). Meta-analyses have provided important insights on the prevalence of driver interactions across many drivers and domains ( Suleiman et al, 2022 ), demonstrating interactive effects, particularly antagonisms, dominate ( Birk et al, 2020 ; Crain et al, 2008 ; Darling & Côté, 2008 ; Harvey et al, 2013 ; Jackson et al, 2016 ; Seifert et al, 2020 ; Tekin et al, 2020 ).…”

“…To do so, researchers usually measure a quantity of interest (e.g., population size or community biomass) and estimate the effects of the individual drivers alone and jointly with statistical models that capture the magnitude and sign of the main and interactive effects ( Rillig et al, 2019 ; Suleiman et al, 2022 ). Meta-analyses have provided important insights on the prevalence of driver interactions across many drivers and domains ( Suleiman et al, 2022 ), demonstrating interactive effects, particularly antagonisms, dominate ( Birk et al, 2020 ; Crain et al, 2008 ; Darling & Côté, 2008 ; Harvey et al, 2013 ; Jackson et al, 2016 ; Seifert et al, 2020 ; Tekin et al, 2020 ). Antagonisms and synergisms may be even more prevalent since current methods do not capture statistical interactions very well ( Tekin et al, 2020 ).…”