The abiotic environment exerts strong effects on plant-associated microbes, shaping their interactions with plants and resulting ecosystem processes. However, these abiotic effects on plant-microbe interactions are often highly specific and contingent on the abiotic driver or microbial group, requiring synthesis work describing general patterns and from this generate hypotheses and guide mechanistic work. To address this, we conducted a meta-analysis of the effects of climate change-related abiotic factors, namely warming, drought, and eCO2, on plant-associated microbes distinguishing by microbial taxonomic or biological group (bacteria, fungi or virus) and the plant part where microbes are found or associated with (phyllosphere or rhizosphere). We found abiotic driver-specific patterns, whereby drought significantly reduced microbial abundance, whereas warming and eCO2 had no significant effects. In addition, these abiotic effects were contingent on the microbial taxonomic group, with fungi being negatively affected by drought but positively affected by warming (eCO2 enrichment had no effect), whereas bacteria and viruses were not significantly affected by any factor. Likewise, rhizopheric microbes were negatively affected by drought but positively affected by warming (eCO2 enrichment had no effect), whereas phyllospheric microbes were not significantly affected by any factor. Collectively, these findings point to important implications for global change research by highlighting contrasting effects of climate change-related abiotic drivers on plant-associated microbes and the contingency of such effects on microbe life histories and the nature of their interactions with plants.

To be included in our analysis, studies had to meet the following criteria: (a) provide a measure of plant-associated microbial abundance (e.g., amount, frequency, disease intensity, transmission rate, virus load) in the phyllosphere or rhizosphere of plants growing under experimental manipulation of climate change-related abiotic conditions (eCO2, warming, drought, etc.), and (b) report treatment level means (abiotic manipulation vs unmanipulated control), variability (i.e., variance, standard error or standard deviation), and the sample size in either the text, figures, tables or appendices. When needed, we extracted data from figures following digitalization using WebPlotDigitizer software. We excluded studies that applied two or more different abiotic manipulations together on the same plants. After applying these criteria, the resulting dataset consisted of 513 case studies from 96 studies (out of the original 5450) from the primary literature published between 1975 and 2021 in 47 scientific journals.

Juan de la Cierva-Formación Research Programme : FJC2020-044296-I

Xunta de Galicia : IN607A 2021/03

Peer reviewed