Adaptation to fluctuations in temperature by nine species of bacteria

Abstract

Rapid environmental fluctuations are ubiquitous in the wild, yet majority of experimental studies mostly consider effects of slow fluctuations on organism. To test the evolutionary consequences of fast fluctuations, we conducted nine independent experimental evolution experiments with bacteria. Experimental conditions were same for all species, and we allowed them to evolve either in fluctuating temperature alternating rapidly between 20°C and 40°C or at constant 30°C temperature. After experimental evolution, we tested the performance of the clones in both rapid fluctuation and in constant environments (20°C, 30°C and 40°C). Results from experiments on these nine species were combined meta-analytically. We found that overall the clones evolved in the fluctuating environment had evolved better efficiency in tolerating fluctuations (i.e., they had higher yield in fluctuating conditions) than the clones evolved in the constant environment. However, we did not find any evidence that fluctuation-adapted clones would have evolved better tolerance to any measured constant environments (20°C, 30°C, and 40°C). Our results back up recent empirical findings reporting that it is hard to predict adaptations to fast fluctuations using tolerance curves.

Keywords: experimental evolution; reaction norm; temperature fluctuation; tolerance curve.

Figure 1

Phylogeny of the study species based on 16S rRNA. The scale bar represents the number of nucleotide substitutions per site. The tree includes the sequences FJ971882 (Enterobacter aerogenes), GQ856082 (Leclercia adecarboxylata), NR_041980 (Serratia marcescens ssp. marcescens), HG326223 (Serratia marcescens ssp. DB11 [whole genome, 16S rRNA part included]), NR_024570 (Escherichia coli), AF094736 (Pseudomonas putida), AF094725 (Pseudomonas fluorescens), AB680102 (Pseudomonas chlororaphis), and NR_025838 (Novosophingobium capsulatum). The sequence accession numbers were obtained from the NCBI nucleotide sequences database

Figure 2

Measured thermal tolerance of the study species (°C) expressed as maximum growth rate (OD 600 nm/hr) (Pink line: measurements, black line: third degree polynomial fitted to the measurement data)

Figure 3

Forest plots of meta‐analyses (corresponds to Table 2) of the biomass yield in the four different measurement temperatures (a) fluctuating (2 hr 20°C, 2 hr 30°C, 2 hr 40°C) (b) constant 20°C (c) constant 30°C (d) constant 40°C for all studied species. If effect sizes are higher than zero, it indicates a better performance of clones adapted to fluctuating temperature than clones adapted to constant (30°C) temperature. Effect sizes and their confidence intervals (±95%) are denoted in the right‐hand side of the figure. RE model indicates estimate for random effect meta‐analysis model. Different sized symbols denote the magnitude of weighing (larger more weight, smaller less)

Figure 4

Forest plots of meta‐analyses (corresponds to Table 2) of the growth rate in different measurement temperatures (a) fluctuating (2 hr 20°C, 2 hr 30°C, 2 hr 40°C) (b) constant 20°C (c) constant 30°C (d) constant 40°C for all studied species. If effect sizes are higher than zero, it indicates a better performance of clones adapted to fluctuating temperature than clones adapted to constant (30°C) temperature. Effect sizes and their confidence intervals (±95%) are denoted in the right‐hand side of the figure. RE model indicates estimate for random effect meta‐analysis model. Different sized symbols denote weighing (larger more weight, smaller less)