DOI: 10.5061/dryad.sn02v6x7q
title: Data and code from: Experimental evolution of environmental tolerance, acclimation, and physiological plasticity in a randomly fluctuating environment

description: Environmental tolerance curves, representing absolute fitness against the
 environment, are an empirical assessment of the fundamental niche, and
 emerge from the phenotypic plasticity of underlying phenotypic traits.
 Dynamic plastic responses of these traits can lead to acclimation effects,
 whereby recent past environments impact current fitness. Theory predicts
 that higher levels of phenotypic plasticity should evolve in environments
 that fluctuate more predictably, but there have been few experimental
 tests of these predictions. Specifically, will still lack experimental
 evidence for evolution of acclimation effects in response to environmental
 predictability. Here, we exposed 25 genetically diverse populations of the
 halotolerant microalgae Dunaliella salina to different constant
 salinities, or to randomly fluctuating salinities, for over 200
 generations. The fluctuating treatments differed in their autocorrelation,
 which determines the similarity of subsequent values, and thus
 environmental predictability. We then measured acclimated tolerance
 surfaces, mapping population growth rate against past (acclimation) and
 current (assay) environments. We found that experimental mean and variance
 in salinity caused the evolution of niche position (optimal salinity) and
 breadth, with respect to not only current but also past (acclimation)
 salinity. We also detected weak but significant evidence for evolutionary
 changes in response to environmental predictability, with higher
 predictability leading notably to an upwards shift in optimal salinities
 and stronger acclimation effect of past environment on current fitness. We
 further showed that these responses are related to the evolution of
 plasticity for intracellular glycerol, the major osmoregulatory mechanism
 in this species. However the direction of plasticity evolution did not
 match simple theoretical predictions. Our results underline the need for a
 more explicit consideration of the dynamics of environmental tolerance and
 its underlying plastic traits to reach a better understanding of ecology
 and evolution in fluctuating environments.
description: <strong>Experimental Evolution</strong>
 25 populations of a mix of two strain of the green microalgae
 <em>Dunaliella salina </em>were exposed to 25 independent,
 constant (6 populations) or randomly fluctuating (19 populations)
 time-series of salinities, with same targeted mean (2.4M), variance (1)
 and four autocorrelation treatments (-0.5, 0, 0.5, 0.9). Populations were
 transferred twice a week for 8 to 12 months. 
 <strong>Acclimated tolerance surface assay and
 analysis</strong> For each population, we
 measured an acclimated tolerance surface, with population growth rate
 parametrized as a bivariate function of present (assay) and past
 (acclimation) salinity. Cultures form the evolution experiment were placed
 for 10 days in 2.4M NaCl, then acclimated during 7 days in 9 salinities
 (0.1, 0.5, 1.1, 1.8, 2.4, 3.2, 3.7, 4.3, 4.7) in culture flask (15mL),
 then transferred to the same 9 salinities in culture plates (0.8 mL). This
 led to 75 cross salinity-treatments, eliminating 6 transfers from very low
 to very high / very high to very low salinities, which could no be
 achieved due to the dilution level used to reach initial population size
 of 5000 cells/mL. Growth was inferred from changes in population sizes
 measured by flow cytometry. Flow cytometry allowed us to detect cells that
 were alive versus recently dead or dying. Initial population sizes (after
 dilution but before any growth and mortality) were measured just after
 dilution in 0.36M (salinity known to not generate mortality) and final
 population sizes were measured after 3 days. We partitioned population growth rate into an
 apparent growth rate and an instantaneous apparent mortality ratio, and
 used the R package TMB (Template Model Builder) to estimate apparent
 growth rates and apparent death ratio in a negative binomial and a beta
 binomial model, respectively. Apparent growth was modelled by a bivariate
 tolerance surface parametrized by the assay and acclimation optima, the
 assay and acclimation niche width, and the acclimation strength
 (quantifying the impact of previous salinity on current salinity optimum).
 Apparent death, occurring only in transfers from low to high salinities,
 was a linear function of acclimation and assay salinities, and their
 interaction. We analyzed the evolution of the acclimated tolerance surface
 by quantifying the effect of the mean, variance, autocorrelation, and
 predictability of environments during evolution on each parameter of the
 apparent growth (ex. assay salinity optimum) and apparent death
 functions. <strong>Dynamics of
 glycerol content assay</strong> We analyzed the
 dynamics of intracellular and total glycerol content after a salinity
 transfer in 8 population that evolved in fluctuating salinities. After 10
 days at 2.4M, populations were acclimated for 7 days in 0.5 or 3.5M NaCl.
 They were then transferred to 0.5, 2 and 3.5M NaCl and glycerol content
 was assessed 1h, 8h and 3 days after this transfer. 800 µl Free Glycerol
 Reagent (Sigma Aldrich) was mixed with 200-µl culture (total glycerol
 concentration) or 200µl of culture where cells had been removed by
 centrifugation (5min 2000rpm on 0.2µm filtration plates), to measure the
 extracellular glycerol concentration. We measured optical density at 540
 nm after 5 min incubation at
 37°C. <em>Dunaliella</em> densities were then estimated by
 flow cytometry to yield measurements per unit cell, and we made two
 independent replicates for each measure. Glycerol
 concentration was interpolated from a linear standard curve (Appendix),
 and intracellular glycerol concentrations (mean and standard errors) were
 computed by subtracting extracellular to total amount of glycerol. We
 assessed the evolution of plasticity by measuring the effect of
 environmental autocorrelation on the environmental variance in glycerol
 content.  More details on the protocols and analyses
 can be found in the paper.
description: See README file.