The timescale of plant acclimation

When you climb a mountain, you would notice that there may exist the same species all along the way. Do you ever wonder if there are some differences between them? For example, leaf nitrogen content of Abies fabri increases almost 90% more along elevation (data collected in Gongga Mountain, China). Why’s that?

Fig. 1 Left panel is the picture of Abies fabri. Right panel is leaf nitrogen content per area of Abies fabri along elevation gradient.

It’s the ability of plant to adjust themselves to the local environment, which is crucial for their survival when environmental stress strikes. This adjustment includes two processes: one is adaptation involving the acquisition or recombination of genetic traits that improve performance or survival over multiple generations; and the other is acclimation which are the physiological or behavioral changes that occur within the lifetime of an organism to reduce or enhance tolerance of the strain (Demmig-Adams et al. 2008). Unlike adaptation, acclimation of a trait can sometimes only take minutes for a plant to respond to fluctuating environment rapidly and adjust to their best fitness, such as stomatal conductance and photosynthetic rate (Downton et al. 1987), which is more important for an individual plant to survive at the current condition.

How can we tell how quickly a trait can acclimate to the environment? Usually many people would conduct some experiments to measure the its timescale. Whereas, with growing researches on optimality theory, we now have another simple way to speculate it —— optimality model. The optimality models considering traits adaptation and acclimation to environmental conditions can predict traits well, especially in the future scenarios (Franklin et al. 2020; Wang et al. 2017). Take the model to predict photosynthetic capacities for carboxylation at 25 degree (Vcmax25) for example (Wang et al. 2017), the inputs are the average climate data of a certain period and its accuracy determines the predicted trait values. Thus, using climate data during the “right” period (theoretically during the acclimation time) should produce the best predictions, which also indicates that this certain period may reflect the timescale of trait adaptation to some extent. Fig. 2 shows that predicted Vcmax25 using temperature in July (Tj, before measurement time) are better than that using mean temperature during the whole growing season (Tg). Several studies have shown that photosynthetic traits can acclimate fast to temperature changes (Smith and Dukes, 2017; Smith et al., 2017), by regulating intrinsic biochemical characteristics, such as Rubisco content or catalytic turnover rate (Cavanagh and Kubien, 2014).

Fig. 2 The comparison between observations and predicted photosynthetic capacities for carboxylation at 25 degree (Vcmax25) using temperature in July (TdJ) and mean temperature during the whole growing season (Tg). Black line is the 1:1 line.

Trait acclimation is ignored by using fixed parameters in most vegetation models, let alone the timescale of trait acclimation. As we try to incorporate trait prediction into the current models, the need to explore trait acclimation timescale becomes more urgent.


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