Optimality and resilience in patterns of carbon allocation and growth in vegetation under acclimation response to climate change

The earth's climate has changed significantly in the last 250 years due to activities arising from a rapidly growing human population such as, agriculture, industrialization, deforestation, pollution etc. While the pre-industrial atmospheric concentration of the greenhouse gas carbon dioxide was 280 ppm, it is currently at 400 ppm and is expected to reach 550 ppm by 2050. Under such a changing environmental condition plants undergo acclimation, by which they adjust to the altered environment and in doing so enhance their probability of survival. The acclimation response of plants significantly alters the land surface fluxes of water, carbon and energy thereby impacting the hydrologic cycle. Furthermore, the consequences of this acclimation on agricultural ecosystems is critical in tackling future food security issues. In this study we investigate the acclimation response of an agricultural ecosystem and its impact on the hydrologic cycle. We further explore the effects of plant acclimation on optimizing seed yield under climate change. In this study we consider 4 types of plant acclimation to one aspect of climate change, which is the elevated atmospheric carbon dioxide concentration. These are: a) eco-physiological, b) allocation c) biochemical and d) structural acclimation. We employ a multi-layer canopy, soil, and root system vegetation model to capture the effects of plant acclimation. This vegetation model is coupled with a teleonomic carbon allocation and growth model that we develop in this study to specifically capture the acclimation of carbon allocation. Our modeling results are tested using field experiments performed in a soybean agricultural system at the SoyFACE research facility in Illinois. Our modeling results indicate that the acclimation response of plants significantly alters the land surface fluxes of water, carbon and energy thereby impacting the hydrologic cycle. They confirm the widely observed effects of decreased transpiration (latent heat) fluxes, increased sensible heat fluxes, and increased plant carbon uptake. More interestingly, our results illustrate that each of the four acclimation responses cause a decrease in plant carbon uptake. Furthermore, under acclimation to elevated carbon dioxide, the increased carbon uptake is not proportionally allocated to different plant parts according to prior carbon allocation patterns, that is allometry. Compared to the vegetative parts, a significantly lower proportion of carbon is allocated to the reproductive parts of the plant. This result has significant consequences in obtaining projections of future crop yield under a changing climate, where we now project lower than expected yield increases for our crops. Further optimality analysis indicates that while plants are sub-optimal in terms of maximizing seed yield under current climate conditions, the extent of sub-optimality increases under future climate scenarios. This is because plants allocate more carbon to vegetative parts compared to reproductive parts. We test this result through a set of canopy thinning field experiments, and the results illustrate that plants which are artificially modified to have fewer leaves have a higher seed yield compared to plants growing under control conditions under both ambient and elevated carbon dioxide conditions. These results indicate the existence of a potential to increase seed yield by upto 23% through canopy modification alone. We hypothesize that the reason for this observed sub-optimality is a resilience trade off, whereby plants need to maintain resilience against extreme disturbance events such as drought, hail, herbivory and diseases. Through the help of simple non-linear systems, we illustrate how different attributes of resilience can be investigated and quantified. Finally, we propose a combined optimality versus resilience trade off framework which can be used to manage risk and optimize productivity in agricultural ecosystems.