The influence of acclimation to density on organisms' traits and population growth in Synechococcus sp. RS 9909

Abstract

In a constantly changing environment, it is essential to understand how different factors can affect the growth of populations, including its dependence on density, commonly referred to as density dependence. These factors can affect population growth directly, by inducing a higher mortality rate for example, but also indirectly by changing organisms’ traits, such as their size or their pigment content, enabling them to better cope with the changing environmental conditions in which they find themselves. This process, also known as acclimation, can have a strong influence on the density dependence of populations. It is therefore necessary, when studying population growth and density dependence, to take into account the initial conditions in which individuals have lived. Surprisingly, while many studies have looked at how acclimation to external conditions (light, temperature, pH) can affect density dependence, the impact of initial population density conditions on growth has never been studied. In this master’s thesis, we studied how acclimation of a cyanobacterium of the genus Synechococcus to different population densities could impact organisms’ traits and population growth, including the density dependence. We found that populations acclimated to higher densities showed higher mean trait values for all but one trait, but less variability. Growth modelling showed the emergence of a non-linear density dependence from a certain initial acclimation density. In addition, comparison of these functions showed that populations acclimated to higher densities had higher growth rates and lower density dependence. This study is the first to show how initial population density conditions can affect individual characteristics and population growth, including density dependence, and demonstrates that it is important to take a more general interest in all the historical conditions experienced by populations in order to better predict their dynamics.

Date of Award	2024
Original language	English
Awarding Institution	University of Namur Université Catholique de Louvain
Supervisor	Frederik De Laender (Supervisor)