Microbes are constantly exposed to a wide range of environmental stresses. To cope,
all microbial species have developed protective mechanisms that can safeguard against
potential damages due to stress. While stress protection is a beneficial contributor to
microbial survival, it also carries a cost. The launch of a stress response diverts cellular
resources to the synthesis of energetically expensive stress protectants. Consequently,
responding and adapting to stress reduces growth in nutrient-poor environments. Hence
the ability to balance allocation of available resources between stress protection and
nutritional capacity in response to environmental signals is a fundamental property required
for microbial survival with important consequences for species abundance and
distribution in nature. This thesis deals with microbial survival strategies and their ecological
impacts in two parts: First, we investigate the balance between stress-protection
and nutrition as a driver of intra-species evolutionary divergence. Using a simple mathematical
model we show that the protection-nutrition balance itself is sufficient to generate
diversity within an initially monomorphic microbial population growing in a spatially
homogeneous environment containing a single limiting resource. From experimental data
we then estimate resource allocation between nutritional and stress resistant properties
in glucose-limited E. coli chemostat populations subject to a range of environmental
challenges and find that the evolutionary trajectories of multiple types can be predicted
using the mathematical model. Second, we investigate the impact of inter-species differential
survival strategies on microbial community structure within a Candida infection
niche. Focusing on the lifestyles of Candida albicans and Candida glabrata under antifungal
stress we build a mathematical model of niche competition within an infection.
Calibrating the model using experimental data, we then generate predictions for the
long term Candida ecology and find that the model is indeed predictive of equilibrium
population distribution within a given infection niche.