Gaining Mechanistic Insights into Multiple Stressor Effects Using Freshwater Microbial Communities

Abstract

Understanding and predicting the combined effects of multiple environmental stressors has become a major goal in the field of global change biology. Whilst a plethora of multiple stressor research exists in the literature, a mechanistic understanding of how stressors interact is distinctly lacking, hampering our ability to explain and predict their combined effects. In this thesis, concepts and theories from various branches of ecology were utilised to gain mechanistic insights into multi-stressor effects across levels of biological organisation and over eco-evolutionary timescales, employing freshwater microbial communities as a model system.

Starting at the population level, the initial study demonstrated how a response curve approach provides us with a holistic view of multiple stressor effects, revealing generalities across taxa stress responses. Additionally, it highlighted how small changes in the stressor levels measured along non-linear response curves can dramatically impact assigned stressor interactions, reconciling variability across multiple stressor studies to date.

The second study then scaled from the population to the community level, using the biodiversity-ecosystem functioning framework to evaluate the combined effects of multiple stressors and biodiversity loss on community functioning. The study revealed that the distribution of taxa response traits within communities can explain alterations in community composition and functioning across various stressor and biodiversity loss scenarios.

Building upon this, the final study explored how, over short-term ecological timescales, adaptation of taxa to multiple stressors can influence effects at higher levels of organisation. The presence of an adapted taxon significantly enhanced the functioning of communities under multi-stressor conditions compared to those with naïve taxa, highlighting the importance of incorporating evolutionary dynamics into multi-stressor studies. Collectively, the findings demonstrate how adopting a mechanistic approach deepens our knowledge of multiple stressor effects, providing improved predictive capabilities that are crucial for making well informed conservation and ecosystem management decisions in the future.