River macroinvertebrate community response to environmental change

With the increasing pressure of climate change, it is expected that flood events will become more frequent and intense in the coming decades. Current literature provides minimal consensus on how freshwater ecosystems respond to disturbance events. Building a more robust understanding of how ecosystems respond to disturbance events, and how anthropogenic stressors can exacerbate these responses, is crucial. This thesis explores the use of data science techniques to develop a broader understanding, and to build tools that can be used in freshwater management. In Chapter Two, multiple metrics of ecosystem response are modelled against flood parameters, such as magnitude and duration. Incremental contribution analysis is used to assess the sensitivity of each metric over a 1-year period following extreme flood events. Flow-based metrics are found to be highly sensitive. However, taxonomic metrics exhibit more complex patterns over the period. In Chapter Three, a novel Monte-Carlo simulation is produced to integrate into the RIVPACS model, that can generate simulated macroinvertebrate assemblages based on the reference conditions for any given site. This model was used to assess trends between 2010-2019 and found that most sites are moving away from their predicted reference assemblages. There are indications that taxonomic trends are sensitive to novel stressors and might be a useful management metric. In Chapter Four, a novel workflow is developed that can summarise the impact of multistressor interactions in an accessible manner. This workflow is demonstrated by assessing resistance of macroinvertebrates to a major flood event, finding that the impact of urbanisation could be mitigated approximately 20% by management strategies including alleviating abstraction and removing weirs. The findings of this thesis have direct management implications and potential, developing novel tools that can develop taxonomic-based targets for the management of freshwater ecosystems, and allow for rapid assessments of multi-stressor responses to disturbance events.