Response to environmental change by bacterial communities in stream biofilms

Abstract

Stream biofilms play an important ecological role in the supply of energy and organic matter to aquatic food webs. Bacterial community composition in stream biofilms is shaped by biotic interactions as well as physicochemical conditions which vary across space and time. It is, therefore, crucial to understand how natural and human-driven activities can impact stream bacterial community composition. This thesis applied next-generation high throughput sequencing approaches to investigate stream biofilm bacterial community dynamics and their differential patterns from small to large spatial gradients as well as demonstrate the existence of temporal and repeatable seasonal patterns in bacterial communities. Additionally, it was also discovered how stream bacterial communities can be used as indicators of stream quality. The relative importance of spatial and temporal heterogeneity in biofilm bacterial community composition was investigated by extracting DNA from stream biofilm samples from six different streams of Auckland region for 30 months period (February 2013-July 2015) draining three different catchment types. It was hypothesised that bacterial community composition would change monthly/seasonally in accordance with an annual cyclical trend and that regional environmental factors would be significant drivers of the observed community change. Bacterial 16S rRNA gene sequencing of these samples revealed that stream bacterial community composition was explained better by monthly changes than by differences observed among streams and catchment types and followed a discernible cyclical seasonal pattern. 16S rRNA data was obtained after further inclusion of DNA extracted from stream biofilm samples (February 2013- February 2016) to determine the existence of temporal and repeatable seasonal patterns associated with stream bacterial communities. To evaluate this ecological modelling approach, temporal cyclicity models, such as Generalised Linear Models in R were utilised to discover the associations between bacterial community differences/shifts and environmental heterogeneity. This study selected for specific bacterial communities responding to bloom and seasonal changes throughout time, where a bloom represented a significant seasonal occurrence that can be influenced by climate change or by a combination of environmental conditions such as temperature, light, rainfall, and humidity. Finally, to address changes in bacterial communities as a result of land use alterations in large-scale spatial investigation in stream biofilms, 16S rRNA sequencing data from seven regions nationwide in New Zealand were extracted from NCBI database. Machine-learning approaches were used to associate bacterial community relationships with a suite of potential stressors known to alter stream ecological conditions. The national-scale association between stream bacterial communities confirmed that bacterial community composition was closely linked to land use differences and was demonstrated to be predictable, with bacterial community composition correctly identifying sites based on their crude land use classifications with 62 % accuracy. This thesis greatly advances our understanding of the important drivers that shape stream bacterial community composition and trigger significant shifts and differences in their composition across New Zealand. The necessity for microbial pathogens and indicators to be included in freshwater monitoring programmes derives from the fact that stream bacterial communities, including pathogens and related indicators, can represent substantial health concerns to humans. As a result, their presence, absence, survival or die-off in the environment, and sampling all necessitate unique monitoring strategies. Importantly, the implications of spatial, temporal gradients and the impact of land use changes on stream bacterial communities are highlighted in this thesis. This study provides evidence that incorporating stream bacterial community data as an alternate way to stream monitoring can help ensure the long-term sustainability of stream ecosystems impacted by agriculture and anthropogenic activities while maintaining healthy natural ecosystems.