Temporal dynamics of geothermal microbial communities in Aotearoa-New Zealand

Abstract

Microbial biogeography studies, in particular for geothermal-associated habitats, have focused on spatial patterns and/or individual sites, which have limited ability to describe the dynamics of ecosystem behaviour. Here, we report the first comprehensive temporal study of bacterial and archaeal communities from an extensive range of geothermal features in Aotearoa-New Zealand. One hundred and fifteen water column samples from 31 geothermal ecosystems were taken over a 34-month period to ascertain microbial community stability (control sites), community response to both natural and anthropogenic disturbances in the local environment (disturbed sites) and temporal variation in spring diversity across different pH values (pH 3, 5, 7, 9) all at a similar temperature of 60-70°C (pH sites). Identical methodologies were employed to measure microbial diversity via 16S rRNA gene amplicon sequencing, along with 44 physicochemical parameters from each feature, to ensure confidence in comparing samples across timeframes. Our results indicated temperature and associated groundwater physicochemistry were the most likely parameters to vary stochastically in these geothermal features, with community abundances rather than composition more readily affected by a changing environment. However, variation in pH (pH ±1) had a more significant effect on community structure than temperature (±20°C), with alpha diversity failing to adequately measure temporal microbial disparity in geothermal features outside of circumneutral conditions. While a substantial physicochemical disturbance was required to shift community structures at the phylum level, geothermal ecosystems were resilient at this broad taxonomic rank and returned to a pre-disturbed state if environmental conditions re-established. These findings highlight the diverse controls between different microbial communities within the same habitat-type, expanding our understanding of temporal dynamics in extreme ecosystems.

Keywords: Aotearoa-New Zealand; extremophiles; geothermal microbiology; hot springs; microbial biogeography; temporal biogeography.

Figure 1

Short-term, natural disturbance of Waimangu Stream by Inferno Crater Lake. (A) Waimangu Stream (outlined by the solid blue line) originates from Frying Pan Lake and flows down a rift valley to Lake Rotomahana. Sampling locations along the stream (Waimangu Features 2–7) are depicted by white circles, along with Inferno Crater Lake which overflows into Waimangu Stream every ~30–40 days. Waimangu Features 2 and 3 are upstream of the Inferno Crater Lake overflow (by ~150 and 10 m, respectively), while Waimangu Features 4–7 are downstream (by ~35, 200, 1,400, and 1,950m, respectively). (B) Microbial community composition and relative abundance of Waimangu Stream features and Inferno Crater Lake (ICL) before (August 2013) and during (October 2013) the disturbance, measured by amplicon sequencing of the 16S rRNA gene. Only phyla >1% average relative abundance across all samples in this category are shown.

Figure 2

Temporal microbial diversity and physicochemistry of the Waikite wetland during restoration. (A) Aerial map of the Waikite geothermal field, including the wetland (dashed white line) and Otamakokore Stream (outlined by the solid blue line). The location of the weir is highlighted by the star, with stream sites and geothermal features sampled distinguished by colour. (B) Non-metric multidimensional scaling (NMDS; n = 25, stress = 0.11) of beta diversity, calculated using Bray-Curtis dissimilarities between 16S rRNA gene sequences of microbial communities in these features. Samples are coloured by feature name, with time indicated by arrows. (C) Variation in alpha diversity of samples measured via OTU richness (i.e., the number of OTUs per community). (D) Alpha diversity was also measured by the Shannon diversity index to indicate evenness of the communities over time. (E) The pH of features at the time of sampling. (F) Temperature variation of all sites during the wetland restoration. The black dashed line indicates the construction of a weir in the southwest corner of the wetland.

Figure 3

Temporal microbial community composition and relative abundance of control features in the Taupō Volcanic Zone (TVZ), Aotearoa-New Zealand. Amplicon sequencing of the 16S rRNA gene was used to measure read abundance of taxa in spring communities, with only phyla >1% average relative abundance across all samples in this category shown. Control features were sampled from the Wairakei-Tauhara (Wairakei Features 5, 13 and 14), Ngatamariki (Radiata Pool) and Waiotapu (Champagne Pool) geothermal fields.

Figure 4

Temporal microbial community composition and relative abundance of geothermal features during the Waikite wetland restoration. Amplicon sequencing of the 16S rRNA gene was used to measure read abundance of taxa in spring communities, with only phyla >1% average relative abundance across all samples in this category shown. The vertical dashed line indicates the construction of a weir near the outlet of the wetland.

Figure 5

Temporal microbial community composition and relative abundance of geothermal features across the pH scale. Amplicon sequencing of the 16S rRNA gene was used to measure read abundance of taxa in spring communities, with only phyla >1% average relative abundance across all samples in this category shown. Geothermal features are grouped according to their respective pH group (pH 3, 5, 7, and 9).

Figure 6

Temporal diversity and physicochemistry of geothermal features from across the pH range. (A) The microbial communities of 12 geothermal springs were measured six times over 1 year by amplicon sequencing of the 16S rRNA gene, with variation in alpha diversity between temporal samples indicated by OTU richness (i.e., the number of OTUs per community). (B) Alpha diversity was also measured by the Shannon diversity index to indicate evenness of the communities over time. (C) Spring pH of the features at the time of sampling. (D) Spring temperature of the geothermal features. (E) Beta diversity of the 12 features is shown by a non-metric multidimensional scaling (NMDS; n = 62, stress = 0.15) of Bray-Curtis dissimilarities between all temporal samples, with data ellipses generated from multivariate t-distribution with a 95% confidence level. All samples are coloured according to feature name, with alpha diversity and physicochemistry further grouped by pH group.