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Lincoln University Digital Thesis 

 

 

Copyright Statement 

The digital copy of this thesis is protected by the Copyright Act 1994 (New Zealand). 

This thesis may be consulted by you, provided you comply with the provisions of the Act 

and the following conditions of use: 

you will use the copy only for the purposes of research or private study  

you will recognise the author's right to be identified as the author of the thesis and   due acknowledgement will be made to the author where appropriate   you will obtain the author's permission before publishing any material from the   thesis.     Spatial Patterns in Bacterial Community Structure and Function within Shallow Alpine Tarns A thesis submitted in partial fulfilment of the requirements for the Degree of Master of Science at Lincoln University by Julia Ruth Bellamy Lincoln University 2013 Spatial Patterns in Bacterial Community Structure and Function within Shallow Alpine Tarns by Julia Ruth Bellamy Small scale spatial variation in bacterial community structure and function in freshwater ecosystems is poorly understood. I investigated the spatial variation of bacterial communities within three tarns located at Tekapo Scientific Reserve using automated ribosomal intergenic spacer analysis (ARISA). I examined the variability in bacterial community structure both within and among tarn locations and explored whether bacterial communities adhere to the same biogeographical patterns commonly reported for communities of larger organisms; these were the taxa-area and distance-decay relationships.

I also attempted to identify physicochemical variables that were significantly related to the observed community heterogeneity. To achieve these aims, I collected more than 100 samples in total across the three tarns and measured a range of physicochemical variables (pH, conductivity, total carbon and anion concentrations) for each sample. The ARISA data revealed significant variability in bacterial community structure among the tarns and some variation within the tarns that was related to correlated spatial variability in a range of physicochemical variables such as, pH, total carbon and conductivity. Distance-decay and taxa-area relationships in bacterial community similarity were also observed. There was no correlation between the structural and functional attributes (i.e., carbon substrate utilisation patterns) of the bacterial communities, suggesting that there was some functional redundancy in these bacterial communities in terms of carbon substrate utilisation. This study provides valuable information about how freshwater bacterial biodiversity is maintained and expands our understanding of the link between bacterial community structure and function.

Keywords: Biogeographical patterns, taxa-area, z-value, distance-decay, ARISA, BIOLOG EcoPlates™, contour plots, PRIMER, Tekapo, New Zealand, DistLM, variance partitioning

–  –  –

First and foremost, I would like to thank Dr. Gavin Lear at the University of Auckland, NZ, for his patience and guidance throughout the duration of this study. I would also like to thank Dr. Hannah Buckley at Lincoln University, NZ, for her significant contribution towards this study, especially in regards to data analysis and interpretation. I am grateful to Bradley Case, also at Lincoln University, NZ, for his help with the GIS program, ArcMap 10. Finally, the kind assistance with sample collection by Jack Lee and Taiga Yamamura was much appreciated.

This work was supported by the RSNZ Marsden Fund (Grant Number LIU0901).

iii Table of Contents

Acknowledgements

Table of Contents

List of Tables

List of Figures

Chapter 1 Introduction

1.1 Taxa-area relationship

1.2 Distance-decay relationship

1.3 Relationship between spatial scaling and efficient sampling strategies

1.4 Link between microbial community structure and function

1.5 Aims, objectives and hypotheses

Chapter 2 Methods

2.1 Experimental design

2.1.1 Study site

2.1.2 Sample locations

2.1.3 Sample collection

2.1.4 Physicochemical properties of the tarns

2.2 Molecular methods

2.2.1 Filtration procedure

2.2.2 DNA extraction

2.2.3 Polymerase chain reation (PCR)

2.2.4 Agarose gel electrophoresis

2.2.5 DNA purification

2.2.6 Automated ribosomal intergenic spacer analysis (ARISA)

2.3 Functional analysis (carbon substrate utilisation)

2.4 Statistical analysis

2.4.1 Evaluation of the taxa-area hypothesis

2.4.2 Evaluation of the distance-decay hypothesis

2.4.3 Evaluation of the space vs environment hypothesis

Chapter 3 Results

3.1 Overview

3.2 Taxa-area relationship

3.3 Distance-decay relationship

3.4 Spatial and environmental variation

3.4.1 Among tarn variation

3.4.2 Within tarn variation

Chapter 4 Discussion

iv

4.2 Taxa-area relationship





4.3 Distance-decay relationship in bacterial community structure

4.4 Distance-decay relationship in bacterial community function

4.5 Spatial and environmental variability among the tarns

4.6 Spatial and environmental variability within the tarns

4.6.1 Spatial variability within the tarns

4.6.2 Environmental variability within the tarns

4.7 Variance partitioning of spatial factors and environmental variables

4.8 Future research

4.9 Conclusions

Appendix A BIOLOG EcoPlate™

Appendix B Automated ribosomal intergenic spacer analysis (ARISA) data

Appendix C R script for taxa-area relationship

Appendix D R script for distance-decay relationship

Appendix E Cluster plots for bacterial community function

Appendix F Variance Partitioning

References

v List of Tables

Table 1. Samples required for analyses and storage conditions.

Table 2. Reagents used to create a mastermix suitable for the PCR amplification of DNA for ARISA for one sample.

Table 3. Conditions under which the PCR was performed.

Table 4. Average water chemistry obtained for samples abstracted from each tarn.

Data are means ± standard error.

Table 5. Relationship of different environmental variables with bacterial community structure.

Environmental variables in bold were identified as being significant (P 0.05). Subscript numbers indicate the order of significance of the environmental variables (forward selection) and the % represents the proportion that each environmental variable contributed towards total variation. Depth was only measured for samples in Tarn 3.

Table 6. Relationship of different environmental variables with bacterial community function.

Environmental variables in bold were identified as being significant (P 0.05). Subscript numbers indicate the order of significance of the environmental variables (forward selection) and the % represents the proportion that each environmental variable contributed towards total variation. Depth was only measured for samples in Tarn 3.

Table A. 1. Carbon sources in BIOLOG EcoPlate™ (BIOLOG Inc, Hayward, CA, U.S.A.). Each plate contains triplicate replicates of the carbon sources.

Table A. 2. Sample of tabulated ARISA data. Columns are samples and the data in each row provides the relative abundance of each bacterial taxon (ARISA peak).

Each number greater than zero represents a taxon that was detected in the respective sample.

vi List of Figures

Figure 1. Taxa-area relationship modelled by the power law and plotted in arithmetic space showing an increase in species richness with increasing area.

Taken directly from Rosenzweig (1995).

Figure 2. Relationship between the compositional similarity of paired Inga communities and geographic distance.

Taken directly from Dexter et al. (2012).

Figure 3. Lake Mendote and Crystal Bog.

Principle coordinate analysis (PCoA) was performed on the bacterial community composition in the lakes and the axis of the ordinations were coloured and this information was plotted on the maps. Similar colours denote sites represented by more similar bacterial communities. Taken directly from Jones et al. (2012).

Figure 4. Schematic of possible relationships between functional and species diversity.

A1, every species has a unique functional role resulting in a ratio of 1:1 between functional and species diversity; A2, multiple species have similar functional attributes; B, at low species richness, functional diversity rapidly increases and then increases at declining rates with high species diversity, and eventually reaches an asymptote; C, relationship between functional diversity and species diversity is sensitive to changes in environmental variables. Taken directly from Guillemot et al. (2011).

Figure 5. Location of tarns near Lake Tekapo, New Zealand.

Figure 6. Sample locations in the three tarns from which samples were collected.

Each dot shows where water samples, and other data were collected.

Figure 7. Schematic showing the components of the sampling apparatus.

The adjustable support poles extended to a height of 2.5 m and the rope was 100 m long giving us the ability to sample across a distance of 50 m (not to scale)............... 15 Figure 8. One of the support poles with attached rope and tubing of the sampling apparatus.

Figure 9. Floatation device that was used to collect water from a depth of 5 cm.

................. 16 Figure 10. Buchner filter showing a Buchner funnel (Nalgene®, Thermo SCIENTIFIC, Rochester, NY, U.

S.A.) and the waste collection container (KIMAX®, KIMBLE, Vineland, NJ, U.S.A.).

Figure 11. Unused BIOLOG EcoPlate™ (left) and BIOLOG EcoPlate™ (right) showing colour development

Figure 12. Example of variance partitioning displayed on a column plot.

Four different components are shown: (hatched) unexplained variation; (black) pure environmental variation that is independent of any spatial factors; (white) pure spatial variation that is independent of any environmental variables;

(grey) spatially structured environmental variables.

Figure 13. Taxa-area relationship for each tarn.

The cumulative number of samples (mean taxa richness was calculated for each combination of samples) was used to represent area. The relationship in each tarn was modelled by the power law (S = cAz, red line = fitted values).

Figure 14. Distance-decay curve for bacterial community structure and function in each tarn.

Each data point represents the Bray-Curtis similarity score for paired samples (y-axis) and their respective geographic distance (x-axis). For bacterial community structure, an exponential curve was fitted to the data in each tarn.

For bacterial community function a linear regression line was fitted.................. 32 vii Figure 15. Differences in bacterial community structure (left) and bacterial community function (right).

Plots are non-metric multidimensional scaling of ARISA data and carbon substrate utilisation data, using Bray-Curtis similarity matrices of samples. Data points relate to samples abstracted from Tarn 1, 2 or 3. Twodimensional stress values are 0.20 and 0.18 for plots of bacterial community structure and function, respectively.

Figure 16. Similarity in bacterial community structure and function among tarns.

Bacterial community data for each tarn were subjected to a data reduction procedure by non-metric multidimensional scaling of Bray-Curtis similarity data. The differences between the highest and lowest 1-d configuration scores for each plot were then used to classify the configuration into ten equally sized classes.

The bacterial community data falling within each class was assigned a different colour, across a gradient from dark blue (lowest 1-d configuration score) to light green (highest 1-d configuration score. The outcome of this approach is that samples hosting more similar bacterial community data are represented by more similar colours on each map

Figure 17. Example of a dendrogram for bacterial community structure in Tarn 1 showing the cut off at 50 % (red line) Bray-Curtis similarity.

Each branch that the red line crosses is a different cluster in the tarn at the 50 % Bray-Curtis similarity level

Figure 18. Variation in bacterial community structure in each tarn.

Plots are derived from non-metric multidimensional scaling of ARISA data using a Bray-Curtis similarity measure. Group average clusters are superimposed on each plot (red circles) at the level of 50 % Bray-Curtis similarity. Each sample that is not constrained was in its own cluster. Two-dimensional stress values are 0.16,

0.13 and 0.13, respectively.

Figure 19. The plots on the left show similarity in bacterial community structure within the tarns.



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