title: Diversity and Distributional Patterns of Ciliates in Guaymas Basin Hydrothermal Vent Sediments

authors: Coyne, Kathryn J.

subjects: Anoxic

abstract: Little is known about protists at deep-sea hydrothermal vents. The vent sites at Guaymas Basin in the Gulf of California are characterized by dense mats of filamentous pigmented or nonpigmented Beggiatoa that serve as markers of subsurface thermochemical gradients. We constructed 18S rRNA libraries to investigate ciliate assemblages in Beggiatoa mats and from bare sediments at the Guaymas vent site. Results indicated a high diversity of ciliates, with 156 operational taxonomic units identified in 548 sequences. Comparison between mat environments demonstrated that ciliate and bacterial assemblages from pigmented mats, nonpigmented mats, and bare sediments were significantly different and highly correlated with bacterial assemblages. Neither bacterial nor ciliate assemblages were correlated with environmental factors. The most abundant ciliates at Guaymas were more likely to be represented in clone libraries from other hydrothermal, deep-sea, and/or anoxic or microaerophilic environments, supporting the hypothesis that these ciliate species are broadly distributed. The orange mat environment included a higher proportion of ciliate sequences that were more similar to those from other environmental studies than to cultured ciliate species, whereas clone libraries from bare sediments included sequences that were the most highly divergent from all other sequences and may represent species that are endemic to Guaymas.

text: Diversity and Distributional Patterns of Ciliates in Guaymas Basin Hydrothermal Vent Sediments Little is known about protists at deep-sea hydrothermal vents. The vent sites at Guaymas Basin in the Gulf of California are characterized by dense mats of filamentous pigmented or nonpigmented Beggiatoa that serve as markers of subsurface thermochemical gradients. We constructed 18S rRNA libraries to investigate ciliate assemblages in Beggiatoa mats and from bare sediments at the Guaymas vent site. Results indicated a high diversity of ciliates, with 156 operational taxonomic units identified in 548 sequences. Comparison between mat environments demonstrated that ciliate and bacterial assemblages from pigmented mats, nonpigmented mats, and bare sediments were significantly different and highly correlated with bacterial assemblages. Neither bacterial nor ciliate assemblages were correlated with environmental factors. The most abundant ciliates at Guaymas were more likely to be represented in clone libraries from other hydrothermal, deep-sea, and/or anoxic or microaerophilic environments, supporting the hypothesis that these ciliate species are broadly distributed. The orange mat environment included a higher proportion of ciliate sequences that were more similar to those from other environmental studies than to cultured ciliate species, whereas clone libraries from bare sediments included sequences that were the most highly divergent from all other sequences and may represent species that are endemic to Guaymas.

year: 2013

title: Random Relational Rules

authors: Anderson, Grant

subjects: relational data mining

abstract: In the field of machine learning, methods for learning from single-table data have received much more attention than those for learning from multi-table, or relational data, which are generally more computationally complex. However, a significant amount of the world's data is relational. This indicates a need for algorithms that can operate efficiently on relational data and exploit the larger body of work produced in the area of single-table techniques.

This thesis presents algorithms for learning from relational data that mitigate, to some extent, the complexity normally associated with such learning. All algorithms in this thesis are based on the generation of random relational rules. The assumption is that random rules enable efficient and effective relational learning, and this thesis presents evidence that this is indeed the case. To this end, a system for generating random relational rules is described, and algorithms using these rules are evaluated. These algorithms include direct classification, classification by propositionalisation, clustering, semi-supervised learning and generating random forests.

The experimental results show that these algorithms perform competitively with previously published results for the datasets used, while often exhibiting lower runtime than other tested systems. This demonstrates that sufficient information for classification and clustering is retained in the rule generation process and that learning with random rules is efficient.

Further applications of random rules are investigated. Propositionalisation allows single-table algorithms for classification and clustering to be applied to the resulting data, reducing the amount of relational processing required. Further results show that techniques for utilising additional unlabeled training data improve accuracy of classification in the semi-supervised setting. The thesis also develops a novel algorithm for building random forests by makingefficient use of random rules to generate trees and leaves in parallel.

text: Random Relational Rules In the field of machine learning, methods for learning from single-table data have received much more attention than those for learning from multi-table, or relational data, which are generally more computationally complex. However, a significant amount of the world's data is relational. This indicates a need for algorithms that can operate efficiently on relational data and exploit the larger body of work produced in the area of single-table techniques.

This thesis presents algorithms for learning from relational data that mitigate, to some extent, the complexity normally associated with such learning. All algorithms in this thesis are based on the generation of random relational rules. The assumption is that random rules enable efficient and effective relational learning, and this thesis presents evidence that this is indeed the case. To this end, a system for generating random relational rules is described, and algorithms using these rules are evaluated. These algorithms include direct classification, classification by propositionalisation, clustering, semi-supervised learning and generating random forests.

The experimental results show that these algorithms perform competitively with previously published results for the datasets used, while often exhibiting lower runtime than other tested systems. This demonstrates that sufficient information for classification and clustering is retained in the rule generation process and that learning with random rules is efficient.

Further applications of random rules are investigated. Propositionalisation allows single-table algorithms for classification and clustering to be applied to the resulting data, reducing the amount of relational processing required. Further results show that techniques for utilising additional unlabeled training data improve accuracy of classification in the semi-supervised setting. The thesis also develops a novel algorithm for building random forests by makingefficient use of random rules to generate trees and leaves in parallel.

year: 2008

title: Contrasting microbial communities across anthropogenic pollution gradients: MV Rena shipwreck versus urban pressures

authors: Reihana, Kiri

subjects: microbial

abstract: With the catastrophic environmental event that was the MV Rena ship wreck and oil spill in the Bay of Plenty area in 2011, a serendipitous opportunity to laterally explore a variety of impacts in the marine biosphere emerged. One of those opportunities is studied here; how microbial communities respond to stress in context of existing ambient local anthropogenic influences. The locational backdrop to this study is also relevant and provides an additional element of complexity as the Bay of Plenty region is located in the middle of the Taupo Volcanic Zone, hence microbial communities are likely to reflect the regional geology. Therefore a research project was possible where a pollutant gradient was selected based on a range of locations reflecting different elements of natural versus human long term and acute impact stresses. The concept was to establish a platform of preliminary study from which more detailed could follow. In addition this study aimed at providing a link between microbial environmental responses to stress in a context that could permit uptake of scientific evidence for cultural impact assessments given the strong cultural focus on MV Rena impacts by government and the public.
A significant element of the exploratory process was to identify methodologies which may be of use in ascribing microbial community composition with the microenvironment in a manner that allows comparison with research reported in the literature.
Review of all results showed that sites from widely varying geologies and environmental/stress condition were more similar than was initially predicted. This is in sharp contrast to expectations generated from the literature. 
At the sites of Astrolabe Reef (Otaiti) (impacted by the ship wreck, major oil spill and heavy metal pollution event) and Maketu (impacted by a eutrophic estuary), similar consortia transpired, despite strongly different sources of stress, substantiating the microbial biogeography theory: ‘Everything is everywhere’ and ‘the environment selects’. However at Tauranga Harbour long term human activity appears to have had a significant ‘additional’ effect on microbial communities. Our challenge has hence become one to separate out natural versus a range of anthropogenic stressors. 
Underlying the general overall trends, subtle community effects such as at Astrolabe Reef, which supported significantly different dominant communities to the other two sites. It was expected that Tauranga Harbour and Maketu Estuary (adjacent coastally), would be more similar. Of all the sites one at the impacted Maketu, site 1 (M1) and the impacted Tauranga site 2 (T2), were highly similar at 96% from statistical analysis suggesting, the original assumption of visually similar site characteristics (impacted sites with low residence time and thick algal matts) do support similar microbial communities. Substantiating the theory that ‘everything is everywhere’ at these two sites, even though the variables and elemental composition were dissimilar. 
Rare and abundant species were separated out and analysed to elucidate patterns or trends in the communities. Overall the abundant species clustered, into impacted versus non impacted sites, for the rare species Tauranga and Maketu clustered clearly following the same trend; while the Astrolabe reef sites clustered together. The clear differentiation in the rare species from the Astrolabe reef sites, suggests that those species may make up the endemic bacterial communities at Astrolabe Reef with additional effects of Rena pollution suggested at some of the sites close to the wreck.
A general discussion of the cultural and economic impacts of the Rena oil spill, gave context to the holistic human impacts sustained by the local Māori, specifically the disconnect of their precious livelihoods and unique relationship with the environment. These holistic concepts could compromise a thesis in themselves so in this report their definitions are by no means finite.     
This study attempted to interrelate both cultural, ecological biology and microbiology science components for the purpose of a more comprehensive investigation of microbial responses to the Rena oil spill and contrast these to local anthropogenic pressures. However being the first attempt to catalogue the previously unknown microbial communities in the Bay of Plenty area, meant that sample regimes, techniques, processing and analysis were all experimental. Despite this, effects attributable to the Rena were identified that lay beyond the large scale variability identified for Bay of Plenty microbial community assemblages. 
These research results provide an important primary contribution to microbial understandings in the New Zealand context, and as a baseline mapping exercise for the Bay of Plenty region.

text: Contrasting microbial communities across anthropogenic pollution gradients: MV Rena shipwreck versus urban pressures With the catastrophic environmental event that was the MV Rena ship wreck and oil spill in the Bay of Plenty area in 2011, a serendipitous opportunity to laterally explore a variety of impacts in the marine biosphere emerged. One of those opportunities is studied here; how microbial communities respond to stress in context of existing ambient local anthropogenic influences. The locational backdrop to this study is also relevant and provides an additional element of complexity as the Bay of Plenty region is located in the middle of the Taupo Volcanic Zone, hence microbial communities are likely to reflect the regional geology. Therefore a research project was possible where a pollutant gradient was selected based on a range of locations reflecting different elements of natural versus human long term and acute impact stresses. The concept was to establish a platform of preliminary study from which more detailed could follow. In addition this study aimed at providing a link between microbial environmental responses to stress in a context that could permit uptake of scientific evidence for cultural impact assessments given the strong cultural focus on MV Rena impacts by government and the public.
A significant element of the exploratory process was to identify methodologies which may be of use in ascribing microbial community composition with the microenvironment in a manner that allows comparison with research reported in the literature.
Review of all results showed that sites from widely varying geologies and environmental/stress condition were more similar than was initially predicted. This is in sharp contrast to expectations generated from the literature. 
At the sites of Astrolabe Reef (Otaiti) (impacted by the ship wreck, major oil spill and heavy metal pollution event) and Maketu (impacted by a eutrophic estuary), similar consortia transpired, despite strongly different sources of stress, substantiating the microbial biogeography theory: ‘Everything is everywhere’ and ‘the environment selects’. However at Tauranga Harbour long term human activity appears to have had a significant ‘additional’ effect on microbial communities. Our challenge has hence become one to separate out natural versus a range of anthropogenic stressors. 
Underlying the general overall trends, subtle community effects such as at Astrolabe Reef, which supported significantly different dominant communities to the other two sites. It was expected that Tauranga Harbour and Maketu Estuary (adjacent coastally), would be more similar. Of all the sites one at the impacted Maketu, site 1 (M1) and the impacted Tauranga site 2 (T2), were highly similar at 96% from statistical analysis suggesting, the original assumption of visually similar site characteristics (impacted sites with low residence time and thick algal matts) do support similar microbial communities. Substantiating the theory that ‘everything is everywhere’ at these two sites, even though the variables and elemental composition were dissimilar. 
Rare and abundant species were separated out and analysed to elucidate patterns or trends in the communities. Overall the abundant species clustered, into impacted versus non impacted sites, for the rare species Tauranga and Maketu clustered clearly following the same trend; while the Astrolabe reef sites clustered together. The clear differentiation in the rare species from the Astrolabe reef sites, suggests that those species may make up the endemic bacterial communities at Astrolabe Reef with additional effects of Rena pollution suggested at some of the sites close to the wreck.
A general discussion of the cultural and economic impacts of the Rena oil spill, gave context to the holistic human impacts sustained by the local Māori, specifically the disconnect of their precious livelihoods and unique relationship with the environment. These holistic concepts could compromise a thesis in themselves so in this report their definitions are by no means finite.     
This study attempted to interrelate both cultural, ecological biology and microbiology science components for the purpose of a more comprehensive investigation of microbial responses to the Rena oil spill and contrast these to local anthropogenic pressures. However being the first attempt to catalogue the previously unknown microbial communities in the Bay of Plenty area, meant that sample regimes, techniques, processing and analysis were all experimental. Despite this, effects attributable to the Rena were identified that lay beyond the large scale variability identified for Bay of Plenty microbial community assemblages. 
These research results provide an important primary contribution to microbial understandings in the New Zealand context, and as a baseline mapping exercise for the Bay of Plenty region.

year: 2016