Search Marsden awards 2008–2017

Recipient(s): Dr DC Laughlin | PI | University of Waikato

Public Summary: The potential of predicting species abundances using physiological principles has generated tremendous interest, inspired vigorous debate, and has been heralded as the Holy Grail of Ecology. However, assembly theory is haunted by a paradox: environmental filtering tends to increase the functional similarity of species within communities leading to trait convergence, whereas competition tends to limit the functional similarity of species within communities leading to trait divergence. I have developed a new mathematical approach that resolves these paradoxical predictions within a Bayesian framework by incorporating intraspecific trait variation. This model unifies evolutionary biology with community ecology and makes assembly theory potentially falsifiable. The framework is generalizable to any ecosystem as it can accommodate any species pool, any set of functional traits, and multiple environmental gradients, and it eliminates some of the criticisms associated with recent modeling approaches. This project will test the predictions of assembly theory along two distinct environmental gradients: at the local scale along a strong soil nutrient gradient, and at the national scale along a broad climatic gradient. The model is founded on general biological principles and may improve our ability to predict species abundances across multiple taxa in any ecosystem on the planet.

Recipient(s): Dr MR Vennell | PI | University of Otago
Assoc Prof MG Gerritsen | AI | Stanford University

Public Summary: Power from turbines in strong tidal currents will contribute to the increasing demand for renewable energy. Currently operating tidal turbines are producing around 1 Mega-Watt of energy. Realizing the Giga-Watt potential of large tidal channels such as Cook Strait could contribute significantly to meeting the NZ Government’s target of increasing electricity generation from renewable sources from 73% in 2010 to 90% by 2025. To do this, tidal turbine farms must scale up from a few turbines, to hundreds of turbines. Unlike wind turbine farms, production from large tidal turbine farms does not increase in direct proportion to the number of turbines, because power extraction slows flows along the entire channel. This complex interaction between power extraction and flow speeds has meant that our understanding of how power production increases as a turbine farm grows is extremely limited. The proposed work uses analytical and computational techniques to develop a fundamental scaling law to underpin the relationship between power production and farm size. This scaling law would constitute a significant step towards addressing a core question for tidal current power: is it realistic to meet a significant fraction of our future energy needs by increasing the scale of tidal turbine farms?

Recipient(s): Assoc Prof S Ganesh | PI | University of Waikato
Dr LJ Kenix | AI | University of Canterbury
A Mead | AI | Victoria University of Wellington
Prof C Stohl | AI | University of California, Santa Barbara

Public Summary: While commentators maintain that digital technologies have made formal activist organisations redundant, this project questions that proposition by examining how emerging and established forms of social justice organising intersect in the new technological environment. It addresses major debates about technology and collective action by asking three questions about (a) relationships between technology and the efficacy of individual activists across formal and informal contexts; (b) the impact of digital technologies on formal and informal forms of participation; and (c) relationships between technology and the formation of collective action goals across formal organisations and informal groups. These questions are addressed in two research domains. Taking an organisation-centric approach, we will survey 2000 formal members of three large advocacy organisations, and also develop a Facebook app to study how nonmembers increasingly engage with and affect these organisations. We will also take a protest-centric approach, studying emerging coalitions between formal and informal groups at three actual protests. To do so, we will not only use interviewing and observation methods, but also participants’ own digital documentation. Analyses in both domains will significantly advance global studies of contemporary social justice by shedding unprecedented insight into dynamics among technology, organisational forms, and collective action.

Recipient(s): Assoc Prof HE Moewaka-Barnes | PI | Massey University
Prof MS Wetherell | PI | The University of Auckland
Ms B Borell | AI | Massey University
Assoc Prof TN McCreanor | AI | Massey University

Public Summary: ‘Race’, culture, and nationhood are continually reproduced in both daily activities and key events, through embodied social meanings and practices. Our research project explores little-studied acts of commemoration/celebration that express nation and community. Waitangi Day, Anzac Day, Matariki, Chinese and Gregorian New Year build and divide, acknowledge and deny, include and exclude and are focal points where we represent ourselves to each other and the world. They are rich in meaning, wairua and emotion for all citizens, whether participating directly or not and have major implications for identity, wellbeing and social cohesion. Our research will focus on the affective politics evoked as people relate, engage and grapple with cultural observances and often charged acts of remembrance in Aotearoa/New Zealand. We will produce new conceptual knowledge around wairua and emotion as neglected dimensions of relationships between Maori and non-Maori. Innovative methods will be produced in a convergence of kaupapa Maori and affect theory approaches, using multiple qualitative techniques to gather rich, diverse, multimodal data from Maori and non-Maori. The project is a strong collaboration between Maori and non- Maori team members that will train two doctoral students, build new theory, method and research capability in a unique, cutting edge investigation.

Recipient(s): Prof RG Downey | PI | Victoria University of Wellington
Dr DC Turetsky | AI | University of Vienna

Public Summary: I seek to improve our understanding of how computation and complexity
(resource bounded computation) interact with mathematical processes.
I especially wish to do this in algebra, model theory, and combinatorics.
I wish to use the ideas of computability and complexity to improve our
understanding of what randomness means, and how it inter-relates with
relative computational strength.

Recipient(s): Dr BJ Brewer | PI | The University of Auckland
Prof GF Lewis | AI | The University of Sydney

Public Summary: In astronomy, data usually do not answer our questions with certainty. Advances in computational methods have made Bayesian Inference practical for handling uncertainty. However, some problems remain unsolved due to a lack of realistic statistical models and the immense scale of the required computations. One such question is how many dark satellites exist around galaxies, the answer to which is crucial for understanding dark matter and structure formation. We will study high quality observations of gravitational lenses to infer the properties of these dark satellites, by implementing realistic substructure models. Simultaneously, widely-applicable Bayesian techniques for complex problems will be developed.

Recipient(s): Prof MC Corballis | PI | The University of Auckland

Public Summary: Since at least the 1970s, the association between right-handedness and the left-cerebral control of speech has been taken as evidence that language evolved from manual gestures. The gestural theory has been expanded to include facial gesture as a transitional phase, with language having evolved from a primate system dedicated to manual grasping, gradually incorporating communicative manual and facial gestures, and culminating in speech (vocal gestures). These processes became increasingly lateralized in the course of hominin evolution. We now know too that handedness itself is a poor measure of cerebral asymmetry for manual action. In the first large-scale study of its kind, we will use fMRI-based measures of cerebral asymmetries for manual action, facial movements, and speech, to test the hypothesis that they are intercorrelated, as predicted from gestural theory. We include equal numbers of left- and right-handers, because genetic models suggest that genetic control of asymmetry is expressed in some but not all individuals, implying that the correlations should be stronger in right- than in left-handers. Whether or not the results support our predictions, the research will add to our understanding of cerebral asymmetry—its dimensionality, genetic basis, and implications for evolution.

Recipient(s): Prof DK Bilkey | PI | University of Otago
Dr KL Hillman | AI | University of Otago

Public Summary: Self-control underlies a range of human behaviour, from saving for retirement to saving the planet. The construct is so influential that it appears across a host of disciplines, variously termed as impulsivity, self-regulation, delay of gratification, willpower and inter-temporal choice. It has become a focus of study in recent years, not least because childhood self-control appears to predict physical health, substance dependence, personal finances, and criminal offending outcomes in adulthood (1). This project examines one of the brain mechanisms underlying self-control, in particular that involved when access to an easily available reward is rejected in favour of an ultimately more valuable option that might require effort or patience to attain. We will use an animal model that allows us to study the activity of brain cells during behaviour and investigate how the brain uses prospective memory to modulate self-control. This study builds on our novel pilot data that shows how the communication of information from the prefrontal cortex to the hippocampus is important for this process. The outcome of this study will be of significant importance as it will provide new insights into one of the brain processes that supports self-control, and how this operates during decision-making.

Recipient(s): Dr J Hennig | PI | University of Otago
Dr F Beyer | AI | University of Otago
Prof J Frauendiener | AI | University of Otago

Public Summary: This project is concerned with cosmological models within general relativity, Albert Einstein's theory of gravitation, and the investigation of their properties and causal structures. The starting point is the interesting observation that there are mathematical solutions to Einstein's field equations with a strange feature: they contain regions where causality is violated and time travel backwards in time is possible. This unexpected behaviour and the resulting philosophical problems are in disagreement with the current picture that physicists have of our universe. Moreover, it might indicate a lack of determinism in the theory of general relativity. Intimately connected to these problems is the famous cosmic censorship conjecture, according to which generic cosmological solutions cannot be extended into acausal regions. Hence, it is believed that acausal models - despite their existence - cannot be realized in nature. However, we claim that there are indeed large families of such peculiar mathematical solutions and our goal is to prove their existence. In particular, we will study their properties with both analytical and numerical methods, and construct exact solutions. Our investigations will provide essential contributions towards a better understanding of the mathematical structure of general relativity and, therefore, the geometry of the universe.

Recipient(s): Dr RA Arnold | PI | Victoria University of Wellington
Dr I Liu | PI | Victoria University of Wellington
Prof SA Pledger | AI | Victoria University of Wellington

Public Summary: Ordinal categorical data can arise in a questionnaire where ordered categorical responses to a question or values of a variable are possible (e.g. better, unchanged, worse, or vegetation low, medium, high). Such data are very common, but only limited methods for their analysis exist. Many existing ordinal data analysis methods make unjustified assumptions, either by assigning numerical scores to observations or by treating them as unordered categories. These approaches do not exploit the true ranked nature of the responses, and thus have unreliable results. Specifically, methods are lacking for the identification of groups, patterns, clusters and associations. Although some methods use ad hoc distance metrics and crude similarity measures based on the rank, they do not fully incorporate the ordinal structure of the data within a probability model.

One goal of analysis may be to identify groups (clusters) of respondents with similar response patterns, and groups of characteristics that tend to occur together. In this project we will develop new fuzzy clustering methods for ordinal data to classify respondents and questions simultaneously using various ordinal response models. In our methods, the subject/question is probabilistically classified across the groups, allowing a richer description of the data.