Breeding ecology of a translocated population of red-crowned kakariki (Cyanoramphus novaezelandiae) on Tiritiri Matangi island, New Zealand : a thesis submitted in partial fulfillment of the requirements for the degree of Master of Sciences in Ecology at Massey University, Albany, New Zealand

The reproductive ecology of a translocated population of red-crowned kakariki (Cyanoramphus novaezelandiae) was monitored during 2004-2006, covering two breeding seasons on Tiritiri Matangi Island. Red-crowned kakariki nested in tree cavities, ground burrows and in vegetation clusters located in forest remnants, grasslands and replanted vegetation as well as in nestboxes. There was a marked difference in reproductive success between the two breeding seasons. In 2004-2005 1.4 fledglings per breeding pair were produced. In contrast. 3.4 fledglings per breeding pair were produced in 2005-2006. This increase was the result of changes in loss rate during the nesting cycle. Nest failure occurred in 57% of nests in 2004-2005 whereas only 8% of nests were affected in 2005-2006. In both breeding seasons, incubation was the main stage of losses. Clutches hatched with various degrees of asynchrony. Brood sizes ranged from one to nine nestlings. Within broods, nestlings of different hatching ranks reached similar mass at fledgling. Likewise, nestlings of different hatching ranks gained similar weight over the linear portion of the growth curve and grew wings at a similar rate. However, last hatched nestlings fledged with shorter wings. Furthermore, mortality was higher for last hatched nestlings. Sex ratios at the clutch level and at fledgling did not deviate from parity. However, at the clutch level there was a higher proportion of males in clutches laid early and middle in the breeding season. Various lines of evidence suggest that food availability has a direct effect on reproductive success of red-crowned kakariki and can exacerbate the costs of asynchronous hatching. Therefore it is a priority to investigate natural changes in food resources of the red-crowned kakariki and to assess the potential of direct management to improve the conservation of the species

Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data

We consider learning, from strictly behavioral data, the structure and parameters of linear influence games (LIGs), a class of parametric graphical games introduced by Irfan and Ortiz (2014). LIGs facilitate causal strategic inference (CSI): Making inferences from causal interventions on stable behavior in strategic settings. Applications include the identification of the most influential individuals in large (social) networks. Such tasks can also support policy-making analysis. Motivated by the computational work on LIGs, we cast the learning problem as maximum-likelihood estimation (MLE) of a generative model defined by pure-strategy Nash equilibria (PSNE). Our simple formulation uncovers the fundamental interplay between goodness-of-fit and model complexity: good models capture equilibrium behavior within the data while controlling the true number of equilibria, including those unobserved. We provide a generalization bound establishing the sample complexity for MLE in our framework. We propose several algorithms including convex loss minimization (CLM) and sigmoidal approximations. We prove that the number of exact PSNE in LIGs is small, with high probability; thus, CLM is sound. We illustrate our approach on synthetic data and real-world U.S. congressional voting records. We briefly discuss our learning framework's generality and potential applicability to general graphical games.Comment: Journal of Machine Learning Research. (accepted, pending publication.) Last conference version: submitted March 30, 2012 to UAI 2012. First conference version: entitled, Learning Influence Games, initially submitted on June 1, 2010 to NIPS 201

Automating functional enzyme screening & characterization

This work has been presented in the 10th IWBDA workshop.Microfluidics continue to gain traction as an inexpensive alternative to standard multi-well plate-based, and flow cytometry- based, assay platforms. These devices are especially useful for the types of ultra-high throughput screens needed for enzyme discovery applications where large numbers (>106) of unique samples must be screened rapidly1. Coupled with cell-free protein synthesis2, microfluidics are being used to identify novel enzymes useful for a variety of applications with unprecedented speed. However, these devices are typically produced using PDMS, and require considerable infrastructure and artisanal skill to fabricate, limiting their accessibility. Likewise, enzyme hits obtained from a screen are often validated manually and would benefit from automation of downstream validation processes. To address these limitations, we propose a workflow which leverages software tools to automate the rapid design and fabrication of low-cost polycarbonate microfluidic devices for use as high-throughput screening platforms for enzyme discovery, as well as an automated DNA assembly tool to streamline validation of screening candidates. Using this workflow, we aim to identify novel oxidoreductase enzymes from environmental metagenomic DNA libraries, for use in electrochemical biosensors