Estimating the population size of two critically endangered South Pacific parakeets : the Tasman Parakeet and Malherbe's Parakeet : a thesis submitted in partial fulfilment of the requirements for the degree of Master of Science in Conservation Biology, Massey University, New Zealand

The Cyanoramphus parakeets are a cryptically coloured group of birds that are distributed across the islands of the South Pacific region. Due to their restricted range and island distributions, species belonging to this genus are considered vulnerable to extinction. However, the extent to which these parrot species are threatened is difficult to determine due to an absence of accurate and reliable population estimates. This research aims to contribute to the conservation of two critically endangered Cyanoramphus parakeets by evaluating the survey methods currently used to estimate population densities. This thesis details the precision and efficiency of distance sampling methods used for monitoring low density parrot populations on small islands. Specifically, examining the annual variation in population size for the critically endangered Tasman Parakeet (Cyanoramphus cookii) and their introduced competitor, the Crimson Rosella (Platycercus elegans), to evaluate the effectiveness of conservation management and species control on Norfolk Island. In addition, the size of three translocated populations of the critically endangered Malherbe’s Parakeet (Cyanoramphus malherbi) were examined to identify if this endemic New Zealand parrot requires further management. Of the distance sampling methods used to monitor parrots, the fixed point survey method was the most suitable method for surveying Tasman Parakeets. This method yielded the highest number of parakeet detections per survey and offered the greatest count precision of the methods examined. On Norfolk Island, the Tasman Parakeet population increased by 126% over four years of intense predator management and nest provisioning. In comparison, the Crimson Rosella population remained stable, despite regular culling to control the population which competes with the Tasman Parakeet. In New Zealand, Malherbe’s Parakeets were detected with varying degrees of success. On Maud Island, no parakeets were detected; however, they were detected on both Blumine Island and Chalky Island. On Blumine Island, the Malherbe’s Parakeet population was moderately abundant, consisting of 202 ± 67 individuals distributed through the mature forest. In comparison, the Chalky Island population of Malherbe’s Parakeet was less extensive and consisted of 84 ± 58 parakeets. This research illustrates the importance of regularly monitoring the size of threatened parrot populations for conservation

Robustly Optimal Monetary Policy in a Microfounded New Keynesian Model

We consider optimal monetary stabilization policy in a New Keynesian model with explicit microfoundations, when the central bank recognizes that private-sector expectations need not be precisely model-consistent, and wishes to choose a policy that will be as good as possible in the case of any beliefs close enough to model-consistency. We show how to characterize robustly optimal policy without restricting consideration a priori to a particular parametric family of candidate policy rules. We show that robustly optimal policy can be implemented through commitment to a target criterion involving only the paths of inflation and a suitably defined output gap, but that a concern for robustness requires greater resistance to surprise increases in inflation than would be considered optimal if one could count on the private sector to have “rational expectations.”robust control , near-rational expectations , belief distortions , target criterion

Look No Further: Adapting the Localization Sensory Window to the Temporal Characteristics of the Environment

Many localization algorithms use a spatiotemporal window of sensory information in order to recognize spatial locations, and the length of this window is often a sensitive parameter that must be tuned to the specifics of the application. This letter presents a general method for environment-driven variation of the length of the spatiotemporal window based on searching for the most significant localization hypothesis, to use as much context as is appropriate but not more. We evaluate this approach on benchmark datasets using visual and Wi-Fi sensor modalities and a variety of sensory comparison front-ends under in-order and out-of-order traversals of the environment. Our results show that the system greatly reduces the maximum distance traveled without localization compared to a fixed-length approach while achieving competitive localization accuracy, and our proposed method achieves this performance without deployment-time tuning.Comment: Pre-print of article appearing in 2017 IEEE Robotics and Automation Letters. v2: incorporated reviewer feedbac