Dr William Chivers

There are major concerns about the ecological impact of extreme weather events. In the oceans, marine heatwaves (MHWs) are an increasing threat causing, for example, recent devastation to coral reefs around the world. We show that these impacts extend to adjacent terrestrial systems and could negatively affect the breeding of endangered species. We demonstrate that during an MHW that resulted in major coral bleaching and mortality in a large, remote marine protected area, anomalously warm temperatures also occurred on sea turtle nesting beaches. Granger causality testing showed that variations in sea surface temperature strongly influenced sand temperatures on beaches. We estimate that the warm conditions on both coral reefs and sandy beaches during the MHW were unprecedented in the last 70 years. Model predictions suggest that the most extreme female-biased hatchling sex ratio and the lowest hatchling survival in nests in the last 70 years both occurred during the heatwave. Our work shows that predicted increases in the frequency and intensity of MHWs will likely have growing impacts on sea turtle nesting beaches as well as other terrestrial coastal environments.

Models of near-exclusive predator-prey systems such as that of the Canadian lynx and snowshoe hare have included factors such as a second prey species, a Holling Type II predator response and climatic or seasonal effects to reproduce sub-sets of six signature patterns in the empirical data. We present an agent-based model which does not require the factors or constraints of previous models to reproduce all six patterns in persistent populations. Our parsimonious model represents a generalised predator and prey species with a small prey refuge. The lack of the constraints of previous models, considered to be important for those models, casts doubt on the current hypothesised mechanisms of exclusive predator-prey systems. The implication for management of the lynx, a protected species, is that maintenance of an heterogeneous environment offering natural refuge areas for the hare is the most important factor for the conservation of this species. © 2014 Elsevier Ltd.

There are many resource allocation mechanisms for grid computing. One trait that many allocation mechanisms have, is that they require the user to estimate how long their task will take to execute on a system. The result of the user's input can have a considerable impact on scheduling, and can affect the grids ability to meet quality of service requirements for other jobs. Incorrect estimates by users could result in other jobs being turned away that should not have, or jobs being accepted, that should not have been. The user's estimation could conceivably be accurate if the user is running the same jobs repeatedly on the same hardware. However this is rarely the case in a grid environment. This estimation is often an estimation that is made with limited or no knowledge of how long a task will take on the underlying hardware and can be considered to be a guess. In this paper we have tested the accuracy of user estimation of task execution, through simulating the grid environment. The simulation includes a simple agent-based batch auction to distribute jobs to resources. The simulated environment contains a number of resources. Tasks are submitted to the environment periodically by agents who estimate the length of time their job will take. Each agent has only one job but submits that job several times throughout the simulation. Each job requires a random amount of processing and each resource can process a random amount per time-step. There are three groups, each group with equal quantities of agents. Each group uses one of three strategies to estimate the length of time their task will take to execute on the grid. One strategy uses the agent's limited memory to estimate the length of execution based on the time previous tasks took for that agent to execute over the grid; a second strategy utilises a history from the assigned resource as to how long previous jobs have taken to execute on the resource; and the third strategy is a zero intelligence strategy, it is a random guess. The results show whether there is any significant difference between the accuracy of an estimation made using historical data from a user, using historical data from the resource, or a random guess. The results have implications for the design of grid resource allocation mechanisms, and for how users interact with current resource allocation mechanisms. It also raised the question, when should a user's input be required and on what will it be based?.