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ItemCost-effective methods in conservationVan Burm, Els Karel Theresia Etienne Henry ( 2018)Conservation resources are scarce, whether it is time, money or effort. Therefore, wise spending is important and decisions need to be made on how to prioritise limited resources between different conservation actions. Effectively targeting declines in biodiversity requires monitoring and management, each of which contribute differently to improved conservation outcomes. Monitoring provides information about the system (whether it is about the state or the dynamics), while management aims to halt downward population trajectories. Often monitoring and management are competing for the same resources, creating trade-offs in how these resources should be spent. In this thesis, I examine trade-offs in conservation, by focusing on how alternative resource allocations within monitoring, and between monitoring and management, impact the ultimate conservation outcome. I illustrate this with two case studies: the endangered growling grass frog (Litoria raniformis) metapopulation around Melbourne, Australia, and the invasive yellow crazy ant (Anoplolepis gracilipes) on Christmas Island, Australia. The first chapter provides a general introduction to the role of monitoring and management in conservation and the trade-offs that exist within and between them. In the second chapter, I examine whether increasing the spatial coverage of a monitoring program for the growling grass frog can replace learning about metapopulation dynamics in terms of population persistence. The advantage of obtaining reliable estimates from spatial monitoring over temporal monitoring is that wasting invaluable time is avoided. In the third chapter, I explore management of the endangered growling grass frog metapopulation, and study how alternative resource allocations between monitoring and management affect the confidence about sufficient offsetting actions. Increasing urbanisation requires habitat offsetting to reduce further declines in the metapopulation. Investing in monitoring might result in more precise estimates of the metapopulation dynamics, and hence allow managers to be more confident about which management strategy might be best. More management, on the other hand, might reduce the extinction risk of the metapopulation directly, provided suitable actions are chosen. Determining the optimal allocation of resources between the two is important to ensure the metapopulation gains as much as possible from the implemented offsetting management. In the fourth chapter, I switch focus to an invasive species, the yellow crazy ant, and investigate how to optimally survey an island for high density super-colonies. I compare effectiveness of a survey strategy that explicitly accounts for the variation in survey cost across the island with alternative ones that ignore survey cost estimates. In the fifth chapter, I determine the amount of monitoring data that contains sufficient information to find the ant super-colonies. Using a habitat suitability model for the super-colonies, combined with survey cost estimates, I prioritise sites, and evaluate the impact of more monitoring data on the amount of super-colonies found. Monitoring a small part of the island is sufficient to find the maximum possible super-colonies for a particular budget, resulting in the effectiveness of the survey strategy levelling off. This suggests that the resources that are currently spent on monitoring the entire island, can be redirected towards management of the invasive species. Finally, in the last chapter, I summarise the main findings of this research and discuss some potential paths for future research.
ItemRobust prediction and decision strategies for managing extinction risks under climate changeBaumgartner, John Bruno ( 2016)Effective management of biodiversity requires decision strategies that are robust to the uncertainty embodied in predictions of habitat suitability and environmental change. This is particularly relevant in the context of climate change, which may interact with existing threats in unexpected ways. Predictive modelling has become important for addressing questions about climate change impacts. In particular, correlative species distribution models (SDMs) are popular for predicting species' fates, and have been noted as effective tools for guiding conservation decisions. However, SDM predictions are uncertain due to our imperfect understanding of the processes underlying species-environment associations, and, crucially, imprecision in predictions of regional climate change. While this is widely recognised, SDM prediction uncertainty is frequently overlooked, and practical approaches to handling this uncertainty are rare. When SDMs are used to investigate questions of species' persistence during times of environmental change, failure to consider uncertainty about the arrangement and quality of habitat may lead to flawed inferences and ineffective management. It is therefore essential that we improve our understanding of key uncertainties, and develop methods that explicitly handle uncertainty in a way that promotes sensible management decisions. In this thesis, I explore these issues through case studies of the mountain pygmy-possum, Burramys parvus, in the alpine region of south-eastern Australia. I draw on a range of quantitative tools and classical decision theory to: (1) determine the magnitude of uncertainty about habitat suitability due to SDM predictor choice, and how this varies under climate change; (2) develop a framework for identifying the optimal spatial allocation of resources for species' conservation under climate change, given uncertain predictions of habitat suitability; (3) explore the utility of abundance time series for improving our understanding of environmental dynamics influencing populations; (4) combine SDMs and models of population dynamics with decision theory to assess the extent to which predictions are refined by explicitly including population processes; and (5) develop a suite of open source software tools that facilitate common ecological modelling tasks, making rigorous investigation of climate change questions more computationally efficient and feasible. I found that standard approaches to model evaluation obscure key differences amongst competing SDMs, suggesting that consideration of ecological relevance during model construction is essential. I showed that despite extensive uncertainty about future habitat, conservation actions can be prioritised in a way that reflects managers' appetites for risk and reward. I demonstrated that for spatially-structured populations, hierarchical models can reveal the spatial scales at which environmental processes control population growth. Regional synchrony in population dynamics is evident for B. parvus, but local, density-independent environmental forces are more important in determining abundance trajectories. Finally, I demonstrated that habitat change is an unreliable surrogate for a species' response to climate change. Predictions about the distribution and quality of future habitat for B. parvus are uncertain. However, this is an inevitable challenge when forecasting species' fates. Importantly, it does not preclude effective management. The way forward is to recognise and account for uncertainty in ecological models, thereby enabling sensible conservation decisions for species impacted by climate change.