School of BioSciences - Theses
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ItemRobust prediction and decision strategies for managing extinction risks under climate change( 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.
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ItemAssessing and managing interacting species at risk of coextinction( 2016)Interactions between organisms are ubiquitous: predators hunt prey, plants compete for light, and pollinators visit flowers to forage on nectar. Through their interactions species influence each other's population dynamics and ultimately their persistence: Darwin was already convinced that if bumblebees became extinct their food plants would follow quickly. Despite their importance, interactions are commonly ignored when we assess species' extinction risk or plan for their conservation management. My thesis is divided into six chapters, addressing two important components of conserving interdependent species. First, I assess if and how we can use a common type of data - observed interaction networks - to assess the coextinction risk of interacting species in networks, and to predict how interactions influence cascading extinctions when interdependent species are lost. Secondly, I investigate how interacting species can be protected in combined management approaches, focussing on the increasingly common method of translocating species for conservation. To answer this questions, I develop a range of statistical and mathematical modelling approaches and apply these to theoretical simulations and empirical data. In chapter 2, I investigate how quantitative methods can help to identify those species in interaction networks that are at risk of coextinction, while incorporating important factors such as uncertainty and imperfect detection of species in the field. I develop a hierarchical $N$-mixture model that accounts for imperfect detection and allows one to disentangle two factors that influence interaction frequencies between species: the probability that two species interact, and the abundances of species. This enables one to estimate with uncertainty the number of interaction partners of a species and the community size of dependents. I fit the model to data that from simulations of different parameter scenarios and to empirical networks of flower-visiting insects found on a threatened ecological community of plants from the Stirling Ranges National Park in Western Australia. In chapter 3, I extend this modelling approach to investigate how imperfect detection and uncertainty influence the progression of extinction through mutualistic networks. Therefore, I apply the modelling approach from chapter 2 to observed networks to correct these networks for sampling bias. Then, I sequentially remove plant species from the networks to investigate how extinction cascades differ between observed and corrected networks. I show that networks corrected for sampling bias, are more densely connected and the interactions between species are more diffusely distributed throughout the networks. This causes corrected networks to be less specialised, and plant species to be more redundant, leading to increased network robustness. The results of chapter 2 and 3 indicate that imperfect detection strongly affects observed interaction networks and suggests that it is unwise to draw strong inferences for the conservation status of species and the robustness of ecosystems without acknowledging imperfect detection and uncertainty. In the second part of this thesis, I investigate management actions for improving the persistence of cothreatened interacting species, with a particular focus on conservation translocations. The fourth chapter investigates how useful current single-species translocation guidelines are for conserving cothreatened species and the interactions between them. I first classify potential systems of cothreatened species and devise appropriate management options for each system. Secondly, I extend current single-species guidelines to incorporate interactions in the assessment, planning and implementation phase for the conservation of multiple interacting species. For each phase of a translocation, I present case studies of threatened interacting species where a combined translocation could save the species. In chapter 5, I examine in detail how different types of interactions influence the optimal size of founder populations and the order in which interacting species should be translocated. I use mathematical models for coupled two-species systems, in which species interact in consumer-resource, competitive or mutualistic interactions. While some common rules in translocating interacting species emerge, most decisions about necessary founder sizes and translocation order are interaction-type specific. In the two chapters about combined translocations of cothreatened species, I show that interspecific interactions are important processes that shape population dynamics, and should therefore be incorporated into the quantitative planning of multi-species translocations. Finally in chapter 6, I synthesise the findings of my work and highlight future research avenues.
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ItemOptimal resource allocation for invasive species management( 2016)Invasive species are responsible for enormous ecological and economic damage worldwide, and resources for managing them are severely limited. Allocating these resources efficiently is therefore a key concern for conservation managers. Unfortunately, the complexity of the problem makes developing cost-effective strategies for managing invasive species extraordinarily difficult. The scale and difficulty of the problem highlights that quantitative approaches are needed to both understand the general properties of a good control strategy and to assist in developing invasive species management plans in specific cases. In this thesis I use mathematical modelling to find optimal strategies for invasive species control. The theoretical work contained here focuses on solving for the optimal resource allocation in spatial, temporal and spatiotemporal systems. This gives insight into the qualitative features of optimal management. Moving from purely spatial or temporal optimal solutions to the full spatiotemporal solution increases the complexity of the models and solutions. However, key ideas about invasive species control in the spatial and temporal sections translate to the spatiotemporal problem, and a good understanding of these results in this thesis allow one to better understand solutions in the more complex spatiotemporal case. As well as general insights, I want to offer specific support to environmental managers; mathematical modelling is applied to three case studies in this thesis. The first two are applications of optimal control theory to feral cat (Felis catus) control in arid Australia and to orange hawkweed control (Hieracium aurantiacum). The third case is about the proposed eradication of tropical fire ants (Solenopsis geminate) from the islands of Ashmore Reef Commonwealth Marine Reserve in the Timor Sea, which is off the Northwest coast of Australia. This work focuses on providing quantitative advice about the management of tropical fire ants. Linked models for the population dynamics and detectability of tropical fire ants are developed. These models quantify how different control methods and schedules affect the probability of eradicating ants and allow the resource allocation for surveillance to be optimised.