Invasive weeds inflict significant harm on native species, ecosystem processes, and natural disturbance regimes. When managing these weed threats, some of the most useful tools are the outputs of predictive distribution models. As they supplement existing distribution data to assess where in the landscape is most susceptible to weed invasion, they allow for more efficient weed management because the areas most suited to weed species may be targeted for control. This research develops a habitat suitability model for the weed lantana (Lantana camara L. sensu lato) in a portion of the Greater Blue Mountains World Heritage Area at present and under forecast warmer climates. A generalised additive model (GAM) is used, which fits the regression curve used for prediction to the calibration data themselves and allows for an exploration of which environmental conditions favour lantana as well as where in the landscape is most suitable for the weed. Temperature was positively correlated with suitable habitat and explained over 90% of the variation in lantana presence predicted by the model. 15% of the study area was found to be suitable for lantana at present, with this figure reaching 58% after a simulated 3??C rise in temperature. Mapping habitat suitability across the study area allowed for the identification of five distinct pathways for lantana to further invade the Blue Mountains. Responding to calls for the integration of weed management with biodiversity conservation, the research also integrates the habitat suitability model with information regarding the distribution of vegetation communities and endangered species in the Blue Mountains. Thirteen native vegetation communities were found to have more than 20% suitable habitat for lantana at present, and an additional three contained more than 80% suitable habitat after a simulated 3??C rise in temperature. Five of these communities are listed as threatened under relevant legislation and harbour at least 27 endangered species, placing additional urgency on their conservation. This research has successfully used modelling techniques to identify areas for targeted weed management integrated with biodiversity conservation. The methods are easily adaptable to other weeds and regions and could thus be used to illustrate the comprehensive threat weeds pose to Australia???s biodiversity.

Invasive weeds inflict significant harm on native species, ecosystem processes, and natural disturbance regimes. When managing these weed threats, some of the most useful tools are the outputs of predictive distribution models. As they supplement existing distribution data to assess where in the landscape is most susceptible to weed invasion, they allow for more efficient weed management because the areas most suited to weed species may be targeted for control. This research develops a habitat suitability model for the weed lantana (Lantana camara L. sensu lato) in a portion of the Greater Blue Mountains World Heritage Area at present and under forecast warmer climates. A generalised additive model (GAM) is used, which fits the regression curve used for prediction to the calibration data themselves and allows for an exploration of which environmental conditions favour lantana as well as where in the landscape is most suitable for the weed. Temperature was positively correlated with suitable habitat and explained over 90% of the variation in lantana presence predicted by the model. 15% of the study area was found to be suitable for lantana at present, with this figure reaching 58% after a simulated 3??C rise in temperature. Mapping habitat suitability across the study area allowed for the identification of five distinct pathways for lantana to further invade the Blue Mountains. Responding to calls for the integration of weed management with biodiversity conservation, the research also integrates the habitat suitability model with information regarding the distribution of vegetation communities and endangered species in the Blue Mountains. Thirteen native vegetation communities were found to have more than 20% suitable habitat for lantana at present, and an additional three contained more than 80% suitable habitat after a simulated 3??C rise in temperature. Five of these communities are listed as threatened under relevant legislation and harbour at least 27 endangered species, placing additional urgency on their conservation. This research has successfully used modelling techniques to identify areas for targeted weed management integrated with biodiversity conservation. The methods are easily adaptable to other weeds and regions and could thus be used to illustrate the comprehensive threat weeds pose to Australia???s biodiversity.

Invasive weeds inflict significant harm on native species, ecosystem processes, and natural disturbance regimes. When managing these weed threats, some of the most useful tools are the outputs of predictive distribution models. As they supplement existing distribution data to assess where in the landscape is most susceptible to weed invasion, they allow for more efficient weed management because the areas most suited to weed species may be targeted for control. This research develops a habitat suitability model for the weed lantana (Lantana camara L. sensu lato) in a portion of the Greater Blue Mountains World Heritage Area at present and under forecast warmer climates. A generalised additive model (GAM) is used, which fits the regression curve used for prediction to the calibration data themselves and allows for an exploration of which environmental conditions favour lantana as well as where in the landscape is most suitable for the weed. Temperature was positively correlated with suitable habitat and explained over 90% of the variation in lantana presence predicted by the model. 15% of the study area was found to be suitable for lantana at present, with this figure reaching 58% after a simulated 3??C rise in temperature. Mapping habitat suitability across the study area allowed for the identification of five distinct pathways for lantana to further invade the Blue Mountains. Responding to calls for the integration of weed management with biodiversity conservation, the research also integrates the habitat suitability model with information regarding the distribution of vegetation communities and endangered species in the Blue Mountains. Thirteen native vegetation communities were found to have more than 20% suitable habitat for lantana at present, and an additional three contained more than 80% suitable habitat after a simulated 3??C rise in temperature. Five of these communities are listed as threatened under relevant legislation and harbour at least 27 endangered species, placing additional urgency on their conservation. This research has successfully used modelling techniques to identify areas for targeted weed management integrated with biodiversity conservation. The methods are easily adaptable to other weeds and regions and could thus be used to illustrate the comprehensive threat weeds pose to Australia???s biodiversity.

Medical cartography can provide valuable insights into the epidemiology and ecology of infectious diseases, providing a quantitative representation of the distribution of these pathogens. Such methods therefore provide a key step in informing public health policy decisions ranging from prioritising sites for further investigation to identifying targets for interventions. By increasing the resolution at which risk is defined, policymakers are provided with an increasingly informed approach for considering next steps as well as evaluating past progress. In spite of their benefits however, global maps of infectious disease are lacking in both quality and comprehensiveness. This thesis sets out to investigate the next steps for medical cartography and details the use of species distribution models in evaluating global distributions of a variety of zoonotic diseases of public health importance. Chapter 2 defines a methodology by which global targets for infectious disease mapping can be quantitatively assessed by comparing the global burden of each disease with the demand from national policymakers, non-governmental organisations and academic communities for global assessments of disease distribution. Chapter 3 introduces the use of boosted regression trees for mapping the distribution of a group of vector-borne diseases identified as being a high priority target, the leishmaniases. Chapter 4 adapts these approaches to consider Ebola virus disease. This technique shows that the West African outbreak was ecologically consistent with past infections and suggests a much wider area of risk than previously considered. Chapter 5 investigates Marburg virus disease and considers the variety of different factors relating to all aspects of the transmission cycle that must be considered in these analyses. Chapters 6 and 7 complete the mapping of the suite of viral haemorrhagic fevers by assessing the distribution of Crimean-Congo haemorrhagic fever and Lassa fever. Finally, Chapter 8 considers the risk that these viral haemorrhagic fevers present to the wider African continent, quantifying potential risk of spillover infections, local outbreaks and more widespread infection. This thesis addresses important information gaps in global knowledge of a number of pathogens of public health importance. In doing so, this work provides a template for considering the global distribution of a number of other zoonotic diseases.

Conservation of biodiversity and sustainable resource use are central aims within ecology. This thesis focuses on the current data and environmental frameworks used to support these aims across different states in Europe. In particular, it examines the impact of geo-political boundaries on data-use, funding and planning for temporal movement of species in response to climate change. It also examines the current environmental framework agreements in Europe and their capacity to deal with trans-boundary aspects of biodiversity change. Through examination of European biodiversity datasets, undertaking species distribution modelling of forest taxa, examining economic data, palaeo-ecological data, and assessing international environmental framework agreements, this thesis identifies a number of important knowledge gaps. Probably unsurprisingly, the distribution of biodiversity in Europe mostly does not match political entities, all of which have individual aims, financial resources, and biodiversity management regimes in place. All have a significant impact on biodiversity conservation planning because i) the use of geo-politically truncated data influences modelling predictions, ii) financial commitment to biodiversity conservation varies between countries influencing success outcomes, iii) biodiversity persistence in current and future climate change does not recognise geo-political boundaries, and iv) many of the key environmental frameworks are implemented within countries and do not considering trans-boundary issues. Overall these findings significantly improve the understanding of conservation and resource management in Europe and fill a number of important knowledge gaps. They highlight the importance of appropriate trans-boundary ecological datasets and the need for more consistency across Europe in financial resources for biodiversity conservation. They also highlight the need for appreciation of areas of high-persistent biodiversity regardless of geo-political boundaries and environmental framework agreements that support cross-border conservation measures.

Biodiversity is a complex concept incorporating genes, species, ecosystems, composition, structure and function. The global scientific and political community has recognized the importance of biodiversity for human well-being, and has set goals and targets for its conservation, sustainable use, and benefit sharing. Monitoring biodiversity will help meet conservation goals and targets, yet observations collected in-situ are limited in space and time, which may bias interpretations and hinder conservation. Remote sensing can provide complementary datasets for monitoring biodiversity that are spatially comprehensive and repeatable. However, further research is needed to demonstrate, for various spatial scales and regions, how remotely sensed datasets represent different aspects of biodiversity. The overall goal of this dissertation is to advance the mapping and monitoring of biodiversity indicators, globally and within Canada, through the use of remote sensing. This research produced maps that were rich with spatially explicit, spatially continuous data, filling gaps in the availability and spatial resolution or scalability of information regarding ecosystem function (primary productivity) at global scales, tree species composition at regional scales (Saskatchewan, Canada), and ecosystem structure at local scales (coastal British Columbia, Canada). Further, the remotely sensed indicator datasets proposed and tested in each of the research chapters are repeatable, ecologically meaningful, translate to specific biodiversity targets globally and within Canada, and are calculable at multiple spatial scales. Challenges and opportunities for fully implementing these or similar remotely sensed biodiversity indicators and indicator datasets at a national level in Canada are discussed, contributing to the advancement of biodiversity monitoring science. / Graduate

In this thesis, I have assessed and modelled invasion of alien plant species in the forest of the southeastern United States. There are over 380 recognized invasive plants in southeastern forests and grasslands with 53 ranked as high-to-medium risk to natural communities. I have focused on ten of these: Chinese lespedeza, tall fescue, Japanese honeysuckle, Chinese privet, autumn olive, princesstree, silktree, chinaberry, tree of heaven, tallowtree. Assessing them at differing scales, locally (Chapter 2 and 3), eco-regionally (Chapter 4 and 5) and regionally (Chapters 6 and 7), using field based measurements integrated with remotely sensed and digital datasets, and applying both parametric and non-parametric modelling approaches. Data from field based measurements as well as digitally available sources was evaluated, bringing together freely available data with time consuming, intensively collected data. Once models were developed application to assessing long term impacts was done by integrating potential climate change scenarios. At the local level Chinese lespedeza and Japanese Honeysuckle were the most prevalent, with models at the local level dominated by remotely sensed variables. At an eco-regional level Japanese honeysuckle was the most prevalent with models primarily dominated by environmental variables. At a regional level, where only trees were assessed, potential distributions of the invasive species ranged from 12 to 33 percent of the southeastern forests under current conditions with this dramatically increasing for chinaberry and tallowtree under most climate change scenarios, up as high as 66 percent of southeastern forest sites. In this thesis information on anthropogenic factors added some value to the models, however it was rarely dominant. Roads and land use (proportion of forest or distance to forest) were the most useful anthropogenic variables. In all models evaluated, only six times did any one anthropogenic variable represent more than 25 percent of the models, four of these were at the local scale. At the regional and eco-regional level, roads had a greater than 25 percent contribution to the silktree models, at a local level, distance to forest and distance roads contributed more than 25 percent to three of the species evaluated, sawtooth oak, Japanese honeysuckle and privet. Human activities have the most influence on invasion progression through dispersal (movement and introduction rate) and disturbance of the landscape (increased resource availability). Anthropogenic variables such as roads are likely to be a mechanism of spread, thus the more a model is driven by anthropogenic variables, the more likely the invasive plant is to be in the early stages of invasion process. Thus our results suggest that many of these species have moved through the first stages of invasion. Environmental characteristics play an important role in determining a site’s vulnerability to invasion. At an eco-region and regional scale, environmental characteristics dominated (>50%) all but one model (silktree at the regional scale). At the eco-region level elevation was the dominant variable, and at a regional level minimum temperature was the dominant variable. These have some correlation, with higher elevation often relating to lower temperatures, particularly at a smaller scale. This confirms the validity of matching the climate ranges of native species with the range of potential invasion, and the approach of integrating elevation, latitude and longitude to estimate potential distribution. It also suggests that climate change will influence the distribution and that variation in climate should be integrated into models. Two different modelling approaches, logistic regression and maximum entropy, were used throughout my thesis, and applied to the same data. Agreement between different modelling types adds strength to conclusions, while disagreement can assist in asking further questions. The inclusion in the models of similar variables with the same direction of relationships gives confidence to any inference about the importance of these variables. The geographical agreement between models adds confidence to the probability of occurrence in the area. Alternatively using the same model but different datasets can give you similar information. Overall for all models created by both logistic regression and MaxEnt, the logistic regression had slightly better omission rates and the MaxEnt model had better AUC’s. Logistic regression models also often predicted larger geographical areas of occurrences when the threshold of maximum sensitivity plus specificity was used, thus the lower omission rates is related to the less stringent model that predicts a larger area. The selection of appropriate data to answer the question was shown to be fundamental in Chapter 7. When data were used outside of the area of interest it generalized the models and increased the potential for invasion significantly. There was more value in the intensive surveyed data but this was less dramatic than in using the defined areas of interest to select the data for models.

Human activities in the form of land use changes, urbanisation and infrastructure developments are major threats to biodiversity. The loss and fragmentation of natural habitats are great obstacles for the long term preservation of biodiversity and nature protection measures alone may not be sufficient to tackle the problem. Environmental impact assessment (EIA) and strategic environmental assessment (SEA) play a central role in identifying, predicting and managing the impacts of human activities on biodiversity. The review of current practice suggests that the complexity of the task is underestimated and that new methodological approaches encompassing the entire landscape are needed. Spatial aspects of the assessment and the lack of information on scale-related issues are particular problems affecting the appropriate assessment of cumulative effects. In parallel with the development and establishment of EIA and SEA, spatial modelling is an expanding field in ecology and many derived applications could be suitable for the prediction and assessment of biodiversity-related impacts. The diversity of modelling methods suggests that a strategy is needed to identify prediction methods appropriate for EIA and SEA. The relevance and potential limitations of GIS-based species distribution and habitat models in predicting impacts on biodiversity were examined in three studies in the greater Stockholm area. Distinct approaches to habitat suitability modelling were compared from the perspective of environmental assessment needs and requirements. The results showed that model performance, validity and ultimate suitability for planning applications were strongly dependent on empirical data and expert knowledge. The methods allowed visual, qualitative and quantitative assessment of habitat loss, thus improving decision support for assessment of impacts on biodiversity. The proposed methods allowed areas of high ecological value and the surrounding landscape to be considered in the same assessment, thereby contributing to better integration of biodiversity issues in physical planning. / QC 20100727

Statistical modelling is often used to relate the presence or abundance of species to environmental predictors, thereby providing a basis for predictive mapping of species or biodiversity. The variables included must thus be relevant and reflect actual changes in the environment. Therefore, the quantification of species–environment relationships is an important aspect of predictive modelling. This thesis examines how phytobenthic species or communities in the Baltic Sea relate to environmental gradients, and if different aspects of phytobenthic species distribution in the Baltic Sea could be explained by spatial or temporal variation in environmental factors. Predictive distribution modelling usually focuses on how environmental variables control the distribution of species or communities. Thus the relative weight of the predictor variables on different scales is of importance. In this thesis, I show that the relative importance of environmental variables depends both on geographic scale and location, and that it also differs between species or species groups. There are no simple explanations to the temporal variability in species occurrence. I here show that the temporal changes in species distribution within the phytobentic zone varies in a spatial context. I also try to find temporal and spatio-temporal patterns in species distribution that could be related to changes in climate or anthropogenic disturbance. However, the findings in this thesis suggest that single factor explanations are insufficient for explaining large-scale changes in species distribution. A greater understanding of the relationship between species and their environment will lead to the development of more sensitive models of species distributions. The predictions can be used to visualise spatial changes in the distribution of plant and animal communities over time. / At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Submitted. Paper 3: Manuscript. Paper 4: Manuscript. Paper 5: Manuscript.

Conservation strategies are mainly focused on species existing in an environment shaped by natural and anthropogenic pressures. Yet, evidence shows that climate is changing faster than ever and expected to continue to change in the near future, which can be devastating for plants with restricted ranges. Turkey harbors many endemic species that might be affected from these changes. However, available data is scarce and biased, complicating the anticipation of future changes. Aim of this study is to improve our understanding of endemic species distributions and forecasting effects of climate change via species distribution modelling (SDM). The study is based on two Anatolian (Crocus ancyrensis and Crataegus tanacetifolia) and two Ankara (Salvia aytachii and Centaurea tchihatcheffii) endemics. Independent presence and absence data (ranging between 19-68 and 38-61, respectively) for each species was collected through fieldwork in and around the Upper Sakarya Basin in 2008 and 2009. With the software Maxent, SDMs were performed by using 8 least correlated environmental features and random presence records (of which 25% were used for confusion matrix). SDMs for current distributions of C. ancyrensis, C. tchihatcheffii and C. tanacetifolia were reliable enough for future extrapolations despite errors originating from scale, non-equilibrium status and biotic interactions, respectively. The model for S. aytachii failed due to absence of limiting factor (soil type) in the model. Future projections of those three species modelled using CCCMA-CGCM2 and HADCM3 climate models indicated three possible responses to climate change: (1) Extinction, especially for habitat specialists / (2) Range expansion, especially for generalist species / and (3) Range contradiction, especially for Euro-Siberian mountainous species. Species modelling can be used to understand possible responses of plant species to climate change in Turkey. Modelling techniques should to be improved, however, especially by integrating other parameters such as biotic interactions and through a better understanding of uncertainties.