For this work the Geo-referenced Regional Exposure Assessment Tool for European Rivers (GREAT-ER) was developed further to support river basin management and the implementation process within the EU Water Framework Directive (WFD). This was achieved through predicting spatially resolved pharmaceutical exposure concentrations in whole watersheds. A major focus of this thesis has been placed on modeling challenging watersheds, whereby challenging can refer to hydrological conditions in a watershed as well as to specific emission patterns that occur within the watersheds. The adapted methodology improves the prediction accuracy in such watersheds with GREAT-ER with respect to pharmaceutical exposures, but can also lead to improved results in other application areas. The possibilities of the latest model version are demonstrated by the extensive inclusion of local and regional conditions. In watersheds with highly variable and seasonally changing hydrological situations, GREAT-ER has been applied satisfactorily for the first time, and additionally, the developed approach can be transferred to equivalent watersheds worldwide. Comparison with monitoring data confirms that some of the adjustments have resulted in significantly improved model predictions, especially when hydrological and local conditions are specifically addressed. For example, explicit consideration of local drug emissions from hospitals or private medical practices (e.g., for x-ray contrast agents) can improve predictions at the local scale without compromising regional exposure estimates. Pharmaceuticals that have low concentrations and are barely detectable with established analytical methods can be evaluated with model simulations. In addition, current management strategies implemented under the WFD has been replicated and evaluated. These management scenarios simulated with the model allow an a priori evaluation of risk reduction measures. In combination with targeted monitoring approaches, it was shown that the GREAT-ER model can serve as a valuable tool for exposure and risk assessment of pharmaceuticals even in challenging watersheds. This and the useful combination of targeted monitoring and GREAT-ER simulations and the ability of the modeling approach to predict the expected range of spatial surface water concentrations is demonstrated by three selected journal articles.

No description available.

Several vector-borne and zoonotic diseases have emerged or re-emerged in Europe over these last decades. Besides climate change that influences disease risk at a regional scale, landscape changes could be responsible for local heterogeneities in disease risk. Spatial epidemiology tries to understand and predict spatial variations in disease risk by using spatial tools and spatially-explicit modelling methods. This study investigated the impact of fine-grained landscape patterns on the transmission of vector-borne and zoonotic infections in terms of habitat suitability for vectors and/or hosts and of exposure of people to infectious agents. This was studied through three human diseases emerging or at risk of re-emergence in Europe: the rodent-borne Puumala hantavirus, the tick-borne Lyme borreliosis and the mosquito-borne malaria infections. Statistical models were first used to study the relationships between environmental variables and host abundance, host prevalence, and human cases of Puumala hantavirus. Environmental factors were also combined with socio-economic factors to explain Puumala hantavirus and Lyme borreliosis incidence rates. The combination of factors explaining disease transmission and the complexity of such systems led to the development of an innovative, spatially-explicit modelling method: multi-agent simulation (MAS). The MALCAM simulation model was developed to assess the risk of malaria re-emergence in southern France and simulates spatial and temporal variations in contact rate between people and potential malaria vectors. The effect of changes in potential drivers of malaria re-emergence was also simulated. The different case studies showed that fine-grained landscape patterns influence the presence and abundance of vectors and hosts. Moreover, environmental conditions may also influence disease transmission through pathogen dispersal and the exposure of people to infectious agents. Finally, this study showed that people-vector contacts not only depend on the spatial distribution of people and potential vectors, but also on their behaviours and interactions.

Vast progress has been made in detecting rates of tropical deforestation, yet the relationship between visible patterns of forest change, multi-scalar human processes and the underlying drivers associated with them is poorly understood. Building on satellite imagery, a household livelihood survey and semi-structured interviews, this research scrutinised changes of forest cover from the mid-1990s to 2015 in a municipality located in southeastern Mexico and investigated the proximate causes and underlying drivers of change at the household and community levels. Emerging evidence indicated that, contrary to the persistent narrative of deforestation for the region, forest cover change is highly dynamic including periods of deforestation and forest recovery. Moreover, a close examination of 24 communities showed forest cover gained terrain, while the agricultural frontier retracted. Drawing on a comparison between the household survey and previous analyses, it could be inferred that forest resurgence was produced by the decrease in the farming area and the increase in the abandonment of farming activities by some communities. Associated with the adaptation of households was the development of formal and informal institutions at the community level in response to macro-global forces linked to the implementation of forest conservation strategies, environmental degradation, market liberalization and increased urbanization. Overall, this research adds not only to our understanding of the complexity of land-use and cover change in emerging globalized economies but also exemplifies the spatial and temporal heterogeneity of tropical forest systems, which challenges partial models of deforestation and policies designed to reduce it. The research may be focused on a narrow region of the globe, nevertheless, the insights and recommendation provided may be useful to further forest conservation schemes in other tropical regions.

To this day, terrorism perpetrated by non-state actors persists as a worldwide threat, as exemplified by the recent lethal attacks in Paris, London, Brussels, and the ongoing massacres perpetrated by the Islamic State in Iraq, Syria and neighbouring countries. In response, states deploy various counterterrorism policies, the costs of which could be reduced through more efficient preventive measures. The literature has not applied statistical models able to account for complex spatio-temporal dependencies, despite their potential for explaining and preventing non-state terrorism at the sub-national level. In an effort to address this shortcoming, this thesis employs Bayesian hierarchical models, where the spatial random field is represented by a stochastic partial differential equation. The results show that lethal terrorist attacks perpetrated by non-state actors tend to be concentrated in areas located within failed states from which they may diffuse locally, towards neighbouring areas. At the sub-national level, the propensity of attacks to be lethal and the frequency of lethal attacks appear to be driven by antagonistic mechanisms. Attacks are more likely to be lethal far away from large cities, at higher altitudes, in less economically developed areas, and in locations with higher ethnic diversity. In contrast, the frequency of lethal attacks tends to be higher in more economically developed areas, close to large cities, and within democratic countries.

Land, water and natural resources are under increasing pressure due to rising demands for food and energy from the rapidly growing global population. Across a catchment there can be multiple stakeholders with conflicting opinions over how space and resources should be used and managed. Consequently, it is important to consider the suitability of a catchment for a particular purpose to optimise use of the area and minimise potential conflicts and impacts on the wider environment. Aquaculture is a significant contributor to world food supply and as fisheries are unlikely to increase it is expected that the industry will continue to grow and expand in the future to help meet food security requirements. As a result, it is essential that the sector aims for sustainable development within the most suitable locations. However, it can be difficult to assess the suitability of multiple large catchments and some issues may not be immediately apparent. This project aimed to show how spatial models could be used as decision support tools to evaluate the suitability of large catchments for sustainable aquaculture. Four large areas of importance to aquaculture were selected; covering 10,148km2, 26,225km2, 48,319km2 and 66,283km2 in Bangladesh, China, Thailand and Vietnam respectively. Asia is by far the most dominant aquaculture region in the world and each of the four study areas contribute to local, regional and global food supplies. The study area in Bangladesh was located in Khulna region in the south west of the country and the main species of focus were prawn and shrimp. The Chinese study area was located in the south eastern province of Guangdong and the main species covered were tilapia and shrimp. Similarly, in Thailand, the main species evaluated were tilapia and shrimp whilst the study area extended across the Central region. Finally, the largest study area was the Mekong Delta in Vietnam and the main species of focus in this area were pangasius catfish and shrimp. One of the challenges in modelling large catchments is model applicability and data availability. Often, the required data are not available (or accessible) and it would be difficult, time consuming and expensive to collect new information. Furthermore, when assessing multiple areas is it vital that a representative and unbiased approach is used where no one catchment is favoured over the other due to higher quality data. Therefore, this study used data that are available for almost any area in the world; allowing future application of the models and enabling effective and unbiased decision support. Four modelling stages were employed in this study to evaluate the suitability of large catchments for sustainable aquaculture development. The first stage was the classification of seasonal land use models from satellite imagery. This provides information on what the land is used for and how aquaculture could impact or be impacted by the wider environment. The second step was the development of seasonal models of site suitability using optimal values within a GIS-based multi-stage framework. These models identify which locations are best for culture and can also be used to estimate the availability of areas for food production. The next stage investigated the use of Maxent as a novel approach in site suitability modelling to evaluate the conditions experienced by existing farms. The information from Maxent can be used to identify trends, opportunities and concerns related to sustainable management and farm locations. Finally, qualitative models of non-point source pollution (NPSP) were developed which assess the risk of NPSP within a catchment. NPSP is an issue which can impact both aquaculture and the wider environment. Thus, it is important to understand the areas within a catchment where NPSP risk is higher enabling the establishment of monitoring and/or mitigation procedures. The models support the ecosystem approach to aquaculture (EAA) and enable objective planning and management strategies to enhance productivity across large catchments without negatively impacting the environment. In order to meet growing food requirements, large areas will need to be used for agriculture and aquaculture; therefore, analysis at a wider catchment level, which complements assessment at a local scale, is required as it allows a holistic view of the situation. The work presented here illustrates the potential use of spatial models across large catchments and considers the suitability of the areas for aquaculture development.

Species Distribution Modelling (SDM) plays a key role in a number of biological applications: assessment of temporal trends in distribution, environmental impact assessment and spatial conservation planning. From a statistical perspective, this thesis develops two methods for increasing the accuracy and reliability of maps of density surfaces and provides a solution to the problem of how to collate multiple density maps of the same region, obtained from differing sources. From a biological perspective, these statistical methods are used to analyse two marine mammal datasets to produce accurate maps for use in spatial conservation planning and temporal trend assessment. The first new method, Complex Region Spatial Smoother [CReSS; Scott-Hayward et al., 2013], improves smoothing in areas where the real distance an animal must travel (`as the animal swims') between two points may be greater than the straight line distance between them, a problem that occurs in complex domains with coastline or islands. CReSS uses estimates of the geodesic distance between points, model averaging and local radial smoothing. Simulation is used to compare its performance with other traditional and recently-developed smoothing techniques: Thin Plate Splines (TPS, Harder and Desmarais [1972]), Geodesic Low rank TPS (GLTPS; Wang and Ranalli [2007]) and the Soap lm smoother (SOAP; Wood et al. [2008]). GLTPS cannot be used in areas with islands and SOAP can be very hard to parametrise. CReSS outperforms all of the other methods on a range of simulations, based on their fit to the underlying function as measured by mean squared error, particularly for sparse data sets. Smoothing functions need to be flexible when they are used to model density surfaces that are highly heterogeneous, in order to avoid biases due to under- or over-fitting. This issue was addressed using an adaptation of a Spatially Adaptive Local Smoothing Algorithm (SALSA, Walker et al. [2010]) in combination with the CReSS method (CReSS-SALSA2D). Unlike traditional methods, such as Generalised Additive Modelling, the adaptive knot selection approach used in SALSA2D naturally accommodates local changes in the smoothness of the density surface that is being modelled. At the time of writing, there are no other methods available to deal with this issue in topographically complex regions. Simulation results show that CReSS-SALSA2D performs better than CReSS (based on MSE scores), except at very high noise levels where there is an issue with over-fitting. There is an increasing need for a facility to combine multiple density surface maps of individual species in order to make best use of meta-databases, to maintain existing maps, and to extend their geographical coverage. This thesis develops a framework and methods for combining species distribution maps as new information becomes available. The methods use Bayes Theorem to combine density surfaces, taking account of the levels of precision associated with the different sets of estimates, and kernel smoothing to alleviate artefacts that may be created where pairs of surfaces join. The methods were used as part of an algorithm (the Dynamic Cetacean Abundance Predictor) designed for BAE Systems to aid in risk mitigation for naval exercises. Two case studies show the capabilities of CReSS and CReSS-SALSA2D when applied to real ecological data. In the first case study, CReSS was used in a Generalised Estimating Equation framework to identify a candidate Marine Protected Area for the Southern Resident Killer Whale population to the south of San Juan Island, off the Pacific coast of the United States. In the second case study, changes in the spatial and temporal distribution of harbour porpoise and minke whale in north-western European waters over a period of 17 years (1994-2010) were modelled. CReSS and CReSS-SALSA2D performed well in a large, topographically complex study area. Based on simulation results, maps produced using these methods are more accurate than if a traditional GAM-based method is used. The resulting maps identified particularly high densities of both harbour porpoise and minke whale in an area off the west coast of Scotland in 2010, that might be a candidate for inclusion into the Scottish network of Nature Conservation Marine Protected Areas.

Over the past decades technological developments have both changed and increased human in influence on the marine environment. We now have greater potential than ever before to introduce disturbance and deplete marine resources. Two of the issues currently under public scrutiny are the exploitation of fish stocks worldwide and levels of anthropogenic noise in the marine environment. The aim of this thesis is to investigate and develop novel analyses and simulations to provide additional insight into some of the challenges facing the marine ecosystem today. These methodologies will improve the management of these risks to marine ecosystems. This thesis first addresses the issue of competition between humans and grey seals (Halichoerus grypus) for marine resources, providing compelling evidence that a substantial proportion of the sandeels consumed by grey seals in the North Sea are in fact H. lanceolatus, which is not commercially exploited, rather than the commercially important A. marinus. In addition, we present quantitative results regarding sources of bias when estimating the total biomass of sandeels consumed by grey seals. Secondly, we investigate spatially adaptive 2-dimensional smoothing to improve the prediction of both the presence and density of marine species, information that is often key in the management of marine ecosystems. Particularly, we demonstrate the benefits of such methods in the prediction of sandeel occurrence. Lastly this thesis provides a quantitative assessment of the protocols for real-time monitoring of marine mammal presence, which require that acoustic operations cease when an animal is detected within a certain distance (i.e. the "monitoring zone") of the sound source. We assess monitoring zones of different sizes with regards to their effectiveness in reducing the risks of temporary and permanent damage to the animals' hearing, and demonstrate that a monitoring zone of 2 km is generally recommendable.

A conversão de vegetação nativa e o uso inadequado das terras convertidas resultam em prejuízos graves para as funções e processos dos ecossistemas, impactando diretamente a provisão de serviços ambientais e o bem-estar da sociedade. Em meio ao elevado nível de degradação e fragmentação de habitats naturais, pesquisadores têm buscado alternativas para promover a sustentabilidade de paisagens modificadas pelo homem, procurando conciliar ações conservacionistas com as necessidades de produção agropecuária e do uso consciente dos recursos naturais. Para contribuir com esse tema, este trabalho apresentou uma metodologia para a priorização de áreas para restauração florestal em paisagens agrícolas. Foram utilizados indicadores biofísicos (perda de solo, escoamento superficial e conectividade da paisagem) e técnicas de modelagem espacial para analisar o efeito de cenários alternativos de uso do solo sobre a conservação do solo, da água e da biodiversidade. Foram criados dois grupos de cenários, sendo que um grupo apresenta cenários com diferentes quantidades e arranjos espaciais da cobertura florestal na paisagem, e outro grupo se refere à cenários com diferentes tamanhos de faixas marginais florestadas no entorno da rede de drenagem. Os resultados do primeiro grupo de cenários demonstraram que o arranjo espacial da cobertura florestal influenciou significativamente a perda de solo e o escoamento superficial, mas não apresentou efeitos significativos para a conectividade da paisagem, que se mostrou dependente apenas da quantidade de cobertura florestal. As maiores sinergias entre os indicadores analisados foram expressas a partir da aplicação conjunta de boas práticas agrícolas e a restauração florestal de áreas críticas para conservação do solo e da água, demonstrando a complementariedade dessas ações no manejo de paisagens agrícolas. Quanto ao segundo grupo de cenários, os resultados demonstraram a influência positiva do tamanho da faixa florestada na capacidade de retenção de sedimentos das zonas ripárias e na conectividade da paisagem, de modo que os melhores resultados foram obtidos com o total recobrimento das Áreas de Preservação Permanente (APPs) por florestas. Observou-se que a quantidade de cobertura florestal influenciou positivamente a capacidade de retenção de sedimentos das APPs, sendo determinante no comportamento dessas áreas como fonte ou filtro de sedimentos para os canais de drenagem. De forma geral, os resultados apresentados neste trabalho demonstraram o potencial de estratégias conservacionistas e de restauração florestal em paisagens agrícolas, ao mesmo tempo que reforçaram as necessidades de planejamento e da inclusão de áreas de produção agropecuária no manejo de paisagens. Em um cenário atual de implementação da Lei de Proteção da Vegetação Nativa e de exigências mais brandas para recomposição de APPs e Reservas Legais em propriedades privadas, os resultados sugerem que apenas as ações de comando e controle podem não ser suficientes para promover a provisão de serviços ambientais e a conservação da biodiversidade em paisagens agrícolas. Desse modo, recomenda-se que haja uma desvinculação do que está previsto em lei com o conceito de paisagens sustentáveis, pois apenas a adequação legal de propriedades rurais pode não ser capaz de assegurar o fornecimento de bens e serviços à sociedade. / The conversion of native vegetation and the inappropriate use of converted land have resulted in severe damage to the natural ecosystem functions and processes, affecting the provision of environmental services and the welfare of society. Due to the high level of degradation and fragmentation of natural habitats, researchers have sought alternatives to promote the sustainability of human modified landscapes, aiming to reconcile conservation actions with the agricultural production needs and the conscious use of natural resources. To contribute to this theme, this thesis presented a methodology for prioritizing areas for forest restoration on agricultural landscapes. Biophysical indicators (soil loss, runoff and landscape connectivity) and spatial modeling techniques were used to analyze the effects of alternative scenarios of land use on the conservation of soil, water and biodiversity. These effects were analyzed on two groups of scenarios, wherein one group presents scenarios with different amounts and spatial arrangements of forest cover on the landscape, and another group refers to scenarios with different sizes of forested riparian buffer zones. The results of the first group of scenarios showed that the spatial arrangement of the forest cover significantly influenced the soil loss and runoff, but did not significantly affect the landscape connectivity, which proved to be dependent only by the amount of forest cover. The highest synergies between the analyzed indicators were expressed with the joint application of best management practices and forest restoration of critical areas for soil and water conservation, demonstrating the complementarity of these actions in the management of agricultural landscapes. Considering the second group of scenarios, the results demonstrated the positive influence of the size of forested riparian buffers on sediment retention capacity of riparian areas and landscape connectivity, and the best results were obtained with the total coverage of the Areas of Permanent Preservation (APPs) by forests. It was observed that the amount of forest cover positively influenced the sediment retention capacity of APPs and determined the behavior of these areas as sediment filter or source for stream channels. Overall, the results presented in this thesis demonstrated the potential of conservation and forest restoration strategies on agricultural landscapes, while it reinforced the needs for planning and for the inclusion of agricultural production areas in landscape management strategies. On current expectations for the implementation of the new Brazilian Forest Act and softer requirements for restoration of APPs and Legal Reserves on private properties, the results suggest that only the command and control actions may not be sufficient to promote the provision of environmental services and biodiversity conservation on agricultural landscapes. Therefore, we recommend the detachment from what is required by law to the concept of sustainable landscapes, because only the legal compliance of rural properties may not be able to ensure the supply of goods and services to society.

Basal Temperature of Snow (BTS) measurements were used as the primary inputs to a high resolution (30 x 30 m grid cells) empirical-statistical regional permafrost probability model for the southern and central Yukon, and northernmost British Columbia (59° - 65°N). Data from seven individual study areas distributed across the region were combined using a blended distance decay technique, with an eighth area used for validation. The model predictions are reasonably consistent with previous permafrost maps for the area with some notable differences and a much higher level of detail. The modelling gives an overall permafrost probability of 52%. North of 62°N, permafrost becomes more extensive in the lowland areas whereas farther south permafrost is typically common only above treeline. Significant differences exist between the mountain environments of the Yukon and the Swiss Alps where the BTS method originated and as a result different modelling approaches had to be developed. This work therefore: (1) develops additional explanatory variables for permafrost probability modelling, the most notable of which is equivalent elevation, (2) confirms the use of ground truthing as a requirement for empirical-statistical modelling in the Yukon and (3) uses a combination of models for the region in order to spatially predict between study areas. The results of this thesis will be of use to linear infrastructure route-planning, geohazard assessment and climate change adaptation strategies. Future work employing the model will allow the effects of scenario-based climate warming to be examined.