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.

Conservation frequently requires immediate responses to prevent further declines of imperilled populations, often in the absence of detailed information. Consequently, population distribution patterns are often used to guide conservation decisions. However, distribution patterns may be misleading if threats have restricted species to low quality habitat. This issue means it is not always apparent where management efforts should be concentrated for maximum conservation gain. My aim was to improve the effectiveness of threatened species conservation by investigating this issue in whio (blue duck - Hymenolaimus malacorhynchos), a New Zealand riverine duck that has undergone serious declines. I used population and spatial modelling to answer three questions: (1) what are the threats to whio, (2) how can these threats be managed, and (3) managing which whio habitats will give the greatest conservation gain? A spatial analysis of contemporary whio habitat using boosted regression trees revealed whio are only secure in 1 % of their historical range, with predation likely causing significantly greater range contraction (83 %) than habitat modification (29 %). In that analysis, I identified 39,000 km of occupiable whio habitat, providing extensive opportunities to expand their contemporary range through management. Intensive monitoring identified stoats (Mustela erminea) as the primary cause of whio population declines, with stoat predation severely reducing whio nest survival (10 % and 54 % in the absence and presence of stoat control, respectively). Population viability analyses indicated whio populations in the absence of stoat control were at high risk of extinction (λ = 0.74) but large-scale, low-intensity predator control was useful for short-term whio conservation. However, whio populations with stoat control still had a declining population growth rate (λ = 0.95) and further intervention may be required to prevent whio extinctions. Such management needs to target high quality habitat to ensure the greatest conservation value. Analyses of habitat quality revealed whio fitness was highest in warm, low gradient rivers, although fitness gradients differed between North and South Islands. Comparisons of fitness relationships with spatial model predictions showed that South Island whio occurred more frequently in poorer habitat, indicating they may occupy a relict distribution. Limited resources for conservation mean identifying effective management techniques is critical for species persistence. My modelling approach enabled the effectiveness of whio management to be assessed and areas of high quality habitat where such management should provide the greatest benefit to be identified. These tools are directly applicable to the conservation management of many threatened species by quickly informing managers in situations where distributions may not follow habitat quality.

Conservation frequently requires immediate responses to prevent further declines of imperilled populations, often in the absence of detailed information. Consequently, population distribution patterns are often used to guide conservation decisions. However, distribution patterns may be misleading if threats have restricted species to low quality habitat. This issue means it is not always apparent where management efforts should be concentrated for maximum conservation gain. My aim was to improve the effectiveness of threatened species conservation by investigating this issue in whio (blue duck - Hymenolaimus malacorhynchos), a New Zealand riverine duck that has undergone serious declines. I used population and spatial modelling to answer three questions: (1) what are the threats to whio, (2) how can these threats be managed, and (3) managing which whio habitats will give the greatest conservation gain? A spatial analysis of contemporary whio habitat using boosted regression trees revealed whio are only secure in 1 % of their historical range, with predation likely causing significantly greater range contraction (83 %) than habitat modification (29 %). In that analysis, I identified 39,000 km of occupiable whio habitat, providing extensive opportunities to expand their contemporary range through management. Intensive monitoring identified stoats (Mustela erminea) as the primary cause of whio population declines, with stoat predation severely reducing whio nest survival (10 % and 54 % in the absence and presence of stoat control, respectively). Population viability analyses indicated whio populations in the absence of stoat control were at high risk of extinction (λ = 0.74) but large-scale, low-intensity predator control was useful for short-term whio conservation. However, whio populations with stoat control still had a declining population growth rate (λ = 0.95) and further intervention may be required to prevent whio extinctions. Such management needs to target high quality habitat to ensure the greatest conservation value. Analyses of habitat quality revealed whio fitness was highest in warm, low gradient rivers, although fitness gradients differed between North and South Islands. Comparisons of fitness relationships with spatial model predictions showed that South Island whio occurred more frequently in poorer habitat, indicating they may occupy a relict distribution. Limited resources for conservation mean identifying effective management techniques is critical for species persistence. My modelling approach enabled the effectiveness of whio management to be assessed and areas of high quality habitat where such management should provide the greatest benefit to be identified. These tools are directly applicable to the conservation management of many threatened species by quickly informing managers in situations where distributions may not follow habitat quality.

Among tools that are used to fill knowledge gaps on natural systems, ecological modelling has been widely applied during the last two decades. Ecological models are simple representations of a complex reality. They allow to highlight environmental drivers of species ecological niche and better understand species responses to environmental changes. However, applying models to Southern Ocean benthic organisms raises several methodological challenges. Species presence datasets are often aggregated in time and space nearby research stations or along main sailing routes. Data are often limited in number to correctly describe species occupied space and physiology. Finally, environmental datasets are not precise enough to accurately represent the complexity of marine habitats. Can we thus generate performant and accurate models at the scale of the Southern Ocean ?What are the limits of such approaches ?How could we improve methods to build more relevant models ?In this PhD thesis, three different model categories have been studied and their performance evaluated. (1) Mechanistic physiological models (Dynamic Energy Budget models, DEB) simulate how the abiotic environment influences individual metabolism and represent the species fundamental niche. (2) Species distribution models (SDMs) predict species distribution probability by studying the relationship between species presences and the environment. They represent the species realised niche. (3) Dispersal lagrangian models predict the drift of propagules in water masses. Results show that physiological models can be developed for marine Southern Ocean species to simulate the metabolic variations in link with the environment and predict population dynamics. However, more data are necessary to highlight detailed physiological contrasts between populations and to accurately evaluate models. Results obtained for SDMs suggest that models generated at the scale of the Southern Ocean and future simulations are not relevant, given the lack of data available to characterise species occupied space, the lack of precision and accuracy of future climate scenarios and the impossibility to evaluate models. Moreover, model extrapolate on a large proportion of the projected area. Adding information on species physiological limits (observations, results from experiments, physiological model outputs) was shown to reduce extrapolation and to improve the capacity of models to estimate the species realised niche. Spatial aggregation of occurrence data, which influenced model predictions and evaluation was also succefully corrected. Finally, dispersal models showed an interesting potential to highlight the role of geographic barriers or conversely of spatial connectivity and also the link between species distribution, physiology and phylogeny history. This PhD thesis provides several methodological advice, annoted codes and tutorials to help implement future modelling works applied to Southern Ocean marine species. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Climate change, global warming and anthropogenic disturbances are threatening freshwater ecosystems globally. The protection and preservation of freshwater environments, its biodiversity and all of its services for human well-being requires comprehensive knowledge of the impacts that climate change and anthropogenic disturbances have on freshwaters and freshwater species. In-depth knowledge needed for conservation strategies can be established through versatile assessments. Quantitative assessments and the investigation of prevailing environmental relationships within ecosystems constitute the basis for sustaining freshwater systems. However, it is a great challenge to quantify the multifaceted effects of climate change and to broaden the understanding of complex environmental relationships. This thesis aims at contributing to an extension of the understanding of climate change impacts on freshwater ecosystems and environmental relationships, which implies the provision of useful guidelines for the protection and preservation of freshwaters. For this, various statistical approaches based on comprehensive data sets are applied at different scales, ranging from local to global assessments. In particular, five research studies investigating the (1) water quality-nutrient and temperature relationships in European lakes, (2) drivers of freshwater fish species distributions across varying scales in the Danube River delta, (3) globally derived thermal response curves and thermal properties of native European freshwater species, (4) differences between thermal properties derived from native and global range data, and (5) thermal performances of freshwater fish species for different life stages and different global future dispersal scenarios are presented to address the effects of environmental change. Main results of this thesis comprise various aspects of conservation implications and planning. (i) The first study outlines drivers influencing water quality through studying multi-dimensional relationships and compares different modelling techniques in order to outline models that are suitable for the identification of complex driver interactions. (ii) The second study addresses scale effects on the performance of species distribution models, which are commonly used for assessments of climate change impacts, and identifies key predictors driving distributions for the varying scales and studied species. (iii) The third study parameterizes thermal responses of species from different taxonomic groups and assesses the potential resilience in terms of warming tolerance and additional thermal properties as well as the influence of future rising temperatures on current distributions. (iv) The fourth study quantifies the differences in thermal response curves and thermal properties for freshwater fishes derived from global and continental data in order to clarify the need for using global range data in studies making suggestions for conservation planning. (v) The last study estimates the impact of changing climatic conditions on species distribution ranges of two fish species for different time periods by including biotic information about thermal performances for various life stages. Overall, this thesis contributes to the broad field of studying consequences and impacts of climate change on freshwater ecosystems. By applying statistical methods tailored to the underlying investigations, useful implications for conservation planning are derived.

Understanding how climate determines species’ geographic distributions is an important question in ecology with direct implications for predicting climate change-driven range shifts. For Lepidoptera, growing degree-days, a measure of growing season length, has been shown to be an important predictor of species’ distributions in some cases. Most studies use a standardized estimate of base development temperature in their calculations of growing degree-days instead of a species-specific threshold so past investigations of the influence of growing degree-days on Lepidoptera distributions may not have been optimal. Species distribution models (SDMs) are a commonly used approach in ecology that typically only implicitly capture the underlying processes that limit a species’ distribution. A species-specific estimate of growing degree-days should better characterize these processes than standard thermal thresholds and thus improve the accuracy of species distribution models. In this thesis, I use species distribution modelling to model the geographic distribution of 30 moth species native to North America. I ask whether a) growing degree-days are the best climatic predictor of these moth species distributions at broad scales; b) a lab-estimated biological threshold (i.e., BDT) can scale up and improve the predictive ability of SDMs; and c) the quality of experiments used to estimate species-specific BDT influences the predictive accuracy of SDMs. To do so, I compare the predictive accuracy of a correlative model based on a commonly-used thermal threshold to define growing degree-days to a hybrid model with degree-days defined based on a species-specific thermal threshold. I found that the predictive performance of the hybrid models was indistinguishable from the correlative models likely because growing degree-days was not the best climatic predictor of the geographic distributions of the majority of these moth species. I also found that there was no link between the quality of the lab experiments and the difference in performance of the hybrid and correlative models. My findings suggest that lab-estimated thermal thresholds may not always scale up to be predictive at a broad scale and that more work is needed to leverage the data from lab experiments into broad scale SDMs. Determining the ultimate factors that limit species’ distributions will be critical in accurately predicting species’ range shifts response to future climate change.

Cord-forming fungi (CFF) are an assemblage of saprotrophic fungi which can use complex foraging organs of longitudinally arranged hyphae to join up disparate substrates in a patchy resource environment. Their importance to woodlands lies, mainly, in their ability to modify nutrient cycling and soil structure. Therefore, in order to enable woodlands to continue to thrive in terms of their health and ecosystem function, it is necessary to understand the factors contributing to the establishment, success and diversity of this group. Whilst work to date on CFF has focussed on their physiology and interactions in laboratory conditions, little work has been carried out on their taxonomy and establishment/presence in the field. The work in this thesis begins to address these crucial unanswered questions in CFF ecology. By carrying out investigations at a range of scales, from phylogenetic analysis to UK wide Species Distribution Modelling, this thesis reaches a number of surprising results with potentially important implications for woodland management. This is most evident in Chapter 3 where our hypothesis that fungal communities develop over time in plantations of different woodland ages was disproved, illustrating that even 13 years after planting, fungal communities in plantations on ex-agricultural land had not begun to reach those in established ASNW. These unexpected results continue into Chapter 4, where the thesis demonstrates that dominant canopy species has a greater impact on community composition than any other woodland factor. Chapter 5 continues this theme, by showing that removal of invasive species is not always beneficial for the cord-forming fungal communities, especially if it involves removing the woody substrate. The work described, detailed and analysed in this thesis has initiated further investigations, proposed changes to woodland management practices and laid the foundations for future work relating to CFF and their role and function in British woodlands.

Biodiversity, the variety of life on the planet, is declining due to climate change, population and species interactions and as the result f demographic and landscape dynamics. Integrated model-based assessments play a key role in understanding and exploring these complex dynamics and have proven use in conservation planning. Model-based assessments using Species Distribution Models constitute an efficient means of translating limited point data to distribution probability maps for current and future scenarios in support of conservation decision making. The aims of this doctoral study were to investigate; (1) the use of a hybrid genetic programming to build high quality models that handle noisy real-world presence and absence data, (2) the extension of this solution to exploit the parallelism inherent to genetic programming for fast scenario based decision making tasks, and (3) a conceptual framework to share models in the hope of enabling research synthesis. Subsequent to this, the quality of the method, evaluated with the true skill statistic, was examined with two case studies. The first with a dataset obtained by defining a virtual species, and the second with data extracted from the North American Breeding Bird Survey relating to mourning dove (Zenaida macroura). In these studies, the produced models effectively predicted the species distribution up to 30% of error rate both presence and absence samples. The parallel implementation based on a twenty-node c3.xlarge Amazon EC2 StarCluster showed a linear speedup due to the multiple-deme coarse-grained design. The hybrid fuzzy genetic programming algorithm generated under certain consitions during the case studies significantly better transferable models. / Biodiversidade, a variedade de vida no planeta, está em declínio às alterações climáticas, mudanças nas interações das populações e espécies, bem como nas alterações demográficas e na dinâmica de paisagens. Avaliações integradas baseadas em modelo desempenham um papel fundamental na compreensão e na exploração destas dinâmicas complexas e tem o seu uso comprovado no planejamento de conservação da biodiversidade. Os objetivos deste estudo de doutorado foram investigar; (1) o uso de técnicas de programação genética e fuzzy para construir modelos de alta qualidade que lida com presença e ausência de dados ruidosos do mundo real, (2) a extensão desta solução para explorar o paralelismo inerente à programação genética para acelerar tomadas de decisão e (3) um framework conceitual para compartilhar modelos, na expectativa de permitir a síntese de pesquisa. Subsequentemente, a qualidade do método, avaliada com a true skill statistic, foi examinado com dois estudos de caso. O primeiro utilizou um conjunto de dados fictícios obtidos a partir da definição de uma espécie virtual, e o segundo utilizou dados de uma espécie de pomba (Zenaida macroura) obtidos do North American Breeding Bird Survey. Nestes estudos, os modelos foram capazes de predizer a distribuição das espécies maneira correta mesmo utilizando bases de dados com até 30% de erros nas amostras de presença e de ausência. A implementação paralela utilizando um cluster de vinte nós c3.xlarge Amazon EC2 StarCluster, mostrou uma aceleração linear devido ao arquitetura de múltiplos deme de granulação grossa. O algoritmo de programação genética e fuzzy gerada em determinadas condições durante os estudos de caso, foram significativamente melhores na transferência do que os algoritmos do BIOMOD.