<p>Biofilms are communities of sessile microorganisms attached to a surface and imbeddedin a matrix of extracellular polysaccharide substances. These communities can be foundin diverse aquatic environments, such as in industrial pipes and in humans. By formingmicrocolony structures, which are highly resistant to adverse physical conditions as wellas antimicrobial agents, biofilms are very problematic when associated with e.g.persistent infections. In order to find new ways of controlling biofilm growth, theprocesses involved in biofilm development must be investigated further. The maininterest of this study is the occurrence of void formation inside biofilms. Thisphenomenon has been observed in several studies and has been correlated to cell deathinside the microcolonies. The occurrence of cell death has recently been associated withthe presence of nitric oxide in the biofilm. In this study, the implications of nitric oxideaccumulation on biofilm development were investigated using an individual-basedmodel. Specifically, the role of nitric oxide in void formation was considered. A largenumber of simulations were run using different parameter settings in order to determine ifnitric oxide could account for the occurrence of void formation observed experimentally.The general predictions made by the model system showed agreement to someexperimental data, but not to others. Sloughing, the detachment of chunks of cells fromthe biofilm, was observed in the majority of simulations. In some cases, the model alsopredicted the presence of live cells inside the voids, which has been observedexperimentally.</p>

This thesis gives a general model formulation of discrete time individual-based models and two specific individual-based models for gregarious behaviour. A spatially explicit individual-based model for a predator, prey and plant ecosystem is the first specific model considered. The movement of the individuals is solely based on nearest-neighbour attraction/repulsion and reproduction is asexual. The second specific model considered is a spatially explicit individual-based model for schooling behaviour. The movement of the individuals is based on the physics of fluids and their reactions (repel/comfort/attract) are solely based on directional light intensities. In the ecosystem model emergent spatial organization of the individuals into clusters or groups is present even though all the individuals (predators and prey) are intra-specifically repelled by each other. The model ecosystem was used to determine whether an intra-specific attraction among the prey could increase their individual fitness. The attraction mechanism considered is such that when a prey is not affected by a predator or a plant then this prey is attracted to its nearest-neighbour prey. Under the assumptions of the parametrized model ecosystem, this mechanism seems highly selective on the individual level. The schooling model assumes that the individuals have absolutely no spatial reasoning and cannot discern other individuals as individuals. In particular, the individuals school without the capacity to match the velocities or orientations of other individuals. Individuals have the ability to perceive their environment using directional photo-sensors and adjust the direction of their acceleration in response to the values of these photo-sensors. This result should fundamentally change the way social aggregations are modeled. The main objective of this thesis is to formalize mathematically individual-based models with the intention that they become more prevalent in the scientific inquiry into assessing evolutionary aspects of social behaviour.

No description available.

<p>I dagens skola är individen och dennes behov i fokus. Detta uttrycks i skolans läroplan,<em> Lpo 94</em>. Läroplanens intention är att undervisningen skall individanpassas utifrån varje elevs behov och förutsättningar. Syftet med studien är att undersöka hur pedagoger individanpassar undervisningen. Vidare är studiens syfte att belysa pedagogers syn på individanpassad undervisning och vad det är som styr deras utformning av den.</p><p>Antalet respondenter i studien har varit fyra pedagoger, som alla är verksamma i skolår 3. Studien utgörs av en fallstudie med observationer och intervjuer, vilka har analyserats och tolkats.</p><p>Resultatet visar att pedagoger använder sig av ett varierande arbetssätt för att möta varje enskild elev. De använder planeringsbok, arbetsschema eller beting i arbetet med att individanpassa undervisningen, vilket innebär att eleverna arbetar självständigt och att uppgifterna ofta är självinstruerande. Pedagogerna anser det enklare att individanpassa i matematik och svenska än i idrott, engelska och de natur- och samhällsorienterande ämnena. Studiens resultat visar att en av fördelarna med individanpassad undervisning är att den utgår just från den enskilde eleven. När undervisningen anpassas efter elevens förmåga och behov, leder det till att eleven känner glädje och en känsla av att den kan. Pedagogerna synliggör i detta sammanhang också möjligheten som ges att kunna utmana de duktiga eleverna. Studiens resultat visar att pedagoger vill arbeta mer individanpassat än vad de gör idag, men ser de yttre faktorerna som hinder för detta.</p> / <p>In the school of today, the individual's needs are the focus. This is expressed in Lpo 94. The intention of the curriculum (Lpo 94) is that education should be adjusted to the needs and conditions of every pupil. The purpose of the study is to examine how pedagogues adjust education to the individual. Furthermore, the purpose is to illustrate pedagogues' view on individually based education, and what dictates how it is carried out.</p><p>Four pedagogues, all working in school year 3, responded to the study. This case study used observations and interviews, which have been analysed.</p><p>The result shows that pedagogues use a variety of ways to meet the needs of every single pupil. They use planning books, working schedules and/or assignments as learning tools. This implies that the pupils work independently, and that work is often self-instructive. The pedagogues state that it is easier to adjust to the individual's needs in mathematics and Swedish than in physical education, foreign languages, social studies and science. All pedagogues see from a pupil's perspective when talking about the advantages with individually based teaching. They emphasize that the advantage is the possibility to adjust to the abilities and needs of each individual pupil, which leads to a more positive pupil and sense of accomplishment. Individually based teaching also gives the pedagogues the possibility to challenge high achieving pupils. All pedagogues want to work more individually based, but see external factors as impediments.</p>

La production porcine européenne fait face à des enjeux économiques et environnementaux. Les outils d’aide à la décision peuvent aider les éleveurs à tester les effets des facteurs d’élevage sur leur système de production. Nous avons développé un modèle individu-centré de l’atelier d’engraissement porcin, qui intègre la variabilité des performances entre individus, la conduite d’élevage, et estime les résultats technico-économiques (ECO) et les impacts environnementaux (ENV) de l’atelier. L’analyse de sensibilité a montré que le comportement du modèle est cohérent et que les variables d’entrée les plus influentes sont les caractéristiques des porcs. La comparaison des valeurs prédites et observées nous a permis de construire un processus de paramétrage et a montré que le modèle fournit des estimations fiables des résultats ECO et ENV de l’atelier.Une expérimentation virtuelle a été effectuée pour évaluer les effets combinés des pratiques d’alimentation, du statut sanitaire du troupeau, et des infrastructures. Le statut sanitaire a un impact majeur sur les résultats ECO et ENV, pouvant être atténué par le choix de la conduite en bandes et de la stratégie d’alimentation. S’approcher au plus près des besoins nutritionnels individuels des porcs semble être une solution appropriée pour améliorer le revenu de l’élevage et réduire ses impacts quel que soit son statut sanitaire. Ce modèle sera inclus dans un outil d’aide à la décision construit en accord avec les résultats de l’analyse de sensibilité et le processus de paramétrage développé. / European pig production is facing economic and environmental challenges. Decision support tools can help farmers testing the effects of rearing factors on their production system. For this purpose, we developed an individual-based model of the pig fattening unit which includes farmers’ practices, individual variability in performance among pigs and, and estimates economic results (ECO) and environmental impacts (ENV) of the unit. Global sensitivity analysis has shown that the model behavior is consistent and that the most influent input variables are related to the pigs’ characteristics. Formal comparison of predicted with observed data has enabled us to produce a process for parameterisation of the model and has shown that the model provides reliable estimates of ECO and ENV.A virtual experiment using the model was designed in order to evaluate the combined effects of feeding practices, health status of the pig herd, and infrastructure on technical performance, ECO and ENV of the unit. Health status has the major impact on unit performance but this effect can be reduced through batch management and feeding strategies. Approaching more closely the individual pigs’ nutritional requirements seems to be an appropriate solution to increase farm income and to reduce environmental impacts regardless the health status. The model will be included into a decision support tool designed according to the influent inputs resulting from the sensitivity analysis and the parameterisation process

No description available.

In this paper we endeavor to test the controversial ideas that exist about the role of fragmentation in a conservation context. In line with earlier understanding, we find that habitat fragmentation alone results in a strong detrimental effect (especially for the predator population). Connecting the fragmented habitats facilitates predator survival and hence prey survival as compared to the unconnected fragmented case. Our main result is counterintuitive: in the presence of a high quality predator, connected fragmented habitats ensure a better chance for coexistence than does even the unfragmented case. We explain why a connected fragmented habitat might thus be beneficial for the stabilization of the system, and how connections between sub-habitats are able to protect prey population from over-exploitation. In the model, habitat fragmentation is separated from the effects of habitat destruction, in order to better understand how populations react to habitat transformation.

Forests are an important component in the earth system. They cover nearly one third of the land surface, store about as much carbon as the entire atmosphere and host more than half of the planet’s biodiversity. Forests provide ecosystem services such as climate regulation and water cycling and they supply resources. However, forests are increasingly at risk worldwide, due to anthropogenic deforestation, degradation and climate change. Concepts for counteracting this development require abilities to monitor forests and predict possible future developments. Given the vast size of forest cover along with the variety of forest types, field measurements and experiments alone cannot provide the solution for this task. Remote sensing and forest modeling enable a broader and deeper understanding of the processes that shape our planet’s forests. Remote sensing from airborne and spaceborne platforms can provide detailed measurements of forest attributes ranging from landscape to global scale. The challenge is to interpret the measurements in an appropriate way and derive biophysical properties. This requires a good understanding of the interaction between radiation and the vegetation. Forest models are tools that synthesize our knowledge about processes, such as tree growth, competition, disturbances and mortality. They allow simulation experiments which go beyond the spatial and temporal scales of field experiments. In this thesis, several major challenges in forest ecology and remote sensing were addressed. The main variable of interest was forest biomass, as it is the most important variable for forest carbon mapping and for understanding the role of vegetation in the global carbon cycle. For the purpose of biomass estimation, remote sensing derived canopy height and structure measurements were combined with field data, forest simulations and remote sensing simulations. The goals were: 1) to integrate remote sensing measurements into a forest model; 2) to understand the effects of spatial scale and disturbances on biomass estimation using a variety of remote sensing metrics; 3) to develop approaches for quantifying biomass changes over time with remote sensing and 4) to overcome differences among forest types by considering several structural aspects in the biomass estimation function. In the first study, a light detection and ranging (lidar) simulator was developed and integrated in the forest model FORMIND. The model was parameterized for the tropical rainforest on Barro Colorado Island (BCI, Panama). The output of the lidar simulator was validated against real airborne lidar data from BCI. Undisturbed and disturbed forests were simulated with FORMIND to identify the most well suited lidar metric for biomass estimation. The objective hereby was to achieve a low normalized root mean squared error (nRMSE) over the entire range of forest structures caused by disturbances and succession. Results identified the mean top-of-canopy height (TCH) as the best lidar-derived predictor. The accuracy strongly depended on spatial scale and relative errors < 10% could be achieved if the spatial resolution of the produced biomass map was ≥ 100 m and the spatial resolution of the remote sensing input was ≤ 10 m. These results could provide guidance for biomass mapping efforts. In the second study, forest simulations were used to explore approaches for estimating changes in forest biomass over time based on observed changes in canopy height. In an ideal situation, remote sensing provides measurements of canopy height above ground which allows the estimation of biomass stocks and changes. However, this requires sensors which are able to detect canopy surface and terrain elevation, and some sensors can only detect the surface (e.g., X-band radar). In such cases, biomass change has to be estimated from height change using a direct relationship. Unfortunately, such a relationship is not constant for forests in different successional stages, which can lead to considerable biases in the estimates of biomass change. A solution to this problem was found, where missing information of canopy height was compensated by integrating metrics of canopy texture. Applying this improved approach enables estimations of biomass losses and gains after disturbances at 1-ha resolution. In mature forests with very small changes in height and biomass all tested approaches have limited capabilities, as was revealed by an application using TanDEM-X derived canopy height from BCI. In the third study, a general biomass estimation function, which links remote sensing-derived structure metrics to forest biomass, was developed. General in this context means that it can be applied in different forest types and different biomes. For this purpose a set of predictor metrics was explored, with each predictor representing one of the following structural aspects: mean canopy height, maximal possible canopy height, maximal possible stand density, vertical canopy structure and wood density. The derived general equation resulted in almost equally accurate biomass estimates across the five considered sites (nRMSE = 12.4%, R² = 0.74) as site-specific equations (nRMSE = 11.7%, R²= 0.77). The contributions of the predictors provide a better understanding of the variability in the height-to-biomass relationship observed across forest types. The thesis has laid foundations for a close link between remote sensing, forest modeling and forest inventories. Several ongoing projects carry this further, by 1) disentangling and quantifying the uncertainty in biomass remote sensing, 2) trying to predict forest productivity based on structure and 3) detecting single trees from lidar to be used as forest model input. These methods can in the future lead to an integrated forest monitoring and information system, which assimilates remote sensing measurements and produces predictions about forest development. Such tools are urgently needed to reduce the risks forests are facing worldwide.

A renewable energy source, biogas, comprises of methane (80%) and carbon dioxide (15%), and is a great alternative to the conventional fossil-based fuels, such as coal, gas and oil. Biogas is created during anaerobic biological digestion of waste materials, such as landfill material, animal manure, wastewater, algal biomass, industrial organic waste etc. A biogas potential from organic waste in the United States is estimated at about 9 million tons per year and technology allows capture of greenhouse gases, such as methane and carbon dioxide, into a form of a fuel. In the light of global climate change and efforts to decrease carbon footprint of fuels in daily life, usage of biogas as an alternative fuel to fossil fuels looks especially promising. The goal of this research was to develop and test an approach for optimization of biogas production by engineering microorganisms digesting organic waste. Specifically, bacteria that can digest algal biomass, collected from the wastewater lagoons or open waterbodies. The research also expands on the previous efforts to analyze microbial interactions in wastewater treatment systems. A computational model is developed to aid with prognosis of microbial consortia ability to form complex aggregates in reactors with upflow mode of feeding substrate. Combining modeling predictions and laboratory experiments in organic matter digestion will lead to more stable engineered systems and higher yields of biogas.

White-tailed deer (Odocoileus virginianus) dispersal and excursion movements impact gene flow, population dynamics, and disease spread. Knowledge of movement characteristics and habitat selection during dispersal could provide the ability to predict how deer may relocate themselves within the landscape while providing managers valuable information regarding corridors for gene flow and disease spread. My objectives were to 1) test the hypothesis that extra-home-range movements occur as a strategy to broaden mating opportunities or as a means of searching for higher quality resources in this fragmented landscape, 2) compare occurrence rates and path movement metrics for dispersal and excursion movements to determine if underlying differences in behavior exist that would allude to mechanisms for accepting the risk of leaving a home range, 3) create and test the performance of expert opinion and step selection function resistance models at predicting deer dispersal movements, and 4) fit single and multiple random walk models to dispersal path data to determine movement states occurring within this behavior. During 2011-2014, I placed GPS collars programmed to take hourly locations on 49 fawn and yearling white-tailed deer in agricultural east-central Illinois to record dispersal and excursion movement paths. Linear mixed effects models were used to test for differences in path characteristics between sexes and ages (e.g., distance, straightness, duration, and speed). I used known-fate models, with demographic, temporal, and home range variables as covariates, to obtain dispersal and excursion occurrence rate estimates. Ten dispersal and 54 excursion movement paths were recorded during the study. Dispersal paths were longer and straighter (P < 0.001), and trended toward being longer in duration (P = 0.080) and faster in speed (P = 0.085), than excursion paths. Dispersal rates differed by sex (annual estimate ± SE with ages pooled: males 0.81 ± 0.12, females 0.16 ± 0.15) and were greatest during the breeding season (14-day estimates for males: winter 0.00 ± 0.01, fawning 0.02 ± 0.1, prebreeding 0.01 ± 0.01, and breeding 0.31 ± 0.15, and females: winter 0.00 ± 0.01, fawning 0.01 ± 0.1, prebreeding 0.01 ± 0.01, and breeding 0.04 ± 0.03). In contrast, I found no evidence that excursion rates were influenced by demographic, temporal, or home range variables (annual: 0.78 ± 0.06). I compared 2 methods of resistance modeling for predicting deer dispersal paths. I created an expert opinion survey and calculated a dispersal step selection function (SSF) to rank habitat variables and create 2 types of resistance maps to dispersal movements. I created least-cost paths with the starting and ending points coinciding with recorded dispersal paths within these 2 resistance maps. I compared the created paths to actual paths and a null straight line path using a path deviation index (PDI), path straightness, and path cost/m as variables of interest. Experts ranked land cover variables differently by season, applying a lower resistance value to agriculture cover during the summer/fall period, so 2 versions of the expert opinion resistance maps were created. For the SSF, I found that both forest cover and streams had significant nonlinear effects on deer dispersal movements. Assuming that all other factors remained constant, deer were more likely (≥ 0.50 probability) to move toward forested habitat when located < 335 m and when > 2795 m away. Deer dispersal movement behavior relating to streams followed a similar trend but with deer always having > 0.56 probability to move toward a stream than away. For least-cost path comparison, I conducted 3 ANOVAs (α = 0.05 throughout) to test for mean differences in calculated path metrics for all paths with path type as a within-subjects effect. I found no difference between the expert opinion survey model, the SSF model, and the null straight line model at predicting dispersal paths. PDI values were similar among all models (F1,9 = 0.004, P = 0.99). The SSF paths (0.91 ± 0.02) were significantly straighter then both the expert opinion (0.57 ± 0.03) and actual deer paths (0.44 ± 0.06; F1, 9 = 32.65, P < 0.001), but the expert opinion path did not differ from the actual path (P = 0.08). Path costs differed within the expert opinion survey resistance map (F1, 9 = 14.21, P < 0.001) with the expert opinion least cost paths (23.64 ± 3.14) having lower resistance/m than both the actual (46.15 ± 3.85) and straight line paths (48.74 ± 3.94; P < 0.001 for both). However, the actual and straight line paths did not differ (P = 0.872). There were no difference in path costs between the actual, SSF least-cost path, and straight line paths within the SSF resistance map (F1, 9 = 0.454, P = 0.64). I constructed and attempted to fit single and multiple random models to collected dispersal locations using WinBUGS v. 1.4.3. I was able to fit a single random walk model to deer dispersal paths but the more complex random walk models did not converge. I used the average parameter values derived from the single model to simulate deer dispersal paths and compared them to observed Net Squared Displacement. My simulated paths underpredicted deer displacement for 0.90 of individuals. Deer in east-central Illinois are very mobile and commonly make excursion movements throughout the year. The fact that I recorded differing dispersal rates within the same study area over a temporally short period from a previous study highlight the need for managers to obtain recent estimates of population parameters when making management decisions. The frequency of excursion movements should not be overlooked by managers as it is a behavior that can influence gene flow and potentially spread disease across the landscape at a localized scale. The preference for forest and stream habitats during dispersal can allow managers to focus surveillance or culling efforts around these types of habitats. The application of the least-cost path modeling technique appears to be ineffective at predicting deer dispersal paths, which emphasizes the importance of validating these types of models with actual data. The results from the random walk analysis highlight the need to collect as many locations as possible during temporally-short movements to understand the mechanisms acting upon them.