The role and value of water in natural capital restoration on the Agulhas Plain

The Agulhas Plain is a low-lying coastal area within the Cape Floristic Region classified as one of the six plant kingdoms of the world. The area is heavily invaded by alien vegetation that infringes upon the sustainable supply of ecosystem goods and services provided by the native fynbos vegetation. Natural capital restoration is expected to recover the supply of ecosystem goods and services, and in particular to increase the amount of water available for consumption. The study conducts cost-benefit analyses to assess whether alien clearing and restoration would add value to the Agulhas Plain. The analyses indicate that the cost of alien clearing and restoration in the area cannot be justified if the additional water released holds no benefit to the Plain. A brief assessment shows that the actual average value of water on the Agulhas Plain, as estimated by other studies, is higher than the economic cost of making the water available through alien clearing and restoration. Thus this would make alien clearing and restoration economically justified.Cost-benefit analysis, Invasive vegetation, Natural capital restoration, Water

Conceptual and statistical modelling of environmental effects in population dynamics

Population dynamics are generally viewed as the result of intrinsic (purely density dependent) and extrinsic (environmental) processes. Both components, and potential interactions between those two, have to be modelled in order to understand and predict dynamics of natural populations; a topic that is of great importance in population management and conservation. This thesis focuses on modelling environmental effects in population dynamics and how effects of potentially relevant environmental variables can be statistically identified and quantified from time series data. Chapter I presents some useful models of multiplicative environmental effects for unstructured density dependent populations. The presented models can be written as standard multiple regression models that are easy to fit to data. Chapters II IV constitute empirical studies that statistically model environmental effects on population dynamics of several migratory bird species with different life history characteristics and migration strategies. In Chapter II, spruce cone crops are found to have a strong positive effect on the population growth of the great spotted woodpecker (Dendrocopos major), while cone crops of pine another important food resource for the species do not effectively explain population growth. The study compares rate- and ratio-dependent effects of cone availability, using state-space models that distinguish between process and observation error in the time series data. Chapter III shows how drought, in combination with settling behaviour during migration, produces asymmetric spatially synchronous patterns of population dynamics in North American ducks (genus Anas). Chapter IV investigates the dynamics of a Finnish population of skylark (Alauda arvensis), and point out effects of rainfall and habitat quality on population growth. Because the skylark time series and some of the environmental variables included show strong positive autocorrelation, the statistical significances are calculated using a Monte Carlo method, where random autocorrelated time series are generated. Chapter V is a simulation-based study, showing that ignoring observation error in analyses of population time series data can bias the estimated effects and measures of uncertainty, if the environmental variables are autocorrelated. It is concluded that the use of state-space models is an effective way to reach more accurate results. In summary, there are several biological assumptions and methodological issues that can affect the inferential outcome when estimating environmental effects from time series data, and that therefore need special attention. The functional form of the environmental effects and potential interactions between environment and population density are important to deal with. Other issues that should be considered are assumptions about density dependent regulation, modelling potential observation error, and when needed, accounting for spatial and/or temporal autocorrelation.Populationers dynamik, d.v.s. tidsmässiga förändringar i populationens täthet eller storlek, anses vanligtvis regleras av populationens egen täthet på grund av konkurrens inom arten och av yttre miljöfaktorer. Miljön innefattar både den biotiska miljön (andra arter) och den abiotiska miljön (t.ex. väder). För att på ett tillfredsställande sätt förstå och förutspå förändringar i storleken på naturliga populationer, måste dessa komponeter beaktas samtidigt. Detta är av stor vikt för naturskydd, vilt- och fiskerivård, bekämpning av skadeinsekter, för att nämna några exempel. Denna avhandling fokuserar på sätt att modellera miljöfaktorers påverkan på populationer och hur effekter av potentiellt relevanta miljövariabler kan identifieras och kvantifieras med statistiska metoder och tidsseriedata. I kapitel I presenteras några användbara modeller för hur miljön kan tänkas påverka ostrukturerade populationer. Dessa modeller kan enkelt anpassas till data statistiskt, med vanlig multipel regression. Kapitlen II IV utgör empiriska undersökningar som statistiskt påvisar och beskriver hur en rad miljöfaktorer påverkar populationsdynamiken av flera fågelarter med olika levnadssätt och flyttningsstrategier. I kapitel II, påvisas att grankottarnas mängd har en stark positiv inverkan på populationstillväxten av större hackspett i södra Finland, medan tallkottarnas mängd inte effektivt hjälper att förklara hur populationerna beter sig, trots att frön från tallkottar är en känd viktig resurs för arten. Studien använder sig av s.k. state-space modeller för att skilja på okända slumpmässiga faktorer i populationsdynamiken och mätfel i häckfågeltaxeringen. Kapitel III visar hur torkan och aktivt val av häckningsområde under flyttningen påverkar den geografiska variationen i populationsdynamiken hos nordamerikanska änder. Kapitel IV undersöker dynamiken i en finsk sånglärkspopulation, och påvisar att regnmängd samt habitatets kvalitet påverkar populationens tillväxt. Tidsserierna av sånglärka och några av miljövariablerna uppvisar starkt positiv autokorrelation, d.v.s. beroende av påföljande observationer. För att räkna ut korrekt statistisk signifikans för dessa samband, används en metod som grundar sig på datorbaserad simulering av autokorrelerade tidsserier. Kapitel V är en simuleringsbaserad studie, som visar att populationsmodeller utan rättframt beaktande av observationsfel ger något missvisande estimat av miljövariablernas effekter, samt dessa effekters osäkerhet (statistiska signifikans), då miljövariablerna av intresse är autokorrelerade. Sammanfattningsvis finns det flera biologiska antaganden och metodologiska frågor som kan påverka resultatet vid estimering av miljöns effekter på populationers dynamik. Resultat som grundar sig på simpel korrelationsanalys mellan populations- och miljötidsserier kan ibland vara mycket missvisande. Det funktionella sambandet och potentiella interaktioner mellan miljöfaktorer och populationstätheten är viktiga att beakta. Andra frågor som bör beaktas är antagandena om konkurrens inom arten, potentiell modellering av mätfel i taxeringsdata, och vid behov även beaktande av rumsligt-och tidsmässigt beroende mellan observationerna i datat

Mathematical analysis for tumor growth model of ordinary differential equations

Special functions occur quite frequently in mathematical analysis and lend itself rather frequently in physical and engineering applications. Among the special functions, gamma function seemed to be widely used. The purpose of this thesis is to analyse the various properties of gamma function and use these properties and its definition to derive and tackle some integration problem which occur quite frequently in applications. It should be noted that if elementary techniques such as substitution and integration by parts were used to tackle most of the integration problems, then we will end up with frustration. Due to this, importance of gamma function cannot be denied

Reanalysis in Earth System Science: Towards Terrestrial Ecosystem Reanalysis

A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modelled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyses, and more in detail biogeochemical ocean and terrestrial reanalyses. In particular, we identify land surface, hydrologic and carbon cycle reanalyses which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic-abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics

Estimating homelessness in the Netherlands using a capture-recapture approach

This study focuses on the homeless population in the Netherlands, as an indicator of social exclusion. By applying the capture-recapture (CRC) methodology to three registers, not only the size of the homeless population could be estimated, but also its composition in terms of gender, age, place of living, and origin could be depicted. Because of the use of three registers and the availability of background characteristics for each of the registers, the usual stringent assumptions of capture recapture methodology is circumvented. This advanced application of CRC to estimate the homeless population on the national level, has led to official figures for five subsequent reference dates (January 1st of 2009, 2010, 2011, 2012 and 2013). In 2009 the size of the total homeless population in the Netherlands was estimated at 17,767, of which 5169 were registered on one of the three lists. Between 2009 and 2012 the estimated size of the population increased, which was largely due to the financial crisis. For all reference dates, the composition of this population showed that generally more men than women were registered and that homeless people in the age category of 30–49 years old were registered more than the younger or older age groups. Compared to the general Dutch population, the homeless population includes relatively many men, many people aged 30–49 years and people with a non-western backgroun

CONTINUUM DISLOCATION DYNAMICS MODELING OF THE DEFORMATION OF FCC SINGLE CRYSTALS

A continuum dislocation dynamics model was developed for simulation of the deformation of Face Centred Cubic (FCC) single crystals. In this model, dislocations are described by a set of vector fields, one per slip system, whose evolution is governed by curl-type kinetic equations describing the transport of dislocation lines. These kinetic equations are closed by specifying the velocity field in terms of a mobility law in which the driving force is obtained by solving the Cauchy¡¯s equilibrium equation for stress. The coupled kinetic equations and crystal mechanics equations are numerically solved in a staggered fashion using a custom finite element approach featuring the use of Galerkin and Least Squares finite element methods for the mechanics and dislocation kinetics parts, respectively, on a mesh generated on an FCC superlattice. The spatial resolution of the mesh was determined based on the annihilation distance between opposite dislocations. Cross slip rates from discrete dislocation simulation have been incorporated into the continuum model by time coarse graining involving time series analysis. The overall model provides a full solution of the crystal deformation problem, including the space and time evolution of the dislocation density and all internal elastic and plastic fields. Under periodic boundary conditions, the model has been applied to predict the stress-strain behaviour of FCC crystal as well as the dislocation patterns for both monotonic and cyclic loading conditions. For monotonic loading, the cell structure is predicted and the wavelength is detected and shown to satisfy the empirical similitude law. The dislocation patterns are found to depend on the loading mode, monotonic versus cyclic, as well as the crystal orientation. For cyclic loading, the famous vein structure was also predicted by the model and the composition of dislocation veins are analysed. All results are compared with experiments and other discrete dislocation dynamics simulations, yielding a good agreement. An important finding of this investigation is that cross slip was found to be critical in triggering cell structure formation under monotonic loading and that the average cell size evolution was found to strongly depend on the cross slip rate

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie