Ostracode relative abundances and geochemistry in surface sediments from Lake Ohrid

Understanding the ecology of bioindicators such as ostracods is essential in order to reconstruct past environmental and climate change from analysis of fossil assemblages preserved in lake sediment cores. Knowledge of the ecology of ancient Lake Ohrid's ostracod fauna is very limited and open to debate. In advance of the Ohrid ICDP-Drilling project, which has potential to generate high-resolution long-term paleoenvironmental data of global importance in paleoclimate research, we sampled Lake Ohrid and a wide range of habitat types in its surroundings to assess 1) the composition of ostracod assemblages in lakes, springs, streams, and short-lived seasonal water bodies, 2) the geographical distribution of ostracods, and 3) the ecological characteristics of individual ostracod species. In total, 40 species were collected alive, and seven species were preserved as valves and empty carapaces. Of the 40 ostracod species, twelve were endemic to Lake Ohrid. The most common genus in the lake was Candona, represented by 13 living species, followed by Paralimnocythere, represented by five living species. The most frequent species was Cypria obliqua. Species with distinct distributions included Heterocypris incongruens, Candonopsis kingsleii, and Cypria lacustris. The most common species in shallow, flooded areas was H. incongruens, and the most prominent species in ditches was C. kingsleii. C. lacustris was widely distributed in channels, springs, lakes, and rivers. Statistical analyses were performed on a "Lake Ohrid" dataset, comprising the subset of samples from Lake Ohrid alone, and an "entire" dataset comprising all samples collected. The unweighted pair group mean average (UPGMA) clustering was mainly controlled by species-specific depth preferences. Canonical Correspondence Analysis (CCA) with forward selection identified water depth, water temperature, and pH as variables that best explained the ostracod distribution in Lake Ohrid. The lack of significance of conductivity and dissolved oxygen in CCA of Ohrid data highlight the uniformity across the lake of the well-mixed waters. In the entire area, CCA revealed that ostracod distribution was best explained by water depth, salinity, conductivity, pH, and dissolved oxygen. Salinity was probably selected by CCA due to the presence of Eucypris virens and Bradleystrandesia reticulata in short-lived seasonal water bodies. Water depth is an important, although indirect, influence on ostracod species distribution which is probably associated with other factors such as sediment texture and food supply. Some species appeared to be indicators for multiple environmental variables, such as lake level and water temperature.

Spring snowmelt and midwinter thaw and refreeze north of 60°N based on Seawinds QuikScat 2000-2009

The scatterometer SeaWinds on QuikSCAT provided regular measurements at Ku-band from 1999 to 2009. Although it was designed for ocean applications, it has been frequently used for the assessment of seasonal snowmelt patterns aside from other terrestrial applications such as ice cap monitoring, phenology and urban mapping. This paper discusses general data characteristics of SeaWinds and reviews relevant change detection algorithms. Depending on the complexity of the method, parameters such as long-term noise and multiple event analyses were incorporated. Temporal averaging is a commonly accepted preprocessing step with consideration of diurnal, multi-day or seasonal averages.

Global impacts of the 1980s regime shift

Despite evidence from a number of Earth systems that abrupt temporal changes known as regime shifts are important, their nature, scale and mechanisms remain poorly documented and understood. Applying principal component analysis, change-point analysis and a sequential t-test analysis of regime shifts to 72 time series, we confirm that the 1980s regime shift represented a major change in the Earth's biophysical systems from the upper atmosphere to the depths of the ocean and from the Arctic to the Antarctic, and occurred at slightly different times around the world. Using historical climate model simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and statistical modelling of historical temperatures, we then demonstrate that this event was triggered by rapid global warming from anthropogenic plus natural forcing, the latter associated with the recovery from the El Chichón volcanic eruption. The shift in temperature that occurred at this time is hypothesized as the main forcing for a cascade of abrupt environmental changes. Within the context of the last century or more, the 1980s event was unique in terms of its global scope and scale; our observed consequences imply that if unavoidable natural events such as major volcanic eruptions interact with anthropogenic warming unforeseen multiplier effects may occur.

Global impacts of the 1980s regime shift

Despite evidence from a number of Earth systems that abrupt temporal changes known as regime shifts are important, their nature, scale and mechanisms remain poorly documented and understood. Applying principal component analysis, change-point analysis and a sequential t-test analysis of regime shifts to 72 time series, we confirm that the 1980s regime shift represented a major change in the Earth's biophysical systems from the upper atmosphere to the depths of the ocean and from the Arctic to the Antarctic, and occurred at slightly different times around the world. Using historical climate model simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and statistical modelling of historical temperatures, we then demonstrate that this event was triggered by rapid global warming from anthropogenic plus natural forcing, the latter associated with the recovery from the El Chichón volcanic eruption. The shift in temperature that occurred at this time is hypothesized as the main forcing for a cascade of abrupt environmental changes. Within the context of the last century or more, the 1980s event was unique in terms of its global scope and scale; our observed consequences imply that if unavoidable natural events such as major volcanic eruptions interact with anthropogenic warming unforeseen multiplier effects may occur.

Det är alltid bäst innan förändring : En studie om motivation vid förändring, individens upplevelse & ledares påverkan

Förändringar är vanligt förekommande i organisationer och forskning visar att många förändringsinitiativ tyvärr misslyckas. Genomförandet av förändringar som fokuserar på individens motivation är lyckosamma i bemärkelsen att personalen trivs och att arbetet inte blir lidande. Studien syftar till att ta reda på hur ledare kan arbeta för att främja medarbetares motivation vid organisationsförändringar. Genom ett kvalitativt perspektiv och en deduktiv ansats har vi undersökt vad som påverkar och främjar medarbetares motivation vid organisationsförändringar. Undersökningen har genomförts på två avdelningar i ett stort företag i Gästrikland som vid tiden för insamling av data, genomgick en förändring. Studien är en fallstudie med avsikt att undersöka den specifika skiftformsförändringen som företaget genomgick. Tillvägagångssättet för insamlingen av material är en process som ämnar att, genom delaktighet och diskussion ta reda på vad som motiverar medarbetare under förändringar. Analysen av resultatet visar att individernas grundläggande behov i arbetet, till viss del inte varit uppfyllda vilket har lett till att en del personal givit uttryck för att de är omotiverade och att de känner ett motstånd mot förändringen. Det upplevda motståndet består i en rad olika känslor, bland annat; chock, oro och förvirring som uppkommer på grund av osäkerhet och brist på kontroll rörande individens arbetssituation. Känslorna hämmar motivationen och kan ge en upplevelse av att de grundläggande behoven i arbetet inte är uppfyllda, även fast de kan vara det. Studien visar att ledare kan påverka motståndet och främja medarbetares motivation under förändringar dels genom att bjuda in till delaktighet i ett så tidigt stadie som möjligt i förändringsprocesssen och dels genom att på ett tydligt sätt kommunicera och informera kring förändringens innebörd. Det gör att individen upplever kontroll över sin framtid i arbetet vilket påverkar upplevelsen av att de grundläggande behoven inte skulle vara uppfyllda och gynnar motivationen. Ledare behöver även vara lyhörda för att alla upplever förändringar olika och läsa av behov och önskemål och anpassa eventuellt stöd efter det.

ImageLearn - Decoding Britain's Landscape

Abstract Ordnance Survey, our national mapping organisation, collects vast amounts of high-resolution aerial imagery covering the entirety of the country. Currently, photogrammetrists and surveyors use this to manually capture real-world objects and characteristics for a relatively small number of features. Arguably, the vast archive of imagery that we have obtained portraying the whole of Great Britain is highly underutilised and could be ‘mined’ for much more information. Over the last year the ImageLearn project has investigated the potential of "representation learning" to automatically extract relevant features from aerial imagery. Representation learning is a form of data-mining in which the feature-extractors are learned using machine-learning techniques, rather than being manually defined. At the beginning of the project we conjectured that representations learned could help with processes such as object detection and identification, change detection and social landscape regionalisation of Britain. This seminar will give an overview of the project and highlight some of our research results.

Signal subspace identification in hyperspectral imagery

Terrestrial remote sensing imagery involves the acquisition of information from the Earth's surface without physical contact with the area under study. Among the remote sensing modalities, hyperspectral imaging has recently emerged as a powerful passive technology. This technology has been widely used in the fields of urban and regional planning, water resource management, environmental monitoring, food safety, counterfeit drugs detection, oil spill and other types of chemical contamination detection, biological hazards prevention, and target detection for military and security purposes [2-9]. Hyperspectral sensors sample the reflected solar radiation from the Earth surface in the portion of the spectrum extending from the visible region through the near-infrared and mid-infrared (wavelengths between 0.3 and 2.5 µm) in hundreds of narrow (of the order of 10 nm) contiguous bands [10]. This high spectral resolution can be used for object detection and for discriminating between different objects based on their spectral xharacteristics [6]. However, this huge spectral resolution yields large amounts of data to be processed. For example, the Airbone Visible/Infrared Imaging Spectrometer (AVIRIS) [11] collects a 512 (along track) X 614 (across track) X 224 (bands) X 12 (bits) data cube in 5 s, corresponding to about 140 MBs. Similar data collection ratios are achieved by other spectrometers [12]. Such huge data volumes put stringent requirements on communications, storage, and processing. The problem of signal sbspace identification of hyperspectral data represents a crucial first step in many hypersctral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction (DR) yelding gains in data storage and retrieval and in computational time and complexity. Additionally, DR may also improve algorithms performance since it reduce data dimensionality without losses in the useful signal components. The computation of statistical estimates is a relevant example of the advantages of DR, since the number of samples required to obtain accurate estimates increases drastically with the dimmensionality of the data (Hughes phnomenon) [13].

A aprendizagem cooperativa no ensino do inglês : um estudo experimental no 1º ciclo do Ensino Básico

Tese de Doutoramento, Educação (Desenvolvimento Curricular), 14 de Junho de 2013, Universidade dos Açores.

Parental exposure to the herbicide diuron results in oxidative DNA damage to germinal cells of the Pacific oyster Crassostrea gigas

Chemical pollution by pesticides has been identified as a possible contributing factor to the massive mortality outbreaks observed in Crassostrea gigas for several years. A previous study demonstrated the vertical transmission of DNA damage by subjecting oyster genitors to the herbicide diuron at environmental concentrations during gametogenesis. This trans-generational effect occurs through damage to genitor-exposed gametes, as measured by the comet-assay. The presence of DNA damage in gametes could be linked to the formation of DNA damage in other germ cells. In order to explore this question, the levels and cell distribution of the oxidized base lesion 8-oxodGuo were studied in the gonads of exposed genitors. High-performance liquid chromatography coupled with UV and electrochemical detection analysis showed an increase in 8-oxodGuo levels in both male and female gonads after exposure to diuron. Immunohistochemistry analysis showed the presence of 8-oxodGuo at all stages of male germ cells, from early to mature stages. Conversely, the oxidized base was only present in early germ cell stages in female gonads. These results indicate that male and female genitors underwent oxidative stress following exposure to diuron, resulting in DNA oxidation in both early germ cells and gametes, such as spermatozoa, which could explain the transmission of diuron-induced DNA damage to offspring. Furthermore, immunostaining of early germ cells seems indicates that damages caused by exposure to diuron on germ line not only affect the current sexual cycle but also could affect future gametogenesis.

Bioeconomic modelling of woody regrowth carbon offset options in productive grazing systems

Agricultural land has been identified as a potential source of greenhouse gas emissions offsets through biosequestration in vegetation and soil. In the extensive grazing land of Australia, landholders may participate in the Australian Government’s Emissions Reduction Fund and create offsets by reducing woody vegetation clearing and allowing native woody plant regrowth to grow. This study used bioeconomic modelling to evaluate the trade-offs between an existing central Queensland grazing operation, which has been using repeated tree clearing to maintain pasture growth, and an alternative carbon and grazing enterprise in which tree clearing is reduced and the additional carbon sequestered in trees is sold. The results showed that ceasing clearing in favour of producing offsets produces a higher net present value over 20 years than the existing cattle enterprise at carbon prices, which are close to current (2015) market levels (~$13 t–1 CO2-e). However, by modifying key variables, relative profitability did change. Sensitivity analysis evaluated key variables, which determine the relative profitability of carbon and cattle. In order of importance these were: the carbon price, the gross margin of cattle production, the severity of the tree–grass relationship, the area of regrowth retained, the age of regrowth at the start of the project, and to a lesser extent the cost of carbon project administration, compliance and monitoring. Based on the analysis, retaining regrowth to generate carbon income may be worthwhile for cattle producers in Australia, but careful consideration needs to be given to the opportunity cost of reduced cattle income.

Postawy pracowników wobec zmiany organizacyjnej. Analiza socjologiczna na przykładzie przedsiębiorstw przechodzących procesy transformacyjne

Wydział Nauk Społecznych: Instytut Socjologii

Utilização de técnica de detecção de mudança aplicada ao sensoriamento espectral para rádios cognitivos

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2015.

Landscape Patterns of Permafrost Disturbance and Degradation in the Canadian High Arctic

Intensification of permafrost disturbances such as active layer detachments (ALDs) and retrogressive thaw slumps (RTS) have been observed across the circumpolar Arctic. These features are indicators of unstable conditions stemming from recent climate warming and permafrost degradation. In order to understand the processes interacting to give rise to these features, a multidisciplinary approach is required; i.e., interactions between geomorphology, hydrology, vegetation and ground thermal conditions. The goal of this research is to detect and map permafrost disturbance, predict landscape controls over disturbance and determine approaches for monitoring disturbance, all with the goal of contributing to the mitigation of permafrost hazards. Permafrost disturbance inventories were created by applying semi-automatic change detection techniques to IKONOS satellite imagery collected at the Cape Bounty Arctic Watershed Observatory (CBAWO). These methods provide a means to estimate the spatial distribution of permafrost disturbances for a given area for use as an input in susceptibility modelling. Permafrost disturbance susceptibility models were then developed using generalized additive and generalized linear models (GAM, GLM) fitted to disturbed and undisturbed locations and relevant GIS-derived predictor variables (slope, potential solar radiation, elevation). These models successfully delineated areas across the landscape that were susceptible to disturbances locally and regionally when transferred to an independent validation location. Permafrost disturbance susceptibility models are a first-order assessment of landscape susceptibility and are promising for designing land management strategies for remote permafrost regions. Additionally, geomorphic patterns associated with higher susceptibility provide important knowledge about processes associated with the initiation of disturbances. Permafrost degradation was analyzed at the CBAWO using differential interferometric synthetic aperture radar (DInSAR). Active-layer dynamics were interpreted using inter-seasonal and intra-seasonal displacement measurements and highlight the importance of hydroclimatic factors on active layer change. Collectively, these research approaches contribute to permafrost monitoring and the assessment of landscape-scale vulnerability in order to develop permafrost disturbance mitigation strategies.

Comparison of multitemporal MODIS-EVI smoothing algorithms and its contribution to crop monitoring.

Crop monitoring and more generally land use change detection are of primary importance in order to analyze spatio-temporal dynamics and its impacts on environment. This aspect is especially true in such a region as the State of Mato Grosso (south of the Brazilian Amazon Basin) which hosts an intensive pioneer front. Deforestation in this region as often been explained by soybean expansion in the last three decades. Remote sensing techniques may now represent an efficient and objective manner to quantify how crops expansion really represents a factor of deforestation through crop mapping studies. Due to the special characteristics of the soybean productions' farms in Mato Grosso (area varying between 1000 hectares and 40000 hectares and individual fields often bigger than 100 hectares), the Moderate Resolution Imaging Spectroradiometer (MODIS) data with a near daily temporal resolution and 250 m spatial resolution can be considered as adequate resources to crop mapping. Especially, multitemporal vegetation indices (VI) studies have been currently used to realize this task [1] [2]. In this study, 16-days compositions of EVI (MODQ13 product) data are used. However, although these data are already processed, multitemporal VI profiles still remain noisy due to cloudiness (which is extremely frequent in a tropical region such as south Amazon Basin), sensor problems, errors in atmospheric corrections or BRDF effect. Thus, many works tried to develop algorithms that could smooth the multitemporal VI profiles in order to improve further classification. The goal of this study is to compare and test different smoothing algorithms in order to select the one which satisfies better to the demand which is classifying crop classes. Those classes correspond to 6 different agricultural managements observed in Mato Grosso through an intensive field work which resulted in mapping more than 1000 individual fields. The agricultural managements above mentioned are based on combination of soy, cotton, corn, millet and sorghum crops sowed in single or double crop systems. Due to the difficulty in separating certain classes because of too similar agricultural calendars, the classification will be reduced to 3 classes : Cotton (single crop), Soy and cotton (double crop), soy (single or double crop with corn, millet or sorghum). The classification will use training data obtained in the 2005-2006 harvest and then be tested on the 2006-2007 harvest. In a first step, four smoothing techniques are presented and criticized. Those techniques are Best Index Slope Extraction (BISE) [3], Mean Value Iteration (MVI) [4], Weighted Least Squares (WLS) [5] and Savitzky-Golay Filter (SG) [6] [7]. These techniques are then implemented and visually compared on a few individual pixels so that it allows doing a first selection between the five studied techniques. The WLS and SG techniques are selected according to criteria proposed by [8]. Those criteria are: ability in eliminating frequent noises, conserving the upper values of the VI profiles and keeping the temporality of the profiles. Those selected algorithms are then programmed and applied to the MODIS/TERRA EVI data (16-days composition periods). Tests of separability are realized based on the Jeffries-Matusita distance in order to see if the algorithms managed in improving the potential of differentiation between the classes. Those tests are realized on the overall profile (comprising 23 MODIS images) as well as on each MODIS sub-period of the profile [1]. This last test is a double interest process because it allows comparing the smoothing techniques and also enables to select a set of images which carries more information on the separability between the classes. Those selected dates can then be used to realize a supervised classification. Here three different classifiers are tested to evaluate if the smoothing techniques as a particular effect on the classification depending on the classifiers used. Those classifiers are Maximum Likelihood classifier, Spectral Angle Mapper (SAM) classifier and CHAID Improved Decision tree. It appears through the separability tests on the overall process that the smoothed profiles don't improve efficiently the potential of discrimination between classes when compared with the original data. However, the same tests realized on the MODIS sub-periods show better results obtained with the smoothed algorithms. The results of the classification confirm this first analyze. The Kappa coefficients are always better with the smoothing techniques and the results obtained with the WLS and SG smoothed profiles are nearly equal. However, the results are different depending on the classifier used. The impact of the smoothing algorithms is much better while using the decision tree model. Indeed, it allows a gain of 0.1 in the Kappa coefficient. While using the Maximum Likelihood end SAM models, the gain remains positive but is much lower (Kappa improved of 0.02 only). Thus, this work's aim is to prove the utility in smoothing the VI profiles in order to improve the final results. However, the choice of the smoothing algorithm has to be made considering the original data used and the classifier models used. In that case the Savitzky-Golay filter gave the better results.

Detection of Change in Fluorescence Between Reactive Cyan and the Yellow Fluorophores Usinga-SiC:H Multilayer Transducers

Optical colour sensors based on multilayered a-SiC:H heterostructures can act as voltage controlled optical filters in the visible range. In this article we investigate the application of these structures for Fluorescence Resonance Energy Transfer (FRET) detection, The characteristics of a-SiC:H multilayered structure are studied both theoretically and experimentally in several wavelengths corresponding to different fluorophores. The tunable optical p-i'(a-SiC:H)-n/p-i(a-Si:H)-n heterostructures were produced by PECVD and tested for a proper fine tuning in the violet, cyan and yellow wavelengths. The devices were characterized through transmittance and spectral response measurements, under different electrical bias and frequencies. Violet, cyan and yellow signals were applied in simultaneous and results have shown that they can be recovered under suitable applied bias. A theoretical analysis supported by numerical simulation is presented.