A Non-Iterative Power Flow Study Technique Based on the Method of Minimal Least Squares

A load flow study is used to determine the state variables of a power system. Load flow studies are essential for analyzing the effects of future load growth, for planning new facilities, and for the normal daily operation of the power system. The goal of this research is to examine an alternative load flow solution to be used in connection with the power system’s data acquisition system. Three standard linear power flow models are proposed which form a redundant set of linear equations. The equations make use of system measurements to help overcome errors introduced in the linear models. The set of redundant equations is then solved in minimal least squares sense. A method to include off-nominal tap ratio transformers and transmission line shunt capacitance is developed. A method to identify optimal measurement placement through a study of singular values is suggested. The non-iterative study technique is evaluated on a thirty-eight bus, forty-four line test system

Extrapolating cetacean densities to quantitatively assess human impacts on populations in the high seas

Funding for this study came from the U.S. Fleet Forces Command (Cooperative Agreement N62470-13-2-8008), NASA (NNX08AK73G) and NOAA/NMFS (EE-133F-14-SE-3558).As human activities expand beyond national jurisdictions to the high seas, there is an increasing need to consider anthropogenic impacts to species inhabiting these waters. The current scarcity of scientific observations of cetaceans in the high seas impedes the assessment of population-level impacts of these activities. We developed plausible density estimates to facilitate a quantitative assessment of anthropogenic impacts on cetacean populations in these waters. Our study region extended from a well-surveyed region within the U.S. Exclusive Economic Zone into a large region of the western North Atlantic sparsely surveyed for cetaceans. We modeled densities of 15 cetacean taxa with available line transect survey data and habitat covariates and extrapolated predictions to sparsely surveyed regions. We formulated models to reduce the extent of extrapolation beyond covariate ranges, and constrained them to model simple and generalizable relationships. To evaluate confidence in the predictions, we mapped where predictions were made outside sampled covariate ranges, examined alternate models, and compared predicted densities with maps of sightings from sources that could not be integrated into our models. Confidence levels in model results depended on the taxon and geographic area and highlighted the need for additional surveying in environmentally distinct areas. With application of necessary caution, our density estimates can inform management needs in the high seas, such as the quantification of potential cetacean interactions with military training exercises, shipping, fisheries, and deep-sea mining and be used to delineate areas of special biological significance in international waters. Our approach is generally applicable to other marine taxa and geographic regions for which management will be implemented but data are sparse.Publisher PDFPeer reviewe

Dispersal and extrapolation on the accuracy of temporal predictions from distribution models for the Darwin's frog

Indexación: Web of Science; Scopus.Climate change is a major threat to biodiversity; the development of models that reliably predict its effects on species distributions is a priority for conservation biogeography. Two of the main issues for accurate temporal predictions from Species Distribution Models (SDM) are model extrapolation and unrealistic dispersal scenarios. We assessed the consequences of these issues on the accuracy of climate-driven SDM predictions for the dispersal-limited Darwin's frog Rhinoderma darwinii in South America. We calibrated models using historical data (1950-1975) and projected them across 40 yr to predict distribution under current climatic conditions, assessing predictive accuracy through the area under the ROC curve (AUC) and True Skill Statistics (TSS), contrasting binary model predictions against temporal-independent validation data set (i.e., current presences/absences). To assess the effects of incorporating dispersal processes we compared the predictive accuracy of dispersal constrained models with no dispersal limited SDMs; and to assess the effects of model extrapolation on the predictive accuracy of SDMs, we compared this between extrapolated and no extrapolated areas. The incorporation of dispersal processes enhanced predictive accuracy, mainly due to a decrease in the false presence rate of model predictions, which is consistent with discrimination of suitable but inaccessible habitat. This also had consequences on range size changes over time, which is the most used proxy for extinction risk from climate change. The area of current climatic conditions that was absent in the baseline conditions (i.e., extrapolated areas) represents 39% of the study area, leading to a significant decrease in predictive accuracy of model predictions for those areas. Our results highlight (1) incorporating dispersal processes can improve predictive accuracy of temporal transference of SDMs and reduce uncertainties of extinction risk assessments from global change; (2) as geographical areas subjected to novel climates are expected to arise, they must be reported as they show less accurate predictions under future climate scenarios. Consequently, environmental extrapolation and dispersal processes should be explicitly incorporated to report and reduce uncertainties in temporal predictions of SDMs, respectively. Doing so, we expect to improve the reliability of the information we provide for conservation decision makers under future climate change scenarios.http://onlinelibrary.wiley.com/doi/10.1002/eap.1556/abstract;jsessionid=1E2084FF99600D0EEC9FA358A3DBC2A3.f02t0

Modelling the introduction and spread of non-native species: international trade and climate change drive ragweed invasion

Biological invasions are a major driver of global change, for which models can attribute causes, assess impacts and guide management. However, invasion models typically focus on spread from known introduction points or non-native distributions and ignore the transport processes by which species arrive. Here, we developed a simulation model to understand and describe plant invasion at a continental scale, integrating repeated transport through trade pathways, unintentional release events and the population dynamics and local anthropogenic dispersal that drive subsequent spread. We used the model to simulate the invasion of Europe by common ragweed (Ambrosia artemisiifolia), a globally invasive plant that causes serious harm as an aeroallergen and crop weed. Simulations starting in 1950 accurately reproduced ragweed's current distribution, including the presence of records in climatically unsuitable areas as a result of repeated introduction. Furthermore, the model outputs were strongly correlated with spatial and temporal patterns of ragweed pollen concentrations, which are fully independent of the calibration data. The model suggests that recent trends for warmer summers and increased volumes of international trade have accelerated the ragweed invasion. For the latter, long distance dispersal because of trade within the invaded continent is highlighted as a key invasion process, in addition to import from the native range. Biosecurity simulations, whereby transport through trade pathways is halted, showed that effective control is only achieved by early action targeting all relevant pathways. We conclude that invasion models would benefit from integrating introduction processes (transport and release) with spread dynamics, to better represent propagule pressure from native sources as well as mechanisms for long-distance dispersal within invaded continents. Ultimately, such integration may facilitate better prediction of spatial and temporal variation in invasion risk and provide useful guidance for management strategies to reduce the impacts of invasion

A moral framework for the practice of companion canine veterinary medicine

In this dissertation I construct and defend a moral framework for the care and medical treatment of companion canines that recognizes and accommodates the moral implications of the relationships among companion canines, their owners, the communities in which they live, and the veterinary clinicians who care for them. The goal is to bring into equilibrium our considered moral judgments concerning particular canine care practices, the moral principles we use to guide our judgments, the professional ethical standards for veterinary medicine, and the legal protections needed to ensure that obligations are met. I argue that humans have moral obligations to companion canines beyond those due to all sentient animals, because of the relationship they have with them. The specification of these obligations is explored using a modified capabilities approach. The result is a list of wellbeing-promoting interests of companion canines that delineates the scope of human responsibilities of care for these animals. Next, I discuss the professional ethical standards and legal protections necessary to ensure that human obligations to companion canines are met. An argument is offered for the establishment of a new category of property, custodial property, to include living things that have the morally significant feature of being created and owned in order to form strong, emotional bonds with their owners. Finally, several ethical problems are explored to show that the recognition of obligations generated by relationships, the list of wellbeing-promoting companion canine interests, and the new category of property combine to form a moral framework that can help dog owners, veterinary clinicians, and legislators in mixed-species communities make difficult decisions concerning the care and medical treatment of some of their most vulnerable members-companion canines.Thesis (Ph. D.)--Michigan State University. Philosophy, 2017Includes bibliographical reference

Chapter 4. In search of relevant predictors for marine species distribution modelling using the MarineSPEED benchmark dataset

Aim: Ideally, datasets for species distribution modelling (SDM) contain evenly sampled records covering the entire distribution of the species, confirmed absences and auxiliary ecophysiological data allowing informed decisions on relevant predictors. Unfortunately, these criteria are rarely met for marine organisms for which distributions are too often only scantly characterized and absences generally not recorded. Here, we investigate predictor relevance as a function of modelling algorithms and settings for a global dataset of marine species.Location: Global marine.Methods: We selected well-studied and identifiable species from all major marine taxonomic groups. Distribution records were compiled from public sources (e.g., OBIS, GBIF, Reef Life Survey) and linked to environmental data from Bio-ORACLE and MARSPEC. Using this dataset, predictor relevance was analysed under different variations of modelling algorithms, numbers of predictor variables, cross-validation strategies, sampling bias mitigation methods, evaluation methods and ranking methods. SDMs for all combinations of predictors from eight correlation groups were fitted and ranked, from which the top five predictors were selected as the most relevant. Results: We collected two million distribution records from 514 species across 18 phyla. Mean sea surface temperature and calcite are, respectively, the most relevant and irrelevant predictors. A less clear pattern was derived from the other predictors. The biggest differences in predictor relevance were induced by varying the number of predictors, the modelling algorithm and the sample selection bias correction. The distribution data and associated environmental data are made available through the R package marinespeed and at http://marinespeed.org.Main conclusions: While temperature is a relevant predictor of global marine species distributions, considerable variation in predictor relevance is linked to the SDM set-up. We promote the usage of a standardized benchmark dataset (MarineSPEED) for methodological SDM studies

Ecological niches of open ocean phytoplankton taxa:Niches of open ocean phytoplankton

International audienceWe characterize the realized ecological niches of 133 phytoplankton taxa in the open ocean based on observations from the MAREDAT initiative and a statistical species distribution model (MaxEnt). The models find that the physical conditions (mixed layer depth, temperature, light) govern large-scale patterns in phyto-plankton biogeography over nutrient availability. Strongest differences in the realized niche centers were found between diatoms and coccolithophores. Diatoms (87 species) occur in habitats with significantly lower temperatures, light intensity and salinity, with deeper mixed layers, and with higher nitrate and silicate concentrations than coccolithophores (40 species). However, we could not statistically separate the realized niches of coccolithophores from those of diazotrophs (two genera) and picophytoplankton (two genera). Phaeocystis (two species) niches only clearly differed from diatom niches for temperature. While the realized niches of diatoms cover the majority of niche space, the niches of picophytoplankton and coccolithophores spread across an intermediate fraction and diazotroph and colonial Phaeocystis niches only occur within a relatively confined range of environmental conditions in the open ocean. Our estimates of the realized niches roughly match the predictions of Reynolds' C-S-R model for the global ocean, namely that taxa classified as nutrient stress tolerant have niches at lower nutrient and higher irradiance conditions than light stress tolerant taxa. Yet, there is considerable within-class variability in niche centers, and many taxa occupy broad niches, suggesting that more complex approaches may be necessary to capture all aspects of phytoplankton ecology