Fractal species distributions do not produce power-law species-area relationships

We derive the species-area relationship (SAR) expected from an assemblage of fractally distributed species. If species have truly fractal spatial distributions with different fractal dimensions, we show that the expected SAR is not the classical power-law function, as suggested recently in the literature. This analytically derived SAR has a distinctive shape that is not commonly observed in nature: upward-accelerating richness with increasing area (when plotted on log-log axes). This suggests that, in reality, most species depart from true fractal spatial structure. We demonstrate the fitting of a fractal SAR using two plant assemblages (Alaskan trees and British grasses). We show that in both cases, when modelled as fractal patterns, the modelled SAR departs from the observed SAR in the same way, in accord with the theory developed here. The challenge is to identify how species depart from fractality, either individually or within assemblages, and more importantly to suggest reasons why species distributions are not self-similar and what, if anything, this can tell us about the spatial processes involved in their generation.

The geographical structure of British bird distributions:diversity, spatial turnover and scale

1. Using data on the spatial distribution of the British avifauna, we address three basic questions about the spatial structure of assemblages: (i) Is there a relationship between species richness (alpha diversity) and spatial turnover of species (beta diversity)? (ii) Do high richness locations have fewer species in common with neighbouring areas than low richness locations?, and (iii) Are any such relationships contingent on spatial scale (resolution or quadrat area), and do they reflect the operation of a particular kind of species-area relationship (SAR)?

2. For all measures of spatial turnover, we found a negative relationship with species richness. This held across all scales, with the exception of turnover measured as beta (sim).

3. Higher richness areas were found to have more species in common with neighbouring areas.

4. The logarithmic SAR fitted better than the power SAR overall, and fitted significantly better in areas with low richness and high turnover.

5. Spatial patterns of both turnover and richness vary with scale. The finest scale richness pattern (10 km) and the coarse scale richness pattern (90 km) are statistically unrelated. The same is true of the turnover patterns.

6. With coarsening scale, locations of the most species-rich quadrats move north. This observed sensitivity of richness 'hotspot' location to spatial scale has implications for conservation biology, e.g. the location of a reserve selected on the basis of maximum richness may change considerably with reserve size or scale of analysis.

7. Average turnover measured using indices declined with coarsening scale, but the average number of species gained or lost between neighbouring quadrats was essentially scale invariant at 10-13 species, despite mean richness rising from 80 to 146 species (across an 81-fold area increase). We show that this kind of scale invariance is consistent with the logarithmic SAR.

Stress and Strain Distributions During Compressive Deformation of Titanium Alloy Affected by Microstructure

In the research of the microstructural influence on dynamic compression, an assumption that the α and the β phases in titanium alloys were linearly strengthened was proposed, and a two-dimensional model using ANSYS (ANSYS, Inc., Canonsburg, PA) focusing on the role of microgeometrical structure was developed. By comparing the stress and strain distributions of different microstructures, the roles of cracks and phase boundaries in titanium compression were studied.

Brightness variation distributions among main belt asteroids from sparse light curve sampling with Pan-STARRS 1

The rotational state of asteroids is controlled by various physical mechanisms including collisions, internal damping and the Yarkovsky-O'Keefe-Radzievskii-Paddack (YORP) effect. We have analysed the changes in magnitude between consecutive detections of ∼ 60,000 asteroids measured by the PanSTARRS 1 survey during its first 18 months of operations. We have attempted to explain the derived brightness changes physically and through the application of a simple model. We have found a tendency toward smaller magnitude variations with decreasing diameter for objects of 1 < D < 8 km. Assuming the shape distribution of objects in this size range to be independent of size and composition our model suggests a population with average axial ratios 1: 0.85 ± 0.13: 0.71 ± 0.13, with larger objects more likely to have spin axes perpendicular to the orbital plane.

A Conditional Approach for Modelling Patient Re-admissions in Hospital using a mixture of Coxian phase-type distributions incorporating Bayes Theorem

The number of elderly patients requiring hospitalisation in Europe is rising. With a greater proportion of elderly people in the population comes a greater demand for health services and, in particular, hospital care. Thus, with a growing number of elderly patients requiring hospitalisation competing with non-elderly patients for a fixed (and in some cases, decreasing) number of hospital beds, this results in much longer waiting times for patients, often with a less satisfactory hospital experience. However, if a better understanding of the recurring nature of elderly patient movements between the community and hospital can be developed, then it may be possible for alternative provisions of care in the community to be put in place and thus prevent readmission to hospital. The research in this paper aims to model the multiple patient transitions between hospital and community by utilising a mixture of conditional Coxian phase-type distributions that incorporates Bayes' theorem. For the purpose of demonstration, the results of a simulation study are presented and the model is applied to hospital readmission data from the Lombardy region of Italy.

Measuring the shape of degree distributions

Degree distribution is a fundamental property of networks. While mean degree provides a standard measure of scale, there are several commonly used shape measures. Widespread use of a single shape measure would enable comparisons between networks and facilitate investigations about the relationship between degree distribution properties and other network features. This paper describes five candidate measures of heterogeneity and recommends the Gini coefficient. It has theoretical advantages over many of the previously proposed measures, is meaningful for the broad range of distribution shapes seen in different types of networks, and has several accessible interpretations. While this paper focusses on degree, the distribution of other node based network properties could also be described with Gini coefficients.

Estudo das possibilidades de resultados anormais através de estratégias momentum no mercado Euronext

Dissertação de mestrado, Finanças Empresariais, Faculdade de Economia, Universidade do Algarve, 2014

Potential Impact of Climate Change on Fungal Distributions: Analysis of 2 Years of Contrasting Weather in the UK

The impact of climate change on fungal growth and spore production is less well documented than for allergenic pollen grains, although similar implications for respiratory tract diseases in humans occur. Fungal spores are commonly described as either “dry” or “wet” according to the type of weather associated with their occurrence in the air. This study examined the distribution of selected fungal spores (Alternaria spp., Cladosporium spp., Didymella spp., Epicoccum spp., Leptosphaeria spp. and rusts) occurring in the West Midlands of UK during 2 years of contrasting weather. Spore specimens were collected using a 7-day volumetric air sampler and then analysed with the aid of light microscopy. Distributions of spores were then studied using normality tests and Mann–Whitney U test, while relationships with meteorological parameters were investigated using Spearman’s rank test and angular-linear correlation for wind direction analysis. Our results showed that so-called wet spores were more sensitive to the weather changes showing statistically significant differences between the 2 years of study, in contrast to “dry” spores. We predict that in following years we will observe accelerated levels in allergenic fungal spore production as well as changes in species diversity. This study could be a starting point to revise the grouping system of fungal spores as either “dry” or “wet” types and their response to climate change

Comparisons of Fungal Spore Distributions Using Air Sampling at Worcester, England (2006-2010)

This study determined annual and monthly fluctuations in concentration of 20 fungal genera. The selection of taxa was made based upon their high frequency in the air as well as their well-known allergenic properties. Air samples were collected using a spore trap of Hirst design at an urban site where the trap continuously worked throughout a 5-year survey. Weather data were acquired from a meteorological station co-located with the air sampler. Influence of several meteorological parameters was then examined to reveal species–environment interactions and the potential location of fungal spore sources within the urban area. The maximum monthly sum of mean daily spore concentration varied between genera, and the earliest peaks were recorded for Pleospora sp. in April and Ustilago sp. in June. However, the majority of investigated spore types occurred in the greatest concentrations between August and September. Out of the 20 studied taxa, the most dominant genus was Cladosporium sp., which exceeded an allergenic threshold of 3000 s m-3 40 times during very rainy years and twice as much during dry years. A Spearman’s rank test showed that statistically significant (p B 0.05) relationships between spore concentration and weather parameters were mainly rs B 0.50. Potential sources of spores at Worcester were likely to be localised outside the city area.

A Review on the Characterization of Signals and Systems by Power Law Distributions

Power laws, also known as Pareto-like laws or Zipf-like laws, are commonly used to explain a variety of real world distinct phenomena, often described merely by the produced signals. In this paper, we study twelve cases, namely worldwide technological accidents, the annual revenue of America׳s largest private companies, the number of inhabitants in America׳s largest cities, the magnitude of earthquakes with minimum moment magnitude equal to 4, the total burned area in forest fires occurred in Portugal, the net worth of the richer people in America, the frequency of occurrence of words in the novel Ulysses, by James Joyce, the total number of deaths in worldwide terrorist attacks, the number of linking root domains of the top internet domains, the number of linking root domains of the top internet pages, the total number of human victims of tornadoes occurred in the U.S., and the number of inhabitants in the 60 most populated countries. The results demonstrate the emergence of statistical characteristics, very close to a power law behavior. Furthermore, the parametric characterization reveals complex relationships present at higher level of description.