Probability distributions and statistical inference for axial data

Observations on axes which lack information on the direction of propagation are referred to as axial data. Such data are often encountered in enviromental sciences, e.g. observations on propagations of cracks or on faults in mining walls. Even though such observations are recorded as angles, circular probability models are inappropriate for such data since the constraint that observations lie only in [0, π) needs to be enforced. Probability models for such axial data are argued here to have a general structure stemming from that of wrapping a circular distribution on a semi-circle. In particular, we consider the most popular circular model, the von Mises or circular normal distribution, and derive the corresponding axial normal distribution. Certain properties of this distribution are established. Maximum likelihood estimation of its parameters are shown to be surprisingly, in contrast to trigonometric moment estimation, numerically quite appealing. Finally we illustrate our results by several real life axial data sets. Received: September 2004/ Revised: December 2004     

The effect of wind direction on ozone levels: a case study

This paper provides an illustrative case study on how the wind direction plays an important role in determining the ozone levels, in a suburb of Houston. Circular correlation and circular regression methods are used in the analysis and the primary goal is to illustrate how circular data analytic methods help in analyzing certain environmental issues. Received: August 2003/Revised: June 2004     

Procedures for outlier detection in circular time series models

It is known that the occurrence of outliers in linear or non-linear time series models may have adverse effects on the modelling and statistical inference of the data. Consequently, extensive research has been conducted on developing outlier detection procedures so that outliers may be properly managed. However, no work has been done on the problem of outliers in circular time series data. This problem is the focus of this paper. The main objective is to develop novel numerical and graphical procedures for detecting these outliers in circular time series data.A number of circular time series models have been proposed including the circular autoregressive model. We extend the iterative outlier detection procedure which has been successfully used in linear time series models to the circular autoregressive model. The proposed procedure shows a good performance when investigated via simulation for the circular autoregressive model of order one. At the same time, several statistical techniques have been used to detect the change of preferred trend in time series data using SLIME and CUSUM plots. While the methods fail to indicate directly the outliers in circular time series data, we use the ideas employed to develop three novel graphical procedures for identifying the outliers. For illustration, we apply the procedures to a particular set of wind direction data. An agreement between the results of the graphical and iterative detection procedures is observed. These procedures could be very useful in improving the modelling and inferential processes for circular time series data.     

The problem of specifying otolith-mass growth parameters in the radiometric estimation of fish age using whole otoliths

A problem in the radiometric estimation of age using whole otoliths is the necessity to specify otolith-mass growth. Unless it can be assumed that otolith-mass growth is linear, parameters describing this growth will occur in the radiometric equation used to estimate age. Previous authors have assumed that the values of these parameters must be known before age can be estimated. This leads to circular reasoning: to estimate the age of a fish (and thus infer its growth) prior knowledge about the growth of the otolith is needed. A reanalysis of published radiometric data for Hoplostethus atlanticus is presented to illustrate two new approaches that avoid this assumption and thus the problem of circular reasoning. The first calculates the age that is most probable for each sample given the radiometric and otolith-mass data; the second estimates a lower bound for the maximum age. These analyses depend on correcting a misinterpretation of the otolith-mass term in a common radiometric equation. The effect of between-individual variability in otolith growth rates on the radiometric method is discussed.     

Remote sensing: Using the spatial domain

Objects in the terrestrial environment interact differentially with electromagnetic radiation according to their essential physical, chemical and biological properties. This differential interaction is manifest as variability in scattered radiation according to wavelength, location, time, geometries of illumination and observation and polarization. If the population of scattered radiation could be measured, then estimation of these essential properties would be straightforward. The only problem would be linking such estimates to environmental variables of interest. This review paper is divided into three parts. Part 1 is an overview of the attempts that have been made to sample the five domains of scattered radiation (spectral, spatial, temporal, geometrical, polarization) and then to use the results of this sampling to estimate environmental variables of interest. Part one highlights three issues: first, that relationships between remotely sensed data and environmental variables of interest are indirect; second, our ability to estimate these environmental variables is dependent upon our ability to capture a sound representation of variability in scattered radiation and third, a considerable portion of the useful information in remotely sensed images resides in the spatial domain (within the relations between the pixels in the image). This final point is developed in Part 2 that explores ways in which the spatial domain is utilized to describe spatial variation in remotely sensed and ground data; to design optimum sampling schemes for image data and ground data and to increase the accuracy with which remotely sensed data can be used to estimate both discontinuous and continuous variables. Part 3 outlines two specific uses of information in the spatial domain; first, to select an optimum spatial resolution and second, to inform an image classification.     

Different ranking methods: potentialities and pitfalls for the case of European opinion poll

Prioritization and ranking of objects are primary needs in various substantive fields. It might be said that ranking and comparison are the first step in every risk assessment procedure, whatever the ‘risk’ is intended as: social, environmental, political or economic. Often objects to be ranked are valued by a multi-dimensional attribute which is usually transformed into a composite numerical score. In spite of conventional solutions, the author agrees with recent recommendations of performing multiple ranking, keeping indicators separated. Different innovative methods are analyzed and compared: Hasse diagrams method, POSAC and Nonlinear PCA. The first one stems directly from partial order theory, the second one may be seen as an approximation of Hasse representation in a two dimensional space, whilst the third one belongs to the wide set of non-linear multivariate techniques and it is particularly suitable in handling data of categorical type. Among them, the first two methods compare objects on the basis only on order property of data, whilst the last one simultaneously performs an optimal scale of qualitative attributes and a ranking of objects. The case study is based on the Eurobarometer survey carried out in 2002, at the request of the European Commission, which collects Europeans opinion about various political and social issues. The analysis is focused on users’ level of satisfaction about access easiness, cost, quality, information received and contracts of various services of general interest, such as telephone services, power (gas and electricity) providers, water and postal utilities, urban and rail transports. Separate indicators are set up for each facet of each service within different European regions. Eventually, the ranking of European regions is performed on the basis of the overall performance of services of general interest, as perceived by users. Selected methods lead to almost alike results, still with some differentiations due to different approaches used. As it frequently occurs, each method has its own advantages and pitfalls which are here explored and compared.     

Bootstrap methods for simultaneous benchmark analysis with quantal response data

A primary objective in quantitative risk assessment is the characterization of risk which is defined to be the likelihood of an adverse effect caused by an environmental toxin or chemcial agent. In modern risk-benchmark analysis, attention centers on the “benchmark dose” at which a fixed benchmark level of risk is achieved, with a lower confidence limits on this dose being of primary interest. In practice, a range of benchmark risks may be under study, so that the individual lower confidence limits on benchmark dose must be corrected for simultaneity in order to maintain a specified overall level of confidence. For the case of quantal data, simultaneous methods have been constructed that appeal to the large sample normality of parameter estimates. The suitability of these methods for use with small sample sizes will be considered. A new bootstrap technique is proposed as an alternative to the large sample methodology. This technique is evaluated via a simulation study and examples from environmental toxicology.

Ecological relevance of associative learning in fruit fly larvae

A few invertebrate models have been used for studying neurobiological and molecular aspects of associative learning. The ecological and evolutionary aspects of associative learning in these invertebrates are not yet well understood. To further this knowledge, I tested fruit fly larvae for their ability to learn to associate odors with preferred environmental conditions. The larvae learned to avoid odors associated with predation and to prefer odors associated with high-quality food, but failed to learn about odors associated with optimal temperature. It appears that the larvae possess a general ability to evaluate a suite of environmental parameters and associate preferred conditions with relevant stimuli. Received: 6 May 1998 / Accepted after revision: 11 October 1998     

Correlating seabird movements with ocean winds: linking satellite telemetry with ocean scatterometry

Satellite telemetry studies of the movements of seabirds are now common and have revealed impressive flight capabilities and extensive distributions among individuals and species at sea. Linking seabird movements with environmental conditions over vast expanses of the world’s open ocean, however, remains difficult. Seabirds of the order Procellariiformes (e.g., petrels, albatrosses, and shearwaters) depend largely on wind and wave energy for efficient flight. We present a new method for quantifying the movements of far-ranging seabirds in relation to ocean winds measured by the SeaWinds scatterometer onboard the QuikSCAT satellite. We apply vector correlation (as defined by Crosby et al. in J Atm Ocean Tech 10:355–367, 1993) to evaluate how the trajectories (ground speed and direction) for five procellariiform seabirds outfitted with satellite transmitters are related to ocean winds. Individual seabirds (Sooty Shearwater, Pink-footed Shearwater, Hawaiian Petrel, Grey-faced Petrel, and Black-footed Albatross) all traveled predominantly with oblique, isotropic crossing to quartering tail-winds (i.e., 105–165° in relation to birds’ trajectory). For all five seabirds, entire track line trajectories were significantly correlated with co-located winds. Greatest correlations along 8-day path segments were related to wind patterns during birds’ directed, long-range migration (Sooty Shearwater) as well as movements associated with mega-scale meteorological phenomena, including Pacific Basin anticyclones (Hawaiian Petrel, Grey-faced Petrel) and eastward-propagating north Pacific cyclones (Black-footed Albatross). Wind strength and direction are important factors related to the overall movements that delineate the distribution of petrels at sea. We suggest that vector correlation can be used to quantify movements for any marine vertebrate when tracking and environmental data (winds or currents) are of sufficient quality and sample size. Vector correlation coefficients can then be used to assess population—or species-specific variability and used to test specific hypotheses related to how animal movements are associated with fluid environments.     

Multiplicity-adjusted Inferences in Risk Assessment: Benchmark Analysis with Continuous Response Data

We develop and study multiplicity adjustments for low-dose inferences in environmental risk assessment. Application is intended for risk analysis studies where human, animal, or ecological data are used to set safe levels of a hazardous environmental agent. A modern method for making inferences in this setting is known as benchmark analysis, where attention centers on the dose at which a fixed benchmark level of risk is achieved. Both upper confidence limits on the risk and lower confidence limits on the “benchmark dose” are of interest. In practice, a number of possible benchmark risks may be under study; if so, corrections must be applied to adjust the limits for multiplicity. In this note, we discuss approaches for doing so with continuous, nonquantal response data.     

Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests

Forestry science has a long tradition of studying the relationship between stand productivity and abiotic and biotic site characteristics, such as climate, topography, soil and vegetation. Many of the early site quality modelling studies related site index to environmental variables using basic statistical methods such as linear regression. Because most ecological variables show a typical non-linear course and a non-constant variance distribution, a large fraction of the variation remained unexplained by these linear models. More recently, the development of more advanced non-parametric and machine learning methods provided opportunities to overcome these limitations. Nevertheless, these methods also have drawbacks. Due to their increasing complexity they are not only more difficult to implement and interpret, but also more vulnerable to overfitting. Especially in a context of regionalisation, this may prove to be problematic. Although many non-parametric and machine learning methods are increasingly used in applications related to forest site quality assessment, their predictive performance has only been assessed for a limited number of methods and ecosystems.In this study, five different modelling techniques are compared and evaluated, i.e. multiple linear regression (MLR), classification and regression trees (CART), boosted regression trees (BRT), generalized additive models (GAM), and artificial neural networks (ANN). Each method is used to model site index of homogeneous stands of three important tree species of the Taurus Mountains (Turkey): Pinus brutia, Pinus nigra and Cedrus libani. Site index is related to soil, vegetation and topographical variables, which are available for 167 sample plots covering all important environmental gradients in the research area. The five techniques are compared in a multi-criteria decision analysis in which different model performance measures, ecological interpretability and user-friendliness are considered as criteria.When combining these criteria, in most cases GAM is found to outperform all other techniques for modelling site index for the three species. BRT is a good alternative in case the ecological interpretability of the technique is of higher importance. When user-friendliness is more important MLR and CART are the preferred alternatives. Despite its good predictive performance, ANN is penalized for its complex, non-transparent models and big training effort.     

Asymmetric circular-linear multivariate regression models with applications to environmental data

We propose asymmetric angular-linear multivariate regression models, which were motivated by the need to predict some environmental characteristics based on some circular and linear predictors. A measure of fit is provided through the residual analysis. Some applications using data from solar energy radiation experiment and wind energy are given. Received: September 2003 / Revised: February 2005     

Potential health impacts of heavy-metal exposure at the Tar Creek Superfund site,Ottawa County,Oklahoma

The potential impact of exposure to heavy metals and health problems was evaluated at the Tar Creek Superfund site, Ottawa County, Oklahoma, USA. Observed versus expected mortality was calculated for selected conditions in the County and exposed cities. Excess mortality was found for stroke and heart disease when comparing the exposed County to the state but not when comparing the exposed cities to the nonexposed rest of the County. However, sample sizes in the exposed area were small, population emigration has been ongoing, and geographic coding of mortality data was incomplete. In an exposed community, 62.5% of children under the age of 6 years had blood lead levels exceeding 10 μg/dl. The relationships between heavy-metal exposure and children’s health and chronic disease in adults are suggestive that a more thorough investigation might be warranted. A number of possible environmental and health studies are suggested, including those focusing on possible central nervous system impacts. Unfortunately, the exposed population is dispersing. One lesson learned at this site is that health studies need to be conducted as soon as possible after an environmental problem is identified to both study the impact of the most acute exposures and to maximize study sample size—including those exposed to higher doses—and minimize the loss of individuals to follow-up.     

Modelling Ecological Presence–absence Data along an Environmental Gradient: Threshold Levels of the Environment

A methodology for estimating environmental thresholds of binary presence–absence data is presented where the level of the threshold is parameterised. Presence–absence data is fitted to three complementary different models: an independent null-model, a monotonically increasing or decreasing model, and an optimum model. The range of the three models is strictly between zero and one and the models are therefore well suited for modelling presence probabilities. The results of the three models may be combined by using Bayesian model selection methodologies. The proposed methodology is exemplified on observed binary presence–absence data of Bauera rubioides along an elevation gradient. Received: May 2005 / Revised: July 2005 An erratum to this article is available at.     

Hidden Markov models for circular and linear-circular time series

We introduce a new class of circular time series based on hidden Markov models. These are compared with existing models, their properties are outlined and issues relating to parameter estimation are discussed. The new models conveniently describe multi-modal circular time series as dependent mixtures of circular distributions. Two examples from biology and meteorology are used to illustrate the theory. Finally, we introduce a hidden Markov model for bivariate linear-circular time series and use it to describe larval movement of the fly Drosophila. Received: September 2003 / Revised: March 2004     

Analysis of the relations between circular economy and sustainable development goals

ABSTRACT

Since the United Nations approved the eight Millennium Development Goals in 2000 and, 15 years later, the 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals (SDGs), the highest political institutions in the world have not stopped worrying about achieving the sustainability of the planet. Also in 2015, the European Commission prepared the European Union Action Plan for the Circular Economy, seeking a transition towards a less linear economy, in which products, materials, and resources are kept in the system for as long as possible and in which the generation of waste is minimized.

Since then, the European Union has continued issuing reports and communications to accelerate this process in search of a circular economy, making continuous references to the fact that, through circular economy initiatives, the SDGs would be fulfilled. In this context, the objectives of this paper are 1) to determine, through exploratory factor analysis and correlation analysis, whether there is a statistically significant relationship between circular economy initiatives undertaken in the EU and compliance with the SDGs; 2) to check, through a cluster analysis, if there are homogeneous groups of countries worldwide in terms of compliance with the SDGs; and 3) using this same technique, to check whether the countries that make up the EU achieve similar results in terms of compliance with the SDGs.     

A new photodegradable molecule as a low impact ballast water biocide: efficacy screening on marine organisms from different trophic levels

Marine species carried by the ships’ ballast waters are a potentially serious environmental problem. Many strategies are being adopted to minimize the transfer of invasive or pathogenic marine species between different aquatic ecosystems. This problem is often addressed by using biocides for ballast water treatment; however, the biocide could be dangerous to native organisms once the ballast water is discharged. Chemical treatments such as chlorination and addition of glutaraldehyde could cause problems related to toxicity and application costs. The search for new effective molecules with a low environmental impact is pressing. This paper presents data from a preliminary efficacy screening of a promising molecule derived from alkylated naphtoquinones on a battery of ballast water model organisms. Results show that this new molecule is very effective in the absence of light and is extremely photodegradable (half-life <6 h). It can thus be easily degraded when released in the environment. Physical and Chemical Impacts on Marine Organisms, a Bilateral Seminar Italy-Japan held in November 2004.     

构建促进循环经济型服务业发展的保障体系

循环经济是促进可持续发展的重要手段,服务业近年来在我国发展迅速,由此造成的环境问题也日见突出,用循环经济的理念对服务业进行引导显得很有意义。发展循环型经济、建立循环型社会是实施可持续发展战略的重要途径和实现形式。剖析了我国在发展循环经济型服务业方面诸多应改进和进行创新的内容,利用循环经济的理论从行政、法律、经济、社会等方面探讨了循环经济型服务业建设实施的保障体系为服务业按循环经济的规律运行作了益的探索。     

Multicriteria prioritization and partial order in environmental sciences

The complexity of the present data-centric world finds its expression in the increasing number of multi-indicator systems. This has led to the development of multicriteria ranking systems based on partial orders. Order theory is a main pillar of structural mathematics. Partial orders help to reveal why an object of interest holds a certain ranking position and how much it is subject to change if a composite indicator is upgraded. Order theory helps to derive linear or weak orders without indicator weighting schemes. Hence, rankings obtained from decision support systems (DSS) which depend on many parameters beyond the data matrix can be checked and discrepancies can lead to examine the parameters of the DSS. Order theory helps discover association and implication structures derived from formal concept lattices. Association and implication networks among the attributes of the data matrix allow more insights into multi-indicator systems and lead to new hypotheses and motivate further research. Some new and innovative concepts, like separated subsets, antagonistic indicators, ranking stability fields are rendered. Separated subsets are the typical outcome of a partial order analysis; their identification leads to antagonistic indicators, which are responsible for the separatedness of object’s subsets. Numerical aggregation can be performed step-by-step and the question which values of a weight lead to an order inversion is of high interest. The concept of stability fields is one possible answer, discussed in this paper. After an outline of partial order theory some more specific theoretical results are shown, then we discuss the role of composite indicators in the light of partial order and give some examples of interesting applications of partial order. Finally examples are selected from real life case studies of watersheds, environmental performance evaluations, child well being, geographic and administrative regions and more.     

Fourth-corner generation of plant functional response groups

Plant functional response groups (PFGs) are now widely established as a tool to investigate plant—environment relationships. Different statistical methods to form PFGs are used in the literature. One way is to derive emergent groups by classifying species based on correlation of biological attributes and subjecting these groups to tests of response to environmental variables. Another way is to search for associations of occurrence data, environmental variables and trait data simultaneously. The fourth-corner method is one way to assess the relationships between single traits and habitat factors. We extended this statistical method to a generally applicable procedure for the generation of plant functional response groups by developing new randomization procedures for presence/absence data of plant communities. Previous PFG groupings used either predefined groups or emergent groups i.e. classifications based on correlations of biological attributes (Lavorel et al Trends Ecol Evol 12:474–478, 1997), of the global species pool and assessed their functional response. However, since not all PFGs might form emergent groups or may be known by experts, we used a permutation procedure to optimise functional grouping. We tested the method using an artificial test data set of virtual plants occurring in different disturbance treatments. Direct trait-treatment relationships as well as more complex associations are incorporated in the test data. Trait combinations responding to environmental variables could be clearly distinguished from non-responding combinations. The results are compared with the method suggested by Pillar (J Veg Sci 10:631–640) for the identification of plant functional groups. After exploring the statistical properties using an artificial data set, the method is applied to experimental data of a greenhouse experiment on the assemblage of plant communities. Four plant functional response groups are formed with regard to differences in soil fertility on the basis of the traits canopy height and spacer length.