Environmental assessment of Kuwait Bay: an integrated approach

Kuwait Bay is an imperative characteristic of the State of Kuwait. It has a number of major activities existing around it, including, business, industrial and recreational ones. The phase of construction and development of projects, and their resulting pollution have led to major change in the features of the area. The purpose of this research is to serve as a managing tool for decision-making through the environmental assessment of Kuwait Bay. Due to the multiplicities and diversities of the man-made activities and the natural environmental setting of the bay, the impacts are out looked on a holistic approach rather than on a single approach. The methodology of assessment including, sampling and analysis of water and sediment, statistical analysis, as well as application of the Rapid Impact Assessment Matrix (RAIM) in order to analysis the impacts in an integrated approach. The southern areas of the bay are the main sources of pollution that distributed northward to cover the central region of the bay. Sulaibikhat Bay (South-West sector of the bay) is the most vulnerable area due to its exposure to anthropogenic activities such as reclamations, sewage inflow and other activities. The area near Shuwaikh Port is suffering due to reclamation processes. The northern and north-west sectors suffer from coastal erosion as well as from pollutants arriving via Shatt Al Arab.     

An integrated approach to mangrove dynamics and management

The main objective of the MADAM project (Mangrove Dynamics and Management) is to generate the scientific basis enabling the sustainable stewardship of the resources of the Caeté mangrove estuary in Northeast Brazil in the sense of integrated coastal (zone) management. To achieve this, it is necessary to acquire in-depth knowledge of natural processes as well as of the relevant institutional, cultural, economic, social and political dynamics. Causal linkages within the ecosystem, as well as between ecosystem, economy and society, are analysed and explained via dynamic and trophic modelling. Scenario construction is intended to forecast the effects of acute or chronic interference on utilized resources, and to answer wider, management-related questions (e.g. restoration of destroyed areas, utilization potential for aquaculture). This paper describes the project strategy as developed and modified in the context of research results from the initial 2-yr project phase. It is argued that a continuous discussion process is essential to assess the validity of the strategies formulated at the beginning of a medium-time project, particularly if the project is of interdisciplinary nature.     

Linking chronic wasting disease to mule deer movement scales: a hierarchical Bayesian approach.

Observed spatial patterns in natural systems may result from processes acting across multiple spatial and temporal scales. Although spatially explicit data on processes that generate ecological patterns, such as the distribution of disease over a landscape, are frequently unavailable, information about the scales over which processes operate can be used to understand the link between pattern and process. Our goal was to identify scales of mule deer (Odocoileus hemionus) movement and mixing that exerted the greatest influence on the spatial pattern of chronic wasting disease (CWD) in northcentral Colorado, USA. We hypothesized that three scales of mixing (individual, winter subpopulation, or summer subpopulation) might control spatial variation in disease prevalence. We developed a fully Bayesian hierarchical model to compare the strength of evidence for each mixing scale. We found strong evidence that the finest mixing scale corresponded best to the spatial distribution of CWD infection. There was also evidence that land ownership and habitat use play a role in exacerbating the disease, along with the known effects of sex and age. Our analysis demonstrates how information on the scales of spatial processes that generate observed patterns can be used to gain insight when process data are sparse or unavailable.     

Dose-response relationships in mutagenicity assays including an appropriate positive control group: a multiple testing approach

The objective of mutagenicity assays in regulatory toxicology is the decision on non-mutagenicity or mutagenicity. An inherent problem of statistical tests is the possibility of false decisions, i.e., a mutagenic substance will be falsely labeled as non-mutagenic or a non-mutagenic substance will be falsely labeled as mutagenic. These probabilities of false negative (consumer's risk=type II error) and/or false positive decision (producer's risk=type I error) can be limited by using suitable testing procedures as well as a design including an appropriate positive control. Using the proof of hazard concept the well-known many-to-one procedures with total order restriction for increasing effect differences are used, while using the proof of safety concept procedures on equivalence with total order restriction are discussed. Both approaches are demonstrated on a real data example.     

Contrasting plant productivity-diversity relationships across latitude: the role of evolutionary history

The relationship between net primary productivity and biological diversity has been a central topic in ecology for several decades. The unimodal ("hump-back") relationship has been the most widely accepted for plants with the decrease in diversity at high productivity usually attributed to competitive exclusion. However, the relatively small species pool size under high productivity conditions may account for this pattern as well. Small species pool sizes for highly productive habitats are characteristic of temperate regions, where productive habitats for speciation and species migration have historically been rare. In contrast, productive habitats in the tropics have been relatively common during evolutionary history, resulting in large species pools. We hypothesize that evolutionary history contributes to the observed productivity-diversity relationship of plants, and that the productivity-diversity relationship differs between temperate and tropical regions. We investigated the productivity-diversity relationship patterns from 163 case studies throughout the world. Latitude described approximately 80% of the variation in the shape of the relationships. The unimodal relationship was found to dominate in the temperate zone, whereas the positive relationship was significantly more common in the tropics. We detected no influence due to methods of productivity measurement, but unimodal or positive productivity-diversity relationships were more likely within larger ranges of productivity. The length of the productivity gradient did not affect the latitudinal influence. In summary, the shape of the productivity-diversity relationship differs between temperate and tropical regions and the different evolutionary history of the local species pools is a probable cause for the difference.     

Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system

A fully non-linear analysis of forcing influences on temperatures is performed in the climate system by means of neural network modelling. Two case studies are investigated, in order to establish the main factors that drove the temperature behaviour at both global and regional scales in the last 140 years. In particular, our neural network model shows the ability to catch non-linear relationships among these variables and to reconstruct temperature records with a high degree of accuracy. In this framework, we clearly show the need of including anthropogenic inputs for explaining the temperature behaviour at global scale and recognise the role of El Niño southern oscillation for catching the inter-annual variability of temperature data. Furthermore, we analyse the relative influence of global forcing and a regional circulation pattern in determining the winter temperatures in Central England, showing that the North Atlantic oscillation represents the driven element in this case study. Our modelling activity and results can be very useful for simple assessments of relationships in the complex climate system and for identifying the fundamental elements leading to a successful downscaling of atmosphere–ocean general circulation models.     

Multiscale advanced raster map analysis system: Definition, design and development

This paper brings together a multidisciplinary initiative to develop advanced statistical and computational techniques for analyzing, assessing, and extracting information from raster maps. This information will provide a rigorous foundation to address a wide range of applications including disease mapping, emerging infectious diseases, landscape ecological assessment, land cover trends and change detection, watershed assessment, and map accuracy assessment. It will develop an advanced map analysis system that integrates these techniques with an advanced visualization toolbox, and use the system to conduct large case studies using rich sets of raster data, primarily from remotely sensed imagery. As a result, it will be possible to study and evaluate raster maps of societal, ecological, and environmental variables to facilitate quantitative characterization and comparative analysis of geospatial trends, patterns, and phenomena. In addition to environmental and ecological studies, these techniques and tools can be used for policy decisions at national, state, and local levels, crisis management, and protection of infrastructure. Geospatial data form the foundation of an information-based society. Remote sensing has been a vastly under-utilized resource involving a multi-million dollar investment at the national levels. Even when utilized, the credibility has been at stake, largely because of lack of tools that can assess, visualize, and communicate accuracy and reliability in timely manner and at desired confidence levels. Consider an imminent 21st century scenario: What message does a multi-categorical map have about the large landscape it represents? And at what scale, and at what level of detail? Does the spatial pattern of the map reveal any societal, ecological, environmental condition of the landscape? And therefore can it be an indicator of change? How do you automate the assessment of the spatial structure and behavior of change to discover critical areas, hot spots, and their corridors? Is the map accurate? How accurate is it? How do you assess the accuracy of the map? How do we evaluate a temporal change map for change detection? What are the implications of the kind and amount of change and accuracy on what matters, whether climate change, carbon emission, water resources, urban sprawl, biodiversity, indicator species, human health, or early warning? And with what confidence? The proposed research initiative is expected to find answers to these questions and a few more that involve multi-categorical raster maps based on remote sensing and other geospatial data. It includes the development of techniques for map modeling and analysis using Markov Random Fields, geospatial statistics, accuracy assessment and change detection, upper echelons of surfaces, advanced computational techniques for geospatial data mining, and advanced visualization techniques.     

Seagrass mortality due to oversedimentation: an experimental approach

Mortality due to oversedimentation of the Mediterranean seagrassPosidonia oceanica was experimentally evaluated by field manipulations of the sediment level. Increased levels of sediment placed over plant shoots and rhizomes induced significant shoot mortality, even at moderate burial levels (ca. 5 cm). When sediment was added to reach levels 15 cm higher than the initial one, a 100 % mortality was observed after 200–300 days. The response of the plant was independent of site and depth. These results can be used in ecological risk assessment of coastal activities which potentially affect sediment deposition.     

ReefBahia, an integrated GIS approach for coral reef conservation in Bahia, Brazil

Coral reefs around the world are facing serious threats. These fragile ecosystems are in need for conservation. The coastal state of Bahia hosts the most extensive and richest area of coral reefs in the South Atlantic Ocean. Assessment, planning and management of coral reef ecosystems are particularly challenging tasks. This work shows how the creation of a GIS improves the process of management, monitoring and conservation of the Bahian reef environments The initial data input started by the vectorization of 1) bathymetric data from the Bureau of Hydrography and Navigation (DHN), 2) shoreline and mangrove areas from Landsat 7 ETM + images, 3) near surface reefs from Quickbird images, and 4) coastal and marine protected areas of federal, state and local administrations. Geological, physical, biological and social information was then included in order to create a suitable marine GIS for conservation aims. The data includes information on sediment granulometry and transport patterns, rocky substrate outcrops, sea surface temperature, wave direction, rain precipitation, major contributing river discharge, artisanal fishery, benthic cover and bleaching data. ReefBahia GIS has provided essential information for a better understanding of coral reefs of the state of Bahia geological and ecological characteristics such as mapping, representation, connectivity and biodiversity of coral reefs, geological facies, Quaternary sedimentation, numeric modeling of wave refraction and monitoring of bleaching events.     

Throughflow analysis: A stochastic approach

Ecological network analysis (ENA), predicated on systems theory and Leontiev input–output analysis, is a method widely used in ecology to reveal ecosystem properties. An important ecosystem property computed in ENA is throughflows, the amount of matter/energy leaving each compartment of the ecosystem. Throughflows are analyzed via a matrix representing their relationships to the driving input at the boundary. Network particle tracking (NPT) builds on ENA to offer a Lagrangian particle method that describes the activity of the ecosystem at the microscopic level. This paper introduces a Lagrangian throughflow analysis methodology using NPT and shows that the NPT throughflow matrix, , agrees with the conventional ENA throughflow matrix, , for ecosystems at steady-state with donor-controlled flows. The matrix is computed solely from the pathways (particles’ histories) generated by NPT simulations and its average over multiple runs of the algorithm with longer simulation time agrees with the Eulerian matrix (Law of Large Numbers). While the traditional NEA throughflow analysis is mostly used with steady-state ecosystem models, the Lagrangian throughflow analysis that we propose can be used with non-steady-state models and paves the way for the development of dynamic throughflow analysis.     

Genuine saving rate: An integrated indicator to measure urban sustainable development towards an ecocity

The construction of an ecocity is gradually gaining attention as an indicator of sustainable development. Genuine saving rate (GSR), which takes account of various impacts of economic activities including depletion of natural resources, costs of environmental pollution and long-term environmental damage, can be used as an integrated indicator to measure the status quo and potential of sustainability for an ecocity. This paper discusses the concept of an ecocity; and analyses the time-series of GSR in Suzhou between 1991–2001 as a case study used to develop a standard method of measuring sustainability. The status quo and trends in urban sustainability in six case study cities in China are then evaluated and compared. The results show the current status: Suzhou (23.6%), Guangzhou(18.9%), Ningbo (14.7%), Yantai (13.1%), Yangzhou (11.7%) and Sanming (7.9%). The study demonstrates that GSR provides not only a linkage by which local governmental departments for resources management, environmental protection, finance and planning can be connected directly, but also an effective analytical tool for the planning and construction of an ecocity as well as decision-making support for local governments.     

Laser-induced fluorescence of polycyclic aromatic hydrocarbons: an approach to gas standards

Rapid methods are needed to analyse air pollutants such as polycyclic aromatic hydrocarbons (PAHs). Reliable semi-quantitative gas standards were required for the development of a laser-induced fluorescence (LIF) method for polycyclic aromatic hydrocarbon analysis, based on sampling of air onto multi-channel polydimethylsiloxane rubber traps. Easily constructed diffusion tubes provided naphthalene vapour at ~2 ng s⁻¹. A gas chromatographic fraction collection method for loading less volatile PAHs onto the traps from a flame ionization detector outlet was developed and optimized. The accuracy of the method, which can be further optimized, was sufficient for initial LIF screening tests to flag samples exceeding threshold PAH levels for subsequent quantitative GC–MS analysis.     

An integrated sustainability decision-support framework Part II: Problem analysis

One of the main goals in decision-making for sustainable development is to identify and choose the most sustainable option among different alternatives. This process usually involves a large number of stakeholders with multiple, often conflicting objectives. Facilitating and resolving such difficult decision situations can be complex, so that a more formal and systematic approach to decision-making may be necessary. This paper proposes an integrated multiple criteria decision-support framework specifically developed to provide a systematic, step-by-step guidance to decision-makers. The framework, which is suitable for both corporate and public policy-making in the context of sustainable development, comprises three steps: problem structuring, problem analysis and problem resolution. This paper concentrates on problem analysis and resolution, where decision-makers articulate their preferences for different decision criteria. A suitable Multiple Criteria Decision Analysis (MCDA) technique, such as multi-objective optimisation, goal programming, value-based and outranking approaches, is then used to model the preferences. These techniques are discussed here in some detail, to provide guidance on the choice of the most appropriate MCDA method. Based on the outcome of preference modelling, which estimates the overall 'value' of each alternative being considered, decision-makers can then choose the 'best' or most sustainable option. Such an integrated decision-support framework is useful for providing structure to the debate, ensuring dialogue among decision-makers and showing trade-offs between conflicting objectives. In this way, it may be possible to create shared understanding about the issues, generate a sense of common purpose and, often, resolve 'difficult' decision problems.     

RETRACTED ARTICLE: Using pyrosequencing and quantitative PCR to analyze microbial communities

New high-throughput technologies continue to emerge for studying complex microbial communities. In particular, massively parallel pyrosequencing enables very high numbers of sequences, providing a more complete view of community structures and a more accurate inference of the functions than has been possible just a few years ago. In parallel, quantitative real-time polymerase chain reaction (QPCR) allows quantitative monitoring of specific community members over time, space, or different environmental conditions. In this review, the principles of these two methods and their complementary applications in studying microbial ecology in bioenvironmental systems are discussed. The parallel sequencing of amplicon libraries and using barcodes to differentiate multiple samples in a pyrosequencing run are explained. The best procedures and chemistries for QPCR amplifications are also described and advantages of applying automation to increase accuracy are addressed. Three examples in which pyrosequencing and QPCR were used together to define and quantify members of microbial communities are provided: in the human large intestine, in a methanogenic digester whose sludge was made more bioavailable by a high-voltage pretreatment, and on the biofilm anode of a microbial electrolytic cell. The key findings in these systems and how both methods were used in concert to achieve those findings are highlighted.     

An improved formal approach to demographic loop analysis

Loop analysis is introduced to demographic analysis as a tool to compare relative contributions of different life-history types to population growth rate. In 1998, G. M. Wardle brought in basic concepts of the graph theory to demographic loop analysis and proposed a methodology to determine the loops from any life-cycle graph based on these concepts. However, the mathematics behind Wardle's methodology cannot readily be used by most population ecologists. A new methodology that is also based on graph theory concepts but both makes ecological sense in its application and is simpler to implement is proposed. Three rules of thumb serve as the basis of the proposed methodology that brings a more systematic approach to loop selection: it identifies only those loops that are ecologically meaningful (i.e., loops that are forward-flowing and with positive elasticity values). Thus, it produces a loop set that is more amenable to answer questions on comparison of different lifehistory types. It is tested on several life-cycle graphs from the literature. Three of these are presented: Vouacapoua americana, Dipsacus sylvestris, and Alcyonium sp. In each case, the methodology successfully produced a loop set that makes sense in terms of the ecology of the species. The methodology is also implemented as a couple of open-source computer codes. It is hoped that the proposed methodology will lead to wider use of loop analysis in demographic population studies.     

Toward an integrative molecular approach to wildlife disease

Pathogens pose serious threats to human health, agricultural investment, and biodiversity conservation through the emergence of zoonoses, spillover to domestic livestock, and epizootic outbreaks. As such, wildlife managers are often tasked with mitigating the negative effects of disease. Yet, parasites form a major component of biodiversity that often persist. This is due to logistical challenges of implementing management strategies and to insufficient understanding of host–parasite dynamics. We advocate for an inclusive understanding of molecular diversity in driving parasite infection and variable host disease states in wildlife systems. More specifically, we examine the roles of genetic, epigenetic, and commensal microbial variation in disease pathogenesis. These include mechanisms underlying parasite virulence and host resistance and tolerance, and the development, regulation, and parasite subversion of immune pathways, among other processes. Case studies of devil facial tumor disease in Tasmanian devils (Sarcophilus harrisii) and chytridiomycosis in globally distributed amphibians exemplify the broad range of questions that can be addressed by examining different facets of molecular diversity. For particularly complex systems, integrative molecular analyses present a promising frontier that can provide critical insights necessary to elucidate disease dynamics operating across scales. These insights enable more accurate risk assessment, reconstruction of transmission pathways, discernment of optimal intervention strategies, and development of more effective and ecologically sound treatments that minimize damage to the host population and environment. Such measures are crucial when mitigating threats posed by wildlife disease to humans, domestic animals, and species of conservation concern.     

A new approach to the analysis of adjacencies: Potentials for landscape insights

Spatial adjacencies are a key-issue in environmental studies. Adjacency effects have been amply observed for biotic (plants and animals) and abiotic components of ecosystems. Particularly well-documented are the effects from human manufactures onto the contiguous vegetation mosaics of natural and semi-natural areas.In this work we first propose and reformulate association rules analysis (ARA), a relatively new data mining algorithm with very limited scientific applications so far, in the form of an in-depth investigation method of the spatial pattern of landcover and vegetation maps. We applied ARA to two very different study areas in Northern Italy, the first (Ceno valley) having a substantial human footprint and mapped at 1:25,000 scale, the second (Foses valley) being almost natural and mapped at 1:5000 scale.We were able to: (a) detect the entire network of spatial adjacencies among landcover types and (b) quantify the frequency and strength of detected adjacencies. Based on our spatial analysis, we also advanced hypotheses on both natural and man-driven vegetation dynamics. In addition, ARA allowed us to propose an index of naturality based on the discovered contiguities.Results show the skill of the proposed approach to characterize landcover spatial patterns for both mid-resolution and high-resolution maps. Furthermore the proposed approach bears a general interest, since it can be applied to the analysis of any landcover map.     

A Bayesian approach to line-transect analysis for estimating abundance

Line-transect analysis is a widely used method of estimating plant and animal density and abundance. A Bayesian approach to a basic line-transect analysis is developed for a half-normal detection function. We extend the model of Karunamuni and Quinn [Karunamuni, R.J., Quinn II, T.J., 1995. Bayesian estimation of animal abundance for line-transect sampling. Biometrics 51, 1325–1337] by including a binomial likelihood function for the number of objects detected. The method computes a joint posterior distribution on the effective strip width and the density of objects in the sampled area. Analytical and computational methods for binned and unbinned perpendicular distance data are provided. Existing information about effective strip width and density can be brought into the analysis via prior distributions. The Bayesian approach is compared to a standard line-transect analysis using both real and simulated data. Results of the Bayesian and non-Bayesian analyses are similar when there are no prior data on effective strip width or density, but the Bayesian approach performs better when such data are available from previous or related studies. Practical methods for including prior data on effective strip width and density are suggested. A numerical example shows how the Bayesian approach can provide valid estimates when the sample size is too small for the standard approach to work reliably. The proposed Bayesian approach can form the basis for developing more advanced analyses.     

A general approach to the analysis of habitat selection

The investigation of animal habitat selection aims at the detection of selective usage of habitat types and the identification of covariates influencing their selection. The results not only allow for a better understanding of the habitat selection process but are also intended to help improve the conservation of animals. Usually, habitat selection by larger animals is assessed by radio-tracking or visual observation studies, where the chosen habitat is determined for some animals at a set of specific points in time. Hence the resulting data often have the following structure: a categorical variable indicating the habitat type selected by an animal at a specific point in time is repeatedly observed and will be explained by covariates. These may either describe properties of the habitat types currently available and/or properties of the animal. In this paper, we present a general approach to the analysis of such data in a categorical regression setup. The proposed model generalizes and improves upon several of the approaches previously discussed in the literature. In particular, it accounts for changing habitat availability due to the movement of animals within the observation area. It incorporates both habitat- and animal-specific covariates, and includes individual-specific random effects to account for correlations introduced by the repeated measurements on single animals. Furthermore, the assumption that the effects are linear can be dropped by including the effects in nonparametric manner based on a penalized spline approach. The methodology is implemented in a freely available software package. We demonstrate the general applicability and the potential of the proposed approach in two case studies: The analysis of a songbird community in South-America and a study on brown bears in Central Europe.