Spatial extremes of wildfire sizes: Bayesian hierarchical models for extremes

In Portugal, due to the combination of climatological and ecological factors, large wildfires are a constant threat and due to their economic impact, a big policy issue. In order to organize efficient fire fighting capacity and resource management, correct quantification of the risk of large wildfires are needed. In this paper, we quantify the regional risk of large wildfire sizes, by fitting a Generalized Pareto distribution to excesses over a suitably chosen high threshold. Spatio-temporal variations are introduced into the model through model parameters with suitably chosen link functions. The inference on these models are carried using Bayesian Hierarchical Models and Markov chain Monte Carlo methods.     

An indexed dispersion criterion for testing the sex-biased dispersal of lek mating behavior of capercaillies

Traditionally it has been assumed by ecologists that the dispersion of positions with respect to leks of female capercaillies during the mating season is superior to the dispersion of positions of male capercaillies. However some recently published articles suggest the idea that dispersion is not sex-biased, but both sexes show a similar dispersion. This article introduces a mathematical model to approach the study of the above question by means of an indexed dispersion criterion. This model is developed in detail thus deriving its main mathematical properties. On this basis, the model is applied to the analysis of the motivating problem with real data of positions of leks and female and male capercaillies. The results derived by means of the model put into doubt the traditional assumption of a superior dispersion of females supporting the new theories, that is, both sexes show a similar dispersion.     

Persistence models for mark-recapture

The stable of models available for analyzing mark-recapture data (Otis et al. Wild Momogr 66:135, 1978) includes those having behavioral characteristics, time variation, heterogeneity, along with combinations of those characteristics. This paper proposes use of a series of models based on the persistence model of Ramsey and Usner (Biometrics 59:331–339, 2003). We show that persistence can be modeled in combination with behavior and with time variation. We apply the persistence model to situations in which capture occasions are not equally-spaced in time. Two case studies illustrate the use of these extended persistence models.     

Biosecurity threats: the design of surveillance systems,based on power and risk

We consider the problem of designing a surveillance system to detect a broad range of invasive species across a heterogeneous sampling frame. We present a model to detect a range of invertebrate invasives whilst addressing the challenges of multiple data sources, stratifying for differential risk, managing labour costs and providing sufficient power of detection. We determine the number of detection devices required and their allocation across the landscape within limiting resource constraints. The resulting plan will lead to reduced financial and ecological costs and an optimal surveillance system.     

The potential impacts of sea level rise along the coastal zone of Kanyakumari District in Tamilnadu,India

Climate change associated with sea level rise (SLR) is one of the major environmental concerns of today. This paper presents an assessment of the impacts of sea level rise on the coastal zone of Kanyakumari District in Tamilnadu, India. Digital Elevation Model (DEM) combined with overlay techniques in GIS are used in determining the inundation zones along the coastal region. The analysis evaluated the impact on coastal fishing villages, landuse, tourist spots and sensitive areas under threat. The vulnerability of the coastal areas in Kanyakumari to inundation was quantified, based on the projected sea level rise scenarios of 0.5 and 1 m. Our findings reveal that approximately 13 km² of the land area of Kanyakumari would be permanently inundated due to SLR. This would result in loss of land, alteration of the coastal zone and affects coastal ecosystem. From the study, the mitigation measures (engineering measures) and Coastal Zone Management practices that can be taken to protect human life and property from sea level rise are suggested.     

Evaluation of a fast response pressure solver for flow around an isolated cube

This work describes and evaluates a pressure solver that has been incorporated into a fast response three-dimensional building-resolving diagnostic wind modeling system. The solver computes the three-dimensional pressure field around buildings and on exterior walls in terms of a coefficient of pressure by solving a simplified pressure Poisson equation (that neglects turbulence stresses in the Navier-Stokes) for incompressible flow. The input to the solver is the three-dimensional mean wind field obtained from a fast response empirical-diagnostic urban wind model. The present study is an evaluation of the pressure solver using wind-tunnel data for flow normal to and at a 45° angle to an isolated cubical building. Results for the normal incident wind angle case indicate that the model satisfactorily reproduces the general spatial patterns and the magnitude of the pressure difference around much of the cube. Details of the flow field that are not satisfactorily predicted include the spatial distribution of pressure on the roof and the lower half of the front side of the building and the magnitude along the sidewalls where pressures are over predicted. The results for the 45° case show reasonable agreement between the model and experiments on the front and the back walls, but over predict pressures on the leading edge of the rooftop. Regions with poor pressure predictions appear to be a result of unsatisfactory mean wind modeling.     

Empirical likelihood confidence intervals for adaptive cluster sampling

Adaptive cluster sampling (ACS) is an efficient sampling design for estimating parameters of rare and clustered populations. It is widely used in ecological research. The modified Hansen-Hurwitz (HH) and Horvitz-Thompson (HT) estimators based on small samples under ACS have often highly skewed distributions. In such situations, confidence intervals based on traditional normal approximation can lead to unsatisfactory results, with poor coverage properties. Christman and Pontius (Biometrics 56:503–510, 2000) showed that bootstrap percentile methods are appropriate for constructing confidence intervals from the HH estimator. But Perez and Pontius (J Stat Comput Simul 76:755–764, 2006) showed that bootstrap confidence intervals from the HT estimator are even worse than the normal approximation confidence intervals. In this article, we consider two pseudo empirical likelihood functions under the ACS design. One leads to the HH estimator and the other leads to a HT type estimator known as the Hájek estimator. Based on these two empirical likelihood functions, we derive confidence intervals for the population mean. Using a simulation study, we show that the confidence intervals obtained from the first EL function perform as good as the bootstrap confidence intervals from the HH estimator but the confidence intervals obtained from the second EL function perform much better than the bootstrap confidence intervals from the HT estimator, in terms of coverage rate.     

Toward an ecosystem approach to ICM: assessing ecological provinces at sea by remote sensing

The ecosystem approach requires that all elements of an ecosystem, and their mutual interactions, be taken into consideration in any management effort. The selection of suitable geographical units, where this approach can be taken, requires the assessment of ecological provinces, characterized by a coherent set of environmental traits. The marine side of coastal zones, where the interaction between atmosphere, land and sea is not bounded by evident geographic markers, represents a critical factor in this assessment. A coastal province can be defined by physical setting, but also by its bio-geo-chemical features, ideally on the basis of synoptic remote sensing data, collected at space/time scales not accessible by other means. Classifications based on indicators such as temperature, wind speed and chlorophyll-like pigments, demonstrate the identification of potential ecological provinces in the Mediterranean Sea. The results suggest remote sensing as the ideal tool to set up the basis for an ecosystem approach to the management of each province.     

Characterization and source apportionment of polycyclic aromatic hydrocarbons in the ambient air (Tehran,Iran)

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous in the urban atmosphere and the atmospheric pollutants to be identified as the most suspected carcinogens. In early measurements of urban PAH concentrations in Tehran area, the correlation between concentrations of different species indicated that automobiles were the predominant source of emission of PAHs in this city. From the diagnostic ratios, the results indicated that PAHs in the ambient air in Tehran originate primarily from diesel and gasoline engines, but contribution of the related diesel emission is apparently higher, or dominant, at the sampling sites and therefore considered as the major emission of PAHs in the ambient air.