The rodent fauna of Long Pine Key, Everglades National Park: a comparison of habitat types

Rodents are often involved at several stages of trophic dynamics. Consequently they often play crucial roles in the structure and function of many complex ecological systems. This study sought to address the lack of baseline data concerning rodents in tropical areas, and south Florida in particular. Live trapping took place in the four major habitat types of the Long Pine Key area of Everglades National Park over the course of one year. I compared population structures and abundance of murid rodents in the four habitat types, and tested multiple weather variables for their effectiveness as predictors of rodent abundance. I found the Long Pine Key area to be depauperate in terms of species diversity. Each of the four species of rodent encountered favored a particular habitat type. The density of the understory vegetation and the avoidance of avian predators in particular appear to be the most important factors in the distribution and abundance of rodents in the Long Pine Key area of Everglades National Park.

Dynamique d’une population faunique en expansion sous différents scénarios climatiques : le dindon sauvage (Meleagris gallopavo)

Les changements climatiques récents ont mené à l’expansion de la répartition de plusieurs espèces méridionales, mais ont aussi causé l’extinction locale d’espèces se retrouvant à la limite de leur tolérance environnementale. Ces populations en expansion peuvent favoriser différentes stratégies d’histoire de vie en répondant à différents facteurs limitants. Dans cette thèse, je vise à déterminer et quantifier l’effet du climat et des évènements extrêmes sur le cycle de vie complet d’une espèce en expansion (le dindon sauvage) pour comprendre les changements au niveau populationnel ainsi que les mécanismes impliqués dans l’expansion de la distribution d’une espèce. J’ai défini les évènements extrêmes de pluie, d’épaisseur de neige au sol et de température, comme un évènement dont la fréquence est plus rare que le 10e et 90e percentile. En utilisant l’approche « Measure-Understand-Predict » (MUP), j’ai tout d’abord suivi trois populations le long d’un gradient latitudinal de sévérité hivernale pour mesurer l’effet de variables météorologiques sur la dynamique des populations. La survie des dindons sauvages diminuait drastiquement lorsque l’accumulation de neige au sol dépassait 30 cm pour une période de 10 jours et diminuait également avec la température. Au printemps, la persistance de la neige affectait négativement le taux d’initiation de la nidification et l’augmentation de la pluie diminuait la survie des nids. Dans une deuxième étape, j’ai examiné l’impact des évènements climatiques extrêmes et des processus démographiques impliqués dans l’expansion du dindon, liés à la théorie des histoires de vie pour comprendre la relation entre la dynamique de ces populations en expansions avec le climat. J’ai démontré que la fréquence des évènements extrêmes hivernaux et, d’une façon moins importante, les évènements extrêmes estivaux limitaient l’expansion nordique des dindons sauvages. J’ai appuyé, à l’aide de données empiriques et de modélisation, les hypothèses de la théorie classique des invasions biologiques en montrant que les populations en établissement priorisaient les paramètres reproducteurs tandis que la survie adulte était le paramètre démographique affectant le plus la dynamique des populations bien établies. De plus, les populations les plus au nord étaient composées d’individus plus jeunes ayant une espérance de vie plus faible, mais avaient un potentiel d’accroissement plus élevé que les populations établies, comme le suggère cette théorie. Finalement, j’ai projeté l’impact de la récolte sur la dynamique des populations de même que le taux de croissance de cette espèce en utilisant les conditions climatiques futures projetées par les modèles de l’IPCC. Les populations en établissement avaient un taux de récolte potentiel plus élevé, mais la proportion de mâles adultes, possédant des caractéristiques recherchées par les chasseurs, diminuait plus rapidement que dans les populations établies. Dans le futur, la fréquence des évènements extrêmes de pluie devrait augmenter tandis que la fréquence des évènements extrêmes de température hivernale et d’accumulation de neige au sol devraient diminuer après 2060, limitant probablement l’expansion nordique du dindon sauvage jusqu’en 2100. Cette thèse améliore notre compréhension des effets météorologiques et du climat sur l’expansion de la répartition des espèces ainsi que les mécanismes démographiques impliqués, et nous a permis de prédire la probabilité de l’expansion nordique de la répartition du dindon sauvage en réponse aux changements climatiques.

Psychosocial and communication variables involved in mediating the individual experience of older adults following a severe weather event

The recent floods in south-east Queensland have focused policy, academic and community attention on the challenges associated with severe weather events (SWE), specifically pre-disaster preparation, disaster-response and post-disaster community resilience. Financially, the cost of SWE was $9 billion in the 2011 Australian Federal Budget (Swan 2011); psychologically and emotionally, the impact on individual mental health and community wellbeing is also significant but more difficult to quantify. However, recent estimates suggest that as many as one in five will subsequently experience major emotional distress (Bonanno et al. 2010). With climate change predicted to increase the frequency and intensity of a wide range of SWE in Australia (Garnaut 2011; The Climate Institute 2011), there is an urgent and critical need to ensure that the unique psychological and social needs of more vulnerable community members - such as older residents - are better understood and integrated into disaster preparedness and response policy, planning and protocols. Navigating the complex dynamics of SWE can be particularly challenging for older adults and their disaster experience is frequently magnified by a wide array of cumulative and interactive stressors, which intertwine to make them uniquely vulnerable to significant short and long-term adverse effects. This current article provides a brief introduction to the current literature in this area and highlights a gap in the research relating to communication tools during and after severe weather events.

Preparation of future weather data to study the impact of climate change on buildings

The dynamic interaction between building systems and external climate is extremely complex, involving a large number of difficult-to-predict variables. In order to study the impact of climate change on the built environment, the use of building simulation techniques together with forecast weather data are often necessary. Since most of building simulation programs require hourly meteorological input data for their thermal comfort and energy evaluation, the provision of suitable weather data becomes critical. In this paper, the methods used to prepare future weather data for the study of the impact of climate change are reviewed. The advantages and disadvantages of each method are discussed. The inherent relationship between these methods is also illustrated. Based on these discussions and the analysis of Australian historic climatic data, an effective framework and procedure to generate future hourly weather data is presented. It is shown that this method is not only able to deal with different levels of available information regarding the climate change, but also can retain the key characters of a “typical” year weather data for a desired period.

Important omitted spatial variables in safety models: Understanding contributing crash causes at intersections

Advances in safety research—trying to improve the collective understanding of motor vehicle crash causation—rests upon the pursuit of numerous lines of inquiry. The research community has focused on analytical methods development (negative binomial specifications, simultaneous equations, etc.), on better experimental designs (before-after studies, comparison sites, etc.), on improving exposure measures, and on model specification improvements (additive terms, non-linear relations, etc.). One might think of different lines of inquiry in terms of ‘low lying fruit’—areas of inquiry that might provide significant improvements in understanding crash causation. It is the contention of this research that omitted variable bias caused by the exclusion of important variables is an important line of inquiry in safety research. In particular, spatially related variables are often difficult to collect and omitted from crash models—but offer significant ability to better understand contributing factors to crashes. This study—believed to represent a unique contribution to the safety literature—develops and examines the role of a sizeable set of spatial variables in intersection crash occurrence. In addition to commonly considered traffic and geometric variables, examined spatial factors include local influences of weather, sun glare, proximity to drinking establishments, and proximity to schools. The results indicate that inclusion of these factors results in significant improvement in model explanatory power, and the results also generally agree with expectation. The research illuminates the importance of spatial variables in safety research and also the negative consequences of their omissions.

On the significance of omitted variables in intersection crash modeling

Advances in safety research—trying to improve the collective understanding of motor vehicle crash causes and contributing factors—rest upon the pursuit of numerous lines of research inquiry. The research community has focused considerable attention on analytical methods development (negative binomial models, simultaneous equations, etc.), on better experimental designs (before-after studies, comparison sites, etc.), on improving exposure measures, and on model specification improvements (additive terms, non-linear relations, etc.). One might logically seek to know which lines of inquiry might provide the most significant improvements in understanding crash causation and/or prediction. It is the contention of this paper that the exclusion of important variables (causal or surrogate measures of causal variables) cause omitted variable bias in model estimation and is an important and neglected line of inquiry in safety research. In particular, spatially related variables are often difficult to collect and omitted from crash models—but offer significant opportunities to better understand contributing factors and/or causes of crashes. This study examines the role of important variables (other than Average Annual Daily Traffic (AADT)) that are generally omitted from intersection crash prediction models. In addition to the geometric and traffic regulatory information of intersection, the proposed model includes many spatial factors such as local influences of weather, sun glare, proximity to drinking establishments, and proximity to schools—representing a mix of potential environmental and human factors that are theoretically important, but rarely used. Results suggest that these variables in addition to AADT have significant explanatory power, and their exclusion leads to omitted variable bias. Provided is evidence that variable exclusion overstates the effect of minor road AADT by as much as 40% and major road AADT by 14%.

A closed-form approximation for pricing temperature-based weather derivatives

This paper develops analytical distributions of temperature indices on which temperature derivatives are written. If the deviations of daily temperatures from their expected values are modelled as an Ornstein-Uhlenbeck process with timevarying variance, then the distributions of the temperature index on which the derivative is written is the sum of truncated, correlated Gaussian deviates. The key result of this paper is to provide an analytical approximation to the distribution of this sum, thus allowing the accurate computation of payoffs without the need for any simulation. A data set comprising average daily temperature spanning over a hundred years for four Australian cities is used to demonstrate the efficacy of this approach for estimating the payoffs to temperature derivatives. It is demonstrated that expected payoffs computed directly from historical records are a particularly poor approach to the problem when there are trends in underlying average daily temperature. It is shown that the proposed analytical approach is superior to historical pricing.

Towards the Use of Radar Winds in Numerical Weather Prediction

Numerical weather prediction (NWP) models provide the basis for weather forecasting by simulating the evolution of the atmospheric state. A good forecast requires that the initial state of the atmosphere is known accurately, and that the NWP model is a realistic representation of the atmosphere. Data assimilation methods are used to produce initial conditions for NWP models. The NWP model background field, typically a short-range forecast, is updated with observations in a statistically optimal way. The objective in this thesis has been to develope methods in order to allow data assimilation of Doppler radar radial wind observations. The work has been carried out in the High Resolution Limited Area Model (HIRLAM) 3-dimensional variational data assimilation framework. Observation modelling is a key element in exploiting indirect observations of the model variables. In the radar radial wind observation modelling, the vertical model wind profile is interpolated to the observation location, and the projection of the model wind vector on the radar pulse path is calculated. The vertical broadening of the radar pulse volume, and the bending of the radar pulse path due to atmospheric conditions are taken into account. Radar radial wind observations are modelled within observation errors which consist of instrumental, modelling, and representativeness errors. Systematic and random modelling errors can be minimized by accurate observation modelling. The impact of the random part of the instrumental and representativeness errors can be decreased by calculating spatial averages from the raw observations. Model experiments indicate that the spatial averaging clearly improves the fit of the radial wind observations to the model in terms of observation minus model background (OmB) standard deviation. Monitoring the quality of the observations is an important aspect, especially when a new observation type is introduced into a data assimilation system. Calculating the bias for radial wind observations in a conventional way can result in zero even in case there are systematic differences in the wind speed and/or direction. A bias estimation method designed for this observation type is introduced in the thesis. Doppler radar radial wind observation modelling, together with the bias estimation method, enables the exploitation of the radial wind observations also for NWP model validation. The one-month model experiments performed with the HIRLAM model versions differing only in a surface stress parameterization detail indicate that the use of radar wind observations in NWP model validation is very beneficial.

Acute Asthma Epidemics, Weather and Pollen in England, 1987-1994

Recent epidemics of acute asthma have caused speculation that, if their causes were known, early warnings might be feasible. In particular, some epidemics seemed to be associated with thunderstorms. We wondered what risk factors predicting epidemics could be identified. Daily asthma admissions counts during 1987-1994, for two age groups (0-14 yrs and > or = 15 yrs), were measured using the Hospital Episodes System (HES). Epidemics were defined as combinations of date, age group and English Regional Health Authority (RHA) with exceptionally high asthma admission counts compared to the predictions of a log-linear autoregression model. They were compared with control days 1 week before and afterwards, regarding seven meteorological variables and 5 day average pollen counts for four species. Fifty six asthma epidemics were identified. The mean density of sferics (lightning flashes), temperature and rainfall on epidemic days were greater than those on control days. High sferics densities were overrepresented in epidemics. Simultaneously high sferics and grass pollen further increased the probability of an epidemic, but only to 15% (95% confidence interval 2-45%). Two thirds of epidemics were not preceded by thunderstorms. Thunderstorms and high grass pollen levels precede asthma epidemics more often than expected by chance. However, most epidemics are not associated with thunderstorms or unusual weather conditions, and most thunderstorms, even following high grass pollen levels, do not precede epidemics. An early warning system based on the indicators examined here would, therefore, detect few epidemics and generate an unacceptably high rate of false alarms.

Interpretation of wind components as compositional variables

The classical statistical study of the wind speed in the atmospheric surface layer is made generally from the analysis of the three habitual components that perform the wind data, that is, the component W-E, the component S-N and the vertical component, considering these components independent. When the goal of the study of these data is the Aeolian energy, so is when wind is studied from an energetic point of view and the squares of wind components can be considered as compositional variables. To do so, each component has to be divided by the module of the corresponding vector. In this work the theoretical analysis of the components of the wind as compositional data is presented and also the conclusions that can be obtained from the point of view of the practical applications as well as those that can be derived from the application of this technique in different conditions of weather

Weather to disperse? Evidence that climatic conditions influence vertebrate dispersal

1. Dispersal is regarded as critical to the stability of existing populations and the spread of invading species, but empirical data on the effect of travelling conditions during the transfer phase are rare. We present evidence that both timing and distance of ex-natal dispersal in buzzards (Buteo buteo) are strongly affected by weather. 2. Dispersal was recorded more often when the wind changed to a more southerly direction from the more common westerly winds, and when minimum temperatures were lower. The effect of wind direction was greatest in the winter and minimum temperature was most important in the autumn. Poor weather did not appear to initiate dispersal. 3. Dispersal distance was most strongly correlated with maximum temperature during dispersal and wind direction in the following 5-day period. Combined with the sex of the buzzard these three variables accounted for 60% of the variation in dispersal distance. 4. These results are important for conservationists who manage species recovery programs and wildlife managers who model biological invasions.

Correlations of control variables in variational data assimilation

Variational data assimilation systems for numerical weather prediction rely on a transformation of model variables to a set of control variables that are assumed to be uncorrelated. Most implementations of this transformation are based on the assumption that the balanced part of the flow can be represented by the vorticity. However, this assumption is likely to break down in dynamical regimes characterized by low Burger number. It has recently been proposed that a variable transformation based on potential vorticity should lead to control variables that are uncorrelated over a wider range of regimes. In this paper we test the assumption that a transform based on vorticity and one based on potential vorticity produce an uncorrelated set of control variables. Using a shallow-water model we calculate the correlations between the transformed variables in the different methods. We show that the control variables resulting from a vorticity-based transformation may retain large correlations in some dynamical regimes, whereas a potential vorticity based transformation successfully produces a set of uncorrelated control variables. Calculations of spatial correlations show that the benefit of the potential vorticity transformation is linked to its ability to capture more accurately the balanced component of the flow.

Incorporation of water vapour transfer in the JULES Land Surface Model: implications for key soil variables and land surface fluxes

This study focuses on the mechanisms underlying water and heat transfer in upper soil layers, and their effects on soil physical prognostic variables and the individual components of the energy balance. The skill of the JULES (Joint UK Land Environment Simulator) land surface model (LSM) to simulate key soil variables, such as soil moisture content and surface temperature, and fluxes such as evaporation, is investigated. The Richards equation for soil water transfer, as used in most LSMs, was updated by incorporating isothermal and thermal water vapour transfer. The model was tested for three sites representative of semi-arid and temperate arid climates: the Jornada site (New Mexico, USA), Griffith site (Australia) and Audubon site (Arizona, USA). Water vapour flux was found to contribute significantly to the water and heat transfer in the upper soil layers. This was mainly due to isothermal vapour diffusion; thermal vapour flux also played a role at the Jornada site just after rainfall events. Inclusion of water vapour flux had an effect on the diurnal evolution of evaporation, soil moisture content and surface temperature. The incorporation of additional processes, such as water vapour flux among others, into LSMs may improve the coupling between the upper soil layers and the atmosphere, which in turn could increase the reliability of weather and climate predictions.

Imbalanced land-surface water budgets in a numerical weather prediction system

There has been a significant increase in the skill and resolution of numerical weather prediction models (NWPs) in recent decades, extending the time scales of useful weather predictions. The land-surface models (LSMs) of NWPs are often employed in hydrological applications, which raises the question of how hydrologically representative LSMs really are. In this paper, precipitation (P), evaporation (E) and runoff (R) from the European Centre for Medium-Range Weather Forecasts (ECMWF) global models were evaluated against observational products. The forecasts differ substantially from observed data for key hydrological variables. In addition, imbalanced surface water budgets, mostly caused by data assimilation, were found on both global (P-E) and basin scales (P-E-R), with the latter being more important. Modeled surface fluxes should be used with care in hydrological applications and further improvement in LSMs in terms of process descriptions, resolution and estimation of uncertainties is needed to accurately describe the land-surface water budgets.

Meteomap: Generation Of Meteorological Variables For Distributed Physically-Based Hydrological Modeling

Distributed energy and water balance models require time-series surfaces of the meteorological variables involved in hydrological processes. Most of the hydrological GIS-based models apply simple interpolation techniques to extrapolate the point scale values registered at weather stations at a watershed scale. In mountainous areas, where the monitoring network ineffectively covers the complex terrain heterogeneity, simple geostatistical methods for spatial interpolation are not always representative enough, and algorithms that explicitly or implicitly account for the features creating strong local gradients in the meteorological variables must be applied. Originally developed as a meteorological pre-processing tool for a complete hydrological model (WiMMed), MeteoMap has become an independent software. The individual interpolation algorithms used to approximate the spatial distribution of each meteorological variable were carefully selected taking into account both, the specific variable being mapped, and the common lack of input data from Mediterranean mountainous areas. They include corrections with height for both rainfall and temperature (Herrero et al., 2007), and topographic corrections for solar radiation (Aguilar et al., 2010). MeteoMap is a GIS-based freeware upon registration. Input data include weather station records and topographic data and the output consists of tables and maps of the meteorological variables at hourly, daily, predefined rainfall event duration or annual scales. It offers its own pre and post-processing tools, including video outlook, map printing and the possibility of exporting the maps to images or ASCII ArcGIS formats. This study presents the friendly user interface of the software and shows some case studies with applications to hydrological modeling.