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1 - 10 of 1137
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(2009)Journal ArticleModeling potential disease spread in wildlife populations is important for predicting, responding to and recovering from a foreign animal disease incursion such as foot and mouth disease (FMD). We conducted a series of simulation experiments to determine how seasonal estimates of the spatial distribution of white-tailed deer impact the predicted magnitude and distribution of potential FMD outbreaks. Outbreaks were simulated in a study area comprising 2 distinct ecoregions in south Texas, USA, using a susceptible-latent-infectious-resistant geographic automata model (Sirca). Seasonal deer distributions were estimated by spatial autoregressive lag models and the normalized difference vegetative index. Significant (P < 0.0001) differences in both the median predicted number of deer infected and number of herds infected were found both between seasons and between ecoregions. Larger outbreaks occurred in winter within the higher deer-density ecoregion, whereas larger outbreaks occurred in summer and fall within the lower deer-density ecoregion. Results of this simulation study suggest that the outcome of an FMD incursion in a population of wildlife would depend on the density of the population infected and when during the year the incursion occurs. It is likely that such effects would be seen for FMD incursions in other regions and countries, and for other diseases, in cases in which a potential wildlife reservoir exists. Study findings indicate that the design of a mitigation strategy needs to take into account population and seasonal characteristics.
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(2009)Journal ArticleWe describe a method of morphometric characterisation of landform from DEMs. The method is implemented by first classifying every location into morphometric classes based on the mathematical shape of a locally fitted quadratic surface and its positional relationship with the analysis window. Single-scale fuzzy terrain indices of peakness, pitness, passness, ridgeness, and valleyness are then calculated based on the distance of the analysis location from the ideal cases. These can then be combined into multi-scale terrain indices to summarise terrain information across different operational scales. The algorithm has four characteristics: (1) the ideal cases of different geomorphometric features are simply and clearly defined; (2) the output is spatially continuous to reflect the inherent fuzziness of geomorphometric features; (3) the output is easily combined into a multi-scale index across a range of operational scales; and (4) the standard general morphometric parameters are quantified as the first and second order derivatives of the quadratic surface. An additional benefit of the quadratic surface is the derivation of the R2 goodness of fit statistic, which allows an assessment of both the reliability of the results and the complexity of the terrain. An application of the method using a test DEM indicates that the single- and multi-scale terrain indices perform well when characterising the different geomorphometric features.
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(2009)Journal ArticleWe analysed the sensitivity of a decision tree derived forest type mapping to simulated data errors in input DEM, geology and remotely sensed (Landsat Thematic Mapper) variables. We used a stochastic Monte Carlo simulation model coupled with a one-at-a-time approach. The DEM error was assumed to be spatially autocorrelated with its magnitude being a percentage of the elevation value. The error of categorical geology data was assumed to be positional and limited to boundary areas. The Landsat data error was assumed to be spatially random and follow a Gaussian distribution. Each layer was perturbed using its error model with increasing levels of error, and the effect on the forest type mapping was assessed. The results of the three sensitivity analyses were markedly different, with the classification being most sensitive to the DEM error, then to the Landsat data errors, but with only a limited sensitivity to the geology data error used. A linear increase in error resulted in non-linear increases in effect for the DEM and Landsat errors, while it was linear for geology. As an example, a DEM error of as small as ±2% reduced the overall test accuracy by more than 2%. More importantly, the same uncertainty level has caused nearly 10% of the study area to change its initial class assignment at each perturbation, on average. A spatial assessment of the sensitivities indicates that most of the pixel changes occurred within those forest classes expected to be more sensitive to data error. In addition to characterising the effect of errors on forest type mapping using decision trees, this study has demonstrated the generality of employing Monte Carlo analysis for the sensitivity and uncertainty analysis of categorical outputs which have distinctive characteristics from that of numerical outputs.
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(2008)Journal ArticleThe Cape bee (Apis mellifera capensis) is unique among honeybees in that workers can lay eggs that instead of developing into males develop into females via thelytokous parthenogenesis. We show that this ability allows workers to compete directly with the queen over the production of new queens. Genetic analyses using microsatellites revealed that 23 out of 39 new queens produced by seven colonies were offspring of workers and not the resident queen. Of these, eight were laid by resident workers, but the majority were offspring of parasitic workers from other colonies. The parasites were derived from several clonal lineages that entered the colonies and successfully targeted queen cells for parasitism. Hence, these parasitic workers had the potential to become genetically reincarnated as queens. Of the daughter queens laid by the resident queen, three were produced asexually, suggesting that queens can 'choose' to produce daughter queens clonally and thus have the potential for genetic immortality.
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(2007)Journal ArticleThe honeybees of South Africa, the Cape honeybee Apis mellifera capensis and the African honeybee Apis mellifera scutellata are separated by a zone of overlap situated mainly within the Great Karoo ecotone (Hepburn and Crewe, 1991). The exact nature of this zone of overlap is unclear, but neither subspecies appears to be able to increase its range, despite capensis’s ability to become a social parasite of other honeybee subspecies (Allsopp, 1992) and scutellata’s potential for invasiveness (Winston, 1992). This status quo (Hepburn and Radloff, 1998) may be due to an asynchrony of mating flight times. Here we test the hypothesis that scutellata and capensis have offset mating flight times by comparing the flight times of drones of the two subspecies at a single locality in Stellenbosch, Western Cape, South Africa.
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(2009)Journal ArticleAustralia is vulnerable to the impacts of tsunamis and exposure along the SE coast of New South Wales is es- pecially high. Significantly, this is the same area reported to have been affected by repeated large magnitude tsunamis during the Holocene. Efforts are under way to complete probabilistic risk assessments for the region but local gov- ernment planners and emergency risk managers need infor- mation now about building vulnerability in order to develop appropriate risk management strategies. We use the newly revised PTVA-3 Model (Dall’Osso et al., 2009) to assess the relative vulnerability of buildings to damage from a “worst case tsunami” defined by our latest understanding of regional risk – something never before undertaken in Australia. We present selected results from an investigation of building vul- nerability within the local government area of Manly – an iconic coastal area of Sydney. We show that a significant proportion of buildings (in particular, residential structures) are classified as having “High” and “Very High” Relative Vulnerability Index scores. Furthermore, other important buildings (e.g., schools, nursing homes and transport struc- tures) are also vulnerable to damage. Our results have seri- ous implications for immediate emergency risk management, longer-term land-use zoning and development, and building design and construction standards. Based on the work un- dertaken here, we recommend further detailed assessment of the vulnerability of coastal buildings in at risk areas, devel- opment of appropriate risk management strategies and a de- tailed program of community engagement to increase overall resilience.
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(2010)Journal ArticleAustralia is at risk from tsunamis and recent work has identified the need for models to assess the vulnerability of exposed coastal areas – a fundamental element of the risk management process. Outputs of vulnerability assessment can be used as a baseline for the generation of tsunami pre- vention and mitigation measures, including evacuation maps. Having noted that no evacuation maps exist for Manly, Syd- ney (an area recently subjected to high resolution building vulnerability assessment by Dall’Osso et al., 2009b), we use the results of the analysis by Dall’Osso et al. (2009b) to “draft” tsunami evacuation maps that could be used by the local emergency service organisations. We then interviewed 500 permanent residents of Manly in order to gain a rapid assessment on their views about the potential usefulness of the draft evacuation maps we generated. Results of the sur- vey indicate that residents think the maps are useful and un- derstandable, and include insights that should be considered by local government planners and emergency risk manage- ment specialists during the development of official evacua- tion maps (and plans) in the future.
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(2010)Journal ArticleWe present here a novel method to assess coastal vulnerability to tsunami based on GIS (Geographical Information System), ASTER imagery (Advanced Spaceborn Thermal Emission and Reflection Radiometer) and SRTM-3 elevation model (Shuttle Radar Topography Mission-3). We developed this method within the CRATER project (Coastal Risk Analysis for Tsunamis and Environmental Remediation) and applied it on the whole western coast of Thailand. As result, we generated a set of vectorial vulnerability maps with a geometrical resolution of 90m (scale 1:450 000). This approach provides a low-cost and quick tool to analyse extended coastal tracts, and prioritize investments for prevention measures or for further high-resolution analysis.
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(2009)Journal ArticleThe Papathoma Tsunami Vulnerability Assess- ment (PTVA) Model (Papathoma, 2003) was developed in the absence of robust, well-constructed and validated build- ing fragility models for assessing the vulnerability of build- ings to tsunami. It has proven to be a useful tool for pro- viding assessments of building vulnerability. We present an enhanced version (PTVA-3) of the model that takes account of new understanding of the factors that influence building vulnerability and significantly, introduce the use of the Ana- lytic Hierarchy Process (AHP) for weighting the various at- tributes in order to limit concerns about subjective ranking of attributes in the original model. We successfully test PTVA- 3 using building data from Maroubra, Sydney, Australia.
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(2010)Journal ArticleThe volcanic archipelago of the Aeolian Islands (Sicily, Italy) is included on the UNESCO World Heritage list and is visited by more than 200 000 tourists per year. Due to its geological characteristics, the risk related to vol- canic and seismic activity is particularly high. Since 1916 the archipelago has been hit by eight local tsunamis. The most recent and intense of these events happened on 30 De- cember 2002. It was triggered by two successive landslides along the north-western side of the Stromboli volcano (Sciara del Fuoco), which poured approximately 2–3×107 m3 of rocks and debris into the Tyrrhenian Sea. The waves im- pacted across the whole archipelago, but most of the damage to buildings and infrastructures occurred on the islands of Stromboli (maximum run-up 11 m) and Panarea. The aim of this study is to assess the vulnerability of build- ings to damage from tsunamis located within the same area inundated by the 2002 event. The assessment is carried out by using the PTVA-3 Model (Papathoma Tsunami Vulner- ability Assessment, version 3). The PTVA-3 Model calcu- lates a Relative Vulnerability Index (RVI) for every build- ing, based on a set of selected physical and structural at- tributes. Run-up values within the area inundated by the 2002 tsunami were measured and mapped by the Istituto Italiano di Geofisica e Vulcanologia (INGV) and the University of Bologna during field surveys in January 2003. Results of the assessment show that if the same tsunami were to oc- cur today, 54 buildings would be affected in Stromboli, and 5 in Panarea. The overall vulnerability level obtained in this analysis for Stromboli and Panarea are “average”/“low” and “very low”, respectively. Nonetheless, 14 buildings in Strom- Correspondence to: F. Dall’Osso (filippodallosso@gmail.com) boli are classified as having a “high” or “average” vulnera- bility. For some buildings, we were able to validate the RVI scores calculated by the PTVA-3 Model through a qualita- tive comparison with photographs taken by INGV and the University of Bologna during the post-tsunami survey. With the exception of a single structure, which is partially cov- ered by a coastal dune on the seaward side, we found a good degree of accuracy between the PTVA-3 Model forecast as- sessments and the actual degree of damage experienced by buildings. This validation of the model increases our confi- dence in its predictive capability. Given the high tsunami risk for the archipelago, our results provide a framework for pri- oritising investments in prevention measures and addressing the most relevant vulnerability issues of the built environ- ment, particularly on the island of Stromboli.