Moose-vehicle collisions (MVC) in the State of Maine from 1992-2004

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ESTIMATED NUMBER AND LOCATION OF FUTURE MOOSE-VEHICLE COLLISIONS (MVC) IN MAINE

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Estimated Number and Location of Future Moose-Vehicle Collisions (MVC) in Maine

Moose (Alces alces) are a keystone herbivore in Maine. Because of the large number of rural roads in Maine, there is a high rate of moose-vehicle collisions (MVCs), which is increasing. On-road encounters with animals resulted in 231 fatalities in the United States in 1999. Because of the fatality of MVCs, it is important to know where they are most likely to occur. I used GIS analysis to estimate where future MVCs would occur, factoring in the variables of land cover suitability for moose, distance from water bodies, locations of past MVCs, and speed limits on the roads. I ran four different analyses, each one weighting the variables equally. I also ran a regression to determine if increasing road speed was associated with the increase in the number of MVCs per length of road. There was not a strong positive relationship between the number of MVCs per length of road and the speed limit, but it was interesting to note that there were more MVCs per length of road on 35mph and 40mph roads than on 45, 50, 55 or 65mph roads. Future research on MVCs would benefit from the inclusion of include moose population density and road traffic data.

Antler Size of Alaskan Moose Alces alces gigas: Effects of Population Density, Hunter Harvest and Use of Guides

Moose Alces alces gigas in Alaska, USA, exhibit extreme sexual dimorphism, with adult males possessing large, elaborate antlers. Antler size and conformation are influenced by age, nutrition and genetics, and these bony structures serve to establish social rank and affect mating success. Population density, combined with anthropogenic effects such as harvest, is thought to influence antler size. Antler size increased as densities of moose decreased, ostensibly a density-dependent response related to enhanced nutrition at low densities. The vegetation type where moose were harvested also affected antler size, with the largest-antlered males occupying more open habitats. Hunts with guides occurred in areas with low moose density, minimized hunter interference and increased rates of success. Such hunts harvested moose with larger antler spreads than did non-guided hunts. Knowledge and abilities allowed guides to satisfy demands of trophy hunters, who are an integral part of the Alaskan economy. Heavy harvest by humans was also associated with decreased antler size of moose, probably via a downward shift in the age structure of the population resulting in younger males with smaller antlers. Nevertheless, density-dependence was more influential than effects of harvest on age structure in determining antler size of male moose. Indeed, antlers are likely under strong sexual selection, but we demonstrate that resource availability influenced the distribution of these sexually selected characters across the landscape. We argue that understanding population density in relation to carrying capacity (K) and the age structure of males is necessary to interpret potential consequences of harvest on the genetics of moose and other large herbivores. Our results provide researchers and managers with a better understanding of variables that affect the physical condition, antler size, and perhaps the genetic composition of populations, which may be useful in managing and modeling moose populations.

MooseJEE: A Moose Extension to Enable the assessment of JEAs

Java Enterprise Applications (JEAs) are large systems that integrate multiple technologies and programming languages. With the purpose to support the analysis of JEAs we have developed MooseJEE an extension of the \emphMoose environment capable to model the typical elements of JEAs.

Landscape and forest structure at moose mortality sites on Isle Royale National Park. A LiDAR based assessment

Landscape structure and heterogeneity play a potentially important, but little understood role in predator-prey interactions and behaviourally-mediated habitat selection. For example, habitat complexity may either reduce or enhance the efficiency of a predator's efforts to search, track, capture, kill and consume prey. For prey, structural heterogeneity may affect predator detection, avoidance and defense, escape tactics, and the ability to exploit refuges. This study, investigates whether and how vegetation and topographic structure influence the spatial patterns and distribution of moose (Alces alces) mortality due to predation and malnutrition at the local and landscape levels on Isle Royale National Park. 230 locations where wolves (Canis lupus) killed moose during the winters between 2002 and 2010, and 182 moose starvation death sites for the period 1996-2010, were selected from the extensive Isle Royale Wolf-Moose Project carcass database. A variety of LiDAR-derived metrics were generated and used in an algorithm model (Random Forest) to identify, characterize, and classify three-dimensional variables significant to each of the mortality classes. Furthermore, spatial models to predict and assess the likelihood at the landscape scale of moose mortality were developed. This research found that the patterns of moose mortality by predation and malnutrition across the landscape are non-random, have a high degree of spatial variability, and that both mechanisms operate in contexts of comparable physiographic and vegetation structure. Wolf winter hunting locations on Isle Royale are more likely to be a result of its prey habitat selection, although they seem to prioritize the overall areas with higher moose density in the winter. Furthermore, the findings suggest that the distribution of moose mortality by predation is habitat-specific to moose, and not to wolves. In addition, moose sex, age, and health condition also affect mortality site selection, as revealed by subtle differences between sites in vegetation heights, vegetation density, and topography. Vegetation density in particular appears to differentiate mortality locations for distinct classes of moose. The results also emphasize the significance of fine-scale landscape and habitat features when addressing predator-prey interactions. These finer scale findings would be easily missed if analyses were limited to the broader landscape scale alone.

Compensatory growth in moose and its relationship to sex, longevity, intraspecific competition, and senescence

Individual life history theory is largely focused on understanding the extent to which various phenotypes of an organism are adaptive and whether they represent life history trade-offs. Compensatory growth (CG) is increasingly appreciated as a phenotype of interest to evolutionary ecologists. CG or catch-up growth involves the ability of an organism to grow at a faster-than-normal rate following periods of under-nutrition once conditions subsequently improve. Here, I examine CG in a population of moose (Alces alces) living on Isle Royale, a remote island in Lake Superior, North America. I gained insights about CG from measurements of skeletal remains of 841 moose born throughout a 52-year period. In particular, I compared the length of the metatarsal bone (ML) with several skull measurements. While ML is an index of growth while the moose is in utero and during the first year or two of life, a moose skull continues to grow until a moose is approximately 5 years of age. Because of these differences, the strength of correlation between ML and skull measurements, for a group of moose (say female moose) is an indication of that group’s capacity for CG. Using this logic, I conducted analyses whose results suggest that the capacity for CG did not differ between sexes, between individuals born during periods of high and low population densities, or between individuals exhibiting signs of senescence and those that do not. The analysis did however suggest that long-lived individuals had a greater capacity for CG than short-lived individuals. These results suggest that CG in moose is an adaptive trait and might not be associated with life history trade-offs.

Workshop on FAMIX and Moose in Software Reengineering (FAMOOSr 2008)

The increasing amount of data available about software systems poses new challenges for re- and reverse engineering research, as the proposed approaches need to scale. In this context, concerns about meta-modeling and analysis techniques need to be augmented by technical concerns about how to reuse and how to build upon the efforts of previous research. Moose is an extensive infrastructure for reverse engineering evolved for over 10 years that promotes the reuse of engineering efforts in research. Moose accommodates various types of data modeled in the FAMIX family of meta-models. The goal of this half-day workshop is to strengthen the community of researchers and practitioners who are working in re- and reverse engineering, by providing a forum for building future research starting from Moose and FAMIX as shared infrastructure.

Workshop on FAMIX and Moose in Software Reengineering (FAMOOSr 2009)

The increasing amount of data available about software systems poses new challenges for re- and reverse engineering research, as the proposed approaches need to scale. In this context, concerns about meta-modeling and analysis techniques need to be augmented by technical concerns about how to reuse and how to build upon the efforts of previous research. MOOSE is an extensive infrastructure for reverse engineering evolved for over 10 years that promotes the reuse of engineering efforts in research. MOOSE accommodates various types of data modeled in the FAMIX family of meta-models. The goal of this half-day workshop is to strengthen the community of researchers and practitioners who are working in re- and reverse engineering, by providing a forum for building future research starting from MOOSE and FAMIX as shared infrastructure.

Embedding Moose Facilities Directly in IDEs

Moose is a powerful reverse engineering platform, but its facilities and means to analyze software are separated from the tools developers typically use to develop and maintain their software systems: development environments such as Eclipse, VisualWorks, or Squeak. In practice, this requires developers to work with two distinct environments, one to actually develop the software, and another one (e.g., Moose) to analyze it. We worked on several different techniques, using both dynamic and static analyzes to provide software analysis capabilities to developers directly in the IDE. The immediate availability of analysis tools in an IDE significantly increases the likelihood that developers integrate software analysis in their daily work, as we discovered by conducting user studies with developers. Finally, we identified several important aspect of integrating software analysis in IDEs that need to be addressed in the future to increase the adoption of these techniques by developers.